Excellence in Research and Innovation for Humanity

International Science Index

Commenced in January 1999 Frequency: Monthly Edition: International Paper Count: 142

Security Enhanced RFID Middleware System
Recently, the RFID (Radio Frequency Identification) technology attracts the world market attention as essential technology for ubiquitous environment. The RFID market has focused on transponders and reader development. But that concern has shifted to RFID software like as high-valued e-business applications, RFID middleware and related development tools. However, due to the high sensitivity of data and service transaction within the RFID network, security consideration must be addressed. In order to guarantee trusted e-business based on RFID technology, we propose a security enhanced RFID middleware system. Our proposal is compliant with EPCglobal ALE (Application Level Events), which is standard interface for middleware and its clients. We show how to provide strengthened security and trust by protecting transported data between middleware and its client, and stored data in middleware. Moreover, we achieve the identification and service access control against illegal service abuse. Our system enables secure RFID middleware service and trusted e-business service.
Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series
In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible.
First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks
Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.
Mixtures of Monotone Networks for Prediction
In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.
An Engineering Approach to Forecast Volatility of Financial Indices
By systematically applying different engineering methods, difficult financial problems become approachable. Using a combination of theory and techniques such as wavelet transform, time series data mining, Markov chain based discrete stochastic optimization, and evolutionary algorithms, this work formulated a strategy to characterize and forecast non-linear time series. It attempted to extract typical features from the volatility data sets of S&P100 and S&P500 indices that include abrupt drops, jumps and other non-linearity. As a result, accuracy of forecasting has reached an average of over 75% surpassing any other publicly available results on the forecast of any financial index.
Advanced Polymorphic Techniques

Nowadays viruses use polymorphic techniques to mutate their code on each replication, thus evading detection by antiviruses. However detection by emulation can defeat simple polymorphism: thus metamorphic techniques are used which thoroughly change the viral code, even after decryption. We briefly detail this evolution of virus protection techniques against detection and then study the METAPHOR virus, today's most advanced metamorphic virus.

The SAFRS System : A Case-Based Reasoning Training Tool for Capturing and Re-Using Knowledge

The paper aims to specify and build a system, a learning support in radiology-senology (breast radiology) dedicated to help assist junior radiologists-senologists in their radiologysenology- related activity based on experience of expert radiologistssenologists. This system is named SAFRS (i.e. system supporting the training of radiologists-senologists). It is based on the exploitation of radiologic-senologic images (primarily mammograms but also echographic images or MRI) and their related clinical files. The aim of such a system is to help breast cancer screening in education. In order to acquire this expert radiologist-senologist knowledge, we have used the CBR (case-based reasoning) approach. The SAFRS system will promote the evolution of teaching in radiology-senology by offering the “junior radiologist" trainees an advanced pedagogical product. It will permit a strengthening of knowledge together with a very elaborate presentation of results. At last, the know-how will derive from all these factors.

A Two-Step Approach for Tree-structured XPath Query Reduction
XML data consists of a very flexible tree-structure which makes it difficult to support the storing and retrieving of XML data. The node numbering scheme is one of the most popular approaches to store XML in relational databases. Together with the node numbering storage scheme, structural joins can be used to efficiently process the hierarchical relationships in XML. However, in order to process a tree-structured XPath query containing several hierarchical relationships and conditional sentences on XML data, many structural joins need to be carried out, which results in a high query execution cost. This paper introduces mechanisms to reduce the XPath queries including branch nodes into a much more efficient form with less numbers of structural joins. A two step approach is proposed. The first step merges duplicate nodes in the tree-structured query and the second step divides the query into sub-queries, shortens the paths and then merges the sub-queries back together. The proposed approach can highly contribute to the efficient execution of XML queries. Experimental results show that the proposed scheme can reduce the query execution cost by up to an order of magnitude of the original execution cost.
Nonlinear Controller for Fuzzy Model of Double Inverted Pendulums

In this paper a method for designing of nonlinear controller for a fuzzy model of Double Inverted Pendulum is proposed. This system can be considered as a fuzzy large-scale system that includes offset terms and disturbance in each subsystem. Offset terms are deterministic and disturbances are satisfied a matching condition that is mentioned in the paper. Based on Lyapunov theorem, a nonlinear controller is designed for this fuzzy system (as a model reference base) which is simple in computation and guarantees stability. This idea can be used for other fuzzy large- scale systems that include more subsystems Finally, the results are shown.

A Local Statistics Based Region Growing Segmentation Method for Ultrasound Medical Images

This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are consisting of the homogeneity and similarity bounds according to the kernel size. The shape and size of the growing regions depend on this look up table entries. The algorithms are implemented by using connected seeded region growing procedure where each pixel is taken as seed point. The region merging after the region growing also suppresses the high frequency artifacts. The updated merged regions produce the output in formed of segmented image. This algorithm produces the results that are less sensitive to the pixel location and it also allows a segmentation of the accurate homogeneous regions.

Computing the Loop Bound in Iterative Data Flow Graphs Using Natural Token Flow
Signal processing applications which are iterative in nature are best represented by data flow graphs (DFG). In these applications, the maximum sampling frequency is dependent on the topology of the DFG, the cyclic dependencies in particular. The determination of the iteration bound, which is the reciprocal of the maximum sampling frequency, is critical in the process of hardware implementation of signal processing applications. In this paper, a novel technique to compute the iteration bound is proposed. This technique is different from all previously proposed techniques, in the sense that it is based on the natural flow of tokens into the DFG rather than the topology of the graph. The proposed algorithm has lower run-time complexity than all known algorithms. The performance of the proposed algorithm is illustrated through analytical analysis of the time complexity, as well as through simulation of some benchmark problems.
An Improved Method to Watermark Images Sensitive to Blocking Artifacts
A new digital watermarking technique for images that are sensitive to blocking artifacts is presented. Experimental results show that the proposed MDCT based approach produces highly imperceptible watermarked images and is robust to attacks such as compression, noise, filtering and geometric transformations. The proposed MDCT watermarking technique is applied to fingerprints for ensuring security. The face image and demographic text data of an individual are used as multiple watermarks. An AFIS system was used to quantitatively evaluate the matching performance of the MDCT-based watermarked fingerprint. The high fingerprint matching scores show that the MDCT approach is resilient to blocking artifacts. The quality of the extracted face and extracted text images was computed using two human visual system metrics and the results show that the image quality was high.
The Wavelet-Based DFT: A New Interpretation, Extensions and Applications

In 1990 [1] the subband-DFT (SB-DFT) technique was proposed. This technique used the Hadamard filters in the decomposition step to split the input sequence into low- and highpass sequences. In the next step, either two DFTs are needed on both bands to compute the full-band DFT or one DFT on one of the two bands to compute an approximate DFT. A combination network with correction factors was to be applied after the DFTs. Another approach was proposed in 1997 [2] for using a special discrete wavelet transform (DWT) to compute the discrete Fourier transform (DFT). In the first step of the algorithm, the input sequence is decomposed in a similar manner to the SB-DFT into two sequences using wavelet decomposition with Haar filters. The second step is to perform DFTs on both bands to obtain the full-band DFT or to obtain a fast approximate DFT by implementing pruning at both input and output sides. In this paper, the wavelet-based DFT (W-DFT) with Haar filters is interpreted as SB-DFT with Hadamard filters. The only difference is in a constant factor in the combination network. This result is very important to complete the analysis of the W-DFT, since all the results concerning the accuracy and approximation errors in the SB-DFT are applicable. An application example in spectral analysis is given for both SB-DFT and W-DFT (with different filters). The adaptive capability of the SB-DFT is included in the W-DFT algorithm to select the band of most energy as the band to be computed. Finally, the W-DFT is extended to the two-dimensional case. An application in image transformation is given using two different types of wavelet filters.

Processing Web-Cam Images by a Neuro-Fuzzy Approach for Vehicular Traffic Monitoring

Traffic management in an urban area is highly facilitated by the knowledge of the traffic conditions in every street or highway involved in the vehicular mobility system. Aim of the paper is to propose a neuro-fuzzy approach able to compute the main parameters of a traffic system, i.e., car density, velocity and flow, by using the images collected by the web-cams located at the crossroads of the traffic network. The performances of this approach encourage its application when the traffic system is far from the saturation. A fuzzy model is also outlined to evaluate when it is suitable to use more accurate, even if more time consuming, algorithms for measuring traffic conditions near to saturation.

A Fuzzy Predictive Filter for Sinusoidal Signals with Time-Varying Frequencies
Prediction of sinusoidal signals with time-varying frequencies has been an important research topic in power electronics systems. To solve this problem, we propose a new fuzzy predictive filtering scheme, which is based on a Finite Impulse Response (FIR) filter bank. Fuzzy logic is introduced here to provide appropriate interpolation of individual filter outputs. Therefore, instead of regular 'hard' switching, our method has the advantageous 'soft' switching among different filters. Simulation comparisons between the fuzzy predictive filtering and conventional filter bank-based approach are made to demonstrate that the new scheme can achieve an enhanced prediction performance for slowly changing sinusoidal input signals.
Towards Clustering of Web-based Document Structures
Methods for organizing web data into groups in order to analyze web-based hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering web-based document structures has many applications, e.g., improving information retrieval on the web, better understanding of user navigation behavior, improving web users requests servicing, and increasing web information accessibility. In this paper we investigate a new approach for clustering web-based hypertexts on the basis of their graph structures. The hypertexts will be represented as so called generalized trees which are more general than usual directed rooted trees, e.g., DOM-Trees. As a important preprocessing step we measure the structural similarity between the generalized trees on the basis of a similarity measure d. Then, we apply agglomerative clustering to the obtained similarity matrix in order to create clusters of hypertext graph patterns representing navigation structures. In the present paper we will run our approach on a data set of hypertext structures and obtain good results in Web Structure Mining. Furthermore we outline the application of our approach in Web Usage Mining as future work.
Simplex Method for Solving Linear Programming Problems with Fuzzy Numbers
The fuzzy set theory has been applied in many fields, such as operations research, control theory, and management sciences, etc. In particular, an application of this theory in decision making problems is linear programming problems with fuzzy numbers. In this study, we present a new method for solving fuzzy number linear programming problems, by use of linear ranking function. In fact, our method is similar to simplex method that was used for solving linear programming problems in crisp environment before.
Dynamic Fuzzy-Neural Network Controller for Induction Motor Drive

In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed control algorithm is robust to parameter variations and external influences. It also assures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the designed controller of induction motor drives which considered as highly non linear dynamic complex systems and variable characteristics over the operating conditions.

