A single-phase, buck-boost based, dual-output AC-DC converter is studied in this thesis. The converter has two DC outputs with opposite polarities, which share the same ground with the input power line. The power stage performance, including the input filter, is studied and procedure to select power components is given. The circuit model is analyzed to develop appropriate control. Zerocrossing distortion of the source input current is addressed and a solution is proposed. Experimental results are satisfactory in that a high power factor line current results for steady-state operation.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (M.S.)--Ohio University, March, 2004. / Title from PDF t.p. Includes bibliographical references (leaves 80-81).

Thesis--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 67-68).

Thesis (M.S.)--Ohio University, November, 1993. / Title from PDF t.p.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Ashraf Saad, Committee Member ; Spyros Reveliotis, Committee Member ; Anthony Yezzi, Committee Member ; Erik Verriest, Committee Member ; Yorai Wardi, Committee Co-Chair ; Magnus Egerstedt, Committee Chair.

by Zhou, Shengli. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 70-75). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background of Gesture Recognition --- p.1 / Chapter 1.2 --- HCI System --- p.2 / Chapter 1.2.1 --- Vision Based HCI System --- p.2 / Chapter 1.2.2 --- Accelerometer Based HCI System --- p.4 / Chapter 1.3 --- Pattern Recognition Methods --- p.6 / Chapter 1.4 --- Thesis Outline --- p.7 / Chapter Chapter 2 --- 2D Hand-Written Character Recognition --- p.8 / Chapter 2.1 --- Introduction to Accelerometer Based Hand-Written Character Recognition --- p.8 / Chapter 2.1.1 --- Character Recognition Based on Trajectory Reconstruction --- p.9 / Chapter 2.1.2 --- Character Recognition Based on Classification --- p.10 / Chapter 2.2 --- Neural Network --- p.11 / Chapter 2.2.1 --- Mathematical Model of Neural Network (NN) --- p.11 / Chapter 2.2.2 --- Types of Neural Network Learning --- p.13 / Chapter 2.2.3 --- Self-Organizing Maps (SOMs) --- p.14 / Chapter 2.2.4 --- Properties of Neural Network --- p.16 / Chapter 2.3 --- Experimental Setup --- p.17 / Chapter 2.4 --- Configuration of Sensing Mote --- p.18 / Chapter 2.5 --- Data Acquisition Methods --- p.19 / Chapter 2.6 --- Data Preprocessing Methods --- p.20 / Chapter 2.6.1 --- Fast Fourier Transform (FFT) --- p.21 / Chapter 2.6.2 --- Discrete Cosine Transform (DCT) --- p.23 / Chapter 2.6.3 --- Problem Analysis --- p.25 / Chapter 2.7 --- Hand-written Character Classification using SOMs --- p.26 / Chapter 2.7.1 --- Recognition of All Characters in the Same Group --- p.27 / Chapter 2.7.2 --- Recognize the Numbers and Letters Respectively --- p.28 / Chapter 2.8 --- Conclusion --- p.29 / Chapter Chapter 3 --- Human Gesture Recognition --- p.32 / Chapter 3.1 --- Introduction to Human Gesture Recognition --- p.32 / Chapter 3.1.1 --- Dynamic Gesture Recognition --- p.32 / Chapter 3.1.2 --- Hidden Markov Models (HMMs) --- p.33 / Chapter 3.1.2.1 --- Applications of HMMs --- p.34 / Chapter 3.1.2.2 --- Training Algorithm --- p.35 / Chapter 3.1.2.3 --- Recognition Algorithm --- p.35 / Chapter 3.2 --- System Architecture --- p.36 / Chapter 3.2.1 --- Experimental Devices --- p.36 / Chapter 3.2.2 --- Data Acquisition Methods --- p.38 / Chapter 3.2.3 --- System Work Flow --- p.39 / Chapter 3.3 --- Real-Time Gesture Spotting --- p.40 / Chapter 3.3.1 --- Introduction --- p.40 / Chapter 3.3.2 --- Gesture Segmentation Based on Standard Deviation Calculation --- p.42 / Chapter 3.3.3 --- Evaluation of Gesture Spotting Program --- p.47 / Chapter 3.4 --- Comparison of Data Processing Methods --- p.48 / Chapter 3.4.1 --- Discrete Cosine Transform (DCT) --- p.48 / Chapter 3.4.2 --- Discrete Wavelet Transform (DWT) --- p.49 / Chapter 3.4.3 --- Zero Bias Compensation and Filtering (ZBC&F) --- p.51 / Chapter 3.4.4 --- Comparison of Experimental Results --- p.52 / Chapter 3.5 --- Data Base Setup --- p.53 / Chapter 3.6 --- Experimental Results Based on the Database Obtained from Ten Test Subjects --- p.53 / Chapter 3.6.1 --- Experimental Results when Gestures are Manually and Automatically “cut´ح --- p.54 / Chapter 3.6.2 --- The Influence of Number of Dominant Frequencies on Recognition --- p.55 / Chapter 3.6.3 --- The Influence of Sampling Frequencies on Recognition --- p.59 / Chapter 3.6.4 --- Influence of Number of Test Subjects on Recognition --- p.62 / Chapter 3.6.4.1 --- Experimental Results When Training and Testing Subjects Are Overlaped --- p.61 / Chapter 3.6.4.2 --- Experimental Results When Training and Testing Subjects Are Not Overlap --- p.62 / Chapter 3.6.4.3 --- Discussion --- p.65 / Chapter Chapter 4 --- Conclusion --- p.68 / Bibliography --- p.70

