Signal processing techniques for mobile multimedia systems

Recent trends in wireless communication systems show a significant demand for the delivery of multimedia services and applications over mobile networks - mobile multimedia - like video telephony, multimedia messaging, mobile gaming, interactive and streaming video, etc. However, despite the ongoing development of key communication technologies that support these applications, the communication resources and bandwidth available to wireless/mobile radio systems are often severely limited. It is well known, that these bottlenecks are inherently due to the processing capabilities of mobile transmission systems, and the time-varying nature of wireless channel conditions and propagation environments. Therefore, new ways of processing and transmitting multimedia data over mobile radio channels have become essential which is the principal focus of this thesis. In this work, the performance and suitability of various signal processing techniques and transmission strategies in the application of multimedia data over wireless/mobile radio links are investigated. The proposed transmission systems for multimedia communication employ different data encoding schemes which include source coding in the wavelet domain, transmit diversity coding (space-time coding), and adaptive antenna beamforming (eigenbeamforming). By integrating these techniques into a robust communication system, the quality (SNR, etc) of multimedia signals received on mobile devices is maximised while mitigating the fast fading and multi-path effects of mobile channels. To support the transmission of high data-rate multimedia applications, a well known multi-carrier transmission technology known as Orthogonal Frequency Division Multiplexing (OFDM) has been implemented. As shown in this study, this results in significant performance gains when combined with other signal-processing techniques such as spa ce-time block coding (STBC). To optimise signal transmission, a novel unequal adaptive modulation scheme for the communication of multimedia data over MIMO-OFDM systems has been proposed. In this system, discrete wavelet transform/subband coding is used to compress data into their respective low-frequency and high-frequency components. Unlike traditional methods, however, data representing the low-frequency data are processed and modulated separately as they are more sensitive to the distortion effects of mobile radio channels. To make use of a desirable subchannel state, such that the quality (SNR) of the multimedia data recovered at the receiver is optimized, we employ a lookup matrix-adaptive bit and power allocation (LM-ABPA) algorithm. Apart from improving the spectral efficiency of OFDM, the modified LM-ABPA scheme, sorts and allocates subcarriers with the highest SNR to low-frequency data and the remaining to the least important data. To maintain a target system SNR, the LM-ABPA loading scheme assigns appropriate signal constella tion sizes and transmit power levels (modulation type) across all subcarriers and is adapted to the varying channel conditions such that the average system error-rate (SER/BER) is minimised. When configured for a constant data-rate load, simulation results show significant performance gains over non-adaptive systems. In addition to the above studies, the simulation framework developed in this work is applied to investigate the performance of other signal processing techniques for multimedia communication such as blind channel equalization, and to examine the effectiveness of a secure communication system based on a logistic chaotic generator (LCG) for chaos shift-keying (CSK)

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology

COMPOSITIONAL EXPLORATIONS OF PLASTIC SOUND

Each piece of music in this research is meant to explore a different aspect of music as a plastic art. Conclusions reached in the review of each new work were used to guide the development of the next. The notions of plasticity in sound, and sound as a plastic material were used to give the overall research a focal point. In exploring different types of composition, reciprocal plasticity between the materials and the developing ideas of the music are discussed in the context of ecological and biological psychology. By restricting all these works within the genre of 'plastic arts' it became necessary to introduce a new technique for instrumental composition. An aural model is used to replace the traditional written score. These instrumental works were developed entirely within an auditory situation.Funded by De Montfort Universit