Extraction of Symbolic Rules from Artificial Neural Networks
Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, such as breast cancer, iris, diabetes, and season classification problems, demonstrate the effectiveness of the proposed approach with good generalization ability.
Multistage Condition Monitoring System of Aircraft Gas Turbine Engine
Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Accurate Calculation of Free Frequencies of Beams and Rectangular Plates
An accurate procedure to determine free vibrations of beams and plates is presented. The natural frequencies are exact solutions of governing vibration equations witch load to a nonlinear homogeny system. The bilinear and linear structures considered simulate a bridge. The dynamic behavior of this one is analyzed by using the theory of the orthotropic plate simply supported on two sides and free on the two others. The plate can be excited by a convoy of constant or harmonic loads. The determination of the dynamic response of the structures considered requires knowledge of the free frequencies and the shape modes of vibrations. Our work is in this context. Indeed, we are interested to develop a self-consistent calculation of the Eigen frequencies. The formulation is based on the determination of the solution of the differential equations of vibrations. The boundary conditions corresponding to the shape modes permit to lead to a homogeneous system. Determination of the noncommonplace solutions of this system led to a nonlinear problem in Eigen frequencies. We thus, develop a computer code for the determination of the eigenvalues. It is based on a method of bisection with interpolation whose precision reaches 10 -12. Moreover, to determine the corresponding modes, the calculation algorithm that we develop uses the method of Gauss with a partial optimization of the "pivots" combined with an inverse power procedure. The Eigen frequencies of a plate simply supported along two opposite sides while considering the two other free sides are thus analyzed. The results could be generalized with the case of a beam by regarding it as a plate with low width. We give, in this paper, some examples of treated cases. The comparison with results presented in the literature is completely satisfactory.
Fast Factored DCT-LMS Speech Enhancement for Performance Enhancement of Digital Hearing Aid
Background noise is particularly damaging to speech intelligibility for people with hearing loss especially for sensorineural loss patients. Several investigations on speech intelligibility have demonstrated sensorineural loss patients need 5-15 dB higher SNR than the normal hearing subjects. This paper describes Discrete Cosine Transform Power Normalized Least Mean Square algorithm to improve the SNR and to reduce the convergence rate of the LMS for Sensory neural loss patients. Since it requires only real arithmetic, it establishes the faster convergence rate as compare to time domain LMS and also this transformation improves the eigenvalue distribution of the input autocorrelation matrix of the LMS filter. The DCT has good ortho-normal, separable, and energy compaction property. Although the DCT does not separate frequencies, it is a powerful signal decorrelator. It is a real valued function and thus can be effectively used in real-time operation. The advantages of DCT-LMS as compared to standard LMS algorithm are shown via SNR and eigenvalue ratio computations. . Exploiting the symmetry of the basis functions, the DCT transform matrix [AN] can be factored into a series of ±1 butterflies and rotation angles. This factorization results in one of the fastest DCT implementation. There are different ways to obtain factorizations. This work uses the fast factored DCT algorithm developed by Chen and company. The computer simulations results show superior convergence characteristics of the proposed algorithm by improving the SNR at least 10 dB for input SNR less than and equal to 0 dB, faster convergence speed and better time and frequency characteristics.
DHT-LMS Algorithm for Sensorineural Loss Patients
Hearing impairment is the number one chronic disability affecting many people in the world. Background noise is particularly damaging to speech intelligibility for people with hearing loss especially for sensorineural loss patients. Several investigations on speech intelligibility have demonstrated sensorineural loss patients need 5-15 dB higher SNR than the normal hearing subjects. This paper describes Discrete Hartley Transform Power Normalized Least Mean Square algorithm (DHT-LMS) to improve the SNR and to reduce the convergence rate of the Least Means Square (LMS) for sensorineural loss patients. The DHT transforms n real numbers to n real numbers, and has the convenient property of being its own inverse. It can be effectively used for noise cancellation with less convergence time. The simulated result shows the superior characteristics by improving the SNR at least 9 dB for input SNR with zero dB and faster convergence rate (eigenvalue ratio 12) compare to time domain method and DFT-LMS.
A Knowledge Engineering Workshop: Application for Choise Car
This paper proposes a declarative language for knowledge representation (Ibn Rochd), and its environment of exploitation (DeGSE). This DeGSE system was designed and developed to facilitate Ibn Rochd writing applications. The system was tested on several knowledge bases by ascending complexity, culminating in a system for recognition of a plant or a tree, and advisors to purchase a car, for pedagogical and academic guidance, or for bank savings and credit. Finally, the limits of the language and research perspectives are stated.
Knowledge-Based Approach and System for Processof School/University Orientation
The school / university orientation interests a broad and often badly informed public. Technically, it is an important multicriterion decision problem, which supposes the combination of much academic professional and/or lawful knowledge, which in turn justifies software resorting to the techniques of Artificial Intelligence. CORUS is an expert system of the "Conseil et ORientation Universitaire et Scolaire", based on a knowledge representation language (KRL) with rules and objects, called/ known as Ibn Rochd. CORUS was developed thanks to DéGSE, a workshop of cognitive engineering which supports this LRC. CORUS works out many acceptable solutions for the case considered, and retains the most satisfactory among them. Several versions of CORUS have extended its services gradually.
On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.

Distributed Detection and Optimal Traffic-blocking of Network Worms
Despite the recent surge of research in control of worm propagation, currently, there is no effective defense system against such cyber attacks. We first design a distributed detection architecture called Detection via Distributed Blackholes (DDBH). Our novel detection mechanism could be implemented via virtual honeypots or honeynets. Simulation results show that a worm can be detected with virtual honeypots on only 3% of the nodes. Moreover, the worm is detected when less than 1.5% of the nodes are infected. We then develop two control strategies: (1) optimal dynamic trafficblocking, for which we determine the condition that guarantees minimum number of removed nodes when the worm is contained and (2) predictive dynamic traffic-blocking–a realistic deployment of the optimal strategy on scale-free graphs. The predictive dynamic traffic-blocking, coupled with the DDBH, ensures that more than 40% of the network is unaffected by the propagation at the time when the worm is contained.
Modeling Corporate Memories using the ReCaRo Model, Some Experiments
This paper presents a model of case based corporate memory named ReCaRo (REsource, CAse, ROle). The approach suggested in ReCaRo decomposes the domain to model through a set of components. These components represent the objects developed by the company during its activity. They are reused, and sometimes, while bringing adaptations. These components are enriched by knowledge after each reuse. ReCaRo builds the corporate memory on the basis of these components. It models two types of knowledge: 1) Business Knowledge, which constitutes the main knowledge capital of the company, refers to its basic skill, thus, directly to the components and 2) the Experience Knowledge which is a specialised knowledge and represents the experience gained during the handling of business knowledge. ReCaRo builds corporate memories which are made up of five communicating ones.
Pervasive Differentiated Services: A QoS Model for Pervasive Systems

In this article, we introduce a mechanism by which the same concept of differentiated services used in network transmission can be applied to provide quality of service levels to pervasive systems applications. The classical DiffServ model, including marking and classification, assured forwarding, and expedited forwarding, are all utilized to create quality of service guarantees for various pervasive applications requiring different levels of quality of service. Through a collection of various sensors, personal devices, and data sources, the transmission of contextsensitive data can automatically occur within a pervasive system with a given quality of service level. Triggers, initiators, sources, and receivers are four entities labeled in our mechanism. An explanation of the role of each is provided, and how quality of service is guaranteed.

Reduction of Search Space by Applying Controlled Genetic Operators for Weight Constrained Shortest Path Problem
The weight constrained shortest path problem (WCSPP) is one of most several known basic problems in combinatorial optimization. Because of its importance in many areas of applications such as computer science, engineering and operations research, many researchers have extensively studied the WCSPP. This paper mainly concentrates on the reduction of total search space for finding WCSP using some existing Genetic Algorithm (GA). For this purpose, some controlled schemes of genetic operators are adopted on list chromosome representation. This approach gives a near optimum solution with smaller elapsed generation than classical GA technique. From further analysis on the matter, a new generalized schema theorem is also developed from the philosophy of Holland-s theorem.
Optimal All-to-All Personalized Communication in All-Port Tori

All-to-all personalized communication, also known as complete exchange, is one of the most dense communication patterns in parallel computing. In this paper, we propose new indirect algorithms for complete exchange on all-port ring and torus. The new algorithms fully utilize all communication links and transmit messages along shortest paths to completely achieve the theoretical lower bounds on message transmission, which have not be achieved among other existing indirect algorithms. For 2D r × c ( r % c ) all-port torus, the algorithm has time complexities of optimal transmission cost and O(c) message startup cost. In addition, the proposed algorithms accommodate non-power-of-two tori where the number of nodes in each dimension needs not be power-of-two or square. Finally, the algorithms are conceptually simple and symmetrical for every message and every node so that they can be easily implemented and achieve the optimum in practice.

A Practical Distributed String Matching Algorithm Architecture and Implementation
Traditional parallel single string matching algorithms are always based on PRAM computation model. Those algorithms concentrate on the cost optimal design and the theoretical speed. Based on the distributed string matching algorithm proposed by CHEN, a practical distributed string matching algorithm architecture is proposed in this paper. And also an improved single string matching algorithm based on a variant Boyer-Moore algorithm is presented. We implement our algorithm on the above architecture and the experiments prove that it is really practical and efficient on distributed memory machine. Its computation complexity is O(n/p + m), where n is the length of the text, and m is the length of the pattern, and p is the number of the processors.
Comparison of Multi-User Detectors of DS-CDMA System
DS-CDMA system is well known wireless technology. This system suffers from MAI (Multiple Access Interference) caused by Direct Sequence users. Multi-User Detection schemes were introduced to detect the users- data in presence of MAI. This paper focuses on linear multi-user detection schemes used for data demodulation. Simulation results depict the performance of three detectors viz-conventional detector, Decorrelating detector and Subspace MMSE (Minimum Mean Square Error) detector. It is seen that the performance of these detectors depends on the number of paths and the length of Gold code used.
Implementing an Adaptive Behavior for Spread Spectrum Watermarking Procedures
The advances in multimedia and networking technologies have created opportunities for Internet pirates, who can easily copy multimedia contents and illegally distribute them on the Internet, thus violating the legal rights of content owners. This paper describes how a simple and well-known watermarking procedure based on a spread spectrum method and a watermark recovery by correlation can be improved to effectively and adaptively protect MPEG-2 videos distributed on the Internet. In fact, the procedure, in its simplest form, is vulnerable to a variety of attacks. However, its security and robustness have been increased, and its behavior has been made adaptive with respect to the video terminals used to open the videos and the network transactions carried out to deliver them to buyers. In fact, such an adaptive behavior enables the proposed procedure to efficiently embed watermarks, and this characteristic makes the procedure well suited to be exploited in web contexts, where watermarks usually generated from fingerprinting codes have to be inserted into the distributed videos “on the fly", i.e. during the purchase web transactions.
DODR : Delay On-Demand Routing

As originally designed for wired networks, TCP (transmission control protocol) congestion control mechanism is triggered into action when packet loss is detected. This implicit assumption for packet loss mostly due to network congestion does not work well in Mobile Ad Hoc Network, where there is a comparatively high likelihood of packet loss due to channel errors and node mobility etc. Such non-congestion packet loss, when dealt with by congestion control mechanism, causes poor TCP performance in MANET. In this study, we continue to investigate the impact of the interaction between transport protocols and on-demand routing protocols on the performance and stability of 802.11 multihop networks. We evaluate the important wireless networking events caused routing change, and propose a cross layer method to delay the unnecessary routing changes, only need to add a sensitivity parameter α , which represents the on-demand routing-s reaction to link failure of MAC layer. Our proposal is applicable to the plain 802.11 networking environment, the simulation results that this method can remarkably improve the stability and performance of TCP without any modification on TCP and MAC protocol.

Business Scenarios Assessment in Healthcare and Education for 21st Century Networks in Asia Pacific

Business scenario is an important technique that may be used at various stages of the enterprise architecture to derive its characteristics based on the high-level requirements of the business. In terms of wireless deployments, they are used to help identify and understand business needs involving wireless services, and thereby to derive the business requirements that the architecture development has to address by taking into account of various wireless challenges. This study assesses the deployment of Wireless Local Area Network (WLAN) and Broadband Wireless Access (BWA) solutions for several business scenarios in Asia Pacific region. This paper focuses on the overview of the business and technology environments, whereby examples of existing (or suggested) wireless solutions (to be) adopted in Asia Pacific region will be discussed. Interactions of several players, enabling technologies, and key processes in the wireless environments are studied. The analysis and discussions associated to this study are divided into two divisions: healthcare and education, where the merits of wireless solutions in improving living quality are highlighted.

