Search Results - (((((((want OR pants) OR granting) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Advances in intelligent transportation : system and technology : selected, peer reviewed papers from the 1st International Doctoral Annual Symposium on Intelligent Transportation T...

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Information engineering for mechanics and materials research

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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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Artificial intelligence and data mining approaches in security frameworks

Table of Contents: “…87 -- 5.1.2 Purpose of Spamming 88 -- 5.1.3 Spam Filters Inputs and Outputs 88 -- 5.2 Content-Based Spam Filtering Techniques 89 -- 5.2.1 Previous Likeness–Based Filters 89 -- 5.2.2 Case-Based Reasoning Filters 89 -- 5.2.3 Ontology-Based E-Mail Filters 90 -- 5.2.4 Machine-Learning Models 90 -- 5.2.4.1 Supervised Learning 90 -- 5.2.4.2 Unsupervised Learning 90 -- 5.2.4.3 Reinforcement Learning 91 -- 5.3 Machine Learning–Based Filtering 91 -- 5.3.1 Linear Classifiers 91 -- 5.3.2 Naïve Bayes Filtering 92 -- 5.3.3 Support Vector Machines 94 -- 5.3.4 Neural Networks and Fuzzy Logics–Based Filtering 94 -- 5.4 Performance Analysis 97 -- 5.5 Conclusion 97 -- References 98 -- 6 Artificial Intelligence in the Cyber Security Environment 101 Jaya Jain -- 6.1 Introduction 102 -- 6.2 Digital Protection and Security Correspondences Arrangements 104 -- 6.2.1 Operation Safety and Event Response 105 -- 6.2.2 AI2 105 -- 6.2.2.1 CylanceProtect 105 -- 6.3 Black Tracking 106 -- 6.3.1 Web Security 107 -- 6.3.1.1 Amazon Macie 108 -- 6.4 Spark Cognition Deep Military 110 -- 6.5 The Process of Detecting Threats 111 -- 6.6 Vectra Cognito Networks 112 -- 6.7 Conclusion 115 -- References 115 -- 7 Privacy in Multi-Tenancy Frameworks Using AI 119 Shweta Solanki -- 7.1 Introduction 119 -- 7.2 Framework of Multi-Tenancy 120 -- 7.3 Privacy and Security in Multi-Tenant Base System Using AI 122 -- 7.4 Related Work 125 -- 7.5 Conclusion 125 -- References 126 -- 8 Biometric Facial Detection and Recognition Based on ILPB and SVM 129 Shubhi Srivastava, Ankit Kumar and Shiv Prakash -- 8.1 Introduction 129 -- 8.1.1 Biometric 131 -- 8.1.2 Categories of Biometric 131 -- 8.1.2.1 Advantages of Biometric 132 -- 8.1.3 Significance and Scope 132 -- 8.1.4 Biometric Face Recognition 132 -- 8.1.5 Related Work 136 -- 8.1.6 Main Contribution 136 -- 8.1.7 Novelty Discussion 137 -- 8.2 The Proposed Methodolgy 139 -- 8.2.1 Face Detection Using Haar Algorithm 139 -- 8.2.2 Feature Extraction Using ILBP 141 -- 8.2.3 Dataset 143 -- 8.2.4 Classification Using SVM 143 -- 8.3 Experimental Results 145 -- 8.3.1 Face Detection 146 -- 8.3.2 Feature Extraction 146 -- 8.3.3 Recognize Face Image 147 -- 8.4 Conclusion 151 -- References 152 -- 9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155 S K Rajesh Kanna, O. …”
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Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics

Table of Contents: “…3.3.1.2 Computation Task Models 68 -- 3.3.1.3 Quality of Experience 71 -- 3.3.2 Computation Offloading Game 71 -- 3.3.2.1 Game Formulation 71 -- 3.3.2.2 Algorithm Development 74 -- 3.3.2.3 Price of Anarchy 74 -- 3.3.2.4 Performance Evaluation 75 -- 3.4 Conclusion 80 -- References 80 -- 4 Pricing Tradeoffs for Data Analytics in Fog-Cloud Scenarios 83 /Yichen Ruan, Liang Zheng, Maria Gorlatova, Mung Chiang, and Carlee Joe-Wong -- 4.1 Introduction: Economics and Fog Computing 83 -- 4.1.1 Fog Application Pricing 85 -- 4.1.2 Incentivizing Fog Resources 86 -- 4.1.3 A Fogonomics Research Agenda 86 -- 4.2 Fog Pricing Today 87 -- 4.2.1 Pricing Network Resources 87 -- 4.2.2 Pricing Computing Resources 89 -- 4.2.3 Pricing and Architecture Trade-offs 89 -- 4.3 Typical Fog Architectures 90 -- 4.3.1 Fog Applications 90 -- 4.3.2 The Cloud-to-Things Continuum 90 -- 4.4 A Case Study: Distributed Data Processing 92 -- 4.4.1 A Temperature Sensor Testbed 92 -- 4.4.2 Latency, Cost, and Risk 95 -- 4.4.3 System Trade-off: Fog or Cloud 98 -- 4.5 Future Research Directions 101 -- 4.6 Conclusion 102 -- Acknowledgments 102 -- References 103 -- 5 Quantitative and Qualitative Economic Benefits of Fog 107 /Joe Weinman -- 5.1 Characteristics of Fog Computing Solutions 108 -- 5.2 Strategic Value 109 -- 5.2.1 Information Excellence 110 -- 5.2.2 Solution Leadership 110 -- 5.2.3 Collective Intimacy 110 -- 5.2.4 Accelerated Innovation 111 -- 5.3 Bandwidth, Latency, and Response Time 111 -- 5.3.1 Network Latency 113 -- 5.3.2 Server Latency 114 -- 5.3.3 Balancing Consolidation and Dispersion to Minimize Total Latency 114 -- 5.3.4 Data Traffic Volume 115 -- 5.3.5 Nodes and Interconnections 116 -- 5.4 Capacity, Utilization, Cost, and Resource Allocation 117 -- 5.4.1 Capacity Requirements 117 -- 5.4.2 Capacity Utilization 118 -- 5.4.3 Unit Cost of Delivered Resources 119 -- 5.4.4 Resource Allocation, Sharing, and Scheduling 120 -- 5.5 Information Value and Service Quality 120 -- 5.5.1 Precision and Accuracy 120.…”
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The language of mathematics education : an expanded glossary of key terms and concepts in mathematics teaching and learning by Dingman, Shannon W., 1977-

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Mastering Active Directory by Francis, Dishan

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Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 by Pastor Sanz, Iván

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Experimental Economics and Culture by Gunnthorsdottir, Anna

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Normal and abnormal fetal face atlas : ultrasonographic features by Levaillant, Jean-Marc, Bault, Jean-Philippe, Benoit, Bernard, Couly, G. (Gérard)

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Big Data Analytics with R. by Walkowiak, Simon

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Official Google Cloud Certified Professional Data Engineer study guide by Sullivan, Dan, 1962-

