❇️پردازش تصاویر دیجیتال

🔸فصل یازدهم:نمایش و توصیف
🔸فصل دوازدهم:تشخیص اشیا

—افزودن متن به عکس
—تشخیص دایره در تصویر
—تشخیص اشیا و تعیین مساحت آن ها
—تعیین زاویه قسمتی از شکل
—امتداد تصویر و تعیین مرکز
—ساختن ماسک دایره ای و مربعی
—شماره بندی
—پیدا کردن ماشین های قرمز در تصاویر ماهواره ای
—تعقیب شی قرمز در ویدیو(وبکم)
—تشخیص و تعقیب صورت در ویدیو(وبکم)
—تشخیص اشیا(وبکم)
—پروژه نهایی


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#متلب #پردازش_سیگنال #پردازش_تصویر #فیلتر #بازسازی_تصویر #بازیابی_تصویر #ویولت #فشرده_سازی #بخشبندی #سگمنتیشن #استخراج_ویژگی #کلاسبندی #بازشناسی_الگو #دیپ_لرنینگ #یادگیری_عمیق
#MATLAB #SourceCode #ImageProcessing #SignalProcessing #Segmentation #FeatureExtraction #DeepLearning #Classification #Wavelet #Filtering #Compression #ReStoration #Reconstruction #CNN

In this video, we demonstrate how to use MATLAB and MQL4 programming languages to forecast the price of gold in the forex market. We'll walk you through the process of time series analysis, which involves analyzing and modeling patterns in historical price data to make predictions about future trends.
https://www.youtube.com/watch?v=7zSKoqd1LXs

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In this MATLAB programming example, we solve an optimal control problem using the Pontryagin's Maximum Principle. We use the state equations, cost function, Hamiltonian, and costate equations to obtain the optimal control. The solution is obtained using the "dsolve" function, and the results are visualized using MATLAB plots. This example is taken from the "Crack Optimal Control" book.

Watch the video on YouTube:
https://www.youtube.com/watch?v=eN9qZ-dOskM

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Genetic Algorithm Optimization in MATLAB: Visualizing Fitness Progression

In this video, we showcase the implementation of a Genetic Algorithm (GA) optimization technique using MATLAB. The GA is applied to optimize a 2-variable function by iteratively evolving a population of candidate solutions. The video demonstrates the fitness progression over generations, with the best, worst, and average fitness values plotted. The algorithm incorporates selection, crossover, and mutation operations to drive the evolution of the population. The function landscape, population, and elite individuals are visualized using contour plots and scatter points. Watch this video to gain insights into how a GA can be utilized for optimization tasks and witness the evolution of the population towards finding optimal solutions.

YouTube: https://youtu.be/SJ1zXyEbl0M

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#GeneticAlgorithm #Optimization #MATLAB #FitnessProgression #EvolutionaryAlgorithms #AlgorithmVisualization

MATLAB online

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#MATLAB #online #MATLAB_online

❇️Transfer Function Coefficient Identification with MATLAB: Markov Series and Hankel Matrix Method

Description:

"Dive into MATLAB as we explore the Markov series and Hankel matrix method for identifying transfer function coefficients. This tutorial guides you through the step-by-step process of extracting numerator and denominator coefficients, shedding light on the intricacies of system identification. Perfect for engineers and enthusiasts looking to enhance their understanding of transfer function modeling.
To watch in
🔻YouTube: https://youtu.be/tuHf-3MOGiM
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#MATLAB #TransferFunction #SystemIdentification #ControlSystems #Engineering #MarkovSeries #HankelMatrix #MATLABCoding #CoefficientIdentification

🟢R2024a Release Highlights🟢
#MATLAB

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❇️Major Updates:
- Computer Vision Toolbox: Deploy YOLOX object detection; conduct team-based labeling; perform real-time visual SLAM.
- Deep Learning Toolbox: Support architectures such as transformers; import and co-simulate PyTorch and TensorFlow models.
- GPU Coder: Generate generic CUDA for deep learning; use single memory manager and profile code for MEX code generation.
- Instrument Control Toolbox: Use the Instrument Explorer app to manage devices with IVI and VXIplug&play drivers without writing code.
- Satellite Communications Toolbox: Model multiplatform scenarios and perform visibility and communications link analyses on them.
- UAV Toolbox: Design and deploy flight controller for a vertical take-off and landing (VTOL) UAV with PX4 hardware-in-the-loop simulation; interface with PX4 Cube Orange Plus and Pixhawk 6c autopilots.

