Search Results - (((((((ken OR want) OR semantic) OR wind) OR cantor) OR anne) OR share) OR hints) algorithms.

Power system monitoring and control by Bevrani, Hassan

Full text (MFA users only)

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.…”
Full text (MFA users only)

PID and predictive control of electrical drives and power supplies using MATLAB/Simulink by Wang, Liuping

Full text (MFA users only)

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? …”
Full text (MFA users only)

Insight into fuzzy modeling by Novák, Vilém, 1951-, Perfilieva, Irina, 1953-, Dvořák, Antonín (Mathematician)

Full text (MFA users only)

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 ??…”
Full text (MFA users only)

Unmanned aircraft systems

Table of Contents: “…Unmanned Aircraft Systems -- Contents -- Contributors -- Foreword -- Preface -- Part 1: Introductory -- Chapter 1: UAS Uses, Capabilities, Grand Challenges -- 1 Introduction -- 2 Uses -- Missions and Applications -- 2.1 Early evolution -- 2.2 Dull, dirty, and dangerous -- 2.3 Emergence of civil and commercial applications -- 3 Emerging Capabilities And A Look Ahead -- 3.1 Expanding the design space and operational envelope -- 3.2 Autonomy -- 4 Grand Challenges Ahead -- 4.1 Access to the airspace -- 4.2 The quest for trust -- 4.3 Integration -- 5 Summary -- References -- Part 2: Missions -- Chapter 2: Remote Sensing Methodology for Unmanned Aerial Systems -- 1 Introduction -- 2 UAS Remote Sensing Methodology -- 3 Core Concepts in UAS Remote Sensing Applications -- 3.1 Detection/Counting Applications -- 3.2 Identification/Localization Applications -- 3.3 Analysis Applications -- 4 UAS Imaging Equipment -- 4.1 Video Systems -- 4.2 Digital Cameras -- 4.3 Calibrated Digital Imagers -- 4.3.1 Digital Cameras as Calibrated Imagers -- 4.3.2 Multispectral and Hyperspectral Imagers -- 4.3.3 Spectral Sensitivity -- 5 Conclusion -- References -- Chapter 3: Autonomous Parachute-Based Precision Delivery Systems -- 1 Introduction -- 2 Concept of Operations and Key Requirements -- 3 Pads Family and Steady-State Performance -- 4 Modeling -- 4.1 Governing equations -- 4.2 Apparent mass and inertia -- 4.3 PADS aerodynamics -- 4.4 Effect of the control inputs -- 4.5 Linearized models and stability -- 5 Pads Gnc -- 5.1 Maneuver-based guidance -- 5.2 Accounting for the variable winds -- 5.3 Optimal precision placement guidance -- 6 Other Developments -- 6.1 Glide slope angle control -- 6.2 Reduced cost PADS -- 7 Conclusion -- References -- Chapter 4: Networked Multiple UAS -- 1 Introduction -- 2 Principles of Radio Links -- 3 Air-to-Ground Communications.…”
Full text (MFA users only)

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. …”
Full text (MFA users only)

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.…”
Full text (MFA users only)

Penetration testing : a hands-on introduction to hacking by Weidman, Georgia

Table of Contents: “…Metasploit Payloads / Peter Van Eeckhoutte -- Meterpreter / Peter Van Eeckhoutte -- Exploiting WebDAV Default Credentials / Peter Van Eeckhoutte -- Running a Script on the Target Web Server / Peter Van Eeckhoutte -- Uploading a Msfvenom Payload / Peter Van Eeckhoutte -- Exploiting Open phpMyAdmin / Peter Van Eeckhoutte -- Downloading a File with TFTP / Peter Van Eeckhoutte -- Downloading Sensitive Files / Peter Van Eeckhoutte -- Downloading a Configuration File / Peter Van Eeckhoutte -- Downloading the Windows SAM / Peter Van Eeckhoutte -- Exploiting a Buffer Overflow in Third-Party Software / Peter Van Eeckhoutte -- Exploiting Third-Party Web Applications / Peter Van Eeckhoutte -- Exploiting a Compromised Service / Peter Van Eeckhoutte -- Exploiting Open NFS Shares / Peter Van Eeckhoutte -- Summary / Peter Van Eeckhoutte -- 9. …”
Full text (MFA users only)

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.…”
Full text (MFA users only)

Electric Power System Fundamentals. by Acha Daza, Salvador

Full text (MFA users only)

Engineering autonomous vehicles and robots : the DragonFly modular-based approach by Liu, Shaoshan

Full text (MFA users only)

The language of mathematics education : an expanded glossary of key terms and concepts in mathematics teaching and learning by Dingman, Shannon W., 1977-

Full text (MFA users only)

Disobedient Aesthetics : Surveillance, Bodies, Control by Stagliano, Anthony, 1973-

Full text (MFA users only)