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A ressonância estocástica (RE) mostra que certos níveis de ruído ajudam na detecção e transmissão de sinais subliminares. Melhorias no desempenho do sistema somato-sensorial e motor (dentre outros) têm sido obtidos por meio da RE gerada pela utilização de sinais aditivos de intensidade ótima. O sinal aditivo (SA) mais comumente utilizado é o ruído branco gaussiano (RBG). Este estudo teve como objetivo verificar se é possível gerar RE no sistema sensorial tátil usando como SA um sinal senoidal e comparar estes resultados com os obtidos realizando o mesmo experimento com SA de tipo RBG. Os sinais usados no experimento foram definidos como sinal de estímulo (SE) de 3Hz a ser reconhecido com a ajuda dos SA, sinal aditivo senoidal (SAS) de 150Hz e sinal aditivo de ruido branco gaussiano (SARBG) filtrado a 150Hz. Na primeira parte do estudo foi feita uma simulação do modelo de neurônio de Hodgkin e Huxley para verificar se na teoria podia se obter RE para SE e SA senoidais. Foi injetado um sinal senoidal de 3Hz no modelo com uma intensidade para a qual o neurônio não conseguia gerar potencial de ação (PA). Quando a este sinal inicial foi adicionado um sinal senoidal de frequência superior, o neurônio conseguiu responder. A mesma resposta foi obtida quando o SA usado foi RBG, conseguindo mostrar de forma qualitativa a nossa hipótese a partir de um modelo simulado. Posteriormente foi realizado um estudo psicofísico com 20 voluntários (11 homens e 9 mulheres) para verificar o desempenho do SAS e comparar este com o desempenho de SARBG para a detecção sensorial do SE. Primeiro foi achado o limiar de detecção (LD) para cada um dos sinais usados e no experimento este valor foi usado para determinar a intensidade de estímulo. No caso do SE a intensidade foi definida como 80% do LD de cada voluntário. No caso dos SA a intensidade foi variando entre 0% até 80% do LD, com o objetivo de se encontrar a melhor proporção de SA adicionado para detectar o SE. Em 90% dos casos conseguiu-se gerar RE tanto empregando um sinal senoidal de frequência rápida como SA, quanto utilizando-se RBG. Ambos SAs apresentaram uma melhoria estatisticamente significativa na proporção de detecção (PD) do SE. Porém, nenhum dos SA apresentou um melhor desempenho em relação ao outro, de maneira que poderia ser usado tanto um quanto outro tipo de SA para gerar RE no sistema somato-sensorial. Este trabalho é pioneiro em usar uma combinação de senóides para gerar RE e abre as portas à elaboração e desenvolvimento de dispositivos biomédicos que contenham uma parte geradora de RE e consigam melhorar a estabilidade e controle postural em pessoas com deficiência motora ou somato-sensorial. / Stochastic ressonance (SR) shows that certain levels of noise help to detect and transmit subliminal signals. Improvements in the performance of the somatosensory and motor systems (among others) have been obtained through the SR generated using additive signals with optimal intensity. The most commonly used additive signal (AS) is white Gaussian noise (WGN). This study aimed to verify whether it is possible to generate SR in the tactile sensory system using a sinusoidal signal as the AS and, at the same time, compare the results when the AS was WGN. The signals used in the experiments were defined as 3Hz for the stimulus signal (SS), to be recognized with the aid of ASs. These were either a sinuoid of 150Hz additive sinusoidal signal (ASS) or a white Gaussian noise additive signal (WGNAS) filtered at 150Hz. In the first part of the study a simulation of the Hodgkin and Huxley neuron model was made to verify if it could undergo SR for the same types of SS and AS mentioned before. A 3Hz sine signal was injected into the model with an intensity at which the neuron could not generate action potentials. When a higher frequency sine wave was added to this initial signal, the neuron could respond. The same behaviour was obtained when the additive signal used was WGN, giving, hence, a qualitative confirmation of our hypothesis. A psychophysical study was then carried out with 20 volunteers (11 men and 9 women) to verify the performance of the ASS and compare it with the WGNAS for the sensory detection of the sinusoidal SS. Initially, the detection threshold (DT) was found for each of the signals used. During the experiment, this value was used to determine the stimulus intensity. In the case of the SS the intensity was defined as 80 % of the DT of each volunteer. In the case of ASs, the intensity varied from 0% to 80% of the DT, in order to find the best proportion of AS added to detect the SS. In 90% of the cases it was possible to generate SR using either a fast frequency ASS or the WGNAS. Both ASs showed a statistically significant improvement in the detection rate (DR) of the SS. However, none of ASs performed better than the other, so that both types could be used to generate SR in the somatosensory system. This work has pioneered the use of a combination of sinusoids to generate SR and opens the door to the development of biomedical devices that help generate SR to provide stability improvement and better postural control for people with motor or somatosensory impairment.

