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.
[Github][Website]
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.
[Github]
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.
[Github]
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.
[Github]
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.
[Github]
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.
[Paper][Link]
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.
[Paper]
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.
[Journal paper][Link]