Search Results - ("algorithm" OR "algorithms")
Suggested Topics within your search.
Suggested Topics within your search.
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3101
Milling simulation : metal milling mechanics, dynamics and clamping principles
Published 2016Full text (MFA users only)
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3102
Anesthesia and perioperative care for organ transplantation
Published 2017Full text (MFA users only)
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3103
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3106
Test Signal Generation for Service Diagnosis Based on Local Structure Graphs.
Published 2015Full text (MFA users only)
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3107
Global flood hazard : applications in modeling, mapping and forecasting
Published 2018Full text (MFA users only)
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3108
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3109
Big data analytics and computing for digital forensic investigations
Published 2020Full text (MFA users only)
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3110
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3111
Smart grid communication infrastructures : big data, cloud computing, and security
Published 2018Table of Contents: “…3.4.3 Evaluation of the Global Uplink Transmission Rates 58 -- 3.4.4 Evaluation of the Global Power Consumption 59 -- 3.4.5 Evaluation of the Minimum Cost Problem 59 -- 3.5 Case Study 63 -- 3.6 Summary 65 -- 4 Reliable Energy-Efficient Uplink Transmission Power Control Scheme in NAN 67 -- 4.1 Background and RelatedWork 67 -- 4.1.1 Motivations and Background 67 -- 4.1.2 RelatedWork 69 -- 4.2 SystemModel 70 -- 4.3 Preliminaries 71 -- 4.3.1 Mathematical Formulation 72 -- 4.3.2 Energy Efficiency Utility Function 73 -- 4.4 Hierarchical Uplink Transmission Power Control Scheme 75 -- 4.4.1 DGD Level Game 76 -- 4.4.2 BGD Level Game 77 -- 4.5 Analysis of the Proposed Schemes 78 -- 4.5.1 Estimation of B and D 78 -- 4.5.2 Analysis of the Proposed Stackelberg Game 80 -- 4.5.3 Algorithms to Approach NE and SE 84 -- 4.6 Numerical Results 85 -- 4.6.1 Simulation Settings 85 -- 4.6.2 Estimate of D and B 86 -- 4.6.3 Data Rate Reliability Evaluation 87 -- 4.6.4 Evaluation of the Proposed Algorithms to Achieve NE and SE 88 -- 4.7 Summary 90 -- 5 Design and Analysis of a Wireless Monitoring Network for Transmission Lines in the Smart Grid 91 -- 5.1 Background and RelatedWork 91 -- 5.1.1 Background and Motivation 91 -- 5.1.2 RelatedWork 93 -- 5.2 Network Model 94 -- 5.3 Problem Formulation 96 -- 5.4 Proposed Power Allocation Schemes 99 -- 5.4.1 Minimizing Total Power Usage 100 -- 5.4.2 Maximizing Power Efficiency 101 -- 5.4.3 Uniform Delay 104 -- 5.4.4 Uniform Transmission Rate 104 -- 5.5 Distributed Power Allocation Schemes 105 -- 5.6 Numerical Results and A Case Study 107 -- 5.6.1 Simulation Settings 107 -- 5.6.2 Comparison of the Centralized Schemes 108 -- 5.6.3 Case Study 111 -- 5.7 Summary 113 -- 6 A Real-Time Information-Based Demand-Side Management System 115 -- 6.1 Background and RelatedWork 115 -- 6.1.1 Background 115 -- 6.1.2 RelatedWork 117 -- 6.2 System Model 118 -- 6.2.1 The Demand-Side Power Management System 118 -- 6.2.2 MathematicalModeling 120 -- 6.2.3 Energy Cost and Unit Price 122.…”
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3112
Energy efficient technologies for sustainability : selected, peer reviewed papers from the International Conference on Energy Efficient Technologies for Sustainability (ICEETS 2013...
Published 2013Table of Contents: “…Seyezhai -- Modeling of Photovoltaic Array and Simulation of MPPT Algorithm / R. Ramprakash -- A Review of Mathematical Models for Performance Analysis of Hybrid Solar Photovoltaic -- Thermal (PV/T) Air Heating Systems / G. …”
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3113
Radar for Fully Autonomous Driving.