A ressonância estocástica (RE) mostra que certos níveis de ruído ajudam na detecção e transmissão de sinais subliminares. Melhorias no desempenho do sistema somato-sensorial e motor (dentre outros) têm sido obtidos por meio da RE gerada pela utilização de sinais aditivos de intensidade ótima. O sinal aditivo (SA) mais comumente utilizado é o ruído branco gaussiano (RBG). Este estudo teve como objetivo verificar se é possível gerar RE no sistema sensorial tátil usando como SA um sinal senoidal e comparar estes resultados com os obtidos realizando o mesmo experimento com SA de tipo RBG. Os sinais usados no experimento foram definidos como sinal de estímulo (SE) de 3Hz a ser reconhecido com a ajuda dos SA, sinal aditivo senoidal (SAS) de 150Hz e sinal aditivo de ruido branco gaussiano (SARBG) filtrado a 150Hz. Na primeira parte do estudo foi feita uma simulação do modelo de neurônio de Hodgkin e Huxley para verificar se na teoria podia se obter RE para SE e SA senoidais. Foi injetado um sinal senoidal de 3Hz no modelo com uma intensidade para a qual o neurônio não conseguia gerar potencial de ação (PA). Quando a este sinal inicial foi adicionado um sinal senoidal de frequência superior, o neurônio conseguiu responder. A mesma resposta foi obtida quando o SA usado foi RBG, conseguindo mostrar de forma qualitativa a nossa hipótese a partir de um modelo simulado. Posteriormente foi realizado um estudo psicofísico com 20 voluntários (11 homens e 9 mulheres) para verificar o desempenho do SAS e comparar este com o desempenho de SARBG para a detecção sensorial do SE. Primeiro foi achado o limiar de detecção (LD) para cada um dos sinais usados e no experimento este valor foi usado para determinar a intensidade de estímulo. No caso do SE a intensidade foi definida como 80% do LD de cada voluntário. No caso dos SA a intensidade foi variando entre 0% até 80% do LD, com o objetivo de se encontrar a melhor proporção de SA adicionado para detectar o SE. Em 90% dos casos conseguiu-se gerar RE tanto empregando um sinal senoidal de frequência rápida como SA, quanto utilizando-se RBG. Ambos SAs apresentaram uma melhoria estatisticamente significativa na proporção de detecção (PD) do SE. Porém, nenhum dos SA apresentou um melhor desempenho em relação ao outro, de maneira que poderia ser usado tanto um quanto outro tipo de SA para gerar RE no sistema somato-sensorial. Este trabalho é pioneiro em usar uma combinação de senóides para gerar RE e abre as portas à elaboração e desenvolvimento de dispositivos biomédicos que contenham uma parte geradora de RE e consigam melhorar a estabilidade e controle postural em pessoas com deficiência motora ou somato-sensorial. / Stochastic ressonance (SR) shows that certain levels of noise help to detect and transmit subliminal signals. Improvements in the performance of the somatosensory and motor systems (among others) have been obtained through the SR generated using additive signals with optimal intensity. The most commonly used additive signal (AS) is white Gaussian noise (WGN). This study aimed to verify whether it is possible to generate SR in the tactile sensory system using a sinusoidal signal as the AS and, at the same time, compare the results when the AS was WGN. The signals used in the experiments were defined as 3Hz for the stimulus signal (SS), to be recognized with the aid of ASs. These were either a sinuoid of 150Hz additive sinusoidal signal (ASS) or a white Gaussian noise additive signal (WGNAS) filtered at 150Hz. In the first part of the study a simulation of the Hodgkin and Huxley neuron model was made to verify if it could undergo SR for the same types of SS and AS mentioned