Publications
You can also find my articles on my Google Scholar profile.
POSTER: Analysis of Latency for Wireless Connectivity in Networked Robots
This study presents a comparative analysis of various types of communication latency in networked robotic setup.
Recommended citation: A. Kharel, R. Shakya, E. Barrientos, G. Singh, X. Zhang and D. Roy, "POSTER: Analysis of Latency for Wireless Connectivity in Networked Robots," 2025 IEEE 26th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Fort Worth, TX, USA, 2025, pp. 154-156, doi: 10.1109/WoWMoM65615.2025.00036. https://ieeexplore.ieee.org/document/11027042
SauRON: Smart Surveillance using Robotic Swarms with Optimized Networks
This paper discusses the strategic placement of swarm of robots within the dynamic environments to ensure full coverage implemented using Reinforcement Learning.
Recommended citation: Singh, Gaurav and Amatare, Sunday and Roy, Debashri, SauRON: Smart Surveillance using Robotic Swarms with Optimized Networks (January 01, 2025). Available at SSRN: https://ssrn.com/abstract=5143487 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143487
Spec-SCAN: Spectrum Learning in Shared Channel using Neural Networks
This paper presents a novel supervised deep learning framework for radar detection in CBRS Band.
Recommended citation: R. Hazari et al., "Spec-SCAN: Spectrum Learning in Shared Channel using Neural Networks," 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2025, pp. 1-6, doi: 10.1109/CCNC54725.2025.10976013. keywords: {YOLO;Training;Time-frequency analysis;Wireless networks;Radar detection;Radar;Interference;Spread spectrum communication;Spectrogram;Signal to noise ratio;CBRS;object detection;radar detection;spectrum learning;YOLO}, https://ieeexplore.ieee.org/document/10976013
Real-Time Localization of Objects using Radio Frequency Propagation in Digital Twin
This paper introduces an innovative real-time object localization system within a digital twin, designed to detect and locate objects within an environment.
Recommended citation: S. Amatare, G. Singh, A. Kharel and D. Roy, "Real-Time Localization of Objects using Radio Frequency Propagation in Digital Twin," MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), Washington, DC, USA, 2024, pp. 653-654, doi: 10.1109/MILCOM61039.2024.10774060. keywords: {Location awareness;Radio frequency;Military communication;Shape;Training data;Real-time systems;Digital twins;Sensors;Reliability;Object recognition;Digital Twin;RF propagation;Object localization}, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143487
Speclearn: Spectrum Learning in Shared Band Under Extreme Noise Conditions
This paper focuses on the detection of radar signals within the shared spectrum such as the Citizen Broadband Radio Service band employing YOLO algorithms under the influence of various noisy conditions.
Recommended citation: M. H. Rahman, G. Singh and D. Roy, "Speclearn: Spectrum Learning in Shared Band under Extreme Noise Conditions," 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 2024, pp. 1-2, doi: 10.1109/DySPAN60163.2024.10632806. keywords: {Noise;Pipelines;Radar detection;Radar;Object detection;Machine learning;Robustness}, https://ieeexplore.ieee.org/document/10632806
Sentiment Analysis of Code-Mixed Social Media Text (Hinglish)
This paper discusses the results of applying various machine learning algorithms and vectorization techniques to perform the sentiment analysis of Hinglish.
Recommended citation: Singh, G. (2021). "Sentiment Analysis of Code-Mixed Social Media Text (Hinglish)." arXiv:2102.12149v1 . https://arxiv.org/abs/2102.12149
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)
This paper discusses the application of the WEKA as the tool for performing Sentiment Analysis of Hinglish.
Recommended citation: Singh, G. (2020). "Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)." arXiv:2008.11398v1. http://arxiv.org/abs/2008.11398