Hartley transform and the use of the Whitened Hartley spectrum as a tool for phase spectral processing

The Hartley transform is a mathematical transformation which is closely related to the better known Fourier transform. The properties that differentiate the Hartley Transform from its Fourier counterpart are that the forward and the inverse transforms are identical and also that the Hartley transform of a real signal is a real function of frequency. The Whitened Hartley spectrum, which stems from the Hartley transform, is a bounded function that encapsulates the phase content of a signal. The Whitened Hartley spectrum, unlike the Fourier phase spectrum, is a function that does not suffer from discontinuities or wrapping ambiguities. An overview on how the Whitened Hartley spectrum encapsulates the phase content of a signal more efficiently compared with its Fourier counterpart as well as the reason that phase unwrapping is not necessary for the Whitened Hartley spectrum, are provided in this study. Moreover, in this study, the product–convolution relationship, the time-shift property and the power spectral density function of the Hartley transform are presented. Finally, a short-time analysis of the Whitened Hartley spectrum as well as the considerations related to the estimation of the phase spectral content of a signal via the Hartley transform, are elaborated

Computation of the one-dimensional unwrapped phase

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 101-102). "Cepstrum bibliography" (p. 67-100).In this thesis, the computation of the unwrapped phase of the discrete-time Fourier transform (DTFT) of a one-dimensional finite-length signal is explored. The phase of the DTFT is not unique, and may contain integer multiple of 27r discontinuities. The unwrapped phase is the instance of the phase function chosen to ensure continuity. This thesis presents existing algorithms for computing the unwrapped phase, discussing their weaknesses and strengths. Then two composite algorithms are proposed that use the existing ones, combining their strengths while avoiding their weaknesses. The core of the proposed methods is based on recent advances in polynomial factoring. The proposed methods are implemented and compared to the existing ones.by Zahi Nadim Karam.S.M

Text-Independent Automatic Speaker Identification Using Partitioned Neural Networks

This dissertation introduces a binary partitioned approach to statistical pattern classification which is applied to talker identification using neural networks. In recent years artificial neural networks have been shown to work exceptionally well for small but difficult pattern classification tasks. However, their application to large tasks (i.e., having more than ten to 20 categories) is limited by a dramatic increase in required training time. The time required to train a single network to perform N-way classification is nearly proportional to the exponential of N. In contrast, the binary partitioned approach requires training times on the order of N2. Besides partitioning, other related issues were investigated such as acoustic feature selection for speaker identification and neural network optimization. The binary partitioned approach was used to develop an automatic speaker identification system for 120 male and 130 female speakers of a standard speech data base. The system performs with 100% accuracy in a text-independent mode when trained with about nine to 14 seconds of speech and tested with six to eight seconds of speech

Optophone design: optical-to-auditory vision substitution for the blind

An optophone is a device that turns light into sound for the benefit of blind people. The present project is intended to produce a general-purpose optophone to be worn on the head about the house and in the street, to give the wearer a detailed description in sound of the'scene he is facing. The device will therefore consist'of an'electronic camera, some signal-processing electronics, earphones`, and a battery. The two major problems are the derivation of (a) the most suitable mapping from images to sounds, and (b) an algorithm to perform the mapping in real'time on existing electronic components. This thesis concerns problem (a). Chapter 2 goes into the general scene-to-sound mapping problem in some detail'and presents the work of earlier investigators. Chapter 3 1- discusses the design of tests to evaluate the performance of candidate mappings. A theoretical performance test (TPT) is derived. Chapter 4 applies the TPT to the most obvious mapping, the cartesian piano transform. Chapter 5 applies the TPT to a mapping based on the cosine transform. Chapter 6 attempts to derive a mapping by principal component analysis, using the inaccuracies of human sight and hearing and the statistical properties of real scenes and sounds. Chapter 7 presents a complete scheme, implemented in software, for representing digitised colour scenes by audible digitised stereo sound. Chapter 8 tries to decide how'many numbers are required to specify a steady spectrum with no noticeable degradation. Chapter 9 looks'at a scheme designed to produce more natural-sounding sounds related to more meaningful portions of the scene. This scheme maps windows in the scene to steady spectral patterns of short duration, the location of the window being conveyed by simulated free-field listening. Chapter 10 gives detailed recommendations as to further work

About intelligent maintenance and diagnosis techniques for mechatronic systems : case study using fractional order calculus