Techniques for Decentralized and Dynamic Resource Allocation

abstract: This thesis investigates three different resource allocation problems, aiming to achieve two common goals: i) adaptivity to a fast-changing environment, ii) distribution of the computation tasks to achieve a favorable solution. The motivation for this work relies on the modern-era proliferation of sensors and devices, in the Data Acquisition Systems (DAS) layer of the Internet of Things (IoT) architecture. To avoid congestion and enable low-latency services, limits have to be imposed on the amount of decisions that can be centralized (i.e. solved in the ``cloud") and/or amount of control information that devices can exchange. This has been the motivation to develop i) a lightweight PHY Layer protocol for time synchronization and scheduling in Wireless Sensor Networks (WSNs), ii) an adaptive receiver that enables Sub-Nyquist sampling, for efficient spectrum sensing at high frequencies, and iii) an SDN-scheme for resource-sharing across different technologies and operators, to harmoniously and holistically respond to fluctuations in demands at the eNodeB' s layer. The proposed solution for time synchronization and scheduling is a new protocol, called PulseSS, which is completely event-driven and is inspired by biological networks. The results on convergence and accuracy for locally connected networks, presented in this thesis, constitute the theoretical foundation for the protocol in terms of performance guarantee. The derived limits provided guidelines for ad-hoc solutions in the actual implementation of the protocol. The proposed receiver for Compressive Spectrum Sensing (CSS) aims at tackling the noise folding phenomenon, e.g., the accumulation of noise from different sub-bands that are folded, prior to sampling and baseband processing, when an analog front-end aliasing mixer is utilized. The sensing phase design has been conducted via a utility maximization approach, thus the scheme derived has been called Cognitive Utility Maximization Multiple Access (CUMMA). The framework described in the last part of the thesis is inspired by stochastic network optimization tools and dynamics. While convergence of the proposed approach remains an open problem, the numerical results here presented suggest the capability of the algorithm to handle traffic fluctuations across operators, while respecting different time and economic constraints. The scheme has been named Decomposition of Infrastructure-based Dynamic Resource Allocation (DIDRA).Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

A Multiple-Systems Approach in the Symbolic Modelling of Human Vision

For most of the thirty years or so of machine vision research, activity has been concentrated mainly in the domain of metric-based approaches: there has been negligible attention to the psychological factors in human vision. With the recent resurgence of interest in neural systems, that is now changing. This thesis discusses relevant aspects of basic visual neuroanatomy, and psychological phenomena, in an attempt to relate the concepts to a model of human vision and the prospective goals of future machine vision systems. It is suggested that, while biological vision is complex, the underlying mechanisms of human vision are more tractable than is often believed. We also argue here that the controversial subject of direct vision plays a crucial role in natural vision, and we attempt to relate this to the model. The recognition of massive parallelism in natural vision has led to proposals for emulating aspects of neural networks in technology. The systems model developed in this work demonstrates software-simulated cellular automata (CAs) in the role of mainly low-level image processing. It is shown that CAs are able to efficiently provide both conventional and neurally-inspired vision functions. The thesis also discusses the use of Prolog as the means of realising higher level image understanding. The symbolic processing developed is basic, but is nevertheless sufficient for the purposes of the present. demonstrations. Extensions to the concepts can be easily achieved. The modular systems approach adopted blends together several ideas and processes, and results in a more robust model of human vision that is able to translate a noisy real image into an accessible symbolic form for expert-domain interpretation

Supervisory control of machine tool feed drives.

While motion control of machine tool feed drives is the targeted application. The goal of this study is to explore the relative performance potentials of supervisory control systems against the classical servo control systems; Reconfiguration aspects at the control level are the scope of this study. One of the most essential nonlinear problems faced during modeling and control stages of the CNC machining systems is called backlash. Reversal of motion for each moving axis can lead to that area of disengagement where backlash occurs due to inherently unavoidable clearance between linkages of the machine tool feed drive system. Due to backlash, efficiency of machine tools will be undesirably turned down causing higher vibrations, lower contouring accuracy, and may draw the whole system into instability region. A Switching control scheme is designed to manage the control process where two different controllers with two different control functionalities, acting differently in two vital zones---one of them where the backlash lies, and the other when moving past the backlash---seem to be an important need. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .S53. Source: Masters Abstracts International, Volume: 43-03, page: 0961. Adviser: Waguih ElMaraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004

