Adaptive Lattice Algorithms for Passive Array Data

This paper reports the development of an algorithm for the processing of data from an array of broad-band sensors using lattice type processor. The problem is to enhance the look-direction signal in the presence of spatially distributed interference sources and sensor self noise by employing a multi-channel processor subject to the constraint that it has a desired response for look-direction signals. The multichannel lattice algorithm proposed here possess stage by stage decoupling, and do not involve an arbitrary size of the step length, unlike conventional tapped-delay-line algorithms

Adaptive Constraint Solving for Information Flow Analysis

In program analysis, unknown properties for terms are typically represented symbolically as variables. Bound constraints on these variables can then specify multiple optimisation goals for computer programs and nd application in areas such as type theory, security, alias analysis and resource reasoning. Resolution of bound constraints is a problem steeped in graph theory; interdependencies between the variables is represented as a constraint graph. Additionally, constants are introduced into the system as concrete bounds over these variables and constants themselves are ordered over a lattice which is, once again, represented as a graph. Despite graph algorithms being central to bound constraint solving, most approaches to program optimisation that use bound constraint solving have treated their graph theoretic foundations as a black box. Little has been done to investigate the computational costs or design e cient graph algorithms for constraint resolution. Emerging examples of these lattices and bound constraint graphs, particularly from the domain of language-based security, are showing that these graphs and lattices are structurally diverse and could be arbitrarily large. Therefore, there is a pressing need to investigate the graph theoretic foundations of bound constraint solving. In this thesis, we investigate the computational costs of bound constraint solving from a graph theoretic perspective for Information Flow Analysis (IFA); IFA is a sub- eld of language-based security which veri es whether con dentiality and integrity of classified information is preserved as it is manipulated by a program. We present a novel framework based on graph decomposition for solving the (atomic) bound constraint problem for IFA. Our approach enables us to abstract away from connections between individual vertices to those between sets of vertices in both the constraint graph and an accompanying security lattice which defines ordering over constants. Thereby, we are able to achieve significant speedups compared to state-of-the-art graph algorithms applied to bound constraint solving. More importantly, our algorithms are highly adaptive in nature and seamlessly adapt to the structure of the constraint graph and the lattice. The computational costs of our approach is a function of the latent scope of decomposition in the constraint graph and the lattice; therefore, we enjoy the fastest runtime for every point in the structure-spectrum of these graphs and lattices. While the techniques in this dissertation are developed with IFA in mind, they can be extended to other application of the bound constraints problem, such as type inference and program analysis frameworks which use annotated type systems, where constants are ordered over a lattice

Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Automatic speech recognition: from study to practice

Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work

Image correlation and sampling study

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed

Multi-Agent Fitness Functions For Evolutionary Architecture

The dynamics of crowd movements are self-organising and often involve complex pattern formations. Although computational models have recently been developed, it is unclear how well their underlying methods capture local dynamics and longer-range aspects, such as evacuation. A major part of this thesis is devoted to an investigation of current methods, and where required, the development of alternatives. The main purpose is to utilise realistic models of pedestrian crowds in the design of fitness functions for an evolutionary approach to architectural design. We critically review the state-of-the-art in pedestrian and evacuation dynamics. The concept of 'Multi-Agent System' embraces a number of approaches, which together encompass important local and longer-range aspects. Early investigations focus on methods-cellular automata and attractor fields-designed to capture these respective levels. The assumption that pattern formations in crowds result from local processes is reflected in two dimensional cellular automata models, where mathematical rules operate in local neighbourhoods. We investigate an established cellular automata and show that lane-formation patterns are stable only in a low-valued density range. Above this range, such patterns suddenly randomise. By identifying and then constraining the source of this randomness, we are only able to achieve a small degree of improvement. Moreover, when we try to integrate the model with attractor fields, no useful behaviour is achieved, and much of the randomness persists. Investigations indicate that the unwanted randomness is associated with 2-lattice phase transitions, where local dynamics get invaded by giant-component clusters during the onset of lattice percolation. Through this in-depth investigation, the general limits to cellular automata are ascertained-these methods are not designed with lattice percolation properties in mind and resulting models depend, often critically, on arbitrarily chosen neighbourhoods. We embark on the development of new and more flexible methodologies. Rather than treating local and global dynamics as separate entities, we combine them. Our methods are responsive to percolation, and are designed around the following principles: 1) Inclusive search provides an optimal path between a pedestrian origin and destination. 2) Dynamic boundaries protect search and are based on percolation probabilities, calculated from local density regimes. In this way, more robust dynamics are achieved. Simultaneously, longer-range behaviours are also specified. 3) Network-level dynamics further relax the constraints of lattice percolation and allow a wider range of pedestrian interactions. Having defined our methods, we demonstrate their usefulness by applying them to lane-formation and evacuation scenarios. Results reproduce the general patterns found in real crowds. We then turn to evolution. This preliminary work is intended to motivate future research in the field of Evolutionary Architecture. We develop a genotype-phenotype mapping, which produces complex architectures, and demonstrate the use of a crowd-flow model in a phenotype-fitness mapping. We discuss results from evolutionary simulations, which suggest that obstacles may have some beneficial effect on crowd evacuation. We conclude with a summary, discussion of methodological limitations, and suggestions for future research