Decodificación de códigos LDPC en canal de Rayleigh con algoritmos genéticos

En este trabajo se propone la decodificación de Códigos Low Density Parity Check (LDPC) mediante un decodificador que utiliza una combinación de Algorimos Genéticos (GA, Genetic Algorithms) con Lógica Mayoritaria. La selección de GA obedece a la capacidad de los mismos para resolver problemas de optimización complejos, basándose en principios de la evolución natural de una población cuyos individuos se evalúan de acuerdo a una función de ajuste. El tipo de decodificación propuesto en este trabajo ha arrojado valores de Probabilidad Binaria de Error (BER, Bit Error Rate) comparables con la comportamiento del algoritmo tradicional de decodificación suma-producto para un canal de comunicaciones inalámbricas. La ventaja adicional de la decodificación propuesta frente a la tradicional es que no precisa conocer información de la relación Señal a Ruido en el canal.Sociedad Argentina de Informática e Investigación Operativ

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Proceedings of the Mobile Satellite Conference

A satellite-based mobile communications system provides voice and data communications to mobile users over a vast geographic area. The technical and service characteristics of mobile satellite systems (MSSs) are presented and form an in-depth view of the current MSS status at the system and subsystem levels. Major emphasis is placed on developments, current and future, in the following critical MSS technology areas: vehicle antennas, networking, modulation and coding, speech compression, channel characterization, space segment technology and MSS experiments. Also, the mobile satellite communications needs of government agencies are addressed, as is the MSS potential to fulfill them

Towards Efficient Intrusion Detection using Hybrid Data Mining Techniques

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but also helps classifiers make accurate decisions during attack recognition time, especially when coping with large-scale and heterogeneous data such as network traffic data. Furthermore, the continued evolution of network attack patterns has resulted in the emergence of zero-day cyber attacks, which nowadays has considered as a major challenge in cyber security. In this threat environment, traditional security protections like firewalls, anti-virus software, and virtual private networks are not always sufficient. With this in mind, most of the current intrusion detection systems (IDSs) are either signature-based, which has been proven to be insufficient in identifying novel attacks, or developed based on absolute datasets. Hence, a robust mechanism for detecting intrusions, i.e. anomaly-based IDS, in the big data setting has therefore become a topic of importance. In this dissertation, an empirical study has been conducted at the initial stage to identify the challenges and limitations in the current IDSs, providing a systematic treatment of methodologies and techniques. Next, a comprehensive IDS framework has been proposed to overcome the aforementioned shortcomings. First, a novel hybrid dimensionality reduction technique is proposed combining information gain (IG) and principal component analysis (PCA) methods with an ensemble classifier based on three different classification techniques, named IG-PCA-Ensemble. Experimental results show that the proposed dimensionality reduction method contributes more critical features and reduced the detection time significantly. The results show that the proposed IG-PCA-Ensemble approach has also exhibits better performance than the majority of the existing state-of-the-art approaches

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Optimización de cuantificadores vectoriales basada en algoritmos genéticos y técnicas heurísticas

