The aim of speech scrambling algorithms is to transform clear speech into an unintelligible signal so that it is difficult to decrypt it in the absence of the key. Most of the existing speech scrambling algorithms tend to retain considerable residual intelligibility in the scrambled speech and are easy to break. Typically, a speech scrambling algorithm involves permutation of speech segments in time, frequency or time-frequency domain or permutation of transform coefficients of each speech block. The time-frequency algorithms have given very low residual intelligibility and have attracted much attention. We first study the uniform filter bank based time-frequency scrambling algorithm with respect to the block length and number of channels. We use objective distance measures to estimate the departure of the scrambled speech from the clear speech. Simulations indicate that the distance measures increase as we increase the block length and the number of chan­nels. This algorithm derives its security only from the time-frequency segment permutation and it has been estimated that the effective number of permutations which give a low residual intelligibility is much less than the total number of possible permutations. In order to increase the effective number of permutations, we propose a time-frequency scrambling algorithm based on wavelet packets. By using different wavelet packet filter banks at the analysis and synthesis end, we add an extra level of security since the eavesdropper has to choose the correct analysis filter bank, correctly rearrange the time-frequency segments, and choose the correct synthesis bank to get back the original speech signal. Simulations performed with this algorithm give distance measures comparable to those obtained for the uniform filter bank based algorithm. Finally, we introduce the 2-channel perfect reconstruction circular convolution filter bank and give a simple method for its design. The filters designed using this method satisfy the paraunitary properties on a discrete equispaced set of points in the frequency domain.

The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation. Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohen’s class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator. Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohen’s class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier. The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohen’s class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohen’s class classifier. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

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Various methods describes an image by specific shapes, which are called basis or frames. With these basis can be transformed the image into a representation by transformation coefficients. The aim is that the image can be described by a small number of coefficients to obtain so-called sparse representation. This feature can be used for example for image compression. But basis are not able to describe all the shapes that may appear in the image. This lack increases the number of transformation coefficients describing the image. The aim of this thesis is to study the general principle of calculating the transformation coefficients and to compare classical methods of image analysis with some of the new methods of image analysis. Compares effectiveness of method for image reconstruction from a limited number of coefficients and a noisy image. Also, compares image interpolation method using characteristics of two different transformations with bicubic transformation. Theoretical part describes the transformation methods. Describes some methods from aspects of multi/resolution, localization in time and frequency domains, redundancy and directionality. Furthermore, gives examples of transformations on a particular image. The practical part of the thesis compares efficiency of the Fourier, Wavelet, Contourlet, Ridgelet, Radon, Wavelet Packet and WaveAtom transform in image recontruction from a limited number of the most significant transformation coefficients. Besides, ability of image denoising using these methods with thresholding techniques applied to transformation coefficients. The last section deals with the interpolation of image interpolation by combining of two methods and compares the results with the classical bicubic interpolation.

No / An investigation into the wavelet packet transform (WPT) modulation scheme for Multiple Input Multiple Output (MIMO) band-limited systems is presented. The implementation involves using the WPT as the base multiplexing technology at baseband, instead of the traditional Fast Fourier Transform (FFT) common in Orthogonal Frequency Division Multiplexing (OFDM) systems. An investigation for a WPT-MIMO multicarrier system, using the Alamouti diversity technique, is presented. Results are consistent with those in the original Alamouti work. The scheme is then implemented for WPT-MIMO and FFTMIMO cases with extended receiver diversity, namely 2 ×Nr MIMO systems, where Nr is the number of receiver elements. It is found that the diversity gain decreases with increasing receiver diversity and that WPT-MIMO systems can be more advantageous than FFT-based MIMO-OFDM systems.

Dans cette thèse, nous proposons une méthodologie de conception d’architectures de communication dédiées aux réseaux de capteurs basées sur la technique de radio impulsionnelle pour les transmissions ultralarge bande (ULB). La technique impulsionnelle proposée ici repose sur la modulation de forme d’impulsion. L’approche de conception architecturale présentée dans cette thèse se focalise plus particulièrement sur la forme des impulsions et leur génération, qui revêt un intérêt majeur puisqu’elle constitue le support de l’information échangée. L’étude sur le choix de la forme d’impulsion nous a conduit à proposer deux architectures différentes. Une première architecture repose sur les polynômes orthogonaux, et plus particulièrement sur les polynômes d’Hermite, pour la génération des impulsions, et sur une architecture de corrélation pour la détection et la reconnaissance des trains d’impulsions transmis. La deuxième architecture est basée sur la transformée en paquets d’ondelettes discrète et peut être exploitée selon deux modes d’utilisation différents, mono et multiutilisateurs. L’utilisation d’une architecture de synthèse à l’émission et d’analyse à la réception ouvre une nouvelle orientation pour les communications numériques, permettant à la transformée en ondelettes d’assurer à la fois la génération des impulsions à l’émission et leur reconnaissance à la réception. Un intérêt immédiat de la technique proposée permet notamment de faciliter l’accès multiutilisateurs au canal ultralarge bande, et d’autoriser des communications simultanées (Many-to-one, des nœuds vers le puits) ou du broadcast (One-to-many, du puits vers les nœuds) sans surcharger la couche MAC. L’architecture proposée s’inscrit donc à l’interface des couches PHY et MAC et permet de relâcher les contraintes de conception spécifiques à ces couches / In this thesis, we propose a design methodology for communication architectures dedicated to wireless sensor network based on impulse radio techniques for UWB communications. The impulse technique proposed in this work relies on pulse shape modulation. The architecture design approach proposed in this thesis focuses on pulses shape and their generation, which is of major interest as it constitutes the carrier of the information exchanged. The study on the choice of pulse shape led us to propose two different architectures. The first one is based on orthogonal polynomials, more especially on the Hermite polynomials, for impulse generation, and on a correlation architecture for detection and recognition of transmitted impulses. The second architecture is based on discrete wavelet packet transform and can be used according two different modes, mono and multi-users. The use of both synthesis and analysis architectures for emitter and receiver, respectively, offers a new way for digital communications and allows the wavelet transform to ensure the impulses generation on the transmitter and their recognition on the receiver. A major interest point of the proposed technique is to facilitate the multi-users access to the ultra-wideband channel and to allow simultaneous communications (many-to-one, from the sensors to the sink) or broadcast (one-to-many, from the coordinator to the nodes) but without overloading the MAC layer. Hence, the proposed architecture is part of the interface between both PHY and MAC layers, and allows to release their specific design constraints