Techniques in secure chaos communication

In today's climate of increased criminal attacks on the privacy of personal or confidential data over digital communication systems, a more secure physical communication link is required. Chaotic signals which have bifurcation behavior (depending on some initial condition) can readily be exploited to enhance the security of communication systems. A chaotic generator produces disordered sequences that provide very good auto- and cross- correlation properties similar to those of random white noise. This would be an important feature in multiple access environments. These sequences are used to scramble data in spread spectrum systems as they can produce low co-channel interference, hence improve the system capacity and performance. The chaotic signal can be created from only a single mathematical relationship and is neither restricted in length nor is repetitive/ cyclic. On the other hand, with the progress in digital signal processing and digital hardware, there has been an increased interest in using adaptive algorithms to improve the performance of digital systems. Adaptive algorithms provide the system with the ability to self-adjust its coefficients according to the signal condition, and can be used with linear or non-linear systems; hence, they might find application in chaos communication. There has been a lot of literature that proposed the use of LMS adaptive algorithm in the communication arena for a variety of applications such as (but not limited to): channel estimation, channel equalization, demodulation, de-noising, and beamforming. In this thesis, we conducted a study on the application of chaos theory in communication systems as well as the application of adaptive algorithms in chaos communication. The First Part of the thesis tackled the application of chaos theory in com- munication. We examined different types of communication techniques utilizing chaos theory. In particular, we considered chaos shift keying (CSK) and mod- ified kind of logistic map. Then, we applied space-time processing and eigen- beamforming technique to enhance the performance of chaos communication. Following on, we conducted a study on CSK and Chaos-CDMA in conjunction with multi-carrier modulation (MCM) techniques such as OFDM (FFT/ IFFT) and wavelet-OFDM. In the Second Part of the thesis, we tried to apply adaptivity to chaos com- munication. Initially, we presented a study of multi-user detection utilizing an adaptive algorithm in a chaotic CDMA multi-user environment, followed by a study of adaptive beamforming and modified weight-vector adaptive beam- forming over CSK communication. At last, a study of modified time-varying adaptive filtering is presented and a conventional adaptive filtering technique is applied in chaotic signal environment. Twelve papers have been published during the PhD candidature, include two journal papers and ten refereed conference papers

Visual perception an information-based approach to understanding biological and artificial vision

The central issues of this dissertation are (a) what should we be doing — what problems should we be trying to solve — in order to build computer vision systems, and (b) what relevance biological vision has to the solution of these problems. The approach taken to tackle these issues centres mostly on the clarification and use of information-based ideas, and an investigation into the nature of the processes underlying perception. The primary objective is to demonstrate that information theory and extensions of it, and measurement theory are powerful tools in helping to find solutions to these problems. The quantitative meaning of information is examined, from its origins in physical theories, through Shannon information theory, Gabor representations and codes towards semantic interpretations of the term. Also the application of information theory to the understanding of the developmental and functional properties of biological visual systems is discussed. This includes a review of the current state of knowledge of the architecture and function of the early visual pathways, particularly the retina, and a discussion of the possible coding functions of cortical neurons. The nature of perception is discussed from a number of points of view: the types and function of explanation of perceptual systems and how these relate to the operation of the system; the role of the observer in describing perceptual functions in other systems or organisms; the status and role of objectivist and representational viewpoints in understanding vision; the philosophical basis of perception; the relationship between pattern recognition and perception, and the interpretation of perception in terms of a theory of measurement These two threads of research, information theory and measurement theory are brought together in an overview and reinterpretation of the cortical role in mammalian vision. Finally the application of some of the coding and recognition concepts to industrial inspection problems are described. The nature of the coding processes used are unusual in that coded images are used as the input for a simple neural network classifier, rather than a heuristic feature set The relationship between the Karhunen-Loève transform and the singular value decomposition is clarified as background the coding technique used to code the images. This coding technique has also been used to code long sequences of moving images to investigate the possibilities of recognition of people on the basis of their gait or posture and this application is briefly described