Tabla de Contenidos: “…5.2.2.2 Limit and trend checking -- 5.2.2.3 Partial least squares regression -- 5.2.2.4 Bayesian network -- 5.2.2.5 Artificial neural network -- 5.2.2.6 K-means clustering -- 5.2.2.7 Attribute oriented induction -- 5.2.2.8 Hidden Markov model -- 5.3 Remaining Useful Life Identification of Wearing Components -- 5.3.1 Theoretical Background -- 5.3.2 Techniques Catalogue -- 5.3.3 Physical Modelling -- 5.3.3.1 Industrial automation -- 5.3.3.2 Fleet's maintenance -- 5.3.3.3 Eolic systems -- 5.3.3.4 Medical systems -- 5.3.4 Artificial Neural Networks -- 5.3.4.1 Deep neural networks -- 5.3.5 Life Expectancy Models -- 5.3.5.1 Time series analysis with attribute oriented induction -- 5.3.5.2 Application to a pump -- 5.3.5.3 Application to industrial forklifts -- 5.3.5.4 Application to a gearbox -- 5.3.6 Expert Systems -- 5.4 Alerting and Prediction of Failures -- 5.4.1 Theoretical Background -- 5.4.2 Techniques Catalogue -- 5.4.2.1 Nearest neighbour cold-deck imputation -- 5.4.2.2 Support vector machine -- 5.4.2.3 Linear discriminant analysis -- 5.4.2.4 Pattern mining -- 5.4.2.5 Temporal pattern mining -- 5.4.2.6 Principal component analysis -- 5.4.2.7 Hidden Semi-Markov model with Bayes classification -- 5.4.2.8 Autoencoders -- 5.4.2.9 Convolutional neural network with Gramian angular fields -- 5.4.2.10 Recurrent neural network with long-short-term memory -- 5.4.2.11 Change detection algorithm -- 5.4.2.12 Fisher's exact test -- 5.4.2.13 Bonferroni correction -- 5.4.2.14 Hypothesis testing using univariate parametric statistics -- 5.4.2.15 Hypothesis testing using univariate non-parametricstatistics -- 5.4.2.16 Mean, thresholds, normality tests -- 5.5 Examples -- 5.5.1 Usage Patterns/k-means -- 5.5.1.1 Data analysis -- 5.5.1.2 Results -- 5.5.1.2.1 Plotting -- 5.5.1.2.2 Replicability of results -- 5.5.1.2.3 Summary of results…”

Tabla de Contenidos: “…-- 7.3.3 Vollbildmodus -- 7.3.4 Event-Verarbeitung -- 7.3.5 Autostart -- 7.4 Projekt: Ein digitaler Bilderrahmen -- 7.4.1 Zugriff auf das Dateisystem: Das Modul os -- 7.4.2 Python Imaging Library (PIL) -- 7.4.3 Die Programmierung -- 7.5 Projekt: Wahrnehmungstest -- 7.5.1 Die Programmierung -- 7.6 Projekt: Stoppuhr mit Gong -- 7.7 Aufgaben -- 7.8 Lösungen -- Kapitel 8: Objektorientierte Programmierung -- 8.1 Überall Objekte -- 8.2 Klassen und Vererbung bei Python -- 8.2.1 Einführendes Beispiel: Alphabet -- 8.2.2 Qualitätsmerkmal Änderbarkeit -- 8.2.3 Vererbung -- 8.3 Pong revisited -- 8.3.1 Bau eines Fußschalters -- 8.3.2 Die Klasse Canvas -- 8.3.3 Die Programmierung -- 8.4 Renn, Lola renn! …”

Tabla de Contenidos: “…-- 19.3 RF Amplifier Topology -- 19.4 Op Amp Parameters for RF Designers -- 19.4.1 Stage Gain -- 19.4.2 Phase Linearity -- 19.4.3 Frequency Response Peaking -- 19.4.4 −1dB Compression Point -- 19.4.5 Noise Figure -- 19.5 Wireless Systems -- 19.5.1 Broadband Amplifiers -- 19.5.2 IF Amplifiers -- 19.6 High-Speed Analog Input Drive Circuits -- 19.7 Conclusions -- 20 - Designing Low-Voltage Op Amp Circuits -- 20.1 Introduction -- 20.2 Critical Specifications -- 20.2.1 Output Voltage Swing -- 20.2.2 Dynamic Range -- 20.2.3 Input Common-Mode Range -- 20.2.4 Signal-to-Noise Ratio -- 20.3 Summary…”

