Designed a recommender system to assist customers in upgrading their personal computer (PC) hardware components by optimizing value and performance. Built a data pipeline to extract the performance benchmarks and prices of different hardware components to train machine learning models. Implemented a random forest regression model to predict the component performance and deployed the web app using streamlit on AWS.
Spoken digit recognition using the Mel-frequency cepstral coefficients (MFCCs) and convolution neural networks (CNN). The model can recognize 0-9 spoken digits from .wav audio files by passing their MFCCs as an image input. My model achieves an accuracy of 94% on the test audio files.
Given a set of RGB images (32x32 pixels) with one (and only one) of the following objects: aves, flights, bucks, felines (labels 0, 1, 2 and 3, respectively). The goal is to train a Neural Network model to recognize which of the objects is present in an image. Also, Understand the efficiency, performance, and perform model selection using cross-validation.
Unsupervised learning algorithms attempt to learn some structure of the data using unlabeled samples like images. Here we will use K-Means to achieve optimal compression for efficient reconstruction of images.
Train decision trees with different maximum depths, nearest neighbors with different number of neighbors, and linear models with different regularization parameters to predict the air quality.
Analysis on the effect of non-linear delay model (exhibited by Feed-forward PUF) on performance parameters of PUFs i.e. Reliability and Uniqueness under different operating voltage conditions.
Implemented a G-Share branch predictor using N3ASIC, a nanofabric using combination of crosspoint nanowire FETs integrated using metal interconnects.
The usage of N3ASIC reduces area and the improves performance of the predictor when compared to the conventional CMOS.
A novel intellectual-property (IP) identication using System-on-a-Chip (SOC) watermarking scheme.
The principle is embedding dierent Advanced Encryption Standard (AES) encoders in to a System-on-a-Chip (SOC) based watermarking scheme at behavior design level.