Linga Reddy Cenkeramaddi

TL;DR: Paluru et al. as discussed by the authors proposed anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images, which has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point of care) platforms.

TL;DR: The spoofing scenario results show that using the predicted fusion state provides the same accuracy as a GPS receiver in a clean environment, and because the innovation is calculatedUsing the predicted fused state, there is no effect on the number of satellite signals on PRMSE.

TL;DR: In this paper, the authors developed a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images, which can achieve the highest accuracy of 83.2% and requires a training time of only 24 min.

TL;DR: A novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications.

TL;DR: This paper presents a proof of concept for selfpowered Internet of Things (IoT) device, which is maintenance free and completely self-sustainable through energy harvesting, which can potentially last for more than 5 months on the coin cell battery without any energy harvesting.