Theoretical Considerations for Software Component Metrics
We have defined two suites of metrics, which cover static and dynamic aspects of component assembly. The static metrics measure complexity and criticality of component assembly, wherein complexity is measured using Component Packing Density and Component Interaction Density metrics. Further, four criticality conditions namely, Link, Bridge, Inheritance and Size criticalities have been identified and quantified. The complexity and criticality metrics are combined to form a Triangular Metric, which can be used to classify the type and nature of applications. Dynamic metrics are collected during the runtime of a complete application. Dynamic metrics are useful to identify super-component and to evaluate the degree of utilisation of various components. In this paper both static and dynamic metrics are evaluated using Weyuker-s set of properties. The result shows that the metrics provide a valid means to measure issues in component assembly. We relate our metrics suite with McCall-s Quality Model and illustrate their impact on product quality and to the management of component-based product development.
Attenuation in Transferred RF Power to a Biomedical Implant due to the Absorption of Biological Tissue
In a transcutanious inductive coupling of a biomedical implant, a new formula is given for the study of the Radio Frequency power attenuation by the biological tissue. The loss of the signal power is related to its interaction with the biological tissue and the composition of this one. A confrontation with the practical measurements done with a synthetic muscle into a Faraday cage, allowed a checking of the obtained theoretical results. The supply/data transfer systems used in the case of biomedical implants, can be well dimensioned by taking in account this type of power attenuation.
Attenuation in Transferred RF Power to a Biomedical Implant due to the Misalignment Coils
In biomedical implant field, a new formula is given for the study of Radio Frequency power attenuation by simultaneous effects of side and angular misalignment of the supply/data transfer coils. A confrontation with the practical measurements done into a Faraday cage, allowed a checking of the obtained theoretical results. The DC supply systems without material connection and the data transmitters used in the case of biomedical implants, can be well dimensioned by taking into account the possibility of power attenuation by misalignment of transfer coils
Review and Experiments on SDMSCue
In this work, I present a review on Sparse Distributed Memory for Small Cues (SDMSCue), a variant of Sparse Distributed Memory (SDM) that is capable of handling small cues. I then conduct and show some cognitive experiments on SDMSCue to test its cognitive soundness compared to SDM. Small cues refer to input cues that are presented to memory for reading associations; but have many missing parts or fields from them. The original SDM failed to handle such a problem. SDMSCue handles and overcomes this pitfall. The main idea in SDMSCue; is the repeated projection of the semantic space on smaller subspaces; that are selected based on the input cue length and pattern. This process allows for Read/Write operations using an input cue that is missing a large portion. SDMSCue is augmented with the use of genetic algorithms for memory allocation and initialization. I claim that SDM functionality is a subset of SDMSCue functionality.
Evolutionary Approach for Automated Discovery of Censored Production Rules
In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski & Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations, in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the 'If P Then D' part of the CPR expresses important information, while the Unless C part acts only as a switch and changes the polarity of D to ~D. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of CPRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a CPR. Appropriate genetic operators are suggested and a fitness function is proposed that incorporates the basic constraints on CPRs. Experimental results are presented to demonstrate the performance of the proposed algorithm.
Investigating the Possible use of Session Initiation Protocol for Extending Mobility Service to the Biomedical Engineers
Today, the Internet based communication has widen the opportunity of event monitoring system in the medical field. There is always a need of analyzing and designing secure and reliable mobile communication between the hospital and biomedical engineers mobile units. This study has been carried out to find possible solution using SIP-based event notification for alerting the technical staff about the Biomedical Device (BMD) status and Patients treatment session. The Session Initiation Protocol (SIP) can be used to create a medical event notification system. SIP can work on a variety of devices. Its adoption as the protocol of choice for third generation wireless networks allows for a robust and scalable environment. One of the advantages of SIP is that it supports personal mobility through the separation of user addressing and device addressing. The solution for Telemed alert notification system is based on SIP - Specific Event Notification. The aim of this project is to extend mobility service to the hospital technicians who are using Telemedicine system.
Implementation of TinyHash based on Hash Algorithm for Sensor Network
In recent years, it has been proposed security architecture for sensor network.[2][4]. One of these, TinySec by Chris Kalof, Naveen Sastry, David Wagner had proposed Link layer security architecture, considering some problems of sensor network. (i.e : energy, bandwidth, computation capability,etc). The TinySec employs CBC_mode of encryption and CBC-MAC for authentication based on SkipJack Block Cipher. Currently, This TinySec is incorporated in the TinyOS for sensor network security. This paper introduces TinyHash based on general hash algorithm. TinyHash is the module in order to replace parts of authentication and integrity in the TinySec. it implies that apply hash algorithm on TinySec architecture. For compatibility about TinySec, Components in TinyHash is constructed as similar structure of TinySec. And TinyHash implements the HMAC component for authentication and the Digest component for integrity of messages. Additionally, we define the some interfaces for service associated with hash algorithm.
Implementation and Analysis of Elliptic Curve Cryptosystems over Polynomial basis and ONB
Polynomial bases and normal bases are both used for elliptic curve cryptosystems, but field arithmetic operations such as multiplication, inversion and doubling for each basis are implemented by different methods. In general, it is said that normal bases, especially optimal normal bases (ONB) which are special cases on normal bases, are efficient for the implementation in hardware in comparison with polynomial bases. However there seems to be more examined by implementing and analyzing these systems under similar condition. In this paper, we designed field arithmetic operators for each basis over GF(2233), which field has a polynomial basis recommended by SEC2 and a type-II ONB both, and analyzed these implementation results. And, in addition, we predicted the efficiency of two elliptic curve cryptosystems using these field arithmetic operators.
Development of State Model Theory for External Exclusive NOR Type LFSR Structures
Using state space technique and GF(2) theory, a simulation model for external exclusive NOR type LFSR structures is developed. Through this tool a systematic procedure is devised for computing pseudo-random binary sequences from such structures.
Tests for Gaussianity of a Stationary Time Series
One of the primary uses of higher order statistics in signal processing has been for detecting and estimation of non- Gaussian signals in Gaussian noise of unknown covariance. This is motivated by the ability of higher order statistics to suppress additive Gaussian noise. In this paper, several methods to test for non- Gaussianity of a given process are presented. These methods include histogram plot, kurtosis test, and hypothesis testing using cumulants and bispectrum of the available sequence. The hypothesis testing is performed by constructing a statistic to test whether the bispectrum of the given signal is non-zero. A zero bispectrum is not a proof of Gaussianity. Hence, other tests such as the kurtosis test should be employed. Examples are given to demonstrate the performance of the presented methods.
Security Engine Management of Router based on Security Policy
Security management has changed from the management of security equipments and useful interface to manager. It analyzes the whole security conditions of network and preserves the network services from attacks. Secure router technology has security functions, such as intrusion detection, IPsec(IP Security) and access control, are applied to legacy router for secure networking. It controls an unauthorized router access and detects an illegal network intrusion. This paper relates to a security engine management of router based on a security policy, which is the definition of security function against a network intrusion. This paper explains the security policy and designs the structure of security engine management framework.
Distributed Splay Suffix Arrays: A New Structure for Distributed String Search
As a structure for processing string problem, suffix array is certainly widely-known and extensively-studied. But if the string access pattern follows the “90/10" rule, suffix array can not take advantage of the fact that we often find something that we have just found. Although the splay tree is an efficient data structure for small documents when the access pattern follows the “90/10" rule, it requires many structures and an excessive amount of pointer manipulations for efficiently processing and searching large documents. In this paper, we propose a new and conceptually powerful data structure, called splay suffix arrays (SSA), for string search. This data structure combines the features of splay tree and suffix arrays into a new approach which is suitable to implementation on both conventional and clustered computers.
Factors Influencing Rote Student's Intention to Use WBL: Thailand Study

Conventional WBL is effective for meaningful student, because rote student learn by repeating without thinking or trying to understand. It is impossible to have full benefit from conventional WBL. Understanding of rote student-s intention and what influences it becomes important. Poorly designed user interface will discourage rote student-s cultivation and intention to use WBL. Thus, user interface design is an important factor especially when WBL is used as comprehensive replacement of conventional teaching. This research proposes the influencing factors that can enhance student-s intention to use the system. The enhanced TAM is used for evaluating the proposed factors. The research result points out that factors influencing rote student-s intention are Perceived Usefulness of Homepage Content Structure, Perceived User Friendly Interface, Perceived Hedonic Component, and Perceived (homepage) Visual Attractiveness.

Semantic Web Agent Communication Capable of Reasoning with Ontology and Agent Locations
Multi-agent communication of Semantic Web information cannot be realized without the need to reason with ontology and agent locations. This is because for an agent to be able to reason with an external semantic web ontology, it must know where and how to access to that ontology. Similarly, for an agent to be able to communicate with another agent, it must know where and how to send a message to that agent. In this paper we propose a framework of an agent which can reason with ontology and agent locations in order to perform reasoning with multiple distributed ontologies and perform communication with other agents on the semantic web. The agent framework and its communication mechanism are formulated entirely in meta-logic.
Word Stemming Algorithms and Retrieval Effectiveness in Malay and Arabic Documents Retrieval Systems
Documents retrieval in Information Retrieval Systems (IRS) is generally about understanding of information in the documents concern. The more the system able to understand the contents of documents the more effective will be the retrieval outcomes. But understanding of the contents is a very complex task. Conventional IRS apply algorithms that can only approximate the meaning of document contents through keywords approach using vector space model. Keywords may be unstemmed or stemmed. When keywords are stemmed and conflated in retrieving process, we are a step forwards in applying semantic technology in IRS. Word stemming is a process in morphological analysis under natural language processing, before syntactic and semantic analysis. We have developed algorithms for Malay and Arabic and incorporated stemming in our experimental systems in order to measure retrieval effectiveness. The results have shown that the retrieval effectiveness has increased when stemming is used in the systems.
Enhancing Retrieval Effectiveness of Malay Documents by Exploiting Implicit Semantic Relationship between Words

Phrases has a long history in information retrieval, particularly in commercial systems. Implicit semantic relationship between words in a form of BaseNP have shown significant improvement in term of precision in many IR studies. Our research focuses on linguistic phrases which is language dependent. Our results show that using BaseNP can improve performance although above 62% of words formation in Malay Language based on derivational affixes and suffixes.

Object-Oriented Programming Strategies in C# for Power Conscious System

Low power consumption is a major constraint for battery-powered system like computer notebook or PDA. In the past, specialists usually designed both specific optimized equipments and codes to relief this concern. Doing like this could work for quite a long time, however, in this era, there is another significant restraint, the time to market. To be able to serve along the power constraint while can launch products in shorter production period, objectoriented programming (OOP) has stepped in to this field. Though everyone knows that OOP has quite much more overhead than assembly and procedural languages, development trend still heads to this new world, which contradicts with the target of low power consumption. Most of the prior power related software researches reported that OOP consumed much resource, however, as industry had to accept it due to business reasons, up to now, no papers yet had mentioned about how to choose the best OOP practice in this power limited boundary. This article is the pioneer that tries to specify and propose the optimized strategy in writing OOP software under energy concerned environment, based on quantitative real results. The language chosen for studying is C# based on .NET Framework 2.0 which is one of the trendy OOP development environments. The recommendation gotten from this research would be a good roadmap that can help developers in coding that well balances between time to market and time of battery.