before. A 3Hz sine signal was injected into the model with an intensity at which the neuron could not generate action potentials. When a higher frequency sine wave was added to this initial signal, the neuron could respond. The same behaviour was obtained when the additive signal used was WGN, giving, hence, a qualitative confirmation of our hypothesis. A psychophysical study was then carried out with 20 volunteers (11 men and 9 women) to verify the performance of the ASS and compare it with the WGNAS for the sensory detection of the sinusoidal SS. Initially, the detection threshold (DT) was found for each of the signals used. During the experiment, this value was used to determine the stimulus intensity. In the case of the SS the intensity was defined as 80 % of the DT of each volunteer. In the case of ASs, the intensity varied from 0% to 80% of the DT, in order to find the best proportion of AS added to detect the SS. In 90% of the cases it was possible to generate SR using either a fast frequency ASS or the WGNAS. Both ASs showed a statistically significant improvement in the detection rate (DR) of the SS. However, none of ASs performed better than the other, so that both types could be used to generate SR in the somatosensory system. This work has pioneered the use of a combination of sinusoids to generate SR and opens the door to the development of biomedical devices that help generate SR to provide stability improvement and better postural control for people with motor or somatosensory impairment.

Στην παρούσα διπλωματική εργασία μελετήθηκε και κατασκευάστηκε ένα σύστημα προσομοίωσης και εξομοίωσης των χαρακτηριστικών διαφόρων καναλιών επικοινωνίας ως προς την υποβάθμιση του σήματος και το θόρυβο που αυτά εισάγουν. Για τη διαδικασία σχεδίασης, υλοποίησης και ελέγχου του συστήματος, καθώς και για την εκτέλεση των πειραμάτων χρησιμοποιήθηκε η αναπτυξιακή πλατφόρμα ZedBoard. Στο πρώτο μέ- ρος της εργασίας παρουσιάζονται και αναλύονται τα μοντέλα των υποσυστημάτων που απαρτίζουν το κανάλι, καθώς και η υλοποίησή τους σε λογισμικό και υλικό. Οι δύο κύ- ριες μονάδες επεξεργασίας στο κανάλι είναι ένα FIR φίλτρο και μία γεννήτρια θορύβου. Το φίλτρο χρησιμοποιείται ως το στοιχείο που υποβαθμίζει το σήμα εισόδου, ενώ η γεννήτρια θορύβου εισάγει το τυχαίο σήμα στο κανάλι επικοινωνίας. Στο δεύτερο μέρος της εργασίας αναλύεται η αρχιτεκτονική του τελικού συστήματος, οι διεπαφές Ε/Ε που χρησιμοποιήθηκαν καθώς και η λογική ελέγχου που ακολουθήθηκε. Τέλος, περιγράφουμε την επικοινωνία και τη λογική ελέγχου του συστήματος, σε επίπεδο εντολών από το περιβάλλον της MATLAB. / The purpose of this thesis is to study and implement a simulation and emulation system of the characteristics of different communications channels concerning the degradation of the signal and the noise they introduce. For the process of design, implementation and monitoring of the system as well as to perform the experiment the ZedBoard development platform was used.In the first part we present and analyze the models of the subsystems which constitute the communication channel and also their implementation in software and hardware. The two main processing units in the channel is a FIR filter and a noise generator. The filter is used as the element that lowers the power of the input signal and the noise generator introduces the random signal to the communication channel.In the second part we analyze the architecture of the final system, the I / O interfaces used and the control logic followed.Finally, we describe the communication and control logic of the system. The commands used for this purpose are developed using the computing environment of MATLAB.