Tabla de Contenidos: “…Putting it All Together: Analyzing a Malware Executable -- 5.1 Static Analysis of the Sample -- 5.2 Dynamic Analysis of the Sample -- 6. Dynamic-Link Library (DLL) Analysis -- 6.1 Why Attackers Use DLLs -- 6.2 Analyzing the DLL Using rundll32.exe -- 6.2.1 Working of rundll32.exe -- 6.2.2 Launching the DLL Using rundll32.exe -- Example 1 - Analyzing a DLL With No Exports -- Example 2 - Analyzing a DLL Containing Exports -- Example 3 - Analyzing a DLL Accepting Export Arguments -- 6.3 Analyzing a DLL with Process Checks -- Summary -- Chapter 4: Assembly Language and Disassembly Primer -- 1. …”

Tabla de Contenidos: “…Tagging data to a specific region -- 10.3.2. Linear referencing: snapping points to the closest linestring -- 10.4. …”

Tabla de Contenidos: “…-- 2.3 Abschreibung -- 2.3.1 Wie berechnen Sie die lineare Abschreibung‌ für Ihr Auto? -- 2.3.2 Wie berechnen Sie die arithmetisch-degressive Abschreibung‌ für eine Maschine? …”

Tabla de Contenidos: “…4.5.3 Satellite Communication Based on Quantum Computing -- 4.5.4 Machine Learning &amp -- Artificial Intelligence -- 4.6 Optical Quantum Computing -- 4.7 Experimental Realisation of Quantum Computer -- 4.7.1 Hetero-Polymers -- 4.7.2 Ion Traps -- 4.7.3 Quantum Electrodynamics Cavity -- 4.7.4 Quantum Dots -- 4.8 Challenges of Quantum Computing -- 4.9 Conclusion and Future Scope -- References -- Chapter 5 Feature Engineering for Flow-Based IDS -- 5.1 Introduction -- 5.1.1 Intrusion Detection System -- 5.1.2 IDS Classification -- 5.2 IP Flows -- 5.2.1 The Architecture of Flow-Based IDS -- 5.2.2 Wireless IDS Designed Using Flow-Based Approach -- 5.2.3 Comparison of Flow- and Packet-Based IDS -- 5.3 Feature Engineering -- 5.3.1 Curse of Dimensionality -- 5.3.2 Feature Selection -- 5.3.3 Feature Categorization -- 5.4 Classification of Feature Selection Technique -- 5.4.1 The Wrapper, Filter, and Embedded Feature Selection -- 5.4.2 Correlation, Consistency, and PCA-Based Feature Selection -- 5.4.3 Similarity, Information Theoretical, Sparse Learning, and Statistical-Based Feature Selection -- 5.4.4 Univariate and Multivariate Feature Selection -- 5.5 Tools and Library for Feature Selection -- 5.6 Literature Review on Feature Selection in Flow-Based IDS -- 5.7 Challenges and Future Scope -- 5.8 Conclusions -- Acknowledgement -- References -- Chapter 6 Environmental Aware Thermal (EAT) Routing Protocol for Wireless Sensor Networks -- 6.1 Introduction -- 6.1.1 Single Path Routing Protocol -- 6.1.2 Multipath Routing Protocol -- 6.1.3 Environmental Influence on WSN -- 6.2 Motivation Behind the Work -- 6.3 Novelty of This Work -- 6.4 Related Works -- 6.5 Proposed Environmental Aware Thermal (EAT) Routing Protocol -- 6.5.1 Sensor Node Environmental Modeling and Analysis -- 6.5.2 Single Node Environmental Influence Modeling -- 6.5.3 Multiple Node Modeling…”