El intrincado problema del diseño de cuantificadores vectoriales, esto es, la obtención de librerias de código con las que la codificación de señales tenga las menores distorsiones posibles, se hace aun mas complejo cuando son considerados los efectos del ruido en el canal, plasmados en la Tasa de Error por Bit (Bit Error Rate 0 BER). Muchos de los algoritmos de diseño de cuantificadores vectoriales, entre los que destaca el GLA (Algoritmo de Lloyd Generalizado), no son capaces de sortear los numerosos minimos locales subóptimos que presenta la funci6n de distorsión media de la cuantificaci6n en el espacio de las librerias de c6digo. Por ello es preciso ejecutarlos repetidas veces, partiendo de puntos de inicio diferentes. En esta Tesis se han querido explorar las posibilidades que brindan los Algoritmos Genéticos y otras tecnicas heuristicas en el diseño óptimo de cuantificadores vectoriales sujetos a errores de canal. Los Algoritmos Geneticos (AG) son procedimientos de optimizaci6n global iterativos y estocasticos inspirados en algunos mecanismos que rigen la dinamica de la Naturaleza, en particular la selección natural, la codificación genética y la reproducción heterosexual. Un AG contiene una población de individuos pertenecientes al espacio de posibles soluciones, que compiten entre si y evolucionan tratando de maximizar alguna función de prestaciones o minimizar alguna función de coste definida sobre ese espacio. Esta evoluci6n se basa en la selección de los mejores individuos y la eliminación de los peores, junto con diversos mecanismos para procrear nuevos individuos (hijos) a partir de los anteriormente seleccionados (padres). Se plantean tres metodos distintos: - El AGCV (Algoritmo Genetico para la Cuantificaci6n Vectorial): es un genetico en el que los individuos de la poblaci6n son tentativas librerias de vectores código. Para facilitar su evolución hacia puntos de minima distorsión, se incorpora, como mecanismo de busqueda local, el algoritmo GLA. - EI ARL (Algoritmo Refinado de Lloyd): es un algoritmo heuristico en el que se ejecuta sucesivas veces el algoritmo GLA, preservando siempre los mejores vectores c6digo hallados hasta el momento. A medida que el algoritmo progresa, el numero de vectores nuevos (no preservados) se va haciendo menor, con el objeto de que la busqueda vaya siendo progresivamente mas local. - El AHCV (Algoritmo Hibrido para la Cuantificación Vectorial): es otro genético en el que se parte de una libreria de códigos ya conocido, y lo que se optimiza es la asignación de los códigos binarios disponibles, a los vectores código de la libreria. Los dos primeros son sometidos a extensas pruebas de simulación y contrastados con tres algoritmos de reputado nombre, corrobonindose su adecuación al disefio de cuantificadores vectoriales. EI tercero se plantea como una tecnica posible, aun sin explorar ni probar exhaustivamente, que abre el camino a una nueva manera de utilizar los AG en este problema. Al margen de estos metodos, una segunda cuestión abordada en esta Tesis es la reformulación de los principios de la Cuantificación Vectorial cuando las condiciones del canal no se suponen fijas o bien conocidas, sino que son descritas mediante la función densidad de probabilidad del BER. A este respecto se determina analiticamente la nueva función de distorsión y las reglas de optimalidad para el diseño de cuantificadores óptimos. Esto constituye un nuevo punto de partida para el diseño de cuantificadores vectoriales con planteamientos mas realistas que los normalmente considerados.The involved problem of Vector Quantization (VQ) design, i. e. the search for codebooks which yield as minimum distortions as possible, turns even more complicated when channel noise effects, characterised by the Bit Error Rate (BER), are considered. Many VQ design techniques, included the most famous GLA (Generalized Lloyd Algorithm), are unable to avoid the sub-optimum local minima present in the quantification distortion function. Thus repeated executions, with different starting points are needed. In this Thesis the possibilities offered by Genetic Algorithms and other heuristic techniques for noisy channel VQ design are explored. Genetic Algorithms (GA) are stochastic and iterative global optimisation procedures, inspired on various mechanisms which rule Nature dynamics, such as natural selection, genetic coding and heterosexual reproduction. A GA contains a population of individuals belonging to the solution space, which compete each other and evolve towards maximisation of some performance function or minimisation of some cost function defined throughout this space. This evolution is carried out by means of selecting the fittest individuals in the population while removing the worst ones, as well as by several mechanisms for creating new individuals (offsprings) from selected ones (parents). Three new methods are proposed: - AGCV (Vector Quantization Genetic Algorithm): a GA in which individuals are tentative codebooks of the VQ scheme. To ease the evolution towards minimum distortion points, the GLA is included as a local search mechanism. - ARL (Lloyd Refinement Algorithm): an heuristic algorithm in which GLA is run several times, preserving the best codevectors encountered so far. As the algorithm progresses, the number of codevectors removed and replaced by new ones is decreases, making the search progressively local. - AHCV (Vector Quantization Hybrid Algorithm): another GA which starts from an initial fixed code book and tries to optimise the assignment of the available binary codes to the vectors in the code book. The two first ones are submitted to extensive simulation tests and compared to three well-reputed methods in the field, confirming their adequacy to the problem under study. The third one is given as a possible technique, without having been exhaustively explored or tested so far; it only leads the way to a new manner of using GA for VQ design. Apart from this, a second question faced in this Thesis is the reformulation of the VQ principles when channel conditions are not supposed fixed or well known, but are described by the probability density function of the BER. To this respect, a new distortion function and optimality laws are analytically determined. This constitutes a new starting point for VQ design with more realistic basis than normally considered

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Solid State Circuits Technologies

The evolution of solid-state circuit technology has a long history within a relatively short period of time. This technology has lead to the modern information society that connects us and tools, a large market, and many types of products and applications. The solid-state circuit technology continuously evolves via breakthroughs and improvements every year. This book is devoted to review and present novel approaches for some of the main issues involved in this exciting and vigorous technology. The book is composed of 22 chapters, written by authors coming from 30 different institutions located in 12 different countries throughout the Americas, Asia and Europe. Thus, reflecting the wide international contribution to the book. The broad range of subjects presented in the book offers a general overview of the main issues in modern solid-state circuit technology. Furthermore, the book offers an in depth analysis on specific subjects for specialists. We believe the book is of great scientific and educational value for many readers. I am profoundly indebted to the support provided by all of those involved in the work. First and foremost I would like to acknowledge and thank the authors who worked hard and generously agreed to share their results and knowledge. Second I would like to express my gratitude to the Intech team that invited me to edit the book and give me their full support and a fruitful experience while working together to combine this book

A MS-GS VQ codebook design for wireless image communication using genetic algorithms

An image compression technique is proposed that attempts to achieve both robustness to transmission bit errors common to wireless image communication, as well as sufficient visual quality of the reconstructed images, Error robustness is achieved by using biorthogonal wavelet subband image coding with multistage gain-shape vector quantization (MS-GS VQ) which uses three stages of signal decomposition in an attempt to reduce the effect of transmission bit errors by distributing image information among many blocks, Good visual quality of the reconstructed images is obtained by applying genetic algorithms (GA's) to codebook generation to produce reconstruction capabilities that are superior to the conventional techniques, The proposed decomposition scheme also supports the use of GA's because decomposition reduce the problem size, Some simulations for evaluating the performance of the proposed coding scheme on both transmission bit errors and distortions of the reconstructed images are performed. Simulation results show that the proposed MS-GS VQ with good codebooks designed by GA's provides not only better robustness to transmission bit errors but also higher peak signal-to-noise ratio even under high bit error rate conditions.X118sciescopu