Published 2022Table of Contents: “…4.2.1 Waveform Orthogonality via TDM -- 4.2.2 Waveform Orthogonality via DDM -- 4.2.3 Waveform Orthogonality via FDM -- 4.3 Angle Finding in Automotive MIMO Radar -- 4.3.1 High Resolution Angle Finding with ULA -- 4.3.2 High Resolution Angle Finding with SLA -- 4.4 High Resolution Imaging Radar for Autonomous Driving -- 4.4.1 Cascade of Multiple Radar Transceivers -- 4.4.2 Examples of Cascaded Imaging Radars -- 4.4.3 Design Challenges of Imaging Radar -- 4.5 Challenges in Automotive MIMO Radar -- 4.5.1 Angle Finding in the Presence of Multipath Reflections -- 4.5.2 Waveform Orthogonality in Automotive MIMO Radar -- 4.5.3 Efficient, High Resolution Angle Finding Algorithms Are Needed -- References -- Chapter 5 Synthetic Aperture Radar for Automotive Applications -- 5.1 Introduction -- 5.1.1 Historical Background -- 5.1.2 Comparison to Traditional Radar Systems -- 5.1.3 SAR and Point Cloud Imaging Performance -- 5.1.4 Applications for Automotive Use -- 5.2 Mathematical Foundation -- 5.2.1 Key Assumptions -- 5.2.2 Signal Model -- 5.2.3 Slow Time -- 5.3 Building an Automotive SAR -- 5.3.1 Measuring Ego-Motion -- 5.3.2 SAR Image Formation -- 5.3.3 Coexistence with Point Cloud Pipeline -- 5.3.4 Elevation Information -- 5.4 Future Directions -- 5.4.1 Forward-Facing SAR -- 5.4.2 SAR for Moving Objects -- 5.4.3 Gapped SAR -- 5.5 Conclusion -- References -- Chapter 6 Radar Transceiver Technologies -- 6.1 Background and Introduction to Automotive Radar -- 6.2 Block Diagram Overview of an FMCW Radar Transceiver -- 6.3 Challenges with Deeply Scaled CMOS -- 6.4 Active Devices in CMOS -- 6.5 Passives in CMOS -- 6.6 Circuit Architectures Suitable for Advanced CMOS -- 6.6.1 The Transmit Power Amplifier -- 6.6.2 The TX Phase Shifter -- 6.7 The LO/FMCW Chirp Generator -- 6.8 The Receiver Signal Chain -- 6.8.1 RX Frontend -- 6.8.2 Radar RX Baseband -- 6.9 Summary.…”
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3114
Advances in applied materials and electronics engineering III : selected, peer reviewed papers from the 2014 3rd International Conference on Applied Materials and Electronics Engin...
Published 2014Full text (MFA users only)
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3115
Application of diamond and related materials : selected, peer reviewed papers from the 4th Conference on Application of Diamond and Related Materials in China (CADRM2010) and the 1...
