Matlab Major Projects brings you a collection of novel ideas and innovative concepts mined with the help of top experts. Major projects does not signify the task or size of the project, it signifies the underlying innovative concept, which will make your research a remarkable success. We have membership with top 500+ journals worldwide which makes us updated with every new concept registered. On the other hands, our developers update themselves with all the recent areas and tools. Both this approach makes us informative and knowledgeable, which makes us a knowledge hub for students searching for innovative ideas. Join with us; we will be your support for your successful research completion.


     Matlab Major Projects offers you a wide collection of research topics, which will upgrade your career profile. Matlab is one of the best platforms due to its advanced features like data integration, Numerical computation, Programming interface and wide toolbox support. We have provided below complete information about Matlab programming, tools, metrics and algorithms for students to have a glance over it, before taking up a project in Matlab. After you get an idea about Matlab, you can refer the topics enumerated below, which will give you an initiative to undertake your project.

Matlab programming features:

  • Programming using OOPS
  • GPU and GUI programming
  • Interfacing with C,C++, java and Fortran
  • Problem solving , iterative exploration and design using Interactive environment
  • Support for mathematical functions(Optimization, solving ODE, numerical integration)
  • Built in graphics for visualizing data and tools to enhance performance
  • Application building tools
  • Development tools for enhancing code quality

 Platform support in Matlab:

Latest version (R2016a- ver. 9.4) features

  • Superpixel support(using SLIC)
  • Calculate 3D gradient magnitude, elevations and directions
  • Image batch processor support
  • Image segmenter for flood-fill and adaptive thresholding

Works on Windows (XP, 7 and 8), Linux, Ubuntu and MAC OS X (64 bits).

For security analysis- False positive rate, detection rate, true positive rate.

Tools and languages integrated with Matlab:

  • OpenCV
  • Optisystems
  • Weka
  • Java using JAR files
  • C,C++ , Python and Fortran integration using MEX functions
  • VLfeat

Algorithm Development using Matlab (Using Matlab toolbox):

  • Machine learning algorithms
  • Artificial neural network algorithms
  • Image processing algorithms
  • Pattern recognition algorithms
  • AI algorithms
  • DSP algorithm development

Performance Metrics used in Matlab:

  • For segmentation- True Positive, False Positive, True Negative, False Negative, Similarity, F1 Measure
  • For classification – Sensitivity, specificity, Accuracy, ROC Curve, Confusion Matrix
  • For feature extraction- Entropy, Correlation, Contrast, Homogeneity
  • For denoising – PSNR(Peak signal to noise ratio), MSE(Mean square error)

Research Topics in Matlab:

  • The process of micro bio-me sample visualization based on UniFrac distance and Laplace matrix by using an efficient algorithm
  • The performance of Wind Power Generation based on a  Novel Multi phase Brush-less Power-Split Transmission System
  • An efficient approach for Fast Regression-Based Super-Resolution based on Antipodally Invariant Metrics
  • The new process of Full Diversity Uncoordinated Cooperative Transmission perform on Asynchronous Relay Networks
  • An efficient process Optimality of Fast Matching Algorithms for Random Networks with Applications to Structural Controllability