Research & Publications

Thaw explores the use of wireless signals to distinguish between solid and liquid states of water inside enclosed spaces such as freezers and microwave ovens. This proof-of-concept system, utilizes ultra-wideband (UWB) technology, demonstrating how changes in wireless reflection patterns can indicate transition between ice and water states.

This work was presented at ACM HotMobile 2024 and SCS PhD Visit Day. 

This project was advised by Prof. Ashutosh Dhekne. Additionally, this work is partially supported by the NSF under CAREER-2145278. Submitted a US provisional patent for this work. 

For more information on this project feel free to look at the paper abstract and poster below:

Rahul Bulusu, Ashutosh Dhekne (2024). Thaw: A UWB-based Ice-Water State Detector,", ACM International Workshop on Mobile Computing Systems and Applications (HotMobile), San Diego, CA, 2024. 

An extension to this work has been submitted as a full paper in IEEE Sensors Letters Journal; currently under review. 

This project is still ongoing and next steps will be to make the system a fusion system using millimeter-wave and making it full-scale.

[Paper Abstract][Poster][Video Demo]

This project has been done in fulfillment for CS 8803: Mobile Computing and IoT. This project was a group project done by me and the following team members: Eric Greenlee, Aadesh Madnaik, and Jason Cox. 

We have built a low-power backscatter system (<100 uW) with practical ranges of ~100s+ meters by applying digital communication techniques such as forward error correction and spread spectrum. Additionally, we have integrated a temperature sensor targeting Atlanta urban heat islands. 

Submitted proposal for Center for the Development and Application of Internet of Things Technologies (CDAIT) IoT Innovation Challenge and a proposal to WILDLABS.

THIS PROJECT IS STILL UNDER WORKS! STAY TUNED FOR MORE INFO! 

[Repository][Video]

This project proposes to use a frequency-modulated continuous wave (FMCW) radar implemented with a Millimeter-Wave transceiver integrated on a drone platform with depth sensors for ground truth. Using properties of FMCW and constant false-alarm rate (CFAR) detection techniques we are able to generate small-scale PCDs in outdoor environments. This project is still in the beginning works and is still on-going.

So far, this project has been presented as a poster at the USC CSE Research Symposium and Discover USC 2023.

This project was advised by Prof. Sanjib Sur and the work was co-authored with Ian McDowell (currently a Masters Student at the University of South Carolina).

For more information on this project feel free to look at the poster below:

[Poster]

Theia is an extension to MilliDrone which uses a drone-based system that predicts outdoor Millimeter-Wave (mmWave) Signal Reflection Profiles (SRPs) and facilitates picocell placement for optimal network coverage. The drone platform integrates optical systems and a mmWave transceiver to collect depth images and mmWave SRPs of the environment. The datasets are fed into a machine learning model that maps the depth data to SRPs, allowing SRPs to be predicted at previously unseen parts of the environment. Theia then leverages these predictions to identify optimal picocell locations that maximize network coverage and minimize link outages. Theia has been evaluated in three large-scale outdoor environments and demonstrates that the proposed design can generalize the deployment method with a little refinement of the model.

This work has been accepted as a full paper to The 32nd International Conference on Computer Communications and Networks (ICCCN 2023) and has also been presented as a poster both at the USC CSE Research Symposium and Discover USC 2023. The poster received the First Place Award at Discover USC 2023.

This project was advised by Prof. Sanjib Sur and the work was co-authored with Ian McDowell (currently a Masters Student at the University of South Carolina) and Hem Regmi (currently a PhD Student at the University of South Carolina).

This work is partially supported by the NSF under grants CAREER-2144505, CNS-1910853, and MRI-2018966.

For more information on this project feel free to look at the full paper and poster below:

Ian C. McDowell, Rahul Bulusu, Hem Regmi, Sanjib Sur (2023). Outdoor Millimeter-Wave Picocell  Placement using Drone-based Surveying and Machine Learning, The 32nd International Conference on  Computer Communications and Networks (ICCCN), Honolulu, HI, 2023. 

[Paper] [Slides] [Talk] [Poster]

MilliDrone is a Drone-based system equipped with a mmWave transceiver and a Guidance platform, and is synchronized to collect depth, greyscale, and mmWave reflection profiles by following a specified programmed path in an outdoor environment.

This work was presented at ACM HotMobile 2022 and was awarded Best Poster Runner-Up. Additionally, it was also presented at Discover UofSC 2022. 

This project was advised by Prof. Sanjib Sur and the work was co-authored with Ian McDowell (then an Undergraduate Senior at University of South Carolina).

This work is partially supported by the NSF under grants CNS1910853 and MRI-2018966.

For more information on this project feel free to look at the paper abstract and poster below:

Ian C. McDowell, Rahul Bulusu, Sanjib Sur (2022). MilliDrone: A Drone Platform to Facilitate Scalable  Survey of Outdoor Millimeter-Wave Signal Propagation, ACM International Workshop on Mobile Computing Systems and Applications (HotMobile), Tempe, AZ, 2022. 

[Paper Abstract] [Poster]