Table of Contents: “…Row Key Access 13 -- Unstructured Data 15 -- Google's Storage Decision Tree 16 -- Schema Design Considerations 16 -- Relational Database Design 17 -- NoSQL Database Design 20 -- Exam Essentials 23 -- Review Questions 24 -- Chapter 2 Building and Operationalizing Storage Systems 29 -- Cloud SQL 30 -- Configuring Cloud SQL 31 -- Improving Read Performance with Read Replicas 33 -- Importing and Exporting Data 33 -- Cloud Spanner 34 -- Configuring Cloud Spanner 34 -- Replication in Cloud Spanner 35 -- Database Design Considerations 36 -- Importing and Exporting Data 36 -- Cloud Bigtable 37 -- Configuring Bigtable 37 -- Database Design Considerations 38 -- Importing and Exporting 39 -- Cloud Firestore 39 -- Cloud Firestore Data Model 40 -- Indexing and Querying 41 -- Importing and Exporting 42 -- BigQuery 42 -- BigQuery Datasets 43 -- Loading and Exporting Data 44 -- Clustering, Partitioning, and Sharding Tables 45 -- Streaming Inserts 46 -- Monitoring and Logging in BigQuery 46 -- BigQuery Cost Considerations 47 -- Tips for Optimizing BigQuery 47 -- Cloud Memorystore 48 -- Cloud Storage 50 -- Organizing Objects in a Namespace 50 -- Storage Tiers 51 -- Cloud Storage Use Cases 52 -- Data Retention and Lifecycle Management 52 -- Unmanaged Databases 53 -- Exam Essentials 54 -- Review Questions 56 -- Chapter 3 Designing Data Pipelines 61 -- Overview of Data Pipelines 62 -- Data Pipeline Stages 63 -- Types of Data Pipelines 66 -- GCP Pipeline Components 73 -- Cloud Pub/Sub 74 -- Cloud Dataflow 76 -- Cloud Dataproc 79 -- Cloud Composer 82 -- Migrating Hadoop and Spark to GCP 82 -- Exam Essentials 83 -- Review Questions 86 -- Chapter 4 Designing a Data Processing Solution 89 -- Designing Infrastructure 90 -- Choosing Infrastructure 90 -- Availability, Reliability, and Scalability of Infrastructure 93 -- Hybrid Cloud and Edge Computing 96 -- Designing for Distributed Processing 98 -- Distributed Processing: Messaging 98 -- Distributed Processing: Services 101 -- Migrating a Data Warehouse 102 -- Assessing the Current State of a Data Warehouse 102 -- Designing the Future State of a Data Warehouse 103 -- Migrating Data, Jobs, and Access Controls 104 -- Validating the Data Warehouse 105 -- Exam Essentials 105 -- Review Questions 107 -- Chapter 5 Building and Operationalizing Processing Infrastructure 111 -- Provisioning and Adjusting Processing Resources 112 -- Provisioning and Adjusting Compute Engine 113 -- Provisioning and Adjusting Kubernetes Engine 118 -- Provisioning and Adjusting Cloud Bigtable 124 -- Provisioning and Adjusting Cloud Dataproc 127 -- Configuring Managed Serverless Processing Services 129 -- Monitoring Processing Resources 130 -- Stackdriver Monitoring 130 -- Stackdriver Logging 130 -- Stackdriver Trace 131 -- Exam Essentials 132 -- Review Questions 134 -- Chapter 6 Designing for Security and Compliance 139 -- Identity and Access Management with Cloud IAM 140 -- Predefined Roles 141 -- Custom Roles 143 -- Using Roles with Service Accounts 145 -- Access Control with Policies 146 -- Using IAM with Storage and Processing Services 148 -- Cloud Storage and IAM 148 -- Cloud Bigtable and IAM 149 -- BigQuery and IAM 149 -- Cloud Dataflow and IAM 150 -- Data Security 151 -- Encryption 151 -- Key Management 153 -- Ensuring Privacy with the Data Loss Prevention API 154 -- Detecting Sensitive Data 154 -- Running Data Loss Prevention Jobs 155 -- Inspection Best Practices 156 -- Legal Compliance 156 -- Health Insurance Portability and Accountability Act (HIPAA) 156 -- Children's Online Privacy Protection Act 157 -- FedRAMP 158 -- General Data Protection Regulation 158 -- Exam Essentials 158 -- Review Questions 161 -- Chapter 7 Designing Databases for Reliability, Scalability, and Availability 165 -- Designing Cloud Bigtable Databases for Scalability and Reliability 166 -- Data Modeling with Cloud Bigtable 166 -- Designing Row-keys 168 -- Designing for Time Series 170 -- Use Replication for Availability and Scalability 171 -- Designing Cloud Spanner Databases for Scalability and Reliability 172 -- Relational Database