Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries

Table of Contents: “…Extreme Development of Dragon Fruit Agriculture with Nighttime Lighting in Southern Vietnam (Shenyue Jia, Son V. Nghiem, Seung-Hee Kim, Laura Krauser, Andrea E. …”
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Corrosion Science by Kumar, N. Suresh

Table of Contents: “…Urethane Type -- 6.4. Ultraviolet Light Curable Coatings -- 6.5. Silicone Type Coatings -- 7. …”
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Distributed fiber sensing and dynamic ratings of power cable by Cherukupalli, Sudhakar Ellapragada, 1954-, Anders, George J.

Table of Contents: “…Preface xiii -- Acknowledgments xvi -- 1 Application of Fiber Optic Sensing 1 -- 1.1 Types of Available FO Sensors 2 -- 1.2 Fiber Optic Applications for Monitoring of Concrete Structures 4 -- 1.3 Application of FO Sensing Systems in Mines 7 -- 1.4 Composite Aircraft Wing Monitoring 8 -- 1.5 Application in the Field of Medicine 9 -- 1.6 Application in the Power Industry 9 -- 1.6.1 Brief Literature Review 10 -- 1.6.2 Monitoring of Strain in the Overhead Conductor of Transmission Lines 15 -- 1.6.3 Temperature Monitoring of Transformers 16 -- 1.6.4 Optical Current Measurements 17 -- 1.7 Application for Oil, Gas, and Transportation Sectors 17 -- 2 Distributed Fiber Optic Sensing 20 -- 2.1 Introduction 20 -- 2.2 Advantages of the Fiber Optic Technology 20 -- 2.3 Disadvantages of the Distributed Sensing Technology 22 -- 2.4 Power Cable Applications 23 -- 3 Distributed Fiber Optic Temperature Sensing 26 -- 3.1 Fundamental Physics of DTS Measurements 26 -- 3.1.1 Rayleigh Scattering 26 -- 3.1.2 Raman Spectroscopy 27 -- 3.1.3 Brillouin Scattering 27 -- 3.1.4 Time and Frequency Domain Reflectometry 30 -- 4 Optical Fibers, Connectors, and Cables 32 -- 4.1 Optical Fibers 32 -- 4.1.1 Construction of the Fiber Optic Cable and Light Propagation Principles 33 -- 4.1.2 Protection and Placement of Optical Fibers in Power Cable Installations 38 -- 4.1.3 Comparison of Multiple and Single-Mode Fibers 44 -- 4.2 Optical Splicing 45 -- 4.3 Fiber Characterization 47 -- 4.4 Standards for Fiber Testing 55 -- 4.4.1 Fiber Optic Testing 56 -- 4.4.2 Fiber Optic Systems and Subsystems 56 -- 4.5 Optical Connectors 68 -- 4.6 Utility Practice for Testing of Optical Fibers 74 -- 4.7 Aging and Maintenance 75 -- 5 Types of Power Cables and Cable with Integrated Fibers 77 -- 5.1 Methods of Incorporating DTS Sensing Optical Fibers (Cables) into Power Transmission Cable Corridors 77 -- 5.1.1 Integration of Optical Cable into Land Power Cables 77 -- 5.1.2 Integration of Optical Cable into Submarine Power Cables 78.…”
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Rethinking justice : restoring our humanity by Bell, Richard H.

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Thirty years of the HIV/AIDS epidemic in Argentina : an assessment of the national health response

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George Romney.

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Caste at birth by Hamermesh, Mira

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Agro-product processing technology : principles and practice by Bala, B. K. (Bilash Kanti)

