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This chapter provides background information and problem descriptions of the applications treated in this book. Most of the applications include real data and many of them are used as case studies examined throughout the book with different algorithms. This chapter serves both as a reference chapter and as a motivation for the area of adaptive filtering and change detection. The applications are divided here according to the model structures that are used. The real data applications include signals from: airbag, paper refinery, photon emission, econometrics, altitude sensors, EEG from rats and humans, EKG, ash recycling, earthquake and speech data, electronic nose and DC motor. The examples are used as benchmarks throughout the book. [ABSTRACT FROM PUBLISHER]

In this chapter we study the drift-diffusion (DD) model, which is the simplest version of the Boltzmann transport equation (BTE) coupled with Poisson's equation. The DD model has handled most engineering problems to date reasonably well. Having studied the DD model, we then use spherical expansion and Galerkin's method to solve the 1D BTE, obtaining more advanced information of hot carrier effects and ballistic transport for deep-submicron SMOS devices, or high-frequency compound semiconductor devices. [ABSTRACT FROM PUBLISHER]

Chapter 7 is dedicated to the description of frequency-selective stochastic and deterministic channel models beginning with the ellipses model introduced by Parsons and Bajwa, illustrating the path geometry for multipath fading channels (Section 7.1). In Section 7.2, a description of linear time-variant systems is given. With the help of system theory, four important system functions are introduced, allowing us to describe the input-output behaviour of linear time-variant systems in different ways. Section 7.3 is devoted to the theory of linear time-variant stochastic systems going back to Bello. In connection with this, stochastic system functions and characteristic quantities derivable from these are defined. In addition, the reference to frequency-selective stochastic channel models is established and, moreover, the channel models for typical propagation areas specified in the European work group COST 207 are given. Section 7.4 deals with the derivation and analysis of frequency-selective deterministic channel models. Chapter 7 ends with the design of deterministic simulation models for the channel models according to COST 207. [ABSTRACT FROM PUBLISHER]

Chapter 3 presents real-time scheduling and schedulability analysis, covering topics in computation time prediction, uniprocessor scheduling, scheduling preemptable and independent tasks, fixed-priority schedulers, rate-monotonic and deadline-monotonic algorithms, dynamic-priority schedulers, earliest-deadline-first (EDF) algorithm, least-laxity-first (LL) algorithm, scheduling nonpreemptable sporadic tasks, nonpreemptable tasks with precedence constraints, periodic tasks with precedence constraints, communicating periodic tasks, Deterministic Rendezvous Model, periodic tasks with critical sections, Kernelized Monitor Model, multiprocessor scheduling, schedule representations, Scheduling Game Board, sufficient conditions for conflict-free task sets, scheduling periodic tasks on a multiprocessor, PERTS, PerfoRMAx, TimeWiz, and real-time operating systems (RTOSs). [ABSTRACT FROM PUBLISHER]

Chapter 7 develops simple and efficient redundancy methods for optimizing the reliability of large systems represented at the top level by a series structure. Apportionment is introduced as an approximate method and a newly developed, bounded enumeration approach yields an exact solution with modest computation. Greedy and dynamic programming approaches are compared. [ABSTRACT FROM PUBLISHER]

Chapter 6 describes Real-Time Logic (RTL), graph-theoretic analysis, and Modechart, covering specification and safety assertions, Event-Action Model, restricted RTL formulas, constraint-graph construction, unsatisfiability check, analysis complexity and optimization, NASA X-38 Crew Return Vehicle X-38 Avionics Architecture, Modechart, verification of timing properties of Modechart specifications, system computations, and computation graph. It presents techniques for finding the minimum and maximum distance between endpoints, and exclusion and inclusion of endpoint and interval. [ABSTRACT FROM PUBLISHER]

Chapter 4 describes model checking of finite-state systems. Topics covered include: system specification, Clarke-Emerson-Sistla Model Checker, CTL, Complete CTL Model Checker in C, Symbolic Model Checking, Binary Decision Diagrams, Real-Time CTL, minimum and maximum delays, minimum and maximum number of condition occurrences, and state graphs with non-unit transition time. [ABSTRACT FROM PUBLISHER]

This chapter starts by reviewing some of the early research efforts in independent component analysis (ICA), especially the technique based on nonlinear decorrelation, that was successfully used by Jutten, Hérault, and Ans to solve the first ICA problems. The authors show that independent components can in some cases be found as nonlinearly uncorrelated linear combinations. The nonlinear functions used in this approach introduce higher order statistics into the solution method, making ICA possible. [ABSTRACT FROM PUBLISHER]

In the preceding chapters, the authors introduced several different estimation principles and algorithms for independent component analysis (ICA). In this chapter, they provide an overview of these methods. First, they show that all these estimation principles are intimately connected, and the main choices are between cumulant-based vs. negentropy/likelihood-based estimation methods, and between one-unit vs. multi-unit methods. They compare the algorithms experimentally, and show that the main choice here is between on-line (adaptive) gradient algorithms vs. fast batch fixed-point algorithms. At the end of the chapter, they provide a short summary of basic ICA estimation. [ABSTRACT FROM PUBLISHER]

This chapter deals with blind deconvolution and blind separation of convolutive mixtures. The authors first consider the simpler blind deconvolution problem, and after that separation of convolutive mixtures. Many techniques for convolutive mixtures have in fact been developed by extending methods designed originally for either the blind deconvolution or standard ICA/BSS problems. In the appendix, certain basic concepts of discrete-time filters needed in this chapter are briefly reviewed. [ABSTRACT FROM PUBLISHER]