Search Results - (((((((kant OR frantzis) OR wantsa) OR makart) OR cantor) OR anne) OR shared) OR hints) algorithms.

Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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Intelligent IoT for the Digital World : Incorporating 5G Communications and Fog/Edge Computing Technologies. by Yang, Yang

Table of Contents: “…1.3.1 Data Collection Technologies -- 1.3.1.1 mmWave -- 1.3.1.2 Massive MIMO -- 1.3.1.3 Software Defined Networks -- 1.3.1.4 Network Slicing -- 1.3.1.5 Time Sensitive Network -- 1.3.1.6 Multi-user Access Control -- 1.3.1.7 Muti-hop Routing Protocol -- 1.3.2 Computing Power Network -- 1.3.2.1 Intelligent IoT Computing Architecture -- 1.3.2.2 Edge and Fog Computing -- 1.3.3 Intelligent Algorithms -- 1.3.3.1 Big Data -- 1.3.3.2 Artificial Intelligence -- 1.4 Typical Applications -- 1.4.1 Environmental Monitoring -- 1.4.2 Public Safety Surveillance -- 1.4.3 Military Communication…”
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Advanced reliability modeling : proceedings of the 2004 Asian International Workshop (AIWARM 2004) : Hiroshima, Japan, 26-27 August 2004

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The spirit of the hive : the mechanisms of social evolution by Page, Robert E.

Table of Contents: “…The regulatory architecture of pollen hoarding -- 9.1. Loading algorithms -- 9.2. Heritability of the pollen-hoarding syndrome -- 9.3. …”
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Stochastic optimization models in finance

Table of Contents: “…The Main Theorem and an Algorithm; V. Nonterminating Processes; ACKNOWLEDGMENT; REFERENCES; CHAPTER5. …”
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Adventures in Authentic Learning : 21 Step-by-Step Projects From an Edtech Coach. by Harrington, Kristin

Table of Contents: “…Lesson Plans -- Lesson Plan 2.1 PechaKucha Presentations -- Lesson Plan 2.2 Life Cycles Jigsaw Research Project -- Lesson Plan 2.3 Math Jigsaw Project -- Lesson Plan 2.4 Student-Created Tutorial Videos -- Coach's Connection -- CHAPTER 3: Collaborate for Success -- Some Lesser-Known Educator Sharing Tools -- Enhance Projects with Content Experts -- Provide an Authentic Audience -- Engaging Students in Peer Review -- Lesson Plans -- Lesson Plan 3.1 Algorithmic Thinking Project -- Lesson Plan 3.2 Pick Your Path Stories -- Lesson Plan 3.3 Invent It Challenge…”
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Pediatric incontinence : evaluation and clinical management

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Corporate strategy for dramatic productivity surge

Table of Contents: “…Instantaneous and spontaneous information sharing / Hiromichi Yasuoka -- ch. 7. The development of a three-minute battery charger / Toru Fujii -- ch. 8. …”
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Accelerating MATLAB with GPU computing : a primer with examples by Suh, Jung W., Kim, Youngmin

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Swift 2 design patterns : build robust and scalable iOS and Mac OS X game applications by Lange, Julien

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Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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Tumor board review : guideline and case reviews in oncology

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Concise encyclopaedia of bioinformatics and computational biology 2e

Table of Contents: “….; Akaike Information Criterion; Pedro Larranaga and Concha Bielza; Algorithm; Matthew He; Alignment (Domain Alignment, Repeats Alignment); Jaap Heringa; Alignment Score; Laszlo Patthy; Allele-Sharing Methods (Non-parametric Linkage Analysis).…”
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Networks-on-chip : from implementations to programming paradigms by Ma, Sheng, Huang, Libo, 1976-, Lai, Mingche, Shi, Wei

Table of Contents: “…Front Cover; Networks-on-Chip: From Implementations to Programming Paradigms; Copyright; Contents in Brief; Contents; Preface; About the Editor-in-Chief and Authors; Editor-in-Chief; Authors; Part I: Prologue; Chapter 1: Introduction; 1.1 The dawn of the many-core era; 1.2 Communication-centric cross-layer optimizations; 1.3 A baseline design space exploration of NoCs; 1.3.1 Topology; 1.3.2 Routing algorithm; 1.3.3 Flow control; 1.3.4 Router microarchitecture; 1.3.5 Performance metric; 1.4 Review of NoC research; 1.4.1 Research on topologies; 1.4.2 Research on unicast routing.…”
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Optimized cloud resource management and scheduling : theories and practice by Tian, Wenhong

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Open science by design : realizing a vision for 21st century research

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Integration of swarm intelligence and artificial neural network

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Large Scale Network-Centric Distributed Systems

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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Computational models of argument : Proceedings of COMMA 2012

Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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