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 Totsky, Alexander V., 1952
 Berlin ; Boston : Walter de Gruyter GmbH & Co. KG, [2015]
 Description
 Book — x, 199 pages : illustrations ; 25 cm
 Summary

 General properties of bispectrumbased digital signal processing
 Unknown noisy signal shape estimation by bispectrumfiltering techniques
 Bispectrumbased digital image reconstruction using tapering predistortion
 Signal detection by using thirdorder test statistics in communications and radar applications.
(source: Nielsen Book Data) 9783110374568 20160618
 Online
Engineering Library (Terman)
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TK5102.9 .T738 2015  Unknown 
2. Medical imaging signals and systems [2015]
 Prince, Jerry L., author.
 Second edition.  Boston : Pearson, [2015]
 Description
 Book — xvii, 519 pages : illustrations ; 27 cm
 Summary

 Part I Basic Imaging Principles
 1
 1 Introduction
 5 1.1 History of Medical Imaging
 5 1.2 Physical Signals
 6 1.3 Imaging Modalities
 7 1.4 Projection Radiography
 7 1.5 Computed Tomography
 9 1.6 Nuclear Medicine
 10 1.7 Ultrasound Imaging
 11 1.8 Magnetic Resonance Imaging
 12 1.9 Multimodality Imaging
 13 1.10 Summary and Key Concepts
 13
 2 Signals and Systems
 15 2.1 Introduction
 15 2.2 Signals
 16 2.2.1 Point Impulse
 16 2.2.2 Line Impulse
 19 2.2.3 Comb and Sampling Functions
 19 2.2.4 Rect and Sinc Functions
 20 2.2.5 Exponential and Sinusoidal Signals
 22 2.2.6 Separable Signals
 23 2.2.7 Periodic Signals
 23 2.3 Systems
 24 2.3.1 Linear Systems
 24 2.3.2 Impulse Response
 25 2.3.3 Shift Invariance
 25 2.3.4 Connections of LSI Systems
 28 2.3.5 Separable Systems
 30 2.3.6 Stable Systems
 31 2.4 The Fourier Transform
 31 2.5 Properties of the Fourier Transform
 36 2.5.1 Linearity
 36 2.5.2 Translation
 37 2.5.3 Conjugation and Conjugate Symmetry
 37 2.5.4 Scaling
 37 2.5.5 Rotation
 38 2.5.6 Convolution
 38 2.5.7 Product
 39 2.5.8 Separable Product
 40 2.5.9 Parseval's Theorem
 40 2.5.10 Separability
 40 2.6 Transfer Function
 41 2.7 Circular Symmetry and the Hankel Transform
 43 2.8 Summary and Key Concepts
 47
 3 Image Quality
 54 3.1 Introduction
 54 3.2 Contrast
 55 3.2.1 Modulation
 56 3.2.2 Modulation Transfer Function
 56 3.2.3 Local Contrast
 60 3.3 Resolution
 61 3.3.1 Line Spread Function
 61 3.3.2 Full Width at Half Maximum
 62 3.3.3 Resolution and Modulation Transfer Function
 63 3.3.4 Subsystem Cascade
 65 3.3.5 Resolution Tool
 68 3.3.6 Temporal and Spectral Resolution
 68 3.4 Noise
 69 3.4.1 Random Variables
 70 3.4.2 Continuous Random Variables
 70 3.4.3 Discrete Random Variables
 72 3.4.4 Independent Random Variables
 75 3.5 SignaltoNoise Ratio
 76 3.5.1 Amplitude SNR
 77 3.5.2 Power SNR
 77 3.5.3 Differential SNR
 79 3.5.4 Decibels
 80 3.6 Sampling
 80 3.6.1 Signal Model for Sampling
 81 3.6.2 Nyquist Sampling Theorem
 83 3.6.3 AntiAliasing Filters
 85 3.7 Other Effects
 86 3.7.1 Artifacts
 86 3.7.2 Distortion
 88 3.8 Accuracy
 88 3.8.1 Quantitative Accuracy
 89 3.8.2 Diagnostic Accuracy
 89 3.9 Summary and Key Concepts
 92 Part II Radiographic Imaging
 101
 4 Physics of Radiography
 106 4.1 Introduction
 106 4.2 Ionization
 107 4.2.1 Atomic Structure
 107 4.2.2 Electron Binding Energy
 109 4.2.3 Ionization and Excitation
 109 4.3 Forms of Ionizing Radiation
 110 4.3.1 Particulate Radiation
 110 4.3.2 Electromagnetic Radiation
 112 4.4 Nature and Properties of Ionizing Radiation
 113 4.4.1 Primary Energetic Electron Interactions
 114 4.4.2 Primary Electromagnetic Radiation Interactions
 116 4.5 Attenuation of Electromagnetic Radiation
 120 4.5.1 Measures of XRay Beam Strength
 121 4.5.2 Narrow Beam, Monoenergetic Photons
 123 4.5.3 Narrow Beam, Polyenergetic Photons
 125 4.5.4 Broad Beam Case
 127 4.6 Radiation Dosimetry
 127 4.6.1 Exposure
 127 4.6.2 Dose and Kerma
 128 4.6.3 Linear Energy Transfer (LET)
 128 4.6.4 The f Factor
 128 4.6.5 Dose Equivalent
 129 4.6.6 Effective Dose
 130 4.7 Summary and Key Concepts
 131
 5 Projection Radiography
 135 5.1 Introduction
 135 5.2 Instrumentation
 136 5.2.1 XRay Tubes
 136 5.2.2 Filtration and Restriction
 139 5.2.3 Compensation Filters and Contrast Agents
 141 5.2.4 Grids, Airgaps, and Scanning Slits
 143 5.2.5 FilmScreen Detectors
 146 5.2.6 XRay Image Intensifiers
 148 5.2.7 Digital Radiography
 149 5.2.8 Mammography
 154 5.3 Image Formation
 154 5.3.1 Basic Imaging Equation
 154 5.3.2 Geometric Effects
 155 5.3.3 Blurring Effects
 162 5.3.4 Film Characteristics
 166 5.4 Noise and Scattering
 169 5.4.1 SignaltoNoise Ratio
 169 5.4.2 Quantum Efficiency and Detective Quantum Efficiency
 171 5.4.3 Compton Scattering
 173 5.5 Summary and Key Concepts
 175
 6 Computed Tomography
 186 6.1 Introduction
 186 6.2 CT Instrumentation
 188 6.2.1 CT Generations
 188 6.2.2 XRay Source and Collimation
 194 6.2.3 DualEnergy CT
 194 6.2.4 CT Detectors
 195 6.2.5 Gantry, Slip Ring, and Patient Table
 196 6.3 Image Formation
 197 6.3.1 Line Integrals
 197 6.3.2 CT Numbers
 198 6.3.3 ParallelRay Reconstruction
 198 6.3.4 FanBeam Reconstruction
 208 6.3.5 Helical CT Reconstruction
 212 6.3.6 Cone Beam CT
 213 6.3.7 Iterative Reconstruction
 213 6.4 Image Quality in CT
 213 6.4.1 Resolution
 214 6.4.2 Noise
 216 6.4.3 Artifacts
 221 6.5 Summary and Key Points
 223 Part III Nuclear Medicine Imaging
 235
 7 The Physics of Nuclear Medicine
 239 7.1 Introduction
 239 7.2 Nomenclature
 240 7.3 Radioactive Decay
 240 7.3.1 Mass Defect and Binding Energy
 240 7.3.2 Line of Stability
 242 7.3.3 Radioactivity
 243 7.3.4 Radioactive Decay Law
 243 7.4 Modes of Decay
 245 7.4.1 Positron Decay and Electron Capture
 245 7.4.2 Isomeric Transition
 246 7.5 Statistics of Decay
 247 7.6 Radiotracers
 249 7.7 Summary and Key Concepts
 251
 8 Planar Scintigraphy
 255 8.1 Introduction
 255 8.2 Instrumentation
 255 8.2.1 Collimators
 256 8.2.2 Scintillation Crystal
 258 8.2.3 Photomultiplier Tubes
 258 8.2.4 Positioning Logic
 260 8.2.5 Pulse Height Analyzer
 260 8.2.6 Gating Circuit
 262 8.2.7 Image Capture
 263 8.2.8 Solid State and Other New Cameras
 264 8.3 Image Formation
 264 8.3.1 Event Position Estimation
 264 8.3.2 Acquisition Modes
 266 8.3.3 Anger Camera Imaging Equation
 269 8.4 Image Quality
 272 8.4.1 Resolution
 273 8.4.2 Sensitivity
 276 8.4.3 Uniformity
 278 8.4.4 Energy Resolution
 279 8.4.5 Noise
 280 8.4.6 Factors Affecting Count Rate
 281 8.5 Summary and Key Concepts
 282
 9 Emission Computed Tomography
 293 9.1 Instrumentation
 294 9.1.1 SPECT Instrumentation
 294 9.1.2 PET Instrumentation
 298 9.2 Image Formation
 304 9.2.1 SPECT Image Formation
 304 9.2.2 PET Image Formation
 309 9.2.3 Iterative Reconstruction
 313 9.3 Image Quality in SPECT and PET
 317 9.3.1 Spatial Resolution
 318 9.3.2 Attenuation and Scatter
 319 9.3.3 Random Coincidences
 320 9.3.4 Contrast
 320 9.3.5 Noise and SignaltoNoise Ratio
 321 9.4 Summary and Key Concepts
 321 Part IV Ultrasound Imaging
 331
 10 The Physics of Ultrasound
 335 10.1 Introduction
 335 10.2 The Wave Equation
 336 10.2.1 ThreeDimensional Acoustic Waves
 336 10.2.2 Plane Waves
 338 10.2.3 Spherical Waves
 340 10.3 Wave Propagation
 341 10.3.1 Acoustic Energy and Intensity
 341 10.3.2 Reflection and Refraction at Plane Interfaces
 342 10.3.3 Transmission and Reflection Coefficients at Plane Interfaces
 343 10.3.4 Attenuation
 344 10.3.5 Scattering
 347 10.3.6 Nonlinear Wave Propagation
 347 10.4 Doppler Effect
 349 10.5 Beam Pattern Formation and Focusing
 353 10.5.1 Simple Field Pattern Model
 354 10.5.2 Diffraction Formulation
 355 10.5.3 Focusing
 361 10.6 Summary and Key Concepts
 362
 11 Ultrasound Imaging Systems
 367 11.1 Introduction
 367 11.2 Instrumentation
 367 11.2.1 Ultrasound Transducer
 367 11.2.2 Ultrasound Probes
 372 11.3 PulseEcho Imaging
 374 11.3.1 The PulseEcho Equation
 374 11.4 Transducer Motion
 377 11.5 Ultrasound Imaging Modes
 380 11.5.1 AMode Scan
 380 11.5.2 MMode Scan
 381 11.5.3 BMode Scan
 381 11.6 Steering and Focusing
 386 11.6.1 Transmit Steering and Focusing
 386 11.6.2 Beamforming and Dynamic Focusing
 388 11.7 ThreeDimensional Ultrasound Imaging
 391 11.8 Image Quality
 392 11.8.1 Resolution
 392 11.8.2 Noise and Speckle
 395 11.9 Summary and Key Concepts
 396 Part V Magnetic Resonance Imaging
 407
 12 Physics of Magnetic Resonance
 410 12.1 Introduction
 410 12.2 Microscopic Magnetization
 410 12.3 Macroscopic Magnetization
 412 12.4 Precession and Larmor Frequency
 414 12.5 Transverse and Longitudinal Magnetization
 416 12.5.1 NMR Signals
 417 12.5.2 Rotating Frame
 419 12.6 RF Excitation
 419 12.7 Relaxation
 422 12.8 The Bloch Equations
 425 12.9 Spin Echoes
 426 12.10 Basic Contrast Mechanisms
 429 12.11 Summary and Key Concepts
 433
 13 Magnetic Resonance Imaging
 439 13.1 Instrumentation
 439 13.1.1 System Components
 439 13.1.2 Magnet
 441 13.1.3 Gradient Coils
 442 13.1.4 Radio Frequency Coils
 445 13.1.5 Scanning Console and Computer
 446 13.2 MRI Data Acquisition
 447 13.2.1 Encoding Spatial Position
 447 13.2.2 Slice Selection
 449 13.2.3 Frequency Encoding
 455 13.2.4 Polar Scanning
 460 13.2.5 Gradient Echoes
 461 13.2.6 Phase Encoding
 462 13.2.7 Spin Echoes
 465 13.2.8 Pulse Repetition Interval
 467 13.2.9 Realistic Pulse Sequences
 467 13.3 Image Reconstruction
 469 13.3.1 Rectilinear Data
 470 13.3.2 Polar Data
 471 13.3.3 Imaging Equations
 472 13.4 Image Quality
 475 13.4.1 Sampling
 475 13.4.2 Resolution
 477 13.4.3 Noise
 479 13.4.4 SignaltoNoise Ratio
 481 13.4.5 Artifacts
 482 13.5 Advanced Contrast Mechanisms
 483 13.6 Summary and Key Concepts
 487 Index 497.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780132145183 20160618
 Online
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RC78.7 .D53 P755 2015  Unknown 
3. Foundations of signal processing [2014]
 Vetterli, Martin, author.
 Cambridge : Cambridge University Press, 2014.
 Description
 Book — xxvii, 715 pages : illustrations ; 26 cm
 Summary

