Enhancing the Error-Correcting Performance of LDPC Codes through an Efficient Use of Decoding Iterations
The decoding of Low-Density Parity-Check (LDPC) codes is operated over a redundant structure known as the bipartite graph, meaning that the full set of bit nodes is not absolutely necessary for decoder convergence. In 2008, Soyjaudah and Catherine designed a recovery algorithm for LDPC codes based on this assumption and showed that the error-correcting performance of their codes outperformed conventional LDPC Codes. In this work, the use of the recovery algorithm is further explored to test the performance of LDPC codes while the number of iterations is progressively increased. For experiments conducted with small blocklengths of up to 800 bits and number of iterations of up to 2000, the results interestingly demonstrate that contrary to conventional wisdom, the error-correcting performance keeps increasing with increasing number of iterations.
Autonomous Robots- Visual Perception in Underground Terrains Using Statistical Region Merging
Robots- visual perception is a field that is gaining
increasing attention from researchers. This is partly due to emerging
trends in the commercial availability of 3D scanning systems or
devices that produce a high information accuracy level for a variety of
applications. In the history of mining, the mortality rate of mine workers
has been alarming and robots exhibit a great deal of potentials to
tackle safety issues in mines. However, an effective vision system
is crucial to safe autonomous navigation in underground terrains.
This work investigates robots- perception in underground terrains
(mines and tunnels) using statistical region merging (SRM) model.
SRM reconstructs the main structural components of an imagery
by a simple but effective statistical analysis. An investigation is
conducted on different regions of the mine, such as the shaft, stope
and gallery, using publicly available mine frames, with a stream of
locally captured mine images. An investigation is also conducted on a
stream of underground tunnel image frames, using the XBOX Kinect
3D sensors. The Kinect sensors produce streams of red, green and
blue (RGB) and depth images of 640 x 480 resolution at 30 frames per
second. Integrating the depth information to drivability gives a strong
cue to the analysis, which detects 3D results augmenting drivable and
non-drivable regions in 2D. The results of the 2D and 3D experiment
with different terrains, mines and tunnels, together with the qualitative
and quantitative evaluation, reveal that a good drivable region can be
detected in dynamic underground terrains.
Making Data Structures and Algorithms more Understandable by Programming Sudoku the Human Way
Data Structures and Algorithms is a module in most
Computer Science or Information Technology curricula. It is one of
the modules most students identify as being difficult. This paper
demonstrates how programming a solution for Sudoku can make
abstract concepts more concrete. The paper relates concepts of a
typical Data Structures and Algorithms module to a step by step
solution for Sudoku in a human type as opposed to a computer
A Discrete Choice Modeling Approach to Modular Systems Design
The paper proposes an approach for design of modular
systems based on original technique for modeling and formulation of
combinatorial optimization problems. The proposed approach is
described on the example of personal computer configuration design.
It takes into account the existing compatibility restrictions between
the modules and can be extended and modified to reflect different
functional and users- requirements. The developed design modeling
technique is used to formulate single objective nonlinear mixedinteger
optimization tasks. The practical applicability of the
developed approach is numerically tested on the basis of real modules
data. Solutions of the formulated optimization tasks define the
optimal configuration of the system that satisfies all compatibility
restrictions and user requirements.