Dans cette thèse, nous présenterons une architecture de communication multi-utilisateurs à base des réseaux de capteurs sans fil dans un environnement industriel fortement bruité. Deux modes de fonctionnement de cette architecture sont présentés ; un mode Many-To-One reliant plusieurs capteurs émetteurs à un seul récepteur et un mode One-To-Many reliant un émetteur à plusieurs capteurs récepteurs. La couche physique de ce système est basée sur une modulation par transformée par paquets d'ondelettes inverse (IDWPT) à l'émission et une transformée par paquets d'ondelette discrète (DWPT) en réception. Pour tester notre architecture, un modèle de canal industriel est proposé qui tient compte des phénomènes des multi-trajets et des évanouissements en plus du bruit additif. Ce dernier étant modélisé comme un bruit gaussien auquel s'additionne un bruit impulsionnel causant une dégradation significative des signaux. L'architecture est testée pour différentes configurations de communications sans fil et pour différentes formes d'ondelettes afin de proposer un mode de communication optimal. Aussi, une amélioration de la robustesse de notre système est effectuée en ajoutant un codage correcteur d'erreur du canal et un seuillage du bruit impulsionnel à la réception pour minimiser les effets du bruit industriel sur les signaux reçus. En utilisant un code correcteur d'erreur, la détection et reconstitution des signaux se fait sans erreur à partir d'un SNR de 8dB pour un taux de codage 1/4 pour une transmission sur à canal à évanouissement. Pour un récepteur optimal à base du seuillage du bruit, les performances en termes de taux d'erreur binaires sont améliorées de 10dB pour une transmission sur un canal à bruit industriel. Enfin, une comparaison de la robustesse de notre architecture impulsionnelle avec un système à base d'une modulation multi-porteuse classique OFDM est effectuée. Ceci nous amène à proposer un système de communication multi-utilisateurs robuste à base des réseaux de capteurs sans fil pour des communications en milieu industriel difficile. / In this thesis, we will present a multi-user communication architecture based on wireless sensor networks in a noisy industrial environment. Two modes of operation of this architecture are presented ; a Many-To-One mode linking several transmitter sensors to a single receiver and a One-To-Many mode linking a transmitter to several receiver sensors. The physical layer of this system is based on the inverse transform (IDWPT) at transmission and the discrete wavelet packet transform (DWPT) at reception. To test our architecture, an industrial channel model is proposed that takes into account the phenomena of multipath and fading in addition to additive noise. The latter being modelled as Gaussian noise to which is added an impulse noise causing significant signal degradation. The architecture is tested for different wireless communication configurations and wavelet shapes to provide an optimal communication mode. Also, an improvement in the robustness of our system is achieved by adding channel error correction coding and pulse noise thresholding at reception to minimize the effects of industrial noise on the received signals. Using an error-correcting code, the detection and reconstruction of signals is error-free from an SNR of 8dB for a coding rate of 1/4 for transmission on a fading channel. For an optimal receiver based on noise thresholding, the performance in terms of binary error rates is improved by 10dB for transmission over an industrial noise channel. Finally, a comparison of the robustness of our pulse architecture with a system based on a conventional OFDM multi-carrier modulation is carried out. This leads us to propose a robust multi-user communication system based on wireless sensor networks for communications in difficult industrial environments.

