Purabi Sharma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Purabi Sharma has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Kandarpa Kumar Sarma
Department of Electronics and Communication Engineering, Gauhati University, Guwahati, India
6 shared publications - Aradhana Misra
Gauhati University, India
1 shared publication - Debashis Dev Misra
Assam Down Town University, India
1 shared publication - Jacopo Iannacci
Fondazione Bruno Kessler (FBK), Trento, Italy
1 shared publication Kandarpa Kumar Sarma
1 shared publication- Koushik Guha
Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, India
1 shared publication
Latest Publications
- Open Access
- 8 November 2024
- Published by MDPI AG in Preprints.org
Considerations on Novel Network Edge Physical Nodes Architectures Leveraging A Bottom-Up Technology-Centric Approach Empowered by Micro/Nanotechnologies (MEMS/NEMS) and Federated Learning
The forthcoming paradigms of 6G, Future Networks (FN) and Super-Internet of Things (IoT), will bring disruption at various levels of the physical infrastructure. This work focuses on the network edge, stressing how the forecasted proliferation and technology innovation of the physical systems at the boundary of the network (edge), will pose crucial issues to be addressed in the next years. In particular, the continuity from the edge to the core of the network will be addressed, suggesting unprecedented design and development approaches. Along such a direction, the impact of Micro and Nanotechnologies as a Key Enabling Technology (KET) for the network edge of the future is sketched, including numerous examples of already existing micro/nano devices, components and systems. Afterwards, a practical study of how improving hardware technologies at the edge can be beneficial in terms of more efficient operation is reported. To this end, scattered intelligence shall be a key enabler with federated learning at the base station providing decisive assistance to adaptive downlink beamforming codebook design for mmWave massive MIMO set-ups as essential ingredients for enhanced link reliability in edge networks.
- 8 November 2024
- Published by MDPI AG in Preprints.org
Open AccessConsiderations on Novel Network Edge Physical Nodes Architectures Leveraging A Bottom-Up Technology-Centric Approach Empowered by Micro/Nanotechnologies (MEMS/NEMS) and Federated Learning
The forthcoming paradigms of 6G, Future Networks (FN) and Super-Internet of Things (IoT), will bring disruption at various levels of the physical infrastructure. This work focuses on the network edge, stressing how the forecasted proliferation and technology innovation of the physical systems at the boundary of the network (edge), will pose crucial issues to be addressed in the next years. In particular, the continuity from the edge to the core of the network will be addressed, suggesting unprecedented design and development approaches. Along such a direction, the impact of Micro and Nanotechnologies as a Key Enabling Technology (KET) for the network edge of the future is sketched, including numerous examples of already existing micro/nano devices, components and systems. Afterwards, a practical study of how improving hardware technologies at the edge can be beneficial in terms of more efficient operation is reported. To this end, scattered intelligence shall be a key enabler with federated learning at the base station providing decisive assistance to adaptive downlink beamforming codebook design for mmWave massive MIMO set-ups as essential ingredients for enhanced link reliability in edge networks.
- 11 September 2024
- Published by Elsevier in Physical Communication
- Vol. 67
Intra-pulse modulation discrimination using a self-supervised attention-driven CNN-BiLSTM-VAE combination
- 11 September 2024
- Published by Elsevier in Physical Communication
- Vol. 67
Intra-pulse modulation discrimination using a self-supervised attention-driven CNN-BiLSTM-VAE combination
- 21 November 2023
- Published by Elsevier in Physical Communication
- Vol. 62
Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation
- 21 November 2023
- Published by Elsevier in Physical Communication
- Vol. 62
Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation
- 1 November 2023
- Published by Springer Nature
- p. 453-462
PRI Modulation Classification in EW Systems Using Deep Learning
- 1 November 2023
- Published by Springer Nature
- p. 453-462
PRI Modulation Classification in EW Systems Using Deep Learning
- Open Access
- 14 December 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 224761-224780
Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications
Electronic warfare (EW) is one of the most important characteristics of modern battles. EW can affect a military force's use of the electromagnetic spectrum to detect targets or to provide information. Recent developments in artificial intelligence (AI) suggest that this emerging technology will have a deterministic and potentially transformative influence on military power. AI driven algorithms can be very effective in diverse domain of EW like processing of radar signals for efficient recognition and classification of emitters, detection of jammer and its characteristics and for developing efficient anti-jamming algorithms. AI techniques can also enable an EW system to operate autonomously. This paper provides a description of various branches of EW, the role of AI in EW systems and different AI techniques that have been deployed in EW systems.
- 14 December 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 224761-224780
Open AccessArtificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications
Electronic warfare (EW) is one of the most important characteristics of modern battles. EW can affect a military force's use of the electromagnetic spectrum to detect targets or to provide information. Recent developments in artificial intelligence (AI) suggest that this emerging technology will have a deterministic and potentially transformative influence on military power. AI driven algorithms can be very effective in diverse domain of EW like processing of radar signals for efficient recognition and classification of emitters, detection of jammer and its characteristics and for developing efficient anti-jamming algorithms. AI techniques can also enable an EW system to operate autonomously. This paper provides a description of various branches of EW, the role of AI in EW systems and different AI techniques that have been deployed in EW systems.
- 29 January 2014
- Published by Springer Nature in Network Modeling Analysis in Health Informatics and Bioinformatics
- Vol. 3 (1) , 1-9
A system for grading diabetic maculopathy severity level
- 29 January 2014
- Published by Springer Nature in Network Modeling Analysis in Health Informatics and Bioinformatics
- Vol. 3 (1) , 1-9
A system for grading diabetic maculopathy severity level