Στην παρούσα διπλωματική εργασία επιχειρείται η ανάλυση και η εκτίμηση της συχνότητας απλών ημιτονοειδών σημάτων υπό την παρουσία λευκού Γκαουσιανού θορύβου (AWGN).Η εκτίμηση παραμέτρων απλών ημιτονοειδών σημάτων υπό την παρουσία προσθετικού Γκαουσιανού θορύβου αποτελεί ένα κλασσικό πρόβλημα και σημαντικό αντικείμενο μελέτης εξαιτίας της πληθώρας των εφαρμογών που έχει στην θεωρία ελέγχου, στην επεξεργασία σημάτων, στις ψηφιακές επικοινωνίες, στην βιοϊατρική τεχνολογία κ.α.Η εκτίμηση της συχνότητας είναι συνήθως το θέμα ‘ζωτικής σημασίας’ του προβλήματος για δύο σημαντικούς λόγους. Αφ’ενός οι συχνότητες πρέπει να εκτιμηθούν διότι αποτελούν μη-γραμμικές συναρτήσεις στην ληφθείσα ακολουθία δεδομένων και αφ’ ετέρου έχοντας καθοριστεί οι συχνότητες, οι υπόλοιπες παράμετροι του σήματος όπως είναι το πλάτος και η φάση του, μπορούν να υπολογιστούν άμεσα. Αρχικά γίνεται μία σύντομη εισαγωγή στις βασικές έννοιες πάνω στις οποίες δομείται η εκτίμηση παραμέτρων ενός ημιτονοειδούς σήματος και έπειτα παρουσιάζονται μερικοί αλγόριθμοι εκτίμησης. Πιο συγκεκριμένα παρουσιάζεται η διαδικασία κατασκευής τους και αναλύονται οι επιδόσεις τους. Τέλος παραθέτουμε και προσομοιώσεις μέσω υπολογιστή για κάθε αλγόριθμο ξεχωριστά και συγκρίνουμε την επίδοση του καθενός με τους υπόλοιπους. Από την σύγκριση αυτή γίνεται εξαγωγή χρήσιμων συμπερασμάτων σχετικά με τον προσδιορισμό των παραμέτρων κάθε αλγόριθμου αλλά και με την καταλληλότητα κάθε αλγόριθμου για συγκεκριμένες συνθήκες θορύβου. / In this thesis attempts to analyze and estimate the frequency of single sinusoid signals in Additive White Gaussian Noise (AWGN). Parameter estimation of sinusoids has been a classical problem and it is still an important research topic because of its numerous applications in multiple disciplines such as control theory, signal processing, digital communications, biomedical engineering etc. Estimation of the frequencies is often the crucial step in the problem for two principally reasons. Firstly, frequencies should be estimated because they are nonlinear functions in the received data sequence and secondly, once frequencies have been determined, the remaining parameters, such as amplitude and phase, can then be computed straightforwardly. Primarily we introduce some basic concepts on parameters estimation of sinusoid signals and then several estimation algorithms. More specifically shows the fabrication process of these algorithms and analyze their performance. Finally, we quote computer simulations for each algorithm separately and compare their performance. From these comparisons we can draw conclusions on the determination of parameters for each algorithm and the appropriateness of algorithms for specific noise conditions.