 1. On rainbows and spectra
 2. From Euclid to Hilbert: 2.1. Introduction 2.2. Vector spaces 2.3. Hilbert spaces 2.4. Approximations, projections, and decompositions 2.5. Bases and frames 2.6. Computational aspects 2.A. Elements of analysis and topology 2.B. Elements of linear algebra 2.C. Elements of probability 2.D. Basis concepts Exercises with solutions Exercises
 3. Sequences and discretetime systems: 3.1. Introduction 3.2. Sequences 3.3. Systems 3.4. Discretetime Fourier Transform 3.5. zTransform 3.6. Discrete Fourier Transform 3.7. Multirate sequences and systems 3.8. Stochastic processes and systems 3.9. Computational aspects 3.A. Elements of analysis 3.B. Elements of algebra Exercises with solutions Exercises
 4. Functions and continuoustime systems: 4.1. Introduction 4.2. Functions 4.3. Systems 4.4. Fourier Transform 4.5. Fourier series 4.6. Stochastic processes and systems Exercises with solutions Exercises
 5. Sampling and interpolation: 5.1. Introduction 5.2. Finitedimensional vectors 5.3. Sequences 5.4. Functions 5.5. Periodic functions 5.6. Computational aspects Exercises with solutions Exercises
 6. Approximation and compression: 6.1. Introduction 6.2. Approximation of functions on finite intervals by polynomials 6.3. Approximation of functions by splines 6.4. Approximation of functions and sequences by series truncation 6.5. Compression 6.6. Computational aspects Exercises with solutions Exercises
 7. Localization and uncertainty: 7.1. Introduction 7.2. Localization for functions 7.3. Localization for sequences 7.4. Tiling the timefrequency plane 7.5. Examples of local Fourier and wavelet bases 7.6. Recap and a glimpse forward Exercises with solutions Exercises.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781139898942 20160618
 Online
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TK5102.9 .V479 2014  Unknown 
 Jauregui, Juan Carlos, author.
 Cambridge : Elsevier/Woodhead Publishing, [2014]
 Description
 Book — xviii, 208 pages : ill. (some color) ; 24 cm.
 Summary

