Search Results - (((((((ant OR antti) OR semantic) OR arts) OR cantor) OR anne) OR maarten) OR mantis) algorithms.

Real-time database systems : architecture and techniques

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The Fifth Decade of Cardiac Pacing. by Barold, S. Serge

Table of Contents: “…; 11 Permanent and Temporary Single-Lead VDD and DDD Pacing: State of the Art; 12 Pacemaker Memory: Basic Concepts and New Technology; 13 Future Trends in Pacemaker Technology; 14 Diagnosis of Supraventricular Tachyarrhythmias by Automatic Mode-Switching Algorithms of Dual-Chamber Pace.…”
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Digital signal processing with kernel methods by Rojo-Álvarez, José Luis, 1972-, Martínez-Ramón, Manel, 1968-, Muñoz Marí, Jordi, Camps-Valls, Gustavo, 1972-

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Integrated and collaborative product development environment : technologies and implementations by Li, W. D.

Table of Contents: “…5.4 Experimental Results5.4.1 Sample parts; 5.4.2 Computation results; 5.4.3 Comparisons of constraint handling methods; 5.4.4 Algorithm implementation; 5.5 Summary; 6. Collaborative Computer-Aided Design -- State-of-the-Art; 6.1 Introduction; 6.2 Visualisation-based Collaborative Systems; 6.2.1 3D representations for Web applications; 6.2.2 System architectures and implementation strategies; 6.3 Co-design Collaborative Systems; 6.3.1 System architectures; 6.3.2 Design coordination and team management; 6.3.3 Optimised feature and assembly-based representations.…”
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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Table of Contents: “…Solving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer.…”
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Constraint Solving over Multi-valued Logics : Application to Digital Circuits.

Table of Contents: “…-- 3.2 Test Generation -- 3.3 TG Modelling Approaches and Algorithms.…”
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HTML5 Multimedia Development Cookbook. by Cruse, Dale

Table of Contents: “…More flexible footer contentApplying the outline algorithm; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Are you sure?…”
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Circuit analysis

Table of Contents: “…CIRCUIT ANALYSIS; ELECTRICAL ENGINEERINGDEVELOPMENTS; CONTENTS; PREFACE; ELEMENT STAMP ALGORITHM FOR MATRIXFORMULATION OF SYMBOLIC CIRCUITS; Abstract; 1. …”
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Remote Sensing of Energy Fluxes and Soil Moisture Content. by Petropoulos, George P.

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Modelling for coastal hydraulics and engineering by Chau, Kwok Wing

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From text to political positions : text analysis across disciplines

Table of Contents: “…Conditional probabilities and associative framing ; Semantic network analysis.…”
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Challenges in information technology management

Table of Contents: “…Improved data mining algorithms for frequent patterns with composite items / Ke Wang, James N.K. …”
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Cryptography 101 : From Theory to Practice. by Oppliger, Rolf

