Tabla de Contenidos: “…-- Teil II: Eine Klangquelle, ein Mikrofon -- Kapitel 4: Grundlagen der Gesangsaufnahme -- 4.1 Das Mikrofon -- 4.1.1 Das Kondensatormikrofon -- 4.1.2 Nierenförmige Richtcharakteristik -- 4.1.3 Röhren und Wandler -- 4.1.4 Kosten und Reputation -- 4.1.5 »Beste Schätzung« versus »Ideal« -- 4.2 Anschließen und Verkabeln -- 4.2.1 Stabilität -- 4.2.2 Schwingungsdämpfer und Mikrofonclips -- 4.2.3 Kabelsicherung -- 4.3 Die Positionierung des Mikrofons -- 4.3.1 Der Winkel des Mikrofons -- 4.3.2 Die Distanz zum Mikrofon -- 4.3.3 Die Ausrichtung des Mikrofons -- 4.3.4 Konsistenz bei der Positionierung -- 4.4 Überlegungen zur Akustik -- 4.4.1 Reflexionen des Raums -- 4.4.2 Resonanzen -- 4.5 Überarbeitungen beim Monitoring -- 4.5.1 Akustisches Gesangs-Monitoring -- 4.5.2 Vocal-Foldback -- 4.5.3 Gesangskomprimierung -- 4.5.4 Den Backing-Track austarieren und bearbeiten -- 4.5.5 Komforteffekte -- 4.6 Während der Session -- 4.6.1 Die Akustik überarbeiten -- 4.6.2 Takes und Comping -- 4.6.3 Hinweise für Talkback und Performance -- 4.7 Und es geht noch weiter ... -- 4.8 Auf den Punkt gebracht -- Kapitel 5: Weiterentwicklung von Gesangsaufnahmen -- 5.1 Alternative Mikrofontypen -- 5.1.1 Andere Polardiagramme -- 5.1.2 Dynamische Mikrofone -- 5.1.3 Bändchenmikrofone -- 5.2 Akustische Reflexion -- 5.3 Lautsprecher-Monitoring -- 5.3.1 Lautsprecher im Mikrofon-Nullpunkt -- 5.3.2 Techniken zur Polaritätsumkehr -- 5.3.3 Reflexionen von Lautsprechern…”

Tabla de Contenidos: “…9 Style Recognition and Kinship Understanding -- 9.1 Style Classi cation by Deep Learning -- 9.1.1 Background -- 9.1.2 Preliminary Knowledge of Stacked Autoencoder (SAE) -- 9.1.3 Style Centralizing Autoencoder -- 9.1.3.1 One Layer Basic SCAE -- 9.1.3.2 Stacked SCAE (SSCAE) -- 9.1.3.3 Visualization of Encoded Feature in SCAE -- 9.1.3.4 Geometric Interpretation of SCAE -- 9.1.4 Consensus Style Centralizing Autoencoder -- 9.1.4.1 Low-Rank Constraint on the Model -- 9.1.4.2 Group Sparsity Constraint on the Model -- 9.1.4.3 Rank-Constrained Group Sparsity Autoencoder -- 9.1.4.4 Ef cient Solutions for RCGSAE -- 9.1.4.5 Progressive CSCAE -- 9.1.5 Experiments -- 9.1.5.1 Dataset -- 9.1.5.2 Compared Methods -- 9.1.5.3 Experimental Results -- 9.2 Visual Kinship Understanding -- 9.2.1 Background -- 9.2.2 Related Work -- 9.2.3 Family Faces -- 9.2.4 Regularized Parallel Autoencoders -- 9.2.4.1 Problem Formulation -- 9.2.4.2 Low-Rank Reframing -- 9.2.4.3 Solution -- 9.2.5 Experimental Results -- 9.2.5.1 Kinship Veri cation -- 9.2.5.2 Family Membership Recognition -- 9.3 Research Challenges and Future Works -- References -- 10 Image Dehazing: Improved Techniques -- 10.1 Introduction -- 10.2 Review and Task Description -- 10.2.1 Haze Modeling and Dehazing Approaches -- 10.2.2 RESIDE Dataset -- 10.3 Task 1: Dehazing as Restoration -- 10.4 Task 2: Dehazing for Detection -- 10.4.1 Solution Set 1: Enhancing Dehazing and/or Detection Modules in the Cascade -- 10.4.2 Solution Set 2: Domain-Adaptive Mask-RCNN -- Experiments -- 10.5 Conclusion -- References -- 11 Biomedical Image Analytics: Automated Lung Cancer Diagnosis -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Methodology -- Metrics for Scoring Images -- 11.4 Experiments -- 11.5 Conclusion -- Acknowledgments -- References -- Index -- Back Cover…”