A Study on RFID Privacy Mechanism using Mobile Phone
This paper is about hiding RFID tag identifier (ID) using handheld device like a cellular phone. By modifying the tag ID of objects periodically or manually using cellular phone built-in a RFID reader chip or with a external RFID reader device, we can prevent other people from gathering the information related with objects querying information server (like an EPC IS) with a tag ID or deriving the information from tag ID-s code structure or tracking the location of the objects and the owner of the objects. In this paper, we use a cryptographic algorithm for modification and restoring of RFID tag ID, and for one original tag ID, there are several different temporary tag ID, periodically.
Tag Broker Model for Protecting Privacy in RFID Environment

RFID system, in which we give identification number to each item and detect it with radio frequency, supports more variable service than barcode system can do. For example, a refrigerator with RFID reader and internet connection will automatically notify expiration of food validity to us. But, in spite of its convenience, RFID system has some security threats, because anybody can get ID information of item easily. One of most critical threats is privacy invasion. Existing privacy protection schemes or systems have been proposed, and these schemes or systems defend normal users from attempts that any attacker tries to get information using RFID tag value. But, these systems still have weakness that attacker can get information using analogous value instead of original tag value. In this paper, we mention this type of attack more precisely and suggest 'Tag Broker Model', which can defend it. Tag broker in this model translates original tag value to random value, and user can only get random value. Attacker can not use analogous tag value, because he/she is not able to know original one from it.

Information Security Risk in Financial Institutions
The history of technology and banking is examined as it relates to risk and technological determinism. It is proposed that the services that banks offer are determined by technology and that banks must adopt new technologies to be competitive. The adoption of technologies paradoxically forces the adoption of other new technologies to protect the bank from the increased risk of technology. This cycle will lead to bank examiners and regulators to focus on human behavior, not on the ever changing technology.
Surrogate based Evolutionary Algorithm for Design Optimization
Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.
Design of an Authentication Protocol for Secure Electronic Seals
Electronic seal is an electronic device to check the authenticity and integrity of freight containers at the point of arrival. While RFID-based eSeals are gaining more acceptances and there are also some standardization processes for these devices, a recent research revealed that the current RFID-based eSeals are vulnerable to various attacks. In this paper, we provide a feasible solution to enhance the security of active RFID-based eSeals. Our approach is to use an authentication and key agreement protocol between eSeal and reader device, enabling data encryption and integrity check. Our protocol is based on the use of block cipher AES, which is reasonable since a block cipher can also be used for many other security purposes including data encryption and pseudo-random number generation. Our protocol is very simple, and it is applicable to low-end active RFID eSeals.
A 10 Giga VPN Accelerator Board for Trust Channel Security System
This paper proposes a VPN Accelerator Board (VPN-AB), a virtual private network (VPN) protocol designed for trust channel security system (TCSS). TCSS supports safety communication channel between security nodes in internet. It furnishes authentication, confidentiality, integrity, and access control to security node to transmit data packets with IPsec protocol. TCSS consists of internet key exchange block, security association block, and IPsec engine block. The internet key exchange block negotiates crypto algorithm and key used in IPsec engine block. Security Association blocks setting-up and manages security association information. IPsec engine block treats IPsec packets and consists of networking functions for communication. The IPsec engine block should be embodied by H/W and in-line mode transaction for high speed IPsec processing. Our VPN-AB is implemented with high speed security processor that supports many cryptographic algorithms and in-line mode. We evaluate a small TCSS communication environment, and measure a performance of VPN-AB in the environment. The experiment results show that VPN-AB gets a performance throughput of maximum 15.645Gbps when we set the IPsec protocol with 3DES-HMAC-MD5 tunnel mode.
Enhancing the Peer-To-Peer Architecture with a Roaming Service and OWL
This paper addresses the problem of building a unified structure to describe a peer-to-peer system. Our approach uses the well-known notations in the P2P area, and provides a global architecture that puts a separation between the platform specific characteristics and the logical ones. In order to enable the navigation of the peer across platforms, a roaming layer is added. The latter provides a capability to define a unique identification of peer and assures the mapping between this identification and those used in each platform. The mapping task is assured by special wrapper. In addition, ontology is proposed to give a clear presentation of the structure of the P2P system without interesting in the content and the resource managed by the peer. The ontology is created according to the web semantic paradigm and using OWL language; so, the structure of the system is considered as a web resource.
Adaptation of State/Transition-Based Methods for Embedded System Testing

In this paper test generation methods and appropriate fault models for testing and analysis of embedded systems described as (extended) finite state machines ((E)FSMs) are presented. Compared to simple FSMs, EFSMs specify not only the control flow but also the data flow. Thus, we define a two-level fault model to cover both aspects. The goal of this paper is to reuse well-known FSM-based test generation methods for automation of embedded system testing. These methods have been widely used in testing and validation of protocols and communicating systems. In particular, (E)FSMs-based specification and testing is more advantageous because (E)FSMs support the formal semantic of already standardised formal description techniques (FDTs) despite of their popularity in the design of hardware and software systems.

Evaluation of Graph-based Analysis for Forest Fire Detections
Spatial outliers in remotely sensed imageries represent observed quantities showing unusual values compared to their neighbor pixel values. There have been various methods to detect the spatial outliers based on spatial autocorrelations in statistics and data mining. These methods may be applied in detecting forest fire pixels in the MODIS imageries from NASA-s AQUA satellite. This is because the forest fire detection can be referred to as finding spatial outliers using spatial variation of brightness temperature. This point is what distinguishes our approach from the traditional fire detection methods. In this paper, we propose a graph-based forest fire detection algorithm which is based on spatial outlier detection methods, and test the proposed algorithm to evaluate its applicability. For this the ordinary scatter plot and Moran-s scatter plot were used. In order to evaluate the proposed algorithm, the results were compared with the MODIS fire product provided by the NASA MODIS Science Team, which showed the possibility of the proposed algorithm in detecting the fire pixels.
A Copyright Protection Scheme for Color Images using Secret Sharing and Wavelet Transform

This paper proposes a copyright protection scheme for color images using secret sharing and wavelet transform. The scheme contains two phases: the share image generation phase and the watermark retrieval phase. In the generation phase, the proposed scheme first converts the image into the YCbCr color space and creates a special sampling plane from the color space. Next, the scheme extracts the features from the sampling plane using the discrete wavelet transform. Then, the scheme employs the features and the watermark to generate a principal share image. In the retrieval phase, an expanded watermark is first reconstructed using the features of the suspect image and the principal share image. Next, the scheme reduces the additional noise to obtain the recovered watermark, which is then verified against the original watermark to examine the copyright. The experimental results show that the proposed scheme can resist several attacks such as JPEG compression, blurring, sharpening, noise addition, and cropping. The accuracy rates are all higher than 97%.

Establishing of Education Strategy in New Technological Environments with using Student Feedback

According to the new developments in the field of information and communication technologies, the necessity arises for active use of these new technologies in education. It is clear that the integration of technology in education system will be different for primary-higher education or traditional- distance education. In this study, the subject of the integration of technology for distance education was discussed. The subject was taken from the viewpoint of students. With using the information of student feedback about education program in which new technological medias are used, how can survey variables can be separated into the factors as positive, negative and supporter and how can be redesigned education strategy of the higher education associations with the examining the variables of each determinated factor is explained. The paper concludes with the recommendations about the necessitity of working as a group of different area experts and using of numerical methods in establishing of education strategy to be successful.

Obfuscation Studio Executive
New software protection product called “Obfuscation Studio" is presented in the paper. Several obfuscating modules that are already implemented are described. Some theoretical data is presented, that shows the potency and effectiveness of described obfuscation methods. “Obfuscation Studio" is being implemented for protecting programs written for .NET platform, but the described methods can also be interesting for other applications.
Analysis and Prototyping of Biological Systems: the Abstract Biological Process Model
The aim of a biological model is to understand the integrated structure and behavior of complex biological systems as a function of the underlying molecular networks to achieve simulation and forecast of their operation. Although several approaches have been introduced to take into account structural and environment related features, relatively little attention has been given to represent the behavior of biological systems. The Abstract Biological Process (ABP) model illustrated in this paper is an object-oriented model based on UML (the standard object-oriented language). Its main objective is to bring into focus the functional aspects of the biological system under analysis.
Markov Game Controller Design Algorithms
Markov games are a generalization of Markov decision process to a multi-agent setting. Two-player zero-sum Markov game framework offers an effective platform for designing robust controllers. This paper presents two novel controller design algorithms that use ideas from game-theory literature to produce reliable controllers that are able to maintain performance in presence of noise and parameter variations. A more widely used approach for controller design is the H∞ optimal control, which suffers from high computational demand and at times, may be infeasible. Our approach generates an optimal control policy for the agent (controller) via a simple Linear Program enabling the controller to learn about the unknown environment. The controller is facing an unknown environment, and in our formulation this environment corresponds to the behavior rules of the noise modeled as the opponent. Proposed controller architectures attempt to improve controller reliability by a gradual mixing of algorithmic approaches drawn from the game theory literature and the Minimax-Q Markov game solution approach, in a reinforcement-learning framework. We test the proposed algorithms on a simulated Inverted Pendulum Swing-up task and compare its performance against standard Q learning.
Improving Air Temperature Prediction with Artificial Neural Networks
The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters.
Neural Networks Learning Improvement using the K-Means Clustering Algorithm to Detect Network Intrusions
In the present work, we propose a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism. The K-means algorithm is first applied to the training dataset to reduce the amount of samples to be presented to the neural network, by automatically selecting an optimal set of samples. The obtained results demonstrate that the proposed technique performs exceptionally in terms of both accuracy and computation time when applied to the KDD99 dataset compared to a standard learning schema that use the full dataset.
Grid Coordination with Marketmaker Agents
Market based models are frequently used in the resource allocation on the computational grid. However, as the size of the grid grows, it becomes difficult for the customer to negotiate directly with all the providers. Middle agents are introduced to mediate between the providers and customers and facilitate the resource allocation process. The most frequently deployed middle agents are the matchmakers and the brokers. The matchmaking agent finds possible candidate providers who can satisfy the requirements of the consumers, after which the customer directly negotiates with the candidates. The broker agents are mediating the negotiation with the providers in real time. In this paper we present a new type of middle agent, the marketmaker. Its operation is based on two parallel operations - through the investment process the marketmaker is acquiring resources and resource reservations in large quantities, while through the resale process it sells them to the customers. The operation of the marketmaker is based on the fact that through its global view of the grid it can perform a more efficient resource allocation than the one possible in one-to-one negotiations between the customers and providers. We present the operation and algorithms governing the operation of the marketmaker agent, contrasting it with the matchmaker and broker agents. Through a series of simulations in the task oriented domain we compare the operation of the three agents types. We find that the use of marketmaker agent leads to a better performance in the allocation of large tasks and a significant reduction of the messaging overhead.
Navigation of Multiple Mobile Robots using Rule-based-Neuro-Fuzzy Technique
This paper deals with motion planning of multiple mobile robots. Mobile robots working together to achieve several objectives have many advantages over single robot system. However, the planning and coordination between the mobile robots is extremely difficult. In the present investigation rule-based and rulebased- neuro-fuzzy techniques are analyzed for multiple mobile robots navigation in an unknown or partially known environment. The final aims of the robots are to reach some pre-defined goals. Based upon a reference motion, direction; distances between the robots and obstacles; and distances between the robots and targets; different types of rules are taken heuristically and refined later to find the steering angle. The control system combines a repelling influence related to the distance between robots and nearby obstacles and with an attracting influence between the robots and targets. Then a hybrid rule-based-neuro-fuzzy technique is analysed to find the steering angle of the robots. Simulation results show that the proposed rulebased- neuro-fuzzy technique can improve navigation performance in complex and unknown environments compared to this simple rulebased technique.
Control of Vibrations in Flexible Smart Structures using Fast Output Sampling Feedback Technique
This paper features the modeling and design of a Fast Output Sampling (FOS) Feedback control technique for the Active Vibration Control (AVC) of a smart flexible aluminium cantilever beam for a Single Input Single Output (SISO) case. Controllers are designed for the beam by bonding patches of piezoelectric layer as sensor / actuator to the master structure at different locations along the length of the beam by retaining the first 2 dominant vibratory modes. The entire structure is modeled in state space form using the concept of piezoelectric theory, Euler-Bernoulli beam theory, Finite Element Method (FEM) and the state space techniques by dividing the structure into 3, 4, 5 finite elements, thus giving rise to three types of systems, viz., system 1 (beam divided into 3 finite elements), system 2 (4 finite elements), system 3 (5 finite elements). The effect of placing the sensor / actuator at various locations along the length of the beam for all the 3 types of systems considered is observed and the conclusions are drawn for the best performance and for the smallest magnitude of the control input required to control the vibrations of the beam. Simulations are performed in MATLAB. The open loop responses, closed loop responses and the tip displacements with and without the controller are obtained and the performance of the proposed smart system is evaluated for vibration control.
The Application of an Ensemble of Boosted Elman Networks to Time Series Prediction: A Benchmark Study

