Search Results - "algorithms"

Machine learning for protein subcellular localization prediction by Wan, Shibiao, Mak, M. W.

Table of Contents: “…-- 3.4 More reasons for using GO information -- 4 Single-location protein subcellular localization -- 4.1 Extracting GO from the Gene Ontology Annotation Database -- 4.1.1 Gene Ontology Annotation Database -- 4.1.2 Retrieval of GO terms -- 4.1.3 Construction of GO vectors -- 4.1.4 Multiclass SVM classification -- 4.2 FusionSVM: Fusion of gene ontology and homology-based features -- 4.2.1 InterProGOSVM: Extracting GO from InterProScan -- 4.2.2 PairProSVM: A homology-based method -- 4.2.3 Fusion of InterProGOSVM and PairProSVM -- 4.3 Summary -- 5 From single- to multi-location -- 5.1 Significance of multi-location proteins -- 5.2 Multi-label classification -- 5.2.1 Algorithm-adaptation methods.…”
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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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Ecological modelling for sustainable development

Table of Contents: “…. -- Effects of Endozoochorous Seed Dispersal on the Soil Seed Bank and Vegetation in the Woodland Area of the Wolfhezerheide-Saragih Evi,Bokdam Jan and Braakhekke Wim -- The State of the Environment, Biodiversity and Fisheries in the Southern Caspian Sea-Hassan Ghadirnejad and Siti Azizah Mohd Nor -- SECTION 2: ORAL PRESENTATION VI: Water Quality and Pollutant Removal/Treatment -- TSS Measurements in the Straits of Penang, Malaysia, using Multispectral Algorithm-Sami Gumaan Daraigan, Mohd Zubir Mat Jafri, Khiruddin Abdullah, Lim Hwee San, A.N. …”
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Penetration testing : a hands-on introduction to hacking by Weidman, Georgia

Table of Contents: “…Online Password Attacks / Peter Van Eeckhoutte -- Wordlists / Peter Van Eeckhoutte -- Guessing Usernames and Passwords with Hydra / Peter Van Eeckhoutte -- Offline Password Attacks / Peter Van Eeckhoutte -- Recovering Password Hashes from a Windows SAM File / Peter Van Eeckhoutte -- Dumping Password Hashes with Physical Access / Peter Van Eeckhoutte -- LM vs. NTLM Hashing Algorithms / Peter Van Eeckhoutte -- The Trouble with LM Password Hashes / Peter Van Eeckhoutte -- John the Ripper / Peter Van Eeckhoutte -- Cracking Linux Passwords / Peter Van Eeckhoutte -- Cracking Configuration File Passwords / Peter Van Eeckhoutte -- Rainbow Tables / Peter Van Eeckhoutte -- Online Password-Cracking Services / Peter Van Eeckhoutte -- Dumping Plaintext Passwords from Memory with Windows Credential Editor / Peter Van Eeckhoutte -- Summary / Peter Van Eeckhoutte -- 10. …”
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Public safety networks from LTE to 5G by Yarali, Abdulrahman

Table of Contents: “…6.3.2 Priority Control 100 -- 6.4 Public Safety Standardization 100 -- 6.5 Flawless Mobile Broadband for Public Safety and Security 101 -- 6.6 Applications in Different Scenarios 102 -- 6.7 Public Safety Systems and Architectures 103 -- 6.7.1 Airwave 103 -- 6.7.2 LMR 104 -- 6.7.3 TETRA Security Analysis 105 -- 6.7.4 TETRA Services System 106 -- 6.7.5 The Architecture of TETRA 106 -- 6.7.5.1 The Interfaces of TETRA Network 106 -- 6.7.6 TETRA Network Components 106 -- 6.7.6.1 The Mobile Station 108 -- 6.7.6.2 TETRA Line Station 108 -- 6.7.6.3 The Switching Management Infrastructure 108 -- 6.7.6.4 Network Management Unit 108 -- 6.7.6.5 The Gateways 108 -- 6.7.6.6 How the TETRA System Operates 108 -- 6.7.7 TETRA Mobility Management 109 -- 6.7.8 The Security of TETRA Networks 109 -- 6.7.8.1 Confidentiality 109 -- 6.7.8.2 Integrity 109 -- 6.7.8.3 Reliability 109 -- 6.7.8.4 Non-repudiation 109 -- 6.7.8.5 Authentication 110 -- 6.7.9 The Process of Authentication in TETRA 110 -- 6.7.10 The Authentication Key 110 -- 6.7.11 Symmetric Key Algorithms 110 -- 6.7.12 The Process of Authentication Key Generation 111 -- 6.7.12.1 ESN (In United Kingdom) 111 -- 6.8 Emergency Services Network (ESN) in the United Kingdom 112 -- 6.8.1 Overview of the ESN 112 -- 6.8.2 The Deliverables of ESN 112 -- 6.8.3 The Main Deliverables of ESN 112 -- 6.9 SafeNet in South Korea 113 -- 6.10 FirstNet (in USA) 115 -- 6.10.1 The Benefits of FirstNet 117 -- 6.10.2 Public Safety Core of SafetyNet 117 -- 6.10.2.1 End-to-End Encryption 117 -- 6.10.3 Round the Clock Security Surveillance 118 -- 6.10.4 User Authentication 118 -- 6.10.5 Mission Critical Functionalities 118 -- 6.10.5.1 Tactical LTE Coverage 118 -- 6.11 Canadian Interoperability Technology Interest Group (CITIG) 118 -- 6.12 Centre for Disaster Management and Public Safety (CDMPS) at the University of Melbourne 119 -- 6.13 European Emergency Number Association (EENA) 120 -- 6.13.1 European Standardization Organization (ESO) 121 -- 6.13.2 Public Safety Communications -- Europe (PSCE) 121.…”
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Analyzing neural time series data : theory and practice by Cohen, Mike X., 1979-

