Engineers who ride.

Smart Actuation Platform

A comprehensive ARAS platform architecture engineered for high fidelity simulation, complex scenario modelling, structural physics analysis, and deep system performance metrics.

• →Real-time 3D Environment Simulation
• →Sensor Modelling (Camera, LiDAR, Radar, IMU)
• →Physics & Vehicle Dynamics Engineering
• →Scenario Editor & Automated Validation Suite

Simulation to Product to Performance

An end-to-end digital thread pipeline architecture that seamlessly connects cross-platform simulation, hardware product development, and physical real-world edge telemetry validation.

• →Closed-loop Digital Twin Simulation Continuum
• →Model-based Systems Engineering Frameworks
• →Hardware-in-the-loop Virtual Validation Integration
• →Over-the-air (OTA) Fleet Performance Telemetry Optimization

Lean-Aware Intelligence

Calculates exact system dynamics based on dynamic angular pitch and variance.

Rider-Centric UI

Adaptive HMI interfaces tailored to non-intrusive ergonomic warning systems.

Real-Time Hazard

Predictive telemetry computes traction loss probabilities 250ms ahead.

Advanced Sensor Processing

Integrated tracking metrics parsing real-time raw edge streams across Camera, LiDAR, Radar, IMU, and localized active system data arrays.

Vehicle Edge Dynamics

Comprehensive tracking mechanisms parsing real-time angular metrics, multi-point stability mechanics, suspension tracking, and continuous braking profiles.

Control System Logic

High performance computational execution layers interacting dynamically with on-vehicle systems including VCU, MCU, BMS, ABS, TCS, and ESC logic.

Next-Gen Connected HMI

Adaptive rider and driver interface systems delivering context-aware alerts, fleet telemetry, and OTA-updatable experience layers.

Advanced Rider Assistance System (ARAS)

Developing an Advanced Rider Assistance System integrating camera, radar, and vehicle/sensor data for real-time perception, sensor fusion, and behavioral driving analysis. The platform explores cost-effective edge architectures for bringing capabilities from the autonomous-driving stack to resource-constrained mobility platforms.

Smart Lock Hardware Security

Investigated the security of commercial smart-lock hardware through hardware reverse engineering, PCB analysis, attack-point identification, and electromagnetic fault injection. Developed a contactless “wireless spiking” technique capable of manipulating internal control circuitry using intentional electromagnetic interference (IEMI).

Vehicle Fingerprinting for Intelligent Transportation

Developed a non-contact vehicle fingerprinting system using existing inductive-loop infrastructure to capture distinctive electromagnetic signatures and machine-learning/deep-learning models to identify vehicle make, model, and year, achieving up to 93% identification accuracy.

Automotive CAN Security

Demonstrated physical-layer data-integrity attacks on automotive CAN networks, including controlled bidirectional bit manipulation validated on laboratory testbeds and a production vehicle. By exploiting CAN’s electrical and transmission-line characteristics, attacks could evade standard error-detection mechanisms.

Wireless PHY & FPGA Development

Industry and graduate-level experience taking wireless PHY algorithms from mathematical models toward hardware implementation, including IEEE 802.15.4 and GNSS physical layers. Work spans MATLAB algorithm development, floating-to-fixed-point conversion, bit-accurate modeling, RTL/FPGA design, verification, and hardware prototyping.

AI-Based Hardware Trojan Detection

Developed an AI-assisted hardware Trojan detection approach that models digital circuit netlists as graphs and applies Graph Convolutional Networks to identify malicious circuit modifications. Combining structural circuit analysis with graph-based deep learning, the work addresses emerging challenges in IC security and semiconductor supply-chain assurance.