Tabla de Contenidos: “…Chapter 8: Load balancing -- 8.1 Introduction -- 8.2 Dynamic load balancing: the Linda legacy -- 8.3 Static Load Balancing: The Divisible LoadTheory Approach -- 8.3.1 Modeling Costs -- 8.3.2 Communication Configuration -- 8.3.3 Analysis -- 8.3.3.1 N-Port, Block-Type, Single-Installment Solution -- 8.3.3.2 One-Port, Block-Type, Single-Installment Solution -- 8.3.4 Summary - Short Literature Review -- 8.4 DLTlib: A library for partitioning workloads -- 8.5 Case studies -- 8.5.1 Hybrid Computation of a Mandelbrot Set "Movie'':A Case Study in Dynamic Load Balancing -- 8.5.2 Distributed Block Cipher Encryption: A Case Study in Static Load Balancing -- Appendix A: Compiling Qt programs -- A.1 Using an IDE -- A.2 The qmake Utility -- Appendix B: Running MPI programs -- B.1 Preparatory Steps -- B.2 Computing Nodes discovery for MPI Program Deployment -- B.2.1 Host Discovery with the nmap Utility -- B.2.2 Automatic Generation of a Hostfile -- Appendix C: Time measurement -- C.1 Introduction -- C.2 POSIX High-Resolution Timing -- C.3 Timing in Qt -- C.4 Timing in OpenMP -- C.5 Timing in MPI -- C.6 Timing in CUDA -- Appendix D: Boost.MPI -- D.1 Mapping from MPI C to Boost.MPI -- Appendix E: Setting up CUDA -- E.1 Installation -- E.2 Issues with GCC -- E.3 Running CUDA without an Nvidia GPU -- E.4 Running CUDA on Optimus-Equipped Laptops -- E.5 Combining CUDA with Third-Party Libraries -- Appendix F: DLTlib -- F.1 DLTlib Functions -- F.1.1 Class Network: Generic Methods -- F.1.2 Class Network: Query Processing -- F.1.3 Class Network: Image Processing -- F.1.4 Class Network: Image Registration -- F.2 DLTlib Files -- Glossary -- Bibliography -- Index…”

Tabla de Contenidos: “…6.3.1 - No-History Reference Tables -- 6.3.2 - History-Based Reference Tables -- 6.3.3 - Code and Descriptions -- 6.3.3.1 - Code and Descriptions with History -- Reference -- Chapter 7 - Dimensional Modeling -- 7.1 - Introduction -- 7.2 - Star Schemas -- 7.2.1 - Fact Tables -- 7.2.1.1 - The Grain of a Fact Table -- 7.2.2 - Dimension Tables -- 7.2.3 - Querying Star Schemas -- 7.3 - Multiple Stars -- 7.3.1 - Conformed Dimensions -- 7.4 - Dimension Design -- 7.4.1 - Slowly Changing Dimensions -- 7.4.2 - Hierarchies -- 7.4.3 - Snowflake Design -- References -- Chapter 8 - Physical Data Warehouse Design -- 8.1 - Database Workloads -- 8.1.1 - Workload Characteristics -- 8.2 - Separate Environments for Development, Testing, and Production -- 8.2.1 - Blue-Green Deployment -- 8.3 - Microsoft Azure Cloud Computing Platform -- 8.4 - Physical Data Warehouse Architecture on Premise -- 8.4.1 - Hardware Architectures and Databases -- 8.4.2 - Processor Options -- 8.4.3 - Memory Options -- 8.4.4 - Storage Options -- 8.4.5 - Network Options -- 8.5 - Database Options -- 8.5.1 - tempdb Options -- 8.5.2 - Partitioning -- 8.5.3 - Filegroups -- 8.5.4 - Data Compression -- 8.6 - Setting up the Data Warehouse -- 8.6.1 - Setting up the Stage Area -- 8.6.1.1 - Hardware Considerations for Stage Area -- 8.6.1.2 - Stage Database Setup -- 8.6.2 - Setting up the Data Vault -- 8.6.2.1 - Hardware Considerations for Data Vault Layer -- 8.6.2.2 - Backing Up the Data Vault -- 8.6.2.3 - Data Vault Database Setup -- 8.6.3 - Setting up Information Marts -- 8.6.3.1 - Hardware Considerations for Information Marts -- 8.6.3.2 - Information Mart Database Setup -- 8.6.4 - Setting up the Meta, Metrics, and Error Marts -- 8.6.4.1 - Hardware Considerations for Meta, Metrics, and Error Marts -- 8.6.4.2 - Meta, Metrics, and Error Marts Database Setup -- References…”