 Introduction: Nonlinear dynamics Nonlinear vibrations Signal processing Parameter identification Application of signal processing to mechanical systems Practical experience and industrial applications Synchronization of nonlinear systems.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781782421658 20160617
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TA355 .J38 2014  Unknown 
 Yao, Kung, author.
 Cambridge ; New York : Cambridge University Press, 2013.
 Description
 Book — x, 322 pages : illustrations ; 26 cm
 Summary

 1. Introduction and motivation to detection and estimation
 2. Review of probability and random processes
 3. Statistical hypothesis testing theory
 4. Detection of deterministic binary signals in Gaussian noises
 5. Mary detection and classification of deterministic signals
 6. Noncoherent detection
 7. Parameter estimation
 8. Analytical and simulation methods for system performance analysis and design.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780521766395 20160615
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TK5102.9 .Y365 2013  Unknown 
 Piscataway, N.J. : IEEE Press ; Hoboken, N.J. : Wiley, c2013.
 Description
 Book — xxiv, 712 p. : ill. ; 25 cm
 Summary

 PREFACE xvii CONTRIBUTORS xxiii PART I FILTERING
 1. AngleOnly Filtering in Three Dimensions
 3 Mahendra Mallick, Mark Morelande, Lyudmila Mihaylova, Sanjeev Arulampalam, and Yanjun Yan 1.1 Introduction
 3 1.2 Statement of Problem
 6 1.3 Tracker and Sensor Coordinate Frames
 6 1.4 Coordinate Systems for Target and Ownship States
 7 1.5 Dynamic Models
 9 1.6 Measurement Models
 14 1.7 Filter Initialization
 15 1.8 Extended Kalman Filters
 17 1.9 Unscented Kalman Filters
 19 1.10 Particle Filters
 23 1.11 Numerical Simulations and Results
 28 1.12 Conclusions
 31
 2. Particle Filtering Combined with Interval Methods for Tracking Applications
 43 Amadou Gning, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic 2.1 Introduction
 43 2.2 Related Works
 44 2.3 Interval Analysis
 46 2.4 Bayesian Filtering
 51 2.5 Box Particle Filtering
 52 2.6 Box Particle Filtering Derived from the Bayesian Inference Using a Mixture of Uniform Probability Density Functions
 56 2.7 BoxPF Illustration over a Target Tracking Example
 65 2.8 Application for a Vehicle Dynamic Localization Problem
 67 2.9 Conclusions
 71
 3. Bayesian Multiple Target Filtering Using Random Finite Sets
 75 BaNgu Vo, BaTuong Vo, and Daniel Clark 3.1 Introduction
 75 3.2 Overview of the Random Finite Set Approach to Multitarget Filtering
 76 3.3 Random Finite Sets
 81 3.4 Multiple Target Filtering and Estimation
 85 3.5 Multitarget Miss Distances
 91 3.6 The Probability Hypothesis Density (PHD) Filter
 95 3.7 The Cardinalized PHD Filter
 105 3.8 Numerical Examples
 111 3.9 MeMBer Filter
 117
 4. The Continuous Time Roots of the Interacting Multiple Model Filter
 127 Henk A.P. Blom 4.1 Introduction
 127 4.2 Hidden Markov Model Filter
 129 4.3 System with Markovian Coefficients
 136 4.4 Markov Jump Linear System
 141 4.5 ContinuousDiscrete Filtering
 149 4.6 Concluding Remarks
 154 PART II MULTITARGET MULTISENSOR TRACKING
 5. Multitarget Tracking Using Multiple Hypothesis Tracking
 165 Mahendra Mallick, Stefano Coraluppi, and Craig Carthel 5.1 Introduction
 165 5.2 Tracking Algorithms
 166 5.3 Track Filtering
 170 5.4 MHT Algorithms
 179 5.5 HybridState Derivations of MHT Equations
 180 5.6 The TargetDeath Problem
 185 5.7 Examples for MHT
 186 5.8 Summary
 189
 6. Tracking and Data Fusion for Ground Surveillance
 203 Michael Mertens, Michael Feldmann, Martin Ulmke, and Wolfgang Koch 6.1 Introduction to Ground Surveillance
 203 6.2 GMTI Sensor Model
 204 6.3 Bayesian Approach to Ground Moving Target Tracking
 209 6.4 Exploitation of Road Network Data
 222 6.5 Convoy Track Maintenance Using Random Matrices
 234 6.6 Convoy Tracking with the Cardinalized Probability Hypothesis Density Filter
 243
 7. Performance Bounds for Target Tracking: Computationally Efficient Formulations and Associated Applications
 255 Marcel Hernandez 7.1 Introduction
 255 7.2 Bayesian Performance Bounds
 258 7.3 PCRLB Formulations in Cluttered Environments
 262 7.4 An Approximate PCRLB for Maneuevring Target Tracking
 269 7.5 A General Framework for the Deployment of Stationary Sensors
 271 7.6 UAV Trajectory Planning
 294 7.7 Summary and Conclusions
 305
 8. TrackBeforeDetect Techniques
 311 Samuel J. Davey, Mark G. Rutten, and Neil J. Gordon 8.1 Introduction
 311 8.2 Models
 318 8.3 Baum Welch Algorithm
 327 8.4 Dynamic Programming: Viterbi Algorithm
 331 8.5 Particle Filter
 334 8.6 MLPDA
 337 8.7 HPMHT
 341 8.8 Performance Analysis
 347 8.9 Applications: Radar and IRST Fusion
 354 8.10 Future Directions
 357
 9. Advances in Data Fusion Architectures
 363 Stefano Coraluppi and Craig Carthel 9.1 Introduction
 363 9.2 DenseTarget Scenarios
 364 9.3 Multiscale Sensor Scenarios
 368 9.4 Tracking in Large Sensor Networks
 370 9.5 Multiscale Objects
 372 9.6 Measurement Aggregation
 378 9.7 Conclusions
 383
 10. Intent Inference and Detection of Anomalous Trajectories: A Metalevel Tracking Approach
 387 Vikram Krishnamurthy 10.1 Introduction
 387 10.2 Anomalous Trajectory Classification Framework
 393 10.3 Trajectory Modeling and Inference Using Stochastic ContextFree Grammars
 395 10.4 Trajectory Modeling and Inference Using Reciprocal Processes (RP)
 403 10.5 Example
 1: Metalevel Tracking for GMTI Radar
 406 10.6 Example
 2: Data Fusion in a Multicamera Network
 407 10.7 Conclusion
 413 PART III SENSOR MANAGEMENT AND CONTROL
 11. Radar Resource Management for Target TrackingA Stochastic Control Approach
 417 Vikram Krishnamurthy 11.1 Introduction
 417 11.2 Problem Formulation
 422 11.3 Structural Results and Lattice Programming for Micromanagement
 431 11.4 Radar Scheduling for Maneuvering Targets Modeled as Jump Markov Linear System
 437 11.5 Summary
 444
 12. Sensor Management for LargeScale MultisensorMultitarget Tracking
 447 Ratnasingham Tharmarasa and Thia Kirubarajan 12.1 Introduction
 447 12.2 Target Tracking Architectures
 451 12.3 Posterior Cram'erRao Lower Bound
 452 12.4 Sensor Array Management for Centralized Tracking
 458 12.5 Sensor Array Management for Distributed Tracking
 473 12.6 Sensor Array Management for Decentralized Tracking
 489 12.7 Conclusions
 507 PART IV ESTIMATION AND CLASSIFICATION
 13. Efficient Inference in General Hybrid Bayesian Networks for Classification
 523 Wei Sun and KuoChu Chang 13.1 Introduction
 523 13.2 Message Passing: Representation and Propagation
 526 13.3 Network Partition and Message Integration for Hybrid Model
 532 13.4 Hybrid Message Passing Algorithm for Classification
 536 13.5 Numerical Experiments
 537 13.6 Concluding Remarks
 544
 14. Evaluating Multisensor Classification Performance with Bayesian Networks
 547 Eswar Sivaraman and KuoChu Chang 14.1 Introduction
 547 14.2 SingleSensor Model
 548 14.3 Multisensor Fusion SystemsDesign and Performance Evaluation
 560 14.4 Summary and Continuing Questions
 564
 15. Detection and Estimation of Radiological Sources
 579 Mark Morelande and Branko Ristic 15.1 Introduction
 579 15.2 Estimation of Point Sources
 580 15.3 Estimation of Distributed Sources
 590 15.4 Searching for Point Sources
 599 15.5 Conclusions
 612 PART V DECISION FUSION AND DECISION SUPPORT
 16. Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks
 619 Qi Cheng, Ruixin Niu, Ashok Sundaresan, and Pramod K. Varshney 16.1 Introduction
 619 16.2 Elements of Detection Theory
 620 16.3 Distributed Detection with Multiple Sensors
 624 16.4 Distributed Detection in Wireless Sensor Networks
 634 16.5 CopulaBased Fusion of Correlated Decisions
 645 16.6 Conclusion
 652
 17. Evidential Networks for Decision Support in Surveillance Systems
 661 Alessio Benavoli and Branko Ristic 17.1 Introduction
 661 17.2 Valuation Algebras
 662 17.3 Local Computation in a VA
 668 17.4 Theory of Evidence as a Valuation Algebra
 672 17.5 Examples of Decision Support Systems
 685 References
 702 Index 705.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780470639054 20160612
Engineering Library (Terman)
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TK7872 .D48 I58 2013  Unknown 
 Brandwood, David.
 2nd ed.  Boston : Artech House, c2012.
 Description
 Book — xv, 264 p. : ill. ; 24 cm. + 1 CDROM (4 3/4 in.)
 Summary