❇️Transitions:
- Simulink 3D Animation: Simulate and visualize dynamic systems in Unreal Engine 5.1 with new prebuilt scenes, actors, and sensors.
- SoC Blockset: Prototype and test on SDR and vision hardware with SoC Blockset Support Package for Xilinx Devices.

❇️MATLAB and Simulink Updates:
- Editor Spell Checker: Check spelling in text and comments in MATLAB code files.
- Simulink Editor: Preserve signal line shape when moving and resizing blocks.

❇️MATLAB:
- Local Functions: Define functions anywhere in scripts and live scripts.
- Python Interface: Convert between MATLAB tables and Python Pandas DataFrames.
- Python Interface: Interactively run Python code with Run Python Live Editor task.
- REST Function Service: Call MATLAB functions from any local or remote client program using REST.
- Secrets in MATLAB Vault: Remove sensitive information from code.
- ode Object: Solve ODEs and perform sensitivity analysis using SUNDIALS solvers.

❇️Simulink:
- Simulink Solver: Use local solvers for components with faster dynamics.
- Simulation Object: Control the execution and tune parameter values of scripted simulations.
- MATLAB Apps: Create a custom app that interfaces with a Simulink model using MATLAB App Designer.

❇️Support Packages
- 6G Exploration Library for 5G Toolbox
- Audio Toolbox Interface for SpeechBrain Library
- Computer Vision Toolbox Model for Pose Mask R-CNN 6-DOF Object Pose Estimation
- Databricks ODBC Driver
- Embedded Coder Support Package for Infineon AURIX TC3x Processors
- Lidar Toolbox Model for RandLA-Net Semantic Segmentation
- Lidar Toolbox Support Package for Hokuyo Lidar Sensors
- MariaDB ODBC Driver
- PostgreSQL ODBC Driver

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#matlab_2024

🔺Fuzzy System Optimization Step-by-Step: Enhancing Interpolation with Genetic Algorithms🔻:

✳️In this detailed tutorial, we dive into the complex world of fuzzy system optimization using a genetic algorithm. Watch as we methodically enhance a fuzzy model to predict outputs more accurately through successive generations of optimization. Starting with the basics, the video explains the generation of membership functions for inputs and outputs and how they evolve through various iterations to minimize error.

🔻YouTube: https://youtu.be/KXM-diXXEyE
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#FuzzyLogic #GeneticAlgorithm #SystemOptimization #MachineLearning #ArtificialIntelligence #DataScience #EngineeringTutorials #MATLAB #OptimizationTechniques #AlgorithmDevelopment #MATLAB_2024

⚜️Neural network course session two::
2️⃣Neuron Model and Network

🔵Explore neuron models and neural network architectures in this comprehensive session. Understand the mathematical foundations of these computational models. Study single and multiple-input neuron models, transfer functions, and how neurons form network building blocks. Discover single-layer, multi-layer, and recurrent network architectures designed for various problem complexities. Learn about feedback loops enabling temporal behavior in recurrent networks.

✅Neuron Model
✅Transfer Functions
✅Network Architectures
✅Recurrent Networks
🔻YouTube: third session
https://youtu.be/DvaMtUP095Q
Download file and codes (in comment)::
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#NeuralNetworks #NeuronModels #NetworkArchitectures #ArtificialNeurons #TransferFunctions #SingleLayerNetworks #MultiLayerNetworks #RecurrentNetworks #DeepLearning #NeuralNetworkDesign #ComputationalModels #MATLAB #MATLABCourse #NeuralNetworkCourse

🔰Linear Control Training Workshop - Session 2

🔹Partial Fraction Expansion: Learn how to use the residue() command to easily perform partial fraction expansion on transfer functions. See examples of expanding proper and improper rational functions.
🔸Transforming Mathematical Models: Discover how to convert between different representations of dynamic systems using commands like tf2ss, ss2tf, zp2tf, etc. Examples show conversions between transfer functions, state-space models, pole-zero form, and discrete-time systems.
🔹Block Diagram Modeling: Master the techniques for representing interconnected systems with transfer function or state-space blocks. Learn the MATLAB syntax for series, parallel, and feedback connections. See how to extract the overall transfer function or state-space model.
🔸Telegram:
🆔Channel: @MATLAB_House

