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Robotics - Sensors and Methods for Autonomous Mobile Robot Positioning
Leonard and Durrant-Whyte [1991] summarized the problem of navigation by three questions: where am I?, where am I going?, and how should I get there? This report surveys the stateof- the-art in sensors, systems, methods, and technologies that aim at answering the first question, that is: robot positioning in its environment.
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Sensors in Manufacturing P1
Manufacturing can be said in a broad sense to be the process of converting raw materials into usable and saleable end products by various processes, machinery, and operations. The important function of manufacturing is, therefore, to add value to the raw materials. It is the backbone of any industrialized nation. Without manufacturing, few nations could afford the amenities that improve the quality of life. In fact, generally, the higher the level of manufacturing activity in a nation, the higher is the standard of living of its people. Manufacturing should also be competitive, not only locally but also on a global basis because of the shrinking of our world....
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Sensors in Manufacturing P2
Sensor Fusion With a specific focus for the monitoring in mind, researchers have developed over the years a wide variety of sensors and sensing strategies, each attempting to predict or detect a specific phenomenon during the operation of the process and in the presence of noise and other environmental contaminants. A good number of these sensing techniques applicable to manufacturing have been reviewed in the early part of this chapter. Although able to accomplish the task for a narrow set of conditions, these specific techniques have almost uniformly failed to be reliable enough to work over the range of operating conditions...
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Signal Processing for Telecommunications and Multimedia P1
Speech separation from noise, given a-priori information, can be viewed as a subspace estimation problem. Some conventional speech enhancement methods are spectral subtraction [1], Wiener filtering [2], blind signal separation [3] and hidden Markov modelling [4]. Hidden Markov Model (HMM) based speech enhancement techniques are related to the problem of performing speech recognition in noisy environments [5,6]. HMM based methods uses a-priori information about both the speech and the noise [4]. Some papers propose HMM speech enhancement techniques applied to stationary noise sources [4,7]....
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The Design Of Manufacturing Systems P1
Recent years have witnessed increasingly growing awareness for long-range planning in all sectors. Companies are concerned more than ever about long-term stability and profitability. The chemical process industries is no exception. New environmental regulations, rising competition, new technology, uncertainty of demand, and fluctuation of prices have all led to an increasing need for decision policies that will be ‘‘best” in a dynamic sense over a wide time horizon. Quantitative techniques have long established their importance in such decision-making problems. It is, therefore, no surprise that there is a considerable number of papers in the literature devoted to the problem of long-range planning in the processing industries. It...
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The Design Of Manufacturing Systems P2
The sequential engineering approach to product design and development typically treats design and manufacturing as isolated activities. In this approach, the design department designs an artifact and throws it “over the wall” to the manufacturing department without taking into consideration the manufacturing capabilities and limitations of the shop floor. The manufacturing department, in turn, studies the design from a manufacturability viewpoint and throws it back “over the wall” to the design department with a list of manufacturing concerns. Typically, the artifact drawings go back and forth between the two departments until, eventually, the drawings are approved for production. Obviously, this situation prolongs the product realization time. Also,...
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Digital Signal Processing Handbook Part 1
Introduction Fourier Series Representation of Continuous Time Periodic Signals Exponential Fourier Series • The Trigonometric Fourier Series • Convergence of the Fourier Series Properties of the Continuous Time Fourier Transform • Fourier Spectrum of the Continuous Time Sampling Model • Fourier Transform of Periodic Continuous Time Signals • The Generalized Complex Fourier Transform 1.3 The Classical Fourier Transform for Continuous Time Signals 1.4 1.5 Properties of the Discrete Time Fourier Transform • Relationship between the Continuous and Discrete Time Spectra Properties of the Discrete Fourier Series • Fourier Block Processing in Real-Time Filtering Applications • Fast Fourier Transform...