 Introduction. Rules and Pairs. Pulse Spectra. Sampling Theory. Periodic Waveforms. Interpolation for Delayed Waveform Time Series. Equalization. Array Beamforming.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781608071975 20160607
 Online
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TK6580 .B68 2012  Unknown 
 Brown, Robert Grover.
 4th ed.  Hoboken, NJ : Wiley, c2012.
 Description
 Book — xii, 383 p. : ill ; 26 cm.
 Summary

 PART 1: RANDOM SIGNALS BACKGROUND
 Chapter 1 Probability and Random Variables: A Review
 Chapter 2 Mathematical Description of Random Signals
 Chapter 3 Linear Systems Response, Statespace Modeling and Monte Carlo Simulation
 PART 2: KALMAN FILTERING AND APPLICATIONS
 Chapter 4 Discrete Kalman Filter Basics
 Chapter 5 Intermediate Topics on Kalman Filtering
 Chapter 6 Smoothing and Further Intermediate Topics
 Chapter 7 Linearization, Nonlinear Filtering and Sampling Bayesian Filters
 Chapter 8 the "GoFree" Concept, Complementary Filter and Aided Inertial Examples
 Chapter 9 Kalman Filter Applications to the GPS and Other Navigation Systems APPENDIX A. Laplace and Fourier Transforms APPENDIX B. The Continuous Kalman Filter.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780470609699 20160607
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TK5102.9 .B75 2012  Unknown 
 Weeks, Michael.
 2nd ed.  Sudbury, Mass. : Jones and Bartlett Publishers, c2011.
 Description
 Book — xix, 492 p. : ill. (some col.) ; 24 cm. + 1 CDROM (4 3/4 in.)
 Summary

Although Digital Signal Processing (DSP) has long been considered an electrical engineering topic, recent developments have also generated significant interest from the computer science community. DSP applications in the consumer market, such as bioinformatics, the MP3 audio format, and MPEGbased cable/satellite television have fueled a desire to understand this technology outside of hardware circles. Designed for upper division engineering and computer science students as well as practicing engineers and scientists, Digital Signal Processing Using MATLAB & Wavelets, Second Edition emphasizes the practical applications of signal processing. Over 100 MATLAB examples and wavelet techniques provide the latest applications of DSP, including image processing, games, filters, transforms, networking, parallel processing, and sound. This Second Edition also provides the mathematical processes and techniques needed to ensure an understanding of DSP theory. Designed to be incremental in difficulty, the book will benefit readers who are unfamiliar with complex mathematical topics or those limited in programming experience. Beginning with an introduction to MATLAB programming, it moves through filters, sinusoids, sampling, the Fourier transform, the ztransform and other key topics. Two chapters are dedicated to the discussion of wavelets and their applications. A CDROM (platform independent) accompanies every new printed copy of the book and contains source code, projects for each chapter, and the figures from the book. (eBook version does not include the CDROM).
(source: Nielsen Book Data) 9780763784225 20160604
 Online
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TK5102.9 .W433 2011  Unknown 
10. Spectral audio signal processing [2011]
 Smith, Julius O. (Julius Orion)
 [Stanford, Calif.?] : W3K, 2011.
 Description
 Book — xx, 654 p. : ill. ; 23 cm.
 Summary

 Fourier transforms and theorems
 Spectrum analysis windows
 FIR digital filter design
 Spectrum analysis of sinusoids
 Spectrum analysis of noise
 Timefrequency displays
 Overlapadd STFT processing
 Filterbank view of the STFT
 Applications of the STFT
 Multirate filter banks.
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TK5102.9 .S567 2011  Unknown 
11. Understanding digital signal processing [2011]
 Lyons, Richard G., 1948
 3rd ed.  Upper Saddle River, NJ : Prentice Hall, c2011.
 Description
 Book — xxiii, 954 p. : ill. ; 24 cm.
 Summary