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#MATLAB #ControlSystems #DynamicSystems #TransferFunctions #StateSpace #BlockDiagrams #ModelConversion #PartialFractions #MATLABTutorial #ModelingAndAnalysis

⚜️Neural network course session three::
3️⃣An Illustrative Example

🔵In this MATLAB tutorial, learn how to implement Principal Component Analysis (PCA) and Anchor Graphs for dimensionality reduction. The video covers the core concepts, provides step-by-step code explanations, and demonstrates how to visualize and compare results. By the end of this tutorial, you'll be able to apply PCA and Anchor Graphs to your own datasets in MATLAB. Suitable for both beginners and experienced users.

✅Visualizing PCA results in MATLAB
✅Introduction to Anchor Graphs and their advantages
✅Constructing Anchor Graphs in MATLAB
✅Using Anchor Graphs for efficient dimensionality reduction
✅Comparing PCA and Anchor Graph results
🔻YouTube: third session

Download file and codes (in comment)::
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#MATLAB #PCA #PrincipalComponentAnalysis #AnchorGraphs #DimensionalityReduction #MachineLearning #DataScience #Tutorial #Eigenvectors #Covariance #DataVisualization #Code #Programming

🔰Linear Control Training Workshop - Session 3

🔵Learn how to analyze the transient response of control systems using MATLAB in this comprehensive tutorial video. We cover step response, impulse response, ramp response, and response to arbitrary inputs. Discover how to obtain key parameters like rise time, peak time, maximum overshoot, and settling time. We also explore generating 3D plots of response curves. Improve your understanding of control system behavior and master transient response analysis with MATLAB.
🔸Telegram:
🆔Channel: @MATLAB_House

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#MATLAB #ControlSystems #TransientResponse #StepResponse #ImpulseResponse #RampResponse #RiseTime #PeakTime #Overshoot #SettlingTime #3DPlots #EngineeringTutorial #ControlTheory

⚜️Neural network course session four::
4️⃣Perceptron Learning Rule

🔵In this MATLAB tutorial video, we dive into the fundamentals of the Perceptron Learning Rule, a powerful algorithm for training single-layer neural networks. Through practical examples and step-by-step explanations, you'll learn how to implement the Perceptron Learning Rule in MATLAB to solve linearly separable classification problems.
We cover key concepts such as:

✅Perceptron architecture and decision boundaries
✅Supervised learning and training sets
✅Weight and bias updates using the Perceptron Learning Rule
✅Convergence and limitations of the Perceptron network
🔻YouTube: third session

Download file and codes (in comment)::
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#MATLAB #MachineLearning #NeuralNetworks #PerceptronLearningRule #AI #ArtificialIntelligence #DeepLearning #DataScience #Programming #Tutorial

🔰Linear Control Training Workshop - Session 4

🔵In this MATLAB tutorial video, we dive into the powerful control systems analysis and design capabilities of MATLAB. Learn how to create and interpret root locus plots to analyze system stability and transient response characteristics. We then explore various control system compensation techniques, including lead, lag, and lag-lead compensation, and how to design compensators using the root locus approach.

🔸Generating root locus plots with MATLAB
🔹Effects of poles and zeros on root locus shape
🔸Finding gain values at points on the root locus
🔹Plotting root loci with damping ratio and natural frequency lines
🔸Lead compensator design
🔹Lag compensator design
🔸Lag-lead compensator design
🔹Analyzing compensated vs. uncompensated system responses
🔸Parallel compensation and velocity feedback
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#MATLAB #ControlSystems #RootLocus #SystemStability #LeadCompensation #LagCompensation #LagLeadCompensation #ControlSystemDesign

❇️ساختن ربات تلگرام مخصوص اطلاع اتمام شبیه سازی ها در متلب ❇️

با توجه به زمان‌بر و پیچیده بودن کدها، ایجاد سیستمی برای اعلام اتمام شبیه‌سازی‌ها اهمیت زیادی دارد. در این ویدیو، نحوه ساخت ربات تلگرامی ساده‌ای را آموزش می‌دهیم که از طریق API با متلب ارتباط برقرار می‌کند و پیغام پایان شبیه‌سازی را ارسال می‌کند. این روش به دلیل سادگی و حفظ حریم خصوصی بهتر از ارسال ایمیل است. هنگام اتمام شبیه‌سازی، تنها کافی است دستور sendTelegramMessage('Simulation completed successfully!'); را فراخوانی کنید. همچنین، کدی برای ایجاد هشدار صوتی در سیستم‌های شخصی پس از اتمام کد نیز قرار داده شده که در تابع soundtest قرار دارد و قابل فراخوانی است. تمامی کدها در کامنت‌ها شرح داده شده‌اند.

