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Best MATLAB projects for ECE which offers excellent training that allows having hands-on experience and knowledge on the projects. The courses are designed to conduct advanced training and practical implementation to engineering students.
MATLAB is data structure used to break numerous specialized computing problems more efficiently based on programming languages. With the MATLAB language, it can analyze and explore data, develop and apply algorithms and create user interfaces.
Pantech is a platform which offers a wide range of MATLAB training programs for engineering students and helps to build skills and designed to emerge technologies. The experts help to grow for engineers who understand the fundamentals of principles and technical conditions of modern MATLAB projects.
It developed a student-centered culture that emphasizes harmonious and high- quality learning and exploration and involves in practical exposure by completing it successfully. It engaged in the service of evolving new ideas in MATLAB projects.
The Brain tumor segmentation using SFCM and CNN of human visual system performs these tasks mostly unconsciously but a computer requires skillful programming and lots of processing power to approach human performance. Manipulating data in the form of an image through several possible techniques.
An image is usually interpreted as a two-dimensional array of brightness values and is most familiarly represented by such patterns as those of a photographic print, slide, television screen or movie screen. An image can be processed optically or digitally with a computer.
When the blood vessels get damaged glaucoma detection using CNN and MATLAB disorder is found. Retinal vessel segmentation are the implementation of screening programs for glaucoma retinopathy, evaluation is done by the prematurity, detection of macular a vascular regions, detection of arteriolar narrowing, measurement of vessel tortuosity to characterize hypertensive retinopathy, measurement of vessel diameter to diagnose hypertension, cardiovascular diseases, and computer-assisted laser surgery.
The applications include automatic generation of retinal maps for the treatment of age-related macular degeneration, extraction of characteristic points of the retinal vasculature for temporal or multimodal image registration, retinal image mosaic synthesis, identification of the optic disc position, and localization of the fovea.
The image forgery detection using MATLAB is one of the critical purposes of photograph forensics. The most essential goal is to present numerous elements of photograph forgery detection; to assessment some overdue and modern methods in pixel-primarily based on picture forgery detection; to give a modern technique with their benefits and downsides.
An assessment of photo forgery detection has presented the particular form of virtual photograph forgery. The virtual photograph forgery detection method introduces unique present strategies of pixel-based completely image forgery detection of numerous algorithms.
Human Cancer is one of the most dangerous diseases which is mainly caused by the genetic instability of multiple molecular alterations. Among many human cancers, skin cancer is the most common one. To identify skin cancer at an early stage and analyze them through various techniques named segmentation and feature extraction. It focuses on malignant melanoma skin cancer detection using ABCD rule technology for malignant melanoma skin cancer detection.
In this system different steps for melanoma skin lesion characterization i.e., image Acquisition Technique, pre-processing, segmentation, define feature for skin feature selection determines lesion characterization, classification methods. The image processing method includes symmetry detection, border detection, color, and diameter detection and also used LBP to extract the texture-based features.
Cuckoo Search (CS) algorithm is one such algorithm that is efficient in solving optimization problems in varied fields. It discovers how it is efficient in detecting tumors and compares the results with the other commonly used optimization algorithms. The basic concepts of the cuckoo search algorithm and its application towards the segmentation of brain tumors from the MRI.
The human brain is the most complex structure where identifying tumor-like diseases is extremely challenging because differentiating the components of the brain is complex. The tumor sometimes occurs with the same intensity as normal tissues. The brain tumor analysis using cuckoo search optimization is detected by radiologists through a comprehensive analysis of MR images, which takes substantially a longer time.
Pedestrian detection is an essential and significant task in surveillance systems, as it provides the fundamental information for semantic understanding of the video footage. It indicates the presence of pedestrians at various scales and locations in the images.
Pedestrian detection using MATLAB involves stages like pre-processing, ROI choice, feature extraction, classification, verification/refinement, and trailing. It implements the feature extraction and classification stages that the extracted between a pedestrian and a non-pedestrian.
It focuses on the implementation of the LBP abstract background changes obtaining and histogram of orientated gradients (HOG) with modified parameters to classifying is achieved exploitation support vector machine (SVM).
It identifies the blind image blur estimation using neutral network algorithm from a mixed input of image patches corrupted by various blurs with different parameters. The effectiveness of the proposed method in several tasks with better or competitive results compared with the state of the art on two standard image data sets.