Table of Contents: “…What Is the Surface Laplacian? -- 22.2. Algorithms for Computing the Surface Laplacian for EEG Data -- 22.3. …”
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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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Unmanned aircraft systems

Table of Contents: “…1.1 Autonomy Levels for UAS -- 1.2 Overview of Architectures for Autonomous Systems -- 2 Autonomy Architecture for UAS -- 2.1 Low-Level Architecture -- 2.2 High-Level Architecture -- 3 Example of Autonomy Architecture: The ARCAS Project -- 3.1 Low-Level ARCAS Architecture -- 3.2 High-Level ARCAS Architecture -- 3.3 Example of ARCAS Complex Mission: Assembly Operations -- 4 Conclusions -- References -- Chapter 17: Obstacle Avoidance: Static Obstacles -- 1 Introduction -- 2 Avoiding Static Obstacles -- 2.1 Voronoi Diagram -- 2.2 Cell Decomposition -- 2.3 Visibility Graph -- 2.4 Potential Field and Sampling-Based Methods -- 3 Research on Obstacle Avoidance -- 4 Avoidance of Static Obstacles -- 5 Reactive Planning -- 6 Summary -- References -- Chapter 18: Guided Weapon and UAV Navigation and Path-Planning -- 1 Problems of GPS and INS for Missiles and UAVs -- 1.1 Global Positioning System (GPS) Navigation -- 1.2 Inertial Navigation System (INS) -- 1.3 Inertial Navigation Algorithm -- 1.4 GPS/INS Integration -- 2 Principles and Practice of TERPROM and TERCOM -- 2.1 Aircraft and UAV Path Planning -- 3 Tactical Missile Guidance Strategies -- 3.1 CLOS Guidance and Variations -- 3.2 Proportional Navigation (PN) Guidance -- 3.3 Miss Distance (MD) -- 4 Conclusions -- Notation -- Nomenclature -- References -- Chapter 19: Embedded UAS Autopilot and Sensor Systems -- 1 Introduction -- 2 Autopilot Architecture -- 3 Inner-Loop Control Structure -- 3.1 Lateral Autopilot -- 3.2 Longitudinal Autopilot -- 4 On-Board Sensors and Sensor Processing -- 4.1 Angular Rates, Airspeed, and Altitude -- 4.2 Roll and Pitch Angles -- 4.3 Inertial Position and Heading -- 5 GPS Navigation -- 5.1 Straight-Line Path Following -- 5.2 Orbit Following -- 6 Summary -- Acknowledgments -- End Notes -- References -- Part 6: Control.…”
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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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Spread spectrum systems for GNSS and wireless communications by Holmes, Jack K. (Jack Kenneth), 1936-

