Дисертації з теми "NON-LINEAR SIGNAL PROCESSING"

This thesis addresses problems in the field of time-varying, non-Gaussian, non-linear signal processing. It concentrates on developing results in the areas of time-frequency signal analysis and higher-order spectra which are linked by the developing area of time-varying higher-order spectra. Motivation comes from applying procedures developed to underwater acoustic signals. Reviews of time-frequency analysis and higher-order spectra precede the research contributions. Three appendices cover: a review of the multiple-window spectrum estimation method, an improved procedure for computing analytic signals frequently used in time-frequency signal analysis, and an updated approach for computing Slepian sequences necessary for the multiple-window spectrogram.

Reported in this thesis are advances in glass poling, a technique which produces second-order optical nonlinearity in glass. Poling of glass has drawn much attention because frequency conversion and electro-optic modulation, operations which are typically restricted to crystals, becomes feasible in glass fibres and waveguides which are widely spread media in photonics thanks to their excellent optical properties and mature manufacturing technology. Poled silica glass, despite showing about 10 times lower second-order nonlinearity than nonlinear crystals, can be competitive with them because longer interaction lengths are possible in glass fibres, owing to the lower chromatic dispersion. -gratings in fibres. This technology led to the first demonstration of an all-fibre frequency doubler of a fibre laser. Milliwatts of red light in the fundamental mode were produced by frequency doubling in a quasi-phase matched periodically poled silica fibre which was directly spliced to the output of the laser source. Efficiency up to 2.5% has been demonstrated in 11.5 cm long device with only about 100W of pump power, which means that 50% conversion efficiency is expected for a 2kW input power.

Cette thèse porte sur la numérisation de signaux hyperfréquences en utilisant une horloge optique, possédant une gigue temporelle très faible en comparaison des horloges électroniques. Une faible gigue est un facteur clé de l’échantillonnage à haute performance, car l’horloge commande l’ouverture d’une “porte” qui extrait les échantillons du signal à intervalles réguliers. Cette thèse décrit deux approches originales : l’échantillonnage purement optique et l’échantillonnage électro-optique.Une porte électro-optique se constitue d’une ligne coplanaire transportant le signal électrique. Cette ligne présente une discontinuité qui devient conductrice sur commande optique grâce à un matériau photoconducteur. Les alliages semi-conducteurs du groupe III-V sont souvent utilisés car la mobilité de ces porteurs photo-générés est suffisamment élevée, ce qui est favorable à une bonne conductivité à l’état “on”. Le GaAs, en particulier, présente l’avantage d’une conductivité faible à l’état “off” du fait de la largeur de la bande interdite électronique. Cela explique l’intérêt de ce matériau, cependant, cela impose aussi une contrainte sur la longueur d’onde de la source laser, expliquant l’utilisation de sources autour 800 nm.Dans cette thèse l’utilisation de sources laser à verrouillage de modes à fibre, développées dans le domaine des Télécoms, donc plus facilement accessibles, tout en gardant le GaAs comme matériaux actif est explorée. Cela est possible en exaltant l’efficacité de l’absorption à deux photons, effet faible dans la plupart des structures. L’approche suivie porte sur l’utilisation d’une cavité à cristaux photoniques. Le très fort confinement et le très faible volume occupé par le mode optique se traduit en une très forte absorption non linéaire. De plus, la nano-structuration du semi-conducteur réduit de manière considérable le temps de vie des porteurs, ce qui permet un retour suffisamment rapide à l’état “off”. L’étude se conclut par la démonstration de la fonction d’échantillonnage.La même fonction a été étudiée dans le cas ou le signal hyperfréquence se trouve sur une porteuse optique. La fonction porte “tout optique” est réalisée par un résonateur à cristaux photoniques. La génération de porteurs par absorption à deux photons induit un déplacement spectral de la résonance, ce qui est exploité pour moduler la transmission du dispositif. Une porte optique rapide, capable en principe de traiter des signaux dépassant les 50 GHz à été montrée. Cette porte requiert une puissance de commande de l’ordre de 200 fJ/impulsion, ce qui est suffisamment faible pour utiliser des sources lasers compactes (diode laser) et, de ce fait, outre sa très faible taille, peut être intégrée facilement
This thesis is focused on the digitalization of radio-frequency signal using optical clock, allowing a low time jitter compared to electronic clocks. A low jitter is a key factor for high performance sampling, as the clock commands the “gate” opening which extracts the signal samples at regular intervals. This thesis describes two original approaches: all-optical sampling and electro-optics one.An electro-optic gate is based on radio-frequency transmission strip-line carrying the electric signal. A discontinuity in this strip-line occurs which become conductive, thanks to the optical command provided by the clock, due to a photo-conductive material. Semiconductor alloys from the III-V groups are widely used thanks to the high mobility of the photo-generated carriers allowing a high “on” state. In particular, GaAs present a good “off” state due to its band-gap energy. However, this restrains the optical clock wavelength explaining the use of optical sources around 800 nm.In this thesis, the focus was made on using mode-locked lasers in the Telecom range, thus using the improvement made on these sources during the past decades, while keeping GaAs as the active material in the electro-optic sampler. This is made possible by exalting the efficiency of two-photon absorption, which is usually weak in common structures. The approach followed here is to use a photonic crystal cavity. Thanks to its high optical mode confinement, non-linear absorption becomes efficient enough to generated carriers to modify the resistivity of the material. In addition, the nano-structuration of the material reduce tremendously the carrier lifetime, owing to switch from an “on” to “off” state fast enough to sample high frequency signals.The same function has been studied in the case where the signal is not carried electrically but optically. The all-optical gate function is realized using two photonic crystal resonators coupled together. The carrier generation by two-photon absorption induces a spectral shift of the resonance, used to modulate the transmission of the device. A fast all-optical gate, enabling signal processing up to 50 GHz is demonstrated here. The gate only requires a control power of about 200 fJ per pulses, which is low enough to use integrated optical sources (laser diodes) and, thanks to the small footprint, be easily integrated

Image filtering methods are designed to enhance noisy images captured in situations that are problematic for the camera sensor. Such noisy images originate from unfavourable illumination conditions, camera motion, or the desire to use only a low dose of ionising radiation in medical imaging. Therefore, in this thesis work I have investigated the theory of partial differential equations (PDE) to design filtering methods that attempt to remove noise from images. This is achieved by modeling and deriving energy functionals which in turn are minimized to attain a state of minimum energy. This state is obtained by solving the so called Euler-Lagrange equation. An important theoretical contribution of this work is that conditions are put forward determining when a PDE has a corresponding energy functional. This is in particular described in the case of the structure tensor, a commonly used tensor in computer vision.A primary component of this thesis work is to model adaptive image filtering such that any modification of the image is structure preserving, but yet is noise suppressing. In color image filtering this is a particular challenge since artifacts may be introduced at color discontinuities. For this purpose a non-Euclidian color opponent transformation has been analysed and used to separate the standard RGB color space into uncorrelated components.A common approach to achieve adaptive image filtering is to select an edge stopping function from a set of functions that have proven to work well in the past. The purpose of the edge stopping function is to inhibit smoothing of image features that are desired to be retained, such as lines, edges or other application dependent characteristics. Thus, a step from ad-hoc filtering based on experience towards an application-driven filtering is taken, such that only desired image features are processed. This improves what is characterised as visually relevant features, a topic which this thesis covers, in particular for medical imaging.The notion of what are relevant features is a subjective measure may be different from a layman's opinion compared to a professional's. Therefore, we advocate that any image filtering method should yield an improvement not only in numerical measures but also a visual improvement should be experienced by the respective end-user
NACIP, VIDI, GARNICS

