Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest, parameters of common interest to a subset of nodes and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.Comment: 13 pages, 6 figure

Randomized Computations for Efficient and Robust Finite Element Domain Decomposition Methods in Electromagnetics

Numerical modeling of electromagnetic (EM) phenomenon has proved to become an effective and efficient tool in design and optimization of modern electronic devices, integrated circuits (IC) and RF systems. However the generality, efficiency and reliability/resilience of the computational EM solver is often criticised due to the fact that the underlying characteristics of the simulated problems are usually different, which makes the development of a general, \u27\u27black-box\u27\u27 EM solver to be a difficult task. In this work, we aim to propose a reliable/resilient, scalable and efficient finite elements based domain decomposition method (FE-DDM) as a general CEM solver to tackle such ultimate CEM problems to some extent. We recognize the rank deficiency property of the Dirichlet-to-Neumann (DtN) operators involved in the previously proposed FETI-2$\lambda$ DDM formulation and apply such principle to improve the computational efficiency and robustness of FETI-2$\lambda$ DDM. Specifically, the rank deficient DtN operator is computed by a randomized computation method that was originally proposed to approximate matrix singular value decomposition (SVD). Numerical results show a up to 35\% run-time and 75% memory saving of the DtN operators computation can be achieved on a realistic example. Later, such rank deficiency principle is incorporated into a new global DDM preconditioner (W-FETI) that is inspired by the matrix Woodbury identity. Numerical study of the eigenspectrum shows the validity of the proposed W-FETI global preconditioner. Several industrial-scaled examples show significant iterative convergence advantage of W-FETI that uses 35%-80% matrix-vector-products (MxVs) than state-of-the-art DDM solvers

Performance Analysis of Adaptive Equalizers Over Multipath Faded Channels: Error Vector Magnitudes

Due to the increasing popularity of digital transmission systems, the need for channel equalizers has been acknowledged. These techniques are designed to counteract the effects of the inter-symbol interference (ISI) caused by the communication channels. An adaptive equalizer is used to operate on the output of a channel in order to provide an approximation of the transmission medium. An adaptive equalizer usually requires a training period to operate successfully. This method eliminates the effects of the wireless transmission channel and allows the subsequent symbol modulation. The paper presents an overview of the various adaptive equalizers, such as the least mean squares (LMS), decision feedback equalizers (DFE), and the Recursive least squares (RLS). It also explores their performance in terms of error vector magnitudes (EVM) over Rician and Rayleigh channels. The paper looks into the effects of adaptive equalizers on various digital modulation techniques for rectangular quadrature phase shift, amplitude modulation, such as the BPSK, QPSK, 4-QAM, 16-QAM, 64-QAM and 256-QAM. These modulations are analysed and measured in terms of symbol error rates and number of incorrect symbols

Entropy coder for audio signals

In the paper an effective entropy coder designed for coding of prediction errors of audio signals is presented. The coder is implemented inside a greater structure which signal modeling part is a lossless coding backward adaptation algorithm consisting of cascaded OLS and NLMS sections is presented. The technique performance is compared to that of 4 other lossless codecs, including MPEG-4 ALS one, and it is shown that indeed, the new method is the best one. The entropy coder is an advanced context adaptive Golomb one followed by two context adaptive arithmetic coders

Streaming and Sketch Algorithms for Large Data NLP

The availability of large and rich quantities of text data is due to the emergence of the World Wide Web, social media, and mobile devices. Such vast data sets have led to leaps in the performance of many statistically-based problems. Given a large magnitude of text data available, it is computationally prohibitive to train many complex Natural Language Processing (NLP) models on large data. This motivates the hypothesis that simple models trained on big data can outperform more complex models with small data. My dissertation provides a solution to effectively and efficiently exploit large data on many NLP applications. Datasets are growing at an exponential rate, much faster than increase in memory. To provide a memory-efficient solution for handling large datasets, this dissertation show limitations of existing streaming and sketch algorithms when applied to canonical NLP problems and proposes several new variants to overcome those shortcomings. Streaming and sketch algorithms process the large data sets in one pass and represent a large data set with a compact summary, much smaller than the full size of the input. These algorithms can easily be implemented in a distributed setting and provide a solution that is both memory- and time-efficient. However, the memory and time savings come at the expense of approximate solutions. In this dissertation, I demonstrate that approximate solutions achieved on large data are comparable to exact solutions on large data and outperform exact solutions on smaller data. I focus on many NLP problems that boil down to tracking many statistics, like storing approximate counts, computing approximate association scores like pointwise mutual information (PMI), finding frequent items (like n-grams), building streaming language models, and measuring distributional similarity. First, I introduce the concept of approximate streaming large-scale language models in NLP. Second, I present a novel variant of the Count-Min sketch that maintains approximate counts of all items. Third, I conduct a systematic study and compare many sketch algorithms that approximate count of items with focus on large-scale NLP tasks. Last, I develop fast large-scale approximate graph (FLAG), a system that quickly constructs a large-scale approximate nearest-neighbor graph from a large corpus

Distributed and Collaborative Processing of Audio Signals: Algorithms, Tools and Applications

