This project deals with 2D and 3D graphics plotting in MATLAB. It also explains the audio signal manipulations using plotting in MATLAB. To use the ‘plot’ function in MATLAB, you should first make sure that the matrices/vectors you are trying to use are of equal dimensions. Familiarization with basic MATLAB objects like vectors and matrices will help in understanding the subject better before delving into details.
MATLAB is a very powerful tool and plays an important role in image processing. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This MATLAB-based project processes images taken by the camera and extracts the position of a red-coloured object.
Real-Time Face Detection
Object detection and tracking are important in many computer vision applications, including activity recognition, automotive safety and surveillance. Presented here is an object detection system that can detect a human face, eyes and upper body. This program can be used to detect on pressing corresponding buttons.
Antenna Analysis and Design
Today, almost all applications are expected to be wireless, consume very little power and have high data retention capability. A good design of the antenna can result in a high degree of efficiency, better directive and more beam width for long-distance transmission without much loss of information. This program written in MATLAB helps in designing antenna arrays.
Huffman Encoding and Decoding
Encoding the information before transmission is necessary to ensure data security and efficient delivery of the information. Huffman algorithm is a popular encoding method used in electronics communication systems. It is widely used in all the mainstream compression formats that you might encounter. The MATLAB project presented here encodes and decodes the information and also outputs the values of entropy, efficiency and frequency probabilities of characters present in the data stream
Artificial Neural Network Simulation
Artificial neural network, in essence, is an attempt to simulate the brain. When the user enters the inputs (say, p1, p2 and p3) and the expected corresponding outputs (say, t1, t2 and t3) in the program, the program trains the system and gives a final weight. The final weight is computed to get the final expected output. This program is an attempt to understand the basics of artificial neural network and how one can use it for further applications.