Search Results - (((((((ant OR canton) OR semantic) OR wind) OR cantor) OR anne) OR blaney) OR hints) algorithms.

Aerospace Sensors. by Nebylov, Alexander

Table of Contents: “…Principles and examples of sensor integration -- 9.1 Sensor systems -- 9.1.1 The sensor system concept -- 9.1.2 Joint processing of readings from identical sensors -- 9.1.3 Joint processing of readings from cognate sensors with different measurement ranges -- 9.1.4 Joint processing of diverse sensors readings -- 9.1.5 Linear and nonlinear sensor integration algorithms -- 9.2 Fundamentals of integrated measuring system synthesis -- 9.2.1 Synthesis problem statement -- 9.2.2 Classes of dynamic system realization -- 9.2.3 Measurement accuracy indices -- 9.2.4 Excitation properties -- 9.2.5 Objective functions for robust system optimisation -- 9.2.6 Methods of dynamic system accuracy index analysis under excitation with given numerical characteristics of derivatives -- 9.2.6.1 Estimation of error variance -- 9.2.6.2 Example of error variance analysis -- 9.2.6.3 Use of equivalent harmonic excitation -- 9.2.6.4 Estimation of error maximal value -- 9.2.7 System optimization under maximum accuracy criteria -- 9.2.8 Procedures for the dimensional reduction of a measuring system -- 9.2.8.1 Determination of an optimal set of sensors -- 9.2.8.2 Analysis of the advantages of invariant system construction -- 9.2.8.3 Advantages of the zeroing of several system parameters -- 9.2.9 Realization and simulation of integration algorithms -- 9.3 Examples of two-component integrated navigation systems -- 9.3.1 Noninvariant robust integrated speed meter -- 9.3.2 Integrated radio-inertial measurement -- 9.3.3 Airborne gravimeter integration -- 9.3.4 The orbital verticant -- References…”
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Mobile magic : the saatchi and saatchi guide to mobile marketing and design by Eslinger, Tom

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Understanding smart sensors by Frank, Randy

Table of Contents: “…ZigBee-Like Wireless -- 8.3.3. ANT+ -- 8.3.4.6LoWPAN -- 8.3.5. Near Field Communication (NFC) -- 8.3.6.Z-Wave -- 8.3.7. …”
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Power system monitoring and control by Bevrani, Hassan

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XML for DB2 information integration

Table of Contents: “…-- 3.4 Creating an XML schema from a database schema -- 3.4.1 The algorithm.…”
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Insight into fuzzy modeling by Novák, Vilém, 1951-, Perfilieva, Irina, 1953-, Dvořák, Antonín (Mathematician)

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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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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.…”
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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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Electric Power System Fundamentals. by Acha Daza, Salvador

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

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