Journal of Telecommunications and Information Technology, 2001, nr 3

kwartalni

System Identification with Applications in Speech Enhancement

As the increasing popularity of integrating hands-free telephony on mobile portable devices and the rapid development of voice over internet protocol, identification of acoustic systems has become desirable for compensating distortions introduced to speech signals during transmission, and hence enhancing the speech quality. The objective of this research is to develop system identification algorithms for speech enhancement applications including network echo cancellation and speech dereverberation. A supervised adaptive algorithm for sparse system identification is developed for network echo cancellation. Based on the framework of selective-tap updating scheme on the normalized least mean squares algorithm, the MMax and sparse partial update tap-selection strategies are exploited in the frequency domain to achieve fast convergence performance with low computational complexity. Through demonstrating how the sparseness of the network impulse response varies in the transformed domain, the multidelay filtering structure is incorporated to reduce the algorithmic delay. Blind identification of SIMO acoustic systems for speech dereverberation in the presence of common zeros is then investigated. First, the problem of common zeros is defined and extended to include the presence of near-common zeros. Two clustering algorithms are developed to quantify the number of these zeros so as to facilitate the study of their effect on blind system identification and speech dereverberation. To mitigate such effect, two algorithms are developed where the two-stage algorithm based on channel decomposition identifies common and non-common zeros sequentially; and the forced spectral diversity approach combines spectral shaping filters and channel undermodelling for deriving a modified system that leads to an improved dereverberation performance. Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased dereverberation techniques. Comprehensive simulations and discussions demonstrate the effectiveness of the aforementioned algorithms. A discussion on possible directions of prospective research on system identification techniques concludes this thesis

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

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

Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

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

Interoperability of wireless communication technologies in hybrid networks: Evaluation of end-to-end interoperability issues and quality of service requirements

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Hybrid Networks employing wireless communication technologies have nowadays brought closer the vision of communication “anywhere, any time with anyone”. Such communication technologies consist of various standards, protocols, architectures, characteristics, models, devices, modulation and coding techniques. All these different technologies naturally may share some common characteristics, but there are also many important differences. New advances in these technologies are emerging very rapidly, with the advent of new models, characteristics, protocols and architectures. This rapid evolution imposes many challenges and issues to be addressed, and of particular importance are the interoperability issues of the following wireless technologies: Wireless Fidelity (Wi-Fi) IEEE802.11, Worldwide Interoperability for Microwave Access (WiMAX) IEEE 802.16, Single Channel per Carrier (SCPC), Digital Video Broadcasting of Satellite (DVB-S/DVB-S2), and Digital Video Broadcasting Return Channel through Satellite (DVB-RCS). Due to the differences amongst wireless technologies, these technologies do not generally interoperate easily with each other because of various interoperability and Quality of Service (QoS) issues. The aim of this study is to assess and investigate end-to-end interoperability issues and QoS requirements, such as bandwidth, delays, jitter, latency, packet loss, throughput, TCP performance, UDP performance, unicast and multicast services and availability, on hybrid wireless communication networks (employing both satellite broadband and terrestrial wireless technologies). The thesis provides an introduction to wireless communication technologies followed by a review of previous research studies on Hybrid Networks (both satellite and terrestrial wireless technologies, particularly Wi-Fi, WiMAX, DVB-RCS, and SCPC). Previous studies have discussed Wi-Fi, WiMAX, DVB-RCS, SCPC and 3G technologies and their standards as well as their properties and characteristics, such as operating frequency, bandwidth, data rate, basic configuration, coverage, power, interference, social issues, security problems, physical and MAC layer design and development issues. Although some previous studies provide valuable contributions to this area of research, they are limited to link layer characteristics, TCP performance, delay, bandwidth, capacity, data rate, and throughput. None of the studies cover all aspects of end-to-end interoperability issues and QoS requirements; such as bandwidth, delay, jitter, latency, packet loss, link performance, TCP and UDP performance, unicast and multicast performance, at end-to-end level, on Hybrid wireless networks. Interoperability issues are discussed in detail and a comparison of the different technologies and protocols was done using appropriate testing tools, assessing various performance measures including: bandwidth, delay, jitter, latency, packet loss, throughput and availability testing. The standards, protocol suite/ models and architectures for Wi-Fi, WiMAX, DVB-RCS, SCPC, alongside with different platforms and applications, are discussed and compared. Using a robust approach, which includes a new testing methodology and a generic test plan, the testing was conducted using various realistic test scenarios on real networks, comprising variable numbers and types of nodes. The data, traces, packets, and files were captured from various live scenarios and sites. The test results were analysed in order to measure and compare the characteristics of wireless technologies, devices, protocols and applications. The motivation of this research is to study all the end-to-end interoperability issues and Quality of Service requirements for rapidly growing Hybrid Networks in a comprehensive and systematic way. The significance of this research is that it is based on a comprehensive and systematic investigation of issues and facts, instead of hypothetical ideas/scenarios or simulations, which informed the design of a test methodology for empirical data gathering by real network testing, suitable for the measurement of hybrid network single-link or end-to-end issues using proven test tools. This systematic investigation of the issues encompasses an extensive series of tests measuring delay, jitter, packet loss, bandwidth, throughput, availability, performance of audio and video session, multicast and unicast performance, and stress testing. This testing covers most common test scenarios in hybrid networks and gives recommendations in achieving good end-to-end interoperability and QoS in hybrid networks. Contributions of study include the identification of gaps in the research, a description of interoperability issues, a comparison of most common test tools, the development of a generic test plan, a new testing process and methodology, analysis and network design recommendations for end-to-end interoperability issues and QoS requirements. This covers the complete cycle of this research. It is found that UDP is more suitable for hybrid wireless network as compared to TCP, particularly for the demanding applications considered, since TCP presents significant problems for multimedia and live traffic which requires strict QoS requirements on delay, jitter, packet loss and bandwidth. The main bottleneck for satellite communication is the delay of approximately 600 to 680 ms due to the long distance factor (and the finite speed of light) when communicating over geostationary satellites. The delay and packet loss can be controlled using various methods, such as traffic classification, traffic prioritization, congestion control, buffer management, using delay compensator, protocol compensator, developing automatic request technique, flow scheduling, and bandwidth allocation