Com o intuito de utilizar uma rede com protocolo IP para a implementação de malhas fechadas de controle, este trabalho propõe-se a realizar um estudo da operação de um sistema de controle dinâmico distribuído, comparando-o com a operação de um sistema de controle local convencional. Em geral, a decisão de projetar uma arquitetura de controle distribuído é feita baseada na simplicidade, na redução dos custos e confiabilidade; portanto, um diferencial bastante importante é a utilização da rede IP. O objetivo de uma rede de controle não é transmitir dados digitais, mas dados analógicos amostrados. Assim, métricas usuais em redes de computadores, como quantidade de dados e taxa de transferências, tornam-se secundárias em uma rede de controle. São propostas técnicas para tratar os pacotes que sofrem atrasos e recuperar o desempenho do sistema de controle através da rede IP. A chave para este método é realizar a estimação do conteúdo dos pacotes que sofrem atrasos com base no modelo dinâmico do sistema, mantendo o sistema com um nível adequado de desempenho. O sistema considerado é o controle de um manipulador antropomórfico com dois braços e uma cabeça de visão estéreo totalizando 18 juntas. Os resultados obtidos mostram que se pode recuperar boa parte do desempenho do sistema. / ln order to enable the use of IP networks to implement closed-Ioop control systems, this work compares a distributed control system to its conventional counterpart, which is based on local controI. Usually the option for a distributed control architecture is motivated by simplicity, cost reduction and reliability, hence the use of an IP network. The goal of a control network is not to transmit pure digital data, but to transmit digitized control and sensor signals. Therefore, metrics common in computer networks, such as amount of transfered data and transfer rates, become of little importance. This work proposes a method to compensate for the delays imposed to network packets and to recover the control system performance. The key for this method is to estimate the contents of delayed network packets based on the system dymanic modeI. The example system is an antropormorphic robot with two arms and a stereo vision head. Results show that the system performance can be recovered.

[pt] Este trabalho descreve um sistema destinado à síntese de quatro sons simultâneos controlado a microcomputador utilizando a técnica de síntese substantiva. O hardware utiliza blocos analógicos que realizam as funções de geração, filtragem e conformação, e a execução em tempo real é possível através de um teclado musical. A programação desenvolvida permite ao usuário atuar nestes blocos determinando parâmetros básicos do som, que podem ser armazenados e recuperados de discos flexíveis. O hardware permite a síntese da voz humana através de um software a ser desenvolvido, que pode utilizar uma biblioteca de fonemas em discos flexíveis, contendo as diversas funções de controle necessárias. / [en] This work describes a system intendent to perform synthesis of four simultaneous sonds controlled by microcomputer using sustrative syntesis technique. Hardware employs analogical performing generating, filtering and conformative functions and performance in real time is allowed throug use of a Keyboard. Programming developed allows user to act upon those blocks, determining basic sound parameters which can be stored recovered from diskettes. Human voice synthesis is possible by means of a futurely developed software wuch will use a phonem library in flexible disks containing the different needed controlling functions.

Com o intuito de utilizar uma rede com protocolo IP para a implementação de malhas fechadas de controle, este trabalho propõe-se a realizar um estudo da operação de um sistema de controle dinâmico distribuído, comparando-o com a operação de um sistema de controle local convencional. Em geral, a decisão de projetar uma arquitetura de controle distribuído é feita baseada na simplicidade, na redução dos custos e confiabilidade; portanto, um diferencial bastante importante é a utilização da rede IP. O objetivo de uma rede de controle não é transmitir dados digitais, mas dados analógicos amostrados. Assim, métricas usuais em redes de computadores, como quantidade de dados e taxa de transferências, tornam-se secundárias em uma rede de controle. São propostas técnicas para tratar os pacotes que sofrem atrasos e recuperar o desempenho do sistema de controle através da rede IP. A chave para este método é realizar a estimação do conteúdo dos pacotes que sofrem atrasos com base no modelo dinâmico do sistema, mantendo o sistema com um nível adequado de desempenho. O sistema considerado é o controle de um manipulador antropomórfico com dois braços e uma cabeça de visão estéreo totalizando 18 juntas. Os resultados obtidos mostram que se pode recuperar boa parte do desempenho do sistema. / ln order to enable the use of IP networks to implement closed-Ioop control systems, this work compares a distributed control system to its conventional counterpart, which is based on local controI. Usually the option for a distributed control architecture is motivated by simplicity, cost reduction and reliability, hence the use of an IP network. The goal of a control network is not to transmit pure digital data, but to transmit digitized control and sensor signals. Therefore, metrics common in computer networks, such as amount of transfered data and transfer rates, become of little importance. This work proposes a method to compensate for the delays imposed to network packets and to recover the control system performance. The key for this method is to estimate the contents of delayed network packets based on the system dymanic modeI. The example system is an antropormorphic robot with two arms and a stereo vision head. Results show that the system performance can be recovered.