Search Results - ("algorithm" OR "algorithms")

The smart city in a digital world by Mosco, Vincent

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DeFi and the Future of Finance. by Harvey, Campbell R.

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Clinical Anesthesia Fundamentals : Ebook Without Multimedia. by Sharar, Sam R.

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Designing stock market trading systems : with and without soft computing by Vanstone, Bruce

Table of Contents: “…5.1 Introduction5.1.1 Types of soft computing -- 5.1.2 Expert systems -- 5.1.3 Case-based reasoning -- 5.1.4 Genetic algorithms -- 5.1.5 Swarm intelligence -- 5.1.6 Artificial neural networks -- 5.2 Review of research -- 5.2.1 Soft computing classifications -- 5.2.2 Research into time series prediction -- 5.2.3 Research into pattern recognition and classification -- 5.2.4 Research into optimisation -- 5.2.5 Research into ensemble approaches -- 5.3 Conclusion -- 5.4 The next step -- Chapter 6: Creating Artificial Neural Networks -- 6.1 Introduction…”
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Techniques for Noise Robustness in Automatic Speech Recognition. by Virtanen, Tuomas

Table of Contents: “…9.1.3 Gaussian Mixture Models 231 -- 9.2 MMSE-SPLICE 232 -- 9.2.1 Parameter Estimation 233 -- 9.2.2 Results 236 -- 9.3 Discriminative SPLICE 237 -- 9.3.1 The MMI Objective Function 238 -- 9.3.2 Training the Front-End Parameters 239 -- 9.3.3 The Rprop Algorithm 240 -- 9.3.4 Results 241 -- 9.4 Model-Based Feature Enhancement 242 -- 9.4.1 The Additive Noise-Mixing Equation 243 -- 9.4.2 The Joint Probability Model 244 -- 9.4.3 Vector Taylor Series Approximation 246 -- 9.4.4 Estimating Clean Speech 247 -- 9.4.5 Results 247 -- 9.5 Switching Linear Dynamic System 248 -- 9.6 Conclusion 249 -- References 249 -- 10 Reverberant Speech Recognition 251 / Reinhold Haeb-Umbach, Alexander Krueger -- 10.1 Introduction 251 -- 10.2 The Effect of Reverberation 252 -- 10.2.1 What is Reverberation? …”
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Modulares Multisensorielles Indoor Navigationssystem. by Ascher, Christian

Table of Contents: “…6.6.1 Literatur6.6.2 UWB Funksystem; 6.6.3 Tightly Coupled UWB/INS Navigationsfilter; 6.6.4 Szenarien mit Ergebnissen; 6.6.5 Fazit; 7 Laser OrthoSLAM; 7.1 SLAM-Methoden; 7.2 Laserentfernungsmesser; 7.3 Linienextraktion; 7.3.1 Incremental Line Extraction; 7.3.2 Split-And-Merge Algorithmen; 7.3.3 Adaptive Line Extraction Algorithm; 7.4 Datenassoziation; 7.4.1 Datenassoziation Nearest-Neighbour; 7.4.2 Datenassoziation: Maximum-Likelihood; 7.4.3 Joint Compatibilty Branch and Bound; 7.5 SLAM-Verfahren; 7.5.1 EKF-SLAM; 7.5.2 FastSLAM; 7.5.3 Thin Junction Tree Filters.…”
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A Primer on Machine Learning Applications in Civil Engineering by Deka, Paresh Chandra