Approved for public release, distribution is unlimited / Despite the increase in the demand for high speed Internet services, the last-mile solutions currently available neither are inexpensive enough to attract the majority of the population, nor are they available in low density populated areas. This thesis examines Fixed Broadband Wireless (FBW) as an alternative technology to the current last-mile solutions. The analysis shows that LMDS and MMDS are the most promising emerging FBW technologies and that they are able, by utilizing microwave radio as their fundamental transport medium and using high modulation schemes, to provide digital two-way voice, data, video and Internet services. The thesis shows that both technologies are constrained by free space loss and line-of-sight impairments with rain absorption being the most significant cause of attenuation in the LMDS case, while vegetation and multipath fading play a significant role mostly in the MMDS case. Additionally, it is shown that there is a positive association between the data rate achieved and the level of influence due to Additive White Gaussian Noise (AWGN). Based on the analysis and using the coverage areas, the total capacity, the achieved data rates, the weather and line-of-sight limitations as well as the cost as the most important criteria, it is concluded that LMDS is a preferable solution for enterprise end-users in densely populated urban areas outside the reach of fiber networks, while MMDS targets residential end-users in rural or suburban areas that are not able to receive service through high-speed wireline connections. / Lieutenant Junior Grade, Hellenic Navy

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 2011

We address the problem of image denoising for additive white Gaussian noise (AWGN), Poisson noise, and Chi-squared noise scenarios. Thermal noise in electronic circuitry in camera hardware can be modeled as AWGN. Poisson noise is used to model the randomness associated with photon counting during image acquisition. Chi-squared noise statistics are appropriate in imaging modalities such as Magnetic Resonance Imaging (MRI). AWGN is additive, while Poisson noise is neither additive nor multiplicative. Although Chi-squared noise is derived from AWGN statistics, it is non-additive. Mean-square error (MSE) is the most widely used metric to quantify denoising performance. In parametric denoising approaches, the optimal parameters of the denoising function are chosen by employing a minimum mean-square-error (MMSE) criterion. However, the dependence of MSE on the noise-free signal makes MSE computation infeasible in practical scenarios. We circumvent the problem by adopting an MSE estimation approach. The ground-truth-independent estimates of MSE are Stein’s unbiased risk estimate (SURE), Poisson unbiased risk estimate (PURE) and Chi-square unbiased risk estimate (CURE) for AWGN, Poison and Chi-square noise models, respectively. The denoising function is optimized to achieve maximum noise suppression by minimizing the MSE estimates. We have chosen the bilateral filter as the denoising function. Bilateral filter is a nonlinear edge-preserving smoother. The performance of the bilateral filter is governed by the choice of its parameters, which can be optimized to minimize the MSE or its estimate. However, in practical scenarios, MSE cannot be computed due to inaccessibility of the noise-free image. We derive SURE, PURE, and CURE in the context of bilateral filtering and compute the parameters of the bilateral filter that yield the minimum cost (SURE/PURE/CURE). On processing the noisy input with bilateral filter whose optimal parameters are chosen by minimizing MSE estimates (SURE/PURE/CURE), we obtain the estimate closest to the ground truth. We denote the bilateral filter with optimal parameters as SURE-optimal bilateral filter (SOBF), PURE-optimal bilateral filter (POBF) and CURE-optimal bilateral filter (COBF) for AWGN, Poisson and Chi-Squared noise scenarios, respectively. In addition to the globally optimal bilateral filters (SOBF and POBF), we propose spatially adaptive bilateral filter variants, namely, SURE-optimal patch-based bilateral filter (SPBF) and PURE-optimal patch-based bilateral filter (PPBF). SPBF and PPBF yield significant improvements in performance and preserve edges better when compared with their globally-optimal counterparts, SOBF and POBF, respectively. We also propose the SURE-optimal multiresolution bilateral filter (SMBF) where we couple SOBF with wavelet thresholding. For Poisson noise suppression, we propose PURE-optimal multiresolution bilateral filter (PMBF), which is the Poisson counterpart of SMBF. We com-pare the performance of SMBF and PMBF with the state-of-the-art denoising algorithms for AWGN and Poisson noise, respectively. The proposed multiresolution-based bilateral filtering techniques yield denoising performance that is competent with that of the state-of-the-art techniques.