Tabla de Contenidos: “…Geocoding using normalized addresses -- 8.4.3. Batch geocoding -- 8.5. Reverse geocoding -- 8.6. …”

Tabla de Contenidos: “…Additional Resources -- Chapter 9 Depth Analysis On DoS &amp -- DDoS Attacks -- 9.1 Introduction -- 9.1.1 Objective and Motivation -- 9.1.2 Symptoms and Manifestations -- 9.2 Literature Survey -- 9.3 Timeline of DoS and DDoS Attacks -- 9.4 Evolution of Denial of Service (DoS) &amp -- Distributed Denial of Service (DDoS) -- 9.5 DDoS Attacks: A Taxonomic Classification -- 9.5.1 Classification Based on Degree of Automation -- 9.5.2 Classification Based on Exploited Vulnerability -- 9.5.3 Classification Based on Rate Dynamics of Attacks -- 9.5.4 Classification Based on Impact -- 9.6 Transmission Control Protocol -- 9.6.1 TCP Three-Way Handshake -- 9.7 User Datagram Protocol -- 9.7.1 UDP Header -- 9.8 Types of DDoS Attacks -- 9.8.1 TCP SYN Flooding Attack -- 9.8.2 UDP Flooding Attack -- 9.8.3 Smurf Attack -- 9.8.4 Ping of Death Attack -- 9.8.5 HTTP Flooding Attack -- 9.9 Impact of DoS/DDoS on Various Areas -- 9.9.1 DoS/DDoS Attacks on VoIP Networks Using SIP -- 9.9.2 DoS/DDoS Attacks on VANET -- 9.9.3 DoS/DDoS Attacks on Smart Grid System -- 9.9.4 DoS/DDoS Attacks in IoT-Based Devices -- 9.10 Countermeasures to DDoS Attack -- 9.10.1 Prevent Being Agent/Secondary Target -- 9.10.2 Detect and Neutralize Attacker -- 9.10.3 Potential Threats Detection/Prevention -- 9.10.4 DDoS Attacks and How to Avoid Them -- 9.10.5 Deflect Attack -- 9.10.6 Post-Attack Forensics -- 9.11 Conclusion -- 9.12 Future Scope -- References -- Chapter 10 SQL Injection Attack on Database System -- 10.1 Introduction -- 10.1.1 Types of Vulnerabilities -- 10.1.2 Types of SQL Injection Attack -- 10.1.3 Impact of SQL Injection Attack -- 10.2 Objective and Motivation -- 10.3 Process of SQL Injection Attack -- 10.4 Related Work -- 10.5 Literature Review -- 10.6 Implementation of the SQL Injection Attack -- 10.6.1 Access the Database Using the 1=1 SQL Injection Statement…”

