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To provide a single, affordable, easy-to-use POC device for simultaneous measurement of multiple critical biomarkers.

Generate a verified physical design of the Pinaka Shakti processor as a baseline for evaluating a custom low-power standard cell library.

To develop a unified, intuitive, mobile-first digital interface for students, integrating various campus services.

Mystery Lab is an experiential STEM edutainment initiative designed to foster curiosity and scientific thinking among children aged 5–14 by transforming science into engaging, hands-on experiences. The project directly addresses the persistent gap between theoretical classroom learning and practical scientific exposure, delivering interactive science through live demonstrations, DIY workshops, portable experiment labs, and gamified activities. During this final semester, the project progressed from initial validation to scalable implementation — expanding across birthday events, school workshops, NGO collaborations, and high-footfall public platforms. These activities enabled strong user engagement, real-world testing, and consistent lead generation. To extend learning beyond events, Mystery Lab introduced modular DIY science kits, designed for safety, usability, and cost efficiency. Trained facilitators and standardized operating procedures were developed to ensure consistent delivery and reduce dependency on the core team. With multiple revenue streams, growing market presence, and a structured operational model, Mystery Lab demonstrates a scalable and sustainable approach to experiential STEM education. The project highlights strong potential for future expansion through institutional partnerships, product-based offerings, and digital platforms — positioning it as a promising venture at the intersection of education, entertainment, and entrepreneurship. === Page 4 === Table of Contents Student's Declaration.....................................................................................................................2 Acknowledgements........................................................................................................................2 Abstract..........................................................................................................................................3

3 Acknowledgments 4 Work Distribution 5

To design, simulate, and fabricate a smart, touchless letter processing machine tailored for enterprise desktop use, eliminating manual mail processing inefficiencies and risks.

To address the fragmented ESDM procurement gap by creating a lifecycle-aware platform for structured, transparent, and efficient hardware product development.

This B.Tech project, undertaken on the Entrepreneurial Track, presents a semester-long inves- tigation into the lithium-ion battery (LIB) recycling and reuse value chain in India. The study began with the problem of automated battery sorting and followed a field-driven methodol- ogy involving visits to a hydrometallurgy equipment manufacturer in Faridabad, the Servotech lithium battery assembly plant, and the industrial cluster in Bhiwadi, Rajasthan. Insights were further strengthened through interactions with industry leaders, including Bharat B Mehta (Reliance Industries), Pravin Saraf (Exide Energy), and the Vice Chairman of JBM Auto, along with engagement with the startup Nunam. The project also received external validation through a grant at the Delhi Startup Yuva Festival and selection for Y Combinator Startup School. A technical review of hydrometallurgical processing was conducted to build process-level un- derstanding, complemented by a detailed value proposition analysis of a black-mass production facility operating at commercial scale. The study found that battery sorting is largely managed within the informal scrap ecosystem, reducing its viability as a standalone innovation oppor- tunity. Consequently, the project pivots to the opportunity of second-life battery reuse. The proposeddirectionfocusesondevelopingaUniversalBatteryManagementSystem(BMS) for end-of-life EV battery packs,enabling safe and scalable redeployment of retired EV bat- teries across stationary storage and low-power mobility applications, contributing to improved resource utilisation and supporting India’s transition toward a circular battery economy.

Large academic campuses experience a persistent and largely unsolved problem: the recovery of lost personal belongings. Existing institutional mechanisms rely on email- based broadcasting, which suffers from low engagement, manual matching, and poor scalability. A comprehensive user study conducted across the campus community confirmed widespread dissatisfaction with the current system, with the substantial majority of respondents reporting that they either ignore or skim lost-and-found notifications entirely, and only a small fraction successfully recovering their lost items through existing channels. ThisreportpresentstheSmart Lost & Found System,afull-stackintelligentplatform that replaces the email-based workflow with a centralized, AI-powered solution. The core technical contribution is the systematic evaluation and deployment of state-of-the- art vision-language models for multimodal item retrieval. We benchmarked four leading models—OpenCLIP, SigLIP, DINOv2, and BLIP-2—using a purpose-built evaluation dataset spanning 20 campus-relevant item categories. SigLIP [15] was selected as the backbone, achieving 97.0% Precision@1 with an inference latency of 89.9ms per image, representing a superior accuracy-to-speed trade-off compared to alternatives. The system supports both image-based and text-based item search through a shared embedding space, enabling users to search by uploading a photo or by typing a natural- language description. User feedback confirms strong adoption intent, with the large majority of surveyed campus members expressing high interest in using the platform.