Tesis por compendio[ES] Esta tesis se enmarca en el campo de las Tecnologías de la Información y las Comunicaciones (TIC), especialmente en el área del procesado digital de la señal. En la actualidad, y debido al auge del Internet de los cosas (IoT), existe un creciente interés por las redes de sensores inalámbricos (WSN), es decir, redes compuestas de diferentes tipos de dispositivos específicamente distribuidos en una determinada zona para realizar diferentes tareas de procesado de señal. Estos dispositivos o nodos suelen estar equipados con transductores electroacústicos así como con potentes y eficientes procesadores con capacidad de comunicación. En el caso particular de las redes de sensores acústicos (ASN), los nodos se dedican a resolver diferentes tareas de procesado de señales acústicas. El desarrollo de potentes sistemas de procesado centralizado han permitido aumentar el número de canales de audio, ampliar el área de control o implementar algoritmos más complejos. En la mayoría de los casos, una topología de ASN distribuida puede ser deseable debido a varios factores tales como el número limitado de canales utilizados por los dispositivos de adquisición y reproducción de audio, la conveniencia de un sistema escalable o las altas exigencias computacionales de los sistemas centralizados. Todos estos aspectos pueden llevar a la utilización de nuevas técnicas de procesado distribuido de señales con el fin de aplicarlas en ASNs. Para ello, una de las principales aportaciones de esta tesis es el desarrollo de algoritmos de filtrado adaptativo para sistemas de audio multicanal en redes distribuidas. Es importante tener en cuenta que, para aplicaciones de control del campo sonoro (SFC), como el control activo de ruido (ANC) o la ecualización activa de ruido (ANE), los nodos acústicos deben estar equipados con actuadores con el fin de controlar y modificar el campo sonoro. Sin embargo, la mayoría de las propuestas de redes distribuidas adaptativas utilizadas para resolver problemas de control del campo sonoro no tienen en cuenta que los nodos pueden interferir o modificar el comportamiento del resto. Por lo tanto, otra contribución destacable de esta tesis se centra en el análisis de cómo el sistema acústico afecta el comportamiento de los nodos dentro de una ASN. En los casos en que el entorno acústico afecta negativamente a la estabilidad del sistema, se han propuesto varias estrategias distribuidas para resolver el problema de interferencia acústica con el objetivo de estabilizar los sistemas de ANC. En el diseño de los algoritmos distribuidos también se han tenido en cuenta aspectos de implementación práctica. Además, con el objetivo de crear perfiles de ecualización diferentes en zonas de escucha independientes en presencia de ruidos multitonales, se han presentado varios algoritmos distribuidos de ANE en banda estrecha y banda ancha sobre una ASN con una comunicación colaborativa y compuesta por nodos acústicos. Se presentan además resultados experimentales para validar el uso de los algoritmos distribuidos propuestos en el trabajo para aplicaciones prácticas. Para ello, se ha diseñado un software de simulación acústica que permite analizar el rendimiento de los algoritmos desarrollados en la tesis. Finalmente, se ha realizado una implementación práctica que permite ejecutar aplicaciones multicanal de SFC. Para ello, se ha desarrollado un prototipo en tiempo real que controla las aplicaciones de ANC y ANE utilizando nodos acústicos colaborativos. El prototipo consiste en dos sistemas de control de audio personalizado (PAC) compuestos por un asiento de coche y un nodo acústico, el cual está equipado con dos altavoces, dos micrófonos y un procesador con capacidad de comunicación entre los dos nodos. De esta manera, es posible crear dos zonas independientes de control de ruido que mejoran el confort acústico del usuario sin necesidad de utilizar auriculares.[CA] Aquesta tesi s'emmarca en el camp de les Tecnologies de la Informació i les Comunicacions (TIC), especialment en l'àrea del processament digital del senyal. En l'actualitat, i a causa de l'auge de la Internet dels coses (IoT), existeix un creixent interés per les xarxes de sensors sense fils (WSN), és a dir, xarxes compostes de diferents tipus de dispositius específicament distribuïts en una determinada zona per a fer diferents tasques de processament de senyal. Aquests dispositius o nodes solen estar equipats amb transductors electroacústics així com amb potents i eficients processadors amb capacitat de comunicació. En el cas particular de les xarxes de sensors acústics (ASN), els nodes es dediquen a resoldre diferents tasques de processament de senyals acústics. El desenvolupament de potents sistemes de processament centralitzat han permés augmentar el nombre de canals d'àudio, ampliar l'àrea de control o implementar algorismes més complexos. En la majoria dels casos, una topologia de ASN distribuïda pot ser desitjable a causa de diversos factors tals com el nombre limitat de canals utilitzats pels dispositius d'adquisició i reproducció d'àudio, la conveniència d'un sistema escalable o les altes exigències computacionals dels sistemes centralitzats. Tots aquests aspectes poden portar a la utilització de noves tècniques de processament distribuït de senyals amb la finalitat d'aplicar-les en ASNs. Per a això, una de les principals aportacions d'aquesta tesi és el desenvolupament d'algorismes de filtrat adaptatiu per a sistemes d'àudio multicanal en xarxes distribuïdes. És important tindre en compte que, per a aplicacions de control del camp sonor (SFC), com el control actiu de soroll (ANC) o l'equalització activa de soroll (ANE), els nodes acústics han d'estar equipats amb actuadors amb la finalitat de controlar i modificar el camp sonor. No obstant això, la majoria de les propostes de xarxes distribuïdes adaptatives utilitzades per a resoldre problemes de control del camp sonor