Tabla de Contenidos: “…7.4.3 Future Generation and Extraction -- 7.4.4 Wavelet Energy -- 7.5 Feature Selection and Optimization -- 7.5.1 Genetic Algorithm -- 7.6 Machine Learning-Based Classification of PQ Disturbances -- 7.6.1 Support Vector Machine Classifier -- 7.6.2 Artificial Neural Network Classifier -- 7.6.2.1 Back-Propagation Neural Network -- 7.6.2.2 Probabilistic Neural Network -- 7.6.3 Performance Prediction of the ML Classifiers -- 7.7 Summary and Conclusion -- References -- Chapter 8 Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection -- 8.1 Introduction -- 8.1.1 Objective of the Work -- 8.1.2 Scope of the Project -- 8.2 Literature Survey -- 8.2.1 Problem Identification -- 8.3 Proposed Methodology -- 8.3.1 Different Kinds of Machine Learning Approaches -- 8.3.1.1 Supervised Learning -- 8.3.1.2 Unsupervised Learning -- 8.3.1.3 Semi-Supervised Learning -- 8.3.1.4 Reinforcement Learning -- 8.4 Artificial Neural Network -- 8.4.1 ANN Classification -- 8.4.1.1 Input Layer -- 8.4.1.2 Hidden Layer -- 8.4.1.3 Output Layer -- 8.4.2 Spotted Hyena Optimization -- 8.4.2.1 Searching Behavior -- 8.4.2.2 Encircling Behavior -- 8.4.2.3 Hunting Behavior -- 8.4.2.4 Attacking Behavior -- 8.4.3 SHO-Based ANN -- 8.4.4 Benefits of SHO in ANN -- 8.5 Software Implementation Requirements -- 8.5.1 Results and Discussion -- 8.6 Conclusion -- References -- Chapter 9 The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic -- 9.1 Introduction -- 9.2 Discussions on the Coronavirus -- 9.2.1 Coronavirus -- 9.2.2 COVID-19 -- 9.2.3 Origin of COVID-19 and Its Symptoms -- 9.2.4 Mode of Spreading -- 9.2.5 Steps Taken by the Government to Prevent the Spread of COVID-19 -- 9.3 Bad Impacts of the Coronavirus -- 9.3.1 Social Impact…”

Tabla de Contenidos: “…10.4.2 Results of Association Rule Mining on Original Sequences -- 10.4.3 Results of the Proposed Approach -- 10.5 Conclusion and Future Work -- Notes -- References -- Chapter 11: Geoinformatics and Social Media: New Big Data Challenge -- 11.1 Introduction: Social Media and Ambient Geographic Information -- 11.2 Characteristics of Big Geosocial Data -- 11.3 Geosocial Complexity -- 11.4 Modeling and Analyzing Geosocial Multimedia: Heterogeneity and Integration -- 11.5 Outlook: Grand Challenges and Opportunities for Big Geosocial Data -- Notes -- References -- Chapter 12: Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern -- 12.1 Introduction -- 12.2 Trajectory Modeling -- 12.2.1 TMC-Pattern -- 12.2.1.1 Determining Meaningful Location -- 12.2.2 Time Correlation -- 12.2.3 Location Context Awareness -- 12.2.4 Relevance Measures of a Region -- 12.2.5 TMC-Pattern -- 12.2.5.1 Determining Residence Mode of a Region -- 12.2.6 Trajectory Extraction -- 12.3 Trajectory Mining -- 12.3.1 Frequent Locations from TMC-Pattern -- 12.3.2 TMC-Pattern and Markov Chain for Prediction -- 12.3.2.1 Markov Chains -- 12.3.2.2 Markov Chain from TMC-Pattern -- 12.3.2.3 Computation of Markov Chain Transition Probability -- 12.3.2.4 Computation of Scores from TMC-Pattern -- 12.4 Empirical Evaluations -- 12.4.1 Experimental Dataset -- 12.4.2 Evaluation of TMC-Pattern Extraction -- 12.4.2.1 Single-User Data -- 12.4.2.2 Multiuser Data -- 12.4.3 Frequent Patterns -- 12.4.4 Location Prediction -- 12.5 Summary -- References -- Chapter 13: Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web -- 13.1 Introduction -- 13.2 Big Data Challenges on the Worldwide Sensor Web -- 13.3 Worldwide Sensor Web Architecture -- 13.4 GeoCENS Architecture -- 13.4.1 OGC-Based Sensor Web servers…”