Table of Contents: “…Intro -- Cryptography 101: From Theory to Practice -- Contents -- Foreword -- Preface -- References -- Acknowledgments -- Chapter 1 Introduction -- 1.1 CRYPTOLOGY -- 1.2 CRYPTOGRAPHIC SYSTEMS -- 1.2.1 Classes of Cryptographic Systems -- 1.2.2 Secure Cryptographic Systems -- 1.3 HISTORICAL BACKGROUND INFORMATION -- 1.4 OUTLINE OF THE BOOK -- References -- Chapter 2 Cryptographic Systems -- 2.1 UNKEYED CRYPTOSYSTEMS -- 2.1.1 Random Generators -- 2.1.2 Random Functions -- 2.1.3 One-Way Functions -- 2.1.4 Cryptographic Hash Functions -- 2.2 SECRET KEY CRYPTOSYSTEMS -- 2.2.1 Pseudorandom Generators -- 2.2.2 Pseudorandom Functions -- 2.2.3 Symmetric Encryption -- 2.2.4 Message Authentication -- 2.2.5 Authenticated Encryption -- 2.3 PUBLIC KEY CRYPTOSYSTEMS -- 2.3.1 Key Establishment -- 2.3.2 Asymmetric Encryption Systems -- 2.4 FINAL REMARKS -- References -- Part I UNKEYEDC RYPTOSYSTEMS -- Chapter 3 Random Generators -- 3.1 INTRODUCTION -- 3.2 REALIZATIONS AND IMPLEMENTATIONS -- 3.2.1 Hardware-Based Random Generators -- 3.2.2 Software-Based Random Generators -- 3.2.3 Deskewing Techniques -- 3.3 STATISTICAL RANDOMNESS TESTING -- References -- Chapter 4 Random Functions -- 4.1 INTRODUCTION -- 4.2 IMPLEMENTATION -- 4.3 FINAL REMARKS -- Chapter 5 One-Way Functions -- 5.1 INTRODUCTION -- 5.2 CANDIDATE ONE-WAY FUNCTIONS -- 5.2.1 Discrete Exponentiation Function -- 5.2.2 RSA Function -- 5.2.3 Modular Square Function -- 5.3 INTEGER FACTORIZATION ALGORITHMS -- 5.3.1 Special-Purpose Algorithms -- 5.3.2 General-Purpose Algorithms -- 5.3.3 State of the Art -- 5.4 ALGORITHMS FOR COMPUTING DISCRETE LOGARITHMS -- 5.4.1 Generic Algorithms -- 5.4.2 Nongeneric (Special-Purpose) Algorithms -- 5.4.3 State of the Art -- 5.5 ELLIPTIC CURVE CRYPTOGRAPHY -- 5.6 FINAL REMARKS -- References -- Chapter 6 Cryptographic Hash Functions -- 6.1 INTRODUCTION.…”
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Meeting security challenges through data analytics and decision support

Table of Contents: “…Counter Terrorism: Methodology and Applications; Arguing About Uncertain Heterogeneous Information for Threat Assessment; Intelligence Analysis: Needs and Solutions; Building Agile Human/Machine Teams with Controlled Natural Language; Military Usages of Speech and Language Technologies: A Review; Comparative Studies on Using Semantic Filtering for Open Relation Identification; Towards an Integration of Fusion of Information and Analytics Technologies (FIAT) to Improve Dependability and Security in Complex Systems; Data Fusion and Response Management.…”
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Fuzzy systems and data mining II : proceedings of FSDM 2016

Table of Contents: “…Adaptive Fuzzy Sliding-Mode Control of Robot and SimulationHesitant Bipolar Fuzzy Set and Its Application in Decision Making; Chance Constrained Twin Support Vector Machine for Uncertain Pattern Classification; Set-Theoretic Kripke-Style Semantics for Monoidal T-Norm (Based) Logics; Data Mining; Dynamic Itemset Mining Under Multiple Support Thresholds; Deep Learning with Large Scale Dataset for Credit Card Data Analysis; Probabilistic Frequent Itemset Mining Algorithm over Uncertain Databases with Sampling; Priority Guaranteed and Energy Efficient Routing in Data Center Networks.…”
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Smart buildings, smart communities and demand response

Table of Contents: “…Demand Response in Smart Zero Energy Buildings and Grids / Nikos Kampelis -- DR in Smart and Near-zero Energy Buildings: The Leaf Community / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- Performance of Industrial and Residential Near-zero Energy Buildings / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response / Nikos Kampelis, Nikolaos Sifakis, Denia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions / Nikos Kampelis, Elisavet Tsekeri, Denia Kolokotsa, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Conclusions and Recommendations.…”
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Mobile opportunistic networks : architectures, protocols and applications

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Evolutionary optimization

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Big Data and HPC. by Grandinetti, L.

Table of Contents: “…Analysis and Design of IoT Based Physical Location Monitoring SystemAutonomous Task Scheduling for Fast Big Data Processing; Adaptive Resource Management for Distributed Data Analytics; HPC Challenges; High-Performance Massive Subgraph Counting Using Pipelined Adaptive-Group Communication; Final Parallel and Distributed Computing Assignment for Master Students: Description of the Properties and Parallel Structure of Algorithms; Parallel Motion Estimation Based on GPU and Combined GPU-CPU; GPU-Based Iterative Hill Climbing Algorithm to Solve Symmetric Traveling Salesman Problem.…”
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Wavelet Methods in Mathematical Analysis and Engineering. by Damlamian, Alain

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