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 |  موضوع: كتاب Mastering MATLAB - A Comprehensive Journey Through Coding and Analysis الثلاثاء 11 يونيو 2024, 12:19 am | |
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أحضرت لكم كتاب
Mastering MATLAB - A Comprehensive Journey Through Coding and Analysis
Kameron Hussain and Frahaan Hussain
و المحتوى كما يلي :
TABLE OF CONTENTS
Title Page
Copyright Page
Mastering MATLAB: A Comprehensive Journey Through Coding.
and AnalysisTable of Contents
Chapter 1: Introduction to MATLAB
1.1 Understanding the MATLAB Environment
What is MATLAB?
Launching MATLAB
The MATLAB Workspace
Command Line vs. Scripting
Help and Documentation
1.2 History and Evolution of MATLAB
Early Beginnings
MATLAB’s Commercialization
Growth and Expanding Capabilities
Graphical User Interface (GUI)
Advancements in Parallel Computing.
Modern Era and Beyond1.3 Basic Syntax and Operations
MATLAB Syntax
Variables and Assignments
Basic Operations
Comments
Built-in Functions
Displaying Results
Scripting and Functions
1.4 MATLAB vs. Other Programming Languages
High-Level vs. Low-Level Languages
Numerical Computing Focus
Interactivity and Quick Prototyping
Extensive Toolbox Support
Simplicity of Syntax
Integration with Other Languages
License and Cost1.5 Setting Up and Navigating the MATLAB Interface
Launching MATLAB
MATLAB Desktop
Creating and Running MATLAB Scripts
MATLAB Documentation and Help
Customizing MATLAB
Online Community and Resources
Closing MATLAB
Chapter 2: Fundamental Programming Concepts
2.1 Variables and Data Types
Variables in MATLAB
Data Types
Variable Assignment and Naming Conventions
Displaying Variables
Clearing Variables
Checking Variable Type2.2 Operators and Expressions
Arithmetic Operators
Relational Operators
Logical Operators
Assignment Operators
Operator Precedence
2.3 Control Structures: If-Else, Switch-Case
Conditional Statements
Nested if-else Statements
Logical Operators in Conditionals
Using break and continue
2.4 Loops: For and While
The for Loop
The while Loop
Loop Control Statements
Vectorization and Efficiency2.5 Functions and Script Files
Functions in MATLAB
Script Files in MATLAB
Function vs. Script
Chapter 3: Advanced Programming Techniques
3.1 Writing Efficient MATLAB Code
Vectorization
Preallocation
Avoiding Global Variables
Profiling and Optimization
Parallel Computing.
Memory Management
Efficient Algorithms
3.2 Debugging and Error Handling.
Debugging Techniques
Error Handling.Common Debugging Scenarios
3.3 Object-Oriented Programming in MATLAB
Classes and Objects
Inheritance
Encapsulation and Access Control
Benefits of Object-Oriented Programming
3.4 Graphical User Interface Development
MATLAB’s GUI Development Tools
GUIDE-Based GUI Development
Programmatic GUI Development
Benefits of GUI Development in MATLAB
3.5 Integrating MATLAB with Other Languages
Interoperability with C/C+ +
Integration with Python
Java Integration
MATLAB Engine APIsWeb Services and REST APIs
Benefits of Integration
Chapter 4: Mathematical Operations and Techniques
4.1 Matrix and Vector Operations
Basics of Matrices and Vectors
Solving Linear Systems
Eigenvalue and Eigenvector Computations
Singular Value Decomposition (SVD)
4.2 Linear Algebra Applications
Solving Systems of Linear Equations
Eigenvalue Problems
Singular Value Decomposition (SVD)
Linear Transformations and Geometry
Applications in Machine Learning.
4.3 Differential Equations and IntegrationSolving Ordinary Differential Equations (ODEs)
Solving Partial Differential Equations (PDEs)
Integration Techniques
4.4 Fourier Transforms and Signal Processing
Discrete Fourier Transform (DFT)
Inverse Fourier Transform
Spectrogram and Short-Time Fourier Transform (STFT)
Filtering and Signal Processing.
