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Researcher profile

Shivam Kumar

Shivam Kumar contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionCryptography and Securityeess.SPProgramming Languages

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Published work

4 published item(s)

preprint2026arXiv

An IoT-Based Controlled Environment Storage for Prevention of Spoilage of Onion (Allium Cepa) During Post-Harvest with UV-C Disinfection

India is the second largest producer of onions in the world, contributing over 26 million tonnes annually. However, during storage, approximately 30-40% of onions are lost due to rotting, sprouting, and weight loss. Despite being a major producer, conventional storage methods are either low-cost but ineffective (traditional storage with 40% spoilage) or highly effective but prohibitively expensive for small farmers (cold storage). This paper presents a low-cost IoT-based smart onion storage system that monitors and automatically regulates environmental parameters including temperature, humidity, and spoilage gases using ESP32 microcontroller, DHT22 sensor, MQ-135 gas sensor, and UV-C disinfection technology. The proposed system aims to reduce onion spoilage to 15-20% from the current 40-45% wastage rate while remaining affordable for small and marginal farmers who constitute the majority in India. The system is designed to be cost-effective (estimated 60k-70k INR), energy-efficient, farmer-friendly, and solar-powered.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes

We introduce ShapeCodeBench, a synthetic benchmark for perception-to-program reconstruction: given a rendered raster image, a model must emit an executable drawing program that a deterministic evaluator re-renders and compares with the target. The v1 DSL has four primitives on a 512 x 512 black-on-white canvas, but every instance is generated from a seeded RNG, so fresh held-out sets can be created to reduce exact-instance contamination. We release a frozen eval_v1 split with 150 samples across easy, medium, and hard tiers, scored by exact match, pixel accuracy, foreground IoU, parse success, and execution success. We evaluate an empty-program floor, a classical computer-vision heuristic, Claude Opus 4.7 at high and max effort, and GPT-5.5 at medium and extra_high reasoning effort. The heuristic is competitive on easy scenes but collapses when overlaps fuse components; the strongest multimodal configuration preserves much of the foreground structure but still misses exact match because of small parameter errors. Best overall exact match remains low, so ShapeCodeBench is far from saturated. The benchmark code, frozen dataset, run artifacts, and paper sources are released to support independent replication and extension.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient Data Race Detection of Async-Finish Programs Using Vector Clocks

Existing data race detectors for task-based programs incur significant run time and space overheads. The overheads arise because of frequent lookups in fine-grained tree data structures to check whether two accesses can happen in parallel. This work shows how to efficiently apply vector clocks for dynamic data race detection of async-finish programs with locks. Our proposed technique, FastRacer, builds on the FastTrack algorithm with per-task and per-variable optimizations to reduce the size of vector clocks. FastRacer exploits the structured parallelism of async-finish programs to use a coarse-grained encoding of the dynamic task inheritance relations to limit the metadata in the presence of many concurrent readers. Our evaluation shows that FastRacer substantially improves time and space overheads over FastTrack, and is competitive with the state-of-the-art data race detectors for async-finish programs with locks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Smart Voltage Monitoring: Centralised and Blockchain-based Decentralised Approach

Voltage controls the majority of the processes around us, starting from lighting an incandescent lamp to running huge machines in industries. Therefore, voltage monitoring becomes essential, which demands efficient measurement and storage of voltage data. However, there is hardly any system till date that fulfils both the goals of voltage monitoring and voltage data storage. To achieve this goal, we propose the application of the Internet of Things along with the server-based framework and Distributed Ledger Technology to build systems for smart voltage monitoring. Two models - a centralised model and a decentralised model have been presented and analysed thoroughly in this paper. The centralised model is built on client-server architecture, whereas the decentralised model is based on a peer-to-peer architecture. Blockchain and InterPlanetary File System have been used for the implementation of the decentralised system. Potential improvements to make these systems robust have also been discussed. The methods proposed in this paper for voltage monitoring are novel; ensure efficient data storage and can be used for IoT data storage of any form.

0 citations0 reviewsNo code linkedOpen access