Linear Systems

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

Linear systems theory is the cornerstone of control theory and a prerequisite for essentially
all graduate courses in this area. It is a well-established discipline that focuses
on linear differential equations from the perspective of control and estimation.

The context of this PhD thesis is the modelling and design of mechatronic systems. The study is positioned in the early design stage of the conception cycle (V-Cycle), where the main efforts have to be produced in terms of methodology, to enhance the quality and the functionality of the products, and based on virtual prototyping (modelling and simulation). One of the possible methodology is to reformulate the design problem as an inverse problem, in order to directly use the design specification of the product, usually given in terms of the system outputs, and then solve the design problem. In this context, the Ampere laboratory of INSA Lyon has developed a conception and design methodology, based on inverse approach and using the bond graph formalism, to propose a step-by-step method based on dynamic and energetic criteria, with a structural analysis phase that allows hierarchical analysis steps, depending on the structural physical layout of the model (topological, phenomenological, parameter set). The aim of the present works is to contribute to the development of this methodology, by enhancing it to the class of descriptor systems. This choice is led by the aim to apply the methodology in the context of chassis design and vehicle dynamics, where, among other, multi-body models represented as a differential-algebraic equation (DAE) system could occur. The contributions are proposed at the level of the topology of the model, as well as at the level of the phenomenological / behavioral aspects. A preliminary step is to enhance the existing algebraic framework to support graphical extension (in term of digraph and bond graph). The overall methodological extensions allow, firstly, a generalization of the approach to the class of descriptor systems, and, secondly, to reach a standardization of the procedures, previously dedicated to direct or inverse models, so as no mandatory differences between those models have to be done anymore.

Scientists, engineers and the like are a strange lot. Unperturbed by societal norms, they direct their energies to finding better alternatives to existing theories and concocting solutions to unsolved problems. Driven by an insatiable curiosity, they record their observations and crunch the numbers. This tome is about the science of crunching. It’s about digging out something of value from the detritus that others tend to leave behind. The described approaches involve constructing models to process the available data. Smoothing entails revisiting historical records in an endeavour to understand something of the past. Filtering refers to estimating what is happening currently, whereas prediction is concerned with hazarding a guess about what might happen next.
This book describes the classical smoothing, filtering and prediction techniques together with some more recently developed embellishments for improving performance within applications. It aims to present the subject in an accessible way, so that it can serve as a practical guide for undergraduates and newcomers to the field. The material is organised as an eleven-lecture course. The foundations are laid in Chapters 1 and 2, which explain minimum-mean-square-error solution construction and asymptotic behaviour. Chapters 3 and 4 introduce continuous-time and discrete-time minimum-variance filtering. Generalisations for missing data, deterministic inputs, correlated noises, direct feedthrough terms, output estimation and equalisation are described. Chapter 5 simplifies the minimum-variance filtering results for steady-state problems. Observability, Riccati equation solution convergence, asymptotic stability and Wiener filter equivalence are discussed. Chapters 6 and 7 cover the subject of continuous-time and discrete-time smoothing. The main fixed-lag, fixed-point and fixed-interval smoother results are derived. It is shown that the minimum-variance fixed-interval smoother attains the best performance. Chapter 8 attends to parameter estimation. As the above-mentioned approaches all rely on knowledge of the underlying model parameters, maximum-likelihood techniques within expectation-maximisation algorithms for joint state and parameter estimation are described. Chapter 9 is concerned with robust techniques that accommodate uncertainties within problem specifications. An extra term within Riccati equations enables designers to trade-off average error and peak error performance. Chapter 10 applies the afore-mentioned linear techniques to nonlinear estimation problems. It is demonstrated that step-wise linearisations can be used within predictors, filters and smoothers, albeit by forsaking optimal performance guarantees. Chapter 11 rounds off the course by exploiting knowledge about transition probabilities. HMM and minimum-variance-HMM filters and smoothers are derived. The improved performance offered by these techniques needs to be reconciled against the significantly higher calculation overheads.

This paper presents a parallel implementation of the hybrid BiCGStab(2) (bi-conjugate gradient stabilized) iterative method in a GPU (graphics processing unit) for solution of large and sparse linear systems. This implementation uses the CUDA-Matlab integration, in which the method operations are performed in a GPU core using Matlab built-in functions. The goal is to show that the exploitation of parallelism by using this new technology can provide a significant computational performance. For the validation of the work, we compared the proposed implementation with a BiCGStab(2) sequential and parallelized implementation in the C and CUDA-C languages. The results showed that the proposed implementation is more efficient and can be viable for simulations being carried out with quality and in a timely manner. The gains in computational efficiency were 76x and 6x compared to the implementation in C and CUDA-C, respectively.

A new approach to analyze injection locking mode of oscillators under small external excitation is proposed. The proposed approach exploits existence conditions of the solution of HB linear system with degenerate matrix. The method allows one to obtain the locking range for an arbitrary oscillator circuit with an arbitrary periodic injection waveform. The approach can be easily implemented into a circuit simulator. Examples are given to confirm the correctness of the new approach.