Table of Contents: “…ContentsForeword--Preface--Author--Chapter 1 Introduction--1.1 Introduction--1.2 Impotence of Postharvest Technology--1.3 Importance of Postharvest Losses--1.4 Postharvest Technology--References--Chapter 2 Physical, Thermal, and Chemical Properties of Food and Biological Materials--2.1 Introduction--2.2 Physical Properties--2.2.1 Physical Dimensions--2.2.2 1000-Grain Weight--2.2.3 Bulk Density--2.2.4 Shrinkage--2.2.5 Friction--2.2.5.1 Angle of Internal Friction and Angle of Repose--2.2.5.2 Coefficient of Friction--2.3 Thermal Properties--2.3.1 Specific Heat--2.3.2 Thermal Conductivity--2.3.2.1 Theory--2.3.2.2 Apparatus and Measurement--2.3.3 Latent Heat of Vaporization--2.3.3.1 Determination of Latent Heat of Vaporization--2.3.4 Heat Transfer Coefficient of Product Bed--2.3.4.1 Dimensional Analysis--2.3.4.2 Comparison of Theory and Experiment--2.3.4.3 Theory--2.3.4.4 Determination of Volumetric Heat Transfer Coefficient--2.4 Chemical Properties--2.4.1 Starch--2.4.2 Protein--2.4.3 Fat--2.4.4 Vitamin--Key to Symbols--Exercises--References--Chapter 3 Cleaning, Grading, and Sorting--3.1 Grade Factor--3.2 Washing--3.3 Sorting Fruits and Vegetables--3.4 Sorting Grain--3.5 Spiral Separator--3.6 Indent Cylinder Separator--3.7 Color Separator--3.8 Centrifugal Separation--3.8.1 Stokes' Equation--3.9 The Centrifuge--3.10 The Cream Separator--3.11 Cyclone Separator--3.12 Machine Vision--3.12.1 Image Acquisition--3.12.1.1 Computer Vision System--3.12.1.2 Ultrasound and Infrared--3.12.1.3 Tomographic Imaging--3.12.2 Preprocessing--3.12.3 Segmentation--3.12.4 Feature Extraction--3.12.4.1 Color Features--3.12.4.2 Morphological Features--3.12.4.3 Texture Features--3.12.5 Classification--Bibliography--Chapter 4 Psychrometry--4.1 Introduction--4.2 Psychrometric Terms--4.2.1 Humidity Ratio--4.2.2 Relative Humidity--4.2.3 Specific Volume--4.2.4 Vapor Pressure--4.2.5 Dry-Bulb Temperature--4.2.6 Dew Point Temperature--4.2.7 Wet-Bulb Temperature--4.2.8 Enthalpy--4.2.9 Adiabatic Wet-Bulb Temperature--4.2.10 Psychrometric Wet-Bulb Temperature--4.3 Construction of Psychrometric Chart--4.4 Use of Psychrometric Chart--4.4.1 Sensible Heating and Cooling--4.4.2 Heating with Humidification--4.4.3 Cooling with Humidification--4.4.4 Cooling with Dehumidification--4.4.5 Drying--4.4.6 Mixing of Airstreams--4.4.7 Heat Addition with Air Mixing--4.4.8 Drying with Recirculation--Key to Symbols--Exercises--Bibliography--Chapter 5 Drying of Agro Products--5.1 Principles of Drying--5.2 Importance of Drying--5.3 Moisture Content--5.3.1 Moisture Content Representation--5.3.2 Determination of Moisture Content--5.3.2.1 Direct Methods--5.3.2.2 Indirect Methods--5.4 Equilibrium Moisture Content--5.4.1 Determination of Static Equilibrium Moisture Content--5.4.2 Static Equilibrium Moisture Content Models--5.5 Mechanism of Drying--5.6 Thin-Layer Drying--5.6.1 Thin-Layer Drying Equations--5.6.1.1 Empirical Drying Equations--5.6.1.2 Theoretical Drying Equations--5.6.1.3 Semi-Theoretical Drying Equations--5.6.2 Drying Rate--5.6.3 Drying Parameters--5.6.4 Drying Rate Constant and Diffusion Coefficient--5.6.4.1 Drying Rate Constant--5.6.5 Half Response Time--5.7 Deep-Bed Drying--5.7.1 Logarithmic Model--5.7.2 Partial