Signal processing architectures for automotive high-resolution MIMO radar systems

To date, the digital signal processing for an automotive radar sensor has been handled in an efficient way by general purpose signal processors and microcontrollers. However, increasing resolution requirements for automated driving on the one hand, as well as rapidly growing numbers of manufactured sensors on the other hand, can provoke a paradigm change in the near future. The design and development of highly specialized hardware accelerators could become a viable option - at least for the most demanding processing steps with data rates of several gigabits per second. In this work, application-specific signal processing architectures for future high-resolution multiple-input and multiple-output (MIMO) radar sensors are designed, implemented, investigated and optimized. A focus is set on real-time performance such that even sophisticated algorithms can be computed sufficiently fast. The full processing chain from the received baseband signals to a list of detections is considered, comprising three major steps: Spectrum analysis, target detection and direction of arrival estimation. The developed architectures are further implemented on a field-programmable gate array (FPGA) and important measurements like resource consumption, power dissipation or data throughput are evaluated and compared with other examples from literature. A substantial dataset, based on more than 3600 different parametrizations and variants, has been established with the help of a model-based design space exploration and is provided as part of this work. Finally, an experimental radar sensor has been built and is used under real-world conditions to verify the effectiveness of the proposed signal processing architectures.Bisher wurde die digitale Signalverarbeitung für automobile Radarsensoren auf eine effiziente Art und Weise von universell verwendbaren Mikroprozessoren bewältigt. Jedoch können steigende Anforderungen an das Auflösungsvermögen für hochautomatisiertes Fahren einerseits, sowie schnell wachsende Stückzahlen produzierter Sensoren andererseits, einen Paradigmenwechsel in naher Zukunft bewirken. Die Entwicklung von hochgradig spezialisierten Hardwarebeschleunigern könnte sich als eine praktikable Alternative etablieren - zumindest für die anspruchsvollsten Rechenschritte mit Datenraten von mehreren Gigabits pro Sekunde. In dieser Arbeit werden anwendungsspezifische Signalverarbeitungsarchitekturen für zukünftige, hochauflösende, MIMO Radarsensoren entworfen, realisiert, untersucht und optimiert. Der Fokus liegt dabei stets auf der Echtzeitfähigkeit, sodass selbst anspruchsvolle Algorithmen in einer ausreichend kurzen Zeit berechnet werden können. Die komplette Signalverarbeitungskette, beginnend von den empfangenen Signalen im Basisband bis hin zu einer Liste von Detektion, wird in dieser Arbeit behandelt. Die Kette gliedert sich im Wesentlichen in drei größere Teilschritte: Spektralanalyse, Zieldetektion und Winkelschätzung. Des Weiteren werden die entwickelten Architekturen auf einem FPGA implementiert und wichtige Kennzahlen wie Ressourcenverbrauch, Stromverbrauch oder Datendurchsatz ausgewertet und mit anderen Beispielen aus der Literatur verglichen. Ein umfangreicher Datensatz, welcher mehr als 3600 verschiedene Parametrisierungen und Varianten beinhaltet, wurde mit Hilfe einer modellbasierten Entwurfsraumexploration erstellt und ist in dieser Arbeit enthalten. Schließlich wurde ein experimenteller Radarsensor aufgebaut und dazu benutzt, die entworfenen Signalverarbeitungsarchitekturen unter realen Umgebungsbedingungen zu verifizieren