Table of Contents: “…4.3.1 Convolutional Code Encoder Characterization -- 4.3.2 The Transfer Function of a Convolutional Code and the Free Distance -- 4.3.3 Decoding of Convolutional Codes -- 4.3.4 The Viterbi Algorithm -- 4.3.5 Error Probabilities for Viterbi Decoding of Convolutional Codes -- 4.3.6 Sequential Decoding of Convolutional Codes -- 4.3.7 Threshold Decoding of Convolutional Codes -- 4.3.8 Nonbinary Convolutional Codes -- 4.4 ITERATIVELY DECODED CODES -- 4.4.1 Turbo Codes -- 4.4.2 A Serial Concatenated Convolutional Code -- 4.4.3 Serial Concatenated Block Codes -- 4.4.4 Parallel Concatenated Block Codes -- 4.4.5 Low-Density Parity Check Codes -- 4.5 SELECTED RESULTS FOR SOME ERROR CORRECTION CODES -- 4.5.1 Bose, Chaudhuri, and Hocquenghem Codes -- 4.5.2 Reed-Solomon Codes -- 4.5.3 Convolutional Codes with Maximum Free Distance -- 4.5.4 Hard- and Soft-Decision FFH/MFSK with Repeat Coding BER Performance -- 4.6 SHANNON'S CAPACITY THEOREM, THE CHANNEL CODING THEOREM, AND BANDWIDTH EFFICIENCY -- 4.6.1 Shannon's Capacity Theorem -- 4.6.2 Channel Coding Theorem -- 4.6.3 Bandwidth Efficiency -- 4.7 APPLICATIONS OF ERROR CONTROL CODING -- 4.8 SUMMARY -- References -- Selected Bibliography -- Problems -- CHAPTER 5 Carrier Tracking Loops and Frequency Synthesizers -- 5.0 INTRODUCTION -- 5.1 TRACKING OF RESIDUAL CARRIER SIGNALS -- 5.2 PLL FOR TRACKING A RESIDUAL CARRIER COMPONENT -- 5.2.1 The Likelihood Function for Phase Estimation -- 5.2.2 The Maximum-Likelihood Estimation of Carrier Phase -- 5.2.3 Long Loops and Short Loops -- 5.2.4 The Stochastic Differential Equation of Operation -- 5.2.5 The Linear Model of the PLL with Noise -- 5.2.6 The Various Loop Filter Types -- 5.2.7 Transient Response of a Second-Order Loop -- 5.2.8 Steady State Tracking Error When the Phase Error Is Small -- 5.2.9 The Variance of the Linearized PLL Phase Error Due to Thermal Noise.…”
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Machine Learning for Risk Calculations : A Practitioner's View. by Ruiz, Ignacio

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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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Industrial automation technologies by Dey, Chanchal, Sen, Sunit Kumar

Table of Contents: “…Digital PID Control Algorithm -- Positional and Velocity Forms -- 3.3.4. …”
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IBM TotalStorage : SAN product, design, and optimization guide

Table of Contents: “…-- 3.7.8 FC-PH-2 and speed -- 3.7.9 1, 2 and 4 Gbps and beyond -- 3.7.10 FC-PH, FC-PH-2, and FC-PH-3 -- 3.7.11 Layers -- 3.8 Zoning -- 3.8.1 Hardware zoning -- 3.8.2 Software zoning -- 3.9 Trunking -- 3.9.1 Frame filtering -- 3.9.2 Oversubscription -- 3.9.3 Congestion -- 3.9.4 Information units -- 3.9.5 The movement of data -- 3.9.6 Data encoding -- 3.10 Ordered set, frames, sequences, and exchanges -- 3.10.1 Ordered set -- 3.10.2 Frames -- 3.10.3 Sequences -- 3.10.4 Exchanges -- 3.10.5 Frames -- 3.10.6 In order and out of order -- 3.10.7 Latency -- 3.10.8 Heterogeneousness -- 3.10.9 Open Fiber Control -- 3.11 Fibre Channel Arbitrated Loop (FC-AL) -- 3.11.1 Loop protocols -- 3.11.2 Fairness algorithm -- 3.11.3 Loop addressing -- 3.11.4 Private devices on NL_Ports -- 3.12 Factors and considerations -- 3.12.1 Limits -- 3.12.2 Security -- 3.12.3 Interoperability -- 3.13 Standards -- 3.14 SAN industry associations and organizations -- 3.14.1 Storage Networking Industry Association -- 3.14.2 Fibre Channel Industry Association -- 3.14.3 SCSI Trade Association -- 3.14.4 International Committee for Information Technology Standards -- 3.14.5 INCITS technical committee T11.…”
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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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Cryogenic Systems : Advanced Monitoring, Fault Diagnostics, and Predictive Maintenance. by Arpaia, Pasquale

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Evolutionary Robust Control. by Feyel, Philippe

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Real-time Endoscopic Image Stitching for Cystoscopy. by Bergen, Tobias

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Hidden Markov processes : theory and applications to biology by Vidyasagar, M. (Mathukumalli), 1947-

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Imaging by numbers : a historical view of the computer print