Differential Equation Model--5.7.2.1 Method of Solution--5.7.2.2 Comparisons of Simulated and Observed Results--5.8 Fluidized Bed Drying Model--5.8.1 Heat Balance Equation--5.8.2 Drying Rate Equation--5.8.3 Mass Balance Equation--5.9 Agro-Product Drying Systems--5.9.1 Solar Drying Systems--5.9.1.1 Solar Dryers--5.9.2 Batch Drying Systems--5.9.2.1 Flatbed Dryer--5.9.3 Continuous Flow Drying Systems--5.9.3.1 Cross-Flow Dryer--5.9.3.2 Cross-Flow Batch Dryer--5.9.3.3 Concurrent Flow Dryer--5.9.3.4 Counterflow Dryer--5.9.3.5 Mixed-Flow Dryer--5.10 Safe Temperature for Drying Grain--5.11 Selection of Dryers--Key to Symbols--Exercises--Bibliography--Chapter 6 Parboiling of Rice--6.1 Introduction--6.2 Principles of Parboiling--6.3 Soaking--6.3.1 Kinetics of Soaking--6.3.2 Finite Element Modeling of Soaking of Water by Paddy--6.3.3 Half Response Time--6.3.4 Kinetics of Water Diffusion and Starch Gelatinization--6.4 Steaming--6.5 Drying--6.6 Effect of Parboiling on Milling, Nutritional, and Cooking Qualitiesof Rice--6.7 Parboiling Methods--6.7.1 Traditional Methods--6.7.1.1 Single Stage Parboiling--6.7.1.2 Double Stage Parboiling--6.7.2 Modern Methods--6.7.2.1 CFTRI (Central Food Technological ResearchIndustries) Method--6.7.2.2 Jadavpur University Method--6.7.2.3 Malek Process--6.7.2.4 Schule Process--6.7.2.5 Crystal Rice Process--6.7.2.6 Rice Conversion Process--6.7.2.7 Avorio Process--6.7.3 Estimation of Heat Required for Parboiling--6.7.3.1 Soaking Operation--6.7.3.2 Steaming Operation--6.7.3.3 Drying Operation--Exercises--Bibliography--Chapter 7 Milling of Rice and Wheat--7.1 Introduction--7.2 Rice Milling--7.3 Traditional Methods--7.3.1 Home Pounding--7.3.2 Huller Mills--7.3.3 Sheller Mills--7.3.4 Rubber Roll Sheller Mills--7.4 The Modern Rice Milling Process--7.5 Modern Rice Milling Machinery--7.5.1 Paddy Cleaner--7.5.2 Stoner--7.5.3 Rubber Roll Sheller--7.5.4 Paddy Separator--7.5.5 Whitening or Polishing--7.5.5.1 Cone-Type Polisher--7.5.5.2 Horizontal Abrasive-Type Polisher--7.5.5.3 Friction-Type Polisher--7.5.6 Bran and Polished Rice Separator--7.5.7 Rice Grader--7.5.8 Rice Mixing--7.6 Wheat Milling--7.6.1 Conditioning/Hydrothermal Treatment--7.6.2 Milling--7.6.3 Storage of Finished Products--7.7 Size Characteristics--7.7.1 Sieve--7.7.2 Fineness Modulus--7.7.3 Energy Requirements--Bibliography--Chapter 8 By-Product Utilization--8.1 Introduction--8.2 Fuels and Combustion--8.2.1 Furnaces--8.3 Pyrolysis and Gasification--8.3.1 Pyrolysis (Destructive Distillation) and Gasification--8.3.2 Types of Gasifiers--8.3.2.1 Countercurrent Moving Bed Gasifiers--8.3.2.2 Concurrent Moving Bed Gasifiers--8.3.2.3 Crosscurrent Moving Bed Gasifiers--8.3.2.4 Fluidized Bed Gasifiers--8.3.3 Gasification Process--8.3.3.1 Oxidation--8.3.3.2 Reduction--8.3.3.3 Drying--8.3.4 Gasifier Units--8.4 Liquefaction--8.5 Hydrolysis Followed by Fermentation--8.6 Biochar Production and Utilization--8.6.1 Biochar Carbonizer--8.6.2 Types of Carbonizers--8.6.2.1 Application--8.7 Rice Husk Pelletizing and Briquetting--8.7.1 Need for Briquetting--8.7.2 Principle and Technology--8.7.3 Types of Briquetting Machines--8.7.3.1 High- and Medium-Pressure Compaction--8.7.3.2 Screw Press--8.7.3.3 Piston Press--8.7.3.4 Low-Pressure