Parallel Genetic Algorithm Decoder Scheme Based on DP-LDPC codes for industrial IoT scenarios

The new concept of Industry 4.0 has been developed: it includes both Internet of Things (IoT) structure and the local networks that are still needed to carry out real-time tasks. Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances when solving large optimization problems. This article proposes a decoder based on parallel Genetic Algorithms (PGAD) for Decoding Low Density Parity Check (LDPC) codes. The proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency, the best performances are obtained for Ring Crossover (RC) as a type of crossover and the tournament as a type of selection. Furthermore, the performances of the new decoder are improved using Multi-criteria method. For the LDPC code, simulation results showed that our Proposed PGAD exceeds the sum-product by a gain of 1.5 dB at BER = 10-4, and the PGAWS exceeds the sum-product by 2.5 dB

Information Theoretic Methods For Biometrics, Clustering, And Stemmatology

This thesis consists of four parts, three of which study issues related to theories and applications of biometric systems, and one which focuses on clustering. We establish an information theoretic framework and the fundamental trade-off between utility of biometric systems and security of biometric systems. The utility includes person identification and secret binding, while template protection, privacy, and secrecy leakage are security issues addressed. A general model of biometric systems is proposed, in which secret binding and the use of passwords are incorporated. The system model captures major biometric system designs including biometric cryptosystems, cancelable biometrics, secret binding and secret generating systems, and salt biometric systems. In addition to attacks at the database, information leakage from communication links between sensor modules and databases is considered. A general information theoretic rate outer bound is derived for characterizing and comparing the fundamental capacity, and security risks and benefits of different system designs. We establish connections between linear codes to biometric systems, so that one can directly use a vast literature of coding theories of various noise and source random processes to achieve good performance in biometric systems. We develop two biometrics based on laser Doppler vibrometry: LDV) signals and electrocardiogram: ECG) signals. For both cases, changes in statistics of biometric traits of the same individual is the major challenge which obstructs many methods from producing satisfactory results. We propose a ii robust feature selection method that specifically accounts for changes in statistics. The method yields the best results both in LDV and ECG biometrics in terms of equal error rates in authentication scenarios. Finally, we address a different kind of learning problem from data called clustering. Instead of having a set of training data with true labels known as in identification problems, we study the problem of grouping data points without labels given, and its application to computational stemmatology. Since the problem itself has no true answer, the problem is in general ill-posed unless some regularization or norm is set to define the quality of a partition. We propose the use of minimum description length: MDL) principle for graphical based clustering. In the MDL framework, each data partitioning is viewed as a description of the data points, and the description that minimizes the total amount of bits to describe the data points and the model itself is considered the best model. We show that in synthesized data the MDL clustering works well and fits natural intuition of how data should be clustered. Furthermore, we developed a computational stemmatology method based on MDL, which achieves the best performance level in a large dataset

Group testing:an information theory perspective

The group testing problem concerns discovering a small number of defective items within a large population by performing tests on pools of items. A test is positive if the pool contains at least one defective, and negative if it contains no defectives. This is a sparse inference problem with a combinatorial flavour, with applications in medical testing, biology, telecommunications, information technology, data science, and more. In this monograph, we survey recent developments in the group testing problem from an information-theoretic perspective. We cover several related developments: efficient algorithms with practical storage and computation requirements, achievability bounds for optimal decoding methods, and algorithm-independent converse bounds. We assess the theoretical guarantees not only in terms of scaling laws, but also in terms of the constant factors, leading to the notion of the {\em rate} of group testing, indicating the amount of information learned per test. Considering both noiseless and noisy settings, we identify several regimes where existing algorithms are provably optimal or near-optimal, as well as regimes where there remains greater potential for improvement. In addition, we survey results concerning a number of variations on the standard group testing problem, including partial recovery criteria, adaptive algorithms with a limited number of stages, constrained test designs, and sublinear-time algorithms.Comment: Survey paper, 140 pages, 19 figures. To be published in Foundations and Trends in Communications and Information Theor

Journal of Telecommunications and Information Technology

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