 Preface xv About the Author xxiii
 Chapter 1: Discrete Sequences and Systems
 1 1.1 Discrete Sequences and their Notation
 2 1.2 Signal Amplitude, Magnitude, Power
 8 1.3 Signal Processing Operational Symbols
 10 1.4 Introduction to Discrete Linear TimeInvariant Systems
 12 1.5 Discrete Linear Systems
 12 1.6 TimeInvariant Systems
 17 1.7 The Commutative Property of Linear TimeInvariant Systems
 18 1.8 Analyzing Linear TimeInvariant Systems
 19 References
 21
 Chapter 1 Problems
 23
 Chapter 2: Periodic Sampling
 33 2.1 Aliasing: Signal Ambiguity in the Frequency Domain
 33 2.2 Sampling Lowpass Signals
 38 2.3 Sampling Bandpass Signals
 42 2.4 Practical Aspects of Bandpass Sampling
 45 References
 49
 Chapter 2 Problems
 50
 Chapter 3: The Discrete Fourier Transform
 59 3.1 Understanding the DFT Equation
 60 3.2 DFT Symmetry
 73 3.3 DFT Linearity
 75 3.4 DFT Magnitudes
 75 3.5 DFT Frequency Axis
 77 3.6 DFT Shifting Theorem
 77 3.7 Inverse DFT
 80 3.8 DFT Leakage
 81 3.9 Windows
 89 3.10 DFT Scalloping Loss
 96 3.11 DFT Resolution, Zero Padding, and FrequencyDomain Sampling
 98 3.12 DFT Processing Gain
 102 3.13 The DFT of Rectangular Functions
 105 3.14 Interpreting the DFT Using the DiscreteTime Fourier Transform
 120 References
 124
 Chapter 3 Problems
 125
 Chapter 4: The Fast Fourier Transform
 135 4.1 Relationship of the FFT to the DFT
 136 4.2 Hints on Using FFTs in Practice
 137 4.3 Derivation of the Radix2 FFT Algorithm
 141 4.4 FFT Input/Output Data Index Bit Reversal
 149 4.5 Radix2 FFT Butterfly Structures
 151 4.6 Alternate SingleButterfly Structures
 154 References
 158
 Chapter 4 Problems
 160
 Chapter 5: Finite Impulse Response Filters
 169 5.1 An Introduction to Finite Impulse Response (FIR) Filters
 170 5.2 Convolution in FIR Filters
 175 5.3 Lowpass FIR Filter Design
 186 5.4 Bandpass FIR Filter Design
 201 5.5 Highpass FIR Filter Design
 203 5.6 ParksMcClellan Exchange FIR Filter Design Method
 204 5.7 Halfband FIR Filters
 207 5.8 Phase Response of FIR Filters
 209 5.9 A Generic Description of Discrete Convolution
 214 5.10 Analyzing FIR Filters
 226 References
 235
 Chapter 5 Problems
 238
 Chapter 6: Infinite Impulse Response Filters
 253 6.1 An Introduction to Infinite Impulse Response Filters
 254 6.2 The Laplace Transform
 257 6.3 The zTransform
 270 6.4 Using the zTransform to Analyze IIR Filters
 274 6.5 Using Poles and Zeros to Analyze IIR Filters
 282 6.6 Alternate IIR Filter Structures
 289 6.7 Pitfalls in Building IIR Filters
 292 6.8 Improving IIR Filters with Cascaded Structures
 295 6.9 Scaling the Gain of IIR Filters
 300 6.10 Impulse Invariance IIR Filter Design Method
 303 6.11 Bilinear Transform IIR Filter Design Method
 319 6.12 Optimized IIR Filter Design Method
 330 6.13 A Brief Comparison of IIR and FIR Filters
 332 References
 333
 Chapter 6 Problems
 336
 Chapter 7: Specialized Digital Networks and Filters
 361 7.1 Differentiators
 361 7.2 Integrators
 370 7.3 Matched Filters
 376 7.4 Interpolated Lowpass FIR Filters
 381 7.5 Frequency Sampling Filters: The Lost Art
 392 References
 426
 Chapter 7 Problems
 429
 Chapter 8: Quadrature Signals
 439 8.1 Why Care about Quadrature Signals?
 440 8.2 The Notation of Complex Numbers
 440 8.3 Representing Real Signals Using Complex Phasors
 446 8.4 A Few Thoughts on Negative Frequency
 450 8.5 Quadrature Signals in the Frequency Domain
 451 8.6 Bandpass Quadrature Signals in the Frequency Domain
 454 8.7 Complex DownConversion
 456 8.8 A Complex DownConversion Example
 458 8.9 An Alternate DownConversion Method
 462 References
 464
 Chapter 8 Problems
 465
 Chapter 9: The Discrete Hilbert Transform
 479 9.1 Hilbert Transform Definition
 480 9.2 Why Care about the Hilbert Transform?
 482 9.3 Impulse Response of a Hilbert Transformer
 487 9.4 Designing a Discrete Hilbert Transformer
 489 9.5 TimeDomain Analytic Signal Generation
 495 9.6 Comparing Analytical Signal Generation Methods
 497 References
 498
 Chapter 9 Problems
 499
 Chapter 10: Sample Rate Conversion
 507 10.1 Decimation
 508 10.2 TwoStage Decimation
 510 10.3 Properties of Downsampling
 514 10.4 Interpolation
 516 10.5 Properties of Interpolation
 518 10.6 Combining Decimation and Interpolation
 521 10.7 Polyphase Filters
 522 10.8 TwoStage Interpolation
 528 10.9 zTransform Analysis of Multirate Systems
 533 10.10 Polyphase Filter Implementations
 535 10.11 Sample Rate Conversion by Rational Factors
 540 10.12 Sample Rate Conversion with Halfband Filters
 543 10.13 Sample Rate Conversion with IFIR Filters
 548 10.14 Cascaded IntegratorComb Filters
 550 References
 566
 Chapter 10 Problems
 568
 Chapter 11: Signal Averaging
 589 11.1 Coherent Averaging
 590 11.2 Incoherent Averaging
 597 11.3 Averaging Multiple Fast Fourier Transforms
 600 11.4 Averaging Phase Angles
 603 11.5 Filtering Aspects of TimeDomain Averaging
 604 11.6 Exponential Averaging
 608 References
 615
 Chapter 11 Problems
 617
 Chapter 12: Digital Data Formats and their Effects
 623 12.1 FixedPoint Binary Formats
 623 12.2 Binary Number Precision and Dynamic Range
 632 12.3 Effects of Finite FixedPoint Binary Word Length
 634 12.4 FloatingPoint Binary Formats
 652 12.5 Block FloatingPoint Binary Format
 658 References
 658
 Chapter 12 Problems
 661
 Chapter 13: Digital Signal Processing Tricks
 671 13.1 Frequency Translation without Multiplication
 671 13.2 HighSpeed Vector Magnitude Approximation
 679 13.3 FrequencyDomain Windowing
 683 13.4 Fast Multiplication of Complex Numbers
 686 13.5 Efficiently Performing the FFT of Real Sequences
 687 13.6 Computing the Inverse FFT Using the Forward FFT
 699 13.7 Simplified FIR Filter Structure
 702 13.8 Reducing A/D Converter Quantization Noise
 704 13.9 A/D Converter Testing Techniques
 709 13.10 Fast FIR Filtering Using the FFT
 716 13.11 Generating Normally Distributed Random Data
 722 13.12 ZeroPhase Filtering
 725 13.13 Sharpened FIR Filters
 726 13.14 Interpolating a Bandpass Signal
 728 13.15 Spectral Peak Location Algorithm
 730 13.16 Computing FFT Twiddle Factors
 734 13.17 Single Tone Detection
 737 13.18 The Sliding DFT
 741 13.19 The Zoom FFT
 749 13.20 A Practical Spectrum Analyzer
 753 13.21 An Efficient Arctangent Approximation
 756 13.22 Frequency Demodulation Algorithms
 758 13.23 DC Removal
 761 13.24 Improving Traditional CIC Filters
 765 13.25 Smoothing Impulsive Noise
 770 13.26 Efficient Polynomial Evaluation
 772 13.27 Designing Very HighOrder FIR Filters
 775 13.28 TimeDomain Interpolation Using the FFT
 778 13.29 Frequency Translation Using Decimation
 781 13.30 Automatic Gain Control (AGC)
 783 13.31 Approximate Envelope Detection
 784 13.32 AQuadrature Oscillator
 786 13.33 Specialized Exponential Averaging
 789 13.34 Filtering Narrowband Noise Using Filter Nulls
 792 13.35 Efficient Computation of Signal Variance
 797 13.36 Realtime Computation of Signal Averages and Variances
 799 13.37 Building Hilbert Transformers from Halfband Filters
 802 13.38 Complex Vector Rotation with Arctangents
 805 13.39 An Efficient Differentiating Network
 810 13.40 LinearPhase DCRemoval Filter
 812 13.41 Avoiding Overflow in Magnitude Computations
 815 13.42 Efficient Linear Interpolation
 815 13.43 Alternate Complex Downconversion Schemes
 816 13.44 Signal Transition Detection
 820 13.45 Spectral Flipping around Signal Center Frequency
 821 13.46 Computing Missing Signal Samples
 823 13.47 Computing Large DFTs Using Small FFTs
 826 13.48 Computing Filter Group Delay without Arctangents
 830 13.49 Computing a Forward and Inverse FFT Using a Single FFT
 831 13.50 Improved Narrowband Lowpass IIR Filters
 833 13.51 A Stable Goertzel Algorithm
 838 References
 840 Appendix A: The Arithmetic of Complex Numbers
 847 A.1 Graphical Representation of Real and Complex Numbers
 847 A.2 Arithmetic Representation of Complex Numbers
 848 A.3 Arithmetic Operations of Complex Numbers
 850 A.4 Some Practical Implications of Using Complex Numbers
 856 Appendix B: Closed Form of a Geometric Series
 859 Appendix C: Time Reversal and the DFT
 863 Appendix D: Mean, Variance, and Standard Deviation
 867 D.1 Statistical Measures
 867 D.2 Statistics of Short Sequences
 870 D.3 Statistics of Summed Sequences
 872 D.4 Standard Deviation (RMS) of a Continuous Sinewave
 874 D.5 Estimating SignaltoNoise Ratios
 875 D.6 The Mean and Variance of Random Functions
 879 D.7 The Normal Probability Density Function
 882 Appendix E: Decibels (DB and DBM)
 885 E.1 Using Logarithms to Determine Relative Signal Power
 885 E.2 Some Useful Decibel Numbers
 889 E.3 Absolute Power Using Decibels
 891 Appendix F: Digital Filter Terminology
 893 Appendix G: Frequency Sampling Filter Derivations
 903 G.1 Frequency Response of a Comb Filter
 903 G.2 Single Complex FSF Frequency Response
 904 G.3 Multisection Complex FSF Phase
 905 G.4 Multisection Complex FSF Frequency Response
 906 G.5 Real FSF Transfer Function
 908 G.6 TypeIV FSF Frequency Response
 910 Appendix H: Frequency Sampling Filter Design Tables
 913
 Appendix I: Computing Chebyshev Window Sequences
 927 I.1 Chebyshev Windows for FIR Filter Design
 927 I.2 Chebyshev Windows for Spectrum Analysis
 929 Index 931.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780137027415 20160605
Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks  
TK5102.9 .L96 2011  Unknown 
 Cambridge, UK ; New York : Cambridge University Press, 2010.
 Description
 Book — xiv, 498 p. : ill. ; 26 cm.
 Summary