no tenen en compte que els nodes poden modificar el comportament de la resta. Per tant, una altra contribució destacable d'aquesta tesi se centra en l'anàlisi de com el sistema acústic afecta el comportament dels nodes dins d'una ASN. En els casos en què l'entorn acústic afecta negativament a l'estabilitat del sistema, s'han proposat diverses estratègies distribuïdes per a resoldre el problema d'interferència acústica amb l'objectiu d'estabilitzar els sistemes de ANC. En el disseny dels algorismes distribuïts també s'han tingut en compte aspectes d'implementació pràctica. A més, amb l'objectiu de crear perfils d'equalització diferents en zones d'escolta independents en presència de sorolls multitonales, s'han presentat diversos algorismes distribuïts de ANE en banda estreta i banda ampla sobre una ASN amb una comunicació col·laborativa i composta per nodes acústics. Es presenten a més resultats experimentals per a validar l'ús dels algorismes distribuïts proposats en el treball per a aplicacions pràctiques. Per a això, s'ha dissenyat un programari de simulació acústica que permet analitzar el rendiment dels algorismes desenvolupats en la tesi. Finalment, s'ha realitzat una implementació pràctica que permet executar aplicacions multicanal de SFC. Per a això, s'ha desenvolupat un prototip en temps real que controla les aplicacions de ANC i ANE utilitzant nodes acústics col·laboratius. El prototip consisteix en dos sistemes de control d'àudio personalitzat (PAC) compostos per un seient de cotxe i un node acústic, el qual està equipat amb dos altaveus, dos micròfons i un processador amb capacitat de comunicació entre els dos nodes. D'aquesta manera, és possible crear dues zones independents de control de soroll que milloren el confort acústic de l'usuari sense necessitat d'utilitzar auriculars.[EN] This thesis fits into the field of Information and Communications Technology (ICT), especially in the area of digital signal processing. Nowadays and due to the rise of the Internet of Things (IoT), there is a growing interest in wireless sensor networks (WSN), that is, networks composed of different types of devices specifically distributed in some area to perform different signal processsing tasks. These devices, also referred to as nodes, are usually equipped with electroacoustic transducers as well as powerful and efficient processors with communication capability. In the particular case of acoustic sensor networks (ASN), nodes are dedicated to solving different acoustic signal processing tasks. These audio signal processing applications have been undergone a major development in recent years due in part to the advances made in computer hardware and software. The development of powerful centralized processing systems has allowed the number of audio channels to be increased, the control area to be extended or more complex algorithmms to be implemented. In most cases, a distributed ASN topology can be desirable due to several factors such as the limited number of channels used by the sound acquisition and reproduction devices, the convenience of a scalable system or the high computational demands of a centralized fashion. All these aspects may lead to the use of novel distributed signal processing techniques with the aim to be applied over ASNs. To this end, one of the main contributions of this dissertation is the development of adaptive filtering algorithms for multichannel sound systems over distributed networks. Note that, for sound field control (SFC) applications, such as active noise control (ANC) or active noise equalization (ANE), acoustic nodes must be not only equipped with sensors but also with actuators in order to control and modify the sound field. However, most of the adaptive distributed networks approaches used to solve soundfield control problems do not take into account that the nodes may interfere or modify the behaviour of the rest. Therefore, other important contribution of this thesis is focused on analyzing how the acoustic system affects the behavior of the nodes within an ASN. In cases where the acoustic environment adversely affects the system stability, several distributed strategies have been proposed for solving the acoustic interference problem with the aim to stabilize ANC control systems. These strategies are based on both collaborative and non-collaborative approaches. Implementation aspects such as hardware constraints, sensor locations, convergenge rate or computational and communication burden, have been also considered on the design of the distributed algorithms. Moreover and with the aim to create independent-zone equalization profiles in the presence of multi-tonal noises, distributed narrowband and broadband ANE algorithms over an ASN with a collaborative learning and composed of acoustic nodes have been presented. Experimental results are presented to validate the use of the distributed algorithms proposed in the work for practical applications. For this purpose, an acoustic simulation software has been specifically designed to analyze the performance of the developed algorithms. Finally, the performance of the proposed distributed algorithms for multichannel SFC applications has been evaluated by means of a real practical implementation. To this end, a real-time prototype that controls both ANC and ANE applications by using collaborative acoustic nodes has been developed. The prototype consists of two personal audio control (PAC) systems composed of a car seat and an acoustic node, which is equipped with two loudspeakers, two microphones and a processor with communications capability. In this way, it is possible to create two independent noise control zones improving the acoustic comfort of the user without the use of headphones.Antoñanzas Manuel, C. (2019). Distributed and Collaborative Processing of Audio Signals: Algorithms, Tools and Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130209TESISCompendi