Atul Bansal

dblp:31/5915 · DBLP profile ↗
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13ranked-venue papers
2as first author
10since 2021 · last 2024
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 8 · 6 since 2021Computer networks · 4 · 2 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
YearPublicationVenuePosition
2024 Towards Ubiquitous IoT through Long Range Wireless Energy Harvesting
abstract
Extending the range of RF energy harvesting can revolutionize battery-free/low-power sensing and networking. This paper explores the design space for RF infrastructure to charge battery-free devices (e.g. RFID) or devices with coin-cell batteries (e.g. water and security sensors) over much longer range than the state-of-the-art. Rather than rely completely on ambient RF (e.g. TV towers) or dedicated infrastructure (e.g. RFID readers), we explore a middle path - combine RF energy from (nearly) all available major wireless frequency bands and then supplement this with low-cost specially designed RF charging infrastructure to fill in any gaps.
Mohamed Ibrahim Ahmed 0001, Atul Bansal, Kuang Yuan, Junbo Zhang 0001, Swarun Kumar
MobiHoc2
2023 Battery-free Wideband Spectrum Mapping using Commodity RFID Tags
abstract
This paper introduces RFIMap, a system that aims to inexpensively characterize the spatial and temporal distribution of RF spectrum occupancy of any indoor space at fine granularity (tens of centimeters). RFIMap builds rich wide-band indoor spectrum occupancy maps using low-cost and battery-free commodity RFID tags. RFIMap's spectrum maps have wide-ranging applications such as monitoring ambient interference in smart manufacturing, and smart hospitals. RFIMap relies on the observation that commodity RFID tags naturally reflect ambient transmission at other frequency bands, without any modification. RFIMap uses these reflections to estimate the ambient signal power originally received at these tags. RFIMap further performs a careful modeling of indoor multipath to build a dense spectrum map with fine spatial granularity. Our experiments demonstrate spatial spectrum measurement with 2.15 dB of median error at 2.4 GHz, 4.45 dB of median error at 470-700 MHz TV whitespace band, 2.1 dB of median error at 1.8-1.9 GHz in diverse industrial and university settings.
Mohamed Ibrahim Ahmed 0001, Atul Bansal, Kuang Yuan, Swarun Kumar, Peter Steenkiste
MobiCom2
2023 Machine learning & computer vision-based optimum black tea fermentation detection
Anuja Bhargava, Atul Bansal, Vishal Goyal, Aasheesh Shukla
Multim. Tools Appl.2
2022 Machine learning-based automatic detection of novel coronavirus (COVID-19) disease
Anuja Bhargava, Atul Bansal, Vishal Goyal
Multim. Tools Appl.2
2022 A novel diabetic retinopathy grading using modified deep neural network with segmentation of blood vessels and retinal abnormalities
Paresh Chandra Sau, Atul Bansal
Multim. Tools Appl.2
2022 An alignment-free non-invertible transformation-based method for generating the cancellable fingerprint template
Diwakar Agarwal, Atul Bansal
Pattern Anal. Appl.2
2021 OwLL: Accurate LoRa Localization using the TV Whitespaces
abstract
LoRa is a popular Low-Power Wide-Area Networking (LP-WAN) technology that allows devices powered by a ten year AA battery to connect to radio infrastructure miles away. One of the most promising features of LoRa is the ability to track the location of radios from a distance, enabling applications ranging from inventory tracking, smart infrastructure monitoring and structural health sensing. Yet, state-of-the-art LoRa localization systems experience errors of several tens or even hundreds of meters in location tracking, owing to the narrow bandwidth and limited battery life of LoRa devices.
Atul Bansal, Akshay Gadre, Vaibhav Singh 0001, Anthony Rowe 0001, Bob Iannucci, Swarun Kumar
IPSN1
2021 A utility of pores as level 3 features in latent fingerprint identification
Diwakar Agarwal, Atul Bansal
Multim. Tools Appl.2
2021 Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review
Anuja Bhargava, Atul Bansal
Multim. Tools Appl.2
2021 A comprehensive review on soil classification using deep learning and computer vision techniques
Pallavi Srivastava, Aasheesh Shukla, Atul Bansal
Multim. Tools Appl.3
2020 Does ambient RF energy suffice to power battery-free IoT?
abstract
Recent years have witnessed novel designs of battery-free IoT tags using RF backscatter. Traditionally, they require a dedicated transmitter to excite the tag. However, such a deployment is infeasible at large scale. To counter this problem, researchers have proposed using ambient RF energy to power up the battery-free tag. In this poster, we evaluate if this ambient RF energy is sufficient to meet the power requirements of a battery-free tag in today's urban and rural areas. We also compare available ambient RF energy across different frequencies. Finally, we discuss open challenges in realising ambient backscatter systems in real-world.
Atul Bansal, Swarun Kumar, Bob Iannucci
MobiSys1
2020 Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging
Anuja Bhargava, Atul Bansal
Multim. Tools Appl.2
2020 Machine learning based quality evaluation of mono-colored apples
Anuja Bhargava, Atul Bansal
Multim. Tools Appl.2