This MATLAB app simplifies FIR filter design by integrating Genetic Algorithms for optimization. Users can choose filter types (Low Pass, High Pass, Band Pass, or Band Stop), configure specifications like passband/stopband frequencies and filter order, and visualize the design process. Dynamic updates, including frequency response and pole-zero diagrams, provide real-time insights into the optimization, making filter design both efficient and intuitive.

In this blog post, we will discuss about the Harris Corner Detector and its implementation. The Harris Corner Detector is a popular feature detection algorithm that is widely used in computer vision and image processing applications. The algorithm is used to detect corners in an image by analyzing the intensity variations in different directions.

Canny is one of the most popular edge detection algorithms. It is known for its good performance and simplicity. This project is an analysis of the Canny edge detector and its implementation.

This project aims to compare the performance of various models on a binary classification task to distinguish between images of apples and bananas. The models being evaluated include different configurations of the VGG architecture, as well as transfer learning approaches.

This project involves designing a 8 bit processor using verilog and implementing it on FPGA. This 8-bit processor uses a register file with 16 registers and a versatile instruction set that enables it to perform a wide array of operations—from arithmetic calculations to logical manipulations. With a comprehensive testbench ensuring robust functionality, the Tiny Processor exemplifies the key principles of digital design.

This project focuses on training a Multi-Layer Perceptron (MLP) on the MNIST dataset, evaluating its performance against Random Forest (RF) and Logistic Regression models. The analysis includes F1-score and confusion matrix metrics, alongside visualizations using t-SNE to understand the embeddings from the MLP’s hidden layers.

This project leverages Multi-Layer Perceptrons (MLP) for next character prediction. Using a rich dataset of texts from William Shakespeare, this implementation is designed for text generation. This involves training a model that involves an embedding layer and multiple fully connected layers.

This project explores different models to tackle this binary classification task on XOR Dataset. With 200 training instances and 200 test instances, the project showcases model performance and decision surfaces, offering insight into the strengths and weaknesses of each approach.

Introducing our Fuzzy Logic-Based Fall Detection and Danger Estimation App. This app continuously monitors accelerometer data to detect falls and assess potential dangers. If danger escalates, the app alerts a designated contact, sending crucial information like GPS location and audio recordings.

Second-order systems with our interactive MATLAB app! Visualize how changes in damping ratio, natural frequency, and gain impact system behavior in real time. This tool enhances your understanding of control theory through dynamic plots and immediate feedback.