This thesis covers the development of a series of new methods and the application of adaptive filter theory which are combined to produce a generalised adaptive filter system which may be used to perform such tasks as pattern recognition. Firstly, the relevant background adaptive filter theory is discussed in Chapter 1 and methods and results which are important to the rest of the thesis are derived or referenced. Chapter 2 of this thesis covers the development of a new adaptive algorithm which is designed to give faster convergence than the LMS algorithm but unlike the Recursive Least Squares family of algorithms it does not require storage of a matrix with n2 elements, where n is the number of filter taps. In Chapter 3 a new extension of the LMS adaptive notch filter is derived and applied which gives an adaptive notch filter the ability to lock and track signals of varying pitch without sacrificing notch depth. This application of the LMS filter is of interest as it demonstrates a time varying filter solution to a stationary problem. The LMS filter is next extended to the multidimensional case which allows the application of LMS filters to image processing. The multidimensional filter is then applied to the problem of image registration and this new application of the LMS filter is shown to have significant advantages over current image registration methods. A consideration of the multidimensional LMS filter as a template matcher and pattern recogniser is given. In Chapter 5 a brief review of statistical pattern recognition is given, and in Chapter 6 a review of relevant connectionist models. In Chapter 7 the generalised adaptive filter is derived. This is an adaptive filter with the ability to model non-linear input-output relationships. The Volterra functional analysis of non-linear systems is given and this is combined with adaptive filter methods to give a generalised non-linear adaptive digital filter. This filter is then considered as a linear adaptive filter operating in a non-linearly extended vector space. This new filter is shown to have desirable properties as a pattern recognition system. The performance and properties of the new filter is compared with current connectionist models and results demonstrated in Chapter 8. In Chapter 9 further mathematical analysis of the networks leads to suggested methods to greatly reduce network complexity for a given problem by choosing suitable pattern classification indices and allowing it to define its own internal structure. In Chapter 10 robustness of the network to imperfections in its implementation is considered. Chapter 11 finishes the thesis with some conclusions and suggestions for future work.

This research develops efficient solution methods for linear systems with scalar and multi-level Toeplitz structure. Toeplitz systems are common in one-dimensional signal-processing applications, and typically correspond to temporal- or spatial-invariance in the underlying physical phenomenon. Over time, a number of algorithms have been developed to solve these systems economically by exploiting their structure. These developments began with the Levinson-Durbin recursion, a classical fast method for solving Toeplitz systems that has become a standard algorithm in signal processing. Over time, more advanced routines known as superfast algorithms were introduced that are capable of solving Toeplitz systems with even lower asymptotic complexity. For multi-dimensional signals, temporally- and spatially-invariant systems have linear-algebraic descriptions characterized by multi-level Toeplitz matrices, which exhibit Toeplitz structure on multiple levels. These matrices lack the same algebraic properties and structural simplicity of their scalar analogs. As a result, it has proven exceedingly difficult to extend the existing scalar Toeplitz algorithms for their treatment. This research presents algorithms to solve scalar and two-level Toeplitz systems through a constructive approach, using methods devised for specialized cases to build more general solution methods. These methods extend known scalar Toeplitz inversion results to more general scalar least-squares problems and to multi-level Toeplitz problems. The resulting algorithms have the potential to provide substantial computational gains for a large class of problems in signal processing, such as image deconvolution, non-uniform resampling, and the reconstruction of spatial volumes from non-uniform Fourier samples.

Esistono molti sistemi audio reali e ciascuno ha le proprie caratteristiche ma tutti sono accomunati dal fatto che sono sistemi in grado di generare o modificare un suono. Se un sistema naturale o artificiale può essere definito come sistema sonoro, allora è possibile applicare le tecniche del digital signal processing per studiare il sistema ed emularne il comportamento. In questo lavoro di tesi si propone di introdurre delle metodologie innovative di processamento del segnale applicate ad alcuni sistemi sonori reali. In particolare, vengono studiati e discussi tre diversi sistemi: il mondo dei dispositivi non lineari basati su valvole, con particolare attenzione agli amplificatori per chitarra e hi-fi, l'ambiente acustico di una stanza ed il suo effetto sulla propagazione del suono ed infine il suono emesso dalle api in un alveare. Per quanto riguarda il primo sistema, vengono proposti dei contributi innovativi per l'identificazione di modelli come la serie di Volterra ed il modello di Hammerstein; in particolare viene discusso un approccio per superare alcune limitazioni dell'identificazione tramite serie di Volterra e l'applicazione di una struttura in sottobande per ridurre il costo computazionale e incrementare la velocità di convergenza di un algoritmo adattativo per l'identificazione del modello di Hammerstein. In ultima analisi, viene proposto un approccio innovativo in grado di stimare con una singola misura vari parametri di distorsione sfruttando un modello di Hammerstein generallizato. Per quanto riguarda il secondo ambito, vengono proposti i risultati relativi a due applicazioni di equalizzazione multipunto: nel primo caso si mostrerà come l'equalizzazione può essere usata non solo per compensare le anomalie sonore generate all'interno di una stanza, ma anche per migliorare la risposta in frequenza di particolari trasduttori a vibrazione ancorati ad un pannello rigido; nel secondo caso si illustra come un approccio in sottobande può migliorare l'efficienza computazionale e la velocità di un algoritmo di equalizzazione adattativo multipunto e multicanale. Infine, viene presentato un sistema sonoro naturale, ovvero quello generato da un alveare. In questo caso si illustrerà un sistema di acquisizione innovativo sviluppato per monitorare gli alveari con particolare attenzione al suono; succesivamente si mostrano gli approcci messi a punto per analizzare il suono registrato in due condizioni reali ed infine verranno si illustrano i risultati ottenuti grazie allo studio del suono usando algoritmi di classificazione. Inoltre, nella parte finale dell'elaborato sono presenti dei contributi secondari ma che hanno comunque come focus principale il signal processing applicato ad ambienti acustici reali, in particolare si discute di un'implementazione di un algoritmo di cancellazione attiva del rumore e di due algoritmi per effetti digitali in cui il primo migliora le performance sonore di altoparlanti compatti, ed il secondo genera un effetto stereofonico per chitarra elettrica.
Many real word audio systems exist, each has its own characteristics but almost all of them can be identified from the fact that they are able to generate or modify a sound. If a natural or artificial system can be defined as a sound system, then it is possible to apply the techniques of digital signal processing for the studying and the emulation of the system. In this thesis, innovative methodologies for digital signal processing applied to real audio systems will be discussed. In particular, three different audio systems will be considered: the world of vacuum-based non linear audio devices with particular attention to guitar and hi-fi amplifiers; the room acoustic environment and its effect on the sound propagation; and finally the sound emitted by honey bees in a beehive. Regarding the first system, innovative approaches for the identification of the Volterra series and Hammerstein models will be proposed, in particular an approach to overcome some limitation of Volterra series identification. The application of a sub-band structure to reduce the computational cost and increase the convergence speed of an adaptive Hammerstein model identification will be proposed as well. Finally, an innovative approach for the measurement of several distortion parameters using a single measure, exploiting a generalized Hammerstein model, will be presented. For the second system, the results of the application of a multi-point equalizer to two different situations will be exposed. In particular, in the first case, it will be shown how a multi-point equalization can be used not only to compensate the acoustical anomalies of a room, but also to improve the frequency response of vibrating transducers mounted on a rigid surface. The second contribution will show how a sub-band approach can be used to improve the computational cost and the speed of an adaptive algorithm for a multi-point and multi channel equalizer. At the end, the focus will be on a natural sound system, i.e., a honey bees colony. In this case, an innovative acquisition system for honey bees sound monitoring will be presented. Then, the approaches developed for sound analysis will be exposed and applied to the recorded sounds in two different situations. Finally, the obtained results, achieved with the application of classification algorithms, will be exposed. In the final part of the work some minor contributions still related to signal processing applied to real sound systems are presented. In particular, an implementation of an active noise control system is discussed, and two algorithms for digital effects where the former improves the sound performances of compact loudspeakers and the latter generates a stereophonic effect for electric guitars are exposed.