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Building Relationship Network for Machine Analysis from Wear Debris Measurements
Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear debris analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self-organizing maps. This is achieved using relationship measurements among corresponding attributes of various measurements for wear debris. Finally, visualization technique is proposed that helps the viewer in understanding and utilizing these relationships that enable accurate diagnostics.
On the Efficient Implementation of a Serial and Parallel Decomposition Algorithm for Fast Support Vector Machine Training Including a Multi-Parameter Kernel

This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Genetic Programming Approach for Multi-Category Pattern Classification Appliedto Network Intrusions Detection
This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher-s Iris dataset, and then, the KDD-99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods [1],[2] and [3], and give a very accepted results compared to other existing techniques proposed in [4],[5],[6],[7] and [8].
A Neurofuzzy Learning and its Application to Control System

A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Rapid Finite-Element Based Airport Pavement Moduli Solutions using Neural Networks
This paper describes the use of artificial neural networks (ANN) for predicting non-linear layer moduli of flexible airfield pavements subjected to new generation aircraft (NGA) loading, based on the deflection profiles obtained from Heavy Weight Deflectometer (HWD) test data. The HWD test is one of the most widely used tests for routinely assessing the structural integrity of airport pavements in a non-destructive manner. The elastic moduli of the individual pavement layers backcalculated from the HWD deflection profiles are effective indicators of layer condition and are used for estimating the pavement remaining life. HWD tests were periodically conducted at the Federal Aviation Administration-s (FAA-s) National Airport Pavement Test Facility (NAPTF) to monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test gear trafficking on the structural condition of flexible pavement sections. In this study, a multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function. The synthetic database generated using an advanced non-linear pavement finite-element program was used to train the ANN to overcome the limitations associated with conventional pavement moduli backcalculation. The changes in ANN-based backcalculated pavement moduli with trafficking were used to compare the relative severity effects of the aircraft landing gears on the NAPTF test pavements.
Development of Heterogeneous Parallel Genetic Simulated Annealing Using Multi-Niche Crowding
In this paper, a new hybrid of genetic algorithm (GA) and simulated annealing (SA), referred to as GSA, is presented. In this algorithm, SA is incorporated into GA to escape from local optima. The concept of hierarchical parallel GA is employed to parallelize GSA for the optimization of multimodal functions. In addition, multi-niche crowding is used to maintain the diversity in the population of the parallel GSA (PGSA). The performance of the proposed algorithms is evaluated against a standard set of multimodal benchmark functions. The multi-niche crowding PGSA and normal PGSA show some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder genetic algorithm (BGA).
Multi-Context Recurrent Neural Network for Time Series Applications

this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.

Auto-regressive Recurrent Neural Network Approach for Electricity Load Forecasting

this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects the demand of energy load. So the AR-MCRN is used to learn the relationship between past, previous, and future exogenous and endogenous variables. Experimental results show that using the change in weather components and the change that occurred in past load as inputs to the AR-MCRN, rather than the basic weather parameters and past load itself as inputs to the same network, produce higher accuracy of predicted load. Experimental results also show that using exogenous and endogenous variables as inputs is better than using only the exogenous variables as inputs to the network.

Replicating Data Objects in Large-scale Distributed Computing Systems using Extended Vickrey Auction
This paper proposes a novel game theoretical technique to address the problem of data object replication in largescale distributed computing systems. The proposed technique draws inspiration from computational economic theory and employs the extended Vickrey auction. Specifically, players in a non-cooperative environment compete for server-side scarce memory space to replicate data objects so as to minimize the total network object transfer cost, while maintaining object concurrency. Optimization of such a cost in turn leads to load balancing, fault-tolerance and reduced user access time. The method is experimentally evaluated against four well-known techniques from the literature: branch and bound, greedy, bin-packing and genetic algorithms. The experimental results reveal that the proposed approach outperforms the four techniques in both the execution time and solution quality.
Measuring the Structural Similarity of Web-based Documents: A Novel Approach

Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOM-Trees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of web-based documents represented by so called generalized trees which are more general than DOM-Trees which represent only directed rooted trees.We will design a new similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, we first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based documents.

No one Set of Parameter Values Can Simulate the Epidemics Due to SARS Occurring at Different Localities

A mathematical model for the transmission of SARS is developed. In addition to dividing the population into susceptible (high and low risk), exposed, infected, quarantined, diagnosed and recovered classes, we have included a class called untraced. The model simulates the Gompertz curves which are the best representation of the cumulative numbers of probable SARS cases in Hong Kong and Singapore. The values of the parameters in the model which produces the best fit of the observed data for each city are obtained by using a differential evolution algorithm. It is seen that the values for the parameters needed to simulate the observed daily behaviors of the two epidemics are different.

Effect of Time Delay on the Transmission of Dengue Fever
The effect of a time delay on the transmission on dengue fever is studied. The time delay is due to the presence of an incubation period for the dengue virus to develop in the mosquito before the mosquito becomes infectious. The conditions for the existence of a Hopf bifurcation to limit cycle behavior are established. The conditions are different from the usual one and they are based on whether a particular third degree polynomial has positive real roots. A theorem for determining whether for a given set of parameter values, a critical delay time exist is given. It is found that for a set of realistic values of the parameters in the model, a Hopf bifurcation can not occur. For a set of unrealistic values of some of the parameters, it is shown that a Hopf bifurcation can occur. Numerical solutions using this last set show the trajectory of two of the variables making a transition from a spiraling orbit to a limit cycle orbit.
Eukaryotic Gene Prediction by an Investigation of Nonlinear Dynamical Modeling Techniques on EIIP Coded Sequences

Many digital signal processing, techniques have been used to automatically distinguish protein coding regions (exons) from non-coding regions (introns) in DNA sequences. In this work, we have characterized these sequences according to their nonlinear dynamical features such as moment invariants, correlation dimension, and largest Lyapunov exponent estimates. We have applied our model to a number of real sequences encoded into a time series using EIIP sequence indicators. In order to discriminate between coding and non coding DNA regions, the phase space trajectory was first reconstructed for coding and non-coding regions. Nonlinear dynamical features are extracted from those regions and used to investigate a difference between them. Our results indicate that the nonlinear dynamical characteristics have yielded significant differences between coding (CR) and non-coding regions (NCR) in DNA sequences. Finally, the classifier is tested on real genes where coding and non-coding regions are well known.

A Maximum Parsimony Model to Reconstruct Phylogenetic Network in Honey Bee Evolution
Phylogenies ; The evolutionary histories of groups of species are one of the most widely used tools throughout the life sciences, as well as objects of research with in systematic, evolutionary biology. In every phylogenetic analysis reconstruction produces trees. These trees represent the evolutionary histories of many groups of organisms, bacteria due to horizontal gene transfer and plants due to process of hybridization. The process of gene transfer in bacteria and hybridization in plants lead to reticulate networks, therefore, the methods of constructing trees fail in constructing reticulate networks. In this paper a model has been employed to reconstruct phylogenetic network in honey bee. This network represents reticulate evolution in honey bee. The maximum parsimony approach has been used to obtain this reticulate network.
Effect of the Seasonal Variation in the Extrinsic Incubation Period on the Long Term Behavior of the Dengue Hemorrhagic Fever Epidemic
The incidences of dengue hemorrhagic disease (DHF) over the long term exhibit a seasonal behavior. It has been hypothesized that these behaviors are due to the seasonal climate changes which in turn induce a seasonal variation in the incubation period of the virus while it is developing the mosquito. The standard dynamic analysis is applied for analysis the Susceptible-Exposed- Infectious-Recovered (SEIR) model which includes an annual variation in the length of the extrinsic incubation period (EIP). The presence of both asymptomatic and symptomatic infections is allowed in the present model. We found that dynamic behavior of the endemic state changes as the influence of the seasonal variation of the EIP becomes stronger. As the influence is further increased, the trajectory exhibits sustained oscillations when it leaves the chaotic region.
UD Covariance Factorization for Unscented Kalman Filter using Sequential Measurements Update
Extended Kalman Filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, not only it has difficulties arising from linearization but also many times it becomes numerically unstable because of computer round off errors that occur in the process of its implementation. To overcome linearization limitations, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. Kalman filter that uses UT for calculation of the first two statistical moments is called Unscented Kalman Filter (UKF). Square-root form of UKF (SRUKF) developed by Rudolph van der Merwe and Eric Wan to achieve numerical stability and guarantee positive semi-definiteness of the Kalman filter covariances. This paper develops another implementation of SR-UKF for sequential update measurement equation, and also derives a new UD covariance factorization filter for the implementation of UKF. This filter is equivalent to UKF but is computationally more efficient.
Data Transformation Services (DTS): Creating Data Mart by Consolidating Multi-Source Enterprise Operational Data
Trends in business intelligence, e-commerce and remote access make it necessary and practical to store data in different ways on multiple systems with different operating systems. As business evolve and grow, they require efficient computerized solution to perform data update and to access data from diverse enterprise business applications. The objective of this paper is to demonstrate the capability of DTS [1] as a database solution for automatic data transfer and update in solving business problem. This DTS package is developed for the sales of variety of plants and eventually expanded into commercial supply and landscaping business. Dimension data modeling is used in DTS package to extract, transform and load data from heterogeneous database systems such as MySQL, Microsoft Access and Oracle that consolidates into a Data Mart residing in SQL Server. Hence, the data transfer from various databases is scheduled to run automatically every quarter of the year to review the efficient sales analysis. Therefore, DTS is absolutely an attractive solution for automatic data transfer and update which meeting today-s business needs.
Dynamic Authenticated Secure Group Communication
Providing authentication for the messages exchanged between group members in addition to confidentiality is an important issue in Secure Group communication. We develop a protocol for Secure Authentic Communication where we address authentication for the group communication scheme proposed by Blundo et al. which only provides confidentiality. Authentication scheme used is a multiparty authentication scheme which allows all the users in the system to send and receive messages simultaneously. Our scheme is secure against colluding malicious parties numbering fewer than k.
String Searching in Dispersed Files using MDS Convolutional Codes
In this paper, we propose use of convolutional codes for file dispersal. The proposed method is comparable in complexity to the information Dispersal Algorithm proposed by M.Rabin and for particular choices of (non-binary) convolutional codes, is almost as efficient as that algorithm in terms of controlling expansion in the total storage. Further, our proposed dispersal method allows string search.
Chua’s Circuit Regulation Using a Nonlinear Adaptive Feedback Technique

Chua’s circuit is one of the most important electronic devices that are used for Chaos and Bifurcation studies. A central role of secure communication is devoted to it. Since the adaptive control is used vastly in the linear systems control, here we introduce a new trend of application of adaptive method in the chaos controlling field. In this paper, we try to derive a new adaptive control scheme for Chua’s circuit controlling because control of chaos is often very important in practical operations. The novelty of this approach is for sake of its robustness against the external perturbations which is simulated as an additive noise in all measured states and can be generalized to other chaotic systems. Our approach is based on Lyapunov analysis and the adaptation law is considered for the feedback gain. Because of this, we have named it NAFT (Nonlinear Adaptive Feedback Technique). At last, simulations show the capability of the presented technique for Chua’s circuit.

