Search Results - (((((((ant OR pwwants) OR antii) OR make) OR cantor) OR anne) OR share) OR hints) algorithms.

Signal processing for 5G : algorithms and implementations

Table of Contents: “…An Introduction to Modulations and Waveforms for 5G Networks / Stefano Buzzi, Alessandro Ugolini, Alessio Zappone, Giulio Colavolpe -- Faster-than-Nyquist Signaling for 5G Communication / John B Anderson -- From OFDM to FBMC: Principles and Comparisons / Wei Jiang, Thomas Kaiser -- Filter Bank Multicarrier for Massive MIMO / Arman Farhang, Nicola Marchetti, Behrouz Farhang-Boroujeny -- Bandwidth-compressed Multicarrier Communication: SEFDM / Izzat Darwazeh, Tongyang Xu, Ryan C Grammenos -- Non-orthogonal Multi-User Superposition and Shared Access / Yifei Yuan -- Non-Orthogonal Multiple Access (NOMA): Concept and Design / Anass Benjebbour, Keisuke Saito, Anxin Li, Yoshihisa Kishiyama, Takehiro Nakamura -- Major 5G Waveform Candidates: Overview and Comparison / Hao Lin, Pierre Siohan -- NEW SPATIAL SIGNAL PROCESSING FOR 5G. …”
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Graph algorithms and applications 3

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Heuristics in analytics : a practical perspective of what influences our analytical world by Reis Pinheiro, Carlos Andre, 1940-, McNeill, Fiona

Subjects: “…Decision making Statistical methods.…”
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Metaheuristic optimization for the design of automatic control laws by Sandou, Guillaume

Table of Contents: “…Ant colony optimization…”
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Optimization of Computer Networks : Modeling and Algorithms: a Hands-On Approach. by Pavón Mariño, Pablo

Table of Contents: “…7.1 Introduction -- 7.2 Node Location Problems -- 7.3 Full Topology Design Problems -- 7.4 Multilayer Network Design -- 7.5 Notes and Sources -- 7.6 Exercises -- References -- Part Two: Algorithms -- Chapter 8: Gradient Algorithms in Network Design -- 8.1 Introduction -- 8.2 Convergence Rates -- 8.3 Projected Gradient Methods -- 8.4 Asynchronous and Distributed Algorithm Implementations -- 8.5 Non-Smooth Functions -- 8.6 Stochastic Gradient Methods -- 8.7 Stopping Criteria -- 8.8 Algorithm Design Hints -- 8.9 Notes and Sources -- 8.10 Exercises -- References…”
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Introduction to programming : learn to program in Java with data structures, algorithms, and logic by Samoylov, Nick

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Nearest-neighbor methods in learning and vision : theory and practice

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Building winning algorithmic trading systems : a trader's journey from data mining to Monte Carlo simulation to live trading by Davey, Kevin J., 1966-

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Network and discrete location : models, algorithms, and applications by Daskin, Mark S., 1952-

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Body sensor networking, design, and algorithms by Sanei, Saeid, Jarchi, Delaram, 1983-, Constantinides, A. G.

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Patently Mathematical : Picking Partners, Passwords, and Careers by the Numbers by Suzuki, Jeff

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Patently mathematical : picking partners, passwords, and careers by the numbers by Suzuki, Jeff

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Blind Equalization in Neural Networks : Theory, Algorithms and Applications. by Zhang, Liyi

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Sharing economy and big data analytics by Sedkaoui, Soraya, Khelfaoui, Mounia

Table of Contents: “…The Sharing Economy or the Emergence of a New Business Model. …”
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Decision making : a psychophysics application of network science, Center for Nonlinear Science, University of North Texas, USA, 10-13 January 2010

Table of Contents: “…Overview of ARO program on network science for human decision making B.J. West; 1. Introduction; 2. Background; 2.1. …”
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Uberland : how algorithms are rewriting the rules of work by Rosenblat, Alex, 1987-

Table of Contents: “…Introduction : using an app to go to work-Uber as a symbol of the new economy -- Workers as digital pawns : how Uber uses the sharing economy to exploit everyone -- Motivations to drive : how Uber creates jobs for many at the expense of a few -- Grandiose promises : how Uber proposes entrepreneurship to the masses -- The shady middleman : how Uber plays broker to line its pockets -- Behind the curtain : how Uber rules drivers with algorithms -- In the big leagues : how Uber plays ball -- Conclusion : the new age of Uber-how technology consumption rewrote the rules of work -- Appendix 1. methodology : how I studied Uber -- Appendix 2. ridehailing beyond Uber : meet Lyft, the younger twin.…”
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Meta-heuristic and evolutionary algorithms for engineering optimization by Bozorg-Haddad, Omid, 1974-, Solgi, Mohammad, 1989-, Loaiciga, Hugo A.

Table of Contents: “…Overview of optimization -- Introduction to meta-heuristic and evolutionary algorithms -- Pattern search (PS) -- Genetic algorithm (GA) -- Simulated annealing (SA) -- Tabu search (TS) -- Ant colony optimization (ACO) -- Particle swarm optimization (PSO) -- Differential evolution (DE) -- Harmony search (HS) -- Shuffled frog-leaping algorithm (SFLA) -- Honey-bee mating optimization (HBMO) -- Invasive weed optimization (IWO) -- Central force optimization (CFO) -- Biogeography-based optimization (BBO) -- Firefly algorithm (FA) -- Gravity search algorithm (GSA) -- Bat algorithm (BA) -- Plant propagation algorithm (PPA) -- Water cycle algorithm (WCA) -- Symbiotic organisms search (SOS) -- Comprehensive evolutionary algorithm (CEA).…”
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The discrete fourier transform : theory, algorithms and applications by Sundararajan, D.

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Engineering an Algorithm for Reducing Variation in Manufacturing Processes. by Steiner, Stefan H.

Table of Contents: “…11 Investigations to Compare Three or More Families of Variation12 Investigations Based on Single Causes; Chapter 13 Verifying a Dominant Cause; PART IV Assessing Feasibility and Implementing a Variation Reduction Approach; 14 Revisiting the Choice of Variation Reduction Approach; 15 Moving the Process Center; 16 Desensitizing a Process to Variation in a Dominant Cause; 17 Feedforward Control Based on a Dominant Cause; 18 Feedback Control; 19 Making a Process Robust; 20 100% Inspection; 21 Validating a Solution and Holding the Gains; References; Index; Case Studies.…”
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The ultimate algorithmic trading system toolbox + website : using today's technology to help you become a better trader by Pruitt, George, 1967-

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