Published 2011Full text (MFA users only)
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3116
Binary decision diagrams and extensions for system reliability analysis
Published 2015Table of Contents: “…4.6.1 Prime Implicants Based Method -- 4.6.2 BDD Based Method -- 4.7 Disjoint Failures -- 4.8 Dependent Failures -- 4.8.1 Common-Cause Failures (CCFs) -- 4.8.2 Functional Dependent Failures -- 5 Phased-Mission Systems -- 5.1 System Description -- 5.2 Rules of Phase Algebra -- 5.3 BDD-Based Method for PMS Analysis -- 5.3.1 Input Variable Ordering -- 5.3.2 Single-Phase BDD Generation -- 5.3.3 PMS BDD Generation -- 5.3.4 PMS BDD Evaluation -- 5.4 Mission Performance Analysis -- 6 Multi-State Systems -- 6.1 Assumptions -- 6.2 An Illustrative Example -- 6.3 MSS Representation -- 6.3.1 MSS Representation Using MFT -- 6.3.2 MSS Representation Using MRBD -- 6.3.3 Equivalency of MRBD and MFT Representations -- 6.4 Multi-State BDD (MBDD) -- 6.4.1 Step 1 -- State Variable Encoding -- 6.4.2 Step 2 -- Generating MBDD from MFT -- 6.4.3 Step 3 -- MBDD Evaluation -- 6.4.4 Example Illustration -- 6.5 Logarithmically-Encoded BDD (LBDD) -- 6.5.1 Step 1 -- Variable Encoding -- 6.5.2 Step 2 -- Generating LBDD from MFT -- 6.5.3 Step 3 -- LBDD Evaluation -- 6.5.4 Example Illustration -- 6.6 Multi-State Multi-Valued Decision Diagrams (MMDD) -- 6.6.1 Step 1 -- Variable Encoding -- 6.6.2 Step 2 -- Generating MMDD from MFT -- 6.6.3 Step 3 -- MMDD Evaluation -- 6.6.4 Example Illustration -- 6.7 Performance Evaluation and Benchmarks -- 6.7.1 Example Analyses -- 6.7.2 Benchmark Studies -- 6.7.3 Performance Comparison and Discussions -- 6.7.3.1 Comparing Model Size -- 6.7.3.2 Comparing Runtime Complexity of Model Construction -- 6.7.3.3 Comparing Runtime Complexity of Model Evaluation -- 6.8 Summary -- 7 Fault Tolerant Systems and Coverage Models -- 7.1 Basic Types -- 7.2 Imperfect Coverage Model -- 7.3 Applications to Binary-State Systems -- 7.3.1 BDD Expansion Method -- 7.3.2 Simple and Efficient Algorithm -- 7.4 Applications to Multi-State Systems.…”
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3117
Machine learning for protein subcellular localization prediction
Published 2015Table 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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3119
High Accuracy Computing Methods : Fluid Flows and Wave Phenomena
Published 2013Table of Contents: “…Parallel and cluster computing -- 1.3.2. Algorithmic issues of HPC -- 1.4.Computational Fluid Mechanics -- 1.5. …”
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3120
Reproducibility : principles, problems, practices, and prospects
Published 2016Table of Contents: “…6.3 Extending BMS (and NML#): BMS* -- 6.4 Replication Variance and Reproducibility -- 6.4.1 Within- and Between-Setting Replication Variance and the True State of the World -- 6.4.2 Reproducibility -- 6.4.3 A Toy Example -- 6.5 Final Remark -- References -- Chapter 7 Reproducibility from the Perspective of Meta-Analysis -- 7.1 Introduction -- 7.2 Basics of Meta-Analysis -- 7.2.1 Conceptual Preliminaries -- 7.2.2 Systematic Reviews -- 7.2.3 Fixed-Effects and Random-Effects Meta-Analysis -- 7.2.4 Biases in Meta-Analysis -- 7.3 Meta-Analysis of Mind-Matter Experiments: A Case Study -- 7.3.1 Statistical Modeling -- 7.3.2 Analysis of the Ramp -- N Data -- 7.4 Summary -- References -- Chapter 8 Why Are There So Many Clustering Algorithms, and How Valid Are Their Results? -- 8.1 Introduction -- 8.1.1 Data Mining and Knowledge Discovery -- 8.1.2 Choices and Assumptions -- 8.2 Supervised and Unsupervised Learning -- 8.3 Cluster Validity as Easiness in Classification -- 8.3.1 Instance Easiness for Supervised Learning -- 8.3.2 Clustering-Quality Measures Based on Supervised Learning -- 8.3.3 Using the Clustering-Quality Measures mp and mc -- 8.4 Applying Clustering-Quality Measures to Data -- 8.4.1 Clustering Based on Prediction Strength -- 8.4.2 Studies with Synthetic Data -- 8.4.3 Studies with Empirical Data -- 8.5 Other Clustering Models -- 8.5.1 Hierarchical Clustering -- 8.5.2 Fuzzy Clustering -- 8.6 Summary -- References -- Part III: Physical Sciences -- Chapter 9 Facilitating Reproducibility in ScientificComputing: Principles and Practice -- 9.1 Introduction -- 9.2 A Culture of Reproducibility -- 9.2.1. …”
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