The Paper titled “Localization and Activity Classification of Unmanned Aerial Vehicle using mmWave FMCW Radars,” has been accepted for publication in the IEEE Sensors Journal.

“Localization and Activity Classification of Unmanned Aerial Vehicle using mmWave FMCW Radars,” has been accepted for publication in the IEEE Sensors Journal. 

Prabhat Kumar Rai, Henning Idsøe, Rajesh Reddy Yakkati, Abhinav Kumar, Mohammed Zafar Ali Khan, Phaneendra K. Yalavarthy and Linga Reddy Cenkeramaddi, “”Localization and Activity Classification of Unmanned Aerial Vehicle using mmWave FMCW Radars,” has been accepted for publication in the IEEE Sensors Journal. “

Visiting Researcher, Yeduri Sreenivasa Reddy

Yeduri Sreenivasa Reddy is a visiting researcher at the ACPS research group during 2021.

Yeduri Sreenivasa Reddy received the B.E. degree in electronics and communication engineering from Andhra University, Visakhapatnam-India, in 2013, and the M.Tech. degree from ABV-Indian Institute of Information Technology, Gwalior-India, in 2016. He is currently pursuing the Ph.D. degree with the Department of Electronics and Communication Engineering, National Institute of Technology, Goa-India. His research interests are Machine type communications, Internet of Things, LTE MAC, 5G MAC, optimization in communication, wireless networks, power line communications, visible light communications, hybrid communication systems, spectrum cartography, spectrum sensing, V2X communication, V2V communication, wireless sensor networks, Long Range communications for UAV, mmWave RADAR, sensor fusion techniques, aerial vehicle traffic control management, low-latency communications for UAV, real-time implementation of communication protocols using USRPs, WSN motes, and mmWAVE radars. He has published quality articles that include IEEE Transactions on Vehicular Technology.

Mini-COVIDNet: Mobile friendly point-of-care detection of COVID19 using Ultrasound Images

We (Medical Imaging Group (MIG), CDS, IISc Bangalore and ACPS Group, ICT, UiA Campus Grimstad) have developed a Mobile-friendly deep learning model for point-of-care detection of COVID19 using Ultrasound Images for better triaging of patients. This manuscript is accepted for publication in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (Special issue on Ultrasound in COVID-19 and Lung Diagnostics).

Brief Summary of the work:

Lung ultrasound imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we developed a lightweight mobile-friendly efficient deep learning model for the detection of COVID-19 using lung ultrasound images. The developed method was shown to be sensitive to the damage to the pleural surface of the lung, which has been proven to have prognostic value, commonly observed in intensive care unit–admitted and deceased patients. The developed model has utility in the context of a massive COVID-19 pandemic, where it can better triage patients with pulmonary symptoms (suspected of infection).

Reference:
Navchetan Awasthi, Aveen Dayal, Linga R. Cenkeramaddi, and Phaneendra K. Yalavarthy, “Mini-COVIDNet : Efficient Light Weight Deep Neural Network for Ultrasound based Point-of-Care Detection of COVID-19,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (Special issue on Ultrasound in COVID-19 and Lung Diagnostics) 2021 (in press). [Reprint is available at: http://cds.iisc.ac.in/faculty/yalavarthy/Publications.html]

Project Repository: https://github.com/navchetan-awasthi/Mini-COVIDNet

The article, “Anam-Net : Anamorphic Depth Embedding based Light-Weight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images” by Paluru, Naveen; Dayal, Aveen; Jenssen, Havard ; Sakinis, Tomas; Cenkeramaddi, Linga Reddy; Prakash, Jaya; Yalavarthy, Phaneendra, has been accepted for publication as a [Fast Track: COVID-19 Focused Papers] in the IEEE Transactions on Neural Networks and Learning Systems.

Naveen Paluru, Aveen Dayal, Havard B. Jenssen, Tomas Sakinis, Linga R. Cenkeramaddi, Jaya Prakash, and Phaneendra K. Yalavarthy, “Anam-Net : Anamorphic Depth Embedding based Light-Weight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images,” IEEE Transactions on Neural Networks and Learning Systems (Fast Track: COVID-19 Focused Papers) 2021 (in press).

The article “Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures” has been accepted for publication in MDPI Electronics.

A. Dayal, N. Paluru, L. R. Cenkeramaddi, S. J., and P. K. Yalavarthy, “Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures,” MDPI Electronics (Artificial Intelligence Circuits and Systems (AICAS)), vol. 10, no. 2, p. 182, Jan. 2021.