Fast Fourier Transform (FFT) for Efficiency
4.5 Optimization Techniques
Linear Programming (LP)
Nonlinear Optimization
Global Optimization
Mixed-Integer Linear Programming (MILP) and Mixed-Integer
Nonlinear Programming (MINLP)Chapter 5: Data Analysis and Visualization
5.1 Importing and Exporting Data
Importing Data
Exporting Data
Data Exploration and Preprocessing.
Data Visualization
5.2 Data Cleaning and Preprocessing
Handling Missing Data
Data Transformation
Data Aggregation and Resampling.
Handling Categorical Data
Data Validation
5.3 Statistical Analysis with MATLAB
Descriptive Statistics
Hypothesis Testing.
Regression AnalysisTime Series Analysis
Data Distributions and Probability
Multivariate Analysis
5.4 Creating Plots and Graphs
Basic Plotting.
Scatter Plots
Bar Charts
Histograms
Box Plots
3D Plots
Customizing Plots
5.5 Advanced Visualization Techniques
Geographic Data Visualization
3D Visualization
Interactive Plotting with App Designer
AnimationCustom Plot Interactions
Specialized Visualization Toolboxes
Chapter 6: Simulink: An Introduction
6.1. Overview of Simulink
Simulink Basics
Block Diagram Modeling
Simulation and Analysis
MATLAB Integration
Targeting Hardware
Application Areas
6.2. Simulink vs. MATLAB: Understanding the Differences
Purpose and Usage
Modeling Paradigm
Representation of Systems
Simulation and Analysis
Customization and ExtensibilityTarget Applications
Integration
6.3. Basic Simulink Models
Blocks in Simulink
Connecting Blocks
Parameters and Block Configuration
Simulink Canvas
Example: Simple Mass-Spring-Damper System
6.4. Simulating Dynamic Systems
Setting Up Simulations
Defining Input Signals
Simulating the System
Analyzing Simulation Results
Example: Pendulum Simulation
6.5. Simulink for Control Systems
Control System Modeling.Controller Design
Simulation and Analysis
Control System Tuning.
Real-Time Testing and Hardware-in-the-Loop (HIL)
Example: Quadcopter Control
Chapter 7: Specialized MATLAB Toolboxes
7.1. Image Processing Toolbox
Basic Image Operations
Filtering and Noise Reduction
Image Segmentation
Morphological Operations
Object Analysis and Measurements
Advanced Image Processing.
Computer Vision and Machine Learning.
7.2. Signal Processing Toolbox
Signal AnalysisFiltering and Filtering Design
Spectral Analysis
Time-Frequency Analysis
Filter Banks and Multirate Signal Processing.
Signal Processing for Communications
Real-World Applications
7.3. Financial Toolbox
Financial Instruments
Risk Assessment and Management
Financial Modeling.
Time Series Analysis
Financial Economics
Real-World Applications
7.4. Robotics Toolbox
Robot Modeling
Robot ControlKinematic and Dynamic Analysis
Visualization and Simulation
Real-World Applications
7.5. Machine Learning Toolbox
Data Preprocessing.
Supervised Learning
Unsupervised Learning.
Deep Learning.
Model Evaluation
Deployment and Integration
Real-World Applications
Chapter 8: Scientific Computing with MATLAB
8.1. Computational Biology
Sequence Analysis
Genomic Data Analysis
Protein Structure and FunctionSystems Biology
Data Visualization
Resources and Toolboxes
Real-World Applications
8.2. Chemical Engineering Applications
Process Modeling and Simulation
Optimization and Design
Data Analysis and Visualization
Chemical Engineering Toolboxes
Real-World Applications
8.3. Physics Simulations
Numerical Simulations
Computational Physics
Mathematical Tools
Visualization and Analysis
Computational Tools and ToolboxesReal-World Applications
8.4. Mathematics and Computational Algorithms
Mathematical Computations
Computational Algorithms
Mathematical Modeling.
Advanced Mathematical Capabilities
Coding and Scripting.