Table of Contents: “…3.10.4 A Linguistic Variable -- IF-THEN Rules -- IF X is High, Then Y is High -- 3.10.5 Membership Functions -- 3.10.6 Strategy of Fuzzy Logic Systems -- 3.10.7 Summary -- References -- 4: Support Vector Machine -- 4.1 Introduction to Statistical Learning Theory -- 4.2 Support Vector Classification -- 4.2.1 Hard Margin SVM -- 4.2.2 Soft Margin SVM -- 4.2.3 Mapping to High-Dimensional Space -- 4.2.3.1 Kernel Tricks -- 4.2.3.2 Normalizing Kernels -- 4.2.4 Properties of Mapping Functions Associated with Kernels -- 4.2.5 Summary -- 4.3 Multi-Class SVM -- 4.3.1 Introduction -- 4.3.2 Conventional SVM -- 4.3.3 Decision Tree-Based SVM -- 4.3.4 Pairwise SVM -- 4.3.5 Summary -- 4.4 Various SVMs -- 4.4.1 Introduction -- 4.4.2 Least Square SVM -- 4.4.3 Linear Programming SVM -- 4.4.4 Sparse SVM -- 4.4.5 Robust SVM -- 4.4.6 Bayesian SVM -- 4.4.7 Summary -- 4.5 Kernel-Based Methods -- 4.5.1 Introduction -- 4.5.2 Kernel Least Squares -- 4.5.3 Kernel Principal Component Analysis -- 4.5.4 Kernel Discriminate Analysis -- 4.5.5 Summary -- 4.6 Feature Selection and Extraction -- 4.6.1 Introduction -- 4.6.2 Initial Set of Features -- 4.6.3 Procedure for Feature Selection -- 4.6.4 Feature Extraction -- 4.6.5 Clustering -- 4.6.6 Summary -- 4.7 Function Approximation -- 4.7.1 Introduction -- 4.7.2 Optimal Hyperplanes -- 4.7.3 Margin Support Vector Regression -- 4.7.4 Model Selection -- 4.7.5 Training Methods -- 4.7.6 Variants of SVR -- 4.7.7 Variable Selections -- 4.7.8 Summary -- References -- 5: Genetic Algorithm (GA) -- 5.1 Introduction -- 5.1.1 Basic Operators and Terminologies in GA -- Key Elements -- Breeding (Crossover) -- Selection -- Crossover (Recombination) -- 5.1.2 Traditional Algorithm and GA -- 5.1.3 General GA -- 5.1.4 The Schema Theorem -- Theorem: Schema Theorem (Holland) -- 5.1.5 Optimal Allocation of Trails -- 5.1.6 Summary -- 5.2 Classification of GA.…”
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Introduction to EEG- and speech-based emotion recognition by Abhang, Priyanka A., Gawali, Bharti W., Mehrotra, Suresh C.

Table of Contents: “…3.1 INTRODUCTION TO BRAIN-WAVE FREQUENCIES -- 3.1.1 Gamma Waves -- 3.1.2 Beta Waves -- 3.1.3 Alpha Waves -- 3.1.4 Theta Waves -- 3.1.5 Delta Waves -- 3.2 SPEECH PROSODIC FEATURES -- 3.2.1 Acoustic Features for Emotions -- 3.2.1.1 Prosody-Related Signal Measures -- 3.2.1.1.1 ENERGY -- 3.2.1.1.2 PITCH -- 3.2.1.1.3 FORMANT -- 3.2.1.1.4 INTENSITY -- 3.2.1.1.5 LOUDNESS -- 3.2.1.1.6 DURATION -- 3.2.1.1.7 SAMPLING RATE -- 3.2.1.2 Spectral Characteristics Measures -- 3.2.1.2.1 MEL-FREQUENCY CEPSTRAL COEFFICIENTS -- 3.2.1.2.2 MEL FILTER BANK ENERGY BASED SLOPE FEATURES -- 3.2.1.3 Voice Quality-related Measures -- 3.2.1.3.1 JITTER -- 3.2.1.3.2 SHIMMER -- 3.2.1.3.3 HARMONIC TO NOISE RATIO -- 3.3 SIGNAL PROCESSING ALGORITHMS -- 3.3.1 Preprocessing Algorithms -- 3.3.1.1 Common Spatial Patterns (CSP) -- 3.3.1.2 Independent Component Analysis -- 3.3.2 Feature Extraction -- 3.3.2.1 Principal Components Analysis -- 3.3.2.2 Mel Frequency Cepstral Coefficients for Speech Feature Extraction -- 3.3.3 Feature Classification -- 3.3.3.1 Linear Discriminative Analysis -- 3.3.3.2 Support Vector Machine -- 3.3.3.2.1 LINEAR CLASSIFICATION -- 3.3.3.2.2 NON-LINEAR CLASSIFICATION -- 3.4 CONCLUSION -- References -- 4 -- Time and Frequency Analysis -- 4.1 INTRODUCTION -- 4.2 FOURIER TRANSFORMATION -- 4.2.1 Theoretical Background -- 4.2.2 Aliasing -- 4.3 GABOR TRANSFORMATION (SHORT-TIME FOURIER TRANSFORMATION)5-7 -- 4.3.1 Theoretical Considerations -- 4.3.2 Limitations of Gabor Transformation5,6,9 -- 4.4 SHORT-TIME FOURIER TRANSFORMATION -- 4.4.1 Window Size for Short-Term Spectral Analysis10,11 -- 4.5 WAVELET TRANSFORMATION -- 4.5.1 Theoretical Background -- 4.5.1.1 Continuous Wavelet Transformation -- 4.5.1.2 Dyadic Wavelet Transformation -- 4.5.1.3 Multiresolution Analysis -- 4.5.1.4 Discrete Wavelet (Haar) Transformation -- 4.5.1.5 The Morlet Wavelet.…”
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Advances in data science : symbolic, complex, and network data