Tabla de Contenidos: “…Create a store -- 12.1 Overview -- 12.2 Package and verify store archive -- 12.2.1 Back up workspace and databases -- 12.2.2 Create the Packaging project -- 12.2.3 Package a store archive (SAR) -- 12.2.4 Publish the store archive (SAR) -- 12.2.5 Verify the store after publish -- 12.3 Import store assets into CVS -- 12.3.1 Create a CVS module from the project -- 12.3.2 Add the files to CVS -- 12.4 Required customization of basic store assets -- 12.4.1 Store directory and identifier -- 12.4.2 Hardcoded references -- 12.4.3 Store address -- 12.4.4 Catalog data -- 12.4.5 Store front-end assets -- 12.5 Further customization of basic store assets -- 12.5.1 Default and supported currencies -- 12.5.2 Default and supported locales -- 12.5.3 Organizations -- 12.5.4 Business accounts -- 12.5.5 Contracts -- 12.5.6 Taxes, shipping couriers and shipping prices -- 12.5.7 Payment information -- 12.6 Publish the store archive to the workspace -- 12.6.1 Package the customized store archive -- 12.6.2 Publish the customized store archive to runtime -- 12.6.3 Verify the customized store archive -- 12.6.4 Publish the store archive to the workspace -- 12.6.5 Verify customized store in the WebSphere Test Environment -- 12.7 Add the store front files to CVS…”

Tabla de Contenidos: “…8.3.2 Drawbacks of CMOS Power Amplifier -- 8.3.3 Design of CMOS Power Amplifier -- 8.3.3.1 Common Cascode PA Design -- 8.3.3.2 Self-Bias Cascode PA Design -- 8.3.3.3 Differential Cascode PA Design -- 8.3.3.4 Power Combining PA Design -- 8.4 Linearization Principles: Predistortion Technique, Phase-Correcting Feedback, Envelope Elimination and Restoration (EER), Cartesian Feedback -- 8.4.1 Predistortion Linearization Technique -- 8.4.2 Phase Correcting Feedback Technique -- 8.4.3 Cartesian Feedback Technique -- 8.4.4 Envelope Elimination and Restoration Technique -- Acknowledgement -- References -- Chapter 9 RF Oscillators -- 9.1 Introduction -- 9.2 Specifications -- 9.2.1 Frequency and Tuning -- 9.2.2 Tuning Constant and Linearity -- 9.2.3 Power Dissipation -- 9.2.4 Phase to Noise Ratio -- 9.2.5 Reciprocal Mixing -- 9.2.6 Signal to Noise Degradation of FM Signals Spurious Emission -- 9.2.7 Harmonics, I/Q Matching, Technology and Chip Area -- 9.3 LC Oscillators -- 9.3.1 Frequency, Tuning and Phase Noise Frequency Tuning Phase Noise to Carrier Ratio -- 9.3.2 Topologies -- 9.3.3 NMOS Only Cross-Coupled Structure -- 9.3.4 RC Oscillators -- 9.4 Design Examples -- 9.4.1 830 MHz Monolithic LC Oscillator Circuit Design Measurements -- 9.4.2 A 10 GHz I/Q RC Oscillator with Active Inductors -- 9.5 Conclusion -- Acknowledgement -- References -- Part III: RF Circuit Applications -- Chapter 10 mmWave Highly-Linear Broadband Power Amplifiers -- 10.1 Basics of PAs -- 10.1.1 Single Transistor Amplifier -- 10.1.2 Trade-Offs Among Power Amplifier Design Parameters (P0, PAE and Linearity) -- 10.1.3 Harmonic Terminations and Switching Amplifiers -- 10.1.4 Challenges at Millimeter-Wave -- 10.2 Millimeter Wave-Based AB Class PA -- 10.2.1 Efficiency at Power Back-Off -- 10.2.2 Sources of AM-PM Distortion -- 10.2.3 Distortion Cancellation Techniques…”

