This book is a technical publication for students, scholars and engineers in electrical engineering, focusing on the pulse-width-modulation (PWM) technologies in power electronics area. Based on an introduction of basic PWM principles this book analyzes three major challenges for PWM on system performance: power losses, voltage/current ripple and electromagnetic interference (EMI) noise, and the lack of utilization of control freedoms in conventional PWM technologies. Then, the model of PWM's impact on system performance is introduced, with the current ripple prediction method for voltage source converter as example. With the prediction model, two major advanced PWM methods are introduced: variable switching frequency PWM and phase-shift PWM, which can reduce the power losses and EMI for the system based on the prediction model. Furthermore, the advanced PWM can be applied in advanced topologies including multilevel converters and paralleled converters. With more control variables in the advanced topologies, performance of PWM can be further improved. Also, for the special problem for common-mode noise, this book introduces modified PWM method for reduction. Especially, the paralleled inverters with advanced PWM can achieve good performance for the common-mode noise reduction. Finally, the implementation of PWM technologies in hardware is introduced in the last part.

Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, including lower-level depth and skeleton features, higher-level representations to model the temporal structure and human-object interactions, and feature selection techniques for occlusion handling. This work enables the reader to quickly familiarize themselves with the latest research, and to gain a deeper understanding of recently developed techniques. It will be of great use for both researchers and practitioners.

The two volume set LNCS 4841 and LNCS 4842 constitutes the refereed proceedings of the Third International Symposium on Visual Computing, ISVC 2007, held in Lake Tahoe, NV, USA in November 2007.

The 77 revised full papers and 42 poster papers presented together with 32 full and 5 poster papers of 6 special tracks were carefully reviewed and selected from more than 270 submissions. The papers cover the four main areas of visual computing: vision, graphics, visualization, and virtual reality. There 6 additional special tracks address issues such as intelligent algorithms for smart monitoring of complex environments, object recognition, image databases, algorithms for the understanding of dynamics in complex and cluttered scenes, medical data analysis, and soft computing in image processing. The papers of this volume are organized in topical sections on motion and tracking, computer graphics, virtual reality, medical data analysis, calibration/reconstruction, visualization, computer vision applications, algorithms for the understanding, face reconstruction and processing, object recognition, and shape/motion/tracking.

The two volume set LNCS 4841 and LNCS 4842 constitutes the refereed proceedings of the Third International Symposium on Visual Computing, ISVC 2007, held in Lake Tahoe, NV, USA in November 2007.

The 77 revised full papers and 42 poster papers presented together with 32 full and 5 poster papers of 6 special tracks were carefully reviewed and selected from more than 270 submissions. The papers cover the four main areas of visual computing: vision, graphics, visualization, and virtual reality. There 6 additional special tracks address issues such as intelligent algorithms for smart monitoring of complex environments, object recognition, image databases, algorithms for the understanding of dynamics in complex and cluttered scenes, medical data analysis, and soft computing in image processing. The papers of this volume are organized in topical sections on motion and tracking, segmentation, feature extraction, classification, intelligent algorithms for smart monitoring of shape/recognition, image databases, soft computing in image processing, and posters.

This book is a technical publication for students, scholars and engineers in electrical engineering, focusing on the pulse-width-modulation (PWM) technologies in power electronics area. Based on an introduction of basic PWM principles this book analyzes three major challenges for PWM on system performance: power losses, voltage/current ripple and electromagnetic interference (EMI) noise, and the lack of utilization of control freedoms in conventional PWM technologies. Then, the model of PWM's impact on system performance is introduced, with the current ripple prediction method for voltage source converter as example. With the prediction model, two major advanced PWM methods are introduced: variable switching frequency PWM and phase-shift PWM, which can reduce the power losses and EMI for the system based on the prediction model. Furthermore, the advanced PWM can be applied in advanced topologies including multilevel converters and paralleled converters. With more control variables in the advanced topologies, performance of PWM can be further improved. Also, for the special problem for common-mode noise, this book introduces modified PWM method for reduction. Especially, the paralleled inverters with advanced PWM can achieve good performance for the common-mode noise reduction. Finally, the implementation of PWM technologies in hardware is introduced in the last part.

This book will provide a comprehensive overview on human action analysis with randomized trees. It will cover both the supervised random trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast method for space-time interest point matching. When labeled data is minimal as in the case of example-based action search, unsupervised random trees is used to leverage the unlabelled data. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction. We will also describe techniques for space-time action localization including branch-and-bound sub-volume search and propagative Hough voting.