Optimal Switching Strategies for Tracking of Currents of Voltage Source Converters
This paper proposes a new optimal feedback controller for voltage source converters VSC's, for current regulated voltage source converters, which allows compensate the harmonics of current produced by nonlinear loads and load reactive power. The aim of the present paper is to describe a novel switching signal generation technique called optimal controller which guarantees that the injected currents follow the reference currents determined by the compensation strategy, with the smallest possible tracking error and fixed switching frequency. It is compared with well-known hysteresis current controller HCC. The validity of presented method and its comparison with HCC is studied through simulation results.
Analysis of Web User Identification Methods
Web usage mining has become a popular research area, as a huge amount of data is available online. These data can be used for several purposes, such as web personalization, web structure enhancement, web navigation prediction etc. However, the raw log files are not directly usable; they have to be preprocessed in order to transform them into a suitable format for different data mining tasks. One of the key issues in the preprocessing phase is to identify web users. Identifying users based on web log files is not a straightforward problem, thus various methods have been developed. There are several difficulties that have to be overcome, such as client side caching, changing and shared IP addresses and so on. This paper presents three different methods for identifying web users. Two of them are the most commonly used methods in web log mining systems, whereas the third on is our novel approach that uses a complex cookie-based method to identify web users. Furthermore we also take steps towards identifying the individuals behind the impersonal web users. To demonstrate the efficiency of the new method we developed an implementation called Web Activity Tracking (WAT) system that aims at a more precise distinction of web users based on log data. We present some statistical analysis created by the WAT on real data about the behavior of the Hungarian web users and a comprehensive analysis and comparison of the three methods
The Application of Six Sigma to Integration of Computer Based Systems

This paper introduces a process for the module level integration of computer based systems. It is based on the Six Sigma Process Improvement Model, where the goal of the process is to improve the overall quality of the system under development. We also present a conceptual framework that shows how this process can be implemented as an integration solution. Finally, we provide a partial implementation of key components in the conceptual framework.

Experimental Studies on the Mechanical Property of Laminated Bamboo in Thailand
A new generation product made from bamboo strips, known as laminated bamboo, has gained importance. The objective of this research was to experiment the effect of three factors on the mechanical property of laminated bamboo. The interested factors for experimental design were (A) four bamboo species, namely Bambusa blumeana Schultes (Pai See Suk), Dendrocalamus asper Backer (Pai Tong), Dendrocalamus hamiltonii Nees (Pai Hok) and Dendrocalamus sericeus Munro (Pai Sang Mon), (B) two types of glue adhesive, polyvinyl acetate emulsion (PVAC) fortified with urea-formaldehyde (UF) and urea-formaldehyde (UF) to make parallel-oriented bamboo strips laminates and (C) glue weight per strip area, 150 g/m2 and 190 g/m2. Experimental results showed that Dendrocalamus asper Backer (Pai Tong) and Dendrocalamus sericeus Munro (Pai Sang Mon) were best used for manufacturing due to their highest MOR and MOE. The amount of glue weight 150 g/m2 yielded higher MOR and MOE than the amount of glue weight 190 g/m2. At the conclusion, the laminated bamboo manufacturers can benefit from this research in order to select right materials according to strength, cost and accessibility.
Stealthy Network Transfer of Data
Users of computer systems may often require the private transfer of messages/communications between parties across a network. Information warfare and the protection and dominance of information in the military context is a prime example of an application area in which the confidentiality of data needs to be maintained. The safe transportation of critical data is therefore often a vital requirement for many private communications. However, unwanted interception/sniffing of communications is also a possibility. An elementary stealthy transfer scheme is therefore proposed by the authors. This scheme makes use of encoding, splitting of a message and the use of a hashing algorithm to verify the correctness of the reconstructed message. For this proof-of-concept purpose, the authors have experimented with the random sending of encoded parts of a message and the construction thereof to demonstrate how data can stealthily be transferred across a network so as to prevent the obvious retrieval of data.
Multiple Sensors and JPDA-IMM-UKF Algorithm for Tracking Multiple Maneuvering Targets
In this paper, we consider the problem of tracking multiple maneuvering targets using switching multiple target motion models. With this paper, we aim to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In this paper we propose to avoid the Extended Kalman filter because of its limitations and substitute it with the Unscented Kalman filter which seems to be more efficient especially according to the simulation results obtained with the nonlinear IMM algorithm (IMMUKF). To resolve the problem of data association, the JPDA approach is combined with the IMM-UKF algorithm, the derived algorithm is noted JPDA-IMM-UKF.
Sloshing Control in Tilting Phases of the Pouring Process
We propose a control design scheme that aims to prevent undesirable liquid outpouring and suppress sloshing during the forward and backward tilting phases of the pouring process, for the case of liquid containers carried by manipulators. The proposed scheme combines a partial inverse dynamics controller with a PID controller, tuned with the use of a “metaheuristic" search algorithm. The “metaheuristic" search algorithm tunes the PID controller based on simulation results of the plant-s linearization around the operating point corresponding to the critical tilting angle, where outpouring initiates. Liquid motion is modeled using the well-known pendulumtype model. However, the proposed controller does not require measurements of the liquid-s motion within the tank.
A Design-Based Cohesion Metric for Object-Oriented Classes
Class cohesion is an important object-oriented software quality attribute. It indicates how much the members in a class are related. Assessing the class cohesion and improving the class quality accordingly during the object-oriented design phase allows for cheaper management of the later phases. In this paper, the notion of distance between pairs of methods and pairs of attribute types in a class is introduced and used as a basis for introducing a novel class cohesion metric. The metric considers the methodmethod, attribute-attribute, and attribute-method direct interactions. It is shown that the metric gives more sensitive values than other well-known design-based class cohesion metrics.
Reliability of Digital FSO Links in Europe

The paper deals with an analysis of visibility records collected from 210 European airports to obtain a realistic estimation of the availability of Free Space Optical (FSO) data links. Commercially available optical links usually operate in the 850nm waveband. Thus the influence of the atmosphere on the optical beam and on the visible light is similar. Long-term visibility records represent an invaluable source of data for the estimation of the quality of service of FSO links. The model used characterizes both the statistical properties of fade depths and the statistical properties of individual fade durations. Results are presented for Italy, France, and Germany.

A Robust Wavelet-Based Watermarking Algorithm Using Edge Detection
In this paper, a robust watermarking algorithm using the wavelet transform and edge detection is presented. The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The watermark embedding process is carried over the subband coefficients that lie on edges, where distortions are less noticeable, with a subband level dependent strength. Also, the watermark is embedded to selected coefficients around edges, using a different scale factor for watermark strength, that are captured by a morphological dilation operation. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency to various attacks such as median filtering, Gaussian noise, JPEG compression and geometrical transformations.
A Study on the Mobile Web Generating using Element of User Experience
As mobile service's subscriber is increasing; mobile contents services are getting more and more variables. So, mobile contents development needs not only contents design but also guideline for just mobile. And when mobile contents are developed, it is important to pass the limit and restriction of the mobile. The restrictions of mobile are small browser and screen size, limited download size and uncomfortable navigation. So each contents of mobile guideline will be presented for user's usability, easy of development and consistency of rule. This paper will be proposed methodology which is each contents of mobile guideline. Mobile web will be developed by mobile guideline which I proposed.
Data Mining Techniques in Computer-Aided Diagnosis: Non-Invasive Cancer Detection
Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.
The System Architecture of the Open European Nephrology Science Centre

The amount and heterogeneity of data in biomedical research, notably in interdisciplinary research, requires new methods for the collection, presentation and analysis of information. Important data from laboratory experiments as well as patient trials are available but come out of distributed resources. The Charite Medical School in Berlin has established together with the German Research Foundation (DFG) a new information service center for kidney diseases and transplantation (Open European Nephrology Science Centre - OpEN.SC). The system is based on a service-oriented architecture (SOA) with main and auxiliary modules arranged in four layers. To improve the reuse and efficient arrangement of the services the functionalities are described as business processes using the standardised Business Process Execution Language (BPEL).

Study of Measures to Secure Video Phone Service Safety through a Preliminary Evaluationof the Information Security of the New IT Service
The rapid advance of communication technology is evolving the network environment into the broadband convergence network. Likewise, the IT services operated in the individual network are also being quickly converged in the broadband convergence network environment. VoIP and IPTV are two examples of such new services. Efforts are being made to develop the video phone service, which is an advanced form of the voice-oriented VoIP service. However, the new IT services will be subject to stability and reliability vulnerabilities if the relevant security issues are not answered during the convergence of the existing IT services currently being operated in individual networks within the wider broadband network environment. To resolve such problems, this paper attempts to analyze the possible threats and identify the necessary security measures before the deployment of the new IT services. Furthermore, it measures the quality of the encryption algorithm application example to describe the appropriate algorithm in order to present security technology that will have no negative impact on the quality of the video phone service.
Edge Detection with the Parametric Filtering Method (Comparison with Canny Method)
In this paper, a new method of image edge-detection and characterization is presented. “Parametric Filtering method" uses a judicious defined filter, which preserves the signal correlation structure as input in the autocorrelation of the output. This leads, showing the evolution of the image correlation structure as well as various distortion measures which quantify the deviation between two zones of the signal (the two Hamming signals) for the protection of an image edge.
A Rule-based Approach for Anomaly Detection in Subscriber Usage Pattern
In this report we present a rule-based approach to detect anomalous telephone calls. The method described here uses subscriber usage CDR (call detail record) data sampled over two observation periods: study period and test period. The study period contains call records of customers- non-anomalous behaviour. Customers are first grouped according to their similar usage behaviour (like, average number of local calls per week, etc). For customers in each group, we develop a probabilistic model to describe their usage. Next, we use maximum likelihood estimation (MLE) to estimate the parameters of the calling behaviour. Then we determine thresholds by calculating acceptable change within a group. MLE is used on the data in the test period to estimate the parameters of the calling behaviour. These parameters are compared against thresholds. Any deviation beyond the threshold is used to raise an alarm. This method has the advantage of identifying local anomalies as compared to techniques which identify global anomalies. The method is tested for 90 days of study data and 10 days of test data of telecom customers. For medium to large deviations in the data in test window, the method is able to identify 90% of anomalous usage with less than 1% false alarm rate.
Modeling of Random Variable with Digital Probability Hyper Digraph: Data-Oriented Approach
In this paper we introduce Digital Probability Hyper Digraph for modeling random variable as the hierarchical data-oriented model.
A Hybrid Search Algorithm for Solving Constraint Satisfaction Problems
In this paper we present a hybrid search algorithm for solving constraint satisfaction and optimization problems. This algorithm combines ideas of two basic approaches: complete and incomplete algorithms which also known as systematic search and local search algorithms. Different characteristics of systematic search and local search methods are complementary. Therefore we have tried to get the advantages of both approaches in the presented algorithm. The major advantage of presented algorithm is finding partial sound solution for complicated problems which their complete solution could not be found in a reasonable time. This algorithm results are compared with other algorithms using the well known n-queens problem.
Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform
This paper describes new computer vision algorithms that have been developed to track moving objects as part of a long-term study into the design of (semi-)autonomous vehicles. We present the results of a study to exploit variable kernels for tracking in video sequences. The basis of our work is the mean shift object-tracking algorithm; for a moving target, it is usual to define a rectangular target window in an initial frame, and then process the data within that window to separate the tracked object from the background by the mean shift segmentation algorithm. Rather than use the standard, Epanechnikov kernel, we have used a kernel weighted by the Chamfer distance transform to improve the accuracy of target representation and localization, minimising the distance between the two distributions in RGB color space using the Bhattacharyya coefficient. Experimental results show the improved tracking capability and versatility of the algorithm in comparison with results using the standard kernel. These algorithms are incorporated as part of a robot test-bed architecture which has been used to demonstrate their effectiveness.
Public R and D Risk and Risk Management Policy
R&D risk management has been suggested as one of the management approaches for accomplishing the goals of public R&D investment. The investment in basic science and core technology development is the essential roles of government for securing the social base needed for continuous economic growth. And, it is also an important role of the science and technology policy sectors to generate a positive environment in which the outcomes of public R&D can be diffused in a stable fashion by controlling the uncertainties and risk factors in advance that may arise during the application of such achievements to society and industry. Various policies have already been implemented to manage uncertainties and variables that may have negative impact on accomplishing public R& investment goals. But we may derive new policy measures for complementing the existing policies and for exploring progress direction by analyzing them in a policy package from the viewpoint of R&D risk management.
Preparation and Characterisation of Chemically Activated Almond Shells by Optimization of Adsorption Parameters for Removal of Chromium VI from Aqueous Solutions

