Search Results - (((((((ant OR want) OR mantis) OR when) OR cantor) OR anne) OR shane) OR hints) algorithms.

Robust statistics : theory and methods (with R) by Maronna, Ricardo A.

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Microbiological sensors for the drinking water industry

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A Statistical approach to genetic epidemiology : concepts and applications, with an e-Learning Platform. by Ziegler, Andreas, 1966-

Table of Contents: “…; 4.3 HOW SHOULD GENOTYPING ERRORS BE CHECKED IN POPULATION-BASED STUDIES USING THE HARDY-WEINBERG EQUILIBRIUM?; Algorithm 4.1.; Algorithm 4.2.; Algorithm 4.3.; 4.4 HOW CAN GENOTYPING ERRORS BE DETECTED IN HIGH-THROUGHPUT GENOTYPING STUDIES?…”
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Drones and other unmanned weapons systems under international law by Casey-Maslen, Stuart, Homayounnejad, Maziar, Stauffer, Hilary, Weizmann, Nathalie

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The Maudsley Prescribing Guidelines in Psychiatry by Taylor, David, 1963-, Paton, Carol, Kapur, Shitij

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Digital journalism, drones, and automation : the language and abstractions behind the news by Dowd, Cate

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QoS in integrated 3G networks by Lloyd-Evans, Robert

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Applied mechanics and industrial technologies : selected, peer reviewed papers from the 2012 International Conference on Applied Mechanics and Manufacturing Technology (AMMT 2012),...

Table of Contents: “…An Assessment of Wet Compression Process in Gas Turbine Systems with an Analytical ModelingAssessment of Hole Quality and Thrust Force when Drilling CFRP/Al Stack Using Carbide Tools; Multi-Objective Optimization of Filament Wound Composite Pressure Vessels Based on Weight and Matrix Cracking/ Burst Pressure Using Imperialist Competitive Algorithm; In Situ Laser Build-Up Welding of Shipboard Crankshafts; Stress and Deformation Analysis for Caster Segment Based on ANSYS; Instable Crack Propagation Analysis with PDS-FEM; Reducing the Manufacturing Cost by Using Combination Die.…”
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Hierarchical Protection for Smart Grids. by Ma, Jing

Table of Contents: “…2.3.4 Adaptive Overload Identification Method Based on the Complex Phasor Plane2.3.5 Novel Fault Phase Selection Scheme Utilizing Fault Phase Selection Factors; 2.4 Summary; References; Chapter 3 Local Area Protection for Renewable Energy; 3.1 Introduction; 3.2 Fault Transient Characteristics of Renewable Energy Sources; 3.2.1 Mathematical Model and LVRT Characteristics of the DFIG; 3.2.2 DFIG Fault Transient Characteristics When Crowbar Protection Is Not Put into Operation; 3.2.3 DFIG Fault Transient Characteristics When Crowbar Protection Is Put into Operation.…”
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Comprehensive Ruby Programming. by Hudgens, Jordan

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Integration of demand response into the electricity chain : challenges, opportunities and smart grid solutions

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Modern aerodynamic methods for direct and inverse applications by Chin, Wilson C.

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Image processing and jump regression analysis by Qiu, Peihua, 1965-

Table of Contents: “…Cover -- Contents -- Preface -- 1 Introduction -- 1.1 Images and image representation -- 1.2 Regression curves and sugaces with jumps -- 1.3 Edge detection, image restoration, and jump regression analysis -- 1.4 Statistical process control and some other related topics -- 1.5 Organization of the book -- Problems -- 2 Basic Statistical Concepts and Conventional Smoothing Techniques -- 2.1 Introduction -- 2.2 Some basic statistical concepts and terminologies -- 2.2.1 Populations, samples, and distributions -- 2.2.2 Point estimation of population parameters -- 2.2.3 Confidence intervals and hypothesis testing -- 2.2.4 Maximum likelihood estimation and least squares estimation -- 2.3 Nadaraya- Watson and other kernel smoothing techniques -- 2.3.1 Univariate kernel estimators -- 2.3.2 Some statistical properties of kernel estimators -- 2.3.3 Multivariate kernel estimators -- 2.4 Local polynomial kernel smoothing techniques -- 2.4.1 Univariate local polynomial kernel estimators -- 2.4.2 Some statistical properties -- 2.4.3 Multivariate local polynomial kernel estimators -- 2.4.4 Bandwidth selection -- 2.5 Spline smoothing procedures -- 2.5.1 Univariate smoothing spline estimation -- 2.5.2 Selection of the smoothing parameter -- 2.5.3 Multivariate smoothing spline estimation -- 2.5.4 Regression spline estimation -- 2.6 Wavelet transformation methods -- 2.6.1 Function estimation based on Fourier transformation -- 2.6.2 Univariate wavelet transformations -- 2.6.3 Bivariate wavelet transformations -- Problems -- 3 Estimation of Jump Regression Curves -- 3.1 Introduction -- 3.2 Jump detection when the number of jumps is known -- 3.2.1 Difference kernel estimation procedures -- 3.2.2 Jump detection based on local linear kernel smoothing -- 3.2.3 Estimation of jump regression functions based on semiparametric modeling -- 3.2.4 Estimation of jump regression functions by spline smoothing -- 3.2.5 Jump and cusp detection by wavelet transformations -- 3.3 Jump estimation when the number of jumps is unknown -- 3.3.1 Jump detection by comparing three local estimators -- 3.3.2 Estimation of the number of jumps by a sequence of hypothesis tests -- 3.3.3 Jump detection by DAKE -- 3.3.4 Jump detection by local polynomial regression -- 3.4 Jump-preserving curve estimation -- 3.4.1 Jump curve estimation by split linear smoothing -- 3.4.2 Jump-preserving curve fitting based on local piecewise-linear kernel estimation -- 3.4.3 Jump-preserving smoothers based on robust estimation -- 3.5 Some discussions -- Problems -- 4 Estimation of Jump Location Curves of Regression Surfaces -- 4.1 Introduction -- 4.2 Jump detection when the number of jump location curves is known -- 4.2.1 Jump detection by RDKE -- 4.2.2 Minimax edge detection -- 4.2.3 Jump estimation based on a contrast statistic -- 4.2.4 Algorithms for tracking the JLCs -- 4.2.5 Estimation of JLCs by wavelet transformations -- 4.3 Detection of arbitrary jumps by local smoothing -- 4.3.1 Treat JLCs as a pointset in the design space -- 4.3.2 Jump detection by local linear estimation -- 4.3.3 Two modijication procedures -- 4.4 Jump detection in two or more given directions -- 4.4.1 Jump detection in two given directions -- 4.4.2 Measuring the p.…”
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Bayesian Analysis with Python. by Osvaldo Martin

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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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Applied Artificial Intelligence : Proceedings of the 7th International FLINS Conference. by Arena, Paolo, Fortuna, Luigi

Table of Contents: “…An extended branch-and-bound algorithm for fuzzy linear bilevel programming / G. …”
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Automatic indexing and abstracting of document texts by Moens, Marie-Francine, 1957-

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Fast finance : does the financial world allow social loafing? by Williams, Gervais

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Google's PageRank and Beyond - the Science of Search Engine Rankings. by Langville, Amy N., Meyer, Carl D.

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Fundamentals of patenting and licensing for scientists and engineers by Ma, Matthew Y.

Table of Contents: “…When final rejection is received. 8.9. Appeal. 8.10. …”
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