Orientadores: João Maurício Rosário, José António Tenreiro MachadoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: A competitividade no mercado global exige cada vez mais a fabricação de produtos de alta qualidade em curto tempo de fabricação, evitando tempos de parada para manutenção e reparo de máquinas e equipamentos, exigindo assim um eficiente controle de qualidade do processo e dos produtos para evitar a ocorrência de falhas de fabricação e utilização. A integração de novas tecnologias em produtos industriais (ex. tecnologias mecatrônicas) exige a utilização de técnicas avançadas para o diagnóstico de falhas, a partir de análise dos sinais obtidos a partir do sensoriamento dos equipamentos, minimizando assim os custos de utilização de mão de obra especializada para controle de qualidade do produto. Neste trabalho é apresentado inicialmente, um estudo sobre o estado da arte em técnicas de manutenção industrial, com ênfase nas estratégias utilizadas para manutenção corretiva, periódica e baseada no comportamento com ênfase no estudo das técnicas de processamento do sinal e identificação de sistemas, frequentemente utilizadas no diagnóstico de sistemas mecatrônicos, que exigem uma grande quantidade de informações, e forte dependência da análise criteriosa de um técnico especializado. Assim, neste trabalho são utilizados sistemas de ordem fracionária, que permite a aproximação do comportamento real do sistema por meio de modelos com menos coeficientes que o sistema real, simplificando a análise do sistema em estudo. Um estudo experimental de caso para validação do trabalho é realizado a partir de uma bancada experimental de um sistema de transmissão por engrenagens que permitiu introduzir falhas particulares no sistema e sua identificaçãoAbstract: The global market competitiveness requires to make high quality products in a short time of manufacturing, avoiding stop-times due to maintenance and repairing of machines and devices, therefore, demanding an efficient quality control of the manufacturing process, in order to shun failures in fabrication and utilization. The integration of new technologies into industrial products (e.g. mechatronics technologies) requires the use of advanced techniques to a precise failure diagnosis. They are typically based on signal analyses, which are obtained from the machines' instrumentation, and consequently, reduce the manpower costs associated to quality control of particular products. In this work it is introduced a literature review of industrial maintenance techniques, focusing in the strategies used into corrective, periodic and condition based maintenance, specially using signal processing and system identification. Those paradigms are frequently applied into the mechatronics systems diagnosis, but requires a huge amount of information and it is strongly dependent on the specialist criterion. In this sense, we introduced a fractional order system approach, which results in a better approximation of the actual system through an few parameters architecture, hence simplifying the analysis of the actual system. A real experimental setup was used to validate the strategies studied in this work. It consist in a gear transmission that lets to introduce particular failures for a posterior identificationDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Towards segmentation into surfaces

Image segmentation is a fundamental problem of low level computer vision and is also used as a preprocessing step for a number of higher level tasks (e.g. object detection and recognition, action classification, optical flow and stereo computation etc). In this dissertation we study the image segmentation problem focusing on the task of segmentation into surfaces. First we present our unifying framework through which mean shift, bilateral filtering and anisotropic diffusion can be described. Three new methods are also described and implemented and the most prominent of them, called Color Mean Shift (CMS), is extensively tested and compared against the existing methods. We experimentally show that CMS outperforms the other methods i.e., creates more uniform regions and retains equally well the edges between segments. Next we argue that color based segmentation should be a two stage process; edge preserving filtering, followed by pixel clustering. We create novel segmentation algorithms by coupling the previously described filtering methods with standard grouping techniques. We compare all the segmentation methods with current state of the art grouping methods and show that they produce better results on the Berkeley and Weizmann segmentation datasets. A number of other interesting conclusions are also drawn from the comparison. Then we focus on surface normal estimation techniques. We present two novel methods to estimate the parameters of a planar surface viewed by a moving robot when the odometry is known. We also present a way of combining them and integrate the measurements over time using an extended Kalman filter. We test the estimation accuracy by demonstrating the ability of the system to navigate in an indoor environment using exclusively vision. We conclude this dissertation with a discussion on how color based segmentation can be integrated into a structure from motion framework that computes planar surfaces using homographies

Automated Epileptic Seizure Onset Detection

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state

Automated classification of humpback whale (Megaptera novaeangliae) songs using hidden Markov models

Humpback whales songs have been widely investigated in the past few decades. This study proposes a new approach for the classification of the calls detected in the songs with the use of Hidden Markov Models (HMMs). HMMs have been used once before for such task but in an unsupervised algorithm with promising results. Here HMMs were trained and two models were employed to classify the calls into their component units and subunits. The results show that classification of humpback whale songs from one year to another is possible even with limited training. The classification is fully automated apart from the labelling of the training set and the input of the initial HMM prototype models. Two different models for the song structure are considered: one based on song units and one based on subunits. The latter model is shown to achieve better recognition results with a reduced need for updating when applied to a variety of recordings from different years and different geographic locations