Real-World Applications
8.5. Environmental Science Modeling
Data Analysis and Processing.
Mathematical Modeling and Simulation
GIS Integration
Environmental Monitoring and Management
Visualization and Reporting.
Case Studies
Chapter 9: MATLAB for Engineers9.1. Electrical Engineering Applications
Circuit Analysis and Design
Signal Processing and Communication
Electrical Machine Analysis
Power Systems Analysis
Renewable Energy Systems
Hardware-in-the-Loop (HIL) Testing.
Robotics and Automation
Electromagnetic Analysis
9.2. Mechanical Engineering Simulations
Finite Element Analysis (FEA)
Multibody Dynamics
Structural Analysis and Design
9.3. Civil Engineering and MATLAB
Structural Analysis and Design
Geotechnical Engineering.Transportation Engineering.
Environmental Engineering.
Project Management and Optimization
9.4. Aerospace Engineering: Modeling and Analysis
Aircraft and Spacecraft Design
Flight Dynamics and Control
Orbital Mechanics and Space Missions
Structural Analysis and Materials
Propulsion Systems and Rocketry
9.5. Automotive Engineering: Modeling and Analysis
Vehicle Dynamics and Handling.
Powertrain and Engine Simulation
Control System Design
Autonomous and Connected Vehicles
Noise, Vibration, and Harshness (NVH) Analysis
Data Analysis and Testing.10.1. Writing Academic Papers with MATLAB Figures
Benefits of Using MATLAB for Academic Figures
Creating Publication-Quality Figures in MATLAB
Including MATLAB Figures in Academic Documents
10.2. MATLAB in Thesis and Dissertation Research
Data Analysis and Visualization
Simulation and Modeling
Writing and Documentation
Collaboration and Presentation
10.3. Collaborative Research Using MATLAB
Version Control with Git
Sharing Code and Data
Simulink Collaboration
Real-Time Collaboration
10.4. Data Management and Archiving.
Organizing DataVersion Control for Data
Data Archiving.
Data Licensing.
Data Privacy and Ethics
10.5. Ethical Considerations in Computational Research
1. Data Privacy and Security
2. Research Integrity
3. Responsible AI and Machine Learning.
4. Inclusivity and Accessibility
5. Ethical Use of MATLAB Toolboxes
6. Collaboration and Attribution
7. Compliance with Regulations
8. Ethics Review
Chapter 11: Scripting and Automation
Section 11. 1: Automating Repetitive Tasks
Writing MATLAB ScriptsDefining MATLAB Functions
Control Structures for Automation
Automation in MATLAB Projects
Section 11. 2: Building Custom Functions
Defining Custom Functions
Input and Output Arguments
Local Variables
Best Practices
Section 11. 3: Batch Processing with MATLAB
Understanding Batch Processing.
Example: Batch Processing Data Files
Custom Analysis Function
Batch Processing Advantages
Section 11. 4: Scheduler and Time-Based Operations
Timer Objects
Scheduled TasksReal-Time Data Acquisition
Task Scheduling.
Task Scheduler Toolbox
Section 11. 5: Interfacing with Databases
Database Connectivity
Querying the Database
Updating Database Records
Error Handling.
Closing the Database Connection
Chapter 12: MATLAB for Business and Finance
Section 12. 1: Analyzing Financial Markets
Data Retrieval
Data Visualization
Technical Analysis
Portfolio Optimization
Backtesting Trading StrategiesRisk Management
Section 12. 2: Risk Management Models
Value at Risk (VaR)
Conditional Value at Risk (CVaR)
Portfolio Risk Metrics
Stress Testing
Monte Carlo Simulation
Section 12. 3: Econometrics and Economic Modeling.
Time Series Analysis
Economic Modeling.
Economic Data Visualization
Economic Forecasting
Section 12. 4: Operations Research and Optimization
Linear and Nonlinear Optimization
Integer Programming
Network OptimizationSection 12. 5: Business Intelligence and Data Analysis
Data Exploration
Statistical Analysis
Predictive Analytics
Interactive Reporting.