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Digital Signal Processing Handbook P2
A function containing variables and their derivatives is called a differential expression, and an equation involving differential expressions is called a differential equation. A differential equation is an ordinary differential equation if it contains only one independent variable; it is a partial differential equation if it contains more than one independent variable. We shall deal here only with ordinary differential equations. In the
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Digital Signal Processing Handbook P3
Bomar, B.W. “Finite Wordlength Effects” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c 1999 by CRC Press LLC .3 Finite Wordlength Effects 3.1 3.2 3.3 3.4 3.5 Introduction Number Representation Fixed-Point Quantization Errors Floating-Point Quantization Errors Roundoff Noise Roundoff Noise in FIR Filters • Roundoff Noise in Fixed-Point IIR Filters • Roundoff Noise in Floating-Point IIR Filters Bruce W. Bomar University of Tennessee Space Institute 3.6 Limit Cycles 3.7 Overflow Oscillations 3.8 Coefficient Quantization Error 3.9 Realization Considerations References 3.1 Introduction Practical digital filters must be implemented with finite precision numbers and arithmetic. As a result, both the filter coefficients and...
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Digital Signal Processing Handbook P4
Sampling of Continuous Functions From Infinite Sequences to Finite Sequences Ton Kalker Philips Research Laboratories, Eindhoven 4.5 Lattice Chains 4.6 Change of Variables 4.7 An Extended Example: HDTV-to-SDTV Conversion 4.8 Conclusions References Appendix A.1 Proof of Theorem 4.3 A.2 Proof of Theorem 4.5 A.3 Proof of Theorem 4.6 A.4 Proof of Theorem 4.7 A.5 Proof of Theorem 4.8 Glossary of Symbols and Expressions This chapter gives an overview of the most relevant
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Digital Signal Processing Handbook P5
Digital signal processing methods fundamentally require that signals are quantized at discrete time instances and represented as a sequence of words consisting of 1’s and 0’s. In nature, signals are usually nonquantized and
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Digital Signal Processing Handbook P6
Signals are usually classified into four categories. A continuous time signal x(t) has the field of real numbers R as its domain in that t can assume any real value. If the range of x(t) (values that x(t) can assume) is also R, then x(t) is said to be a continuous time, continuous amplitude signal. If the
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Digital Signal Processing Handbook P7
Introduction A Historical Perspective The Cooley-Tukey Mapping • Radix-2 and Radix-4 Algorithms • Split-Radix Algorithm • Remarks on FFTs with Twiddle Factors Basic Tools • Prime Factor Algorithms [95] • Winograd’s Fourier Transform Algorithm (WFTA) [56] • Other Members of This Class [38] • Remarks on FFTs Without Twiddle Factors Multiplicative Complexity • Additive Complexity Inverse FFT • In-Place Computation • Regularity, Parallelism • Quantization Noise DFT Algorithms for Real...
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Digital Signal Processing Handbook P8
Overlap-Add and Overlap-Save Methods for Fast Convolution 8.3 8.4 Block Convolution Block Recursion Overlap-Add • Overlap-Save • Use of the Overlap Methods Short and Medium Length Convolution The Toom-Cook Method • Cyclic Convolution • Winograd Short Convolution Algorithm • The Agarwal-Cooley Algorithm • The Split-Nesting Algorithm 8.5 8.6 8.7 8.8 Multirate Methods for Running Convolution Convolution in Subbands Distributed Arithmetic Multiplication is Convolution • Convolution is Two Dimensional • Distributed Arithmetic by Table Lookup Ivan W. Selesnick Polytechnic University Fast Convolution by Number Theoretic Transforms Number Theoretic Transforms C. Sidney Burrus Rice University 8.9...
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Digital Signal Processing Handbook P9
Complexity theory of computation attempts to determine how “inherently” difficult are certain tasks. For example, how inherently complex is the task of computing an inner product of two vectors of length N? Certainly one can compute the inner product N=1 xj yj by computing the j N products xj yj and then summing them. But can one compute this inner product with fewer than N multiplications? The answer is no, but the proof of this assertion is no trivial matter. One first abstracts
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Digital Signal Processing Handbook P10
Yagle, A.E. “Fast Matrix Computations” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c 1999 by CRC Press LLC .10 Fast Matrix Computations 10.1 Introduction 10.2 Divide-and-Conquer Fast Matrix Multiplication Strassen Algorithm • Divide-and-Conquer • Arbitrary Precision Approximation (APA) Algorithms • Number Theoretic Transform (NTT) Based Algorithms Overview • The Wavelet Transform • Wavelet Representations of Integral Operators • Heuristic Interpretation of Wavelet Sparsification 10.3 Wavelet-Based Matrix Sparsification Andrew E. Yagle University of Michigan References 10.1 Introduction This chapter presents two major approaches to fast matrix multiplication. We restrict our attention to matrix multiplication, excluding matrix addition and matrix inversion, since matrix addition...