The objective of this research is to study various methods for censoring state estimate updates generated from radar measurements. The generated 2-D radar data are sent to a fusion center using the J-Divergence metric as the means to assess the quality of the data. Three different distributed sensor network architectures are considered which include different levels of feedback. The Extended Kalman Filter (EKF) and the Gaussian Particle Filter (GPF) were used in order to test the censoring methods in scenarios which vary in their degrees of non-linearity. A derivation for the direct calculation of the J-Divergence using a particle filter is provided. Results show that state estimate updates can be censored using the J-Divergence as a metric controlled via feedback, with higher J-Divergence thresholds leading to a larger covariance at the fusion center.

O principal objetivo do presente trabalho é propor, desenvolver, testar e apresentar um método para a classificação do grau de desenvolvimento da capacidade cognitiva espacial de diferentes indivíduos. 37 alunos de graduação tiveram seus eletroencefalogramas (EEGs) capturados enquanto estavam engajados em tarefas de rotação mental de imagens tridimensionais. Seu grau de desenvolvimento da capacidade cognitiva espacial foi avaliado utilizando-se um teste psicológico BPR-5. O maior expoente de Lyapunov (LLE) foi calculado a partir de cada um dos 8 canais dos EEGs capturados. OS LLEs foram então utilizados como tuplas de entrada para 5 diferentes classificadores: i) perceptron de múltiplas camadas, ii) rede neural artificial de funções de base radial, iii) perceptron votado, iv) máquinas de vetor de suporte, e v) k-vizinhos. O melhor resultado foi obtido utilizando-se uma RBF com 4 clusters e a função de kernel Puk. Também foi realizada uma análise estatística das diferenças de atividade cerebral, baseando-se nos LLEs calculados, entre os dois grupos de interesse: SI+ (indivíduos com um suposto maior grau de desenvolvimento da sua capacidade cognitiva espacial) e SI- (grupo de controle) durante a realização de tarefas de rotação mental de imagens tridimensionais. Uma diferença média de 16% foi encontrada entre os dois grupos. O método de classificação proposto pode vir a contribuir e a interagir com outros processos na analise e no estudo da capacidade cognitiva espacial humana, assim como no entendimento da inteligência humana como um todo. Um melhor entendimento e avaliação das capacidades cognitivas de um indivíduo podem sugerir a este elementos de motivação, facilidade ou de inclinações naturais suas, podendo, provavelmente, afetar as decisões da sua vida e carreira de uma forma positiva.
The main objective of the present work is to propose, develop, test, and show a method for classifying the spatial cognition degree of development on different individuals. Thirty-Seven undergraduate students had their electroencephalogram (EEG) recorded while engaged in 3-D images mental rotation tasks. Their spatial cognition degree of development was evaluated using a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 electrodes recorded in each EEG. The LLEs were used as input for five different classifiers: i) multi-layer perceptron artificial neural network, ii) radial base functions artificial neural network, iii) voted perceptron artificial neural network, iv) support vector machines, and v) K-Nearest Neighbors. The best result was achieved by using a RBF with 4 clusters and Puk kernel function. Also a statistical analysis of the brain activity, based in the calculated LLEs, differences between two interest groups: SI+ (participants with an alleged higher degree of development of their spatial cognition) and SI- (control group) during the performing of mental rotation of tridimensional images tasks was done.. An average difference of 16% was found between both groups. The proposed classification method can contribute and interact with other processes in the analysis and study of human spatial cognition, as in the understanding of the human intelligence at all. A better understanding and evaluation of the cognitive capabilities of an individual could suggest him elements of motivation, ease or natural inclinations, possibly affecting the decisions of his life and carrier positively.

Sparse approximation is an important class of optimization problem in signal and image processing applications. This thesis presents an analog solution to this problem, based on the Locally Competitive Algorithm (LCA). A Hopfield-Network-like analog system, operating on sub-threshold currents is proposed as a solution. The results of the circuit components' implementation on the RASP2.8a chip, a Field Programmable Analog Array, are presented.

Ce rapport concerne l'application de modeles non lineaires a des problemes classiques de traitement du signal: identification de filtres et modelisation de processus. Pour etudier ces modeles, nous introduisons d'abord les outils appropries, principalement les statistiques d'ordre superieur a deux et les algorithmes d'identification de parametres. Nous nous consacrons ensuite a l'etude de differents filtres non lineaires: filtres de hammerstein, de volterra, narmax, bilineaires et reseaux de neurones. Ces filtres sont compares dans le cas de la soustraction de bruit. Cette application necessite des hypotheses specifiques qui sont detaillees pour chaque modele. Ces travaux sont valides sur des signaux sonar reels. Enfin, nous nous interessons a la modelisation de processus non lineaires. Le principe est de caracteriser une serie temporelle comme le resultat du filtrage non lineaire d'un bruit blanc fictif. Nous nous sommes plus particulierement interesses aux processus bilineaires

