Search Results - (((((((ant OR alter) OR find) OR kantor) OR cantor) OR anne) OR salted) OR granting) algorithms.

HTML5 Multimedia Development Cookbook. by Cruse, Dale

Table of Contents: “…; How it works ... ; There's more ... ; Validation as an aid, not a crutch; Eric Meyer's funny; Where to find validators; See also.…”
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Mastering OpenCV with Practical Computer Vision Projects. by Emami, Shervin

Table of Contents: “…Marker-less Augmented Reality; Marker-based versus marker-less AR; Using feature descriptors to find an arbitrary image on video; Feature extraction; Definition of a pattern object.…”
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Trading the Measured Move : a Path to Trading Success in a World of Algos and High Frequency Trading. by Halsey, David

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Securing SQL server : protecting Your database from attackers by Cherry, Denny

Table of Contents: “…Server FirewallsWindows Firewall Inbound Rules; Windows Firewall Outbound Rules; Special Requirements for Clustering; Direct Internet Access; Public IP addresses versus private IP addresses; vLANs; Accessing SQL server from home; Setting up Routing and Remote Access; Allowing Users to VPN in to the Network; Setting up Client VPN Connection; Physical security; Keep Your Hands Off My Box; Open Network Ports; Unlocked Workstations; Automatically Locking Computers; Social engineering; Finding the instances; Testing the network security; Antivirus installation on SQL servers; Summary; References.…”
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Pattern discovery in biomolecular data : tools, techniques, and applications

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Risk and Safety Analysis of Nuclear Systems by Lee, John C., 1941-, McCormick, Norman J.

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Biological computation by Lamm, Ehud

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Radar for Fully Autonomous Driving. by Markel, Matt

Table of Contents: “…4.2.1 Waveform Orthogonality via TDM -- 4.2.2 Waveform Orthogonality via DDM -- 4.2.3 Waveform Orthogonality via FDM -- 4.3 Angle Finding in Automotive MIMO Radar -- 4.3.1 High Resolution Angle Finding with ULA -- 4.3.2 High Resolution Angle Finding with SLA -- 4.4 High Resolution Imaging Radar for Autonomous Driving -- 4.4.1 Cascade of Multiple Radar Transceivers -- 4.4.2 Examples of Cascaded Imaging Radars -- 4.4.3 Design Challenges of Imaging Radar -- 4.5 Challenges in Automotive MIMO Radar -- 4.5.1 Angle Finding in the Presence of Multipath Reflections -- 4.5.2 Waveform Orthogonality in Automotive MIMO Radar -- 4.5.3 Efficient, High Resolution Angle Finding Algorithms Are Needed -- References -- Chapter 5 Synthetic Aperture Radar for Automotive Applications -- 5.1 Introduction -- 5.1.1 Historical Background -- 5.1.2 Comparison to Traditional Radar Systems -- 5.1.3 SAR and Point Cloud Imaging Performance -- 5.1.4 Applications for Automotive Use -- 5.2 Mathematical Foundation -- 5.2.1 Key Assumptions -- 5.2.2 Signal Model -- 5.2.3 Slow Time -- 5.3 Building an Automotive SAR -- 5.3.1 Measuring Ego-Motion -- 5.3.2 SAR Image Formation -- 5.3.3 Coexistence with Point Cloud Pipeline -- 5.3.4 Elevation Information -- 5.4 Future Directions -- 5.4.1 Forward-Facing SAR -- 5.4.2 SAR for Moving Objects -- 5.4.3 Gapped SAR -- 5.5 Conclusion -- References -- Chapter 6 Radar Transceiver Technologies -- 6.1 Background and Introduction to Automotive Radar -- 6.2 Block Diagram Overview of an FMCW Radar Transceiver -- 6.3 Challenges with Deeply Scaled CMOS -- 6.4 Active Devices in CMOS -- 6.5 Passives in CMOS -- 6.6 Circuit Architectures Suitable for Advanced CMOS -- 6.6.1 The Transmit Power Amplifier -- 6.6.2 The TX Phase Shifter -- 6.7 The LO/FMCW Chirp Generator -- 6.8 The Receiver Signal Chain -- 6.8.1 RX Frontend -- 6.8.2 Radar RX Baseband -- 6.9 Summary.…”
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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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Blockchain-based peer-to-peer transactions in energy systems by Nwulu, Nnamdi, Damisa, Uyikumhe

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The linguistic cerebellum

Table of Contents: “…VOICE RECORDING MATERIALVOICE RECORDING SESSION; ANALYSIS ALGORITHM AND EFFECTS; SPINOCEREBELLAR ATAXIA DIAGNOSIS USING SPEECH ANALYSIS; ACOUSTIC FINDINGS IN CEREBELLAR PATIENTS; CONCLUSION; REFERENCES; 8 -- Cerebellum and Writing; INTRODUCTION; WRITING; AGRAPHIA; CEREBELLUM; DISCUSSION; CONCLUSION; REFERENCES; 9 -- The Role of the Cerebellum in Developmental Dyslexia; INTRODUCTION; IS THE CEREBELLUM PART OF THE READING NETWORK?…”
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Digital Learning in Motion : From Book Culture to the Digital Age. by Kergel, David

Table of Contents: “…4.3 The Urban Counterpublic of the Urban Avant-Garde: From Dadaism to Street Art -- 4.4 Progressive Education as (Counter- )Pedagogy of Modern Urbanism -- 4.5 Fordism -- the Economic Structure of Modern Urbanity -- 4.6 Informal-Accidental Learning Via Unidirectional Mass Media -- 4.7 In the Television Era the Electronic Age Finds its Medial Climax -- 4.8 Reacting on Informal-Accidental Learning -- Media Literacy, Media Competence and Media-Bildung -- 5 Fluid Learning in the Digital Age -- 5.1 The Beginning of the Digital Age -- 5.2 Early Counterculture of the 1990s Net Utopists.…”
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Poetry and Mind : Tractatus Poetico-Philosophicus by Dubreuil, Laurent

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Lung cancer and imaging

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Mobile opportunistic networks : architectures, protocols and applications

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Advanced practice nursing guide to the neurological exam by Armitage, Alex

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Complexus mundi : emergent patterns in nature

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Artificial Intelligence in Wireless Communications. by Rondeau, Thomas W.

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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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Learning Python Design Patterns - Second Edition. by Giridhar, Chetan

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