Neste trabalho, foram utilizadas as vantagens da ferramenta matemática de análise temporal e espectral, a transformada wavelet discreta (DWT), além dos coeficientes de predição linear (LPC) e do algoritmo de inteligência artificial, Least Squares Support Vector Machines (LS-SVM), para aplicações em análise de sinais de voz e classificação de vozes patológicas. Inúmeros trabalhos na literatura têm demonstrado o grande interesse existente por ferramentas auxiliares ao diagnóstico de patologias da laringe. Os componentes da DWT forneceram parâmetros de medida para a análise e classificação das vozes patológicas, principalmente aquelas provenientes de pacientes com edema de Reinke e nódulo nas pregas vocais. O banco de dados com as vozes patológicas foi obtido do Departamento de Otorrinolaringologia e Cirurgia de Cabeça e Pescoço do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto (FMRP-USP). Utilizando-se o algoritmo de reconhecimento de padrões, LS-SVM, mostrou-se que a combinação dos componentes da DWT de Daubechies com o filtro LP inverso levou a um classificador de bom desempenho alcançando mais de 90% de acerto na classificação das vozes patológicas. / The main objective of this work was to use the advantages of the time-frequency analysis mathematical tool, discrete wavelet transform (DWT), besides the linear prediction coefficients (LPC) and the artificial intelligence algorithm, Least Squares Support Vector Machines (LS-SVM), for applications in voice signal analysis and classification of pathological voices. A large number of works in the literature has been shown that there is a great interest for auxiliary tools to the diagnosis of laryngeal pathologies. DWT components gave measure parameters for the analysis and classification of pathological voices, mainly that ones from patients with Reinke\'s edema and nodule in the vocal folds. It was used a data bank with pathological voices from the Otolaryngology and the Head and Neck Surgery sector of the Clinical Hospital of the Faculty of Medicine at Ribeirão Preto, University of Sao Paulo (FMRP-USP), Brazil. Using the automatic learning algorithm applied in pattern recognition problems, LS-SVM, results have showed that the combination of Daubechies\' DWT components and inverse LP filter leads to a classifier with good performance reaching more than 90% of accuracy in the classification of the pathological voices.

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

Une des préoccupations dans le domaine des communications sécurisées est le concept de sécurité de l'information. Aujourd’hui, la réalité a encore prouvé que la communication entre deux parties sur de longues distances a toujours été sujet au risque d'interception. Devant ces contraintes, de nombreux défis et opportunités s’ouvrent pour l'innovation. Afin de pouvoir fournir une communication sécurisée, cela a conduit les chercheurs à développer plusieurs schémas de stéganographie. La stéganographie est l’art de dissimuler un message de manière secrète dans un support anodin. L’objectif de base de la stéganographie est de permettre une communication secrète sans que personne ne puisse soupçonner son existence, le message secret est dissimulé dans un autre appelé medium de couverture qui peut être image, video, texte, audio,…. Cette propriété a motivé les chercheurs à travailler sur ce nouveau champ d’étude dans le but d’élaborer des systèmes de communication secrète résistante à tout type d’attaques. Cependant, de nombreuses techniques ont été développées pour dissimuler un message secret dans le but d’assurer une communication sécurisée. Les contributions majeures de cette thèse sont en premier lieu, de présenter une nouvelle méthode de stéganographie permettant la dissimulation d’un message secret dans un signal de parole. La dissimulation c’est le processus de cacher l’information secrète de façon à la rendre imperceptible pour une partie tierce, sans même pas soupçonner son existence. Cependant, certaines approches ont été étudiées pour aboutir à une méthode de stéganogaraphie robuste. En partant de ce contexte, on s’est intéressé à développer un système de stéganographie capable d’une part de dissimuler la quantité la plus élevée de paramètre tout en gardant la perceptibilité du signal de la parole. D’autre part nous avons opté pour la conception d’un algorithme de stéganographie assez complexe afin d’assurer l’impossibilité d’extraction de l’information secrète dans le cas ou son existence été détecter. En effet, on peut également garantir la robustesse de notre technique de stéganographie à l’aptitude de préservation du message secret face aux tentatives de détection des systèmes de stéganalyse. Notre technique de dissimulation tire son efficacité de l’utilisation de caractéristiques spécifiques aux signaux de parole et àl’imperfection du système auditif humain. Des évaluations comparatives sur des critères objectifs et subjectifs de qualité sont présentées pour plusieurs types de signaux de parole. Les résultats ont révélé l'efficacité du système développé puisque la technique de dissimulation proposée garantit l’imperceptibilité du message secret voire le soupçon de son existence. Dans la suite expérimentale et dans le même cadre de ce travail, la principale application visée par la thèse concerne la transmission de parole sécurisée par un algorithme de stéganographie. Dans ce but il s’est avéré primordial d’utiliser une des techniques de codage afin de tester la robustesse de notre algorithme stéganographique face au processus de codage et de décodage. Les résultats obtenus montrent la possibilité de reconstruction du signal original (contenant des informations secrètes) après codage. Enfin une évaluation de la robustesse de notre technique de stéganographie vis à vis des attaques est faite de façon à optimiser la technique afin d'augmenter le taux de sécurisation. Devant cette nécessité nous avons proposé une nouvelle technique de stéganalyse basée sur les réseaux de neurones AR-TDNN. La technique présentée ici ne permet pas d'extraire l'éventuel message caché, mais simplement de mettre en évidence sa présence. / One of the concerns in the field of secure communication is the concept of information security. Today’s reality is still showing that communication between two parties over long distances has always been subject to interception. Providing secure communication has driven researchers to develop several cryptography schemes. Cryptography methods achieve security in order to make the information unintelligible to guarantee exclusive access for authenticated recipients. Cryptography consists of making the signal look garbled to unauthorized people. Thus, cryptography indicates the existence of a cryptographic communication in progress, which makes eavesdroppers suspect the existence of valuable data. They are thus incited to intercept the transmitted message and to attempt to decipher the secret information. This may be seen as weakness in cryptography schemes. In contrast to cryptography, steganography allows secret communication by camouflaging the secret signal in another signal (named the cover signal), to avoid suspicion. This quality motivated the researchers to work on this burning field to develop schemes ensuring better resistance to hostile attackers. The word steganography is derived from two Greek words: Stego (means cover) and graphy (means writing). The two combined words constitute steganography, which means covert writing, is the art of hiding written communications. Several steganography techniques were used to send message secretly during wars through the territories of enemies. The major contributions of this thesis are the following ones. We propose a new method to secure speech communication using the Discrete Wavelet Transforms (DWT) and the Fast Fourier Transform (FFT). Our method exploits first the high frequencies using a DWT, then exploits the low-pass spectral properties of the speech magnitude spectrum to hide another speech signal in the low-amplitude high-frequencies region of the cover speech signal. The proposed method allows hiding a large amount of secret information while rendering the steganalysis more complex. Comparative evaluation based on objective and subjective criteria is introduced for original speech signal, stego-signal and reconstructed secret speech signal after the hiding process. Experimental simulations on both female and male speakers revealed that our approach is capable of producing a stego speech that is indistinguishable from the cover speech. The receiver is still able to recover an intelligible copy of the secret speech message. We used an LPC10 coder to test the effect of the coding techniques on the stego-speech signals. Experimental results prove the efficiency of the used coding technique since intelligibility of the stego-speech is maintained after the encoding and decoding processes. We also advocate a new steganalysis technique to ensure the robustness of our steganography method. The proposed classifier is called Autoregressive time delay neural network (ARTDNN). The purpose of this steganalysis system is to identify the presence or not of embedded information, and does not actually attempt to extract or decode the hidden data. The low detecting rate prove the robustness of our hiding technique.