Features 173 -- Interleaved Tables 174 -- Primary Keys and Hotspots 174 -- Database Splits 175 -- Secondary Indexes 176 -- Query Best Practices 177 -- Designing BigQuery Databases for Data Warehousing 179 -- Schema Design for Data Warehousing 179 -- Clustered and Partitioned Tables 181 -- Querying Data in BigQuery 182 -- External Data Access 183 -- BigQuery ML 185 -- Exam Essentials 185 -- Review Questions 188 -- Chapter 8 Understanding Data Operations for Flexibility and Portability 191 -- Cataloging and Discovery with Data Catalog 192 -- Searching in Data Catalog 193 -- Tagging in Data Catalog 194 -- Data Preprocessing with Dataprep 195 -- Cleansing Data 196 -- Discovering Data 196 -- Enriching Data 197 -- Importing and Exporting Data 197 -- Structuring and Validating Data 198 -- Visualizing with Data Studio 198 -- Connecting to Data Sources 198 -- Visualizing Data 200 -- Sharing Data 200 -- Exploring Data with Cloud Datalab 200 -- Jupyter Notebooks 201 -- Managing Cloud Datalab Instances 201 -- Adding Libraries to Cloud Datalab Instances 202 -- Orchestrating Workflows with Cloud Composer 202 -- Airflow Environments 203 -- Creating DAGs 203 -- Airflow Logs 204 -- Exam Essentials 204 -- Review Questions 206 -- Chapter 9 Deploying Machine Learning Pipelines 209 -- Structure of ML Pipelines 210 -- Data Ingestion 211 -- Data Preparation 212 -- Data Segregation 215 -- Model Training 217 -- Model Evaluation 218 -- Model Deployment 220 -- Model Monitoring 221 -- GCP Options for Deploying Machine Learning Pipeline 221 -- Cloud AutoML 221 -- BigQuery ML 223 -- Kubeflow 223 -- Spark Machine Learning 224 -- Exam Essentials 225 -- Review Questions 227 -- Chapter 10 Choosing Training and Serving Infrastructure 231 -- Hardware Accelerators 232 -- Graphics Processing Units 232 -- Tensor Processing Units 233 -- Choosing Between CPUs, GPUs, and TPUs 233 -- Distributed and Single Machine Infrastructure 234 -- Single Machine Model Training 234 -- Distributed Model Training 235 -- Serving Models 236 -- Edge Computing with GCP 237 -- Edge Computing Overview 237 -- Edge Computing Components and Processes 239 -- Edge TPU 240 -- Cloud IoT 240 -- Exam Essentials 241 -- Review Questions 244 -- Chapter 11 Measuring, Monitoring, and Troubleshooting Machine Learning Models 247 -- Three Types of Machine Learning Algorithms 248 -- Supervised Learning 248 -- Unsupervised Learning 253 -- Anomaly Detection 254 -- Reinforcement Learning 254 -- Deep Learning 255 -- Engineering Machine Learning Models 257 -- Model Training and Evaluation 257 -- Operationalizing ML Models 262 -- Common Sources of Error in Machine Learning Models 263 -- Data Quality 264 -- Unbalanced Training Sets 264 -- Types of Bias 264 -- Exam Essentials 265 -- Review Questions 267 -- Chapter 12 Leveraging Prebuilt Models as a Service 269 -- Sight 270 -- Vision AI 270 -- Video AI 272 -- Conversation 274 -- Dialogflow 274 -- Cloud Text-to-Speech API 275 -- Cloud Speech-to-Text API 275 -- Language 276 -- Translation 276 -- Natural Language 277 -- Structured Data 278 -- Recommendations AI API 278 -- Cloud Inference API 280 -- Exam Essentials 280 -- Review Questions 282 -- Appendix Answers to Review Questions 285 -- Chapter 1: Selecting Appropriate Storage Technologies 286 -- Chapter 2: Building and Operationalizing Storage Systems 288 -- Chapter 3: Designing Data Pipelines 290 -- Chapter 4: Designing a Data Processing Solution 291 -- Chapter 5: Building and Operationalizing Processing Infrastructure 293 -- Chapter 6: Designing for Security and Compliance 295 -- Chapter 7: Designing Databases for Reliability, Scalability, and Availability 296 -- Chapter 8: Understanding Data Operations for Flexibility and Portability 298 -- Chapter 9: Deploying Machine Learning Pipelines 299 -- Chapter 10: Choosing Training and Serving Infrastructure 301 -- Chapter 11: Measuring, Monitoring, and Troubleshooting Machine Learning Models 303 -- Chapter 12: Leveraging Prebuilt Models as a Service 304 -- Index 307.…”
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Learning Neo4j 3.x - Second Edition. by Baton, Jerome