 1. Automatic code generation for realtime convex optimization J. Mattingley and S. Boyd
 2. Gradientbased algorithms with applications to signal recovery problems A. Beck and M. Teboulle
 3. Graphical models of autoregressive processes J. Songsiri, J. Dahl and L. Vandenberghe
 4. SDP relaxation of homogeneous quadratic optimization Z. Q. Luo and T. H. Chang
 5. Probabilistic analysis of SDR detectors for MIMO systems A. ManCho So and Y. Ye
 6. Semidefinite programming, matrix decomposition, and radar code design Y. Huang, A. De Maio and S. Zhang
 7. Convex analysis for nonnegative blind source separation with application in imaging W. K. Ma, T. H. Chan, C. Y. Chi and Y. Wang
 8. Optimization techniques in modern sampling theory T. Michaeli and Y. C. Eldar
 9. Robust broadband adaptive beamforming using convex optimization M. Rubsamen, A. ElKeyi, A. B. Gershman and T. Kirubarajan
 10. Cooperative distributed multiagent optimization A. Nenadic and A. Ozdaglar
 11. Competitive optimization of cognitive radio MIMO systems via game theory G. Scutari, D. P. Palomar and S. Barbarossa
 12. Nash equilibria: the variational approach F. Facchinei and J. S. Pang.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780521762229 20160603
Engineering Library (Terman)
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QA402.5 .C658 2010  Unknown 
 Diniz, Paulo Sergio Ramirez, 1956
 2nd ed.  Cambridge, UK ; New York : Cambridge University Press, 2010.
 Description
 Book — xxi, 889 p. : ill. ; 26 cm.
 Summary

 1. Discretetime signals and systems
 2. The Z and Fourier transforms
 3. Discrete transforms
 4. Digital filters
 5. FIR filter approximations
 6. IIR filter approximations
 7. Spectral estimation
 8. Multirate systems
 9. Filter banks
 10. Wavelet transforms
 11. Finiteprecision digital signal processing
 12. Efficient FIR structures
 13. Efficient IIR structures.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780521887755 20160604
Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks  
TK5102.9 .D63 2010  Unknown 
14. Discretetime signal processing [2010]
 Oppenheim, Alan V., 1937
 3rd ed.  Upper Saddle River : Pearson, c2010.
 Description
 Book — xxviii, 1108 p. : ill. ; 25 cm.
 Summary

 Previous edition TOC
 1. Introduction.
 2. DiscreteTime Signals and Systems. Introduction. Discretetime Signals: Sequences. Discretetime Systems. Linear TimeInvariant Systems. Properties of Linear TimeInvariant Systems. Linear ConstantCoefficient Difference Equations. FrequencyDomain Representation of DiscreteTime Signals and Systems. Representation of Sequence by Fourier Transforms. Symmetry Properties of the Fourier Transform. Fourier Transform Theorems. DiscreteTime Random Signals. Summary.
 3. The zTransform. Introduction. The zTransform. Properties of the Region of Convergence for the zTransform. The Inverse zTransform. zTransform Properties. Summary.
 4. Sampling of ContinuousTime Signals. Introduction. Periodic Sampling. FrequencyDomain Representation of Sampling. Reconstruction of a Bandlimited Signal from its Samples. DiscreteTime Processing of ContinuousTime Signals. ContinuousTime Processing of DiscreteTime Signals. Changing the Sampling Rate Using DiscreteTime Processing. Practical Considerations. Oversampling and Noise Shaping. Summary.
 5. Transform Analysis of Linear TimeInvariant Systems. Introduction. The Frequency Response of LTI Systems. System Functions for Systems Characterized by Linea. Frequency Response for Rational System Functions. Relationship Between Magnitude and Phase. AllPass Systems. MinimumPhase Systems. Linear Systems with Generalized Linear Phase. Summary.
 6. Structures for DiscreteTime Systems. Introduction. Block Diagram Representation of Linear ConstantCoefficient Difference Equations. Signal Flow Graph Representation of Linear ConstantCoefficient Difference Equations. Basic Structures for IIR Systems. Transposed Forms. Basic Network Structures for FIR Systems. Overview of FinitePrecision Numerical Effects. The Effects of Coefficient Quantization. Effects of Roundoff Noise in Digital Filters. ZeroInput Limit Cycles in FixedPoint Realizations of IIR Digital Filters. Summary.
 7. Filter Design Techniques. Introduction. Design of DiscreteTime IIR Filters from ContinuousTime Filters. Design of FIR Filters by Windowing. Examples of FIR Filter Design by the Kaiser Window Method. Optimum Approximations of FIR Filters. Examples of FIR Equiripple Approximation. Comments on IIR and FIR Digital Filters. Summary.
 8. The Discrete Fourier Transform. Introduction. Representation of Periodic Sequences: the Discrete Fourier Series. Summary of Properties of the DFS Representation of Periodic Sequences. The Fourier Transform of Periodic Signals. Sampling the Fourier Transform. Fourier Representation of FiniteDuration Sequences: The DiscreteFourier Transform. Properties of the Discrete Fourier Transform. Summary of Properties of the Discrete Fourier Transform. Linear Convolution Using the Discrete Fourier Transform. The Discrete Cosine Transform (DCT). Summary.
 9. Computation of the Discrete Fourier Transform. Introduction. Efficient Computation of the Discrete Fourier Transform. The Goertzel Algorithm DecimationinTime FFT Algorithms. DecimationinFrequency FFT Algorithms. Practical Considerations Implementation of the DFT Using Convolution. Summary.
 10. Fourier Analysis of Signals Using the Discrete Fourier Transform. Introduction. Fourier Analysis of Signals Using the DFT. DFT Analysis of Sinusoidal Signals. The TimeDependent Fourier Transform. Block Convolution Using the TimeDependent Fourier Transform. Fourier Analysis of Nonstationary Signals. Fourier Analysis of Stationary Random Signals: the Periodogram. Spectrum Analysis of Random Signals Using Estimates of the Autocorrelation Sequence. Summary.
 11. Discrete Hilbert Transforms. Introduction. Real and Imaginary Part Sufficiency of the Fourier Transform for Causal Sequences. Sufficiency Theorems for FiniteLength Sequences. Relationships Between Magnitude and Phase. Hilbert Transform Relations for Complex Sequences. Summary. Appendix A: Random Signals. DiscreteTime Random Process. Averages. Properties of Correlation and Covariance Sequences. Transform Representation of Random Signals. Appendix B: ContinuousTime Filters. Butterworth Lowpass Filters. Chebyshev Filters. Elliptic Filters.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780131988422 20160606
 Online
Engineering Library (Terman)
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Stacks  
TK5102.5 .O2452 2010  Unknown 
TK5102.5 .O2452 2010  Unknown 
 Starck, J.L. (JeanLuc), 1965
 Cambridge [England] ; New York : Cambridge University Press, 2010.
 Description
 Book — xvii, 316 p., [16] p. of plates : ill. (some col.) ; 27 cm.
 Summary

