Fingerprint Image Encryption Using a 2D Chaotic Map and Elliptic Curve Cryptography
Fingerprints are suitable as long-term markers of human identity since they provide detailed and unique individual features which are difficult to alter and durable over life time. In this paper, we propose an algorithm to encrypt and decrypt fingerprint images by using a specially designed Elliptic Curve Cryptography (ECC) procedure based on block ciphers. In addition, to increase the confusing effect of fingerprint encryption, we also utilize a chaotic-behaved method called Arnold Cat Map (ACM) for a 2D scrambling of pixel locations in our method. Experimental results are carried out with various types of efficiency and security analyses. As a result, we demonstrate that the proposed fingerprint encryption/decryption algorithm is advantageous in several different aspects including efficiency, security and flexibility. In particular, using this algorithm, we achieve a margin of about 0.1% in the test of Number of Pixel Changing Rate (NPCR) values comparing to the-state-of-the-art performances.
Network Coding with Buffer Scheme in Multicast for Broadband Wireless Network
Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.
A Proposed Hybrid Color Image Compression Based on Fractal Coding with Quadtree and Discrete Cosine Transform
Fractal based digital image compression is a specific
technique in the field of color image. The method is best suited for
irregular shape of image like snow bobs, clouds, flame of fire; tree
leaves images, depending on the fact that parts of an image often
resemble with other parts of the same image. This technique has
drawn much attention in recent years because of very high
compression ratio that can be achieved. Hybrid scheme incorporating
fractal compression and speedup techniques have achieved high
compression ratio compared to pure fractal compression. Fractal
image compression is a lossy compression method in which selfsimilarity
nature of an image is used. This technique provides high
compression ratio, less encoding time and fart decoding process. In
this paper, fractal compression with quad tree and DCT is proposed
to compress the color image. The proposed hybrid schemes require
four phases to compress the color image. First: the image is
segmented and Discrete Cosine Transform is applied to each block of
the segmented image. Second: the block values are scanned in a
zigzag manner to prevent zero co-efficient. Third: the resulting image
is partitioned as fractals by quadtree approach. Fourth: the image is
compressed using Run length encoding technique.
An Analysis of Compression Methods and Implementation of Medical Images in Wireless Network
The motivation of image compression technique is to reduce the irrelevance and redundancy of the image data in order to store or pass data in an efficient way from one place to another place. There are several types of compression methods available. Without the help of compression technique, the file size is knowingly larger, usually several megabytes, but by doing the compression technique, it is possible to reduce file size up to 10% as of the original without noticeable loss in quality. Image compression can be lossless or lossy. The compression technique can be applied to images, audio, video and text data. This research work mainly concentrates on methods of encoding, DCT, compression methods, security, etc. Different methodologies and network simulations have been analyzed here. Various methods of compression methodologies and its performance metrics has been investigated and presented in a table manner.
Image Steganography Using Least Significant Bit Technique
In any communication, security is the most important issue in today’s world. In this paper, steganography is the process of hiding the important data into other data, such as text, audio, video, and image. The interest in this topic is to provide availability, confidentiality, integrity, and authenticity of data. The steganographic technique that embeds hides content with unremarkable cover media so as not to provoke eavesdropper’s suspicion or third party and hackers. In which many applications of compression, encryption, decryption, and embedding methods are used for digital image steganography. Due to compression, the nose produces in the image. To sustain noise in the image, the LSB insertion technique is used. The performance of the proposed embedding system with respect to providing security to secret message and robustness is discussed. We also demonstrate the maximum steganography capacity and visual distortion.
A Multi-Level GA Search with Application to the Resource-Constrained Re-Entrant Flow Shop Scheduling Problem
Re-entrant scheduling is an important search problem
with many constraints in the flow shop. In the literature, a number of
approaches have been investigated from exact methods to
meta-heuristics. This paper presents a genetic algorithm that encodes
the problem as multi-level chromosomes to reflect the dependent
relationship of the re-entrant possibility and resource consumption.
The novel encoding way conserves the intact information of the data
and fastens the convergence to the near optimal solutions. To test the
effectiveness of the method, it has been applied to the
resource-constrained re-entrant flow shop scheduling problem.