Nous considérons le problème de débruitage d'un signal ou d'une image observés dans le bruit gaussien. Dans ce problème les estimateurs linéaires classiques sont quasi-optimaux quand l'ensemble des signaux, qui doit être convexe et compact, est connu a priori. Si cet ensemble n'est pas spécifié, la conception d'un estimateur adaptatif qui ``ne connait pas'' la structure cachée du signal reste un problème difficile. Dans cette thèse, nous étudions une nouvelle famille d'estimateurs des signaux satisfaisant certains propriétés d'invariance dans le temps. De tels signaux sont caractérisés par leur structure harmonique, qui est généralement inconnu dans la pratique.Nous proposons des nouveaux estimateurs capables d'exploiter la structure harmonique inconnue du signal è reconstruire. Nous démontrons que ces estimateurs obéissent aux divers "inégalités d'oracle," et nous proposons une implémentation algorithmique numériquement efficace de ces estimateurs basée sur des algorithmes d'optimisation de "premier ordre." Nous évaluons ces estimateurs sur des données synthétiques et sur des signaux et images réelles
We consider the problem of denoising a signal observed in Gaussian noise.In this problem, classical linear estimators are quasi-optimal provided that the set of possible signals is convex, compact, and known a priori. However, when the set is unspecified, designing an estimator which does not ``know'' the underlying structure of a signal yet has favorable theoretical guarantees of statistical performance remains a challenging problem. In this thesis, we study a new family of estimators for statistical recovery of signals satisfying certain time-invariance properties. Such signals are characterized by their harmonic structure, which is usually unknown in practice. We propose new estimators which are capable to exploit the unknown harmonic structure of a signal to reconstruct. We demonstrate that these estimators admit theoretical performance guarantees, in the form of oracle inequalities, in a variety of settings.We provide efficient algorithmic implementations of these estimators via first-order optimization algorithm with non-Euclidean geometry, and evaluate them on synthetic data, as well as some real-world signals and images

Cette thèse porte sur les problèmes de localisation et de surveillance de champ de gaz à l'aide de réseaux de capteurs sans fil. Nous nous intéressons d'abord à la géolocalisation des capteurs et au suivi de cibles. Nous proposons ainsi une approche exploitant la puissance des signaux échangés entre les capteurs et appliquant les méthodes à noyaux avec la technique de fingerprinting. Nous élaborons ensuite une méthode de suivi de cibles, en se basant sur l'approche de localisation proposée. Cette méthode permet d'améliorer la position estimée de la cible en tenant compte de ses accélérations, et cela à l'aide du filtre de Kalman. Nous proposons également un modèle semi-paramétrique estimant les distances inter-capteurs en se basant sur les puissances des signaux échangés entre ces capteurs. Ce modèle est une combinaison du modèle physique de propagation avec un terme non linéaire estimé par les méthodes à noyaux. Les données d'accélérations sont également utilisées ici avec les distances, pour localiser la cible, en s'appuyant sur un filtrage de Kalman et un filtrage particulaire. Dans un autre contexte, nous proposons une méthode pour la surveillance de la diffusion d'un gaz dans une zone d'intérêt, basée sur l'apprentissage par noyaux. Cette méthode permet de détecter la diffusion d'un gaz en utilisant des concentrations relevées régulièrement par des capteurs déployés dans la zone. Les concentrations mesurées sont ensuite traitées pour estimer les paramètres de la source de gaz, notamment sa position et la quantité du gaz libéré
This thesis focuses on the problems of localization and gas field monitoring using wireless sensor networks. First, we focus on the geolocalization of sensors and target tracking. Using the powers of the signals exchanged between sensors, we propose a localization method combining radio-location fingerprinting and kernel methods from statistical machine learning. Based on this localization method, we develop a target tracking method that enhances the estimated position of the target by combining it to acceleration information using the Kalman filter. We also provide a semi-parametric model that estimates the distances separating sensors based on the powers of the signals exchanged between them. This semi-parametric model is a combination of the well-known log-distance propagation model with a non-linear fluctuation term estimated within the framework of kernel methods. The target's position is estimated by incorporating acceleration information to the distances separating the target from the sensors, using either the Kalman filter or the particle filter. In another context, we study gas diffusions in wireless sensor networks, using also machine learning. We propose a method that allows the detection of multiple gas diffusions based on concentration measures regularly collected from the studied region. The method estimates then the parameters of the multiple gas sources, including the sources' locations and their release rates

Non-invasive and fast flow measurement techniques have had increasing importance for the last decades. Scientists are looking for such quick techniques to be able to monitor real velocities without disturbing flow itself. Ultrasound Doppler velocimetry (UDV) being one of such techniques promising with advantages of getting simultaneous velocity measurements from several points and of applicability for opaque liquids as well. UDV is a technique which is still being developed for new applications and analysis of complex flows. In this study effect of sinusoidal oscillating, turbulent (random) and viscoelastic fluid motions on UDV signals were investigated theoretically and experimentally. Obtained mathematical relations for random and viscoelastic motions were utilized to get statistics of flow and distribution of relaxation spectrum, respectively. Analytical analysis and numerical simulation of sinusoidal oscillating flow depicted that there is a critical value for the ratio of oscillation amplitude to oscillation frequency for a specified set of measurement parameters of UDV. Above this critical value UDV is not successful to determine mean flow velocity. Mathematical relations between velocity probability density function (PDF) &ndash
velocity auto correlation function (ACF) and UDV signal spectrum were obtained in the analysis v of flow with random velocity. Comparison of velocity ACFs from direct velocity measurements and from raw in-phase (I) and quadrature (Q) signals through derived relation, revealed that time resolution of UDV technique is not enough for getting a good velocity ACF and thus turbulence spectrum. Using I and Q signals rather than measured velocities to get velocity ACF, increased the time resolution in the order of number of pulses used for getting one velocity value (Nprn). Velocity PDF obtained from UDV spectrum was compared with the one obtained from measured velocities with the assumption of Gaussian PDF. Both velocity PDFs were consistent. Also some parameters of pipe turbulence from literature were compared with the presented findings from velocity ACF obtained from I and Q signals through derived relation. Results showed good compatibility. In the last part of the study, complex viscosity of a linear viscoelastic fluid mathematically related to spectrum of UDV for a pipe flow with small-amplitude oscillating pressure field. Generalized Maxwell model was employed to express complex viscosity terms. Zero frequency (mean flow) component of UDV spectrum was used to obtain an equation for relaxation viscosities of generalized Maxwell model. Results have revealed that UDV technique can also be used to probe some of viscoelastic material functions. In conclusion, UDV is relatively new but a promising technique for the measurement and analysis of complex flows in a non-invasive manner.

This project investigates a laser radar system. The system is based on the principles of time-correlated single photon counting, and by measuring the times-of-flight of reflected photons it can find range profiles and perform three-dimensional imaging of scenes. Because of the photon counting technique the resolution and precision that the system can achieve is very high compared to analog systems. These properties make the system interesting for many military applications. For example, the system can be used to interrogate non-cooperative targets at a safe distance in order to gather intelligence. However, signal processing is needed in order to extract the information from the data acquired by the system. This project focuses on the analysis of different signal processing methods.

The Wiener filter and the Richardson-Lucy algorithm are used to deconvolve the data acquired by the photon counting system. In order to find the positions of potential targets different approaches of non-linear least squares methods are tested, as well as a more unconventional method called ESPRIT. The methods are evaluated based on their ability to resolve two targets separated by some known distance and the accuracy with which they calculate the position of a single target, as well as their robustness to noise and their computational burden.