Evaluation of a PSO Approach for Optimum Design of a First-Order Controllers for TCP/AQM Systems
This paper presents a Particle Swarm Optimization
(PSO) method for determining the optimal parameters of a first-order
controller for TCP/AQM system. The model TCP/AQM is described
by a second-order system with time delay. First, the analytical
approach, based on the D-decomposition method and Lemma of
Kharitonov, is used to determine the stabilizing regions of a firstorder
controller. Second, the optimal parameters of the controller are
obtained by the PSO algorithm. Finally, the proposed method is
implemented in the Network Simulator NS-2 and compared with the
Courses Pre-Required Visualization Using Force Directed Placement Technique
Visualizing “Courses – Pre – Required -
Architecture" on the screen has proven to be useful and helpful for
university actors and specially for students. In fact, these students
can easily identify courses and their pre required, perceive the
courses to follow in the future, and then can choose rapidly the
appropriate course to register in. Given a set of courses and their prerequired,
we present an algorithm for visualization a graph entitled
“Courses-Pre-Required-Graph" that present courses and their prerequired
in order to help students to recognize, lonely, what courses
to take in the future and perceive the contain of all courses that they
will study. Our algorithm using “Force Directed Placement"
technique visualizes the “Courses-Pre-Required-Graph" in such way
that courses are easily identifiable. The time complexity of our
drawing algorithm is O (n2), where n is the number of courses in the
Easy-Interactive Ordering of the Pareto Optimal Set with Imprecise Weights
In the multi objective optimization, in the case when generated set of Pareto optimal solutions is large, occurs the problem to select of the best solution from this set. In this paper, is suggested a method to order of Pareto set. Ordering the Pareto optimal set carried out in conformity with the introduced distance function between each solution and selected reference point, where the reference point may be adjusted to represent the preferences of a decision making agent. Preference information about objective weights from a decision maker may be expressed imprecisely. The developed elicitation procedure provides an opportunity to obtain surrogate numerical weights for the objectives, and thus, to manage impreciseness of preference. The proposed method is a scalable to many objectives and can be used independently or as complementary to the various visualization techniques in the multidimensional case.
Fault Detection and Isolation using RBF Networks for Polymer Electrolyte Membrane Fuel Cell
This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an open-loop scheme. This method uses a radial basis function (RBF) neural network to perform fault identification, classification and isolation. The novelty is that the RBF model of independent mode is used to predict the future outputs of the FC stack. One actuator fault, one component fault and three sensor faults have been introduced to the PEMFC systems experience faults between -7% to +10% of fault size in real-time operation. To validate the results, a benchmark model developed by Michigan University is used in the simulation to investigate the effect of these five faults. The developed independent RBF model is tested on MATLAB R2009a/Simulink environment. The simulation results confirm the effectiveness of the proposed method for FDI under an open-loop condition. By using this method, the RBF networks able to detect and isolate all five faults accordingly and accurately.
Precision Control of Single-Phase PWM Inverter Using M68HC11E Microcontroller
Induction motors are being used in greater numbers
throughout a wide variety of industrial and commercial applications
because it provides many benefits and reliable device to convert the
electrical energy into mechanical motion. In some application it-s
desired to control the speed of the induction motor. Because of the
physics of the induction motor the preferred method of controlling its
speed is to vary the frequency of the AC voltage driving the motor. In
recent years, with the microcontroller incorporated into an appliance
it becomes possible to use it to generate the variable frequency AC
voltage to control the speed of the induction motor.
This study investigates the microcontroller based variable
frequency power inverter. the microcontroller is provide the variable
frequency pulse width modulation (PWM) signal that control the
applied voltage on the gate drive, which is provides the required
PWM frequency with less harmonics at the output of the power
The fully controlled bridge voltage source inverter has been
implemented with semiconductors power devices isolated gate
bipolar transistor (IGBT), and the PWM technique has been
employed in this inverter to supply the motor with AC voltage.
The proposed drive system for three & single phase power inverter
is simulated using Matlab/Simulink. The Matlab Simulation Results
for the proposed system were achieved with different SPWM. From
the result a stable variable frequency inverter over wide range has
been obtained and a good agreement has been found between the
simulation and hardware of a microcontroller based single phase
Research on Hybrid Neural Network in Intrusion Detection System
This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.
Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features
This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
Enhanced Conference Organization Based On Correlation of Web Information and Ontology Based Expertise Search
From the importance of the conference and its
constructive role in the studies discussion, there must be a strong
organization that allows the exploitation of the discussions in opening
new horizons. The vast amount of information scattered across the
web, make it difficult to find experts, who can play a prominent role
in organizing conferences. In this paper we proposed a new approach
of extracting researchers- information from various Web resources
and correlating them in order to confirm their correctness. As a
validator of this approach, we propose a service that will be useful to
set up a conference. Its main objective is to find appropriate experts,
as well as the social events for a conference. For this application we
us Semantic Web technologies like RDF and ontology to represent
the confirmed information, which are linked to another ontology
(skills ontology) that are used to present and compute the expertise.
Stereotype Student Model for an Adaptive e-Learning System
This paper describes a concept of stereotype student
model in adaptive knowledge acquisition e-learning system. Defined
knowledge stereotypes are based on student's proficiency level and
on Bloom's knowledge taxonomy. The teacher module is responsible
for the whole adaptivity process: the automatic generation of
courseware elements, their dynamic selection and sorting, as well as
their adaptive presentation using templates for statements and
questions. The adaptation of courseware is realized according to
student-s knowledge stereotype.
Design of Low Power and High Speed Digital IIR Filter in 45nm with Optimized CSA for Digital Signal Processing Applications
In this paper, a design methodology to implement low-power and high-speed 2nd order recursive digital Infinite Impulse Response (IIR) filter has been proposed. Since IIR filters suffer from a large number of constant multiplications, the proposed method replaces the constant multiplications by using addition/subtraction and shift operations. The proposed new 6T adder cell is used as the Carry-Save Adder (CSA) to implement addition/subtraction operations in the design of recursive section IIR filter to reduce the propagation delay. Furthermore, high-level algorithms designed for the optimization of the number of CSA blocks are used to reduce the complexity of the IIR filter. The DSCH3 tool is used to generate the schematic of the proposed 6T CSA based shift-adds architecture design and it is analyzed by using Microwind CAD tool to synthesize low-complexity and high-speed IIR filters. The proposed design outperforms in terms of power, propagation delay, area and throughput when compared with MUX-12T, MCIT-7T based CSA adder filter design. It is observed from the experimental results that the proposed 6T based design method can find better IIR filter designs in terms of power and delay than those obtained by using efficient general multipliers.
High Level Synthesis of Digital Filters Based On Sub-Token Forwarding
High level synthesis (HLS) is a process which
generates register-transfer level design for digital systems from
behavioral description. There are many HLS algorithms and
commercial tools. However, most of these algorithms consider a
behavioral description for the system when a single token is
presented to the system. This approach does not exploit extra
hardware efficiently, especially in the design of digital filters where
common operations may exist between successive tokens. In this
paper, we modify the behavioral description to process multiple
tokens in parallel. However, this approach is unlike the full
processing that requires full hardware replication. It exploits the
presence of common operations between successive tokens. The
performance of the proposed approach is better than sequential
processing and approaches that of full parallel processing as the
hardware resources are increased.
Semantic Markup for Web Applications
In this paper we would like to introduce some of the
best practices of using semantic markup and its significance in the
success of web applications. Search engines are one of the best ways
to reach potential customers and are some of the main indicators of
web sites' fruitfulness. We will introduce the most important
semantic vocabularies which are used by Google and Yahoo.
Afterwards, we will explain the process of semantic markup
implementation and its significance for search engines and other
semantic markup consumers. We will describe techniques for slow
conceiving RDFa markup to our web application for collecting Call
for papers (CFP) announcements.
An Evaluation of Software Connection Methods for Heterogeneous Sensor Networks
The transfer rate of messages in distributed sensor network applications is a critical factor in a system's performance. The Sensor Abstraction Layer (SAL) is one such system. SAL is a middleware integration platform for abstracting sensor specific technology in order to integrate heterogeneous types of sensors in a network. SAL uses Java Remote Method Invocation (RMI) as its connection method, which has unsatisfying transfer rates, especially for streaming data. This paper analyses different connection methods to optimize data transmission in SAL by replacing RMI. Our results show that the most promising Java-based connections were frameworks for Java New Input/Output (NIO) including Apache MINA, JBoss Netty, and xSocket. A test environment was implemented to evaluate each respective framework based on transfer rate, resource usage, and scalability. Test results showed the most suitable connection method to improve data transmission in SAL JBoss Netty as it provides a performance enhancement of 68%.