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The main goal of this work is to design and to implement a mobile application for drums training. The application must be capable of displaying drum notation of different grooves from various music styles, playing audio examples of those grooves and also analyze and evaluate drumming skills of drummers. The main method for audio processing is discrete wavelet transform. Results show true value rate above 96%.

This work deals with image compression using wavelet transformation. At the beginning , you can find theoretical information about the best known techniques used for image compression , a thorough description of wavelet transormation and the EBCOT algorithm. A significant part of the work is devoted to the library's own implementation . Another chapter of the diploma thesis deals with the comparison and evaluation of the achieved results of the processed library with the JPEG2000 format

This master's thesis deals with electrocardiogram and its distortion, which causes errors, due to which a doctor can make a wrong diagnosis. In the theoretical part electrocardiogram is briefly described, further theory of wavelet transform, introduction to the signal compression and some information about ECG delineation. Methods used for the signal evaluation and therefore determination of the distortion size are described in the main theoretical part. This thesis contains known methods and also a method of own design based on the ECG delineation. Some of the described methods are checked in the practical part. These methods are tested for two different compression algorithms, which cause various distortion. In the final part distortion was tested on the influence on setting the wrong diagnosis.

Dizertační práce se zabývá algoritmy SegDWT pro segmentový výpočet Diskrétní Waveletové Transformace – DWT jedno i vícedimenzionálních dat. Segmentovým výpočtem se rozumí způsob výpočtu waveletové analýzy a syntézy po nezávislých segmentech (blocích) s určitým překryvem tak, že nevznikají blokové artefakty. Analyzující část algoritmu pracuje na principu odstranění přesahu a produkuje vždy část waveletových koeficientů z waveletové transformace celého signálu, které mohou být následně libovolně zpracovány a podrobeny zpětné transformaci. Rekonstruované segmenty jsou pak skládány podle principu přičtení přesahu. Algoritmus SegDWT, ze kterého tato práce vychází, není v současné podobně přímo použitelný pro vícerozměrné signály. Tato práce obsahuje několik jeho modifikací a následné zobecnění pro vícerozměrné signály pomocí principu separability. Kromě toho je v práci představen algoritmus SegLWT, který myšlenku SegDWT přenáší na výpočet waveletové transformace pomocí nekauzálních struktur filtrů typu lifting.

This thesis deals with image compression using wavelet, contourlet and shearlet transformation. It starts with quick look at image compression problem a quality measurement. Next are presented basic concepts of wavelets, multiresolution analysis and scaling function and detailed look at each transform. Representatives of algorithms for coeficients coding are EZW, SPIHT and marginally EBCOT. In second part is described design and implementation of constructed library. Last part compare result of transforms with format JPEG 2000. Comparison resulted in determining type of image in which implemented contourlet and shearlet transform were more effective than wavelet. Format JPEG 2000 was not exceeded.

Práce se zaměřuje na efektivní výpočet dvourozměrné diskrétní vlnkové transformace. Současné metody jsou v práci rozšířeny v několika směrech a to tak, aby spočetly tuto transformaci v jediném průchodu, a to případně víceúrovňově, použitím kompaktního jádra. Tohle jádro dále může být vhodně přeorganizováno za účelem minimalizace užití některých prostředků. Představený přístup krásně zapadá do běžně používaných rozšíření SIMD, využívá hierarchii cache pamětí moderních procesorů a je vhodný k paralelnímu výpočtu. Prezentovaný přístup je nakonec začleněn do kompresního řetězce formátu JPEG 2000, ve kterém se ukázal být zásadně rychlejší než široce používané implementace.