Results show that fitting a curve made of a linear combination of asymmetric super-Gaussians to the data by a method of non-linear least squares manages to accurately resolve targets separated by 1.75 cm, which is the best result of all the methods tested. The accuracy for finding the position of a single target is similar between the methods but ESPRIT has a much faster computation time.

Le récepteur SAIC/MAIC introduit récemment est capable de séparer jusqu'à 2N utilisateurs synchronisés avec N antennes pour les réseaux de radiocommunications utilisant des modulations rectilignes ou quasi-rectilignes. Un tel récepteur, opérationnel dans les téléphones GSM depuis 2006, exploite la non-circularité du second ordre des signaux et met en œuvre un filtre optimal linéaire au sens large (widely linéaire WL). Il s'est montré performant pour les utilisateurs synchrones en absence de résidu de porteuse (l'effet doppler par exemple), mais son comportement en présence d'utilisateurs asynchrones ayant des résidus de porteuse potentiellement non nuls, omniprésents dans de nombreuses applications radio, n'a pas encore fait l'objet d'études théoriques, ce qui ne permet pas d'en connaître les dégradations. Le but de la 1e partie de ce travail consiste à présenter une analyse des performances du SAIC/MAIC, implanté via une approche MMSE avec séquence d'apprentissage, en présence de deux utilisateurs non nécessairement synchronisés et ayant des dérives de fréquence. Pour simplifier, nous avons limité l'analyse théorique à quelques cas particuliers avec des modulations rectilignes. Ainsi des expressions analytiques simples et interprétables ont été données et analysées. Nous avons prouvé que les performances sont moins dégradées dans le cas d'une dérive sur l'utile qu'en présence d'une dérive sur le brouilleur. En outre, la dégradation augmente avec la désynchronisation des deux signaux. Afin de préciser le domaine de validité des approximations analytiques, des illustrations numériques ont été réalisées en prenant comme paramètres ceux du standard GSM dans le but d'appliquer cette étude à ce standard et aux réseaux cellulaires. L'extension de l'analyse aux modulations quasi-rectilignes a nécessité la mise en œuvre d'un filtrage spatio-temporel. Nous avons ainsi étudié l'impact de la taille du filtre spatio-temporel sur les performances du MMSE SAIC/MAIC pour des modulations quasi-rectilignes (MSK et GMSK) en présence de résidus de porteuse. L'étude révèle qu'avec des résidus de porteuse standards, ce récepteur reste relativement robuste et ne nécessite pas de compensation. La 2e partie de ce travail considère des canaux sélectifs en fréquence et consiste à développer un SIMO MLSE pour un signal utile à modulation linéaire quelconque en présence de bruit additif gaussien centré stationnaire coloré temporellement et spatialement et potentiellement non circulaire, en partant d'un problème général de détection d'une forme d'onde. Nous avons démontré que le SIMO MLSE est constitué d'un filtre WL, d'un échantillonneur au rythme symbole et d'une minimisation récursive d'une métrique qui peut se mettre en oeuvre par l'algorithme de Viterbi. Le cas des modulations quasi rectilignes a aussi été considéré. Dans ce cadre, nous avons démontré que le SIMO MLSE a la même structure moyennant un prétraitement de dérotation. Tous ces filtres WL sont interprétés comme des filtres WL adaptés multidimensionnels (WL MMF) au sens où ils maximisent le rapport SNR sur le symbole courant en sortie. Nous avons ensuite étendu la structure du MLSE développée au cas d'un bruit gaussien non circulaire mais cyclostationnaire, de manière à se rapprocher des applications cellulaires. Nous appelons ce nouveau récepteur "pseudo-MLSE" car sa structure a été imposée. En conjecturant que les performances en probabilité d'erreur par symbole sont directement reliées au SNR sur le symbole courant, des expressions générales de celui-ci ont été données pour des MLSE et pseudo-MLSE dans le cadre d'interférences stationnaires et cyclostationnaires potentiellement non circulaires. Des formules interprétables de ces SNR ont été données dans des cas particuliers et des simulations numériques ont été présentées pour montrer les gains en performance des récepteurs introduits par rapport aux MLSE classiques dérivés sous hypothèse de bruit stationnaire circulaire
The SAIC/MAIC (Single/Multiple Antenna Interference Cancellation) receiver, recently introduced, is able to separate up to 2N synchronous users with N antennas for the radiocommunications networks using rectilinear or quasi-rectilinear modulations. A such receiver, operational in GSM handsets since 2006, exploits the second order non-circularity of signals and gives rise to an optimal widely linear (WL) filter. This receiver has been shown to be powerful for synchronous users without any frequency offsets (for example Doppler shift), but its behaviour in the presence of asynchronous users having potentially non zero carrier residues, omnipresent in many radio applications, has not yet been the subject of theoretical studies, which doesn't allow us to know their damage. For this reason, the purpose of the first part of this work is to present an analysis of SAIC/MAIC receiver performances, implemented via an MMSE approach with training sequence, with two users not necessarily synchronized with frequency shifts. To simplify the analytical developments, we limited the theoretical analysis to some particular cases with rectilinear modulations. Hence, simple and interpretable analytical expressions were given and analyzed. We proved in particular that the performance is less degraded in the case of a shift on the useful than in the presence of a drift on the jammer. Furthermore, the degradation increases with desynchronization of the two signals. To precise the range of validity of analytical approximations, numerical illustrations were made using as parameters those of the GSM standard in order to apply this study to this standard in particular and cellular networks in general. Extending the analysis to quasi-rectilinear modulations had required the implementation of a spatio-temporal filtering. We have studied the impact of the size of the spatio-temporal filter on the performance of MMSE SAIC/MAIC receiver for quasi-rectilinear modulations (GMSK and MSK) in the presence of residual frequencies. The study reveals that with standard carrier residues, this receiver remains relatively robust and doesn't require a compensation. The second part of this work considers frequency-selective channels and is developing a SIMO MLSE receiver for a useful signal with any linear modulation in the presence of an additive Gaussian centered stationary temporally and spatially colored and potentially non-circular noise, starting from a general problem of detection of a waveform. We have shown that SIMO MLSE receiver consists of a WL filter, a sampler at the symbol rate and a recursive minimization of a metric that can be solved by the Viterbi algorithm. The case of quasi-rectilinear modulations is also considered. Within this framework, we had proved that the SIMO MLSE receiver has the same structure through a postreatment derotation. All these filters are interpreted as WL multidimensional matched filters (WL MMF) in the sense that they maximize the SNR of the current symbol at output. Then, we extended the MLSE receiver structure developed in case of a noncircular gaussian noise but this time cyclostationary, so as to approximate radio cellular applications. We call this new receiver "pseudo-MLSE" because its structure was imposed. By conjecturing that the performances by per symbol error probability are directly related to the SNR of the current symbol, general expressions of this SNR are given for MLSE and pseudo MLSE receivers with stationary and cyclostationary potentially noncircular interferences. Interpretable formulas of these SNR were given in special cases and numerical simulations were presented to show performance gains of the receivers we have introduced relative to conventional MLSE receivers derived under the assumption of circular stationary noise

Thesis (Ph. D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Dept. of Civil and Environmental Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viability for decision-support in clinical settings. The approach, which focuses on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic signals. Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. For a given desired sensitivity, the proposed model provides a significantly higher accuracy and specificity as compared to the state-of-the-art. Notably, within the range of 80-90% of sensitivity, the method provides about 40% higher specificity. This means that when trained to have the same level of sensitivity, the model will yield far fewer false positives (unnecessary shocks). Also introduced is a new model that predicts recurrence of arrest after a successful countershock is delivered. To date, no other work has sought to build such a model. I validate the method by reporting multiple performance metrics calculated on (blind) test sets.