Query Reformulation Guided by External Resource for Information Retrieval
Reformulating the user query is a technique that aims to improve the performance of an Information Retrieval System (IRS) in terms of precision and recall. This paper tries to evaluate the technique of query reformulation guided by an external resource for Arabic texts. To do this, various precision and recall measures were conducted and two corpora with different external resources like Arabic WordNet (AWN) and the Arabic Dictionary (thesaurus) of Meaning (ADM) were used. Examination of the obtained results will allow us to measure the real contribution of this reformulation technique in improving the IRS performance.
Empirical Evaluation of Performance Optimization Techniques Used in Mobile Applications
Mobile application development is different from regular application development due to the hardware resource limitations existed in the mobile platforms. In the mobile environment, the application needs to be optimized by the developer to produce optimal software with least overhead. This study discussed about performance optimization techniques that are employed in general application development, and how such techniques are performing on mobile platforms through some empirical evaluations on a mobile emulator, Nokia X3-02 and Nokia C5-03devices. The scope of the work is only confined to mobile platform based on Java Mobile edition architecture. The empirical results showed that techniques such as loop unrolling, dependency chain, and linearized getter and setter performed better by a factor of 3 to 7. Whereas declaration and initialization on the same line or separate line did not improve the performance.
Simultaneous Clustering and Feature Selection Method for Gene Expression Data
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work K-Means algorithms has been applied for clustering of Gene Expression Data. Further, rough set based Quick reduct algorithm has been applied for each cluster in order to select the most similar genes having high correlation. Then the ACV measure is used to evaluate the refined clusters and classification is used to evaluate the proposed method. They could identify compact clusters with feature selection method used to genes are selected.
Localization of Anatomical Landmarks in Head CT Images for Image to Patient Registration
The use of anatomical landmarks as a basis for image to patient registration is appealing because the registration may be performed retrospectively. We have previously proposed the use of two anatomical soft tissue landmarks of the head, the canthus (corner of the eye) and the tragus (a small, pointed, cartilaginous flap of the ear), as a registration basis for an automated CT image to patient registration system, and described their localization in patient space using close range photogrammetry. In this paper, the automatic localization of these landmarks in CT images, based on their curvature saliency and using a rule based system that incorporates prior knowledge of their characteristics, is described. Existing approaches to landmark localization in CT images are predominantly semi-automatic and primarily for localizing internal landmarks. To validate our approach, the positions of the landmarks localized automatically and manually in near isotropic CT images of 102 patients were compared. The average difference was 1.2mm (std = 0.9mm, max = 4.5mm) for the medial canthus and 0.8mm (std = 0.6mm, max = 2.6mm) for the tragus. The medial canthus and tragus can be automatically localized in CT images, with performance comparable to manual localization, based on the approach presented.
A Model for Test Case Selection in the Software-Development Life Cycle
Software maintenance is one of the essential processes of Software-Development Life Cycle. The main philosophies of retaining software concern the improvement of errors, the revision of codes, the inhibition of future errors, and the development in piece and capacity. While the adjustment has been employing, the software structure has to be retested to an upsurge a level of assurance that it will be prepared due to the requirements. According to this state, the test cases must be considered for challenging the revised modules and the whole software. A concept of resolving this problem is ongoing by regression test selection such as the retest-all selections, random/ad-hoc selection and the safe regression test selection. Particularly, the traditional techniques concern a mapping between the test cases in a test suite and the lines of code it executes. However, there are not only the lines of code as one of the requirements that can affect the size of test suite but including the number of functions and faulty versions. Therefore, a model for test case selection is developed to cover those three requirements by the integral technique which can produce the smaller size of the test cases when compared with the traditional regression selection techniques.