 1. Introduction to the world of sparsity
 2. The wavelet transform
 3. Redundant wavelet transform
 4. Nonlinear multiscale transforms
 5. The ridgelet and curvelet transforms
 6. Sparsity and noise removal
 7. Linear inverse problems
 8. Morphological diversity
 9. Sparse blind source separation
 10. Multiscale geometric analysis on the sphere
 11. Compressed sensing.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780521119139 20160604
Engineering Library (Terman)
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Stacks  
QA601 .S785 2010  Unknown 
 Candy, James V.
 Hoboken, N.J. : Wiley, c2009.
 Description
 Book — xxiii, 445 p. : ill. ; 25 cm.
 Summary

 Preface. References. Acknowledgments.
 1. Introduction. 1.1 Introduction. 1.2 Bayesian Signal Processing. 1.3 SimulationBased Approach to Bayesian Processing. 1.4 Bayesian ModelBased Signal Processing. 1.5 Notation and Terminology. References. Problems.
 2. Bayesian Estimation. 2.1 Introduction. 2.2 Batch Bayesian Estimation. 2.3 Batch Maximum Likelihood Estimation. 2.4 Batch Minimum Variance Estimation. 2.5 Sequential Bayesian Estimation. 2.6 Summary. References. Problems.
 3. SimulationBased Bayesian Methods. 3.1 Introduction. 3.2 Probability Density Function Estimation. 3.3 Sampling Theory. 3.4 Monte Carlo Approach. 3.5 Importance Sampling. 3.6 Sequential Importance Sampling. 3.7 Summary. References. Problems.
 4. StateSpace Models for Bayesian Processing. 4.1 Introduction. 4.2 ContinuousTime StateSpace Models. 4.3 SampledData StateSpace Models. 4.4 DiscreteTime StateSpace Models. 4.5 GaussMarkov StateSpace Models. 4.6 Innovations Model. 4.7 StateSpace Model Structures. 4.8 Nonlinear (Approximate) GaussMarkov StateSpace Models. 4.9 Summary. References. Problems.
 5. Classical Bayesian StateSpace Processors. 5.1 Introduction. 5.2 Bayesian Approach to the StateSpace. 5.3 Linear Bayesian Processor (Linear Kalman Filter). 5.4 Linearized Bayesian Processor (Linearized Kalman Filter). 5.5 Extended Bayesian Processor (Extended Kalman Filter). 5.6 IteratedExtended Bayesian Processor (IteratedExtended Kalman Filter). 5.7 Practical Aspects of Classical Bayesian Processors. 5.8 Case Study: RLC Circuit Problem. 5.9 Summary. References. Problems.
 6. Modern Bayesian StateSpace Processors. 6.1 Introduction. 6.2 SigmaPoint (Unscented) Transformations. 6.3 SigmaPoint Bayesian Processor (Unscented Kalman Filter). 6.4 Quadrature Bayesian Processors. 6.5 Gaussian Sum (Mixture) Bayesian Processors. 6.6 Case Study: 2DTracking Problem. 6.7 Summary. References. Problems.
 7. ParticleBased Bayesian StateSpace Processors. 7.1 Introduction. 7.2 Bayesian StateSpace Particle Filters. 7.3 Importance Proposal Distributions. 7.4 Resampling. 7.5 StateSpace Particle Filtering Techniques. 7.6 Practical Aspects of Particle Filter Design. 7.7 Case Study: Population Growth Problem. 7.8 Summary. References. Problems.
 8. Joint Bayesian State/Parametric Processors. 8.1 Introduction. 8.2 Bayesian Approach to Joint State/Parameter Estimation. 8.3 Classical/Modern Joint Bayesian State/Parametric Processors. 8.3.1 Classical Joint Bayesian Processor. 8.3.2 Modern Joint Bayesian Processor. 8.4 ParticleBased Joint Bayesian State/Parametric Processors. 8.5 Case Study: Random Target Tracking using a Synthetic Aperture Towed Array. 8.6 Summary. References. Problems.
 9. Discrete Hidden Markov Model Bayesian Processors. 9.1 Introduction. 9.2 Hidden Markov Models. 9.3 Properties of the Hidden Markov Model. 9.4 HMM Observation Probability: Evaluation Problem. 9.5 State Estimation in HMM: The Viterbi Technique. 9.6 Parameter Estimation in HMM: The EM/BaumWelch Technique. 9.7 Case Study: TimeReversal Decoding. 9.8 Summary. References. Problems.
 10. Bayesian Processors for PhysicsBased Applications. 10.1 Optimal Position Estimation for the Automatic Alignment. 10.2 Broadband Ocean Acoustic Processing. 10.3 Bayesian Processing for Biothreats. 10.4 Bayesian Processing for the Detection of Radioactive Sources. References. Appendix A. Probability & Statistics Overview. A.1 Probability Theory. A.2 Gaussian Random Vectors. A.3 Uncorrelated Transformation: Gaussian Random Vectors. Referencess.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780470180945 20160528
 Online

 dx.doi.org Wiley Online Library
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 Google Books (Full view)
Engineering Library (Terman)
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Stacks  
TK5102.9 .C3187 2009  Unknown 
 Mallat, S. G. (Stéphane G.)
 [3rd ed.]  Amsterdam ; Boston : Elsevier /Academic Press, c2009.
 Description
 Book — xx, 805 p. : ill. ; 25 cm.
 Summary

 Sparse representations
 Fourier kingdom
 Discrete revolution
 Time meets frequency
 Frames
 Wavelet zoom
 Wavelet bases
 Wavelet packet and local cosine bases
 Approximations in bases
 Compression
 Denoising
 Sparsity in redundant dictionaries
 Inverse problems
 Mathematical complements
 Bibliography
 Index.
(source: Nielsen Book Data) 9780123743701 20160528
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 www.sciencedirect.com ScienceDirect
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TK5102.9 .M34 2009  Unknown 
 Tan, Li, 1963
 Amsterdam ; New York, N.Y. : Elsevier/AP, c2008.
 Description
 Book — xvi, 816 p., [4] p. of plates : ill. (some col.) ; 25 cm.
 Summary