Computational results show that the proposed GA performs better than
the simulated annealing algorithm in the measure of the makespan
Improving Protein-Protein Interaction Prediction by Using Encoding Strategies and Random Indices
A New features are extracted and compared to
improve the prediction of protein-protein interactions. The basic idea
is to select and use the best set of features from the Tensor matrices
that are produced by the frequency vectors of the protein sequences.
Three set of features are compared, the first set is based on the
indices that are the most common in the interacting proteins, the
second set is based on the indices that tend to be common in the
interacting and non-interacting proteins, and the third set is
constructed by using random indices. Moreover, three encoding
strategies are compared; that are based on the amino asides polarity,
structure, and chemical properties. The experimental results indicate
that the highest accuracy can be obtained by using random indices
with chemical properties encoding strategy and support vector
Design of Encoding Calculator Software for Huffman and Shannon-Fano Algorithms
This paper presents a design of source encoding
calculator software which applies the two famous algorithms in the
field of information theory- the Shannon-Fano and the Huffman
schemes. This design helps to easily realize the algorithms without
going into a cumbersome, tedious and prone to error manual
mechanism of encoding the signals during the transmission. The
work describes the design of the software, how it works, comparison
with related works, its efficiency, its usefulness in the field of
information technology studies and the future prospects of the
software to engineers, students, technicians and alike. The designed
“Encodia" software has been developed, tested and found to meet the
intended requirements. It is expected that this application will help
students and teaching staff in their daily doing of information theory
related tasks. The process is ongoing to modify this tool so that it can
also be more intensely useful in research activities on source coding.
Study of Efficiency and Capability LZW++ Technique in Data Compression
The purpose of this paper is to show efficiency and capability LZWµ in data compression. The LZWµ technique is enhancement from existing LZW technique. The modification the existing LZW is needed to produce LZWµ technique. LZW read one by one character at one time. Differ with LZWµ technique, where the LZWµ read three characters at one time. This paper focuses on data compression and tested efficiency and capability LZWµ by different data format such as doc type, pdf type and text type. Several experiments have been done by different types of data format. The results shows LZWµ technique is better compared to existing LZW technique in term of file size.
Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook
This paper presents a very simple and efficient
algorithm for codebook search, which reduces a great deal of
computation as compared to the full codebook search. The algorithm
is based on sorting and centroid technique for search. The results
table shows the effectiveness of the proposed algorithm in terms of
computational complexity. In this paper we also introduce a new
performance parameter named as Average fractional change in pixel
value as we feel that it gives better understanding of the closeness of
the image since it is related to the perception. This new performance
parameter takes into consideration the average fractional change in
each pixel value.
Solving an Extended Resource Leveling Problem with Multiobjective Evolutionary Algorithms
We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.
Color Image Segmentation Using Kekre-s Algorithm for Vector Quantization
In this paper we propose segmentation approach based
on Vector Quantization technique. Here we have used Kekre-s fast
codebook generation algorithm for segmenting low-altitude aerial
image. This is used as a preprocessing step to form segmented
homogeneous regions. Further to merge adjacent regions color
similarity and volume difference criteria is used. Experiments
performed with real aerial images of varied nature demonstrate that
this approach does not result in over segmentation or under
segmentation. The vector quantization seems to give far better results
as compared to conventional on-the-fly watershed algorithm.
A Comparison of Real Valued Transforms for Image Compression
In this paper we present simulation results for the
application of a bandwidth efficient algorithm (mapping algorithm)
to an image transmission system. This system considers three
different real valued transforms to generate energy compact
coefficients. First results are presented for gray scale and color image
transmission in the absence of noise. It is seen that the system
performs its best when discrete cosine transform is used. Also the
performance of the system is dominated more by the size of the
transform block rather than the number of coefficients transmitted or
the number of bits used to represent each coefficient. Similar results
are obtained in the presence of additive white Gaussian noise. The
varying values of the bit error rate have very little or no impact on
the performance of the algorithm. Optimum results are obtained for
the system considering 8x8 transform block and by transmitting 15
coefficients from each block using 8 bits.