Tabla de Contenidos: “…-- 9.2 Model Selection Strategies -- 9.3 Types of Model Selection -- 9.3.1 Methods of Re-Sampling -- 9.3.2 Random Separation -- 9.3.3 Time Divide -- 9.3.4 K-Fold Cross-Validation -- 9.3.5 Stratified K-Fold -- 9.3.6 Bootstrap -- 9.3.7 Possible Steps -- 9.3.8 Akaike Information Criterion (AIC) -- 9.3.9 Bayesian Information Criterion (BIC) -- 9.3.10 Minimum Definition Length (MDL) -- 9.3.11 Building Risk Reduction (SRM) -- 9.3.12 Excessive Installation (Overfitting) -- 9.4 The Principle of Parsimony -- 9.5 Examples of Model Selection Criterions -- 9.6 Other Popular Properties -- 9.7 Key Considerations -- 9.8 Model Validation -- 9.8.1 Why is Model Validation Important? …”

Tabla de Contenidos: “…16 Gradient-based optimizer: analysis and application of the Berry software product -- 16.1 Introduction -- 16.2 Literature review -- 16.2.1 Gradient-based optimization -- 16.2.1.1 Theoretical background -- 16.2.1.2 Gradient-based optimization -- 16.2.1.2.1 Initialization -- 16.2.1.2.2 Gradient search rule -- 16.3 Results and discussion -- 16.4 Conclusion -- References -- 17 A review of krill herd algorithm: optimization and its applications -- 17.1 Introduction -- 17.2 Krill herd algorithm procedure -- 17.2.1 Krill swarms herding behavior -- 17.2.2 Standard of krill herd -- 17.2.2.1 Movement induced by other instances (Krill) -- 17.2.2.2 Foraging activity -- 17.2.3 Krill herd algorithm -- 17.3 Related work -- 17.4 Conclusion -- References -- 18 Salp swarm algorithm: survey, analysis, and new applications -- 18.1 Introduction -- 18.2 Related work procedure of the algorithm -- 18.2.1 Single-objective optimization problems -- 18.2.2 Single-objective optimization procedures -- 18.2.3 Multiobjective optimization problems -- 18.2.4 Multiobjective optimization procedures -- 18.2.5 Research and studies related to the subject of the study -- 18.3 Methods -- 18.3.1 Stimulation -- 18.3.2 Mathematical model -- 18.3.3 Single-objective SALP swarm algorithm -- 18.3.4 Multiobjective SALP Swarm algorithm -- 18.4 Results -- 18.4.1 Qualitative results of SALP swarm algorithm and discussion -- 18.4.2 Quantitative results of SALP swarm algorithm and discussion -- 18.4.3 On the CEC-BBOB-2015 test functions, SALP swarm algorithm, and harmony search -- 18.4.4 Scalability analysis -- 18.4.5 Results of multipurpose SALP swarm algorithm and discussion -- 18.5 Conclusion -- References -- Index -- Back Cover…”

Tabla de Contenidos: “…Replacement strategies -- 3.1 Replacement strategies for C/C++ applications -- 3.1.1 Auditing -- 3.1.2 Authentication -- 3.1.3 Authorization, PAC, and UUID -- 3.1.4 Backing store -- 3.1.5 Configuration…”

Tabla de Contenidos: “…Control data set records -- 3.1 CDS record types, functions and relationships -- 3.1.1 DFSMShsm record type extended description -- 3.2 Migration records -- 3.2.1 MCD record -- 3.2.2 MCO record -- 3.3 Backup records -- 3.3.1 MCB record -- 3.3.2 MCC record -- 3.4 Other audited records -- 3.4.1 TTOC record -- 3.5 Tapes in failed create status -- 3.5.1 ABR record -- 3.5.2 MCP, MCT and MCV records -- Chapter 4. …”

