matlab experiment

Continue to site >>>

About | Contact | Advertise

MATLAB Projects

Find below some interesting MATLAB projects and tutorials for beginners. These projects are available with source codes and instructions to help you learn and work with MATLAB.

In this project, we are going to learn,

Join 100K+ Subscribers

Your email is safe with us, we don’t spam.

Be a part of our ever growing community.

Copyright © 2023  Circuit Digest . All rights reserved.

Help Center Help Center

• Help Center
• Trial Software
• Product Updates
• Documentation

Manage Experiments

Use the Experiment Manager app to create experiments that run your MATLAB code using a range of parameter values, automatically manage your experiment artifacts, and compare results.

This page contains general information about using Experiment Manager . For information about experiments for your AI workflows, see Experiment Manager (Deep Learning Toolbox) .

Run multiple simultaneous trials or one trial at a time on multiple workers.

Run experiments on a cluster so you can continue working or close MATLAB.

Navigate Experiment Manager using only your keyboard.

Troubleshooting

Debug General-Purpose Experiments

Diagnose problems in your experiment function.

Related Information

• How to Set Up and Manage Experiments in MATLAB

Featured Examples

Convert MATLAB Code into Experiment

Convert your existing MATLAB code into an experiment that you can run using the Experiment Manager app.

• Since R2023b
• Open Script

Experiment with Predator-Prey Equations

Explore different coefficients and initial values for a system of differential equations.

• Open Live Script

Compare Air Resistance Models for Projectile Motion

Calculate projectile trajectories assuming different models for air resistance.

MATLAB Command

You clicked a link that corresponds to this MATLAB command:

Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.

Select a Web Site

Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

• Switzerland (English)
• Switzerland (Deutsch)
• Switzerland (Français)
• 中国 (English)

You can also select a web site from the following list:

How to Get Best Site Performance

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.

• América Latina (Español)
• Canada (English)
• United States (English)
• Belgium (English)
• Denmark (English)
• Deutschland (Deutsch)
• España (Español)
• Finland (English)
• France (Français)
• Ireland (English)
• Italia (Italiano)
• Luxembourg (English)
• Netherlands (English)
• Norway (English)
• Österreich (Deutsch)
• Portugal (English)
• Sweden (English)
• United Kingdom (English)

Asia Pacific

• Australia (English)
• India (English)
• New Zealand (English)

Contact your local office

Top 100 MATLAB Projects for Engineering Students

MATLAB (Matrix Laboratory) is a high-performance language for technical computing that offers a rich set of features for data analysis, visualization, and algorithm development. It is a versatile tool extensively used across various engineering disciplines, from control systems to communications and from computational biology to finance.

Table of Contents

Top 100 MATLAB Projects

For engineering students, MATLAB offers a convenient platform to implement algorithms and simulate systems, providing invaluable practical experience. Below is a compilation of 100 MATLAB projects that provide a diverse range of applications for students in engineering.

• Implement algorithms to filter and analyze ECG data, aiming to identify specific cardiac events.
• Develop image compression algorithms to reduce the file size of various image formats.
• Use MATLAB’s Simulink to create a PID controller and test its efficacy in various control systems.
• Create a predictive model for weather forecasting using historical data.
• Build a speech recognition system to identify specific phrases or commands.
• Use machine learning techniques to predict electric load demand for a specific period.
• Develop a steganography algorithm to embed confidential information within an image.
• Design a digital audio equalizer to adjust the balance of different frequency components in audio signals .
• Implement a Monte Carlo simulation to assess risks in financial or engineering projects.
• Create an algorithm to automatically buy and sell stocks based on predefined conditions.
• Build a system to recognize text characters from images or scanned documents.
• Simulate a wireless communication channel to study the effects of fading, noise, and interference.
• Use neural networks to recognize handwritten digits.
• Create models to simulate the behavior of different electronic circuits.
• Implement the Eigenfaces algorithm for face recognition tasks.
• Use wavelet transform techniques to remove noise from images.
• Simulate radar signals and implement algorithms to detect targets.
• Perform vibration analysis for mechanical systems like bridges, towers, or machinery.
• Solve the Vehicle Routing Problem using optimization techniques to find the most efficient delivery routes.
• Design a voice recognition algorithm that can control smart home devices.
• Implement algorithms like A* or Dijkstra’s algorithm for robotic path planning.
• Use MATLAB to simulate fluid flow in various geometries.
• Use sensor data and machine learning to predict maintenance needs for industrial machines .
• Implement genetic algorithms to find the minimum or maximum of complex functions.
• Create a dashboard for real-time data visualization, using data from various sensors.
• Use natural language processing techniques to analyze the sentiment behind social media posts or customer reviews.
• Model and simulate various queuing systems like bank counters or traffic signals.
• Implement the Particle Swarm Optimization algorithm to solve complex optimization problems.
• Use ant colony optimization algorithms to find optimal routes in a network .
• Develop models to predict the efficiency and output of solar energy systems.
• Use Fourier Transform methods to analyze the frequency content of signals.
• Build a system for automated fingerprint matching and recognition.
• Implement algorithms for real-time noise cancellation in audio signals.
• Use mean-variance analysis to create an optimized investment portfolio.
• Use Dynamic Time Warping algorithms for time-series comparison.
• Build a spam filter for emails using machine learning algorithms.
• Simulate a Brain-Computer Interface for controlling external devices through brain signals.
• Implement algorithms for real-time tracking of moving objects in video feeds.
• Create a product recommendation system for an e-commerce platform.
• Implement the K-means algorithm for clustering large datasets.
• Use machine learning to detect faults or anomalies in electrical systems.
• Implement Huffman coding for lossless data compression.
• Perform non-linear regression analysis on complex datasets.
• Use Kalman filters to combine data from multiple sensors for more accurate readings.
• Study and simulate chaotic behavior in dynamical systems like the Lorenz attractor.
• Build a Convolutional Neural Network (CNN) for classifying images.
• Use cellular automata to generate complex patterns or solve computational problems.
• Implement image processing techniques to detect tumors in medical images.
• Use fractal algorithms for image compression applications.
• Implement machine learning algorithms to detect anomalies in network traffic data.
• Create a numerical solver for the wave equation, suitable for studying phenomena like sound propagation.
• Design energy-efficient communication and computation algorithms for Internet of Things (IoT) devices.
• Use statistical and machine learning techniques to forecast financial market trends.
• Apply machine learning algorithms for segmenting a customer base into distinct categories.
• Simulate different aerofoil shapes to optimize for parameters like lift and drag.
• Develop algorithms for localizing the position of nodes in a wireless sensor network.
• Build predictive models to identify customers who are likely to churn.
• Use spectral analysis techniques to study seismic data for earthquake prediction.
• Implement algorithms for the autonomous navigation of drones in a defined airspace.
• Simulate the behavior of biological neural networks to understand neuron dynamics.
• Develop models to study the behavior and efficiency of photovoltaic cells under different conditions.
• Simulate and optimize the efficiency of wireless power transfer systems.
• Model and simulate heat transfer processes in solids, liquids, and gases.
• Implement a Support Vector Machine (SVM) for classifying data into different categories.
• Use machine learning algorithms to forecast inventory requirements for a retail business.
• Design algorithms for active noise control in environments like factories or automobiles.
• Use sparse coding techniques to reconstruct images from partial or corrupted data.
• Implement algorithms for secure data encryption and decryption.
• Build a system that can interpret and respond to queries posed in natural language .
• Apply hierarchical clustering algorithms to categorize text documents into related groups.
• Use machine learning algorithms to diagnose diseases like diabetes or cancer from medical images.
• Implement a text-to-speech algorithm capable of converting written text into spoken words.
• Build a system to recognize facial expressions in real-time.
• Implement algorithms for 3D surface reconstruction from multiple 2D images.
• Build a basic machine translation system for converting text between different languages.
• Create an algorithm to analyze social networks and measure things like degree centrality or community clustering.
• Implement a real-time traffic monitoring system that adjusts traffic light durations to optimize flow.
• Analyze DNA sequences to identify specific genes or perform alignment operations.
• Develop control systems for autonomous vehicles using sensor fusion and path-planning algorithms.
• Use time-frequency representations like spectrograms to analyze non-stationary audio signals.
• Implement AI algorithms for playing games like Chess, Go, or Tic-Tac-Toe.
• Use survival analysis techniques to study the effect of treatments in medical trials.
• Simulate Multiple-Input, Multiple-Output (MIMO) systems in wireless communication scenarios.
• Create models to optimize various aspects of a supply chain from manufacturing to distribution.
• Implement modern cryptographic algorithms for secure data communication.
• Apply machine learning models to perform semantic segmentation of images into different object categories.
• Solve inverse kinematics problems for robotic arms to find possible joint configurations for given end-effector positions.
• Use Hidden Markov Models (HMMs) to build a speech recognition system.
• Implement algorithms to compute and analyze optical flow in video data.
• Simulate phased array systems used in radar, sonar, or wireless communication.
• Develop a discrete event simulation to model complex manufacturing systems.
• Implement texture analysis algorithms to categorize images based on surface characteristics.
• Optimize the allocation of resources in a cloud computing environment.
• Study and simulate nonlinear control systems for specific industrial applications.
• Implement and compare various real-time scheduling algorithms like Rate-Monotonic or Earliest Deadline First.
• Solve eigenvalue problems applicable in various engineering scenarios like stability analysis or system dynamics.
• Apply machine learning algorithms to detect fraudulent activities in financial transactions.
• Implement deep learning algorithms to detect objects in images or video feeds.
• Use MATLAB to model and analyze environmental data, such as air or water quality.
• Human Activity Recognition Using Sensors – Build a system to recognize human activities like walking, running, or sitting using sensor data.

To conclude, MATLAB offers a rich environment for developing and simulating a myriad of engineering projects. The projects listed above span across various engineering disciplines and levels of complexity, enabling students to choose projects that align with their academic and research interests. These projects not only offer an opportunity to apply theoretical knowledge to real-world applications but also provide valuable hands-on experience that is crucial for an engineering career.

If you liked this article, then please subscribe to our YouTube Channel for Electrical, Electronics, Instrumentation, PLC, and SCADA video tutorials.

You can also follow us on Facebook and Twitter to receive daily updates.

• Top 300 Arduino Engineer Projects
• 100 Wireless Engineering Projects
• Advanced Microcontroller Projects
• Top 100 Counters & Timers Projects
• Top 100 Electrical Machines Projects

Share With Your Friends

Recommended articles.

RFID Projects for Engineering Students

Final Year Engineering Projects for ECE, EEE and EIE Students

300+ Mechanical Engineering Design and Fabrication Projects

Real-time PLC Projects – Industrial Automation

200 Pneumatic and Hydraulic Projects List

500 Electronics Projects for Engineering Students

100 IoT Project Ideas for Beginners – Internet of Things

400+ Power Systems Projects – Best Electrical Project Ideas

100 VHDL Projects for Engineering Students

Top 100 Industrial Internet of Things Projects (IIoT)

Leave a Comment Cancel reply

More articles.

175 Electronics and Communication Engineering Projects

100 MTech Project Ideas for Electronics and Electrical Engineering

Top 100 Biomedical Instrumentation Engineering Projects for Students

Top 300 Arduino Projects for Engineering Students

Top 300 Digital Signal Processing Projects – DSP Project Ideas

300 Power Electronics Projects – Top Electrical Major Project Titles

300+ Best Electronics Mini Projects

600+ Digital Image Processing Projects – DIP Project Ideas

• Onsite training

3,000,000+ delegates

15,000+ clients

1,000+ locations

• KnowledgePass
• Log a ticket

01344203999 Available 24/7

Top 18 MATLAB Projects For Engineers & Beginners

Check out the top 18 MATLAB Project ideas. Start building these projects to gain real-time experience in MATLAB. Read more! Explore exciting hands-on Projects ranging from face recognition systems to image compressors that enhance your skills and understanding of MATLAB and offer a practical way to elevate your MATLAB proficiency.

Exclusive 40% OFF

Training Outcomes Within Your Budget!

We ensure quality, budget-alignment, and timely delivery by our expert instructors.

Share this Resource

• Predictive Analytics Course
• Text Mining Training
• Probability and Statistics for Data Science Training
• SPSS Masterclass

MATLAB is a powerful software tool for Data Analysis, simulation, visualisation, and programming. It is widely used by Engineers, Scientists, and students across various domains and disciplines. MATLAB Projects can help you explore new ideas, demonstrate your creativity and expertise, and showcase your potential to employers and peers. 

Are you seeking exciting and challenging MATLAB Projects to boost your skills and portfolio? Do you want to learn new concepts and apply them to real-world problems using MATLAB? If yes, then you are in the right place. 

This blog will introduce you to the top 18 MATLAB Projects for Engineers and beginners, covering a wide range of topics and applications. So, without further ado, let us dive into them.

Table of Contents 

1) Top 18 MATLAB Projects

    a) Vehicle Number Plate Detection 

    b) Automatic Certificate Generation Using MATLAB 

    c) Light animations with Arduino and MATLAB 

    d) Audio Compression using Wavelets in MATLAB 

    e) Voice-based Biometric System 

    f) Two-level Security System 

    g) Face Recognition System 

    h) Artificial Neural Network simulation 

    i) Hearing Aid System 

    j) JPEG Image Compressor

2) Conclusion 

Top 18 MATLAB Projects

Matrix Laboratory, referred to as ‘MATLAB’, is a software environment used by engineers and researchers for high-power computing and Data Visualisation. Implemented Projects across domains such as science, engineering, computational biology, applied physics and so on.   

Here is a descriptive list of MATLAB Projects for beginners to work on: 

Vehicle Number Plate Detection  

Estimated completion time: A few days to 1 or 2 months 

Project description:

This MATLAB Project can be used to detect the number plate on any vehicle with the help of images stored in a database. The  Project aims to first detect the vehicle’s license plate and then extract the relevant information about that vehicle.

Any government authority will have a mandate for owners to register their motor vehicles. This mandate ensures that a link is established between the owner and the vehicle. This registration number is typically in an alphanumeric format and makes the vehicle a uniquely identifiable entity in the authority’s database.   

Vehicle number plates also comprise features like various colours, fonts, and sizes depending on the country’s regulations. This Project will be of benefit, especially for detecting vehicle license plates at interstate borders and airports. 

The number plate detection Project can be further implemented in the following areas: 

1) City traffic analysis during peak times 

2) Improved vehicle theft prevention  

3) Better enforcement of traffic regulations 

Automatic Certificate Generation Using MATLAB

Estimated completion time:  A few days to 1 or 2 weeks    Project description:

This Project is designed to generate certificates for various events like conferences, seminars, workshops, and so on. The MATLAB source code can also be extended to generate analysis reports for large-size data sets.

Here is a description of how the Project code works: 

1) The code takes input from the blank certificate’s file name.  

2) The details to be entered on the certificate are obtained from an Excel sheet.  

3) The data is then written on the blank certificate, followed by the generation of many similar certificates by the MATLAB source code. 

4) The certificates thus generated by the code are then saved into a folder with a unique name.  

5) The main MATLAB code, the blank certificate and the registration details are saved in the same folder. 

The analysis of large data sets is a time-consuming task in Big Data and modelling sensors. This Project can be extended and customised depending on the application requirements. 

Light animations with Arduino and MATLAB

Estimated completion time: Few hours to several weeks.    Project description:

Light animations are massively utilised for advertising purposes. This Project is intended to control the glow patterns of many light-emitting diodes (LEDs). The code includes a Graphical User Interface (GUI) developed using MATLAB, which will be of great benefit for users to control the illumination patterns.  

This Project lets users create five distinct lighting patterns by clicking the corresponding buttons in the GUI. They can also control the LED’s blinking speed by utilising the GUI buttons. The Project components comprise an Arduino Uno board, eight resistors and eight LEDs. The hardware parts will be programmed through the Arduino IDE (Integrated Development Environment), which can be downloaded for free. The Arduino board used will be the ATmega328P microcontroller, which is a high-performance and low-power consumption hardware.   

The MATLAB package required for this website will be the ‘Legacy MATLAB and Simulink Support for Arduino’ from the MathWorks website. After the installation of the Arduino software and the MATLAB packages for the GUI, the user can then set up the COM port and experiment with the lighting patterns.   

Learn to develop scalable electronic gadgets. Sign up for the Introduction of Embedded C Programming course now!  

Audio Compression using Wavelets in MATLAB

Estimated completion time: A few  hours to several weeks 

Audio compression is a great example of digital signal processing. Compression is a process which controls the dynamic range of an audio signal. This process reduces the level of the signal so that it can be heard clearly and uniformly. The range of audio frequencies covers 20Hz to 20Khz, with every frequency band being heard differently. Humans are most sensitive to the range of 2000 to 5000Hz.  

MATLAB is utilised as one of the best tools for analysing and processing audio signals. It is very important for audio to be distributed and streamed over the internet as quickly as possible. This makes it necessary for audio to be compressed for online transmission. Raw audio files are significantly large and need to be reduced for internet streaming and consumption.  

The most important parameters to be analysed in MATLAB for an audio signal are the Peak Signal to Noise Ratio (PSNR), the ratio of compression and the normalised root-mean-square error (NRMSE). These parameters help measure the quality between the original signal and its compressed version.   

A wavelet is a signal oscillation that starts at zero, increases or decreases in amplitude and returns to zero one or more times. Each of these wavelets generally varies in frequency ranges and shapes. The Project utilises the Haar Wavelet algorithm to perform various functions, and let's explore some of them below:

a) Identifying the signal size

b) Amplitude and frequency

c) Signal spectrum decomposition

d) Psychoacoustic modelling

e) Calculation of the compressed signal’s size

Moreover, the Duabenches wavelet transform is also utilised to compress the audio signal. This wavelet transform is used as a tool to analyse the signal and break it down into smaller chunks. Additional functions are performed, such as adjusting the compression percentages, rewriting the signal and calculating the size of the compressed version. This transform algorithm is best suited to compress signals with minimal loss.  

Users can run the audio compression file on MATLAB through the GUI buttons and compare the file sizes before and after the compression process. The compressed version is generated as a .wav file in the same path as the source file.  

Voice-based Biometric System

Estimated completion time:  A few hours  

Users can create their own voice-based biometric system using speech processing techniquesUsers can create their own voice-based biometric system using speech processing techniques.

This Project lets users develop a system that can take the input of a human voice and compares it to voice samples stored in the database. The system will then grant access if the human input matches any of the stored samples. 

More importantly, the system should also be capable of denying access if the input voice is not matching with the stored samples in the database. The advantage of developing a voice-access-based biometric system is that it can be used with other systems whose access is limited to a few users.  

Users will have to design the system’s algorithm so that it can detect the letters and words from the input voice of the human. Additionally, the user can design the system to detect whether the input voice falls in the frequency range of human speech.  

Systems like this are generally based on Deep Neural Networks (DNN) and Feature fusion, which are technologies initially developed as part of the Defence Advanced Research Projects Agency (DARPA) program in the US. The networks used to process arbitrary inputs in signals make Neural Networks ideal for voice recognition Projects. 

The user can run the voice-based biometric system Project script in MATLAB. The MATLAB environment will then display the original signal based on the directory of .wav files provided by the user. They can then experiment with the accuracy parameters to reduce the noise levels in the audio files.  

Learn about Neural Networks and Deep Learning. Sign up for the Neural Networks with Deep Learning Training course now!  

Two-level Security System

Estimated completion time: A few hours  

This Project lets users create a security system that has two layers: such as password authentication and fingerprint recognition. This means that a user will have to authenticate their credentials twice to be granted access. The design of this system aims to reinforce stringent security measures.  

Multi-layered security systems are well-known to protect confidential data against unauthorised access. An example of a notable security system is the Bosch Access Control, reputed across Europe for its encryption standards. 

Fingerprints of multiple users can be stored in the database, and distinct login credentials can be assigned to each user. The user will provide their fingerprint as input after their entered credentials match the database samples. If a match is not found, the user is denied access. 

The industrial technologies utilised for fingerprint recognition are typically optical, ultrasound and silicon. Minutiae and Pattern matching are algorithms for fingerprint recognition. The former compares details with the input ridges and the latter compares the overall features of the input prints. Storage and comparison are two procedures pivotal to biometric systems.  

The larger dataset demands enhanced digitisation and compression of information. Fingerprint readers generally identify based on specific features like ridges, merging lines and loops instead of the full print. 

The features of MATLAB environment will perform functions such as reading and writing grayscale and colour images, displaying image histograms, converting RGB to grayscale images, and specifying display ranges. 

Face recognition system

Estimated completion time: A few hours 

This MATLAB Project lets users design a recognition system based on Neural Networks and image processing techniques. Facial recognition is known as among the most challenging development tasks with computing systems. The MATLAB array environment helps users create this project by implementing both these algorithms. 

More importantly, users can expect to face a complex process while working on the project. This means that having a bare minimum level of experience with the MATLAB software, along with proficiency in utilizing MATLAB operators , will benefit them for the project work. The project will require the implementation of an Artificial Neural Network (ANN) algorithm combined with an image processing approach, making a solid understanding of MATLAB tools and operations crucial for successful execution.

Moreover, the Project will also involve analysing images of human faces using the Discrete Cosine Transform (DCT) algorithm. The DCT can contain the most important visual information from an image. Additionally, the ANN will help to train the system and identify the different features required to recognise human faces. 

Here are the key components for MATLAB Projects for face recognition: 

1) The face recognition system uses the Local Binary Pattern Histogram (LBPH) algorithm. 

2) The ‘CascadeObjectDetector’ system object is intended for human face detection. 

3) The ATMEL microcontroller and a Zigbee module for Arduino or Raspberry Pi. 

4) Continuous visual monitoring via the mobile robot’s attached camera. 

The mobile robot used above is designed to move across areas guided remotely by a person on a computer. The Zigbee module is used by the robot for navigation and wireless vision. It acts as the medium of communication between the robot and the computer controller. Moreover, this robot can also be used by authorised personnel already existing in the database. The automatic identification and verification of human faces make this system the least obtrusive biometric measure.  

Artificial Neural Network simulation  

Estimated completion time: max 10 hours    Project description:

This MATLAB Project helps users simulate the human brain’s function. Simulating the human brain is deemed a very complex engineering task because of the Artificial Neural Network (ANN) that needs to be implemented. The Neural Network is simulated with the combined use of MATLAB and LabVIEW. LabVIEW is a graphical environment used by engineers for researching and testing systems. 

Users can prepare themselves with the knowledge of Perceptron, a Machine Learning algorithm utilised for supervised learning. This is also one of the easiest ANNs to create, and it will enhance the user’s understanding of Neural Network theory. The user may need to implement the Perceptron algorithm in their project.  

Users will benefit by becoming familiar with the fundamental concepts of Neural Networks before starting their Project. Moreover, having an idea of developing Neural Networks using MATLAB will help immensely.    Users are offered special toolboxes for Machine Learning, Deep Learning, and Computer Vision to be applied in their MATLAB Projects. Users can abide by a four-step process for building any Neural Network, such as: 

1) Cleaning and preparing their data 

2) Designing and tuning AI models 

3) Testing simulations on complex systems 

4) Deployment on enterprise systems  

Users can ensure they gather enough labelled data to train for their Neural Networks. They can use the Statistics and Machine Learning Toolbox for training purposes. 

Learn to create algorithms for applications in Machine Learning. Sign up for the Machine Learning  Course now!  

Hearing Aid System

Estimated completion time: 4 to 6 hours     Project description :

Users can design algorithms for their MATLAB Projects that aim to process input audio signals and produce the modified signal with an applied set of functions. The input audio signal can be given to the hearing aid either through a mic or an audio file. The hearing aid system will then remove noise from the signal with an adaptive filter. 

The adaptive filter is a digital filter in MATLAB that makes the system capable of clipping the noise and improving the audio quality. Furthermore, a bandpass filter can be added to the system to increase the frequency of the filtered audio. This filter improves the overall quality of the audio delivered as an output.   

The features of the audio signal that the hearing aid system will deliver must be fixed prior to designing the system and writing a relevant algorithm in MATLAB. The digital hearing aid system is generally implemented with a noise reduction filter and an amplitude amplifier. The aim of the system is to be adaptable for patients with normal to moderate hearing loss. 

MATLAB Projects can be validated for FDA/CE regulations  if they conform to IEC standards. MATLAB and Simulink can also help users to design advanced DSP algorithms with real-time testing. This Project, among many similar others, can pass low-latency tests and wireless communications with low-energy levels. 

JPEG image compressor

Estimated completion time: 2 to 4 hours    Project description: 

This MATLAB project will help users get familiar with the fundamentals of image visualization and analysis. It is also recommended if a user understands the importance of a JPEG compressor. The majority of photographs captured from digital cameras are in JPEG format and are quite large in memory size. The average size of a RAW image file from a digital camera is about 9MB, which is thrice as large as a high-quality JPEG photograph. As part of this project, you can explore techniques for efficient image compression, including methods related to Matlab Convolation that can contribute to enhancing the understanding of image processing fundamentals.

Large-sized image files make storage tedious because they demand premium storage devices with larger capacities. This Project will help the user design a JPEG compressor capable of reducing the overall size of images stored in JPEG format. More importantly, users should emphasise conducting lossless compressions. This also ensures that the original image can be reconstructed from its compressed version.  

When users develop the algorithm in MATLAB for this JPEG compressor, they will need to use the DCT algorithm and the wavelet transform methods (Haav and Daubenches). 

Modelling and Simulation of an Armature-controlled DC Motor

Estimated completion time: 5 to 6 hours    Project description:

This Project will enable users to simulate an armature-controlled DC motor using the MATLAB environment. This Project is also best suited for users keen to explore the field of electronics and electrical engineering. A DC motor’s speed can be changed by varying the supplied input voltage.  

Users can aim to evaluate the relationship between the DC motor’s speed and the load torque at various input voltages. They can conduct the Project in two distinct phases, such as: 

1) Preparing the mathematical model of the DC motor system. 

2) Simulating the system. 

3) There are two control strategies that users can implement by using the PI (Proportional Integral) controller. These strategies are linear voltage control and PWM (Pulse Width Modulation) control.  

The MATLAB repository will contain files such as: 

1) The Simulink model of the DC motor. 

2) The Simulink model for the PWM block. 

3) The State space system. 

4) Simulink model of an LVC using a PI regulator. 

5) Simulink model of a PWM control using a PI regulator. 

The output graph will plot the value measures of the angular velocity in radians/second against time in seconds.

Learn to write scripts and functions in MATLAB. Sign up for the MATLAB Masterclass now.

Controlling equipment using a MATLAB-based GUI

Estimated completion time: A few days    Project description:

This MATLAB project will let users control a maximum of four pieces of electrical equipment through their computers. Remotely controlling electrical equipment through a control panel is a common application in many Projects.   

A MATLAB-based GUI improves the user-friendliness of Human-Machine Interface (HMI) applications. Users can transmit control signals in real-time. The components used in this Project are: 

1) Arduino Uno board 

2) SPDT (Single Pole, Double Throw) Relay 

3) Arduino IDE 

4) Legacy MATLAB and Simulink support 

Huffman Encoding and Decoding in MATLAB

Estimated completion time: A few days    Project description :

This MATLAB Project helps users implement the Huffman coding method, which is an algorithm to conduct lossless data compression. This algorithm is an encoding method widely used across the mainstream compression formats like GZIP, BZIP2 and PKZIP. Few programs might utilise only the Huffman coding method, while others use it as part of a compression process having many steps. 

The encoding algorithm is typically used to compress data with variable-length codes. The codes with the shortest length are assigned to the most frequently occurring characters, and the longer codes are assigned to characters occurring much less frequently.  

MATLAB provides users with two functions, namely huffmaneco() and huffmandeco(). The former is used for encoding and takes the signal as an input, whereas the latter is used for decoding and takes the code vector as an input. Users can encode and decode information and generate outputs containing entropy values and probabilities of characters. 

Circuit Design Calculator

Users can develop software in MATLAB that helps them design circuits and lets them analyse and determine the design with inductors, diodes, capacitors and so on. The software can also help users solve their questions regarding complex analogue and digital circuit design. 

This Project provides users with four program options that are written in C++ for calculating the values of components utilised in voltage regulator circuits. Users are recommended to study the concepts of the Zener Voltage Regulator and tank circuits before starting with their Project. Each program asks for user input depending on the purpose and output.  

Gain a comprehensive understanding of C++ for software development. Sign up for the C++ Programming Training course now!  

Antenna analysis and design in MATLAB

This program helps users to develop a program intended to design antenna arrays and analyse antennas. Users are recommended to study or revisit the fundamentals of antenna design and analysis. This includes concepts of signal radiation patterns, efficiency, directivity and so on.  

An antenna array is a pair of two or more antennas, where the signals from both are processed to achieve enhanced performance as compared to a single antenna. The ‘Yagi-Uda’ antenna is the most widely used TV antenna for rooftop placements.  

The MATLAB program can be written on MATLAB version R2008b or higher. Entering appropriate offset values in the program will generate good directivity plots and directivity values. 

Analogue Clock 

Estimated completion time: 2 to 3 hours

Project description: 

The MATLAB Analogue Clock Project is an engaging and practical application that combines fundamental MATLAB programming skills with graphical representation. In this Project, you will create a functioning analogue clock with customisable features.

Begin by establishing the clock's graphical interface using MATLAB's plotting functions. Implement the clock's hour, minute, and second hands, ensuring accurate movement corresponding to the current time. Utilise MATLAB's timer functions to update the clock in real time.

To enhance the Project, consider adding user interactivity, allowing users to set the time or choose different clock styles. Implementing features such as colour customisation, time zone adjustment, or alarm settings can further elevate the complexity and usefulness of the analogue clock.

This Project not only hones your MATLAB programming capabilities but also provides a visually appealing result. It serves as an excellent exercise for understanding Graphical User Interface (GUI) components in MATLAB and strengthening your grasp of time-related functions and event-driven programming.

Implementation of Fast Fourier Transform

Estimated completion time: A few days

Description:

This MATLAB Project focuses on the practical implementation of the Fast Fourier Transform (FFT) algorithm, a crucial technique in signal processing and Data Analysis. The Project aims to provide participants with hands-on experience understanding and applying FFT using MATLAB.

The participants will start with a comprehensive overview of the FFT algorithm, emphasising its significance in efficiently converting a signal from its time-domain representation to the frequency domain. The Project involves:

a) Writing MATLAB scripts to perform FFT on sample signals.

b) Examining the spectral analysis.

c) Interpreting the results.

Throughout the Project, participants will gain insights into optimising FFT parameters, handling various types of signals, and addressing common challenges in signal processing. The estimated completion time ensures that participants have sufficient opportunities to grasp the nuances of FFT implementation and apply their knowledge to real-world scenarios.

By the end of the Project, participants will have a deeper understanding of FFT, a widely used tool in disciplines such as communications, audio processing, and image analysis, enhancing their proficiency in MATLAB and signal processing techniques.

Digital FIR filters 

Estimated completion time: 2 weeks

In this MATLAB Project, we will delve into Digital Finite Impulse Response (FIR) filters. FIR filters play a pivotal role in digital signal processing, offering versatility in various applications such as audio processing, image filtering, and communication systems. The primary objective is to design, analyse, and implement FIR filters using MATLAB.

The Project unfolds in stages, commencing with a comprehensive exploration of FIR filter fundamentals, understanding their characteristics, and a grasp of their theoretical underpinnings. Participants will then transition to the practical phase, applying MATLAB's powerful capabilities to design FIR filters based on specified requirements.

Participants will gain hands-on experience in customising filter responses, adjusting parameters for desired frequency characteristics, and evaluating filter performance. The Project culminates in the implementation of the designed FIR filters to process real-world signals, providing a tangible demonstration of the theoretical concepts in a practical context.

Through this Project, participants will sharpen their MATLAB proficiency, deepen their understanding of digital signal processing concepts, and acquire practical skills applicable in diverse engineering domains. The estimated completion time of two weeks ensures a focused yet immersive exploration of FIR filters in the digital signal processing landscape.

Conclusion 

This blog has described the top 15 MATLAB beginner Projects with a diverse range of applications. The Projects are commonly based on domains like Electronic Engineering, Artificial Intelligence, Machine Learning, Neural Networks, etc. Users can prepare themselves beforehand by studying or revisiting the fundamentals before starting any project.  

Learn Technical Computing and Data Visualisation. Sign up for our MATLAB Masterclass now.

Frequently Asked Questions

MATLAB is a high-performance programming language and environment primarily used for Numerical Computing, Data Analysis, and visualisation. It is widely employed in academia, industry, and research for tasks ranging from algorithm development to simulation and modelling.

MATLAB is primarily written in C and C++. While the core functionality and mathematical computations are implemented in C, certain parts, especially the Graphical User Interface (GUI), use C++. This combination ensures efficiency and performance in different aspects of the software.

The Knowledge Academy’s Knowledge Pass , a prepaid voucher, adds another layer of flexibility, allowing course bookings over a 12-month period. Join us on a journey where education knows no bounds.

The Knowledge Academy offers various Office Application Courses , including MATLAB Masterclass and SPSS Masterclass. These courses cater to different skill levels, providing comprehensive insights into Basic MATLAB Commands .  

Our Office Application Blogs cover a range of topics related to MATLAB, offering valuable resources, best practices, and industry insights. Whether you are a beginner or looking to advance your MATLAB skills, The Knowledge Academy's diverse courses and informative blogs have you covered.  

The Knowledge Academy takes global learning to new heights, offering over 30,000 online courses across 490+ locations in 220 countries. This expansive reach ensures accessibility and convenience for learners worldwide.

Alongside our diverse Online Course Catalogue, encompassing 17 major categories, we go the extra mile by providing a plethora of free educational Online Resources like News updates, Blogs , videos, webinars, and interview questions. Tailoring learning experiences further, professionals can maximise value with customisable Course Bundles of TKA .  

Upcoming Office Applications Resources Batches & Dates

Thu 21st Nov 2024

Fri 14th Feb 2025

Fri 11th Apr 2025

Fri 13th Jun 2025

Fri 15th Aug 2025

Fri 10th Oct 2025

Fri 12th Dec 2025

Get A Quote

WHO WILL BE FUNDING THE COURSE?

My employer

By submitting your details you agree to be contacted in order to respond to your enquiry

• Business Analysis
• Lean Six Sigma Certification

Share this course

Biggest black friday sale.

We cannot process your enquiry without contacting you, please tick to confirm your consent to us for contacting you about your enquiry.

By submitting your details you agree to be contacted in order to respond to your enquiry.

We may not have the course you’re looking for. If you enquire or give us a call on 01344203999 and speak to our training experts, we may still be able to help with your training requirements.

Or select from our popular topics

• ITIL® Certification
• Scrum Certification
• ISO 9001 Certification
• Change Management Certification
• Microsoft Azure Certification
• Microsoft Excel Courses
• Explore more courses

Press esc to close

Fill out your  contact details  below and our training experts will be in touch.

Fill out your   contact details   below

Thank you for your enquiry!

One of our training experts will be in touch shortly to go over your training requirements.

Back to Course Information

Fill out your contact details below so we can get in touch with you regarding your training requirements.

* WHO WILL BE FUNDING THE COURSE?

Preferred Contact Method

No preference

Back to course information

Fill out your  training details  below

Fill out your training details below so we have a better idea of what your training requirements are.

HOW MANY DELEGATES NEED TRAINING?

HOW DO YOU WANT THE COURSE DELIVERED?

Online Instructor-led

Online Self-paced

WHEN WOULD YOU LIKE TO TAKE THIS COURSE?

Next 2 - 4 months

WHAT IS YOUR REASON FOR ENQUIRING?

Looking for some information

Looking for a discount

I want to book but have questions

One of our training experts will be in touch shortly to go overy your training requirements.

Your privacy & cookies!

Like many websites we use cookies. We care about your data and experience, so to give you the best possible experience using our site, we store a very limited amount of your data. Continuing to use this site or clicking “Accept & close” means that you agree to our use of cookies. Learn more about our privacy policy and cookie policy cookie policy .

We use cookies that are essential for our site to work. Please visit our cookie policy for more information. To accept all cookies click 'Accept & close'.

Navigation Menu

Search code, repositories, users, issues, pull requests..., provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications You must be signed in to change notification settings

An awesome list of helpful resources for students learning MATLAB & Simulink. List includes tips & tricks, tutorials, videos, cheat sheets, and opportunities to learn MATLAB & Simulink.

mathworks/awesome-matlab-students

Folders and files, repository files navigation.

MATLAB and Simulink for Students 🏫 📚 💻

Are you a new MATLAB user seeking helpful tips and tricks? Are you a member of a student society in search of engaging workshops? Or perhaps you're looking for opportunities to test your MATLAB skills through student competitions or challenges? Look no further! Our awesome list repository below is a resource that caters to all these needs. Whether you're starting from scratch or aiming to enhance your existing knowledge, you'll find a wealth of information to help you learn MATLAB and make progress on your journey as a student. Explore the repository now and unlock the potential of MATLAB! Follow us on Instagram for more student resources, events, and competitions! @matlab_students 📸

Table of Contents

Self-paced onramps.

• Cheat Sheets
• Academic Discipline Specific Resources
• Hackathons and Capstone Projects
• Student Societies and Clubs
• Interactive Examples and Fun Animations
• Use MATLAB with AI
• Student Career Opportunities
• Student License for MATLAB
• Need Support or Help?

New to MATLAB? Start here! 💻 💡

Check out this section to explore what MATLAB is, how it is utilized in education and industry, and how it can benefit engineers and scientists globally.

Discover and Elevate Your Skills with MATLAB and Simulink Onramps

MATLAB and Simulink Onramps offer a remarkable opportunity to explore a wide range of topics according to your interests and preferred pace. These onramps are designed to be flexible, allowing you to complete them at your convenience while effectively guiding you through various learning objectives. By immersing yourself in these onramps, you can unlock the power of MATLAB and Simulink, elevating your engineering and science skills to new heights.

Cheat Sheets 📘 ✏️

​​ Master MATLAB Functions and Commands with Featured Cheat Sheets

Explore this section to find a collection of featured cheat sheets that provide concise references for learning MATLAB functions and commands. Whether you're a beginner or an experienced user, these cheat sheets offer valuable insights and quick reminders to enhance your MATLAB proficiency. To access our complete library of cheat sheets, visit: Cheat Sheets

Quick Tip! Use Keyboard Shortcuts to Navigate MATLAB

Discipline-Specific Resources ✈️ 🏎️ 🤖 🔬

Explore Additional Resources for Your Academic Discipline

Click on the icon in the table below to access a wealth of additional resources tailored to your academic discipline. See how MATLAB & Simulink are used in the Industry by reading one of our customer stories .

Additional Resources:

• MATLAB and Simulink Webinars/Videos
• Discipline or Industry Specific Resources
• Product-Specific Support
• Additional Online Courses with edX and COURSERA!
• Industry User Stories
• External Language Interfaces
• Interactive Discipline Specific Examples

Student Programs 🏆

Explore Exciting Student Competitions, Hackathons, and Minidrone Contests!

Unleash your creativity and passion by discovering a world of student competitions, hackathons, mini drone contests, and more! This is your chance to showcase your skills, collaborate with like-minded individuals, and tackle real-world challenges. Don't miss out on the thrilling opportunities that await you! Explore them all right here!

Related MATLAB GitHub Resources for Students

MATLAB and Simulink Challenge Projects: Contribute to the progress of engineering and science by solving key industry challenges!

Are you in search of a design or research project idea that has real industry relevance and can make a positive societal impact? Look no further!

Explore this GitHub list of challenge projects to stay up-to-date with technology trends, gain practical skills using MATLAB and Simulink, and contribute to the fields of science and engineering. By participating, you'll enhance your problem-solving abilities and receive official recognition for your accomplishments from technology leaders at MathWorks. Plus, there are rewards waiting for you upon project completion!

Awesome MATLAB Hackathons: Participate in an upcoming Hackathon!

If you're interested in joining one of our sponsored Hackathons, we have something for you too! Check out our Student Hackathons GitHub repository to learn more about these exciting events and the opportunity to win fantastic prizes!

Awesome MATLAB and Simulink Robotics

A list of awesome demos, tutorials, utilities, and overall resources for the robotics community that use MATLAB® and Simulink®: Awesome MATLAB Robotics

Deep Learning Resources for MATLAB and Simulink

A list of demos, tutorials, models, and overall resources for the AI community that use MATLAB® and Simulink®: MATLAB Deep Learning

Resources for Student Societies and Student Clubs

Host an Engaging MATLAB or Simulink Workshop for Your Student Society or Club!

If you're part of a student society or club and want to organize an exciting MATLAB or Simulink workshop, we've got you covered! Discover how you can host a captivating MATLAB Onramp Party or a thrilling Cody competition using the valuable resources provided below.

Please note that while MathWorks cannot offer financial support or prizes for these events, we're here to assist you in creating an unforgettable learning experience for your participants.

Interactive and Fun MATLAB Examples

Use this curated collection of interactive examples and animations to further your knowledge of MATLAB. Perfect for both beginners keen on mastering the basics and experienced users in search of entertainment, this assortment offers a unique blend of learning and fun.

🎲 MATLAB Interactive Examples

Embark on an educational journey with interactive MATLAB modules designed to make learning both fun and effective. These modules include theoretical background, interactive illustrations, knowledge exercises, reflection questions, and application examples for the concepts explored. These can be great to use if you are part of a student society or club and are looking to do a workshop with students.

🎥 Fun MATLAB Animations

Take a break with these MATLAB animations and GIFs, perfect for a light-hearted diversion during your coding endeavors.

What's New in MATLAB and Simulink?

How to use matlab with ai 🤖, student career opportunities 💼.

Join MathWorks and Explore Exciting Career Opportunities!

Internships and Recent Graduates:

If you're interested in joining MathWorks, we have a range of exciting full-time and internship opportunities for students. Visit our students and recent graduates careers page to explore the possibilities.

On-Campus Job Opportunities: Become a MATLAB Student Ambassador!

If you're currently enrolled as a student with over a year left before graduation, consider becoming a MATLAB Student Ambassador on your campus. Discover how you can make an impact and represent MathWorks within your academic community.

Discover inspiring stories of how students have leveraged MATLAB and Simulink to achieve success in their careers. Check out their stories here!

Need a Student License of MATLAB?

Discover if Your School Provides Access to MATLAB & Simulink!

Curious to know if your school provides access to MATLAB & Simulink? Visit our Student License page to find out! Alternatively, if that option doesn't work for you, we also provide an educationally priced MATLAB and Simulink Student Suite License. This license is specifically designed for students and offers a comprehensive set of tools at a discounted rate.

🚀 Special Licensing for Student Startups, Accelerators, and Incubators

If you're involved in a student startup, part of an accelerator, or incubator program, we have exciting news for you! We offer special licensing options for MATLAB and Simulink, tailored to meet the needs of emerging companies.

Learn more about how MATLAB and Simulink can support your startup's journey:

Explore MATLAB and Simulink for Startups

Where to go to get help?

Need Assistance? Get in Touch with Our Support Team!

Students: Technical support from MathWorks is available for activation, installation and bug-related issues. For additional help visit our student resources above or contact your instructor. Reach out to our dedicated support team .

Security policy

Contributors 3.

• MATLAB 100.0%

Design of Experiments

Passive data collection leads to a number of problems in statistical modeling. Observed changes in a response variable may be correlated with, but not caused by, observed changes in individual  factors  (process variables). Simultaneous changes in multiple factors may produce interactions that are difficult to separate into individual effects. Observations may be dependent, while a model of the data considers them to be independent.

Designed experiments address these problems. In a designed experiment, the data-producing process is actively manipulated to improve the quality of information and to eliminate redundant data. A common goal of all experimental designs is to collect data as parsimoniously as possible while providing sufficient information to accurately estimate model parameters.

For example, a simple model of a response  y  in an experiment with two controlled factors  x 1  and  x 2  might look like this:

y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 1 x 2 + ε

Here  ε  includes both experimental error and the effects of any uncontrolled factors in the experiment. The terms  β 1 x 1  and  β 2 x 2  are  main effects  and the term  β 3 x 1 x 2  is a two-way  interaction effect . A designed experiment would systematically manipulate  x 1  and  x 2  while measuring  y , with the objective of accurately estimating  β 0 ,  β 1 ,  β 2 , and  β 3 .

Matlabsolutions.com provides guaranteed satisfaction with a commitment to complete the work within time. Combined with our meticulous work ethics and extensive domain experience, We are the ideal partner for all your homework/assignment needs. We pledge to provide 24*7 support to dissolve all your academic doubts. We are composed of 300+ esteemed Matlab and other experts who have been empanelled after extensive research and quality check. Matlabsolutions.com provides undivided attention to each Matlab assignment order with a methodical approach to solution. Our network span is not restricted to US , UK and Australia rather extends to countries like Singapore , Canada and UAE . Our Matlab assignment help services include Image Processing Assignments , Electrical Engineering Assignments , Matlab homework help , Matlab Research Paper help , Matlab Simulink help . Get your work done at the best price in industry.

Our Services

Matlab assignment help, matlab simulation help, matlab projects help, matlab homework help, matlab research paper help, r programming help, python programming help, cnn assignment help.

Desktop Basics - MATLAB & Simulink

Matrices and Arrays

Array Indexing - MATLAB & Simulink

Workspace Variables - MATLAB & Simulink

Text and Characters - MATLAB & Simulink

Calling Functions - MATLAB & Simulink

2-D and 3-D Plots - MATLAB & Simulink

Programming and Scripts - MATLAB & Simulink

Help and Documentation - MATLAB & Simulink

Creating, Concatenating, and Expanding Matrices - MATLAB & Simulink

Removing Rows or Columns from a Matrix

Reshaping and Rearranging Arrays

Multidimensional Arrays

Numeric Types

Characters and Strings

Dates and Time

Cell Arrays

Function Handles

Data Type Conversion

Data Type Identification

Add Title and Axis Labels to Chart

Create Chart with Two y-Axes

Combine Multiple Plots

Specify Axis Limits

Scripts vs. Functions

Add Functions to Scripts

Create Functions in Files

Clipping in Plots and Graphs

Creating Colorbars

Change Color Scheme Using a Colormap

How Surface Plot Data Relates to a Colormap

How Image Data Relates to a Colormap

How Patch Data Relates to a Colormap

Create and Work with Tables

Out-of-Process Execution of Python Functionality

Linear (LTI) Models

Transfer Functions

State-Space Models

Discrete-Time Numeric Models

Time-Domain Response Data and Plots

Time-Domain Responses of Discrete-Time Model

Time-Domain Responses of MIMO Model

Time-Domain Responses of Multiple Models

Introduction: PID Controller Design

Introduction: Root Locus Controller Design

Introduction: Frequency Domain Methods for Controller Design

DC Motor Speed: PID Controller Design

DC Motor Position: PID Controller Design

Cruise Control: PID Controller Design

Suspension: Root Locus Controller Design

Aircraft Pitch: Root Locus Controller Design

Inverted Pendulum: Root Locus Controller Design

Ball & Beam: Root Locus Controller Design

Introduction: System Modeling

Get Started with Deep Network Designer

Create Simple Image Classification Network Using Deep Network Designer

Build Networks with Deep Network Designer

Classify Image Using GoogLeNet

Classify Webcam Images Using Deep Learning

Transfer Learning with Deep Network Designer

Train Deep Learning Network to Classify New Images

Deep Learning Processor Customization and IP Generation

Prototype Deep Learning Networks on FPGA

Deep Learning Processor Architecture

Deep Learning INT8 Quantization

Compiler Output

Quantization of Deep Neural Networks

Custom Processor Configuration Workflow

Estimate Performance of Deep Learning Network by Using Custom Processor Configuration

Generate Custom Bitstream

dlhdl.Workflow class

Preprocess Images for Deep Learning

Preprocess Volumes for Deep Learning

Transfer Learning Using AlexNet

Time Series Forecasting Using Deep Learning

Create Simple Sequence Classification Network Using Deep Network Designer

Classify Image Using Pretrained Network

Create Simple Image Classification Network

Design and simulation of a boost converter

Design and simulation of a boost converter 2

Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble

Using Signal Analyzer App

Extract Voices from Music Signal

Align Signals with Different Start Times

Find a Signal in a Measurement

Find Peaks in Data

Extract Features of a Clock Signal

Filtering Data With Signal Processing Toolbox Software

Take Derivatives of a Signal

Find Periodicity Using Frequency Analysis

Find and Track Ridges Using Reassigned Spectrogram

Classify ECG Signals Using Long Short-Term Memory Networks

Waveform Segmentation Using Deep Learning

Label Signal Attributes, Regions of Interest, and Points

Introduction to Streaming Signal Processing in MATLAB

Filter Frames of a Noisy Sine Wave Signal in MATLAB

Filter Frames of a Noisy Sine Wave Signal in Simulink

Lowpass Filter Design in MATLAB

Tunable Lowpass Filtering of Noisy Input in Simulink

Signal Processing Acceleration Through Code Generation

Signal Visualization and Measurements in MATLAB

Estimate the Power Spectrum in MATLAB

Design of Decimators and Interpolators

Multirate Filtering in MATLAB and Simulink

Estimate the Transfer Function of an Unknown System

View the Spectrogram Using Spectrum Analyzer

Get Instant 20% Off on Your Assignment

Matlab solutions.

Let's discuss about any of your MATLAB Project. You need not worry with your Matlab Project, when we are here.

MATLAB Help

   Matlab Assignment Help    Matlab Simulation Help    Quantum Computing In Matlab    Python Assignment Help    MATLAB Exam Help    Python Exam Help    Maths Exam Help    UAV Simulation

Quick Links

   my assignment help    Pay Now    Blogs    E-Books    Answers    Privacy Policy    Careers    Sitemap

   Data Analysis with R    Data Analysis with RapidMiner    Data Analysis with Oracle Analytics    Data Analysis with Jupyter Notebook    Data Analysis Assignment Help    Data Analysis with Excel    Data Analysis With Power BI

   Do My MATLAB Assignment    Electric Vehicle Simulation In Matlab    Excel Assignment Help    Electronics Assignment Help    Bioinformatics Assignment Help    Calculus Assignment Help

   Simulink Exam Help    Statistical Analysis in R    Matlab Assignment Solutions    My Assignment Help Services    Mechanical Engineering Online Exam Help

   Java Assignment Help    Online Tutor    Matlab Free Codes    Matlab Course Help

   Matpower Assignment Help    Matlab Simulation Help    Genetic Algorithms In Matlab    Data Analysis with Google Data Studio    Data Analysis with Tableau    Data Analysis with Python

Copyright 2016-2024 www.matlabsolutions.com - All Rights Reserved.

Disclaimer : Any type of help and guidance service given by us is just for reference purpose. We never ask any of our clients to submit our solution guide as it is, anywhere.

• Textbooks by Cleve Moler
• Download chapters and code
• Updates to electronic edition
• Download the E-book and code
• Differential Equations and Linear Algebra
• Solving ODEs in MATLAB

Experiments with MATLAB

by Cleve Moler

Copyright 2011, Cleve Moler.

Reproduction of single copies of this Web edition is permitted for individual use. Reproduction or distribution of multiple copies is not permitted without permission from the author via email at [email protected] .

Download Individual Chapters:

• Fibonacci Numbers
• Calendars and Clocks
• Linear Equations
• Fractal Fern
• Google PageRank
• Exponential Function
• Magic Squares
• TicTacToe Magic
• Game of Life
• Mandelbrot Set
• Ordinary Differental Equations
• Predator-Prey Model
• Shallow Water Equations

Download the Entire E-book:

Download the exm toolbox and app.

Learn Differential Equations

Up close with Gilbert Strang and Cleve Moler.

Cleve’s Laboratory

A collection of experiments using interactive MATLAB apps

Select a Web Site

Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

You can also select a web site from the following list

How to Get Best Site Performance

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.

• América Latina (Español)
• Canada (English)
• United States (English)
• Belgium (English)
• Denmark (English)
• Deutschland (Deutsch)
• España (Español)
• Finland (English)
• France (Français)
• Ireland (English)
• Italia (Italiano)
• Luxembourg (English)
• Netherlands (English)
• Norway (English)
• Österreich (Deutsch)
• Portugal (English)
• Sweden (English)
• United Kingdom (English)

Asia Pacific

• Australia (English)
• India (English)
• New Zealand (English)
• 简体中文 Chinese
• 日本 Japanese (日本語)
• 한국 Korean (한국어)

Contact your local office