Model Selection for Latent Force Models

En esta tesis exploramos varias extensiones para los modelos de fuerza latente (MFL). Primero, el numero de fuerzas latentes is seleccionado automaticamente por medio del Proceso del Buffet lndio. Segundo, estimamos la funcic’m de respuesta al impulso (FRI) de sistemas lineales e invariantes en el tiempo usando las funciones de Laguerre sobre los MFL y los MFL secuenciales. Finalmente, se desarrollan métodos enfocados en la estimacién de la fuerza latente y FRI sobre sistemas dinamicos de tipo Wiener

Novel Technologies for Real-Time Fluorescent Lifetime Imagind Data Acquisition and Processing for Clinical Diagnosis

Endogenous Fluorescence Lifetime Imaging (FLIM) is a noninvasive technique that has been explored with promising results in a wide range of biomedical applications, including clinical diagnosis. A central issue for the translation of FLIM into the medical field is the development of a robust, fast and cost-effective FLIM instrumentation suitable for in vivo tissue imaging. This thesis directly addressed some of the technical limitations that must be overcome to enable clinical applications of FLIM. The following specific aims were accomplished. First, endogenous FLIM imaging and high-resolution reflectance confocal microscopy (RCM) were integrated into a multimodal bench-top optical system. This multimodal system was used to image oral epithelial cancer in a hamster cheek pouch model. Second, an endoscopic system for fast (0.5-4 frames/second) endogenous wide-field FLIM imaging of oral lesions was developed. The FLIM endoscope system is being evaluate at Texas A&M University College of Dentistry, where more than 80 patients presenting oral lesions suspected of pre-cancer or cancer have been imaged up to date. Third, a novel fluorescence lifetime estimation algorithm was developed to achieve robust, accurate, and real-time fluorescence lifetime estimation. This algorithm is enabling real-time FLIM image processing and visualization during the endoscopic examination of patients with suspicious oral lesions. Finally, the endoscopic endogenous FLIM data from suspicious oral lesions collected at the Texas A&M College of Dentistry was used to develop machine learning algorithms for automated identification of precancerous and cancerous lesions from benign oral epithelial lesions. Our results indicate that endogenous FLIM endoscopy can detect oral epithelial pre-cancer and cancer from a wider range of benign conditions, with levels of sensitivity and specificity above 85%. Altogether, this work has demonstrated the potentials of endogenous FLIM endoscopy as a clinical tool for early detection of oral epithelial cancer

Generalized linear-in-parameter models : theory and audio signal processing applications

This thesis presents a mathematically oriented perspective to some basic concepts of digital signal processing. A general framework for the development of alternative signal and system representations is attained by defining a generalized linear-in-parameter model (GLM) configuration. The GLM provides a direct view into the origins of many familiar methods in signal processing, implying a variety of generalizations, and it serves as a natural introduction to rational orthonormal model structures. In particular, the conventional division between finite impulse response (FIR) and infinite impulse response (IIR) filtering methods is reconsidered. The latter part of the thesis consists of audio oriented case studies, including loudspeaker equalization, musical instrument body modeling, and room response modeling. The proposed collection of IIR filter design techniques is submitted to challenging modeling tasks. The most important practical contribution of this thesis is the introduction of a procedure for the optimization of rational orthonormal filter structures, called the BU-method. More generally, the BU-method and its variants, including the (complex) warped extension, the (C)WBU-method, can be consider as entirely new IIR filter design strategies.reviewe

Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals

Advanced algorithms are required to reveal the complex relations between neural and behavioral data. In this study, forelimb electromyography (EMG) signals were reconstructed from multi-unit neural signals recorded with multiple electrode arrays (MEAs) from the corticospinal tract (CST) in rats. A six-layer convolutional neural network (CNN) was compared with linear decoders for predicting the EMG signal. The network contained three session-dependent Rectified Linear Unit (ReLU) feature layers and three Gamma function layers were shared between sessions. Coefficient of determination (R2) values over 0.2 and correlations over 0.5 were achieved for reconstruction within individual sessions in multiple animals, even though the forelimb position was unconstrained for most of the behavior duration. The CNN performed visibily better than the linear decoders and model responses outlasted the activation duration of the rat neuromuscular system. These findings suggest that the CNN model implicitly predicted short-term dynamics of skilled forelimb movements from neural signals. These results are encouraging that similar problems in neural signal processing may be solved using variants of CNNs defined with simple analytical functions. Low powered firmware can be developed to house these CNN solutions in real-time applications

High speed data transmission over HF radio links

The thesis describes the results of research work on techniques for high speed data transmission (2.4 kbit/s) over voice-band HF radio channels. This work has been carried out using extensive computer simulation of the various transmission techniques and the HF radio channels. Firstly, the characteristics of HF radio channels are discussed in detail and an HF channel model, suitable for computer simulation, is developed. The first of two techniques for high data rate transmission over HF links is then introduced, namely, multi-channel (or parallel) DPSK transmission. Parallel transmission is a well known technique in this application but it has been studied and simulated, in order to compare its performance with that of the second, more novel, transmission technique. This is a single channel system employing 4 point QAM signalling at the transmitter and maximum likelihood detection at the receiver. Initially, the parallel system is compared with an idealised serial system employing optimum Viterbi detection at the receiver with all other functions of the serial function assumed perfect. However, having shown the vastly superior performance of this serial system, a more practical serial modem is gradually developed, with further performance comparisons at each stage in this development. The final comparison is made with a very practical form of serial modem in which all practical receiver functions are simulated. Theseinclude a simpler, adaptive near maximum likelihood detector, receiver filtering, channel estimator, carrier phase tracking, timing synchronisation and automatic gain control. Finally, the design and implementation of the serial modem is studied and details of the complexity of a digital, processor-based, realisation are given

