Search Results - (((((((kent OR akant) OR wantsa) OR markant) OR cantor) OR anne) OR slave) OR hints) algorithms.

Cellular Neural Networks, Multi-scroll Chaos And Synchronization. by Yalin, Mstak E.

Table of Contents: “…Engineering applications -- 1.4 Main contributions -- Spatial-temporal pattern formation -- Implementation of n-scroll attractors from generalized Chuas circuits -- Families of scroll grid attractors -- Hyperchaotic n-scroll attractors and scroll maps -- Time-delay synchronization scheme -- Experimental confirmations of synchronization schemes -- Chaotic annealing and coupled chaotic annealing -- True random bit generation from a double scroll attractor -- Image and Video authentication on the CNN-UM -- Chapter 2 Cellular Neural/Nonlinear Networks -- 2.1 CNN -- 2.1.1 1-D CNNs -- 2.1.2 2-D CNNs -- 2.2 CNN models -- 2.2.1 Chua-Yang CNN model -- 2.2.2 State controlled CNN (SC-CNN) model -- 2.2.3 Full-range CNN model -- 2.2.4 Reaction-diffusion CNN model -- 2.2.5 Generalized CNN models -- 2.3 CNN universal machine: a visual microprocessor -- 2.4 New research directions in CNNs -- 2.4.1 Wave computing algorithm -- 2.4.2 Coupled local minimizers -- 2.4.3 Pattern formation on the ACE16k CNN Chip -- 2.4.4 Propagation of autowaves on the inhomogeneous CNN arrays -- 2.5 Conclusion -- Chapter 3 Multi-Scroll Chaotic and Hyperchaotic Attractors -- 3.1 Chuas circuit -- 3.1.1 Chaos in Chuas circuit -- 3.1.2 Realization of Chuas circuit -- 3.2 Generalized Chuas circuit -- 3.2.1 Realization of n-scroll attractors from generalized Chuas circuits -- 3.3 Families of scroll grid attractors -- 3.3.1 A new family of n-scroll attractors -- 3.3.2 2-D scroll grid attractors -- 3.3.3 3-D scroll grid attractors -- 3.3.4 Circuit realizations -- 3.3.5 Alternative realizations -- 3.4 Multi-scroll hyperchaotic attractors -- 3.4.1 Hyperchaotic n-scroll attractors -- 3.4.2 n-Scroll hypercube attractors -- 3.5 Scroll maps from n-scroll attractors -- 3.5.1 1-Scroll and 2-scroll maps -- 3.5.2 Circuit realization of a 1-scroll map -- 3.6 Lure representations -- 3.7 Conclusion -- Chapter 4 Synchronization of Chaotic Lure Systems -- 4.1 Synchronization -- Absolute stability -- 4.2 Master-slave synchronization: autonomous case -- 4.2.1 Full static state error feedback -- 4.2.2 Dynamic output feedback -- 4.3 Robust synchronization -- 4.3.1 Full static state error feedback -- 4.3.2 Dynamic output feedback -- 4.4 Time-delay synchronization scheme -- 4.4.1 Error system for the time-delay synchronization scheme -- 4.4.2 Delay-dependent synchronization criterion -- 4.4.3 Full static state feedback together with time delay -- 4.5 Nonlinear H synchronization: non-autonomous case -- 4.5.1 Full static state error feedback -- 4.5.2 Dynamic output feedback -- 4.6 Robust nonlinear H synchronization -- 4.6.1 Full static state error feedback -- 4.6.2 Dynamic output feedback -- 4.7 Impulsive synchronization -- 4.7.1 State feedback case -- 4.7.2 Measurement feedback case -- 4.8 Controller design -- 4.8.1 Master-slave synchronization -- 4.8.2 Robust synchronization -- 4.8.3 Synchronization with time-delay -- 4.8.4 N.…”
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Limits, Limits Everywhere : the Tools of Mathematical Analysis by Applebaum, David, 1956-

Table of Contents: “…Continued Fractions; 9.1 Euclid's Algorithm; 9.2 Rational and Irrational Numbers as Continued Fractions; 10. …”
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Microbiological sensors for the drinking water industry

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Eat, cook, grow : mixing human-computer interactions with human-food interactions

Table of Contents: “…"You don't have to be a gardener to do urban agriculture": understanding opportunities for designing interactive technologies to support urban food production / William Odom -- Augmented agriculture, algorithms, aerospace, and alimentary architectures / Jordan Geiger -- The allure of provenance: tracing food through user-generated production information / Ann Light -- Beyond gardening: a new approach to HCI and urban agriculture / Tad Hirsch -- Hungry for data: metabolic interaction from farm to fork to phenotype / Marc Tuters and Denisa Kera -- Food futures: three provocations to challenge HCI interventions / Greg Hearn and David Lindsay Wright -- Bringing technology to the dining table / Charles Spence -- List of recipes.…”
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A cabinet of philosophical curiosities : a collection of puzzles, oddities, riddles, and dilemmas by Sorensen, Roy A.