Tabla de Contenidos: “…Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking…”

Tabla de Contenidos: “…4.3.2 Bayesian Multisource Classification Mechanism -- 4.3.3 A Refined Multisource Bayesian Model -- 4.3.4 Multisource Classification Using the MRF -- 4.3.5 Assumption of Inter-Source Independence -- 4.4 Evidential Reasoning -- 4.4.1 Concept Development -- 4.4.2 Belief Function and Belief Interval -- 4.4.3 Evidence Combination -- 4.4.4 Decision Rules for Evidential Reasoning -- 4.5 Dealing with Source Reliability -- 4.5.1 Using Classification Accuracy -- 4.5.2 Use of Class Separability -- 4.5.3 Data Information Class Correspondence Matrix -- 4.6 Concluding Remarks and Future Trends -- References -- Chapter 5 Support Vector Machines -- 5.1 Linear Classification -- 5.1.1 The Separable Case -- 5.1.2 The Nonseparable Case -- 5.2 Nonlinear Classification and Kernel Functions -- 5.2.1 Nonlinear SVMs -- 5.2.2 Kernel Functions -- 5.3 Parameter Determination -- 5.3.1 t-Fold Cross-Validations -- 5.3.2 Bound on Leave-One-Out Error -- 5.3.3 Grid Search -- 5.3.4 Gradient Descent Method -- 5.4 Multiclass Classification -- 5.4.1 One-Against-One, One-Against-Others, and DAG -- 5.4.2 Multiclass SVMs -- 5.4.2.1 Vapnik's Approach -- 5.4.2.2 Methodology of Crammer and Singer -- 5.5 Relevance Vector Machines -- 5.6 Twin Support Vector Machines -- 5.7 Deep Support Vector Machines -- 5.8 Concluding Remarks -- References -- Chapter 6 Decision Trees -- 6.1 ID3, C4.5, and SEE5.0 Decision Trees -- 6.1.1 ID3 -- 6.1.2 C4.5 -- 6.1.3 SEE5.0 (C5.0) -- 6.2 CHAID -- 6.3 CART -- 6.4 QUEST -- 6.4.1 Split Point Selection -- 6.4.2 Attribute Selection -- 6.5 Tree Induction from Artic fi ial Neural Networks -- 6.6 Pruning Decision Trees -- 6.6.1 Reduced Error Pruning -- 6.6.2 Pessimistic Error Pruning -- 6.6.3 Error-Based Pruning -- 6.6.4 Cost Complexity Pruning -- 6.6.5 Minimal Error Pruning -- 6.7 Ensemble Methods -- 6.7.1 Boosting -- 6.7.2 Random Forest -- 6.7.3 Rotation Forest…”

Tabla de Contenidos: “…8.3 Differential encoding -- 8.4 Speech compression -- 8.5 A-Law and μ-Law companding -- 8.6 Speech sampling -- 8.7 PCM-TDM systems -- 8.8 Exercises -- AUDIO SIGNALS -- 9.1 Introduction -- 9.2 Principles -- 9.3 Digital audio standards -- 9.4 Error control -- 9.5 Interleaving -- 9.6 CD audio system -- 9.7 Digital audio compression -- 9.8 The 44.1 kHz sampling rate -- 9.9 Exercise -- AUDIO COMPRESSION (MPEG-AUDIO AND DOLBY AC-3) -- 10.1 Introduction -- 10.2 Psycho-acoustic model -- 10.3 MPEG audio coding -- 10.4 Backward/forward adaptive bit allocation methods -- 10.5 Comparison between forward and backward adaptive methods -- 10.6 Dolby AC-1 and AC-2 -- 10.7 Dolby AC-3 coding -- 10.8 AC-3 parameters -- 10.9 Exercises -- ERROR CODING PRINCIPLES -- 11.1 Introduction -- 11.2 Modulo-2 arithmetic -- 11.3 Binary manipulation -- 11.4 Hamming distance -- 11.5 General probability theory -- 11.6 Error probability -- 11.7 Combinations of errors -- 11.8 Linear and cyclic codes -- 11.9 Block and convolutional coding -- 11.10 Systematic and unsystematic coding -- 11.11 Feedforward and feedback error correction -- 11.12 Error types -- 11.13 Coding gain -- 11.14 Exercises -- ERROR CODING (DETECTION) -- 12.1 Introduction -- 12.2 Parity -- 12.3 Block parity -- 12.4 Checksum -- 12.5 Cyclic redundancy checking (CRC) -- 12.6 Exercises -- ERROR CODING (CORRECTION) -- 13.1 Introduction -- 13.2 Longitudinal/vertical redundancy checks (LRC/VRC) -- 13.3 Hamming code -- 13.4 Representations of Hamming code -- 13.5 Single error correction/double error detection Hamming code -- 13.6 Reed-Solomon coding -- 13.7 Convolution codes -- 13.8 Tutorial -- DATA ENCRYPTION PRINCIPLES -- 14.1 Introduction -- 14.2 Government pressure -- 14.3 Cryptography -- 14.4 Legal issues -- 14.5 Basic encryption principles -- 14.6 Exercises -- DATA ENCRYPTION -- 15.1 Introduction…”