Many of the existing ambient modal identification methods based on vibration data process information centrally to calculate the modal properties. Such methods demand relatively large memory and processing capabilities to interrogate the data. With the recent advances in wireless sensor technology, it is now possible to process information on the sensor itself. The decentralized information so obtained from individual sensors can be combined to estimate the global modal information of the structure. The main objective of this thesis is to present a new class of decentralized algorithms that can address the limitations stated above. The completed work in this regard involves casting the identification problem within the framework of underdetermined blind source separation (BSS). Time-frequency transformations of measurements are carried out, resulting in a sparse representation of the signals. Stationary wavelet packet transform (SWPT) is used as the primary means to obtain a sparse representation in the time-frequency domain. Several partial setups are used to obtain the partial modal information, which are then combined to obtain the global structural mode information. Most BSS methods in the context of modal identification assume that the excitation is white and do not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations (e.g., pedestrian bridges) when the excitation is a superposition of narrow-band harmonic(s) and broad-band disturbance. Under such conditions, traditional BSS methods yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In this research, a novel under-determined BSS algorithm is developed involving statistical characterization of the sources which are used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system. Moreover, the issue of computational burden involving an over-complete dictionary of sparse bases is alleviated through a new underdetermined BSS method based on a tensor algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. Finally, the proposed methods are validated using measurements obtained from both wired and wireless sensors on laboratory scale and full scale buildings and bridges.

Many of the existing ambient modal identification methods based on vibration data process information centrally to calculate the modal properties. Such methods demand relatively large memory and processing capabilities to interrogate the data. With the recent advances in wireless sensor technology, it is now possible to process information on the sensor itself. The decentralized information so obtained from individual sensors can be combined to estimate the global modal information of the structure. The main objective of this thesis is to present a new class of decentralized algorithms that can address the limitations stated above. The completed work in this regard involves casting the identification problem within the framework of underdetermined blind source separation (BSS). Time-frequency transformations of measurements are carried out, resulting in a sparse representation of the signals. Stationary wavelet packet transform (SWPT) is used as the primary means to obtain a sparse representation in the time-frequency domain. Several partial setups are used to obtain the partial modal information, which are then combined to obtain the global structural mode information. Most BSS methods in the context of modal identification assume that the excitation is white and do not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations (e.g., pedestrian bridges) when the excitation is a superposition of narrow-band harmonic(s) and broad-band disturbance. Under such conditions, traditional BSS methods yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In this research, a novel under-determined BSS algorithm is developed involving statistical characterization of the sources which are used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system. Moreover, the issue of computational burden involving an over-complete dictionary of sparse bases is alleviated through a new underdetermined BSS method based on a tensor algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. Finally, the proposed methods are validated using measurements obtained from both wired and wireless sensors on laboratory scale and full scale buildings and bridges.

Made available in DSpace on 2014-12-17T14:54:56Z (GMT). No. of bitstreams: 1 NathaleeCA_DISSERT.pdf: 1318151 bytes, checksum: d2471205a640d8428567d06ace6c3b31 (MD5) Previous issue date: 2010-07-23 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The human voice is an important communication tool and any disorder of the voice can have profound implications for social and professional life of an individual. Techniques of digital signal processing have been used by acoustic analysis of vocal disorders caused by pathologies in the larynx, due to its simplicity and noninvasive nature. This work deals with the acoustic analysis of voice signals affected by pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The purpose of this work is to develop a classification system of voices to help pre-diagnosis of pathologies in the larynx, as well as monitoring pharmacological treatments and after surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients (MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT) are applied to extract relevant characteristics of the voice signal. For the classification task is used the Support Vector Machine (SVM), which aims to build optimal hyperplanes that maximize the margin of separation between the classes involved. The hyperplane generated is determined by the support vectors, which are subsets of points in these classes. According to the database used in this work, the results showed a good performance, with a hit rate of 98.46% for classification of normal and pathological voices in general, and 98.75% in the classification of diseases together: edema and nodules / A voz humana ? uma importante ferramenta de comunica??o e qualquer funcionamento inadequado da voz pode ter profundas implica??es na vida social e profissional de um indiv?duo. T?cnicas de processamento digital de sinais t?m sido utilizadas atrav?s da an?lise ac?stica de desordens vocais provocadas por patologias na laringe, devido ? sua simplicidade e natureza n?o-invasiva. Este trabalho trata da an?lise ac?stica de sinais de vozes afetadas por patologias na laringe, especificamente, edemas e n?dulos nas pregas vocais. A proposta deste trabalho ? desenvolver um sistema de classifica??o de vozes para auxiliar no pr?-diagn?stico de patologias na laringe, bem como no acompanhamento de tratamentos farmacol?gicos e p?s-cir?rgicos. Os coeficientes de Predi??o Linear (LPC), Coeficientes Cepstrais de Freq??ncia Mel (MFCC) e os coeficientes obtidos atrav?s da Transformada Wavelet Packet (WPT) s?o aplicados para extra??o de caracter?sticas relevantes do sinal de voz. ? utilizada para a tarefa de classifica??o M?quina de Vetor de Suporte (SVM), a qual tem como objetivo construir hiperplanos ?timos que maximizem a margem de separa??o entre as classes envolvidas. O hiperplano gerado ? determinado pelos vetores de suporte, que s?o subconjuntos de pontos dessas classes. De acordo com o banco de dados utilizado neste trabalho, os resultados apresentaram um bom desempenho, com taxa de acerto de 98,46% para classifica??o de vozes normais e patol?gicas em geral, e 98,75% na classifica??o de patologias entre si: edemas e n?dulos