The blur features that are optimized for a certain uniform blur across the image, are unrealistic in a real blind deconvolution setting where the blur type is often unknown. It aims at identifying the blur type for each input image patch and then estimating the parameter.
The fake biometric detection using DWT technique with secret key analysis features are extracted from the pre-processed images of the iris, face, and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module.
This module consists of three major steps i.e., Pre-Processing, DWT Segmentation, and Image fusion. The final fusion is then used to declare the person as Authenticate or Un-Authenticate with Secret Key Analysis.
The analysis of comparison results illustrated the superior capability of the medical image segmentation using cuckoo search optimization algorithm in optimizing the enhancement functions for digital image processing. It is the process of enhancing the image quality and visual appearance in order to provide preferable transfer representation for future automated images such as medical images which might suffer from poor and bad contrast and noise. The analysis of results illustrated the superior capability of the cuckoo search algorithm in optimizing the enhancement functions for digital image processing.
This method and procedure are to construct an efficient system to assist blinds in everyday life. It provides the instructions to blinds for efficient navigation and safe guidance by incorporating objects in real-time. An efficient person identification and classification using LBP & HOG organization is introduced to sight the multiple road face walking persons in the processing of serial frames changes and classification of the pedestrian over the other moving objects.
An aircraft recognition in satellite image using MATLAB template matching for accurate detection and tracking. This recognition system involves dimensionality reduction, segmentation and aircraft identification with templates. The thresholding is use to detect the desired object from background. Correlation measurement is used for measuring between two different object region features. This is use to locate the aircraft region for tracking it and shows that reliable and compatible method for this process.
Digital images are obtain from the retina and graded by professionals. Diabetic retinopathy detection using ground truth segmentation is assess by its severity, which determines the frequency of examinations. The retinal image analysis through efficient detection of vessels and exudates for retinal vasculature disorder analysis. The detection of some diseases in early stages such as diabetes which can be perform by comparison of retinal blood vessels.
A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. There was a negative effect on recognition performance on fingerprint and palmprint biometrics due to some conditions such as oil on the fingers, moisture, and dirt. Therefore, palm vein patterns recognition using MATLAB stand out from the host of intrinsic biometric traits for development of a recognition system that can meet all these expectations.
The various methods are being increasingly use for liver tumor detection using neural networks and MATLAB disorder to assist doctors in the diagnosis of patients. The liver disease may cause the appearance of malignant or benign tumors in the liver and affects the rest of the body. This excess fat is store in the liver cells, where it accumulates triglycerides in the blood causing FLD and involves the pathogenesis of various common disorders such as diabetes and cardiovascular diseases.
Haze-removal using MATLAB algorithms are use to obtain clean, haze-free images with enhance saturation and contrast. It is cause by microscopic aerosols distribute in the air. The removal of haze from video clips obtain using a fixed camera position wherein some of the unknown variables can be eliminate using a clip containing time series data. In contrast, single image de-hazing methods focus more on image enhancement than a restoration based on strict physical laws.
Pancreatic cancer detection using neural network is associate with poor clinical outcomes primarily due to the advance stage at the time of diagnosis. Endoscopic harmonic ultrasound imaging (HI) is being use to characterize pancreatic masses. It evaluates the feasibility of pancreatic tumor detection by high-intensity focused ultrasound image.
For detecting pancreatic cancer mostly machine learning techniques are use. It provides better solution when its work with ensemble machine learning algorithms for diagnosing Pancreatic cancer even the variables are reduce.
Cardiac arrhythmia indicates abnormal electrical activity of heart that can be a great threat to human. So, it needs to be identify for clinical diagnosis and treatment. Analysis of ECG signal classification using CWT and NN plays an important role in diagnosing cardiac diseases. An efficient method of analyzing ECG signal and predicting heart abnormalities have collected ECG Signal Database and convert signals to Image using CWT Scalo gram and classify with Transfer Learning.
Early fetal brain detection and classification of abnormalities using techniques can improve the quality of diagnosis and treatment planning. The algorithm is capable of detecting and classifying a variety of abnormalities from MRI images with a wide range of fetal gestational age using a flexible and simple method with low computational cost. This method consists of four phases; segmentation, enhancement, feature extraction and classification.