Tabla de Contenidos: “…Persistent business objects -- 4.1 New WebSphere Digital Media Enabler objects -- 4.1.1 Folder objects -- 4.1.2 Library objects -- 4.1.3 Usage pricing objects -- 4.1.4 E-mail objects -- 4.1.5 Query objects -- 4.2 WebSphere Commerce business objects -- 4.2.1 User subsystem…”

Tabla de Contenidos: “…10.7.4 Transferring the keyring file to WebSphere -- 10.7.5 Checking VSE keyring library -- 10.7.6 Defining an SSL connection factory in WebSphere -- 10.7.7 Adding SSL resource reference in EAR file -- 10.7.8 Redeploying the EAR file -- 10.7.9 Configuring VSE Connector Server for SSL -- 10.7.10 Restarting VSE Connector Server -- 10.7.11 Changing your servlet code to support SSL -- 10.7.12 Configuring an SSL JDBC data source -- 10.7.13 Considerations on SSL key lengths -- 10.7.14 Considerations on different SSL scenarios -- 10.8 Problem determination -- 10.8.1 Activating stdout trace in WebSphere -- 10.8.2 Tracing a servlet -- Chapter 11. …”

Tabla de Contenidos: “…Basic and additional spanloads 8.2.2 ... Linearized lifting-surface theory 8.2.3 ... Lifting-line theory 8.2.4 ... 3D lift in ground effect 8.2.5 ... …”

Tabla de Contenidos: “…4.5.2 AlphaGo Zero -- 4.5.3 AlphaZero -- 4.6 Manipulation von Objekten -- 4.7 Populäre Umgebungen für das Deep-Reinforcement-Learning -- 4.7.1 OpenAI Gym -- 4.7.2 DeepMind Lab -- 4.7.3 UnityML-Agents -- 4.8 Drei Arten von KI -- 4.8.1 Artificial Narrow Intelligence -- 4.8.2 Artificial General Intelligence -- 4.8.3 Artificial Super Intelligence -- 4.8.4 Zusammenfassung -- Teil II -- Die nötige Theorie -- 5 Der (Code-)Karren vor dem (Theorie-)Pferd -- 5.1 Voraussetzungen -- 5.2 Installation -- 5.3 Ein flaches Netzwerk in Keras -- 5.3.1 Der MNIST-Datensatz handgeschriebener Ziffern -- 5.3.2 Ein schematisches Diagramm des Netzwerks -- 5.3.3 Die Daten laden -- 5.3.4 Die Daten umformatieren -- 5.3.5 Die Architektur eines neuronalen Netzes entwerfen -- 5.3.6 Trainieren eines Deep-Learning-Modells -- 5.4 Zusammenfassung -- 6 Künstliche Neuronen, die Hotdogs erkennen -- 6.1 Das Einmaleins der biologischen Neuroanatomie -- 6.2 Das Perzeptron -- 6.2.1 Der Hotdog/Nicht-Hotdog-Detektor -- 6.2.2 Die wichtigste Gleichung in diesem Buch -- 6.3 Moderne Neuronen und Aktivierungsfunktionen -- 6.3.1 Das Sigmoid-Neuron -- 6.3.2 Das Tanh-Neuron -- 6.3.3 ReLU: Rectified Linear Units -- 6.4 Ein Neuron auswählen -- 6.5 Zusammenfassung -- Schlüsselkonzepte -- 7 Künstliche neuronale Netze -- 7.1 Die Eingabeschicht -- 7.2 Vollständig verbundene Schichten -- 7.3 Ein vollständig verbundenes Netzwerk zum Erkennen von Hotdogs -- 7.3.1 Forwardpropagation durch die erste verborgene Schicht -- 7.3.2 Forwardpropagation durch nachfolgende Schichten -- 7.4 Die Softmax-Schicht eines Netzwerks zum Klassifizieren von Fastfood -- 7.5 Zurück zu unserem flachen Netzwerk -- 7.6 Zusammenfassung -- Schlüsselkonzepte -- 8 Deep Networks trainieren -- 8.1 Kostenfunktionen -- 8.1.1 Quadratische Kosten -- 8.1.2 Gesättigte Neuronen -- 8.1.3 Kreuzentropie-Kosten…”