Chapter 13: Machine Learning and AI with MATLAB
Section 13. 1: Introduction to Machine Learning in MATLAB
Understanding Machine Learning.
Getting Started with MATLAB for Machine Learning
Example of Supervised Learning in MATLAB
Section 13. 2: Supervised Learning Techniques in MATLAB
Classification and Regression
Example: Classification with MATLAB
Regression with MATLAB
Section 13. 3: Unsupervised Learning Approaches in MATLAB
Clustering TechniquesDimensionality Reduction
Anomaly Detection
Section 13. 4: Deep Learning and Neural Networks in MATLAB
Deep Learning Toolbox
Deep Learning Applications
Deep Learning Visualization and Analysis
Section 13. 5: Natural Language Processing with MATLAB
Text Processing.
Sentiment Analysis
Text Classification
Language Translation
Chatbot Development
Text Generation
Section 14. 1: Digital Signal Processing Fundamentals
Understanding Signals
Signal Representation in MATLABSignal Visualization
Signal Operations
Spectral Analysis
Section 14. 2: Image Enhancement and Filtering.
Image Enhancement Techniques
Image Filtering
Displaying Enhanced Images
Section 14. 3: Feature Extraction Techniques
Edge Detection
Blob Detection
Corner Detection
Texture Analysis
Color Histograms
Fourier Transform for Frequency Features
Section 14. 4: Computer Vision with MATLAB
Image Processing ToolboxObject Detection and Recognition
Optical Character Recognition (OCR)
Motion Detection and Tracking.
3D Vision and Stereo Vision
Augmented Reality (AR)
Section 14. 5: Audio Processing and Analysis with MATLAB
Importing and Reading Audio Files
Basic Audio Manipulations
Audio Spectrogram and Visualization
Speech Recognition
Audio Effects and Filters
Music Analysis and Beat Detection
Real-Time Audio Processing
Chapter 15: Biomedical Applications of MATLAB
Section 15. 1: Biomedical Signal Processing
Importing and Visualizing Biomedical SignalsFiltering and Denoising.
Feature Extraction
Heart Rate Variability Analysis
EEG Signal Processing.
Integration with Machine Learning.
Section 15. 2: Medical Image Analysis
Image Preprocessing.
Segmentation
Feature Extraction
Image Registration
3D Image Analysis
Deep Learning for Medical Image Analysis
Section 15. 3: Modeling Biological Systems
Biochemical Reaction Modeling.
Biological Network Modeling
Pharmacokinetic Modeling.Section 15. 4: Bioinformatics with MATLAB
Sequence Analysis
Structural Biology
Genomics and Transcriptomics
Metabolomics and Systems Biology
Next-Generation Sequencing (NGS) Data Analysis
Section 15. 5: Biomechanics and Movement Analysis
Motion Capture and Analysis
Finite Element Analysis (FEA)
Muscle Simulation
Ergonomics and Human Factors
Sports Biomechanics
Rehabilitation Engineering.
Chapter 16: MATLAB in the Internet of Things (IoT)
