CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

CSI-based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world’s healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a non-wearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the Channel State Information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional Convolutional Neural Networks (1D-CNN) and Bi-directional long-short term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds. First, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The HAR results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. IEEE

Lost, but reconnected virtually – AEF ISOBUS Plugfest is back and better than ever

How do you get a group of ag software engineers excited? Most engineers have similar traits. They want to provide new and better designs, and the best possible products. So, you put them in a room together, and let them connect their software and hardware to check if they function as intended.The Agricultural Industry Electronics Foundation (AEF) came up with a way to make that happen. The event is called Plugfest, and it provides a chance for friends and competitors alike to come from around the globe to put aside that competition to work together for the good of their employers and customers. Started in 2001, Plugfest began with a small group of engineers who had the intent of ensuring that manufacturers of tractors, implements and components were interpreting the ISOBUS standard (ISO 11783) in the same way. Nowadays, more than 200 participants coming together ensuring that every testing attendee has a chance to connect with everyone else at the event. And then, the COVID-19 pandemic stopped all joint activities and the AEF had to cancel the already scheduled Plugfest events. Recreating the Plugfest “connection” became a challenge for the whole industry. In the absence of physical contact, the AEF and Vector discussed new approaches. In a joint experimental project, the possibility was created to carry out such Plugfests "virtually". The principle based on "tunnelling" of the physical CAN communication between ISOBUS devices present at two different locations, by means of the internet. Many technical issues such as delays due to long distances were identified and addressed. Also, organizational issues like strict company-specific internet policies had to be considered as well as human interaction between the different Plugfest parties. Based on a cloud approach most aspects could be satisfied. Pilot Plugfests of different sizes and distances have shown: the chosen approach works. © 2022, VDI Verlag GMBH. All rights reserved.

Report from the LHC Early Career Scientists Fora

Early Career Scientists (ECSs) are non-tenured researchers. ECSs constitute nearly half of every major collaboration at the LHC and contribute to all of scientific projects and activities. This report summarizes the programs of the ECSs committees of ALICE, ATLAS, CMS, and LHCb. An overview of the joint activities of the four committees that make up the LHC Early Career Scientists Fora is given. The impact of Covid-19 on the researchers’ wellbeing is discussed in conjunction with a recent survey from LHCb. © Copyright owned by the author(s)