La deuxième décennie du millénaire actuel peut être résumée en une courte phrase : l'essor des données. Le nombre de sources de données s'est multiplié : du streaming audio-vidéo aux réseaux sociaux et à l'Internet des Objets, en passant par les montres intelligentes, les équipements industriels et les véhicules personnels, pour n'en citer que quelques-unes. Le plus souvent, ces sources forment des réseaux afin d'échanger des informations. En conséquence directe, le domaine du Traitement de Signal sur Graphe a prospéré et a évolué. Son but : traiter et donner un sens à tout le déluge de données environnant. Dans ce contexte, le but principal de cette thèse est de développer des méthodes et des algorithmes capables d'utiliser des flots de données, de manière distribuée, afin d'inférer les réseaux sous-jacents qui relient ces flots. Ensuite, ces topologies de réseau estimées peuvent être utilisées avec des outils développés pour le Traitement de Signal sur Graphe afin de traiter et d'analyser les données supportées par des graphes. Après une brève introduction suivie d'exemples motivants, nous développons et proposons d'abord un algorithme en ligne, distribué et adaptatif pour l'inférence de topologies de graphes pour les flots de données qui sont linéairement dépendants. Une analyse de la méthode s'ensuit, afin d'établir des relations entre les performances et les paramètres nécessaires à l'algorithme. Nous menons ensuite une série d'expériences afin de valider l'analyse et de comparer ses performances avec celles d'une autre méthode proposée dans la littérature. La contribution suivante est un algorithme doté des mêmes capacités en ligne, distribuées et adaptatives, mais adapté à l'inférence de liens entre des données qui interagissent de manière non-linéaire. À ce titre, nous proposons un modèle additif simple mais efficace qui utilise l'usine du noyau reproduisant afin de modéliser lesdites non-linéarités. Les résultats de son analyse sont convaincants, tandis que les expériences menées sur des données biomédicales donnent des réseaux estimés qui présentent un comportement prédit par la littérature médicale. Enfin, une troisième proposition d'algorithme est faite, qui vise à améliorer le modèle non-linéaire en lui permettant d'échapper aux contraintes induites par l'additivité. Ainsi, le nouveau modèle proposé est aussi général que possible, et utilise une manière naturelle et intuitive d'imposer la parcimonie des liens, basée sur le concept de dérivés partiels. Nous analysons également l'algorithme proposé, afin d'établir les conditions de stabilité et les relations entre ses paramètres et ses performances. Une série d'expériences est menée, montrant comment le modèle général est capable de mieux saisir les liens non-linéaires entre les données, tandis que les réseaux estimés se comportent de manière cohérente avec les estimations précédentes
The second decade of the current millennium can be summarized in one short phrase: the advent of data. There has been a surge in the number of data sources: from audio-video streaming, social networks and the Internet of Things, to smartwatches, industrial equipment and personal vehicles, just to name a few. More often than not, these sources form networks in order to exchange information. As a direct consequence, the field of Graph Signal Processing has been thriving and evolving. Its aim: process and make sense of all the surrounding data deluge.In this context, the main goal of this thesis is developing methods and algorithms capable of using data streams, in a distributed fashion, in order to infer the underlying networks that link these streams. Then, these estimated network topologies can be used with tools developed for Graph Signal Processing in order to process and analyze data supported by graphs. After a brief introduction followed by motivating examples, we first develop and propose an online, distributed and adaptive algorithm for graph topology inference for data streams which are linearly dependent. An analysis of the method ensues, in order to establish relations between performance and the input parameters of the algorithm. We then run a set of experiments in order to validate the analysis, as well as compare its performance with that of another proposed method of the literature.The next contribution is in the shape of an algorithm endowed with the same online, distributed and adaptive capacities, but adapted to inferring links between data that interact non-linearly. As such, we propose a simple yet effective additive model which makes use of the reproducing kernel machinery in order to model said nonlinearities. The results if its analysis are convincing, while experiments ran on biomedical data yield estimated networks which exhibit behavior predicted by medical literature.Finally, a third algorithm proposition is made, which aims to improve the nonlinear model by allowing it to escape the constraints induced by additivity. As such, the newly proposed model is as general as possible, and makes use of a natural and intuitive manner of imposing link sparsity, based on the concept of partial derivatives. We analyze this proposed algorithm as well, in order to establish stability conditions and relations between its parameters and its performance. A set of experiments are ran, showcasing how the general model is able to better capture nonlinear links in the data, while the estimated networks behave coherently with previous estimates

This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method.