Tabla de Contenidos: “…6.7.3 Data Transformation -- 6.8 Output Data -- 6.9 Design &amp -- Implementation -- 6.9.1 Integration Design -- 6.9.2 High-Level Process Flow -- 6.9.3 Solution Flow -- 6.10 Dashboard Development -- 6.10.1 Landing Page -- 6.10.2 Approach and Design -- 6.10.3 Helpline Information -- 6.10.3.1 Approach and Design -- 6.10.4 Symptom Detection -- 6.10.4.1 Approach and Design -- 6.10.5 Testing Lab Information -- 6.10.5.1 Approach and Design -- 6.10.6 Hospital Information -- 6.10.6.1 Approach and Design -- 6.10.7 Oxygen Suppliers Information -- 6.10.7.1 Approach and Design -- 6.10.8 COVID Cases Information -- 6.10.8.1 Approach and Design -- 6.10.9 Vaccination Information -- 6.10.9.1 Approach and Design -- 6.10.10 Patients' Information -- 6.10.10.1 Approach and Design -- 6.11 Advantages and its Impact -- 6.12 Conclusion and Future Scope -- References -- Chapter 7 A Complete Study on Machine Learning Algorithms for Medical Data Analysis -- 7.1 Introduction -- 7.1.1 Importance of Machine Learning Algorithms in Medical Data Analysis -- 7.2 Pre-Processing Medical Data for Machine Learning -- 7.3 Supervised Learning Algorithms for Medical Data Analysis -- 7.3.1 Linear Regression Algorithm -- 7.3.2 Logistic Regression Algorithm -- 7.3.3 Decision Trees Algorithm -- 7.3.3.1 Advantages of Decision Tree Algorithm -- 7.3.3.2 Limitations of Decision Tree Algorithm -- 7.3.4 Random Forest Algorithm -- 7.3.4.1 Advantages of Random Forest Algorithm -- 7.3.4.2 Limitations of Random Forest Algorithm -- 7.3.4.3 Applications of Random Forest Algorithm in Medical Data Analysis -- 7.3.5 Support Vector Machine Algorithm -- 7.3.5.1 Advantages of SVM Algorithm -- 7.3.5.2 Limitations of SVM Algorithm -- 7.3.5.3 Applications of SVM Algorithm in Medical Data Analysis -- 7.3.6 Naive Bayes Algorithm -- 7.3.7 KNN (K-Nearest Neighbor Algorithm) -- 7.3.7.1 Applications of K-NN Algorithm…”

