Extensive experience in designing and implementing various analog and digital communication systems. My expertise includes: • Modulation techniques: DSB-SC, FM, AM, ASK, FSK, PSK, QAM • Data encoding: PCM, DPCM, correlative coding • Multiplexing: Time Division Multiplexing (TDM) • Performance analysis: BER, ISI using eye diagrams • Tools: MATLAB, Python, Simulink My work in this field has involved signal processing, spectrum analysis, and performance optimization.
Significant experience in designing and analyzing CMOS circuits, implementing projects using Virtuoso Cadence. My expertise in this field includes: • CMOS Logic Gates: Analysis of CMOS inverter characteristics, noise margins, delay models (Elmore and RC Delay), and transient performance optimization. • VLSI and Fabrication Technology: Understanding of the CMOS fabrication process (n-Well, p-Well, Twin-tub processes) and recent trends like FinFET and ultra-thin body MOSFETs for scaling and addressing short-channel effects. • Combinational and Sequential Circuits: Designing combinational logic networks (Pseudo nMOS, Clocked CMOS, Dynamic CMOS) and sequential circuits such as latches and flip-flops, including clocking and distribution strategies. • Tools and Platforms: Proficient in using Virtuoso Cadence for circuit design and simulation. • Advanced Concepts: Familiarity with cutting-edge VLSI technologies and their applications in modern electronics.
Expertise in operating systems and embedded systems, including practical experience implementing concepts on LPC21xx and LPC17xx development boards. Key areas of focus: • Operating System Concepts: Process management, inter-process communication, CPU scheduling algorithms (FCFS, SJF, Priority, Round Robin), memory management techniques, and system boot structures like monolithic and microkernels. • Real-Time Operating Systems (RTOS): Familiarity with RTOS architecture, task synchronization using semaphores, event flags, message queues, and addressing priority inversion problems. • Programming with RTX Kernel: Developed task synchronization and inter-task communication programs using the RTX kernel. • Embedded C Programming: Extensive experience in writing efficient code for embedded systems. • System Design: Proficiency in designing systems for real-time and embedded applications.
Active member and lead of the KLE Rocketry Club, focusing on various space-related projects: • Satellite Development: Contributed to miniature satellite projects including CanSat and CubeSat, gaining hands-on experience in satellite systems engineering. • Rocket Design: Involved in designing and optimizing rocket structures and propulsion systems. • Electronics for Space: Developed electronic systems for rovers and other space-related applications. • Research Initiatives: Actively participating in research aimed at advancing space technology applications.
Co-authored a research paper with the KLE Rocketry Club, which was published and presented at IEEE NKcon International Conference
Currently working on an innovative space research and development startup
Project: Fusion of Machine Learning and Digital Signal Processing for Landmine Detection Using Ground Penetrating Radar (GPR) Description: This project focused on developing a robust system to detect buried landmines using Ground Penetrating Radar (GPR) by combining Digital Signal Processing (DSP) techniques with Machine Learning (ML) models. The approach involved preprocessing GPR signals to remove noise, extracting key features using wavelet transforms, and classifying anomalies with ML algorithms. Key components of the project included: • Signal Preprocessing: Applied band-pass filtering and time-zero correction for noise removal and depth alignment. • Feature Extraction: Used discrete wavelet transform to identify reflection intensity and time delay, which indicate potential landmines. • Classification: Leveraged support vector machines (SVM) for distinguishing landmines from other subsurface objects. Outcomes: • Improved signal clarity with a 20 dB Signal-to-Noise Ratio (SNR). • Achieved 86% detection accuracy with an 8% false-positive rate. • Validated the approach on real-world datasets, demonstrating its applicability in post-conflict zones. This experiment highlights the potential of integrating DSP and ML techniques for solving complex real-world challenges and ensures scalability for future field implementations.