Table of Contents: “…Online decomposition of covariance matrix 125 -- 6.3.3. Adopted algorithms 128 -- 6.4. Simulation studies 131 -- 6.4.1. …”
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Learning Python Design Patterns - Second Edition. by Giridhar, Chetan

Table of Contents: “…Façade -- System -- Client -- Implementing the Façade pattern in the real world -- The principle of least knowledge -- Frequently asked questions -- Summary -- Chapter 5: The Proxy Pattern -- Controlling Object Access -- Understanding the Proxy design pattern -- A UML class diagram for the Proxy pattern -- Understanding different types of Proxies -- A virtual proxy -- A remote proxy -- A protective proxy -- A smart proxy -- The Proxy pattern in the real world -- Advantages of the Proxy pattern -- Comparing the Façade and Proxy patterns -- Frequently asked questions -- Summary -- Chapter 6: The Observer Pattern -- Keeping Objects in the Know -- Introducing Behavioral patterns -- Understanding the Observer design pattern -- A UML class diagram for the Observer pattern -- The Observer pattern in the real world -- The Observer pattern methods -- The pull model -- The push model -- Loose coupling and the Observer pattern -- The Observer pattern -- advantages and disadvantages -- Frequently asked questions -- Summary -- Chapter 7: The Command Pattern -- Encapsulating Invocation -- Introducing the Command pattern -- Understanding the Command design pattern -- A UML class diagram for the Command pattern -- Implementing the Command pattern in the real world -- Design considerations -- Advantages and disadvantages of Command patterns -- Frequently asked questions -- Summary -- Chapter 8: The Template Method Pattern -- Encapsulating Algorithm -- Defining the Template Method pattern -- Understanding the Template Method design pattern -- A UML class diagram for the Template Method pattern -- The Template Method pattern in the real world -- The Template Method pattern -- hooks -- The Hollywood principle and the Template Method -- The advantages and disadvantages of the Template Method pattern -- Frequently asked questions -- Summary.…”
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Computational models of argument : Proceedings of COMMA 2012

Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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Mobile phones : technology, networks, and user issues

Table of Contents: “…Seamless Sensor Fusion -- 4.1. Particle Filter Algorithm -- 4.2. Motion Model -- 4.3. Measurement Model -- 4.4. …”
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Introduction to Bayesian estimation and copula models of dependence by Shemyakin, Arkady

Table of Contents: “…4 Markov Chain Monte Carlo Methods4.1 Markov Chain Simulations for Sun City and Ten Coins; 4.2 Metropolis-Hastings Algorithm; 4.3 Random Walk MHA; 4.4 Gibbs Sampling; 4.5 Diagnostics of MCMC; 4.5.1 Monitoring Bias and Variance of MCMC; 4.5.2 Burn-in and Skip Intervals; 4.5.3 Diagnostics of MCMC; 4.6 Suppressing Bias and Variance; 4.6.1 Perfect Sampling; 4.6.2 Adaptive MHA; 4.6.3 ABC and Other Methods; 4.7 Time-to-Default Analysis of Mortgage Portfolios; 4.7.1 Mortgage Defaults; 4.7.2 Customer Retention and Infinite Mixture Models; 4.7.3 Latent Classes and Finite Mixture Models.…”
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Electromagnetic nondestructive evaluation (XII)

