Μελετάται η χρήση προσαρμοστικών αλγορίθμων οι οποίοι εφαρμόζονται στο πεδίο των συχνοτήτων και σκοπός τους είναι να ακυρωθεί σε όσον το δυνατόν μεγαλύτερο βαθμό η επίδραση της ηχούς σε ένα περιβάλλον τηλεδιάσκεψης. Όσον αφορά την προσομοίωση του τηλεπικοινωνιακού περιβάλλοντος αυτή θα γίνει με την χρησιμοποίηση κρουστικών αποκρίσεων οι οποίες θα λαμβάνονται χρησιμοποιώντας την μέθοδο των ψηφιακών κυματοδηγών. / Frequency domain adaptive filters are evaluated for use in a teleconferencing environment. The convergence rate, steady state, ability to track changes of the Frequency domain block quasi-Newton algorithm is compared to the Frequency domain block LMS (FD-BLMS)and time domain normalized LMS (TD-NLMS). Finally an algorithm for acoustic simulation of small rooms is derived in order to produce acoustic echo simulation data for use in the evaluation of the algorithms.

The objective of Diploma thesis is design of analysis tool for equalizers used in FMT modulation. The model of transmission channel was designed for simulations with FMT. The transmission path is modeled by test loop, which corresponds to DSL line. For this reason, some principles of DSL technology is described in the thesis. The principles of multicarrier modulation are introduced in first part. The multicarrier modulation with filter bank (FMT) is described in detail.The different methods of design the fiter bank are given and compared. Channel equalization are introduced in second part. The attention was focused on minimum mean square error filtering (MMSE). Decision feedback channel equalizer (DFE) is extended from linear MMSE equalizer. DFE equalizers were programmed in analysis tool. For computation of equalizer coeficients was used also equalizers based on adaptive algorithms and MMSE. The last part describes the results of DFE equalizers used in communication system with FMT modulation. Analysis tool was programmed in MATLAB with a graphical user interface. It allows to show mean square error, signal-to-noise ratio and transmission speed dependence on delay between original and distorted signal. Signal-to-noise ration is displayed also in individual subchannels and users can display mean square error dependence on different orders of DFE filters.

Electroreception is a sensory modality found in some fish, which enables them to sense the environment through the detection of electric fields. Biological experimentation on this ability has built an intricate framework that has identified many of the components involved in electroreception's production, but lack the framework for bringing the details back together into a system-level model of how they operate together. This thesis builds and tests a computational model of the Electrosensory Lateral Line Lobe (ELL) in mormyrid electric fish in an attempt to bring some of electroreception's structural details together to help explain its function. The ELL is a brain region that functions as a primary processing area of electroreception. It acts as an adaptive filter that learns to predict reoccurring stimuli and removes them from its sensory stream, passing only novel inputs to other brain regions for further processing. By creating a model of the ELL, the relevant components which underlie the ELL's functional, electrophysiological patterns can be identified and scientific hypotheses regarding their behavior can be tested. Systems science's approach is adopted to identify the ELL's relevant components and bring them together into a unified conceptual framework. The methodological framework of computational neuroscience is used to create a computational model of this structure of relevant components and to simulate their interactions. Individual activation tendencies of the different included cell types are modeled with dynamical systems equations and are interconnected according to the connectivity of the real ELL. Several of the ELL's input patterns are modeled and incorporated in the model. The computational approach claims that if all of the relevant components of a system are captured and interconnected accurately in a computer program, then when provided with accurate representations of the inputs a simulation should produce functional patterns similar to those of the real system. These simulated patterns generated by the ELL model are compared to recordings from real mormyrid ELLs and their correspondences validate or nullify the model's integrity. By building a computation model that can capture the relevant components of the ELL's structure and through simulation reproduces its function, a systems-level understanding begins to emerge and leads to a description of how the ELL's structure, along with relevant inputs, generate its function. The model can be manipulated more easily than a biological ELL, and allows us to test hypotheses regarding how changes in the structures affect the function, and how different inputs propagate through the structure in a way that produces complex functional patterns.

No description available.

Time Interleaved Analog-to-Digital Converters (TI-ADCs) utilize an architecture which enables conversion rates well beyond the capabilities of a single converter while preserving most or all of the other performance characteristics of the converters on which said architecture is based. Most of the approaches discussed here are independent of architecture; some solutions take advantage of specific architectures. Chapter 1 provides the problem formulation and reviews the errors found in ADCs as well as a brief literature review of available TI-ADC error correction solutions. Chapter 2 presents the methods and materials used in implementation as well as extend the state of the art for post conversion correction. Chapter 3 presents the simulation results of this work and Chapter 4 concludes the work. The contribution of this research is three fold: A new behavioral model was developed in SimulinkTM and MATLABTM to model and test linear and nonlinear mismatch errors emulating the performance data of actual converters. The details of this model are presented as well as the results of cumulant statistical calculations of the mismatch errors which is followed by the detailed explanation and performance evaluation of the extension developed in this research effort. Leading post conversion correction methods are presented and an extension with derivations is presented. It is shown that the data converter subsystem architecture developed is capable of realizing better performance of those currently reported in the literature while having a more efficient implementation.

Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this proposed method. Simulation results on actual ECG signals from MIT-BIH arrhythmia database [30] show this approach yields improvement over the un-filtered signal in terms of signal-to-noise ratio (SNR).

Laser interferometry, commonly used in high-precision motion control systems, is rarely adopted in experimental vibration analysis because its installation and mounting is invasive to dynamical systems. However, metrology systems that already utilize laser interferometry, such as profilometry in semiconductor manufacturing, may benefit from interferometer feedback for signal processing. This study investigates the use of laser interferometry for system identification through a piezoelectrically actuated cantilevered beam. The model of the beam including piezo actuators and optical measurement components are established through the Euler-Bernoulli beam theory. From the method of separation of variables, the continuous system is transformed into a discrete system represented in a state-space form. By performing the Laplace transformation of the state-space form, we obtain the analytical transfer function of interferometer displacement versus actuator input, which is then validated numerically and experimentally. Adaptive filters based on FIR and IIR are designed to identify the transfer function. Because of the slow convergence of such filters, a recursive LMS algorithm is designed to accelerate computation. It is experimentally demonstrated that the precision measurement of interferometer can lead to highly accurate results of system identification.

Human expert drivers have the unique ability to build complex perceptive models using correlated sensory inputs and outputs. In the case of longitudinal vehicle traction, this work will show a direct correlation in longitudinal acceleration to throttle input in a controlled laboratory environment. In fact, human experts have the ability to control a vehicle at or near the performance limits, with respect to vehicle traction, without direct knowledge of the vehicle states; speed, slip or tractive force. Traditional algorithms such as PID, full state feedback, and even sliding mode control have been very successful at handling low level tasks where the physics of the dynamic system are known and stationary. The ability to learn and adapt to changing environmental conditions, as well as develop perceptive models based on stimulus-response data, provides expert human drivers with significant advantages. When it comes to bandwidth, accuracy, and repeatability, automatic control systems have clear advantages over humans; however, most high performance control systems lack many of the unique abilities of a human expert. The underlying motivation for this work is that there are advantages to framing the traction control problem in a manner that more closely resembles how a human expert drives a vehicle. The fundamental idea is the belief that humans have a unique ability to adapt to uncertain environments that are both temporal and spatially varying. In this work, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy. The proposed anthropomimetic traction control algorithm operates on the same correlated input signals that a human expert driver would in order to maximize traction. A gradient ascent approach is at the heart of the proposed anthropomimetic control algorithm, and a real-time implementation is described using linear operator techniques, even though the tire-ground interface is highly non-linear. Performance of the proposed anthropomimetic traction control algorithm is demonstrated using both a longitudinal traction case study and a combined mode traction case study, in which longitudinal and lateral accelerations are maximized simultaneously. The approach presented in this research should be considered as a first step in the development of a truly anthropomimetic solution, where an advanced control algorithm has been designed to be responsive to the same limited input signals that a human expert would rely on, with the objective of maximizing traction. This work establishes the foundation for a general framework for an anthropomimetic control algorithm that is capable of learning and adapting to an uncertain, time varying environment. The algorithms developed in this work are well suited for efficient real time control in ground vehicles in a variety of applications from a driver assist technology to fully autonomous applications. / Ph. D.

Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. An analysis for different PU adaptive filter algorithms is necessary and meaningful. The deficient-length adaptive filter addresses a situation in system identification where the length of the estimated filter is shorter than the length of the actual unknown system. It is related to the partial update adaptive filter, but has different performance. It can be viewed as a PU adaptive filter, in that the deficient-length adaptive filter also updates part of the weight vector. However, it updates the same part of the weight vector for each iteration, while the partial update adaptive filter updates a different part of the weight vector for each iteration. In this work, basic PU methods are applied to the adaptive filter algorithms which have not been fully addressed in the literature, including CG, EDS, and Constant Modulus Algorithm (CMA) based algorithms. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical analysis is shown including convergence condition, steady-state performance, and tracking performance. Computer simulation with proper examples is also shown to further help study the performance. The performance is compared among different PU methods or among different adaptive filtering algorithms. Computational complexity is calculated for each PU method and each adaptive filter algorithm. The deficient-length RLS and EDS are also analyzed and compared to the performance of the PU adaptive filter. In this dissertation, basic partial-update methods are applied to adaptive filter algorithms including CMA1-2, NCMA, Least Squares CMA (LSCMA), EDS, and CG. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. Deficient-length RLS and EDS are studied. The performance of the deficient-length filter is also compared with the partial update filter. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application is using PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application is using PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification. Overall, the PU adaptive filters can usually achieve comparable performance to the full-update filters while reducing the computational complexity significantly. The PU adaptive filters can achieve similar steady-state MSE to the full-update filters. Among different PU methods, the MMax method has a convergence rate very close to the full-update method. The sequential and stochastic methods converge slower than the MMax method. However, the MMax method does not always perform well with the LSCMA algorithm. The sequential LSCMA has the best performance among the PU LSCMA algorithms. The PU CMA may perform better than the full-update CMA in tracking a time-varying system. The MMax EDS can converge faster than the MMax RLS and CG. It can converge to the same steady-state MSE as the MMax RLS and CG, while having a lower computational complexity. The PU LMS and PU EDS can also perform a little better in a system combining hyperspectral image compression and classification. / Ph. D.

Made available in DSpace on 2016-03-22T17:26:45Z (GMT). No. of bitstreams: 1 monica.pdf: 3913704 bytes, checksum: 391eb8287c6e4e8d928a93819e3828ee (MD5) Previous issue date: 2012-03-25 / This work proposes the implementation of dedicated hardware architecture for the Least Mean Square (LMS) adaptive filtering algorithm by using Hybrid encoding, whose main goal is to cancel the interferences in the signal of interest. In the used scheme, from a 60Hz reference signal, the algorithm is able to estimate the superior harmonics, using after these results for the cancelling of interferences related to the signal of interest. One of the techniques that is widely used for the switching activity reduction uses signal encoding. In this work, the proposed adaptive filtering architecture uses the Hybrid encoding in its data buses, whose main idea is to split the operands in group of m-bits, encode each group using the Gray code (that potentially enables reduction of the switching activity into each group) and propagate the carry between the groups as in the Binary encoding. We developed new Hybrid multipliers for signed multiplication, which uses radix-2m encoding. The multipliers are applied to the adaptive filtering architecture. We have implemented 18, 23 and 36 bit-width radix-4 Hybrid array multipliers, as well as a particular case for the radix-8 (m=3) operation. The main results showed that the Hybrid multipliers are more efficient than the Binary ones, by presenting less power consumption in some cases. Moreover, the implemented adaptive filtering architectures were validated and compared in both Binary and Hybrid encoding. The efficiency of the implemented filters for the cancelling of interferences was proved by using both encoding scheme. By the presented results, we conclude that it could be practicable to implement an adaptive filtering architecture operating on Hybrid encoding / Este trabalho tem como proposta a implementação de uma arquitetura de hardware dedicada para o algoritmo LMS (Least Mean Square) de filtragem adaptativa, para o cancelamento de interferências em codificação Híbrida. No esquema utilizado, a partir de um sinal de referência de 60Hz, o algoritmo estima as harmônicas superiores, utilizando esses resultados para o cancelamento da interferência associada ao sinal de interesse. Um dos métodos para a redução da atividade de chaveamento em barramentos de dados que tem sido amplamente utilizado é a codificação de dados. Neste trabalho, a arquitetura de filtragem adaptativa proposta utiliza em seus barramentos de dados a codificação Híbrida, cuja idéia é dividir os operandos em grupos de m bits, codificar cada grupo utilizando o código Gray (que habilita reduções na atividade de chaveamento dentro de cada grupo) e utilizar o comportamento do código Binário para propagar o carry entre os grupos. Dessa forma, são desenvolvidas arquiteturas otimizadas de circuitos multiplicadores array base 2m na codificação Híbrida para a aplicação na arquitetura dedicada de filtro adaptativo. São implementados circuitos multiplicadores array de 18, 23 e 36 bits na codificação Híbrida na base 4 (m=2), bem como um caso particular para a base 8 (m=3). Essas arquiteturas são implementadas em linguagem de descrição de hardware. Os principais resultados mostraram que os multiplicadores Híbridos apresentaram, em alguns casos, menor consumo de potência em relação aos multiplicadores binários. Além disso, foi possível validar e comparar as arquiteturas de filtro adaptativo nas codificações Binária e Híbrida, onde se pôde verificar a eficiência dos filtros para o cancelamento de interferências em ambas as codificações, mostrando-se possível a implementação de um filtro adaptativo em codificação Híbrida