Speech Data Compression using Vector Quantization
Mostly transforms are used for speech data
compressions which are lossy algorithms. Such algorithms are
tolerable for speech data compression since the loss in quality is not
perceived by the human ear. However the vector quantization (VQ)
has a potential to give more data compression maintaining the same
quality. In this paper we propose speech data compression algorithm
using vector quantization technique. We have used VQ algorithms
LBG, KPE and FCG. The results table shows computational
complexity of these three algorithms. Here we have introduced a new
performance parameter Average Fractional Change in Speech
Sample (AFCSS). Our FCG algorithm gives far better performance
considering mean absolute error, AFCSS and complexity as
compared to others.
Selective Intra Prediction Mode Decision for H.264/AVC Encoders
H.264/AVC offers a considerably higher improvement
in coding efficiency compared to other compression standards such
as MPEG-2, but computational complexity is increased significantly.
In this paper, we propose selective mode decision schemes for fast
intra prediction mode selection. The objective is to reduce the
computational complexity of the H.264/AVC encoder without
significant rate-distortion performance degradation. In our proposed
schemes, the intra prediction complexity is reduced by limiting the
luma and chroma prediction modes using the directional information
of the 16×16 prediction mode. Experimental results are presented to
show that the proposed schemes reduce the complexity by up to 78%
maintaining the similar PSNR quality with about 1.46% bit rate
increase in average.
Bridging the Gap Between CBR and VBR for H264 Standard
This paper provides a flexible way of controlling
Variable-Bit-Rate (VBR) of compressed digital video, applicable to
the new H264 video compression standard. The entire video
sequence is assessed in advance and the quantisation level is then set
such that bit rate (and thus the frame rate) remains within
predetermined limits compatible with the bandwidth of the
transmission system and the capabilities of the remote end, while at
the same time providing constant quality similar to VBR encoding.
A process for avoiding buffer starvation by selectively eliminating
frames from the encoded output at times when the frame rate is slow
(large number of bits per frame) will be also described. Finally, the
problem of buffer overflow will be solved by selectively eliminating
frames from the received input to the decoder. The decoder detects
the omission of the frames and resynchronizes the transmission by
monitoring time stamps and repeating frames if necessary.
, Video Streaming
, Digital Video
Database Compression for Intelligent On-board Vehicle Controllers
The vehicle fleet of public transportation companies is often equipped with intelligent on-board passenger information systems. A frequently used but time and labor-intensive way for keeping the on-board controllers up-to-date is the manual update using different memory cards (e.g. flash cards) or portable computers. This paper describes a compression algorithm that enables data transmission using low bandwidth wireless radio networks (e.g. GPRS) by minimizing the amount of data traffic. In typical cases it reaches a compression rate of an order of magnitude better than that of the general purpose compressors. Compressed data can be easily expanded by the low-performance controllers, too.
Encoding and Compressing Data for Decreasing Number of Switches in Baseline Networks
This method decrease usage power (expenditure) in networks on chips (NOC). This method data coding for data transferring in order to reduces expenditure. This method uses data compression reduces the size. Expenditure calculation in NOC occurs inside of NOC based on grown models and transitive activities in entry ports. The goal of simulating is to weigh expenditure for encoding, decoding and compressing in Baseline networks and reduction of switches in this type of networks. KeywordsNetworks on chip, Compression, Encoding, Baseline networks, Banyan networks.
2D Bar Codes Reading: Solutions for Camera Phones
Two-dimensional (2D) bar codes were designed to
carry significantly more data with higher information density and
robustness than its 1D counterpart. Thanks to the popular
combination of cameras and mobile phones, it will naturally bring
great commercial value to use the camera phone for 2D bar code
reading. This paper addresses the problem of specific 2D bar code
design for mobile phones and introduces a low-level encoding
method of matrix codes. At the same time, we propose an efficient
scheme for 2D bar codes decoding, of which the effort is put on
solutions of the difficulties introduced by low image quality that is
very common in bar code images taken by a phone camera.