Search Results - ("algorithm" OR "algorithms")

Milling simulation : metal milling mechanics, dynamics and clamping principles by Zhang, Weihong

Full text (MFA users only)

Anesthesia and perioperative care for organ transplantation

Full text (MFA users only)

Bayesian Analysis with Python. by Osvaldo Martin

Full text (MFA users only)

Robot manipulator redundancy resolution by Zhang, Yunong, Jin, Long, 1988-

Full text (MFA users only)

Handbook of venous thromboembolism

Full text (MFA users only)

Test Signal Generation for Service Diagnosis Based on Local Structure Graphs. by Ungermann, Michael

Full text (MFA users only)

Global flood hazard : applications in modeling, mapping and forecasting

Full text (MFA users only)

Optical cryptosystems by Nishchal, Naveen K.

Full text (MFA users only)

Big data analytics and computing for digital forensic investigations

Full text (MFA users only)

The ABSITE review by Fiser, Steven M., 1971-

Full text (MFA users only)

Smart grid communication infrastructures : big data, cloud computing, and security by Ye, Feng, 1989-, Qian, Yi, 1962-, Hu, Rose Qingyang

Table 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.…”
Full text (MFA users only)

Energy efficient technologies for sustainability : selected, peer reviewed papers from the International Conference on Energy Efficient Technologies for Sustainability (ICEETS 2013...

Table 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. …”
Full text (MFA users only)

Radar for Fully Autonomous Driving. by Markel, Matt

Table 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.…”
Full text (MFA users only)

Advances in applied materials and electronics engineering III : selected, peer reviewed papers from the 2014 3rd International Conference on Applied Materials and Electronics Engin...

Full text (MFA users only)

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...

Full text (MFA users only)

Binary decision diagrams and extensions for system reliability analysis by Xing, Liudong

Table 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.…”
Full text (MFA users only)

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.…”
Full text (MFA users only)

Prognostics and Health Management : a Practical Approach to Improving System Reliability Using Condition-Based Data. by Goodman, Douglas

Full text (MFA users only)

High Accuracy Computing Methods : Fluid Flows and Wave Phenomena by Sengupta, Tapan

Table of Contents: “…Parallel and cluster computing -- 1.3.2. Algorithmic issues of HPC -- 1.4.Computational Fluid Mechanics -- 1.5. …”
Full text (MFA users only)

Reproducibility : principles, problems, practices, and prospects

Table 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. …”
Full text (MFA users only)