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Digital Signal Processing Handbook P11
Digital filters are widely used in processing digital signals of many diverse applications, including speech processing and data communications, image and video processing, sonar, radar, seismic and oil exploration, and consumer electronics. One class of digital filters, the linear shift-invariant (LSI) type, are the most frequently used because they are simple to analyze, design, and implement. This chapter treats the LSI case only; other filter types, such as adaptive filters, require quite different design methodologies....
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Digital Signal Processing Handbook P13
Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing...
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Digital Signal Processing Handbook P14
Important Notions and Definitions Random Processes • Spectra of Deterministic Signals • Spectra of Random Processes 14.3 The Problem of Power Spectrum Estimation 14.4 Nonparametric Spectrum Estimation Periodogram • The Bartlett Method • The Welch Method • Blackman-Tukey Method • Minimum Variance Spectrum Estimator • Multiwindow Spectrum Estimator Spectrum Estimation Based on Autoregressive Models • Spectrum Estimation Based on Moving Average Models • Spectrum Estimation Based on Autoregressive Moving Average Models • Pisarenko Harmonic Decomposition Method • Multiple Signal Classification (MUSIC) 14.5 Parametric Spectrum Estimation ´ Petar M. Djuric State University of New York at Stony...
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Digital Signal Processing Handbook P15
Introduction Least-Squares Estimation Properties of Estimators Best Linear Unbiased Estimation Maximum-Likelihood Estimation Mean-Squared Estimation of Random Parameters Maximum A Posteriori Estimation of Random Parameters The Basic State-Variable Model State Estimation for the Basic State-Variable Model Prediction • Filtering (the Kalman Filter) • Smoothing Jerry M. Mendel University of Southern California 15.10 Digital Wiener Filtering 15.11 Linear Prediction in DSP, and Kalman Filtering 15.12 Iterated Least Squares 15.13 Extended Kalman Filter Acknowledgment References Further Information 15.1 Introduction Estimation is one of four modeling...
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Digital Signal Processing Handbook P16
Linear parametric models of stationary random processes, whether signal or noise, have been found to be useful in a wide variety of signal processing tasks such as signal detection, estimation, filtering, and classification, and in a wide variety of applications such as digital
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Digital Signal Processing Handbook P17
Processes encountered in statistical signal processing, communications, and time series analysis applications are often assumed stationary. The plethora of available
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Digital Signal Processing Handbook P18
Introduction to Adaptive Filters 18.1 18.2 18.3 18.4 18.5 What is an Adaptive Filter? The Adaptive Filtering Problem Filter Structures The Task of an Adaptive Filter Applications of Adaptive Filters System Identification • Inverse Modeling • Linear Prediction • Feedforward Control General Form of Adaptive FIR Algorithms • The MeanSquared Error Cost Function • The Wiener Solution • The Method of Steepest Descent • The LMS Algorithm • Other Stochastic Gradient Algorithms • Finite-Precision Effects and Other Implementation Issues • System Identification Example 18.6 Gradient-Based Adaptive Algorithms Scott C. Douglas University of Utah 18.7 Conclusions References 18.1 What is an Adaptive Filter? An adaptive filter is a computational device...
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Digital Signal Processing Handbook P19
Characterizing the Performance of Adaptive Filters 19.3 Analytical Models, Assumptions, and Definitions System Identification Model for the Desired Response Signal • Statistical Models for the Input Signal • The Independence Assumptions • Useful Definitions 19.4 Analysis of the LMS Adaptive Filter Mean Analysis • Mean-Square Analysis 19.5 Performance Issues Basic Criteria for Performance • Identifying Stationary Systems • Tracking Time-Varying Systems Normalized Step Sizes • Adaptive and Matrix Step Sizes • Other Time-Varying Step Size Methods 19.6 Selecting Time-Varying Step Sizes Scott C. Douglas University of Utah Markus Rupp Bell Laboratories Lucent Technologies 19.7 Other Analyses of the LMS Adaptive Filter 19.8 Analysis of Other Adaptive Filters 19.9...