Table of Contents: “…Elastic Properties of Thermally Aged Fe-Cu Model Alloys Measured by EMAR Method -- Nondestructive Evaluation of Ferromagnetic Structural Materials Using FG Sensor -- Inverse Problem and Imaging -- Standoff Detection and Imaging of Suspicious and Concealed Objects with Electromagnetic Waves in the Centimetre and Millimetre Range -- Pulsed Terahertz Imaging for Nondestructive Evaluation -- On the Imaging of Two-Dimensional Thin Inclusions by a MUSIC-Type Algorithm from Boundary Measurements -- Chance-Constrained Programming: A Tool for Solving Linear Eddy Current Inverse Problem -- A Multiple Frequency Strategy for Reconstruction of Stress Corrosion Crack from ECT Signals -- Estimation Theory Metrics in Electromagnetic NDE -- Three Dimensional Shape Recovery of Fatigue Crack Using Eddy Current Testing Signals -- Application of Electromagnetic Nondestructive Techniques -- Development of Strong Magnetizer and Robust Sensor Mount System to Increase Performance in Detecting Defects on Pipeline -- Electromagnetic Evaluation of Honeycomb Composite Materials -- An Electromagnetic Method for Evaluation of Fatigue and for Detection of Damage at Bjork-Shiley Convexo-Concave Prosthetic Heart Valves -- Electromagnetic Evaluation of Soil Condition -- Investigation of Cu-Pc Thin Films on ITO Substrate by Using a Near-Field Microwave Microprobe -- Noncontact Characterization of Electric Carriers Density at Heterojunction Interfaces -- Electromagnetic Field Interaction with Aqueous Glucose Solution in Dielectric Resonator -- Quantitative Evaluation of Corrosion Shape on Back Surface of SUS Samples by EMAT -- Inspection of 3D Flaws Using EMAT -- Nondestructive Evaluation of Beryllium to Copper Joining for ITER by Using an Electromagnetic Acoustic Transducer (EMAT) -- Subject Index -- Author Index.…”
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Fundamentals of patenting and licensing for scientists and engineers by Ma, Matthew Y.

Table of Contents: “…Patenting beyond core algorithms. 5.4. Innovation harvesting. 5.5. Patent landscaping. 5.6. …”
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Steps towards a unified basis for scientific models and methods by Helland, Inge S.

Table of Contents: “…The partial least squares data algorithms. 8.3. The partial least squares population model. 8.4. …”
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A Systems Approach to Lithium-Ion Battery Management. by Weicker, Phil

Table of Contents: “…16.4.2 Active Methods -- 16.5 Capacity Estimation -- 16.6 Self-Discharge Detection -- 16.7 Parameter Estimation -- 16.8 Dual-Loop System -- 16.9 Remaining Useful Life Estimation -- 16.10 Particle Filters -- Reference -- 17 Fault Detection -- 17.1 Overview -- 17.2 Failure Detection -- 17.2.1 Overcharge/Overvoltage -- 17.2.2 Over-Temperature -- 17.2.3 Overcurrent -- 17.2.4 Battery Imbalance/Excessive Self- -- 17.2.5 Internal Short Circuit Detection -- 17.2.6 Detection of Lithium Plating -- 17.2.7 Venting Detection -- 17.2.8 Excessive Capacity Loss -- 17.3 Reaction Strategies -- References -- 18 Hardware Implementation -- 18.1 Packaging and Product Development -- 18.2 Battery Management System IC Select -- 18.3 Component Selection -- 18.3.1 Microprocessor -- 18.3.2 Other Components -- 18.4 Circuit Design -- 18.5 Layout -- 18.6 EMC -- 18.7 Power Supply Architectures -- 18.8 Manufacturing -- 19 Software Implementation -- 19.1 Safety-Critical Software -- 19.2 Design Goals -- 19.3 Analysis of Safety-Critical Softwar -- 19.4 Validation and Coverage -- 19.5 Model Implementation -- 19.6 Balancing -- 19.7 Temperature Impact on State of Char -- 20 Safety -- 20.1 Functional Safety -- 20.2 Hazard Analysis -- 20.3 Safety Goals -- 20.4 Safety Concepts and Strategies -- 20.5 Reference Design for Safety -- 21 Data Collection -- 21.1 Lifetime Data Gathering -- 22 Robustness and Reliability -- 22.1 Failure Mode Analysis -- 22.2 Environmental Durability -- 22.3 Abuse Conditions -- 22.4 Reliability Engineering -- 23 Best Practice -- 23.1 Engineering System Development -- 23.2 Industry Standards -- 23.3 Quality -- 24 Future Developments -- 24.1 Subcell Modeling -- 24.2 Adaptive Algorithms -- 24.3 Advanced Safety -- 24.4 System Integration -- Endnotes -- About the Author -- Index.…”
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Mastering JBoss Drools 6 : discover the power of Drools 6 and Business Rules for developing complex scenarios in your applications by Aliverti, Esteban

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Artificial Neural Network Applications for Software Reliability Prediction. by Bisi, Manjubala

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Practical applications of Bayesian reliability by Liu, Yan, Abeyratne, Athula I.

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