Failure detection system design methodology

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules

Realtime image noise reduction FPGA implementation with edge detection

The purpose of this dissertation was to develop and implement, in a Field Programmable Gate Array (FPGA), a noise reduction algorithm for real-time sensor acquired images. A Moving Average filter was chosen due to its fulfillment of a low demanding computational expenditure nature, speed, good precision and low to medium hardware resources utilization. The technique is simple to implement, however, if all pixels are indiscriminately filtered, the result will be a blurry image which is undesirable. Since human eye is more sensitive to contrasts, a technique was introduced to preserve sharp contour transitions which, in the author’s opinion, is the dissertation contribution. Synthetic and real images were tested. Synthetic, composed both with sharp and soft tone transitions, were generated with a developed algorithm, while real images were captured with an 8-kbit (8192 shades) high resolution sensor scaled up to 10 × 103 shades. A least-squares polynomial data smoothing filter, Savitzky-Golay, was used as comparison. It can be adjusted using 3 degrees of freedom ─ the window frame length which varies the filtering relation size between pixels’ neighborhood, the derivative order, which varies the curviness and the polynomial coefficients which change the adaptability of the curve. Moving Average filter only permits one degree of freedom, the window frame length. Tests revealed promising results with 2 and 4ℎ polynomial orders. Higher qualitative results were achieved with Savitzky-Golay’s better signal characteristics preservation, especially at high frequencies. FPGA algorithms were implemented in 64-bit integer registers serving two purposes: increase precision, hence, reducing the error comparatively as if it were done in floating-point registers; accommodate the registers’ growing cumulative multiplications. Results were then compared with MATLAB’s double precision 64-bit floating-point computations to verify the error difference between both. Used comparison parameters were Mean Squared Error, Signalto-Noise Ratio and Similarity coefficient.O objetivo desta dissertação foi desenvolver e implementar, em FPGA, um algoritmo de redução de ruído para imagens adquiridas em tempo real. Optou-se por um filtro de Média Deslizante por não exigir uma elevada complexidade computacional, ser rápido, ter boa precisão e requerer moderada utilização de recursos. A técnica é simples, mas se abordada como filtragem monotónica, o resultado é uma indesejável imagem desfocada. Dado o olho humano ser mais sensível ao contraste, introduziu-se uma técnica para preservar os contornos que, na opinião do autor, é a sua principal contribuição. Utilizaram-se imagens sintéticas e reais nos testes. As sintéticas, compostas por fortes e suaves contrastes foram geradas por um algoritmo desenvolvido. As reais foram capturadas com um sensor de alta resolução de 8-kbit (8192 tons) e escalonadas a 10 × 103 tons. Um filtro com suavização polinomial de mínimos quadrados, SavitzkyGolay, foi usado como comparação. Possui 3 graus de liberdade: o tamanho da janela, que varia o tamanho da relação de filtragem entre os pixels vizinhos; a ordem da derivada, que varia a curvatura do filtro e os coeficientes polinomiais, que variam a adaptabilidade da curva aos pontos a suavizar. O filtro de Média Deslizante é apenas ajustável no tamanho da janela. Os testes revelaram-se promissores nas 2ª e 4ª ordens polinomiais. Obtiveram-se resultados qualitativos com o filtro Savitzky-Golay que detém melhores características na preservação do sinal, especialmente em altas frequências. Os algoritmos em FPGA foram implementados em registos de vírgula fixa de 64-bits, servindo dois propósitos: aumentar a precisão, reduzindo o erro comparativamente ao terem sido em vírgula flutuante; acomodar o efeito cumulativo das multiplicações. Os resultados foram comparados com os cálculos de 64-bits obtidos pelo MATLAB para verificar a diferença de erro entre ambos. Os parâmetros de medida foram MSE, SNR e coeficiente de Semelhança

Novel Insights into Orbital Angular Momentum Beams: From Fundamentals, Devices to Applications

It is well-known by now that the angular momentum carried by elementary particles can be categorized as spin angular momentum (SAM) and orbital angular momentum (OAM). In the early 1900s, Poynting recognized that a particle, such as a photon, can carry SAM, which has only two possible states, i.e., clockwise and anticlockwise circular polarization states. However, only fairly recently, in 1992, Allen et al. discovered that photons with helical phase fronts can carry OAM, which has infinite orthogonal states. In the past two decades, the OAM-carrying beam, due to its unique features, has gained increasing interest from many different research communities, including physics, chemistry, and engineering. Its twisted phase front and intensity distribution have enabled a variety of applications, such as micromanipulation, laser beam machining, nonlinear matter interactions, imaging, sensing, quantum cryptography and classical communications. This book aims to explore novel insights of OAM beams. It focuses on state-of-the-art advances in fundamental theories, devices and applications, as well as future perspectives of OAM beams