Smart Vehicles
Browse and strategize across all 4 official problem statements for Smart Vehicles. Complete with verified datasets, tech stack blueprints, and jury defense playbooks.
Problem Statements List (4)
Match My TeamSolar-Powered Smart Mini Cold Storage System for Fresh Vegetables in North Eastern Region (NER)
Horticultural farmers across remote North Eastern hills suffer massive post-harvest vegetable spoilage due to frequent grid power failures and lack of localized cooling facilities. Build an IoT and embedded digital twin monitoring platform for decentralized, solar-powered cold storage units that tracks chamber temperature, humidity, battery autonomy, and storage lifespan.
Development of a Software Program/Application for Generation of Test Reports for Non-Automatic Weighing Instruments (NAWI) as per OIML Recommendation R- 76
National Test House (NTH) and Bureau of Indian Standards (BIS) laboratories manually compile physical test results across multiple scientific disciplines (chemical, mechanical, electrical), causing reporting delays, transcription errors, and risks of fake certificate forgery. Build an automated laboratory test report generation and verification system that ingests instrument data, applies standard-specific pass/fail rules, and issues tamper-evident QR-verifiable test certificates.
To develop an AI/ML-enabled adaptive noise cancellation (ANC) system that effectively suppresses stationary, non-stationary, and impulsive defence noises while maintaining high speech intelligibility and real-time performance on embedded hardware.
Soldiers in tanks, armored combat vehicles, and military cockpits operate under extreme acoustic noise (>115 dB from diesel engines, gunshots, rotor blades) that renders tactical radio communication unintelligible and causes hearing loss. Build an AI-enabled low-latency Adaptive Noise Cancellation (ANC) and speech enhancement system in DSP/FPGA that suppresses non-stationary industrial/combat noise while preserving mission-critical voice clarity in under 5 milliseconds.
Quantum-Inspired Fuel Consumption Prediction and Green Fleet Optimization
Commercial shipping fleets and heavy inter-city freight logistics companies face severe carbon emissions and high fuel costs due to unoptimized voyage speeds, oceanic currents, and non-linear aerodynamic/hydrodynamic drag. Build a Quantum-Inspired Fuel Consumption Prediction and Green Fleet Optimization Platform for Egreen Quanta that uses Quantum Support Vector Regression (QSVR) and Quantum-Inspired Genetic Algorithms (QIGA) to predict vessel hydrodynamic fuel burn and calculate fuel-optimal weather-routing trajectories.