Brain tumor detection with image fusion is a challenging task in medical image analysis. The manual process performs through domain specialists is a more time-consuming task. A technique is present to distinguish between benign and malignant tumor. The method integrates the image fusion, features extraction, and classification methods.
It is use for feature extraction and fusion by using Stationary wavelet transform. The fused feature vector is supply to the multiple classifiers to compare the better prediction rate. The databases are utilize for tumor detection. The performance effectively classifies the abnormal and normal brain regions.
The dry and wet age related macular degeneration classification of condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like micro aneurysms and hemorrhages.
The Project proposes the Retinal image analysis through efficient detection of vessels and exudates for retinal vasculature disorder analysis. It plays important roles in detection of some diseases in early stages, such as diabetes, which can be perform by comparison of the states of retinal blood vessels.
Handwritten digit classification using deep neural network in MATLAB system is require to detect the different types of texts and fonts. This will give problem to security reasons. It implementing the handwriting recognition process by using deep neural network algorithms and techniques. Neural network will give the extraordinary performance to classify images, the images which have the content of our requirements. By combining the database images with input image can classify the results and having database images with different types of writing styles and different types of fonts.
High-resolution satellite images contain a huge amount of information. Shadows in such images generate real problems in classifying and extracting the required information. Background subtraction is a commonly used method to detect moving objects from videos capture by static cameras. However, shadows and reflections significantly affect the output of background subtraction algorithms, and distort the shape of the objects obtained as a result. Thus, accurate shadow detection and removal from high resolution satellite images are a crucial post-processing step to perform accurate object tracking required by different applications.
A combination of airborne and satellite-based remote sensing is currently use for operational oil-spill monitoring worldwide. Space borne satellite-based synthetic aperture radar (SAR) images provides an overview of large ocean areas and surveillance aircraft can be direct to check possible oil-spill locations to verify the spill and catch the polluter. Detection oil-spill in satellite based synthetic aperture radar(SAR) images is most effectively perform on a large-scale due to its all-weather capabilities and good coverage.
The speech emotion recognition using deep learning from signal based on features analysis and NN-classifier. It plays an important role in HCI systems for measuring people’s emotions has dominated psychology by linking expressions to group of basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). The recognition system involves speech emotion detection, features extraction, selection and classification.
These features are useful to distinguish the maximum number of samples accurately and the NN classifier based on discriminate analysis is use to classify the six different expressions. The simulate results will be shown the filter-based feature extraction with use classifier gives better accuracy with lesser algorithmic complexity than other speech emotion expression recognition approaches.
The main objective is to detect the human activity recognition using neural networks MATLAB technique and can use sensors to recognize the human activities like walking, jumping and skipping etc. The main purpose is to detect the human body movement. Human activity recognition (HAR) using non-obtrusive sensing techniques received great attention from both researchers and industry.
With the rapid progress in semiconductor technology, low-cost sensors (e.g., accelerometers and gyroscopes) with small size, light weight and low power consumption could be easily embed hidden inside low obtrusive wearable devices. These wearable devices being use more and more popular in daily life.
Images enhancement in foggy images captured during adverse weather conditions frequently feature degraded visibility and undesirable color cast effects. Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, and remote sensing space cameras, and so on.
One of the central problems in image processing in open-air is the presence of cloud, fog or smoke which fades the colors and reduces the contrast of the observed things. Images of outdoor scenes captured during inclement weather conditions.
The breast cancer detection using deep learning technique is a most common type of cancer. It will easily recognize with the help of MRI scan image. The GLCM feature extraction and discrete wavelet transform, these are comes under image processing and deep learning and classify into two abnormal and normal, if the person is affect in cancer. An automatic support system for stage classification using probabilistic neural network based on the detection of cancer region through clustering method for medical application.
Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image. The system uses captured images for this recognition process. First the license plate recognition using deep learning system starts with character identification based on number plate extraction, splitting characters and template matching.
ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions. It plays an important role in numerous real-life applications, such as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring.
Kidney stone detection using MATLAB is an organ stones area unit tiny and pass impromptu. The patients usually want no any treatment. However, some renal lithalsas patients develop giant stones, which may cause vital morbidity within the variety of acute symptoms and chronic complications if not treated.
The technique throughout the wave remodel has the capability to mix the data at totally different frequency bands and accurately of image options and watershed rule enhance the image within the quality manner and it classifies with the Neural network.