Compaction--8.7.3.5 Hand-Molded Briquettes--8.7.4 Applications--8.7.5 Limitations--8.7.6 Future Prospective--8.8 Biogas Digesters--8.8.1 Anaerobic Digestion Process--8.8.2 Indian-Type Biogas Digester--8.8.3 The Chinese Biogas Digester--8.8.4 Digester Sizing--8.9 Composting--8.9.1 Process of Composting--8.9.2 Mixing of Materials in the Compost--8.9.3 Starting a Composter--8.9.4 Operating a Compost--8.9.5 Simple Thermophile Composting Procedure--8.9.6 Types of Composters--8.10 Utilization of Rice Bran - Stabilizer Design and Oil Extraction--8.11 Bran - Stabilizer Design--8.12 Oil Extraction--8.12.1 Batch Extraction Method--Bibliography--Chapter 9 Storage of Agro Products--9.1 Principles of Storage--9.2 Interactions of Physical, Chemical, and Biological Variables in theDeterioration of Stored Grains--9.3 Computer Simulation Modeling for Stored Grain Pest Management--9.4 Grain Storage Systems--9.4.1 Traditional Storage Systems--9.4.2 Modern Storage Systems--9.4.2.1 Bagged Storage System--9.4.2.2 Silo Storage System--9.4.2.3 Airtight Grain Storage--9.4.2.4 Aerated Storage System--9.4.2.5 Low-Temperature Storage System (Grain Chillingby Refrigeration)--9.4.2.6 Controlled Atmosphere Storage Systems--9.4.2.7 Damp Grain Storage System with Chemicals--9.5 Design of Grain Storages--9.5.1 Structural Requirements--9.5.2 Janssen's Equation--9.5.3 Rankine's Equation--9.5.4 Airy's Equation--9.5.5 Construction Materials--Key to Symbols--Exercises--Bibliography--Chapter 10 Heating and Cooling of Agro Products--10.1 Introduction--10.2 Heat Conduction--10.2.1 The Differential Equation of Heat Conduction in Cartesianand Cylindrical Coordinate Systems--10.2.1.1 The Differential Equation ofHeat Conduction in Cartesian Coordinate System--10.2.1.2 The Differential Equation ofHeat Conduction in Cylindrical Coordinate System--10.2.2 The Composite Wall--10.2.3 Cylinder and Sphere--10.3 Convection--10.3.1 Forced Convection--10.3.2 Natural or Free Convection--10.3.3 Heat Exchangers--10.4 Radiation--10.4.1 Radiation Intensity and Shape Factor--10.4.2 Radiation Exchange between Black Surfaces--10.4.3 Heat Exchange by Radiation between Gray Surfaces--10.5 Cooling--10.5.1 Cooling Rate--10.6 Freezing--10.6.1 Freezing Point Depression--10.7 Heating--10.7.1 Boiling-Point Elevation--Key to Symbols--Exercises--Bibliography--Chapter 11 Refrigeration and Cold Storage--11.1 Introduction--11.2 Vapor Compression Refrigeration Cycle--11.3 Pressure-Enthalpy (p-h) Chart--11.3.1 Unit of Refrigeration--11.4 Refrigerants--11.4.1 Desirable Characteristics of Refrigerants--11.5 Construction of Psychrometric Chart--11.6 Moisture Control and Storage of Vegetables Crops--11.6.1 Potatoes--11.6.1.1 Adequate Volume--11.6.1.2 Adequate Strength--11.6.1.3 Storage Environment--11.6.1.4 Suberization Period--11.6.1.5 Short-Term Storage--11.6.1.6 Long-Term Storage--11.7 Cooling Requirement--Exercises--Bibliography--Chapter 12 Separation--12.1 Introduction--12.2 Contact Equilibrium Process--12.2.1 Absorption--12.2.2 Extraction--12.2.2.1 Rate of Extraction--12.2.2.2 Leaching--12.2.3 Distillation--12.2.3.1 Vapor-Liquid Equilibrium--…”
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Characterization of Minerals, Metals, and Materials 2020