This textbook presents digital signal processing (DSP) principles, applications, and hardware implementation issues, emphasizing achievable results and conclusions through the presentation of numerous worked examples, while reducing the use of mathematics for an easier grasp of the concepts. Features include: realtime implementation of DSP algorithms using DSP processors; MATLAB programs for simulations and C programs for realtime DSP; coverage of adaptive filtering with applications to noise reduction and echo cancellation; applications of DSP to multimedia applications  such as ulaw and adaptive differential pulse code modulation, sampling rate conversions, transform coding, image and video processing  show the relevance of DSP to a key area in industry; and, MATLAB programs, student exercises and Realtime C programs available at website. This text gives students in electronics, computer engineering and bioengineering an understanding of essential DSP principles and implementation, demonstrating how the subject is fundamental to engineering as practiced today. 'Professor Tan has written a comprehensive introduction to DSP, not lacking in theory and yet suitable for tech school as well as seniorlevel university courses. With this text one can move through all the main areas of modern DSP, learn the theory, and see plenty of illustrations in terms of hardware and software. It's an excellent reference for our present age, in which DSP has applications in practically every area of technology'  Samuel D. Stearns, Research Professor of Electrical and Computer Engineering, University of New Mexico. This title covers DSP principles and hardware issues with emphasis on applications and many worked examples. It features website with MATLAB programs for simulation and C programs for realtime DSP. Its end of chapter problems are helpful in ensuring retention and understanding of what was just read.
(source: Nielsen Book Data) 9780123740908 20160528
Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks  
TK5102.9 .T36 2008  Unknown 
 Edlund, Greg.
 Upper Saddle River, NJ : Prentice Hall, c2008.
 Description
 Book — xx, 241 p. : ill. ; 25 cm.
 Summary

 Preface xiii Acknowledgments xvi About the Author xix About the Cover xx
 Chapter 1: Engineering Reliable Digital Interfaces
 1
 Chapter 2: ChiptoChip Timing
 13
 Chapter 3: Inside IO Circuits
 39
 Chapter 4: Modeling 3D Discontinuities
 73
 Chapter 5: Practical 3D Examples
 101
 Chapter 6: DDR2 Case Study
 133
 Chapter 7: PCI Express Case Study
 175 Appendix A: A Short CMOS and SPICE Primer
 209 Appendix B: A Stroll Through 3D Fields
 219 Endnotes
 233 Index 235.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780132365048 20160527
Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks  
TK7874.65 .E36 2008  Unknown 
20. Digital signal processing [2007]
 Proakis, John G.
 4th ed.  Upper Saddle River, N.J. : Pearson Prentice Hall, c2007.
 Description
 Book — xix, 1084 p. : ill. ; 24 cm.
 Summary

 1 Introduction 1.1 Signals, Systems, and Signal Processing 1.2 Classification of Signals 1.3 The Concept of Frequency in ContinuousTime and DiscreteTime Signals 1.4 AnalogtoDigital and DigitaltoAnalog Conversion 1.5 Summary and References
 2 DiscreteTime Signals And Systems 2.1 DiscreteTime Signals 2.2 DiscreteTime Systems 2.3 Analysis of DiscreteTime Linear TimeInvariant systems 2.4 DiscreteTime Systems Described by Difference Equations 2.5 Implementation of DiscreteTime Systems 2.6 Correlation of DiscreteTime Signals 2.7 Summary and References
 3 The ZTransform And Its Application To The Analysis Of Lti Systems 3.1 The zTransform 3.2 Properties of the zTransform 3.3 Rational zTransforms 3.4 Inversion of the zTransform 3.5 Analysis of Linear Time Invariant Systems in the zDomain 3.6 The Onesided zTransform 3.7 Summary and References
 4 Frequency Analysis Of Signals And Systems 4.1 Frequency Analysis of ContinuousTime Signals 4.2 Frequency Analysis of DiscreteTime Signals 4.3 FrequencyDomain and TimeDomain Signal Properties 4.4 Properties of the Fourier Transform for DiscreteTime Signals 4.5 Summary and References
 5 Frequency Domain Analysis Of Lti Systems 5.1 FrequencyDomain Characteristics of Linear TimeInvariant Systems 5.2 Frequency Response of LTI Systems 5.3 Correlation Functions and Spectra at the Output of LTI Systems 5.4 Linear TimeInvariant Systems as FrequencySelective Filters 5.5 Inverse Systems and Deconvolution 5.6 Summary and References
 6 Sampling And Reconstruction Of Signals 6.1 Ideal Sampling and Reconstruction of ContinuousTime Signals 6.2 DiscreteTime Processing of ContinuousTime Signals 6.3 AnalogtoDigital and DigitaltoAnalog Converters 6.4 Sampling and Reconstruction of ContinuousTime Bandpass Signals 6.5 Sampling of DiscreteTime Signals 6.6 Oversampling A/D and D/A Converters 6.7 Summary and References
 7 The Discrete Fourier Transform: Its Properties And Applications 7.1 Frequency Domain Sampling:The Discrete Fourier Transform 7.2 Properties of the DFT 7.3 Linear Filtering Methods Based on the DFT 7.4 Frequency Analysis of Signals Using the DFT 7.5 The Discrete Cosine Transform 7.6 Summary and References
 8 Efficient Computaiton Of The Dft: Fast Fourier Transform Algorithms 8.1 Efficient Computation of the DFT: FFT Algorithms 8.2 Applications of FFT Algorithms 8.3 A Linear Filtering Approach to Computation of the DFT 8.4 Quantization Effects in the Computation of the DFT 8.5 Summary and References
 9 Implementation Of DiscreteTime Systems 9.1 Structures for the Realization of DiscreteTime Systems 9.2 Structures for FIR Systems 9.3 Structures for IIR Systems 9.4 Representation of Numbers 9.5 Quantization of Filter Coefficients 9.6 RoundOff Effects in Digital Filters 9.7 Summary and References
 10 Design Of Digital Filers 10.1 General Considerations 10.2 Design of FIR Filters 10.3 Design of IIR Filters From Analog Filters 10.4 Frequency Transformations 10.5 Summary and References
 11 Multirate Digital Signal Processing 11.1 Introduction 11.2 Decimation by a Factor D 11.3 Interpolation by a Factor I 11.4 Sampling Rate Conversion by a Rational Factor I/D 11.5 Implementation of Sampling Rate Conversion 11.6 Multistage Implementation of Sampling Rate Conversion 11.7 Sampling Rate Conversion of Bandpass Signals 11.8 Sampling Rate conversion by an Arbitrary Factor 11.9 Applications of Sampling Rate Conversion 11.10 Digital Filter Banks 11.11 TwoChannel Quadrature Mirror Filter Bank 11.12 MChannel QMF Bank 11.13 Summary and References
 12 Linear Prediction And Optimum Linear Filters 12.1 Random Signals, Correlation Functions and Power Spectra 12.2 Innovations Representation of a Stationary Random Process 12.3 Forward and Backward Linear Prediction 12.4 Solution of the Normal Equations 12.5 Properties of the Linear PredictionError Filters 12.6 AR Lattice and ARMA LatticeLadder Filters 12.7 Wiener Filters for Filtering and Prediction 12.8 Summary and References
 13 Adaptive Filters 13.1 Applications of Adaptive Filters 13.2 Adaptive DirectForm FIR FiltersThe LMS Algorithm 13.3 Adaptive DirectForm FIR FiltersRLS Algorithms 13.4 Adaptive LatticeLadder Filters 13.5 Summary and References
 14 Power Spectrum Estimation 14.1 Estimation of Spectra from FiniteDuration Observations of Signals 14.2 Nonparametric Methods for Power Spectrum Estimation 14.3 Parametric Methods for Power Spectrum Estimation 14.4 Filter Bank Methods 14.5 Eigenanalysis Algorithms for Spectrum Estimation 14.6 Summary and References Appendices Appendix A Random Number Generators Appendix B Tables of Transition Coefficients for the Design of LinearPhase Filters References and Bibliography Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780131873742 20160528
 Online
Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks  
TK5102.9 .P757 2007  Unknown 