Tabla de Contenidos: “…-- 6.7 Identifying bad data -- 6.8 Kinds of problems -- 6.9 Responses to bad data -- Techniques for fixing bad data -- Cleaning our data set -- 6.11.1 Rewriting bad rows -- 6.11.2 Filtering rows of data -- 6.11.3 Filtering columns of data -- Preparing our data for effective use -- 6.12.1 Aggregating rows of data -- 6.12.2 Combining data from different files using globby -- 6.12.3 Splitting data into separate files -- Building a data processing pipeline with Data-Forge -- Summary -- Chapter 7: Dealing with huge data files -- 7.1 Expanding our toolkit -- 7.2 Fixing temperature data -- 7.3 Getting the code and data -- 7.4 When conventional data processing breaks down -- 7.5 The limits of Node.js -- 7.5.1 Incremental data processing -- 7.5.2 Incremental core data representation -- 7.5.3 Node.js file streams basics primer -- 7.5.4 Transforming huge CSV files -- 7.5.5 Transforming huge JSON files -- 7.5.6 Mix and match -- Summary -- Chapter 8: Working with a mountain of data -- 8.1 Expanding our toolkit -- 8.2 Dealing with a mountain of data -- 8.3 Getting the code and data -- 8.4 Techniques for working with big data -- 8.4.1 Start small -- 8.4.2 Go back to small -- 8.4.3 Use a more efficient representation -- 8.4.4 Prepare your data offline -- 8.5 More Node.js limitations -- 8.6 Divide and conquer -- 8.7 Working with large databases -- 8.7.1 Database setup -- 8.7.2 Opening a connection to the database -- 8.7.3 Moving large files to your database -- 8.7.4 Incremental processing with a database cursor -- 8.7.5 Incremental processing with data windows -- 8.7.6 Creating an index…”

Tabla de Contenidos: “…Focused penetration -- 5 Attacking vulnerable web services -- 5.1 Understanding phase 2: Focused penetration -- 5.1.1 Deploying backdoor web shells -- 5.1.2 Accessing remote management services -- 5.1.3 Exploiting missing software patches -- 5.2 Gaining an initial foothold -- 5.3 Compromising a vulnerable Tomcat server -- 5.3.1 Creating a malicious WAR file -- 5.3.2 Deploying the WAR file -- 5.3.3 Accessing the web shell from a browser -- 5.4 Interactive vs. non-interactive shells -- 5.5 Upgrading to an interactive shell -- 5.5.1 Backing up sethc.exe -- 5.5.2 Modifying file ACLs with cacls.exe -- 5.5.3 Launching Sticky Keys via RDP -- 5.6 Compromising a vulnerable Jenkins server -- 5.6.1 Groovy script console execution -- Summary…”

Tabla de Contenidos: “…5.2.5 Fuzzy Microgrid Wind -- 5.3 Genetic Algorithm -- 5.3.1 Important Aspects of Genetic Algorithm -- 5.3.2 Standard Genetic Algorithm -- 5.3.3 Genetic Algorithm and Its Application -- 5.3.4 Power System and Genetic Algorithm -- 5.3.5 Economic Dispatch Using Genetic Algorithm -- 5.4 Artificial Neural Network -- 5.4.1 The Biological Neuron -- 5.4.2 A Formal Definition of Neural Network -- 5.4.3 Neural Network Models -- 5.4.4 Rosenblatt's Perceptron -- 5.4.5 Feedforward and Recurrent Networks -- 5.4.6 Back Propagation Algorithm -- 5.4.7 Forward Propagation -- 5.4.8 Algorithm -- 5.4.9 Recurrent Network -- 5.4.10 Examples of Neural Networks -- 5.4.10.1 AND Operation -- 5.4.10.2 OR Operation -- 5.4.10.3 XOR Operation -- 5.4.11 Key Components of an Artificial Neuron Network -- 5.4.12 Neural Network Training -- 5.4.13 Training Types -- 5.4.13.1 Supervised Training -- 5.4.13.2 Unsupervised Training -- 5.4.14 Learning Rates -- 5.4.15 Learning Laws -- 5.4.16 Restructured Power System -- 5.4.17 Advantages of Precise Forecasting of the Price -- 5.5 Conclusion -- References -- 6 Recent Advances in Wearable Antennas: A Survey -- 6.1 Introduction -- 6.2 Types of Antennas -- 6.2.1 Description of Wearable Antennas -- 6.2.1.1 Microstrip Patch Antenna -- 6.2.1.2 Substrate Integrated Waveguide Antenna -- 6.2.1.3 Planar Inverted-F Antenna -- 6.2.1.4 Monopole Antenna -- 6.2.1.5 Metasurface Loaded Antenna -- 6.3 Design of Wearable Antennas -- 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design -- 6.3.1.1 Conducting Coating on Substrate -- 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure -- 6.3.1.3 Partial Ground Plane -- 6.3.2 Logo Antennas -- 6.3.3 Embroidered Antenna -- 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap -- 6.3.5 Wearable Reconfigurable Antenna -- 6.4 Textile Antennas -- 6.5 Comparison of Wearable Antenna Designs…”