Table of Contents: “…Colorado -- Characterization of a Brazilian Kaolin and Its Sorption Abilityto Mineral Oils / Gilmar Pinheiro, Thamires Carvalho, Bianca Michel, Jessica Arjona, Margarita Bobadilha, Maria Silva-Valenzuela, Tatiana Costaand Francisco Valenzuela-Diaz -- Fabrication of Ultra-High Molecular Weight Polyethylene Membrane and Evaluation of Physical Characteristics for Wastewater Treatment / Shan Shan Xie, Zhang Fu Yuan and Yuan Tao Shi.…”
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Parametric time-frequency domain spatial audio

Table of Contents: “…8.1 Introduction 201 -- 8.2 Notation and Signal Model 202 -- 8.3 Overview of the Method 203 -- 8.4 Loudspeaker-Based Spatial Sound Reproduction 204 -- 8.4.1 Estimation of the Target Covariance Matrix Cy 204 -- 8.4.2 Estimation of the Synthesis Beamforming Signals Ws 206 -- 8.4.4 Processing the Synthesis Signals (Wsx) to Obtain the Target Covariance Matrix Cy 206 -- Spatial Energy Distribution 207 -- 8.4.5 Listening Tests 208 -- 8.5 Binaural-Based Spatial Sound Reproduction 209 -- 8.5.1 Estimation of the Analysis and Synthesis Beamforming Weight Matrices 210 -- 8.5.2 Diffuse-Field Equalization of HRTFs 210 -- 8.5.3 Adaptive Mixing and Decorrelation 211 -- 8.5.4 Subjective Evaluation 211 -- 8.6 Conclusions 212 -- References 212 -- 9 Source Separation and Reconstruction of Spatial Audio Using Spectrogram Factorization 215 /Joonas Nikunen and Tuomas Virtanen -- 9.1 Introduction 215 -- 9.2 Spectrogram Factorization 217 -- 9.2.1 Mixtures of Sounds 217 -- 9.2.2 Magnitude Spectrogram Models 218 -- 9.2.3 Complex-Valued Spectrogram Models 221 -- 9.2.4 Source Separation by Time Frequency Filtering 225 -- 9.3 Array Signal Processing and Spectrogram Factorization 226 -- 9.3.1 Spaced Microphone Arrays 226 -- 9.3.2 Model for Spatial Covariance Based on Direction of Arrival 227 -- 9.3.3 Complex-Valued NMF with the Spatial Covariance Model 229 -- 9.4 Applications of Spectrogram Factorization in Spatial Audio 231 -- 9.4.1 Parameterization of Surround Sound: Upmixing by Time Frequency Filtering 231 -- 9.4.2 Source Separation Using a Compact Microphone Array 233 -- 9.4.3 Reconstruction of Binaural Sound Through Source Separation 238 -- 9.5 Discussion 243 -- 9.6 Matlab Example 243 -- References 247 -- Part III Signal-Dependent Spatial Filtering 251 -- 10 Time Frequency Domain Spatial Audio Enhancement 253 /Symeon Delikaris-Manias and Pasi Pertila -- 10.1 Introduction 253 -- 10.2 Signal-Independent Enhancement 254 -- 10.3 Signal-Dependent Enhancement 255 -- 10.3.1 Adaptive Beamformers 255.…”
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Case Studies in Bayesian Statistical Modelling and Analysis.

