Search Results - (((((((want OR wanton) OR semantic) OR wien) OR cantor) OR anne) OR shape) OR hints) algorithms.

Big Data Analytics with R. by Walkowiak, Simon

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Flood hazard identification and mitigation in semi- and arid environments

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

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Sigma-Delta Converters. by De la Rosa, José M.

Table of Contents: “…4.5.2 Effect of Finite Slew Rate on CT-Ms 133 -- 4.6 Sources of Distortion in CT-Ms 134 -- 4.6.1 Nonlinearities in the Front-end Integrator 134 -- 4.6.2 Intersymbol Interference in the Feedback DAC 136 -- 4.7 Circuit Noise in CT-Ms 137 -- 4.7.1 Noise Analysis Considering NRZ Feedback DACs 137 -- 4.7.2 Noise Analysis Considering SC Feedback DACs 139 -- 4.8 Clock Jitter in CT-Ms 140 -- 4.8.1 Jitter in Return-to-zero DACs 141 -- 4.8.2 Jitter in Non-return-to-zero DACs 142 -- 4.8.3 Jitter in Switched-capacitor DACs 144 -- 4.8.4 Lingering Effect of Clock Jitter Error 145 -- 4.8.5 Reducing the Effect of Clock Jitter with FIR and Sine-shaped DACs 147 -- 4.9 Excess Loop Delay in CT-Ms 149 -- 4.9.1 Intuitive Analysis of ELD 149 -- 4.9.2 Analysis of ELD based on Impulse-invariant DT-CT Transformation 151 -- 4.9.3 Alternative ELD Compensation Techniques 154 -- 4.10 Quantizer Metastability in CT-Ms 155 -- 4.11 Summary 159 -- References 160 -- 5 Behavioral Modeling and High-level Simulation 165 -- 5.1 Systematic Design Methodology of Modulators 165 -- 5.1.1 System Partitioning and Abstraction Levels 167 -- 5.1.2 Sizing Process 167 -- 5.2 Simulation Approaches for the High-level Evaluation of Ms 169 -- 5.2.1 Alternatives to Transistor-level Simulation 169 -- 5.2.2 Event-driven Behavioral Simulation Technique 171 -- 5.2.3 Programming Languages and Behavioral Modeling Platforms 172 -- 5.3 Implementing M Behavioral Models 173 -- 5.3.1 From Circuit Analysis to Computational Algorithms 173 -- 5.3.2 Time-domain versus Frequency-domain Behavioral Models 175 -- 5.3.3 Implementing Time-domain Behavioral Models in MATLAB 178 -- 5.3.4 Building Time-domain Behavioral Models as SIMULINK C-MEX S-functions 182 -- 5.4 Efficient Behavioral Modeling of M Building Blocks using C-MEX S-functions 188 -- 5.4.1 Modeling of SC Integrators using S-functions 188 -- 5.4.1.1 Capacitor Mismatch and Nonlinearity 190.…”
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Computational fluid-structure interaction : methods and applications by Bazilevs, Yuri

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Polymer extrusion

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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept…”
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IBM TotalStorage : SAN product, design, and optimization guide

Table of Contents: “…SAN design considerations -- 6.1 What do you want to achieve with a SAN? -- 6.1.1 Storage consolidation -- 6.1.2 High availability solutions -- 6.1.3 LAN-free backup -- 6.1.4 Server-free backup -- 6.1.5 Server-less backup -- 6.1.6 Disaster recovery -- 6.1.7 Flexibility -- 6.1.8 Goals -- 6.1.9 Benefits expected -- 6.1.10 TCO/ROI -- 6.1.11 Investment protection -- 6.2 Existing resources needs and planned growth -- 6.2.1 Collecting the data about existing resources -- 6.2.2 Planning for future needs -- 6.2.3 Platforms and storage -- 6.3 Select the core design for your environment -- 6.3.1 Selecting the topology -- 6.3.2 Scalability -- 6.3.3 Performance -- 6.3.4 Redundancy and resiliency -- 6.4 Host connectivity and Host Bus Adapters -- 6.4.1 Selection criteria -- 6.4.2 Multipathing software -- 6.4.3 Storage sizing -- 6.4.4 Management software -- 6.5 Director class or switch technology -- 6.6 General considerations -- 6.6.1 Ports and ASICs -- 6.6.2 Class F.…”
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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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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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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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Queer Data Studies. by Keilty, Patrick

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Pink 2.0 : encoding queer cinema on the internet by Tsika, Noah, 1983-

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Primary surplus behavior and risks to fiscal sustainability in emerging market countries : a "fan-chart" approach by Celasun, Oya, Debrun, Xavier, Ostry, Jonathan D. (Jonathan David), 1962-

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

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Power system monitoring and control by Bevrani, Hassan

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