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Iris Pattern Recognition

Duration: Sep’22 – Jan’23

Team Size: 4

Description: Biometric identification using unique iris patterns for secure and accurate recognition.

4 Band Equalizer

Duration: Feb’23 – Jun’23

Team Size: 4

Description: Implementation of a 4-band audio equalizer using analog linear integrated circuits.

PRECISE HEART: Heart Disease Prediction using Machine Learning

Duration: Jan’24 – Aug’24

Team Size: 4

Description: Developed a heart disease prediction system using clinical data and various machine learning algorithms like SVM, KNN, Naive Bayes, and Decision Trees. The system efficiently predicts heart disease based on multiple evaluation metrics including accuracy, sensitivity, and specificity.

Developing a Key-Guessing Attack Strategy using Deep Learning

Duration: Aug’23 – Jun’24

Team Size: 4

Description: This project involves using Deep Learning to launch key-guessing attacks on Logic Locked circuits, which are designed to protect against piracy in VLSI systems. By extracting features from circuit netlists and applying deep learning algorithms, the system predicts secret keys, uncovering potential vulnerabilities in hardware security.

Implementation of Carry Save Adder using FPGA

Duration: Mar’23 – Jun’23

Team Size: 4

Description: Designed and implemented a Carry Save Adder (CSA) on an FPGA board using the VIVADO tool. The project optimized CSA design for high-performance applications, focusing on metrics like power consumption, area utilization, and speed. Utilized hardware description languages (HDL), synthesis, and place-and-route techniques to demonstrate the advantages of CSA for arithmetic operations on FPGA platforms.

Air Defense System Using AnyLogic

Duration: Jan’24 – Feb’24

Team Size: 4

Description: Developed an agent-based simulation of an air defense system to safeguard coastal assets from bomber aircraft threats. The model utilized three radars with limited coverage to allocate missiles effectively. Integrated differential equations within agents to simulate real-time dynamics and evaluate system resilience under varying attack scenarios. This project provided insights into optimizing radar deployment, resource allocation, and overall defense strategies.