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#MATLAB #TelegramBot #Simulation #Automation #TechTutorial #Engineering #Coding #SoftwareDevelopment #APIIntegration #TechTips

✳️Deep Belief Network Controller: A Modern Alternative to PID in Simulink

🔰Discover how to replace traditional PID controllers with advanced Deep Belief Network (DBN) controllers in Simulink. This tutorial demonstrates the step-by-step process of implementing a DBN controller, showcasing its advantages over PID in complex control systems. Learn how this cutting-edge AI technique can enhance system performance and adaptability across various engineering applications. Whether you're a control systems engineer, an AI enthusiast, or a student exploring advanced control methods, this video offers valuable insights into the future of intelligent control systems."
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#DeepBeliefNetwork #ControlSystems #Simulink #MachineLearning #PIDController #AIControl #EngineeringTutorial #AdvancedControl #MATLAB #IntelligentSystems

✳️ Deep Network Designer in MATLAB - Quick Guide

🔰 In this tutorial, you’ll learn how to use MATLAB's Deep Network Designer to build and train deep neural networks effortlessly. Whether you're a beginner or advanced user, this step-by-step guide will help you design custom networks, import pre-trained models, adjust layers and hyperparameters, and train/evaluate your models with ease.

Produced by Saeed Heibati and Amirhossein Jalali, with consulting by Naser Pakar.

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#DeepLearning #MATLAB #NeuralNetworks #TransferLearning #AI #MachineLearning #DLInMATLAB #DeepNetworkTutorial

✳️ Guidance, Navigation and Control System Design - Matlab / Simulink / FlightGear Tutorial

🔰 In this video, you will learn how to build a complete guidance, navigation, and control (GNC) system for a rocket/missile that starts from a random position and reaches a specified target using LQR/LQG and Kalman filtering methods for control and estimation. You will learn:

1) How to calculate azimuth, latitude, and longitude
2) Calculate guidance commands, range, miss distance, and elevation
3) Design a Linear Quadratic Regulator/Gaussian (LQR) for a 2D state-space model
4) Build a 3-DOF Simulation using the Aerospace Blockset in Simulink
5) Perform simulation with FlightGear

Produced by Hossein Mostafavi with consulting by Naser Pakar.

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#GNC #GuidanceSystem #NavigationAndControl #RocketSimulation #Matlab #Simulink #FlightGear #LQRControl #KalmanFilter #AerospaceEngineering #MissileSimulation #ControlSystemDesign #3DOF

🎬✨ Title:
🚀 How to Run Local AI Models with MATLAB GUI 🖥🤖

📝📌 Description:
Dive into 🌟 local AI using OLLAMA 🦙! Learn to download and run powerful open-source models (DeepSeek-R1 1.5B, Qwen 0.5B) locally and integrate them into an interactive MATLAB GUI chatbot 🛠🎨.

🚀🌟 You'll Learn:
- ⚙️ Install OLLAMA quickly 💻✨
- 📥 Easily download DeepSeek-R1 and Qwen 📂
- 🎨 Build a user-friendly chatbot in MATLAB 🤖💬
- 🧠 Test AI logical reasoning:
- ✅ Logical inference: Apples 🍎 and fruits 🍓
- ✅ Comparative reasoning: Which is smallest? 📏
- 📊 Compare with online models (Claude.ai, ChatGPT, DeepSeek R1)

👥👩‍💻 For:
- AI enthusiasts exploring private AI solutions 🔐
- Researchers integrating AI and MATLAB 👨‍💻
- Students & academics in NLP experiments 🎓📚

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#OLLAMA #MATLAB #LocalAI #Chatbot #AIIntegration #MachineLearning #Tutorial