A Broadcast Channel is a communication path between a single source and two or more receivers or users. The source intends to communicate independent information to the users. A particular case of interest is the Gaussian Broadcast Channel (GBC) where the noise at each user is additive white Gaussian noise (AWGN). The capacity region of GBC is well known and the input to the channel is distributed as Gaussian. The capacity region of another special case of GBC namely Fading Broadcast Channel (FBC)was given in [Li and Goldsmith, 2001]and was shown that superposition of Gaussian codes is optimal for the FBC (treated as a vector degraded Broadcast Channel). The capacity region obtained when the input to the channel is distributed uniformly over a finite alphabet(Constellation)is termed as Constellation Constrained(CC) capacity region [Biglieri 2005]. In this thesis the CC capacity region for two-user GBC and the FBC are obtained. In case of GBC the idea of superposition coding with input from finite alphabet and CC capacity was explored in [Hupert and Bossert, 2007]but with some limitations. When the participating individual signal sets are nearly equal i.e., given total average power constraint P the rate reward α (also the power sharing parameter) is approximately equal to 0.5, we show via simulation that with rotation of one of the signal sets by an appropriate angle the CC capacity region is maximally enlarged. We analytically derive the expression for optimal angle of rotation. In case of FBC a heuristic power allocation procedure called finite-constellation power allocation procedure is provided through which it is shown (via simulation)that the ergodic CC capacity region thus obtained completely subsumes the ergodic CC capacity region obtained by allocating power using the procedure given in[Li and Goldsmith, 2001].It is shown through simulations that rotating one of the signal sets by an optimal angle (obtained by trial and error method)for a given α maximally enlarges the ergodic CC capacity region when finite-constellation power allocation is used. An expression for determining the optimal angle of rotation for the given fading state, is obtained. And the effect of rotation is maximum around the region corresponding to α =0.5. For both GBC and FBC superposition coding is done at the transmitter and successive decoding is carried out at the receivers.

Image De-noising forms an integral part of image processing. It is used as a standalone algorithm for improving the quality of the image obtained through camera as well as a starting stage for image processing applications like face recognition, super resolution etc. Non Local Means (NL-Means) and Bilateral Filter are two computationally complex de-noising algorithms which could provide good de-noising results. Due to its computational complexity, the real time applications associated with these letters are limited. In this thesis, we propose the use of hardware accelerators such as GPU (Graphics Processing Units) and FPGA (Field Programmable Gate Arrays) to speed up the filter execution and efficiently implement using them. GPU based implementation of these letters is carried out using Open Computing Language (Open CL). The basic objective of this research is to perform high speed de-noising without compromising on the quality. Here we implement a basic NL-Means filter, a Fast NL-Means filter, and Bilateral filter using Gauss Polynomial decomposition on GPU. We also propose a modification to the existing NL-Means algorithm and Gauss Polynomial Bilateral filter. Instead of Gaussian Spatial Kernel used in standard algorithm, Box Spatial kernel is introduced to improve the speed of execution of the algorithm. This research work is a step forward towards making the real time implementation of these algorithms possible. It has been found from results that the NL-Means implementation on GPU using Open CL is about 25x faster than regular CPU based implementation for larger images (1024x1024). For Fast NL-Means, GPU based implementation is about 90x faster than CPU implementation. Even with the improved execution time, the embedded system application of the NL-Means is limited due to the power and thermal restrictions of the GPU device. In order to create a low power and faster implementation, we have implemented the algorithm on FPGA. FPGAs are reconfigurable devices and enable us to create a custom architecture for the parallel execution of the algorithm. It was found that the execution time for smaller images (256x256) is about 200x faster than CPU implementation and about 25x faster than GPU execution. Moreover the power requirements of the FPGA design of the algorithm (0.53W) is much less compared to CPU(30W) and GPU(200W).