Urban air pollution in Indian cities, particularly Delhi, presents a severe and persistent public health crisis. Particulate matter concentrations routinely exceed WHO guidelines by 10–20×, contributing to respiratory diseases, cardiovascular conditions, and reduced life expectancy. Ex- isting indoor air purifiers prioritize high single-pass filtration efficiency (H13/H14 HEPA) at the expense of air throughput, resulting in slow room-level cleaning, rapid filter clogging in high- dust environments, and high recurring maintenance costs — making them both ineffective and unaffordable for the majority of Indian households and institutions. This project, undertaken as Vayu Labs, presents the design, prototyping, and validation of an alternativeindoorairpurificationsystemthatmaximizesvolumetricthroughputinsteadofsingle- passfilterefficiency. Thesystemusesa3-stagefiltrationarchitecture—awashableG4pre-filter, an activated carbon with potassium permanganate (ACF-K) sponge for gas-phase pollutant removal, and a medium-efficiency F6/F7 main filter — and improves on existing solutions in every key metric: 42% higher CADR (634 m3/hr vs. 447 m3/hr for H14 HEPA), 29% faster room cleaning (t 90 = 3.95 min vs. 5.60 min), 79% lower annual filter cost (INR 11,553 vs. INR 55,233), and gas-phase pollutant removal (88% VOCs, 85% ozone, 75% SO 2 ) that no HEPA-only system provides. The project combines hardware engineering (airflow system design, filter selection, sensor in- tegration, enclosure CAD) with entrepreneurial validation (customer discovery, competitive analysis, institutional go-to-market strategy, and pitch competition participation). The system targets both residential consumers (INR 8,000–10,000 retail) and institutional buyers (schools, government offices) where the cost-per-CADR advantage is most compelling. A seed grant of INR 1,00,000 was secured through the E-Pitch Caf´e 10th Edition (Budding Star category) to fund prototype fabrication and pilot deployments. Field of Study: Environmental Engineering and Sustainable Development (aligned with UN SDG 9: Industry & Innovation, SDG 11: Sustainable Cities, and SDG 13: Climate Action)

Design, develop, simulate, implement, and validate a low-cost custom ultrasonic distance sensing system to replace LiDAR for indoor drone navigation.

This report presents the exhaustive design, architectural methodology, implementation, and successful production deployment of a comprehensive AI-powered resume selection and candidate matching platform, delivered alongside a full-stack research lab manage- ment platform (VICAS Hub). In the contemporary recruitment landscape, the sheer volume of unstructured candidate data heavily burdens academic placement cells. To mitigate this, the system actively processes exactly 171 student resumes collected from professional networks such as LinkedIn, extracting structured, high-fidelity information using an optimized 5-stage Gemini 2.5 Flash API pipeline. The architecture leverages Facebook AI Similarity Search (FAISS) for sub-millisecond, high-dimensional vector re- trieval. It stores the semantic data in both MongoDB for traditional queries and Neo4j for highly visual, graph-based relationship exploration. A critical enhancement implemented in this final deployment phase is the Docker- ized Placement Cell Integration module, alongside our live cloud deployment. Built via FastAPI and React Vite, this containerized standalone architecture empowers placement officers to securely upload new resumes locally and automatically parse the entire 171- student cohort against complex corporate Job Descriptions (JDs) to instantly generate AI-reasoned, ranked shortlists. Concurrently, the VICAS Hub serves as a fully deployed 3-stage role access portal for managing lab research projects, moderated photo galleries, and features a sophisticated Academic BTP Portal driven by an automated Node-Cron mailing engine. Both systems have been rigorously refined, completed, and deployed to production environments, demonstrating a massive 38% increase in precision for complex multi-criteria queries over traditional TF-IDF keyword-based search methodologies. === Page 3 === Contents