Tabla de Contenidos: “…The redbook example scenario -- 3.1 Scenario overview -- 3.2 Objectives -- 3.2.1 Make effective use of Kerberos -- 3.2.2 Network Authentication Service -- 3.2.3 EIM in action -- 3.2.4 Managing users in EIM -- 3.2.5 Backing up EIM -- 3.2.6 Kerberos enabling an application -- 3.2.7 EIM enabling an application -- 3.2.8 A second iSeries -- Part 2 Building blocks for single signon and Enterprise Identity Mapping -- Chapter 4. …”

Tabla de Contenidos: “…Cover -- Impressum -- Inhaltsverzeichnis -- Einleitung -- Für wen ist dieses Buch geeignet? -- Für wen ist dieses Buch nicht geeignet? …”

Tabla de Contenidos: “…3.3 Types of Algorithms -- 3.3.1 Brute Force Algorithms -- 3.3.2 Divide and Conquer Algorithms -- 3.3.3 Dynamic Programming Algorithms -- 3.3.4 Greedy Algorithms -- 3.3.5 Branch and Bound Algorithms -- 3.3.6 Recursive Algorithms -- 3.3.7 Back Tracking Algorithms -- 3.3.8 Randomized Algorithms -- 3.3.9 Hill Climbing Algorithms -- 3.4 Performance Analysis -- 3.4.1 Properties of the Best Algorithms -- 3.5 Space Complexity -- 3.5.1 Instruction Space -- 3.5.2 Text Section of a Program -- 3.5.3 Data Space -- 3.5.4 Stack Space -- 3.5.5 Calculating the Instruction Space -- 3.5.6 Calculating the Data Space -- 3.5.7 Size of Data Section -- 3.5.8 Size of Rodata Section -- 3.5.9 Size of bss Section -- 3.6 Apriori Analysis -- 3.7 Asymptotic Notation -- 3.7.1 Big oh Notation (O) -- 3.7.2 Omega Notation (Ω) -- 3.7.3 Theta Notation (θ) -- 3.7.4 Little oh Notation(o) -- 3.8 Time Complexity -- 3.8.1 Time Complexity Analysis of Bubble Sort -- 3.8.2 Time Complexity Analysis of Selection Sort -- 3.9 Worst Case, Average Case and Best Case Complexity -- 3.9.1 Worst Case -- 3.9.2 Average Case -- 3.9.3 Best Case -- Summary -- Exercises -- Chapter 4 Arrays -- 4.1 Introduction -- 4.1.1 Array -- 4.2 Array Types -- 4.2.1 Single-dimensional Array -- 4.2.2 Multi-dimensional Array -- 4.2.3 N-dimensional Array -- 4.3 Array Representation -- 4.4 Initializing Arrays -- 4.5 Accessing Values of an Array -- 4.6 Array Operations -- 4.6.1 Traversing -- 4.6.2 Insertion -- 4.6.3 Deletion -- 4.6.4 Sorting -- 4.6.5 Searching -- 4.7 Arrays as Parameters -- 4.8 Character Sequences -- 4.9 Applications -- Summary -- Exercises -- Chapter 5 Linked List -- 5.1 Introduction -- 5.2 Representation of Linked List in Memory -- 5.2.1 Static Representation -- 5.2.2 Dynamic Representation -- 5.3 Singly Linked List -- 5.3.1 Operations -- 5.4 Circular Linked List -- 5.4.1 Merging of Two Circular Lists…”