Abstract The rapid evolution of wireless networks to meet the requirements of explosive data traffic demand is escalating energy consumption beyond sustainable limits. Consequently, energy efficiency (EE) has emerged as a key performance indicator for future wireless networks to address the increasing concern over greenhouse gas emissions and sustainable economic growth. This thesis studies energy-efficient transmission strategies for multiantenna wireless systems. The aim is to develop linear beamforming techniques maximizing the bit-per-Joule EE metric, focusing on three appealing scenarios: a coordinated multicell system; a fronthaul-constrained cloud radio access network (C-RAN); and a multi-pair wireless-powered relaying system. The primary emphasis is on suboptimal but efficient optimization approaches which are attractive for practical implementation. The problem of achieving EE fairness in a multicell multiple-input single-output downlink system is studied first. Specifically, coordinated beamforming is designed to maximize the minimum EE among all base stations. Novel efficient iterative optimization methods solving the design problem in both centralized and decentralized fashions are proposed. In a downlink C-RAN with finite-capacity fronthaul links, the network-wide EE performance is explored via a joint design of beamforming and remote radio head-user association. A relatively realistic power consumption model including rate-dependent circuit power and nonlinear power amplifiers' (PA) efficiency is also considered. To gain an insight into the optimal performance of the design problem, an algorithm achieving globally optimal solutions is devised. Towards practical implementation, two efficient iterative suboptimal methods are proposed aiming at yielding near-optimal performance. Finally, a multi-pair amplify-forward relaying network is considered, in which energy-constrained relays adopting time-switching protocol harvest energy from the radio frequency signals transmitted by users. To maintain EE fairness among all user pairs, joint optimization of system parameters, such as users' transmit power, relay beamforming, and energy harvesting (EH) time, is studied. Impacts of rate-dependent circuit power, nonlinear PAs' efficiency and nonlinear EH circuits on the achievable performance are also addressed
Tiivistelmä Langattomat verkot ovat kehittyneet nopeasti räjähdysmäisesti kasvavan dataliikenteen mahdollistamiseksi, minkä seurauksena energiankulutus on kasvanut kestävän kehityksen rajat ylittävällä tavalla. Siksi energiatehokkuudesta (EE, energy efficiency) on tullut uusien langattomien verkkojen keskeinen suunnittelukriteeri vastauksena kasvavaan huoleen kasvihuonepäästöistä ja kestävästä talouskasvusta. Väitöskirjassa tutkitaan moniantennisten langattomien järjestelmien energiatehokkaita tiedonsiirtostrategioita. Tavoitteena on kehittää lineaarisia keilanmuodostustekniikoita, jotka maksimoivat energiatehokkuuden mitattuna bitteinä joulea kohden, keskittymällä kolmeen kiinnostavaan vaihtoehtoon, joita ovat koordinoitu monisolujärjestelmän lähetys laskevalla siirtotiellä, pilvipohjainen radioliityntäverkko (C-RAN, cloud radio access network), jossa laskentayksikön ja varsinaisen radiolähettimen välinen yhteys (fronthaul) on rahoitettu, ja usean parin relejärjestelmiin, joissa releet toimivat paristoilla. Työn pääpaino on alioptimaalisissa, mutta käytännöllisesti tehokkaissa optimointimenetelmissä. Pääpaino on alioptimaalisissa mutta tehokkaissa optimointitavoissa, jotka ovat kiinnostavia käytännön toteutuksen näkökulmasta. Ensiksi tarkastellaan tasapuolisen energiatehokkuuden saavuttamista monisoluisessa laskevan siirtotien moni-tulo yksi-lähtö (MISO, multiple-input single-output) -järjestelmässä. Koordinoitu keilanmuodostus on suunniteltu erityisesti maksimoimaan energiatehokkuuden minimitaso kaikilla tukiasemilla. Tarkemmin sanottuna pyritään maksimoimaan huonoin energiatehokkuus solmujen välillä, kun käytetään yhteistoiminnallista keilanmuodostusta. Muodostetun ongelman ratkaisemiseksi ehdotetaan edistyksellisiä iteratiivisia menetelmiä käyttämällä sekä keskitettyjä että hajautettuja ratkaisuja. Laskevan siirtosuunnan fronthaul-rajoitetussa C-RAN-järjestelmässä selvitetään verkonlaajuista energiatehokkuutta keilanmuodostuksen ja palvelevan tukiaseman yhteisoptimoinnilla. Tässä käytetään verrattain realistista tehonkulutusmallia, joka sisältää datanopeudesta riippuvan prosessointitehon ja epälineaarisen tehovahvistimen (PA, power amplifier) hyötysuhteen. Jotta saadaan käsitys ongelman optimaalisesta suorituskyvystä, siihen kehitetään globaalisti optimaalinen menetelmä. Lisäksi ehdotetaan kaksi käytännöllisempää iteratiivista menetelmää, jotka saavuttavat lähes optimaalisen suorituskyvyn. Lopuksi keskitytään monen parin vahvista-ja-välitä eteenpäin (AF. amplify and forward) verkkoon, jossa aikajakokytkentää käyttävät energiarajoitetut toistimet keräävät energiaa käyttäjien lähettämistä radiosignaaleista. Jotta saavutetaan EE:n oikeudenmukaisuus kaikkien parien välillä, parametrit, kuten käyttäjien lähetysteho, toistimen keilanmuodostus, ja energiankeräysaika suunnitellaan yhdessä. Tässä tutkitaan nopeusriippuvaisen piirin tehon, epälineaarisen tehovahvistimen hyötysuhteen ja epälineaaristen energiankeräyspiirien tehon vaikutusta suorituskykyyn

La poursuite multi-cibles a pour objet le suivi d'un ensemble de cibles mobiles à partir de données obtenues séquentiellement. Ce problème est particulièrement complexe du fait du nombre inconnu et variable de cibles, de la présence de bruit de mesure, de fausses alarmes, d'incertitude de détection et d'incertitude dans l'association de données. Les filtres PHD (Probability Hypothesis Density) constituent une nouvelle gamme de filtres adaptés à cette problématique. Ces techniques se distinguent des méthodes classiques (MHT, JPDAF, particulaire) par la modélisation de l'ensemble des cibles comme un ensemble fini aléatoire et par l'utilisation des moments de sa densité de probabilité. Dans la première partie, on s'intéresse principalement à la problématique de l'application des filtres PHD pour le filtrage multi-cibles maritime et aérien dans des scénarios réalistes et à l'étude des propriétés numériques de ces algorithmes. Dans la seconde partie, nous nous intéressons à l'étude théorique des processus de branchement liés aux équations du filtrage multi-cibles avec l'analyse des propriétés de stabilité et le comportement en temps long des semi-groupes d'intensités de branchements spatiaux. Ensuite, nous analysons les propriétés de stabilité exponentielle d'une classe d'équations à valeurs mesures que l'on rencontre dans le filtrage non-linéaire multi-cibles. Cette analyse s'applique notamment aux méthodes de type Monte Carlo séquentielles et aux algorithmes particulaires dans le cadre des filtres de Bernoulli et des filtres PHD.

Non-Linear signal processing circuits are used in various streams of engineering such as communication, control system, digital design, etc. Various types of signal processing methods are used according to applications and requirements. CMOS based non-linear signal processing circuit is less power consuming. In this dissertation various types of Non-Linear Circuits have been implemented such as multiplier, divider, vector magnitude calculator, sine shaper, and log and antilog amplifier. These circuits have been implanted using off the shelf available components as well as integrable versions using both bipolar as well as CMOS transistors. The off-the-shelf available building blocks used include uA741, AD844, CA3080. Log and antilog amplifier circuits are implemented using uA741, the four quadrant multiplier and divider circuit are implemented using AD844, CMOS based operational transconductance (OTA) circuit is also presented and implementation of two quadrant multiplier circuit using that OTA is presented. Translinear circuit principle is used to implement vector magnitude calculator, sine shaper circuit, and linearized four quadrant multiplier. Lastly, implementation of CMOS based squarer and multiplier is also presented.

Kalman filter is one of the best filter utilized as a part of the state estimation taking into account optimality criteria utilizing system model and observation model. Kalman filtering, otherwise called linear quadratic estimation (LQE), is an algorithm that uses a progression of estimations over time, having noise with different errors, and gives assessments of obscure variables that have a tendency to be more exact than those in view of a single estimation alone. The Kalman Filter is an extremely well known recursive state estimator for linear systems. By and by the calculation is frequently utilized for nonlinear systems by linearizing the system's procedure and estimation capacities. Another Kalman Filter variations linearize the functions in various ways, one of that is Extended Kalman Filter(EKF). While various nonlinear techniques having robust within the sight of outliers and adjusted to non-Gaussian noise, it keeps up high statistical efficiency which is attractive. To deal with this issue, another robust Kalman filter strategy is suggested that limits the impact of structural, innovation, and observation outliers in a linear system. This filter is planned to set up the perceptions and forecasts together, making it amazingly effective in smothering numerous outliers. In development, it contains another pre-whitening methodology that gives a robust multivariable estimator of covariance and location. Besides, the filter gives state estimates that are robust to outliers while keeping up a high statistical effectiveness at the Gaussian appropriation by applying a (GM)Generalised Maximum likelihood estimator. Finally, the filter combines the correct error covariance matrix that is gathered using the influence function of GM-estimator. By simulations, the execution of GM-KF to various outliers and state estimation for the applications: tracking of the vehicle.