Section 16. 1: Connecting MATLAB to IoT Devices
Section 16. 2: Data Acquisition from SensorsSection 16. 3: Analyzing loT Data
Section 16. 4: Predictive Maintenance Using MATLAB
Section 16. 5: Cloud Integration and Data Storage Solutions
Chapter 17: High-Performance Computing with MATLAB
Section 17. 1: Parallel Computing in MATLAB
Benefits of Parallel Computing in MATLAB
Types of Parallelism in MATLAB
Example of Multi-Core Parallelism in MATLAB
Section 17. 2: GPU Acceleration Techniques in MATLAB
Benefits of GPU Acceleration
GPU Support in MATLAB
Example of GPU Acceleration in MATLAB
When to Use GPU Acceleration
Section 17. 3: Large-Scale Data Handling in MATLAB
1. Tall Arrays
2. Datastore and Parallel Computing.3. Database Connectivity
4. Memory-Mapped Files
5. Distributed Computing.
Section 17. 4: Distributed Computing and MATLAB
1. Parallel Computing Toolbox
2. Parallel Data Processing
3. Distributed Computing on Clusters
4. Task Synchronization
5. Scaling and Load Balancing
Section 17. 5: Performance Optimization Strategies
1. Vectorization
2. Preallocation
3. Memory Management
4. Algorithm Selection
5. Parallel Computing
6. Profiling and Benchmarking.7. Mex Files
8. Optimized Libraries and Toolboxes
9. Hardware Acceleration
10. Code Review and Optimization
Chapter 18: Case Studies and Real-World Applications
Section 18. 1: Case Study: Environmental Monitoring System
Introduction
Problem Statement
MATLAB Implementation
Benefits
Conclusion
Section 18. 2: Case Study: Financial Forecasting Model
Introduction
Problem Statement
MATLAB Implementation
BenefitsConclusion
Section 18. 3: Case Study: Autonomous Vehicle Algorithms
Introduction
Problem Statement
MATLAB Implementation
Benefits
Conclusion
Section 18. 4: Case Study: Medical Diagnostic Software
Introduction
Problem Statement
MATLAB Implementation
Benefits
Conclusion
Section 18. 5: Case Study: Smart Grid Energy Analysis
Introduction
Problem StatementMATLAB Implementation
Benefits
Conclusion
Chapter 19: Best Practices and Tips for MATLAB Users
Section 19. 1: Effective Coding Practices
Section 19. 2: Documentation and Code Sharing
1. Function Headers and Comments
2. Usage Examples
3. MATLAB Publishing
4. GitHub and Version Control
5. ReadMe Files
6. Documentation Generators
7. Online Platforms and Forums
8. Licensing and Permissions
9. Collaboration and Contribution Guidelines
10. Update and MaintainSection 19. 3: Community and Online Resources
1. MATLAB Central
2. Stack Overflow
3. GitHub
4. MATLAB Documentation and Tutorials
5. Online Courses and MOOCs
6. YouTube Tutorials
7. LinkedIn Groups
8. Research Papers and Journals
9. Contribute to Open Source
10. MathWorks Support
Section 19. 4: Career Opportunities and MATLAB Certification
Career Opportunities
MATLAB Certification
Section 19. 5: Continuing Education and Learning Paths
The Importance of Continuing EducationMATLAB Learning Paths
Chapter 20: The Future of MATLAB
Section 20. 1: Emerging Trends in MATLAB Programming.
1. Interoperability with Other Languages
2. Enhanced AI and Machine Learning Capabilities
3. Cloud Integration and Scalability
4. GPU Acceleration and High-Performance Computing.
5. Explainable AI and Ethical Considerations
6. Integration with IoT and Sensor Data
7. Advancements in Simulink
8. Sustainability and Green Technologies
9. Education and Accessibility
10. Community Collaboration and Open Source
Section 20. 2: Future of Computational Sciences with MATLAB
1. Multidisciplinary Research
2. Quantum Computing.3. Computational Chemistry and Materials Science
4. Climate Modeling and Environmental Studies
5. Personalized Medicine
6. Space Exploration and Astronomy
7. Cybersecurity and Data Privacy
8. Fusion of AI and Scientific Computing
9. Human-Machine Collaboration
10. Educational Advancements
Section 20. 3: MATLAB in Space Exploration and Astronomy
1. Mission Planning and Navigation
2. Data Analysis and Image Processing.
3. Simulating Celestial Phenomena
4. Telescope Control and Automation
5. Gravitational Wave Analysis
Section 20. 4: The Role of MATLAB in Sustainable Technologies
1. Renewable Energy Systems2. Electric Vehicle (EV) Development
3. Sustainable Agriculture
4. Environmental Monitoring.
5. Sustainable Building Design
Section 20. 5: MATLAB and the Advancement of AI and
Machine Learning.
1. Deep Learning Frameworks
2. Data Preparation and Preprocessing.
3. Machine Learning Model Development
4. Explainable AI and Interpretability
5. Deployment and Integration
6. AutoML and Hyperparameter Tuning.
7. Research and Innovation
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