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STATISTICAL INFERENCE VIA DATA SCIENCE a moderndive into R and the Tidyverse. by Ismay, Chester

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Mussorgsky-Pictures at an Exhibition

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The hollow years : France in the 1930s by Weber, Eugen, 1925-2007

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Haunting of the Queen Mary

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The Dive

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Gorilla society : conflict, compromise, and cooperation between the sexes by Harcourt, A. H. (Alexander H.)

Table of Contents: “…Predation and association -- Conclusion -- A robust model, and therefore the right answer? -- Added variations -- Are female strategies irrelevant to males? …”
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Learning Linux Binary Analysis. by O'Neill, Ryan "elfmaster"

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Scikit-learn Cookbook - Second Edition. by Avila, Julian

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Signal Processing and Machine Learning with Applications by Richter, Michael M., Paul, Sheuli, Këpuska, Veton, Silaghi, Marius

Table of Contents: “…Part I Realms of Signal Processing -- 1 Digital Signal Representation -- 1.1 Introduction -- 1.2 Numbers -- 1.2.1 Numbers and Numerals -- 1.2.2 Types of Numbers -- 1.2.3 Positional Number Systems -- 1.3 Sampling and Reconstruction of Signals -- 1.3.1 Scalar Quantization -- 1.3.2 Quantization Noise -- 1.3.3 Signal-To-Noise Ratio -- 1.3.4 Transmission Rate -- 1.3.5 Nonuniform Quantizer -- 1.3.6 Companding -- 1.4 Data Representations -- 1.4.1 Fixed-Point Number Representations -- 1.4.2 Sign-Magnitude Format -- 1.4.3 One’s-Complement Format -- 1.4.4 Two’s-Complement Format -- 1.5 Fix-Point DSP’s -- 1.6 Fixed-Point Representations Based on Radix-Point -- 1.7 Dynamic Range -- 1.8 Precision -- 1.9 Background Information -- 1.10 Exercises -- 2 Signal Processing Background -- 2.1 Basic Concepts -- 2.2 Signals and Information -- 2.3 Signal Processing -- ix -- x Contents -- 2.4 Discrete Signal Representations -- 2.5 Delta and Impulse Function -- 2.6 Parseval’s Theorem -- 2.7 Gibbs Phenomenon -- 2.8 Wold Decomposition -- 2.9 State Space Signal Processing -- 2.10 Common Measurements -- 2.10.1 Convolution -- 2.10.2 Correlation -- 2.10.3 Auto Covariance -- 2.10.4 Coherence -- 2.10.5 Power Spectral Density (PSD) -- 2.10.6 Estimation and Detection -- 2.10.7 Central Limit Theorem -- 2.10.8 Signal Information Processing Types -- 2.10.9 Machine Learning -- 2.10.10Exercises -- 3 Fundamentals of Signal Transformations -- 3.1 Transformation Methods -- 3.1.1 Laplace Transform -- 3.1.2 Z-Transform -- 3.1.3 Fourier Series -- 3.1.4 Fourier Transform -- 3.1.5 Discrete Fourier Transform and Fast Fourier Transform -- 3.1.6 Zero Padding -- 3.1.7 Overlap-Add and Overlap-Save Convolution -- Algorithms -- 3.1.8 Short Time Fourier Transform (STFT) -- 3.1.9 Wavelet Transform -- 3.1.10 Windowing Signal and the DCT Transforms -- 3.2 Analysis and Comparison of Transformations -- 3.3 Background Information -- 3.4 Exercises -- 3.5 References -- 4 Digital Filters -- 4.1 Introduction -- 4.1.1 FIR and IIR Filters -- 4.1.2 Bilinear Transform -- 4.2 Windowing for Filtering -- 4.3 Allpass Filters -- 4.4 Lattice Filters -- 4.5 All-Zero Lattice Filter -- 4.6 Lattice Ladder Filters -- Contents xi -- 4.7 Comb Filter -- 4.8 Notch Filter -- 4.9 Background Information -- 4.10 Exercises -- 5 Estimation and Detection -- 5.1 Introduction -- 5.2 Hypothesis Testing -- 5.2.1 Bayesian Hypothesis Testing -- 5.2.2 MAP Hypothesis Testing -- 5.3 Maximum Likelihood (ML) Hypothesis Testing -- 5.4 Standard Analysis