The hand written recognition using MATLAB acknowledgment arrangement of utilizing picture preparing needs to improve tad. The acknowledgment framework is need to identify the various kinds of writings and textual styles. Neural organization will give the remarkable exhibition to order pictures, the pictures which have the substance of necessities.
It assumes a vital part during this technique for change and has been ardently investigate throughout the long term. The quality penmanship acknowledgment utilizes a pre-characterized set of alternatives on the composed data partner degreed utilizes a succession coordinating algorithmic principle.
Lung cancer detection using MATLAB are the disorders that affect the organs that allow to breathe is the most common medical conditions. The diseases such as pleural effusion and normal lung are detect and classify in this work. It aided classification method in Computer Tomography (CT) Images of lungs developed using NN.
The purpose of detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform and GLCM Features. The entire lung is segment from the CT Images and the parameters are calculate from the segment image. It evaluates the network designed for classification of ILD patterns and gives the maximum classification accuracy.
This leaf disease detection using neural networks plays an important role in agriculture field as having disease in plants are quite natural. It causes serious effects on plants and due to quality, quantity or productivity is affect. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops and detects the symptoms of diseases on plant leaves.
The algorithm for image segmentation technique use for automatic detection as well as classification of plant leaf diseases and survey on different diseases classification techniques that can be use for plant leaf disease detection. Image segmentation is an important aspect for disease detection in plant leaf is done by using Neural Network.
This robust is based on creating a human-computer interface (HCI) using the user’s hand gesture. The combination of Hardware and software interfaces-webcam and MATLAB performs the feature extraction process from the image captured from real-time video of hand signs. These features are compare with the features of the database images and after some image processing techniques in MATLAB.
The system generates outputs depending on the prediction of highest resemblance. This system is train to translate one sign language detection using MATLAB representations of predefine sign gestures to voice.
An effective feature encoder to extract robust information from CNN. It consists of two main steps: the embedding step and the aggregation step. Moreover, it applies the multi-task loss function to train the model in order to make the training process more effective. The is helpful to improve the content based image retrieval performance. For comprehensively evaluating the performance of their approach, they conducted ablation experiments with various convolutional NN architectures.
Hyper spectral multi image fusion using MATLAB is effectuate to minimize the redundancy while augmenting the necessary information from the input images acquire using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. The major advantage of using NSCT is to improve upon the shift variance, directionality and phase information in the finally fused image.
The first stage employs a NSCT domain for fusion and second stage to enhance the contrast of the diagnostic features by using Guided filter. A quantitative analysis of fused images is carry out using dedicate fusion metrics. The fusion responses compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.
The Blood cells white, red and platelets are important part of the immune system. These cells help fight infections by attacking bacteria, viruses, and germs that invade the body. White blood cells originate in the bone marrow but circulate throughout the bloodstream, while red blood cell helps transport oxygen to our body and platelets are tiny blood cells that help your body from clots to stop bleeding.
Accurate counting of those may require laboratory testing procedure that is not usual to everyone. Generating codes that will help counting of blood cell classification in microscopic images using MATLAB that produce accurate response gives a relief on this problem.
Automatic retinal blood vessel segmentation using deep learning is very crucial to ophthalmology. It plays a vital role in the detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In deep learning-based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this segment the retinal blood vessels from fundus images of the eye of algorithms are enhance the performance of the system.
Intake of healthy fruits and vegetables is vital as they are the source of energy for all living beings. There is an increasing demand for quality in all the consumed food items. This manual process incurs more time and it is a laborious and tiring task. So, there is a demand for an automated process that quickly examines, detects and sorts according to quality.
There are many factors such as temperature, humidity etc., affect the quality of fruits. The aim is to detect fruit quality analysis using clustering method and segregate low of best quality fruits. It is achieve using the combination of hardware and image processing techniques and machine learning algorithms. The segmentation, feature extraction and classification are done using MATLAB.
Clothing pattern recognition using MATLAB has received considerable attention from many communities, such as multimedia information processing and computer vision, due to its commercial and social applications. However, the large variations in clothing images appearances and styles and complicated formation conditions make the problem challenging. A recognition framework that is based on multiple sources of features and neural networks. The textural features are extract, including DWT and low-level features and taken as the inputs for deep feature-level fusion.