Search Results - (((((((want OR kwantis) OR mantis) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

QoS and Energy Management in Cognitive Radio Network : Case Study Approach. by Mishra, Vishram

Table of Contents: “…; 1.2 Spectrum Regulation; 1.2.1 Licensed Spectrum; 1.2.2 Unlicensed Spectrum; 1.2.3 Open Spectrum; 1.3 Opportunistic Spectrum Usage; 1.4 Software Defined Radio and Cognitive Radio; 1.4.1 IEEE Groups Working on Spectrum Sharing; 1.4.2 Cognition Cycle; 1.4.3 Cognitive Engine and Framework; 1.4.4 Cognitive Radio Network; 1.5 Quality of Service (QoS); 1.5.1 QoS Provisioning for Latency Guarantee; 1.5.2 QoS Provisioning for Throughput Guarantee; 1.6 Channel Selection Techniques in Cognitive Radio Network.…”
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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.…”
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Business Hack : the Wealth Dragon Way to Build a Successful Business in the Digital Age. by Lee, John

Table of Contents: “…Out with the Old, in with the NewThe AI Revolution; Think Laterally; Who Wants to Live Forever?; Who's the Expert Now?; Which Future?…”
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Principles of artificial neural networks by Graupe, Daniel

Table of Contents: “…Fundamentals of biological neural networks -- ch. 3. Basic principles of ANNs and their early structures. 3.1. Basic principles of ANN design. 3.2. …”
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Artificial intelligence in society.

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Advanced Python Programming : Build High Performance, Concurrent, and Multi-Threaded Apps with Python Using Proven Design Patterns. by Lanaro, Gabriele

Table of Contents: “…Cover; Title Page; Copyright; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Benchmarking and Profiling; Designing your application; Writing tests and benchmarks; Timing your benchmark; Better tests and benchmarks with pytest-benchmark; Finding bottlenecks with cProfile; Profile line by line with line_profiler; Optimizing our code; The dis module; Profiling memory usage with memory_profiler; Summary; Chapter 2: Pure Python Optimizations; Useful algorithms and data structures; Lists and deques; Dictionaries; Building an in-memory search index using a hash map; Sets; Heaps…”
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Haptic Feedback Teleoperation of Optical Tweezers by Ni, Zhenjiang

Table of Contents: “…Specific designs for haptic interactions; 1.4.1. Temporal sharing; 1.4.2. Spatial sharing; 1.5. Discussion; 1.6. …”
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Machine Learning with Spark - Second Edition. by Dua, Rajdeep

Table of Contents: “…Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset -- Evaluating the performance of classification models -- Accuracy and prediction error -- Precision and recall -- ROC curve and AUC -- Improving model performance and tuning parameters -- Feature standardization -- Additional features -- Using the correct form of data -- Tuning model parameters -- Linear models -- Iterations -- Step size -- Regularization -- Decision trees -- Tuning tree depth and impurity -- The naive Bayes model -- Cross-validation -- Summary -- Chapter 7: Building a Regression Model with Spark -- Types of regression models -- Least squares regression -- Decision trees for regression -- Evaluating the performance of regression models -- Mean Squared Error and Root Mean Squared Error -- Mean Absolute Error -- Root Mean Squared Log Error -- The R-squared coefficient -- Extracting the right features from your data -- Extracting features from the bike sharing dataset -- Training and using regression models -- BikeSharingExecutor -- Training a regression model on the bike sharing dataset -- Linear regression -- Generalized linear regression -- Decision tree regression -- Ensembles of trees -- Random forest regression -- Gradient boosted tree regression -- Improving model performance and tuning parameters -- Transforming the target variable -- Impact of training on log-transformed targets -- Tuning model parameters -- Creating training and testing sets to evaluate parameters -- Splitting data for Decision tree -- The impact of parameter settings for linear models -- Iterations -- Step size -- L2 regularization -- L1 regularization -- Intercept -- The impact of parameter settings for the decision tree -- Tree depth -- Maximum bins -- The impact of parameter settings for the Gradient Boosted Trees -- Iterations -- MaxBins -- Summary.…”
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World's greatest architect : making, meaning, and network culture by Mitchell, William J. (William John), 1944-2010

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Resources utilization and productivity enhancement case studies by Mital, Anil, Pennathur, Arunkumar