Tabla de Contenidos: “…WebSphere - DB2 environment -- 3.1 Introduction to the sample scenario setup -- 3.2 Introduction to DB2 drivers for Java -- 3.3 Data source definitions in WAS V5 -- 3.4 The IBM DB2 Universal Driver for SQLJ and JDBC -- 3.4.1 Summary of WAS z/OS external changes for the Universal Driver -- 3.5 Configuring Universal JDBC Driver type 2 connectivity -- 3.5.1 Specifying the Universal JDBC Driver provider -- 3.5.2 Defining Data Sources under this provider -- 3.5.3 Setting/verifying the symbolic environment variables -- 3.5.4 Defining DB2 Universal Driver - General properties -- 3.5.5 Searching for the package to execute -- 3.5.6 Linking to the DB2 libraries -- 3.5.7 Creating a new Application Server -- 3.6 Configuring Universal JDBC Driver type 4 connectivity -- 3.6.1 Using the Universal Driver for type 4 (non-XA) -- 3.6.2 Using the Universal Driver for type 4 (XA) connectivity -- 3.7 Summary -- Chapter 4. …”

Tabla de Contenidos: “…2.1.8 Radiation and Hertz Dipole -- 2.1.9 Fundamental Antenna Parameters -- 2.1.9.1 Radiation Power Density -- 2.1.9.2 Radiation Intensity -- 2.1.9.3 Directivity -- 2.1.9.4 Input Impedance, Radiation and Loss Resistance -- 2.1.9.5 Gain and Radiation Ef ciency -- 2.1.10 Dipole Antennas -- 2.1.11 Pocklington Integro-Differential Equation for a Straight Thin Wire -- 2.2 Introduction to Numerical Methods in Electromagnetics -- 2.2.1 Weighted Residual Approach -- 2.2.1.1 Fundamental Lemma of Variational Calculus -- 2.2.2 The Finite Element Method (FEM) -- 2.2.2.1 Basic Concepts of FEM -- 2.2.2.2 One-Dimensional FEM -- 2.2.2.3 Incorporation of Boundary Conditions -- 2.2.2.4 Computational Example: 1D Problem -- 2.2.2.5 Two-Dimensional FEM -- 2.2.2.6 The Weak Formulation for Generalized Helmholtz Equation -- 2.2.2.7 Computation of Fluxes on the Domain Boundary -- 2.2.2.8 Computation of Sources on a Finite Element -- 2.2.2.9 Three-Dimensional Elements -- 2.2.3 The Boundary Element Method (BEM) -- 2.2.3.1 Integral Equation Formulation -- 2.2.3.2 Boundary Element Discretization -- 2.2.3.3 Constant Boundary Elements -- 2.2.3.4 Linear and Quadratic Elements -- 2.2.4 Numerical Solution of Integral Equations Over Unknown Sources -- References -- 3 Incident Electromagnetic Field Dosimetry -- 3.1 Assessment of External Electric and Magnetic Fields at Low Frequencies -- 3.1.1 Fields Generated by Power Lines -- 3.1.1.1 The Electric Field -- 3.1.1.2 The Magnetic Field -- 3.1.2 Fields Generated by Substation Transformers -- 3.1.2.1 The Electric Field -- 3.1.2.2 The Magnetic Field -- 3.1.3 Assessment of Circular Current Density Induced in the Body -- 3.1.4 On the Basic Principles of Measurement of LF Fields -- 3.1.4.1 Measurement of LF Electric Fields -- 3.1.4.2 Measurement of LF Magnetic Fields -- 3.1.4.3 Comparison of Calculated and Experimental Results…”

