Search Results - (((((((want OR kwantis) OR wwantis) OR markant) OR cantor) OR anne) OR share) OR hints) algorithms.

A Primer on Machine Learning Applications in Civil Engineering by Deka, Paresh Chandra

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.…”
Full text (MFA users only)

New Horizons in Mobile and Wireless Communications, Volume 1 : Radio Interfaces. by Prasad, Ramjee

Table of Contents: “…1.3 Research and Standardization Activities Toward New Radio Interfaces1.3.1 European-Funded Research Activities; 1.3.2 Other Activities; 1.4 Preview of the Book; References; Chapter 2 Spectrum-Efficient Radio Interface Technologies; 2.1 Introduction; 2.1.1 Radio Interfaces for Ubiquitous Communications; 2.1.2 OFDM-Based Radio Interfaces in the Scope of Next Generation Systems; 2.1.3 Coexistence and Spectrum Sharing; 2.1.4 Opportunities for Secondary Spectrum Use; 2.1.5 Multiband Transmissions; 2.2 Radio Interfaces Optimized for PANs; 2.2.1 Scenarios and Radio Propagation Models for PANs.…”
Full text (MFA users only)

Heterogeneous networks in LTE-Advanced by Acharya, Joydeep

Table of Contents: “…Chapter 3 LTE Signal Structure and Physical Channels 3.1 Introduction; 3.2 LTE Signal Structure; 3.3 Introduction to LTE Physical Channels and Reference Signals; 3.4 Resource Block Assignment; 3.5 Downlink Physical Channels; 3.5.1 Physical Broadcast Channel (PBCH); 3.5.2 Physical Downlink Shared Channel (PDSCH); 3.5.3 Physical Multicast Channel (PMCH); 3.5.4 Physical Control Format Indicator Channel (PCFICH); 3.5.5 Physical Hybrid ARQ Indicator Channel (PHICH); 3.5.6 Physical Downlink Control Channel (PDCCH); 3.6 Uplink Physical Channels; 3.6.1 Physical Uplink Shared Channel (PUSCH).…”
Full text (MFA users only)

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…”
Full text (MFA users only)

Biomedical image analysis recipes in MATLAB : for life scientists and engineers by Reyes-Aldasoro, Constantino Carlos

Full text (MFA users only)

Intelligent IoT for the Digital World : Incorporating 5G Communications and Fog/Edge Computing Technologies. by Yang, Yang

Table of Contents: “…1.4.4 Intelligent Manufacturing and Interactive Design -- 1.4.5 Autonomous Driving and Vehicular Networks -- 1.5 Requirements and Challenges for Intelligent IoT Services -- 1.5.1 A Generic and Flexible Multi-tier Intelligence IoT Architecture -- 1.5.2 Lightweight Data Privacy Management in IoT Networks -- 1.5.3 Cross-domain Resource Management for Intelligent IoT Services -- 1.5.4 Optimization of Service Function Placement, QoS, and Multi-operator Network Sharing for Intelligent IoT Services -- 1.5.5 Data Time stamping and Clock Synchronization Services for Wide-area IoT Systems -- 1.6 Conclusion…”
Full text (MFA users only)

Revolutionary routines : the habits of social transformation by Pedwell, Carolyn

Full text (MFA users only)

Stochastic optimization models in finance

Table of Contents: “…The Main Theorem and an Algorithm; V. Nonterminating Processes; ACKNOWLEDGMENT; REFERENCES; CHAPTER5. …”
Full text (MFA users only)

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…”
Full text (MFA users only)

Pediatric incontinence : evaluation and clinical management

Full text (MFA users only)

Tumor board review : guideline and case reviews in oncology

Full text (MFA users only)

Normal and abnormal fetal face atlas : ultrasonographic features by Levaillant, Jean-Marc, Bault, Jean-Philippe, Benoit, Bernard, Couly, G. (Gérard)

Full text (MFA users only)

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. …”
Full text (MFA users only)

Systemic architecture : operating manual for the self-organizing city by Poletto, Marco

Full text (MFA users only)

Big Data Analytics with R. by Walkowiak, Simon

Full text (MFA users only)

Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
Full text (MFA users only)

Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
Full text (MFA users only)

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.…”
Full text (MFA users only)

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).…”
Full text (MFA users only)

CUDA Application Design and Development. by Farber, Rob

Table of Contents: “…The Nsight Timeline Analysis -- The NVTX Tracing Library -- Scaling Behavior of the CUDA API -- Tuning and Analysis Utilities (TAU) -- Summary -- 4 The CUDA Execution Model -- GPU Architecture Overview -- Thread Scheduling: Orchestrating Performance and Parallelism via the Execution Configuration -- Relevant computeprof Values for a Warp -- Warp Divergence -- Guidelines for Warp Divergence -- Relevant computeprof Values for Warp Divergence -- Warp Scheduling and TLP -- Relevant computeprof Values for Occupancy -- ILP: Higher Performance at Lower Occupancy -- ILP Hides Arithmetic Latency -- ILP Hides Data Latency -- ILP in the Future -- Relevant computeprof Values for Instruction Rates -- Little's Law -- CUDA Tools to Identify Limiting Factors -- The nvcc Compiler -- Launch Bounds -- The Disassembler -- PTX Kernels -- GPU Emulators -- Summary -- 5 CUDA Memory -- The CUDA Memory Hierarchy -- GPU Memory -- L2 Cache -- Relevant computeprof Values for the L2 Cache -- L1 Cache -- Relevant computeprof Values for the L1 Cache -- CUDA Memory Types -- Registers -- Local memory -- Relevant computeprof Values for Local Memory Cache -- Shared Memory -- Relevant computeprof Values for Shared Memory -- Constant Memory -- Texture Memory -- Relevant computeprof Values for Texture Memory -- Global Memory -- Common Coalescing Use Cases -- Allocation of Global Memory -- Limiting Factors in the Design of Global Memory -- Relevant computeprof Values for Global Memory -- Summary -- 6 Efficiently Using GPU Memory -- Reduction -- The Reduction Template -- A Test Program for functionReduce.h -- Results -- Utilizing Irregular Data Structures -- Sparse Matrices and the CUSP Library -- Graph Algorithms -- SoA, AoS, and Other Structures -- Tiles and Stencils -- Summary -- 7 Techniques to Increase Parallelism -- CUDA Contexts Extend Parallelism -- Streams and Contexts.…”
Full text (MFA users only)