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AI and human thought and emotion by Freed, Sam (Philosophy professor)

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Cinder Creative Coding Cookbook. by Dawid Gorny, Rui Madeira

Table of Contents: “…Creating a simple video controllerSaving window content as an image; Saving window animations as video; Saving window content as a vector graphics image; Saving high resolution images with the tile renderer; Sharing graphics between applications; Building Particle Systems; Introduction; Creating a particle system in 2D; Applying repulsion and attraction forces; Simulating particles flying in the wind; Simulating flocking behavior; Making our particles sound reactive; Aligning particles to a processed image; Aligning particles to the mesh surface; Creating springs.…”
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Frontiers of Artificial Intelligence in Medical Imaging. by Razmjooy, Navid

Table of Contents: “…5.5 Electromagnetic field optimization algorithm -- 5.6 Developed electromagnetic field optimization algorithm -- 5.7 Simulation results -- 5.7.1 Image acquisition -- 5.7.2 Pre-processing stage -- 5.7.3 Processing stage -- 5.7.4 Classification -- 5.8 Final evaluation -- 5.9 Conclusions -- References -- Chapter 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation -- 6.1 Introduction -- 6.2 Context -- 6.3 Methodology -- 6.3.1 COVID-19 database -- 6.3.2 Image conversion and pre-processing -- 6.3.3 Image thresholding…”
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Proceedings of the 2009 International Conference on Computer and Network Technology, Chennai, India, 24-26 July 2009

Table of Contents: “…An Ownership Sharing Protocol for RFID without a Trusted Third Party G. …”
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FRBR, before and after : a look at our bibliographic models by Coyle, Karen

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Signal processing for cognitive radios by Jayaweera, Sudharman K., 1972-

Table of Contents: “…8.1 INTRODUCTION8.2 K-MEANS CLASSIFICATION ALGORITHM; 8.3 X-MEANS CLASSIFICATION ALGORITHM; 8.4 DIRICHLET PROCESS MIXTURE MODEL; 8.5 BAYESIAN NONPARAMETRIC CLASSIFICATION BASED ON THE DPMM AND THE GIBBS SAMPLING; 8.6 SUMMARY; PART III: SIGNAL PROCESSING IN COGNITIVE RADIOS; 9 WIDEBAND SPECTRUM SENSING; 9.1 INTRODUCTION; 9.2 WIDEBAND SPECTRUM SENSING PROBLEM; 9.3 WIDEBAND SPECTRUM SCANNING PROBLEM; 9.4 SPECTRUM SEGMENTATION AND SUBBANDING; 9.5 WIDEBAND SPECTRUM SENSING RECEIVER; 9.6 SUBBAND SELECTION PROBLEM IN WIDEBAND SPECTRUM SENSING.…”
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C# 7 and .NET Core 2.0 High Performance : Build highly performant, multi-threaded, and concurrent applications using C# 7 and .NET Core 2.0. by Mehboob Ahmed Khan, Ovais

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Advances in Computers, Electronics and Mechatronics : Selected, Peer Reviewed Papers from the 2014 International Forum on Computers, Electronics and Mechatronics (IFCEM 2014), Augu...

Table of Contents: “…Similarity Measure between Vague Sets Based on ProductsThe Decision-Making Method of Web Services Composition Based on Action Patterns of Open Fuzzy Petri Net; Barcode-Based Service-Oriented Material Flow Technology for Complex Product Assembly; Research on the Influence of Combination Information-Sharing on Supply Chain Performance; Design of Logistics Management System Based on Workflow; Research on the Change of Automobile Marketing Mode with Development of Mobile Internet.…”
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Mastering mobile learning

Table of Contents: “…Mastering Mobile Learning: Tips and Techniques for Success; Copyright; Contents; Foreword; Preface; Introduction; Part 1: Understanding Mobile Learning; Chapter 1: Enterprise Mobile Learning: A Primer; Business Drivers of Mobile Learning; The Mobile Learning Ecosystem; Mobile Learning Applications; Content Transmission and Retrieval; Capturing Data; Communicating and Interacting with Others; Computing Algorithms; Contextual Inquiry; Designing and Creating Mobile Learning Content; Chapter 2: The Seven Shifts in Enterprise Learning; 1. …”
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Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R by Rai, Bharatendra K.

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