Table of Contents: “…Cover; Half-Title; Series; Title; Copyright; Contents; Dedication; Introduction; Conform to Confound; Razing Hopes; Hidden Messages in Songs; A Blessed Book Curse; Listen for a Counterexample; Schopenhauer's Intelligence Test; A Knucklehead on My Premises; The Tversky Intelligence Test; A Matter of Life and Death; The Identity of Indiscernibles; Indiscernible Pills; Telling a Clover from a Plover; The Emotional Range of Logicians; A Pebble from the Baths of Caracalla; Assassination Proof; How to Succeed Your Successor; Not All Logicians Are Saints; Lewis Carroll's Peek at Meno's Slave Boy.…”
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Debates in the digital humanities 2016

Table of Contents: “…Father Busa Female Punch Card Operatives / Melissa Terras and Julianne Nyhan -- On the Origin of "Hack" and "Yack" / Bethany Nowviskie -- Reflections on a Movement: #transformDH, Growing Up / Moya Bailey, Anne Cong-Huyen, Alexis Lothian, and Amanda Phillips.…”
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Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. by Zaccone, Giancarlo

Table of Contents: “…; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs).…”
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Dermatologic principles and practice in oncology : conditions of the skin, hair, and nails in cancer patients

Table of Contents: “…Borovicka, Jennifer R.S. Gordon, Ann Cameron Haley, Nicole E. Larson and Dennis P. …”
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Computational ecology : artificial neural networks and their applications by Zhang, Wenjun

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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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World's greatest architect : making, meaning, and network culture by Mitchell, William J. (William John), 1944-2010

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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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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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Power and energy systems III : selected, peer reviewed papers from the 2013 3rd International Conference on Power and Energy Systems (ICPES 2013), November 23-24, Bangkok, Thailand

Table of Contents: “…Appropriate Electric Energy Conservation Measures for Big Mosques in Riyadh CityBayesian Algorithm Based on Airborne Power Supply System; Study on the Cooling System of Super-Capacitors for Hybrid Electric Vehicle; A Charging Management of Electric Vehicles Based on Campus Survey Data; Back-EMF Position Detection Technology for Brushless DC Motor; Stress State of Turbine Blade Root and Rim Considering Manufacturing Variations; Analysis on a Gas Turbine Sealing Disk Structure and Material Strength; A Kind of Adjustable Electric Heating Pipe Power Electrode Preparation Equipment.…”
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Mathematical Methods in Interdisciplinary Sciences. by Chakraverty, Snehashish

Table of Contents: “…1.2.2.1 Architecture of Single-Layer LgNN Model -- 1.2.2.2 Training Algorithm of Laguerre Neural Network (LgNN) -- 1.2.2.3 Gradient Computation of LgNN -- 1.3 Methodology for Solving a System of Fredholm Integral Equations of Second Kind -- 1.3.1 Algorithm -- 1.4 Numerical Examples and Discussion -- 1.4.1 Differential Equations and Applications -- 1.4.2 Integral Equations -- 1.5 Conclusion -- References -- Chapter 2 Deep Learning in Population Genetics: Prediction and Explanation of Selection of a Population -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Dataset Description…”
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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.…”
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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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Energy storage for sustainable microgrid by Gao, David Wenzhong

Table of Contents: “…1.2.4.2 Weighted Least Squares Estimation1.2.4.3 Newton-Raphson Algorithm; 1.3 Microgrid Control Methods; 1.3.1 PQ Control; 1.3.2 V/f Control; 1.3.3 Droop Control; 1.3.3.1 Active Power Control; 1.3.3.2 Voltage Control; 1.4 Control Architectures in Microgrids; 1.4.1 Master-Slave Control; 1.4.2 Peer-to-peer Control; 1.4.3 Hierarchy Control; 1.5 Microgrid Protection; 1.6 Three-Phase Circuit for Grid-Connected DG; 1.6.1 LC Filter; 1.6.2 Isolation Transformer; 1.7 Energy Storage Technology in Renewable Microgrids; 1.7.1 Batteries; 1.7.1.1 Lead-Acid Batteries; 1.7.1.2 Lithium-Ion Batteries.…”
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Pediatric incontinence : evaluation and clinical management

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CUDA Application Design and Development. by Farber, Rob

Table of Contents: “…Multiple GPUs -- Explicit Synchronization -- Implicit Synchronization -- The Unified Virtual Address Space -- A Simple Example -- Profiling Results -- Out-of-Order Execution with Multiple Streams -- Tip for Concurrent Kernel Execution on the Same GPU -- Atomic Operations for Implicitly Concurrent Kernels -- Tying Data to Computation -- Manually Partitioning Data -- Mapped Memory -- How Mapped Memory Works -- Summary -- 8 CUDA for All GPU and CPU Applications -- Pathways from CUDA to Multiple Hardware Backends -- The PGI CUDA x86 Compiler -- The PGI CUDA x86 Compiler -- An x86 core as an SM -- The NVIDIA NVCC Compiler -- Ocelot -- Swan -- MCUDA -- Accessing CUDA from Other Languages -- SWIG -- Copperhead -- EXCEL -- MATLAB -- Libraries -- CUBLAS -- CUFFT -- MAGMA -- phiGEMM Library -- CURAND -- Summary -- 9 Mixing CUDA and Rendering -- OpenGL -- GLUT -- Mapping GPU Memory with OpenGL -- Using Primitive Restart for 3D Performance -- Introduction to the Files in the Framework -- The Demo and Perlin Example Kernels -- The Demo Kernel -- The Demo Kernel to Generate a Colored Sinusoidal Surface -- Perlin Noise -- Using the Perlin Noise Kernel to Generate Artificial Terrain -- The simpleGLmain.cpp File -- The simpleVBO.cpp File -- The callbacksVBO.cpp File -- Summary -- 10 CUDA in a Cloud and Cluster Environments -- The Message Passing Interface (MPI) -- The MPI Programming Model -- The MPI Communicator -- MPI Rank -- Master-Slave -- Point-to-Point Basics -- How MPI Communicates -- Bandwidth -- Balance Ratios -- Considerations for Large MPI Runs -- Scalability of the Initial Data Load -- Using MPI to Perform a Calculation -- Check Scalability -- Cloud Computing -- A Code Example -- Data Generation -- Summary -- 11 CUDA for Real Problems -- Working with High-Dimensional Data -- PCA/NLPCA -- Multidimensional Scaling -- K-Means Clustering.…”
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