Search Results - (((((((ant OR manthe) OR fine) OR mantis) OR cantor) OR anne) OR halted) OR branting) algorithms.

Danforth's obstetrics and gynecology.

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Sound waves : propagation, frequencies, and effects

Table of Contents: “…Internal Waves -- 1.2b. Vertical Fine Structure -- 1.2c. Small Scale Turbulence -- 1.3. …”
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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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Principles of GNSS, inertial, and multisensor integrated navigation systems by Groves, Paul D. (Paul David)

Table of Contents: “…Attitude Initialization -- 5.6.3. Fine Alignment -- 5.7. INS Error Propagation -- 5.7.1. …”
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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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Software engineering for embedded systems : methods, practical techniques, and applications

Table of Contents: “…Note continued: Available techniques -- Static and dynamic analysis -- Requirements traceability -- Static analysis-adherence to a coding standard -- Essential knots & essential cyclomatic complexity -- case study -- Understanding dynamic analysis -- The legacy from high-integrity systems -- Defining unit, module and integration tests -- Defining structural coverage analysis -- Achieving code coverage with unit test and system test in tandem -- Retaining the functionality through regression test -- Unit test and test-driven development -- Automatically generating test cases -- Setting the standard -- The terminology of standards -- The evolution of a recognized process standard -- Freedom to choose adequate standards -- Dealing with the unusual -- Working with auto-generated code -- Working with legacy code -- Tracing requirements through to object code verification (OCV) -- Implementing a test solution environment -- Pragmatic considerations -- Considering the alternatives -- Summary and conclusions -- Introduction to debugging tools -- GDB debugging -- Configure the GDB debugger -- Starting GDB -- Compiling the application -- Debugging the application -- Examining data -- Using breakpoints -- Stepping -- Changing the program -- Analyzing core dumps -- Debug agent design -- Use cases -- Debug agent overview -- Starting the application -- Context switch -- Position-independent executables -- Debug event from the application -- Multicore -- Starting the debug agent -- Debugging using JTAG -- Benefits of using JTAG -- Board bring-up using JTAG -- Comparison with the debug agent -- GDB and JTAG -- Debugging tools using Eclipse and GDB -- Linux application debug with GDB -- Linux kernel debug with KGDB -- Instrumented code -- Practical example -- Analysis tools -- Strace -- Mtrace -- Vaigrind -- Hardware capabilities -- Hardware breakpoints -- Hardware watchpoints -- Debugging tips and tricks -- Part 1: Analysis and high-level design -- Analysis -- Improving serial performance -- Understand the application -- High-level design -- Parallel decomposition -- Data dependencies -- Communication and synchronization -- Load balancing -- Choice of algorithm -- Decomposition approaches -- Summary of Part 1 -- Part 2: Implementation and low-level design -- Thread-based implementations -- Kernel scheduling -- Pthreads -- Using PPthreads -- Dealing with thread safety -- Implementing synchronizations and mutual exclusion -- Mutexes, locks, nested locks -- Mutex -- Condition variables -- Granularity -- Fine-grained -- Coarse-grained -- Approach -- Implementing task parallelism -- Creation and join -- Parallel-pipeline computation -- Divide-and-conquer scheme -- Task scheduling considerations -- Thread pooling -- Affinity scheduling -- Event-based parallel programs -- Implementing loop parallelism -- Aligning computation and locality -- Message-passing implementations -- MCAPI -- MRAPI -- MCAPI and MRAPI in multicore systems -- Playing-card recognition and sorting example -- Using a hybrid approach -- References -- Introduction -- Which safety requirements? …”
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Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends. by Ramasubramanian, Karthik

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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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Formal languages, automata and numeration systems. 1, Introduction to combinatorics on words by Rigo, Michel

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Power system monitoring and control by Bevrani, Hassan

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