Wireless Channel Equalization in Digital Communication Systems

Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential. The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation. The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4. For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research. Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases

General Interference Suppression Technique For Diversity Wireless Rece

The area of wireless transceiver design is becoming increasingly important due to the rapid growth of wireless communications market as well as diversified design specifications. Research efforts in this area concentrates on schemes that are capable of increasing the system capacity, providing reconfigurability/reprogrammability and reducing the hardware complexity. Emerging topics related to these goals include Software Defined Radio, Multiple-Input-Multiple-Output (MIMO) Systems, Code Division Multiple Access, Ultra-Wideband Systems, etc. This research adopts space diversity and statistical signal processing for digital interference suppression in wireless receivers. The technique simplifies the analog front-end by eliminating the anti-aliasing filters and relaxing the requirements for IF bandpass filters and A/D converters. Like MIMO systems, multiple antenna elements are used for increased frequency reuse. The suppression of both image signal and Co-Channel Interference (CCI) are performed in DSP simultaneously. The signal-processing algorithm used is Independent Component Analysis (ICA). Specifically, the fixed-point Fast-ICA is adopted in the case of static or slow time varying channel conditions. In highly dynamic environment that is typically encountered in cellular mobile communications, a novel ICA algorithm, OBAI-ICA, is developed, which outperforms Fast-ICA for both linear and abrupt time variations. Several practical implementation issues are also considered, such as the effect of finite arithmetic and the possibility of reducing the number of antennas

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition