30 Best MATLAB Project Ideas for Electronics Students

Here is the list of the Top 30 Best MATLAB Based Project Ideas for Engineering Students brought to you by Listyaan.

Signal processing, image processing, research, academic, and industrial organizations all often use MATLAB projects for engineering students. Control engineering researchers and engineers were the ones who initially used this. It is also quickly spreading into a variety of other fields.

1. Removal of Background from Image using MATLAB:

We use a variety of background reduction techniques to detect moving objects using static and dynamic cameras. Foreground detection is another name for background extraction.

A picture’s foreground is retrieved for further processing using this foreground detection approach, which is used in the disciplines of image processing and computer vision.

The foreground object boundary extraction decreases the quantity of data that has to be processed while still giving valuable object-specific information. This background removal project scans a collection of photographs and uses removing actions to remove the background from several identical images.

2. Defect Detection In Ceramic Tiles:

This study discusses a novel method for identifying tile flaws that avoids this. A crucial responsibility of the ceramic tile executive is quality jurisdiction. The cost of ceramic tiles is also influenced by the arrangement’s youth, color accuracy, format, etc.

Except for the observation technique for ceramic quality classification, which is still handled manually, the production process in ceramic tile factories is now carried out automatically by industrial computerization systems.

3. Fruit Recognition Using Color Analysis:

This technique utilizes RGB color to identify fruits. The system can identify fruits with distinctive colors. Based on a preset RGB range, the system recognizes fruits. We have used picture pre-processing techniques to increase accuracy.

The system finds the pixels that fall inside the RGB range and chooses the related pixels. The system keeps track of the linked pixels. The system will identify the fruit submitted by the user based on the number of linked pixels.

We utilize Matlab to preprocess the input photographs before using color grading to determine which fruit in the supplied image is the closest match.

4. Signature Verification System:

A system that normalizes the signature picture and verifies that it corresponds to the original signature. The system performs picture pre-processing operations. The picture subtly changes to black and white.

The picture is thinned using morphing technology. Black pixels can be extracted to reveal the signature’s curvature. The original image’s X and Y coordinates are recovered.

By bypassing the freshly created coordinates, the signature is rotated. After rotating the image, the Signature may cross the border, thus we determined the moving x and y coordinates. The picture is then cropped.

5. Image Authentication Based On Watermarking Approach:

A program or system that can incorporate data or a file into a picture. We used MATLAB to develop this system. To conceal information in digital material, we used watermarking techniques. The system will encrypt the whole file’s information.

Image watermarking may be used to effectively embed the file into the image and retrieve the information. To read or write files, we will be interacting with files in MATLAB. The user will submit the image and enter the data into the system using the encrypted uploaded image.

Most user data will continue to be protected. For accurate computation, the system will transform the file text to 16-bit values. The communications will be encrypted by the system using an encryption loop.

6. Person Identification Based On Teeth Recognition:

A mechanism for recognizing people by their teeth had been proposed. This system is implemented using MATLAB. We made advantage of a dental picture database. The dataset’s teeth pictures are compared to the person’s teeth image.

Here, we suggested several image processing techniques for identifying dental images. We employed many preprocessing procedures to eliminate noise from photos since noise is more likely to be present. Values from the extracted teeth pictures are kept in the database.

7. Traffic Signs Detection Using MATLAB:

Traffic signs are identified by this system, and the name of the sign will be produced. Here, we put image processing techniques to work finding traffic signs. The system requires user input for the traffic sign graphics.

The system will use a powerful algorithm to find traffic signs. First, we turned the RGB picture into a grayscale version. To perform image processing procedures, the picture is further transformed into a black-and-white image.

The system will use certain filtering and picture pre-processing methods. Finally, the system will compare the values of the query image with the dataset. The similarity system’s result will be shown in text format.

8. Digital Watermarking To Hide Text Messages:

A method for incorporating a message picture into an RGB image. For embedding, we employed digital watermarking in this case. A method called digital watermarking allows users to include different kinds of data in digital files.

Digital watermarks are patterns or signals that are put into digital information. This approach conceals the black-and-white picture behind the RGB image and retrieves the black-and-white image from the RGB image.

The image must be resized for further processing before watermarking is applied. The block value for the watermark will be changed. The machine will then produce the original image after removing the watermark.

9. Symbol Recognition Using MATLAB:

In a system where symbols are identified automatically, users submit symbol pictures and the system uses an algorithm to determine the symbol. In this system, we used a few image-processing techniques to deal with pictures.

We turned the RGB picture into a grayscale version. To perform further image processing operations, the picture is changed into a grayscale version. The system will produce a dataset including these templates.

The user will supply the query image, and the system will resize it. After comparing the query picture values to the template image values in the dataset, the system will present the results in text format. A picture will be used as the system’s input, and the output will be in text format.

10. Hand Gesture Recognition Project:

A system that uses image processing to identify hand gestures. The system counts the fingers. The system recognizes split fingers above the palm. The system uses filters to first identify the skin tone in the picture.

To provide a correct number of fingers, an image must go through a variety of image preparation procedures. The system locates the closest point to the contour point. Based on the centroid point, the system degrades the image.

To ensure that fingers appeared appropriately in the final image, we added further image preprocessing procedures. Finally, the system counts the fingers and shows the user the number.

11. Orange Fruit Recognition Using Image Segmentation:

The apparatus can recognize orange color in ambient light. We’ll employ edge detection and color detection techniques. Image segmentation is used in this process. To identify the color of the image, we will input photographs of orange that were taken in various lighting conditions using image segmentation.

This project will be carried out using the MATLAB image processing toolkit. To recognize the edges of objects and their colors, we built edge-based and color-based detection methods.

The user will enter an orange picture into this system. The system will change the RGB image to a grayscale image for additional processing. The system will use a variety of filtering approaches because the image is taken in a variety of lighting conditions.

12. Optical character extraction under different illumination conditions:

This study describes a method for character extraction from characters written on paper using the image processing toolbox in MATLAB. Key Approach The intended approach uses an adaptive picture thresholding technique to distinguish the characters on the front from the background.

To increase the intra-class variance of the black and white pixels, the adaptive thresholding approach selects the finest threshold value from a single pixel. The chronological approach to text scanning is introduced in this study.

13. Diabetic Retinopathy Detection From Retinal Images:

Blindness is a result of diabetic retinopathy. One eye condition brought on by the removal of retinal blood vessels is diabetic retinopathy. Blood vessels in the retina, a light-sensitive tissue that lines the back of the eye, are impacted by diabetic retinopathy.

It is the primary cause of vision impairment and blindness in working-age individuals as well as the most frequent cause of vision loss in diabetes patients. Here, a method is developed for identifying eye illnesses that involve removing blood vessels from the retina.

Although manually removing the retinal blood vessels is a laborious process, several automated techniques may be used to shorten the process. An algorithm to extract blood vessels from photos is shown here.

14. Cursor Movement On Object Motion:

A system that uses hand gestures to control the cursor’s movement across the desktop and trigger actions. The system will move objects by RGB color. The system will identify any object with RGB color as a mouse.

In this application, we imported java awt. The item movement will cause the system to trigger an event. Computer screen size will be obtained. One frame will be taken from the video by the system.

The system will turn the image into a binary image, with any red, blue, or green items being represented as white. The system will create a bounding box around any object the user moves around the display. The centroid of the bounding box is determined. The system will do mouse point detection.

15. Brain Tumor Detection Using Image Segmentation:

The system will apply image processing techniques to the picture. Image segmentation is used to identify picture edges. This approach uses picture segmentation to find tumors.

Here, many image filtering methods are suggested and a strategy for segmenting images. In Matlab, this system is in use.

16. Audio Frequency Generator & Response Analyzer:

This project’s goal is to test audio amplifiers in the audible frequency range and evaluate their output response using a Matlab interface that also displays a graph of their amplitude vs frequency (Bode plot). The user may determine whether or not to utilize the amplifier based on a visual inspection of the graph.

17. Image Retrieval Using Feature Extraction:

A system that searches for comparable photos based on visual attributes. We employed many preprocessing procedures to eliminate noise from photos since noise is more likely to be present. Using the feature extraction approach, the features of an image are retrieved and saved in a database.

The feature values of the query image are compared to those of other photos in the directory, and a comparable image is then extracted and shown to the user.

The characteristics are taken from the HSV color space after the image has been quantized into equal bins. Image noise is reduced with a filter. When a test image is provided as input to the application, the system locates its closest neighbor from the training set and retrieves that image.

18. Vehicle Number Plate Extraction Using OCR:

A unique system that uses the input number plate picture to extract characters. We employed many image-preprocessing procedures to extract only the text from the number plate image.

Considering that photos are more prone to noise and other undesirable things. An efficient noise removal technique is used to eliminate noise from the picture.

Before beginning picture preprocessing, RGB images are transformed to grayscale images and shrunk while maintaining the original aspect ratio. It uses morphological processing to more precisely recognise text.

19. Railway Track Fault Detection Project:

A cutting-edge method for identifying railway track cracks since this technology uses image processing to do it. To find railway track cracks, many picture preparation procedures are performed. Due to noise in images.

The system transforms the picture to grayscale and applies to filter to eliminate noise. More precise crack detection is made possible by noise reduction. The brightness of the image is boosted, and it is also transformed into a binary image.

This aids the system in detecting just cracks and removing other undesirable things. This method is used for inspecting railroad tracks. The suggested technology has an accuracy rate of 50% to 60% for small cracks and an efficiency rate of 80% for bigger fissures.

20. Bone Fracture Detection System:

This system includes processes for picture preprocessing and fractures type-based detection. The suggested approach has an 80% success rate for detecting bone dislocation, a 60%–70% accuracy rate for detecting severe fractures, and a 50%–60% accuracy rate for detecting small fractures.

21. Image Blurring & Deblurring With Noise Removal:

A system that allows you to blur and deblur photographs with various effects. Here, we use a variety of blurring techniques, including average, disc, motion, Sobel, and Prewitt. You may alter the picture to suit user preferences by utilizing these effects.

The concept can have noise added. For a clearer perspective, the user can eliminate noise from the image. Input from the user is required for various types of a blur; depending on the type chosen, this might be a radius, alpha, or another value.

22. Wall Crack Detection Using MATLAB:

A technology that uses image processing to find wall cracks. We applied various picture preprocessing procedures to better precisely detect cracks because images are subject to noise. It supports the majority of picture formats.

The intensity value is the main system emphasis. In the interest of accuracy, this is done. The system eliminates any unwanted sounds. Images are binarized and holes are patched to make them more readable for crack detection.

23. CCTV Theft Detection & Tracking:

Object detection is the system’s main objective. In this case, we just analyze the first frame of the movie, which separates the backdrop from the moving items.

Here, we utilized picture preparation techniques to reduce unwanted noise and some image processing technology to fill in any gaps left by the items that were recognized.

24. Object Tracker Based on Color:

System for tracking objects based on the color that uses the RGB spectrum. We preprocess each video frame to track RGB-colored objects, and the objects are monitored in real-time. RGB color components are removed from the grayscale picture and unwanted noise is eliminated to detect colored objects.

A filter is used to eliminate picture noise. The system gets rid of all unnecessary things to correctly detect objects. The binary picture is then created using the grayscale image.

25. Dental Caries Detection System:

Dental professionals will find cavities easily with the use of this technology. Dental caries are found via image segmentation so the system can distinguish between the tooth and caries.

Here, caries detection is carried out using MATLAB. For reliable results, picture preparation is done in stages. Because images are susceptible to noise and other environmental interferences, many picture preprocessing techniques are utilized to reduce noise and to properly identify caries.

26. Fake Currency Detection:

This technique consists of many steps including image processing, edge detection, picture segmentation, and image comparison. MATLAB may be used to process images and validate the currency’s properties. Therefore, the result will reveal if a cash note is real or fake.

27. Drowsy Driver Detection using MATLAB:

In this project, a web camera is connected to the computer so that it can monitor a driver’s eye movements to determine when he is weary and then take pictures of him napping. MATLAB may be used to both acquire and process these pictures.

28. Attendance Marking System using MATLAB:

By identifying a face, the suggested method is utilized to construct an attendance marking system in MATLAB. In this project, face recognition is used to carry out the two basic duties of person identification and confirmation.

Professors often handle the attendance procedure at schools and universities and keep records of the information.

29. Hybrid Vehicle Design using MATLAB:

Using MATLAB, the suggested approach is applied to the design of a hybrid automobile. Because there are more cars on the road today and because gasoline is used in them to convert chemical energy to kinetic energy, air pollution is rising in all cities.

Electrical cars are made to eliminate emissions by 100% to reduce pollution. However, because they have a smaller battery capacity than gasoline-powered cars, electrical vehicles are used less frequently.

30. High-Speed Railways Automation using MATLAB:

To reach the necessary speed, this project is utilized to create a control system that automatically regulates and is applied to the railroad transportation system. By applying fuzzy control simulation to detect the train speed & minimize parking error, rail accidents may be decreased by the use of this project.

By researching the process and operation of high-speed trains, this fuzzy control simulation helps to solve issues with train automation.

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