Non-Linear signal processing circuits are used in various streams of engineering such as telecommunication, control system, digital design, biomedical, information security etc. Various types of signal processing methods such as analog or digital are used according to applications and requirements. CMOS non-linear signal processing circuits consume less power whereas bipolar non-linear circuits provide faster response and also provide wide operating range. In this dissertation linear term generator and higher order non-linear blocks such as squarer generator, cubic generator, power four generator and power five generator blocks have been implemented. The functions like sine, cosine, exponential, logarithmic, tangent hyperbolic inverse functions and the other two functions viz. Gaussian and sigmoid, which are extensively used in artificial neural network are also implemented in this dissertation. These functions are implemented in current mode since this mode is found to be more advantageous as compared to voltage mode implementation. The analog implementation of elliptic curve using these non-linear blocks is also presented in this dissertation which can be further used in Cryptography.

The Independent Component Analysis technique has been used in Blind Source separation of non linear mixtures. The project involves the blind source separation of a non linear mixture of signals based on their mutual independence as the evaluation criteria. The linear mixer is modeled by the Fast ICA algorithm while the Non linear mixer is modeled by an odd polynomial function whose parameters are updated by four separate optimization techniques which are Particle Swarm Optimization, Real coded Genetic Algorithm, Binary Genetic Algorithm and Bacterial Foraging Optimization. The separated mixture outputs of each case was studied and the mean square error in each case was compared giving an idea of the effectiveness of each optimization technique.

In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) using some learning rules such as back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The primary aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network the designer is often faced with the problem of choosing a network of the right size for the task. The advantages of using a smaller neural network are cheaper cost of computation and better generalization ability. However, a network which is too small may never solve the problem, while a larger network may even have the advantage of a faster learning rate. Thus it makes sense to start with a large network and then reduce its size. For this reason a Genetic Algorithm (GA) based pruning strategy is reported. GA is based upon the process of natural selection and does not require error gradient statistics. As a consequence, a GA is able to find a global error minimum. Transmission bandwidth is one of the most precious resources in digital communication systems. Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response. When the amplitude and the envelope delay response are not constant within the bandwidth of the filter, the channel distorts the transmitted signal causing intersymbol interference (ISI). The addition of noise during propagation also degrades the quality of the received signal. All the signal processing methods used at the receiver's end to compensate the introduced channel distortion and recover the transmitted symbols are referred as channel equalization techniques.When the nonlinearity associated with the system or the channel is more the number of branches in FLANN increases even some cases give poor performance. To decrease the number of branches and increase the performance a two stage FLANN called cascaded FLANN (CFLANN) is proposed.This thesis presents a comprehensive study covering artificial neural network (ANN) implementation for nonlinear system identification and channel equalization. Three ANN structures, MLP, FLANN, CFLANN and their conventional gradient-descent training methods are extensively studied. Simulation results demonstrate that FLANN and CFLANN methods are directly applicable for a large class of nonlinear control systems and communication problems.

A dissertation submitted in fulfllment for the degree of Masters of Science in the Faculty of Science School of Computer Science and Applied Mathematics October 2017.
This work explores a new method of applying partial di erential equations to audio signal processing, particularly that of noise removal. Two methods are explored and compared to the method of noise removal used in the free software Audacity(R). The rst of these methods uses a non-linear variation of the di usion equation in two dimensions, coupled with a non-linear sink/source term, in order to lter the imaginary and real components of an array of overlapping windows of the signal's Fourier transform. The second model is that of a non-linear di usion function applied to the magnitude of the Fourier transform in order to estimate the noise power spectrum to be used in a spectral subtraction noise removal technique. The technique in this work features nite di erence methods to approximate the solutions of each of the models.
LG2018

Brain signal analysis has a crucial role in the investigation of the neuronal activity for diagnosis of brain diseases and disorders. The electroencephalogram (EEG) is the most efficient biomarker for the analysis of brain signal that assists in the diagnosis of brain disorder medication and also plays an essential role in all the neurosurgery related to the brain. EEG findings illustrate the meticulous condition, and clinical content of the brain dysfunctions, and has an undisputed importance role in the detection of epilepsy condition and sleep disorders and dysfunctions allied to alcohol. The clinicians visually study the EEG recording to determine the manifestation of abnormalities in the brain. The visual EEG assessment is tiresome, fallible, and also high-priced. In this dissertation, a number of frameworks have been developed for the analysis and classification of EEG signals by addressing three different domains named: Epilepsy, Sleep staging, and Alcohol Use Disorder. Epilepsy is a non-contagious chronic disease of the brain that affects around 65 million people worldwide. The sudden onset tendency of the epileptic attacks vulnerable their sufferers to injuries. It is also challenging for the clinical staff to detect the epileptic-seizure activity early enough for determining the semiology associated with the seizure onset. For that reason, automated techniques that can accurately detect the epilepsy from EEG are of great importance to epileptic patients and especially to those patients who are resistive to therapies and medications. In this dissertation, four different techniques (named Weighted Visibility Network, Weighted Horizontal Visibility Network, Weighted Complex Network, and New Weighted Complex Network) have been developed for the automated identification of epileptic activity from the EEG signals. Most of the developed schemes attained 100% classification outcomes in their experimental evaluation for the identification of seizure activity from non-seizure activity. A sleep disorder can increase the menace of seizure incidence or severity, cognitive tasks impairments, mood deviation, diminution in the functionality of the immune system and other brain anomalies such as insomnia, sleep apnoea, etc. Hence, sleep staging is essential to discriminate among distinct sleep stages for the diagnosis of sleep and its disorders. EEG provides vital and inimitable information regarding the sleeping brain. The study of EEG has documented deformities in sleep patterns. This research has developed an innovative graph- theory based framework named weighted visibility network for sleep staging from EEG signals. The developed framework in this thesis, outperforms with 97.93% overall classification accuracy for categorizing distinct sleep states Alcoholism causes memory issues as well as motor skill defects by affecting the different portions of the brain. Excessive use of alcohol can cause sudden cardiac death and cardiomyopathy. Also, alcohol use disorder leads to respiratory infections, Vision impairment, liver damage, and cancer, etc. Research study demonstrates the use of EEG for diagnosis the patient with a high menace of developmental impediments with alcohol. In this current Ph.D. project, I developed a weighted graph-based technique that analyses EEG to distinguish between alcoholic subject and non-alcoholic person. The promising classification outcome demonstrates the effectiveness of the proposed technique.