Techniques -- 5.4.1 Best Linear Unbiased Estimator (BLUE) -- 5.4.2 Maximum Likelihood Estimator (MLE) -- 5.4.3 Least Squares Estimator (LSE) -- 5.4.4 Linear Minimum Mean Square Error Estimator -- (LMMSE) -- 5.5 Exercises -- 6 Adaptive Signal Processing -- 6.1 Introduction -- 6.2 Parametric Signal Modeling -- 6.2.1 Parametric Estimation -- 6.3 Wiener Filtering -- 6.4 Kalman Filter -- 6.4.1 Smoothing -- 6.5 Particle Filter -- 6.6 Fundamentals of Monte Carl -- 6.6.1 Importance Sampling (IS) -- 6.7 Non-Parametric Signal Modeling -- 6.8 Non-Parametric Estimation -- 6.8.1 Correlogram -- 6.8.2 Periodogram -- 6.9 Filter Bank Method -- 6.10 Quadrature Mirror Filter Bank (QMF) -- 6.11 Background Information -- 6.12 Exercises -- 7 Spectral Analysis -- 7.1 Introduction -- 7.2 Adaptive Spectral Analysis -- 7.3 Multivariate Signal Processing -- 7.3.1 Sub-band Coding and Subspace Analysis -- 7.4 Wavelet Analysis -- 7.5 Adaptive Beam Forming -- xii Contents -- 7.6 Independent Component Analysis (ICA) -- 7.7 Principal Component Analysis (PCA) -- 7.8 Best Basis Algorithms -- 7.9 Background Information -- 7.10 Exercises -- Part II Machine Learning and Recognition -- 8 General Learning -- 8.1 Introduction to Learning -- 8.2 The Learning Phases -- 8.2.1 Search and Utility -- 8.3 Search -- 8.3.1 General Search Model -- 8.3.2 Preference relations -- 8.3.3 Different learning methods -- 8.3.4 Similarities -- 8.3.5 Learning to Recognize -- 8.3.6 Learning again -- 8.4 Background Information -- 8.5 Exercises -- 9 Signal Processes, Learning, and Recognition -- 9.1 Learning -- 9.2 Bayesian Formalism -- 9.2.1 Dynamic Bayesian Theory -- 9.2.2 Recognition and Search -- 9.2.3 Influences -- 9.3 Subjectivity -- 9.4 Background Information -- 9.5 Exercises -- 10 Stochastic Processes -- 10.1 Preliminaries on Probabilities -- 10.2 Basic Concepts of Stochastic Processes -- 10.2.1 Markov Processes -- 10.2.2 Hidden Stochastic Models (HSM) -- 10.2.3 HSM Topology -- 10.2.4 Learning Probabilities -- 10.2.5 Re-estimation -- 10.2.6 Redundancy -- 10.2.7 Data Preparation -- 10.2.8 Proper Redundancy Removal -- 10.3 Envelope Detection -- 10.3.1 Silence Threshold Selection -- 10.3.2 Pre-emphasis -- Contents xiii -- 10.4 Several Processes -- 10.4.1 Similarity -- 10.4.2 The Local-Global Principle -- 10.4.3 HSM Similarities -- 10.5 Conflict and Support -- 10.6 Examples and Applications -- 10.7 Predictions -- 10.8 Background Information -- 10.9 Exercises -- 11 Feature Extraction -- 11.1 Feature Extractions -- 11.2 Basic Techniques -- 11.2.1 Spectral Shaping -- 11.3 Spectral Analysis and Feature Transformation -- 11.3.1 Parametric Feature Transformations and Cepstrum -- 11.3.2 Standard Feature Extraction Techniques -- 11.3.3 Frame Energy -- 11.4 Linear Prediction Coe_cients (LPC) -- 11.5 Linear Prediction Cepstral Coe_cients (LPCC) -- 11.6 Adaptive Perceptual Local Trigonometric Transformation -- (APLTT) -- 11.7 Search -- 11.7.1 General Search Model -- 11.8 Predictions -- 11.8.1 Purpose -- 11.8.2 Linear Prediction -- 11.8.3 Mean Squared Error Minimization -- 11.8.4 Computation of Probability of an Observation Sequence -- 11.8.5 Forward and Backward Prediction -- 11.8.6 Forward-Backward Prediction -- 11.9 Background Information -- 11.10Exercises -- 12 Unsupervised Learning -- 12.1 Generalities -- 12.2 Clustering Principles -- 12.3 Cluster Analysis Methods -- 12.4 Special Methods -- 12.4.1 K-means -- 12.4.2 Vector Quantization (VQ) -- 12.4.3 Expectation Maximization (EM) -- 12.4.4 GMM Clustering -- 12.5 Background Information -- 12.6 Exercises -- xiv Contents -- 13 Markov Model and Hidden Stochastic Model -- 13.1 Markov Process -- 13.2 Gaussian Mixture Model (GMM) -- 13.3 Advantages of using GMM -- 13.4 Linear Prediction Analysis -- 13.4.1 Autocorrelation Method -- 13.4.2 Yule-Walker Approach -- 13.4.3 Covariance Method -- 13.4.4 Comparison of Correlation and Covariance methods -- 13.5 The ULS Approach -- 13.6 Comparison of ULS and Covariance Methods -- 13.7 Forward Prediction -- 13.8 Backward Prediction -- 13.9 Forward-Backward Prediction -- 13.10Baum-Welch Algorithm -- 13.11Viterbi Algorithm -- 13.12Background Information -- 13.13Exercises -- 14 Fuzzy Logic and Rough Sets -- 14.1 Rough Sets -- 14.2 Fuzzy Sets -- 14.2.1 Basis Elements -- 14.2.2 Possibility and Necessity -- 14.3 Fuzzy Clustering -- 14.4 Fuzzy Probabilities -- 14.5 Background Information -- 14.6 Exercises -- 15 Neural Networks -- 15.1 Neural Network Types -- 15.1.1 Neural Network Training -- 15.1.2 Neural Network Topology -- 15.2 Parallel Distributed Processing -- 15.2.1 Forward and Backward Uses -- 15.2.2 Learning -- 15.3 Applications to Signal Processing -- 15.4 Background Information -- 15.5 Exercises -- Part III Real Aspects and Applications -- Contents xv -- 16 Noisy Signals -- 16.1 Introduction -- 16.2 Noise Questions -- 16.3 Sources of Noise -- 16.4 Noise Measurement -- 16.5 Weights and A-Weights -- 16.6 Signal to Noise Ratio (SNR) -- 16.7 Noise Measuring Filters and Evaluation -- 16.8 Types of noise -- 16.9 Origin of noises -- 16.10Box Plot Evaluation -- 16.11Individual noise types -- 16.11.1Residual -- 16.11.2Mild -- 16.11.3Steady-unsteady Time varying Noise -- 16.11.4Strong Noise -- 16.12Solution to Strong Noise: Matched Filter -- 16.13Background Information -- 16.14Exercises -- 17 Reasoning Methods and Noise Removal -- 17.1 Generalities -- 17.2 Special Noise Removal Methods -- 17.2.1 Residual Noise -- 17.2.2 Mild Noise -- 17.2.3 Steady-Unsteady Noise -- 17.2.4 Strong Noise -- 17.3 Poisson Distribution -- 17.3.1 Outliers and Shots -- 17.3.2 Underlying probability of Shots -- 17.4 Kalman Filter -- 17.4.1 Prediction Estimates -- 17.4.2 White noise Kalman filtering -- 17.4.3 Application of Kalman filter -- 17.5 Classification, Recognition and Learning -- 17.5.1 Summary of the used concepts -- 17.6 Principle Component Analysis (PCA) -- 17.7 Reasoning Methods -- 17.7.1 Case-Based Reasoning (CBR) -- 17.8 Background Information -- 17.9 Exercises -- xvi Contents -- 18 Audio Signals and Speech Recognition -- 18.1 Generalities of Speech -- 18.2 Categories of Speech Recognition -- 18.3 Automatic Speech Recognition -- 18.3.1 System Structure -- 18.4 Speech Production Model -- 18.5 Acoustics -- 18.6 Human Speech Production -- 18.6.1 The Human Speech Generation -- 18.6.2 Excitation -- 18.6.3 Voiced Speech -- 18.6.4 Unvoiced Speech -- 18.7 Silence Regions -- 18.8 Glottis -- 18.9 Lips -- 18.10Plosive Speech Source -- 18.11Vocal-Tract -- 18.12Parametric and Non-Parametric Models -- 18.13Formants -- 18.14Strong Noise -- 18.15Background Information -- 18.16Exercises -- 19 Noisy Speech -- 19.1 Introduction -- 19.2 Colored Noise -- 19.2.1 Additional types of Colored Noise -- 19.3 Poisson Processes and Shots -- 19.4 Matched Filters -- 19.5 Shot Noise -- 19.6 Background Information -- 19.7 Exercises -- 20 Aspects Of Human Hearing -- 20.1 Human Ear -- 20.2 Human Auditory System -- 20.3 Critical Bands and Scales -- 20.3.1 Mel Scale -- 20.3.2 Bark Scale -- 20.3.3 Erb Scale -- 20.3.4 Greenwood Scale -- 20.4 Filter Banks -- 20.4.1 ICA Network -- 20.4.2 Auditory Filter Banks -- 20.4.3 Filter Banks -- Contents xvii -- 20.4.4 Mel Critical Filter Bank -- 20.5 Psycho-acoustic Phenomena -- 20.5.1 Perceptual Measurement -- 20.5.2 Human Hearing and Perception -- 20.5.3 Sound Pressure Level (SPL) -- 20.5.4 Absolute Threshold of Hearing (ATH) -- 20.6 Perceptual Adaptation -- 20.7 Auditory System and Hearing Model -- 20.8 Auditory Masking and Masking Frequency -- 20.…”
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