Activated carbon was prepared from agricultural waste “almond (Prunus amygdalus) nut shells" by chemical activation with phosphoric acid as an activating agent at 450 °C for 24 hr soaking time. The physical and chemical properties were analyzed. The adsorption of chromium VI from aqueous solution on almond nut shell activated carbon (ASAC) was investigated. The adsorption process parameters pH, agitation speed, agitation time, adsorbent dose were optimized. 98% of Cr VI was sorbed at pH 2 and stirring speed 200 rpm.. Surface structure showed that ASAC has a spongy type structure showing large number of pores

Simulation Study of Radial Heat and Mass Transfer Inside a Fixed Bed Catalytic Reactor

A rigorous two-dimensional model is developed for simulating the operation of a less-investigated type steam reformer having a considerably lower operating Reynolds number, higher tube diameter, and non-availability of extra steam in the feed compared with conventional steam reformers. Simulation results show that reasonable predictions can only be achieved when certain correlations for wall to fluid heat transfer equations are applied. Due to severe operating conditions, in all cases, strong radial temperature gradients inside the reformer tubes have been found. Furthermore, the results show how a certain catalyst loading profile will affect the operation of the reformer.

OCR For Printed Urdu Script Using Feed Forward Neural Network
This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.
Development of Mechanical Properties of Self Compacting Concrete Contain Rice Husk Ash
Self-compacting concrete (SCC), a new kind of high performance concrete (HPC) have been first developed in Japan in 1986. The development of SCC has made casting of dense reinforcement and mass concrete convenient, has minimized noise. Fresh self-compacting concrete (SCC) flows into formwork and around obstructions under its own weight to fill it completely and self-compact (without any need for vibration), without any segregation and blocking. The elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. SCC mixes generally have a much higher content of fine fillers, including cement, and produce excessively high compressive strength concrete, which restricts its field of application to special concrete only. To use SCC mixes in general concrete construction practice, requires low cost materials to make inexpensive concrete. Rice husk ash (RHA) has been used as a highly reactive pozzolanic material to improve the microstructure of the interfacial transition zone (ITZ) between the cement paste and the aggregate in self compacting concrete. Mechanical experiments of RHA blended Portland cement concretes revealed that in addition to the pozzolanic reactivity of RHA (chemical aspect), the particle grading (physical aspect) of cement and RHA mixtures also exerted significant influences on the blending efficiency. The scope of this research was to determine the usefulness of Rice husk ash (RHA) in the development of economical self compacting concrete (SCC). The cost of materials will be decreased by reducing the cement content by using waste material like rice husk ash instead of. This paper presents a study on the development of Mechanical properties up to 180 days of self compacting and ordinary concretes with rice-husk ash (RHA), from a rice paddy milling industry in Rasht (Iran). Two different replacement percentages of cement by RHA, 10%, and 20%, and two different water/cementicious material ratios (0.40 and 0.35), were used for both of self compacting and normal concrete specimens. The results are compared with those of the self compacting concrete without RHA, with compressive, flexural strength and modulus of elasticity. It is concluded that RHA provides a positive effect on the Mechanical properties at age after 60 days. Base of the result self compacting concrete specimens have higher value than normal concrete specimens in all test except modulus of elasticity. Also specimens with 20% replacement of cement by RHA have the best performance.
A Study on Cement-Based Composite Containing Polypropylene Fibers and Finely Ground Glass Exposed to Elevated Temperatures
High strength concrete has been used in situations where it may be exposed to elevated temperatures. Numerous authors have shown the significant contribution of polypropylene fiber to the spalling resistance of high strength concrete. When cement-based composite that reinforced by polypropylene fibers heated up to 170 °C, polypropylene fibers readily melt and volatilize, creating additional porosity and small channels in to the matrix that cause the poor structure and low strength. This investigation develops on the mechanical properties of mortar incorporating polypropylene fibers exposed to high temperature. Also effects of different pozzolans on strength behaviour of samples at elevated temperature have been studied. To reach this purpose, the specimens were produced by partial replacement of cement with finely ground glass, silica fume and rice husk ash as high reactive pozzolans. The amount of this replacement was 10% by weight of cement to find the effects of pozzolans as a partial replacement of cement on the mechanical properties of mortars. In this way, lots of mixtures with 0%, 0.5%, 1% and 1.5% of polypropylene fibers were cast and tested for compressive and flexural strength, accordance to ASTM standard. After that specimens being heated to temperatures of 300, 600 °C, respectively, the mechanical properties of heated samples were tested. Mechanical tests showed significant reduction in compressive strength which could be due to polypropylene fiber melting. Also pozzolans improve the mechanical properties of sampels.
WDM-Based Storage Area Network (SAN) for Disaster Recovery Operations
This paper proposes a Wavelength Division Multiplexing (WDM) technology based Storage Area Network (SAN) for all type of Disaster recovery operation. It considers recovery when all paths failure in the network as well as the main SAN site failure also the all backup sites failure by the effect of natural disasters such as earthquakes, fires and floods, power outage, and terrorist attacks, as initially SAN were designed to work within distance limited environments[2]. Paper also presents a NEW PATH algorithm when path failure occurs. The simulation result and analysis is presented for the proposed architecture with performance consideration.
The Effectiveness of Ultrasound Treatment on the Germination Stimulation of Barley Seed and its Alpha-Amylase Activity
In the present study, the effects of ultrasound as emerging technology were investigated on germination stimulation, amount of alpha-amylase activity on dry barley seeds before steeping stage of malting process. All experiments were carried out at 20 KHz on the ultrasonic generator in 3 different ultrasonic intensities (20, 60 and 100% setting from total power of device) and time (5, 10 and 15 min) at constant temperature (30C). For determining the effects of these parameters on enzyme the Fuwa method assay based on the decreased staining value of blue starch–iodine complexes employed for measurement an activity. The results of these assays were analyzed by Qualitek4 software using the Taguchi statistical method to evaluate the factor-s effects on enzyme activity. It has been found that when malting barley is irradiated with an ultrasonic power, a stimulating effect occurs as to the enzyme activity.
A Real-Time Specific Weed Recognition System Using Statistical Methods
The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 90 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.
A Post Processing Method for Quantum Prime Factorization Algorithm based on Randomized Approach
Prime Factorization based on Quantum approach in two phases has been performed. The first phase has been achieved at Quantum computer and the second phase has been achieved at the classic computer (Post Processing). At the second phase the goal is to estimate the period r of equation xrN ≡ 1 and to find the prime factors of the composite integer N in classic computer. In this paper we present a method based on Randomized Approach for estimation the period r with a satisfactory probability and the composite integer N will be factorized therefore with the Randomized Approach even the gesture of the period is not exactly the real period at least we can find one of the prime factors of composite N. Finally we present some important points for designing an Emulator for Quantum Computer Simulation.
Modeling of Statistically Multiplexed Non Uniform Activity VBR Video
This paper reports the feasibility of the ARMA model to describe a bursty video source transmitting over a AAL5 ATM link (VBR traffic). The traffic represents the activity of the action movie "Lethal Weapon 3" transmitted over the ATM network using the Fore System AVA-200 ATM video codec with a peak rate of 100 Mbps and a frame rate of 25. The model parameters were estimated for a single video source and independently multiplexed video sources. It was found that the model ARMA (2, 4) is well-suited for the real data in terms of average rate traffic profile, probability density function, autocorrelation function, burstiness measure, and the pole-zero distribution of the filter model.
Burstiness Reduction of a Doubly Stochastic AR-Modeled Uniform Activity VBR Video
Stochastic modeling of network traffic is an area of significant research activity for current and future broadband communication networks. Multimedia traffic is statistically characterized by a bursty variable bit rate (VBR) profile. In this paper, we develop an improved model for uniform activity level video sources in ATM using a doubly stochastic autoregressive model driven by an underlying spatial point process. We then examine a number of burstiness metrics such as the peak-to-average ratio (PAR), the temporal autocovariance function (ACF) and the traffic measurements histogram. We found that the former measure is most suitable for capturing the burstiness of single scene video traffic. In the last phase of this work, we analyse statistical multiplexing of several constant scene video sources. This proved, expectedly, to be advantageous with respect to reducing the burstiness of the traffic, as long as the sources are statistically independent. We observed that the burstiness was rapidly diminishing, with the largest gain occuring when only around 5 sources are multiplexed. The novel model used in this paper for characterizing uniform activity video was thus found to be an accurate model.
Distillation Monitoring and Control using LabVIEW and SIMULINK Tools
LabVIEW and SIMULINK are two most widely used graphical programming environments for designing digital signal processing and control systems. Unlike conventional text-based programming languages such as C, Cµ and MATLAB, graphical programming involves block-based code developments, allowing a more efficient mechanism to build and analyze control systems. In this paper a LabVIEW environment has been employed as a graphical user interface for monitoring the operation of a controlled distillation column, by visualizing both the closed loop performance and the user selected control conditions, while the column dynamics has been modeled under the SIMULINK environment. This tool has been applied to the PID based decoupled control of a binary distillation column. By means of such integrated environments the control designer is able to monitor and control the plant behavior and optimize the response when both, the quality improvement of distillation products and the operation efficiency tasks, are considered.
Improved Segmentation of Speckled Images Using an Arithmetic-to-Geometric Mean Ratio Kernel
In this work, we improve a previously developed segmentation scheme aimed at extracting edge information from speckled images using a maximum likelihood edge detector. The scheme was based on finding a threshold for the probability density function of a new kernel defined as the arithmetic mean-to-geometric mean ratio field over a circular neighborhood set and, in a general context, is founded on a likelihood random field model (LRFM). The segmentation algorithm was applied to discriminated speckle areas obtained using simple elliptic discriminant functions based on measures of the signal-to-noise ratio with fractional order moments. A rigorous stochastic analysis was used to derive an exact expression for the cumulative density function of the probability density function of the random field. Based on this, an accurate probability of error was derived and the performance of the scheme was analysed. The improved segmentation scheme performed well for both simulated and real images and showed superior results to those previously obtained using the original LRFM scheme and standard edge detection methods. In particular, the false alarm probability was markedly lower than that of the original LRFM method with oversegmentation artifacts virtually eliminated. The importance of this work lies in the development of a stochastic-based segmentation, allowing an accurate quantification of the probability of false detection. Non visual quantification and misclassification in medical ultrasound speckled images is relatively new and is of interest to clinicians.
Fractal Shapes Description with Parametric L-systems and Turtle Algebra

In this paper, we propose a new method to describe fractal shapes using parametric l-systems. First we introduce scaling factors in the production rules of the parametric l-systems grammars. Then we decorticate these grammars with scaling factors using turtle algebra to show the mathematical relation between l-systems and iterated function systems (IFS). We demonstrate that with specific values of the scaling factors, we find the exact relationship established by Prusinkiewicz and Hammel between l-systems and IFS.