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Towards human-UAV physical interaction and fully actuated aerial vehicles by Rajappa, Sujit

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Goliath's revenge : how established companies turn the tables on digital disruptors by Hewlin, Todd, Snyder, Scott A. (Scott Andrew), 1965-

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Software engineering for embedded systems : methods, practical techniques, and applications

Table of Contents: “…-- Examples of modeling languages -- The V diagram promise -- So, why would you want to model your embedded system? -- When should you model your embedded system? …”
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Sigma-Delta Converters. by De la Rosa, José M.

Table of Contents: “…6.3.1 Hardware Emulation of CT-Ms on an FPGA 257 -- 6.3.2 GPU-accelerated Computing of CT-Ms 258 -- 6.4 Using Multi-objective Evolutionary Algorithms to Optimize Ms 259 -- 6.4.1 Combining MOEA with SIMSIDES 261 -- 6.4.2 Applying MOEA and SIMSIDES to the Synthesis of CT-Ms 262 -- 6.5 Summary 269 -- References 269 -- 7 Electrical Design of ??…”
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Public safety networks from LTE to 5G by Yarali, Abdulrahman

Table of Contents: “…9.2.5 Flexibility 164 -- 9.3 Starting Public Safety Implementation Versus Waiting for 5G 165 -- 9.4 5GVersus 4G Public Safety Services 166 -- 9.4.1 Video Surveillance 167 -- 9.4.2 Computer-Driven Augmented Reality (AR) Helmet 167 -- 9.5 How 5GWill Shape Emergency Services 167 -- 9.6 4G LTE Defined Public Safety Content in 5G 168 -- 9.7 The Linkage Between 4G-5G Evolution and the Spectrum for Public Safety 168 -- 9.8 Conclusion 168 -- References 168 -- 10 Fifth Generation (5G) Cellular Technology 171 -- 10.1 Introduction 171 -- 10.2 Background Information on Cellular Network Generations 172 -- 10.2.1 Evolution of Mobile Technologies 172 -- 10.2.1.1 First Generation (1G) 172 -- 10.2.1.2 Second Generation (2G) Mobile Network 172 -- 10.2.1.3 Third Generation (3G) Mobile Network 172 -- 10.2.1.4 Fourth Generation (4G) Mobile Network 173 -- 10.2.1.5 Fifth Generation (5G) 173 -- 10.3 Fifth Generation (5G) and the Network of Tomorrow 174 -- 10.3.1 5G Network Architecture 176 -- 10.3.2 Wireless Communication Technologies for 5G 177 -- 10.3.2.1 Massive MIMO 177 -- 10.3.2.2 Spatial Modulation 179 -- 10.3.2.3 Machine to Machine Communication (M2M) 179 -- 10.3.2.4 Visible Light Communication (VLC) 180 -- 10.3.2.5 Green Communications 180 -- 10.3.3 5G System Environment 180 -- 10.3.4 Devices Used in 5G Technology 181 -- 10.3.5 Market Standardization and Adoption of 5G Technology 181 -- 10.3.6 Security Standardization of Cloud Applications 183 -- 10.3.7 The Global ICT Standardization Forum for India (GISFI) 184 -- 10.3.8 Energy Efficiency Enhancements 184 -- 10.3.9 Virtualization in the 5G Cellular Network 185 -- 10.3.10 Key Issues in the Development Process 185 -- 10.3.10.1 Challenges of Heterogeneous Networks 186 -- 10.3.10.2 Challenges Caused by Massive MIMO Technology 186 -- 10.3.10.3 Big Data Problem 186 -- 10.3.10.4 Shared Spectrum 186 -- 10.4 Conclusion 187 -- References 187 -- 11 Issues and Challenges of 4G and 5G for PS 189 -- 11.1 Introduction 189 -- 11.2 4G and 5GWireless Connections 190.…”
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Visual Inspection Technology in the Hard Disc Drive Industry. by Muneesawang, Paisarn

Table of Contents: “…Introduction / Suchart Yammen / Paisarn Muneesawang -- 1.2. Algorithm for corrosion detection / Suchart Yammen / Paisarn Muneesawang -- 1.2.1. …”
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Fundamentals of Fluid Power Control. by Watton, John

Table of Contents: “…Control-Volume Flow Continuity -- PRV Flow -- Force Balance at the Spindle -- 5.13.3 Frequency Response from a Linearized Transfer Function Analysis -- 5.14 Servovalve Dynamics -- First-Stage, Armature, and Flapper-Nozzle -- Flapper-Nozzle and Resistance Bridge Flow Characteristic -- Force Balance at the Spool -- 5.15 An Open-Loop Servovalve-Motor Drive with Line Dynamics Modeled by Lumped Approximations -- Servovalve, Dynamics Included, Underlapped Spool -- Lines, Laminar Mean Flow, Two Lump Approximations per Line, Negligible Motor Internal Volume -- Motor Flow and Torque Equations -- 5.16 Transmission Line Dynamics -- 5.16.1 Introduction -- Servovalve-Cylinder with Short Lines and Significant Actuator Volumes -- Servovalve-Motor with Long Lines and Negligible Actuator Volumes -- 5.16.2 Lossless Line Model for Z and Y -- 5.16.3 Average and Distributed Line Friction Models for Z and Y -- 5.16.4 Frequency-Domain Analysis -- 5.16.5 Servovalve-Reflected Linearized Coefficients -- 5.16.6 Modeling Systems with Nonlossless Transmission Lines, the Modal Analysis Method -- 5.16.7 Modal Analysis Applied to a Servovalve-Motor Open-Loop Drive -- 5.17 The State-Space Method for Linear Systems Modeling -- 5.17.1 Modeling Principles -- 5.17.2 Some Further Aspects of the Time-Domain Solution -- 5.17.3 The Transfer Function Concept in State Space -- 5.18 Data-Based Dynamic Modeling -- 5.18.1 Introduction -- 5.18.2 Time-Series Modeling -- 5.18.3 The Group Method of Data Handling (GMDH) Algorithm -- 5.18.4 Artificial Neural Networks -- 5.18.5 A Comparison of Time-Series, GMDH, and ANN Modeling of a Second-Order Dynamic System -- 5.18.6 Time-Series Modeling of a Position Control System -- 5.18.7 Time-Series Modeling for Fault Diagnosis -- 5.18.8 Time-Series Modeling of a Proportional PRV -- 5.18.9 GMDH Modeling of a Nitrogen-Filled Accumulator.…”
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