Search Results - (((((((kant OR want) OR mantis) OR wien) OR cantor) OR anne) OR shape) OR hints) algorithms.

Signal Design for Modern Radar Systems. by Alaee-Kerahroodi, Mohammad

Table of Contents: “…Intro -- Introduction -- Practical Signal Design -- The Why -- The How -- The What -- Radar Application Focus Areas -- Designing Signals with Good Correlation Properties -- Signal Design to Enhance SINR -- Spectral Shaping and Coexistence with Communications -- Automotive Radar Signal Processing and Sensing for Autonomous Vehicles -- What this Book Offers -- References -- Convex and Nonconvex Optimization -- Optimization Algorithms -- Gradient Descent Algorithm -- Newton's Method -- Mirror Descent Algorithm -- Power Method-Like Iterations -- Majorization-Minimization Framework…”
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Focus on artificial neural networks

Table of Contents: “…ARTIFICIAL NEURAL NETWORKS (ANNS) -- 3. MICROEMULSIONS -- 4. APPLICATION OF ANNS IN THE DEVELOPMENT OF MICROEMULSION DRUG DELIVERY SYSTEMS -- 4.1. …”
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Industrial control systems

Table of Contents: “…EXTRACTIVE FERMENTATION PROCESS FOR BIOETHANOL PRODUCTION ; 3. PLANT MODEL BASED ON ANN ; 3.1. ANN Configurations.…”
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Regression Analysis with Python. by Massaron, Luca, Boschetti, Alberto

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Spatial Statistics and Digital Image Analysis.

Table of Contents: “…""Posterior Distribution""""Model Identification""; ""Attribute Estimation""; ""Algorithms""; ""2.4.2 Examples""; ""Image Restoration""; ""Boundary Detection""; ""Single Photon Emission Tomography""; ""Shape-From-Shading""; ""Deformable Templates""; ""Bibliography""; ""3 Oceanographic and Atmospheric Applications of Spatial Statistics and Digital Image Analysis""; ""3.1 INTRODUCTION""; ""3.2 SELECTED ANALYSIS AREAS""; ""3.2.1 Principal Component Analysis""; ""General Concepts""; ""Multispectral Data Application""; ""Multi-Temporal Data Application""…”
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Mathematical Research in Materials Science : Opportunities and Perspectives. by Staff, National Research Council

Table of Contents: “…""STIFF POLYMERS AND LIQUID CRYSTALS""""OTHER PROBLEMS""; ""4 EVOLUTION OF MICROSTRUCTURES""; ""INTRODUCTION""; ""SPINODAL DECOMPOSITION AND NUCLEATION""; ""GRAIN GROWTH AND OTHER INTERFACE MOTION CONTROLLED BY INTERFACE KINETICS""; ""Computer Algorithms""; ""SHAPE EVOLUTION CONTROLLED BY SURFACE DIFFUSION""; ""MORPHOLOGICAL STABILITY""; ""Phase Transformations and Pattern Formation""; ""Dendritic Growth""; ""MUSHY ZONES""; ""PRECIPITATION AND COARSENING""; ""Evolution of Microstructures; Stress and Current Effects""; ""MARTENSITE AND SHAPE-MEMORY MATERIALS""; ""MAGNETIC MATERIALS""…”
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Selected papers from the 11th international symposium on electromagnetics fields in electrical engineering ISEF 2003 by Wiak, S.

Table of Contents: “…Editorial advisory board; Abstracts; Editorial; Application of Haar's wavelets in the method of moments to solve electrostatic problems; A 3D multimodal FDTD algorithm for electromagnetic and acoustic propagation in curved waveguides and bent ducts of varying cross-section; The highly efficient three-phase small induction motors with stator cores made from amorphous iron; Optimal shape design of a high-voltage test arrangement; Cogging torque calculation considering magnetic anisotropy for permanent magnet synchronous motors; Magnetoelastic coupling and Rayleigh damping…”
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Automata and Computability. by Kozen, Dexter, 1951-

Table of Contents: “…Miscellaneous Exercises Turing Machines and Effective ComputabilityHints for Selected Miscellaneous Exercises; Solutions to Selected Miscellaneous Exercises; References; Notation and Abbreviations; Index.…”
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Windows 2000 Graphics API Black Book. by Chandler, Damon

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Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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New developments in lasers and electro-optics research

Table of Contents: “…Cho -- Shape detection by means of a laser line and approximation neural networks / J. …”
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Digital workflows in architecture : designing design -- designing assembly -- designing industry

Table of Contents: “…DIGITAL CRAFTSMANSHIP: FROM THINKING TO MODELING TO BUILDINGWireframe Algorithms (Editor's Notes); ALGORITHMIC WORKFLOWS IN ASSOCIATIVE MODELING; Workflow Teams (Editor's Notes); WORKFLOW CONSULTANCY; THE SCENT OF THE SYSTEM; indeterminacy (Editor's Notes); DESIGNING INDUSTRY; WHAT DO WE MEAN BY BUILDING DESIGN?…”
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Pattern discovery in biomolecular data : tools, techniques, and applications

Table of Contents: “…Discovering patterns in DNA sequences by the algorithmic significance method / Aleksandar Milosavljevic -- Assembling blocks / Jorja G. …”
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Vision Geometry. by Melter, Robert A.

Table of Contents: “…Star-Shapedness of Digitized Planar ShapesAlgorithms for the Decomposition of Convex Polygons -- Decomposition of Discrete Curves into Piecewise Straight Segments in Linear Time -- Digitization Schemes and the Recognition of Digital Straight Lines, Hyperplanes, and Flats in Arbitrary Dimensions -- Computational Geometry and Computer Vision -- Convexity, Visibility, and Orthogonal Polygons…”
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Advanced Analytics with R and Tableau. by Stirrup, Jen

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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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Introduction to graph theory by Voloshin, Vitaly I. (Vitaly Ivanovich), 1954-

Table of Contents: “…What Is Mathematical Induction; 7.2. Graph Theory Algorithms and Their Complexity; 7.3. Answers and Hints to Selected Exercises; 7.4. …”
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In silico dreams : how artificial intelligence and biotechnology will create the medicines of the future by Hilbush, Brian

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Deep Learning Quick Reference : Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras. by Bernico, Michael

Table of Contents: “…Drawbacks to consider when using a neural network for regressionUsing deep neural networks for regression; How to plan a machine learning problem; Defining our example problem; Loading the dataset; Defining our cost function; Building an MLP in Keras; Input layer shape; Hidden layer shape; Output layer shape; Neural network architecture; Training the Keras model; Measuring the performance of our model; Building a deep neural network in Keras; Measuring the deep neural network performance; Tuning the model hyperparameters; Saving and loading a trained Keras model; Summary.…”
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Comorbidity in migraine

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