Response Surface Based Optimization of Toughness of Hybrid Polyamide 6 Nanocomposites

Toughening of polyamide 6 (PA6)/ Nanoclay (NC) nanocomposites with styrene-ethylene/butadiene-styrene copolymer (SEBS) using maleated styrene-ethylene/butadiene-styrene copolymer (mSEBS)/ as a compatibilizer were investigated by blending them in a co-rotating twin-screw extruder. Response surface method of experimental design was used for optimizing the material and processing parameters. Effect of four factors, including SEBS, mSEBS and NC contents as material variables and order of mixing as a processing factor, on toughness of hybrid nanocomposites were studied. All the prepared samples showed ductile behavior and low temperature Izod impact toughness of some of the hybrid nanocomposites demonstrated 900% improvement compared to the PA6 matrix while the modulus showed maximum enhancement of 20% compared to the pristine PA6 resin.

Fusion Filters Weighted by Scalars and Matrices for Linear Systems
An optimal mean-square fusion formulas with scalar and matrix weights are presented. The relationship between them is established. The fusion formulas are compared on the continuous-time filtering problem. The basic differential equation for cross-covariance of the local errors being the key quantity for distributed fusion is derived. It is shown that the fusion filters are effective for multi-sensor systems containing different types of sensors. An example demonstrating the reasonable good accuracy of the proposed filters is given.
Comparative study of the Genetic Algorithms and Hessians Method for Minimization of the Electric Power Production Cost
In this paper, we present a comparative study of the genetic algorithms and Hessian-s methods for optimal research of the active powers in an electric network of power. The objective function which is the performance index of production of electrical energy is minimized by satisfying the constraints of the equality type and inequality type initially by the Hessian-s methods and in the second time by the genetic Algorithms. The results found by the application of AG for the minimization of the electric production costs of power are very encouraging. The algorithms seem to be an effective technique to solve a great number of problems and which are in constant evolution. Nevertheless it should be specified that the traditional binary representation used for the genetic algorithms creates problems of optimization of management of the large-sized networks with high numerical precision.
Adaptive Gait Pattern Generation of Biped Robot based on Human's Gait Pattern Analysis

This paper proposes a method of adaptively generating a gait pattern of biped robot. The gait synthesis is based on human's gait pattern analysis. The proposed method can easily be applied to generate the natural and stable gait pattern of any biped robot. To analyze the human's gait pattern, sequential images of the human's gait on the sagittal plane are acquired from which the gait control values are extracted. The gait pattern of biped robot on the sagittal plane is adaptively generated by a genetic algorithm using the human's gait control values. However, gait trajectories of the biped robot on the sagittal plane are not enough to construct the complete gait pattern because the biped robot moves on 3-dimension space. Therefore, the gait pattern on the frontal plane, generated from Zero Moment Point (ZMP), is added to the gait one acquired on the sagittal plane. Consequently, the natural and stable walking pattern for the biped robot is obtained.

Influence of Textured Clusters on the Goss Grains Growth in Silicon Steels Consideration of Energy and Mobility
In the Fe-3%Si sheets, grade Hi-B, with AlN and MnS as inhibitors, the Goss grains which abnormally grow do not have a size greater than the average size of the primary matrix. In this heterogeneous microstructure, the size factor is not a required condition for the secondary recrystallization. The onset of the small Goss grain abnormal growth appears to be related to a particular behavior of their grain boundaries, to the local texture and to the distribution of the inhibitors. The presence and the evolution of oriented clusters ensure to the small Goss grains a favorable neighborhood to grow. The modified Monte-Carlo approach, which is applied, considers the local environment of each grain. The grain growth is dependent of its real spatial position; the matrix heterogeneity is then taken into account. The grain growth conditions are considered in the global matrix and in different matrixes corresponding to A component clusters. The grain growth behaviour is considered with introduction of energy only, energy and mobility, energy and mobility and precipitates.
Data Oriented Model of Image: as a Framework for Image Processing

This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.

Optical Road Monitoring of the Future Smart Roads – Preliminary Results

It has been shown that in most accidents the driver is responsible due to being distracted or misjudging the situation. In order to solve such problems research has been dedicated to developing driver assistance systems that are able to monitor the traffic situation around the vehicle. This paper presents methods for recognizing several circumstances on a road. The methods use both the in-vehicle warning systems and the roadside infrastructure. Preliminary evaluation results for fog and ice-on-road detection are presented. The ice detection results are based on data recorded in a test track dedicated to tyre friction testing. The achieved results anticipate that ice detection could work at a performance of 70% detection with the right setup, which is a good foundation for implementation. However, the full benefit of the presented cooperative system is achieved by fusing the outputs of multiple data sources, which is the key point of discussion behind this publication.

DRE - A Quality Metric for Component based Software Products
The overriding goal of software engineering is to provide a high quality system, application or a product. To achieve this goal, software engineers must apply effective methods coupled with modern tools within the context of a mature software process [2]. In addition, it is also must to assure that high quality is realized. Although many quality measures can be collected at the project levels, the important measures are errors and defects. Deriving a quality measure for reusable components has proven to be challenging task now a days. The results obtained from the study are based on the empirical evidence of reuse practices, as emerged from the analysis of industrial projects. Both large and small companies, working in a variety of business domains, and using object-oriented and procedural development approaches contributed towards this study. This paper proposes a quality metric that provides benefit at both project and process level, namely defect removal efficiency (DRE).
Combinatorial Optimisation of Worm Propagationon an Unknown Network
Worm propagation profiles have significantly changed since 2003-2004: sudden world outbreaks like Blaster or Slammer have progressively disappeared and slower but stealthier worms appeared since, most of them for botnets dissemination. Decreased worm virulence results in more difficult detection. In this paper, we describe a stealth worm propagation model which has been extensively simulated and analysed on a huge virtual network. The main features of this model is its ability to infect any Internet-like network in a few seconds, whatever may be its size while greatly limiting the reinfection attempt overhead of already infected hosts. The main simulation results shows that the combinatorial topology of routing may have a huge impact on the worm propagation and thus some servers play a more essential and significant role than others. The real-time capability to identify them may be essential to greatly hinder worm propagation.
Application of Exact String Matching Algorithms towards SMILES Representation of Chemical Structure

Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information. NMRShiftDB is one of the main databases that used to represent the chemical structures in 2D or 3D structures. SMILES format is one of many ways to write a chemical structure in a linear format. In this study we extracted Antimicrobial Structures in SMILES format from NMRShiftDB and stored it in our Local Data Warehouse with its corresponding information. Additionally, we developed a searching tool that would response to user-s query using the JME Editor tool that allows user to draw or edit molecules and converts the drawn structure into SMILES format. We applied Quick Search algorithm to search for Antimicrobial Structures in our Local Data Ware House.

The Effects of Software Size on Development Effort and Software Quality

Effective evaluation of software development effort is an important issue during project plan. This study provides a model to predict development effort based on the software size estimated with function points. We generalize the average amount of effort spent on each phase of the development, and give the estimates for the effort used in software building, testing, and implementation. Finally, this paper finds a strong correlation between software defects and software size. As the size of software constantly increases, the quality remains to be a matter which requires major concern.

Strategic Software Development: Productivity Comparisons of General Development Programs

Productivity has been one of the major concerns with the increasingly high cost of software development. Choosing the right development language with high productivity is one approach to reduce development costs. Working on the large database with 4106 projects ever developed, we found the factors significant to productivity. After the removal of the effects of other factors on productivity, we compare the productivity differences of the ten general development programs. The study supports the fact that fourth-generation languages are more productive than thirdgeneration languages.

Separation of Polyphenolics and Sugar by Ultrafiltration: Effects of Operating Conditions on Fouling and Diafiltration
Polyphenolics and sugar are the components of many fruit juices. In this work, the performance of ultra-filtration (UF) for separating phenolic compounds from apple juice was studied by performing batch experiments in a membrane module with an area of 0.1 m2 and fitted with a regenerated cellulose membrane of 1 kDa MWCO. The effects of various operating conditions: transmembrane pressure (3, 4, 5 bar), temperature (30, 35, 40 ºC), pH (2, 3, 4, 5), feed concentration (3, 5, 7, 10, 15 ºBrix for apple juice) and feed flow rate (1, 1.5, 1.8 L/min) on the performance were determined. The optimum operating conditions were: transmembrane pressure 4 bar, temperature 30 ºC, feed flow rate 1 – 1.8 L/min, pH 3 and 10 Brix (apple juice). After performing ultrafiltration under these conditions, the concentration of polyphenolics in retentate was increased by a factor of up to 2.7 with up to 70% recovered in the permeate and with approx. 20% of the sugar in that stream.. Application of diafiltration (addition of water to the concentrate) can regain the flux by a factor of 1.5, which has been decreased due to fouling. The material balance performed on the process has shown the amount of deposits on the membrane and the extent of fouling in the system. In conclusion, ultrafiltration has been demonstrated as a potential technology to separate the polyphenolics and sugars from their mixtures and can be applied to remove sugars from fruit juice.
Hydrothermal Behavior of G-S Magnetically Stabilized Beds Consisting of Magnetic and Non-Magnetic Admixtures
The hydrothermal behavior of a bed consisting of magnetic and shale oil particle admixtures under the effect of a transverse magnetic field is investigated. The phase diagram, bed void fraction are studied under wide range of the operating conditions i.e., gas velocity, magnetic field intensity and fraction of the magnetic particles. It is found that the range of the stabilized regime is reduced as the magnetic fraction decreases. In addition, the bed voidage at the onset of fluidization decreases as the magnetic fraction decreases. On the other hand, Nusselt number and consequently the heat transfer coefficient is found to increase as the magnetic fraction decreases. An empirical equation is investigated to relate the effect of the gas velocity, magnetic field intensity and fraction of the magnetic particles on the heat transfer behavior in the bed.
Industrial Compressor Anti-Surge Computer Control
The paper presents a compressor anti-surge control system, that results in maximizing compressor throughput with pressure standard deviation reduction, increased safety margin between design point and surge limit line and avoiding possible machine surge. Alternative control strategies are presented.
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