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Digital Signal Processing Handbook P20
Motivation and Example Adaptive Filter Structure Performance and Robustness Issues Error and Energy Measures Robust Adaptive Filtering Energy Bounds and Passivity Relations Min-Max Optimality of Adaptive Gradient Algorithms Comparison of LMS and RLS Algorithms Time-Domain Feedback Analysis Ali H. Sayed University of California, Los Angeles Markus Rupp Bell Laboratories Lucent Technologies Time-Domain Analysis • l2 −Stability and the Small Gain Condition • Energy Propagation in the Feedback Cascade • A Deterministic Convergence Analysis 20.10Filtered-Error Gradient Algorithms 20.11References and Concluding Remarks Adaptive filters are systems that adjust themselves to a changing...
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Digital Signal Processing Handbook P21
Recursive Least-Squares Adaptive Filters Array Algorithms Elementary Circular Rotations • Elementary Hyperbolic Rotations • Square-Root-Free and Householder Transformations • A Numerical Example Geometric Interpretation • Statistical Interpretation Geometric Interpretation • Statistical Interpretation Reducing to the Regularized Form • Time Updates Estimation Errors and the Conversion Factor • Update of the Minimum Cost Motivation • A Very Useful Lemma • The Inverse QR Algorithm • The QR Algorithm The Prewindowed Case • Low-Rank Property • A Fast Array Algorithm • The Fast Transversal Filter Joint Process Estimation • The Backward Prediction Error Vectors • The Forward Prediction Error Vectors • A Nonunity...
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Digital Signal Processing Handbook P22
One of the earliest works on transform domain adaptive filtering was published in 1978 by Dentino et al. [4], in which the concept of adaptive filtering in the frequency domain was proposed. Many publications have since appeared that further develop the theory and expand
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Digital Signal Processing Handbook P23
The System Identification Framework for Adaptive IIR Filtering • Algorithms and Performance Issues • Some Preliminaries 23.2 The Equation Error Approach The LMS and LS Equation Error Algorithms • Instrumental Variable Algorithms • Equation Error Algorithms with Unit Norm Constraints Gradient-Descent Algorithms Based on Stability Theory • 23.3 The Output Error Approach Output Error Algorithms 23.4 Equation-Error/Output-Error Hybrids The Steiglitz-McBride Family of Algorithms Geoffrey A. Williamson Illinois Institute of Technology 23.5 Alternate Parametrizations 23.6 Conclusions References 23.1 Introduction In comparison with adaptive finite impulse response (FIR) filters, adaptive infinite impulse response (IIR) filters offer the...
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Digital Signal Processing Handbook P24
Adaptive Filters for Blind Equalization 24.1 Introduction 24.2 Channel Equalization in QAM Data Communication Systems 24.3 Decision-Directed Adaptive Channel Equalizer 24.4 Basic Facts on Blind Adaptive Equalization 24.5 Adaptive Algorithms and Notations 24.6 Mean Cost Functions and Associated Algorithms The Sato Algorithm • BGR Extensions of Sato Algorithm • Constant Modulus or Godard Algorithms • Stop-and-Go Algorithms • Shalvi and Weinstein Algorithms • Summary A Common Analysis Approach • Local Convergence of Blind Equalizers • Initialization Issues Linearly Constrained Equalizer With Convex Cost 24.7 Initialization and Convergence of Blind Equalizers 24.8 Globally Convergent Equalizers 24.9 Fractionally Spaced Blind Equalizers 24.10 Concluding Remarks References Zhi...
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Digital Signal Processing Handbook P25
Signal recovery has been an active area of research for applications in many different scientific disciplines. A central reason for exploring the feasibility of signal recovery is due to the limitations imposed by a physical device on the amount of data one can record. For example, for
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