Tabla de Contenidos: “…to a file 357 Chapter 23: File Management 359 Directory Madness 359 Calling up a directory 359 Gathering more file info 361 Separating files from directories 363 Exploring the directory tree 364 Fun with Files 365 Renaming a file 365 Copying a file 367 Deleting a file 368 Chapter 24: Beyond Mere Mortal Projects 369 The Multi-Module Monster 369 Linking two source code files 370 Sharing variables between modules 372 Creating a custom header file 374 Other Libraries to Link 378 Chapter 25: Out, Bugs! 381 Simple Tricks to Resolve Problems 381 Documenting the flow 382 Talking through your code 382 Writing comments for future-you 382 The Debugger 383 Debugging setup 383 Working the debugger 385 Setting a breakpoint 387 Watching variables 388 Improved Error Messages 390 Part 6: The Part of Tens 393 Chapter 26: Ten Common Boo-Boos 395 Conditional Foul-Ups 395 == v = 396 Dangerous Loop Semicolons 397 Commas in for Loops 398 Missing break in a switch Structure 398 Missing Parentheses and Curly Brackets 399 Don't Ignore a Warning 399 Endless Loops 400 scanf() Blunders 401 Streaming Input Restrictions 402 Chapter 27: Ten Reminders and Suggestions 403 Maintain Good Posture 404 Use Creative Names 404 Write a Function 405 Work on Your Code a Little Bit at a Time 405 Break Apart Larger Projects into Several Modules 406 Know What a Pointer is 406 Add Whitespace before Condensing 407 Know When if-else Becomes switch-case 407 Remember Assignment Operators 408 When You Get Stuck, Read Your Code Out Loud 409 Part 7: Appendices 411 Appendix A: ASCII Codes 413 Appendix B: Keywords 419 Appendix C: Operators 421 Appendix D: Data Types 423 Appendix E: Escape Sequences 425 Appendix F: Conversion Characters 427 Appendix G: Order of Precedence 429 Index 431.…”

Tabla de Contenidos: “…-- 8.5.2 Setting up Jest -- 8.5.3 The math library to test -- 8.5.4 Your first Jest test -- 8.5.5 Running your first test -- 8.5.6 Live reload with Jest -- 8.5.7 Interpreting test failures -- 8.5.8 Invoking Jest with npm -- 8.5.9 Populating your test suite -- 8.5.10 Mocking with Jest -- 8.5.11 What have we achieved? …”

Tabla de Contenidos: “…11.1.4 Public import Declarations -- 11.1.5 Static import Declarations -- 11.1.6 Selective imports -- 11.1.7 Renaming in imports -- 11.1.8 The module Declaration -- 11.1.9 Module Summaries -- 11.2 Safety -- 11.2.1 Defined and Undefined Behavior -- 11.2.2 The @safe, @trusted, and @system Attributes -- 11.3 Module Constructors and Destructors -- 11.3.1 Execution Order within a Module -- 11.3.2 Execution Order across Modules -- 11.4 Documentation Comments -- 11.5 Interfacing with C and C++ -- 11.6 Deprecated -- 11.7 Version Declarations -- 11.8 Debug Declarations -- 11.9 D's Standard Library -- 12 Operator Overloading -- 12.1 Overloading Operators -- 12.2 Overloading Unary Operators -- 12.2.1 Using mixin to Consolidate Operator Definitions -- 12.2.2 Postincrement and Postdecrement -- 12.2.3 Overloading the cast Operator -- 12.2.4 Overloading Ternary Operator Tests and if Tests -- 12.3 Overloading Binary Operators -- 12.3.1 Operator Overloading [sup(2)] -- 12.3.2 Commutativity -- 12.4 Overloading Comparison Operators -- 12.5 Overloading Assignment Operators -- 12.6 Overloading Indexing Operators -- 12.7 Overloading Slicing Operators -- 12.8 The Operator -- 12.9 Overloading foreach -- 12.9.1 Foreach with Iteration Primitives -- 12.9.2 Foreach with Internal Iteration -- 12.10 Defining Overloaded Operators in Classes -- 12.11 And Now for Something Completely Different: opDispatch -- 12.11.1 Dynamic Dispatch with opDispatch -- 12.12 Summary and Quick Reference -- 13 Concurrency -- 13.1 Concurrentgate -- 13.2 A Brief History of Data Sharing -- 13.3 Look, Ma, No (Default) Sharing -- 13.4 Starting a Thread -- 13.4.1 Immutable Sharing -- 13.5 Exchanging Messages between Threads -- 13.6 Pattern Matching with receive -- 13.6.1 First Match -- 13.6.2 Matching Any Message -- 13.7 File Copying-with a Twist -- 13.8 Thread Termination -- 13.9 Out-of-Band Communication…”