Siddharth Gupta, Prabhat Kumar Rai, Abhinav Kumar, Phaneendra K. Yalavarthy, and Linga Reddy Cenkeramaddi “Target Classification by mmWave FMCW Radars using Machine Learning on Range-Angle Images,” has been accepted for publication in the IEEE Sensors Journal, 2021.
The paper titled ““Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-Of-Things” has been accepted for presentation in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 13-16 September 2021.
Sreenivasa Reddy Yeduri, Garima Chopra, Ankit Dubey, Abhinav Kumar, Trilochan Panigrahi and Linga Reddy Cenkeramaddi, “Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-Of-Things” has been accepted for presentation in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 13-16 September 2021.”
P. Veda Bhanu, Rahul Govindan, Rajat Kumar, Vishal Singh, Soumya J, and Linga Reddy Cenkeramaddi, Fault-Tolerant Application-Specific Topology based NoC and its Prototype on an FPGA, Accepted for publication in IEEE Access Journal (2021).
The 1st Indo-Norway Workshop on unmanned Aerial VEhicles (IN-WAVE) from April 30, 2021 to May 2, 2021 (3 day workshop) is organized in a virtual mode. This workshop is an outcome of the multiple ongoing collaborations between Indian Institute of Technology Hyderabad (IITH), India, and University of Agder (UiA), Norway. This includes the Department of Science and Technology (International Bilateral Cooperation Division), Government of India and the Norwegian Research council funded project, Low Altitude UAV Communication and Tracking (LUCAT), and the Norwegian Research council and the Diku (the Norwegian agency for international cooperation and quality enhancement in higher education) funded project, the Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS). There exists an MoU between IITH and UiA to further promote this research and collaboration. Through IN-WAVE 2021, we aim to bring together researchers, industries, and practitioners to present and discuss their latest achievements and innovations in the area of UAV Communication and Tracking. The workshop will be an experience sharing forum and we expect to attract a good mix of academic, industry researchers, practitioners, and students working in this area.
The Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS) aims to establish long-term collaboration between highly reputed Indian universities which include Indian Institute of Science (IISc), Bangalore, Indian Institute of Technology Hyderabad (IITH), International Institute of Information Technology Hyderabad (IIITH) and Birla Institute of Technology and Science (BITS), Hyderabad and Norwegian universities, University of Agder (UiA), Norwegian University of Science and Technology (NTNU) and Norwegian Institute for Water Research (NIVA) in world-class research and education. INCAPS considers broad areas of research which include smart sensing for autonomous systems, mmWave sensor-based system design, de-centralized wireless communications, in-network processing and intelligence for heterogeneous wireless sensor and communication networks, machine learning and deep learning for autonomous systems, data analytics, energy harvesting based smart electronic systems, smart water networks and inference methods for timely detection and prediction, cognitive control and adaptive learning in autonomous cyber-physical systems.
The key objectives of the INCAPS project are to: I). Strengthen collaborative network between industry (both public and private enterprises, small and medium-sized enterprises, and multi-national companies) and academia. II). Increased value creation and enhanced innovation by using smart sensing, machine, and deep learning techniques in autonomous cyber-physical systems. III). Facilitate education and knowledge sharing through better mobility for students and researchers. IV). Create an arena for the generation of research and innovation projects. V). Increased utilization of research and educational infrastructure both in Norway and India. VI). Integrate professionals from industries and academics through workshops, seminars, webinars, and summer/winter schools.
“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. “
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.
A. Jha, M. K. Shah, S. Jha, L. R. Cenkeramaddi and S. Royo, “CURRENT MODULATION INDUCED STABILITY IN LASER DIODE UNDER HIGH OPTICAL FEEDBACK STRENGTH,” in IEEE Access, doi: 10.1109/ACCESS.2021.3069387.
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).
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
P. Veda Bhanu, Rahul Govindan, Plava Kattamuri, Soumya J, and Linga Reddy Cenkeramaddi, “Flexible Spare Core Placement in Torus Topology based NoCs and its validation on an FPGA,” has been accepted for publication in IEEE Access Journal (2021).
Daniel S. Breland, Simen B. Skriubakken, Aveen Dayal, Ajit Jha, Phaneendra K. Yalavarthy, and Linga R. Cenkeramaddi, “Deep Learning based Sign Language Digits Recognition from Thermal Images with Edge Computing System,” IEEE Sensors Journal 2021 (in press).