Farrell Udsen (donnaelbow57)

This paper presents a compact DDA-based fully-differential CMOS instrumentation amplifier dedicated for micro-power ECG monitoring. Only eight transistors are employed to realize a power-efficient current-sharing DDA. A RC network (using MOS pseudo resistors and poly capacitors) forms feedback loops around the DDA creating an ac-only amplification. The proposed amplifier is dc-coupled via gate terminals of the p-channel input transistors. It thus achieves sufficiently high input impedance over the entire ECG frequency range. Fabricated in a 0.35-m CMOS process, the proposed amplifier occupies 0.0712 mm2. It operates from a 2 V dc supply with 336 nA current consumption. Measurements show that the amplifier attains its input resistance greater than 10 G and achieves 1.54 Vrms input-referred noise over 0.1-300 Hz. Noise and power efficiency factors are 2.02 and 8.16, respectively. At 50 Hz, the mean CMRR of 83.24 dB is obtained from 11-chip measurement. Experiments performed on a human subject confirm the functionality of the proposed amplifier in a real measurement scenario.An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID- 19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development.Sports professionals have been increasingly using Virtual Reality (VR) for training and assessment of skill-based sports. Yet fundamental questions about the virtue of VR training for skill-based sports remain unanswered Can the complex motor skills required in these sports be learned in VR If so, do these skills transfer to the real world We have developed a VR table tennis system that incorporates customized physics with realistic audio-visual stimuli, haptics, and motion capture to enhance VR immersion and collect information about the players posture and technique. We have assessed skill acquisition and training transfer by comparing real table tennis performance between a control group (n=7) that received no training and an experimental group (n=8) trained for five sessions in VR. Our results show a significant improvement in technique but no significant changes in the number of the returned balls in the experimental group in the real-life retention session. However, no significant differences are found in the control group. Our findings support the notion that complex skills can be learned in VR and that obtained skills can transfer to the real world. This work offers an inexpensive VR table tennis training platform, enabling effective training via real-time motor and ball returning technique feedback.Deep learning-based in-loop filters have recently demonstrated great improvement for both coding efficiency and subjective quality in video coding. However, most existing deep learning-based in-loop filters tend to develop a sophisticated model in exchange for good performance, and they employ a single network structure to all recons