Using the VENµS Super-Spectral Camera for detecting moving vehicles

Monitoring transportation for planning, management, and security purposes has become a growing interest for various stakeholders. A methodology for detecting moving vehicles is based on the acquisition time gap between the pushbroom detector sub-arrays. However, this technique requires overcoming differences in ground sampling distance and/or spectral features of the sensor’s bands used for change detection. The current work demonstrates a proof of concept for the VENµS satellite’s capability to detect moving vehicles in a single pass with a relatively low spatial resolution. The VENµS Super-Spectral Camera has a unique stereoscopic capability because the two spectral bands, with the same central wavelength and width, are positioned at the extreme ends of the camera’s focal plane. This design enables a 2.7-sec difference in observation time. The normalized difference moving object index (NDMOI) has been designed to detect moving vehicles using these bands without image preprocessing for dimensionality reduction or geometric corrections, as other sensors require. Results show the successful detection of small- to medium-sized moving vehicles. Especially interesting is the detection of private cars that are, on average, 2–3 m smaller than the VENµS ground sampling distance. Vehicle movement was effectively detected in different backgrounds/environments, e.g., on asphalt and unpaved roads, as well as over bare soil and plowed fields. Furthermore, a multitemporal analysis of moving vehicles during the Covid-19 pandemic in 2020 shows the effectiveness of the proposed methodology.

Wearable Piezoelectric BioMEMS-based Sensor for SARS-CoV-2 (COVID-19) Virus Droplets Detection

Viral diagnostic is essential to the fields of medicine and bio-nanotechnology, but such analyses can present some complex analytical challenges. While molecular methods that are mostly used in clinical laboratories, for instance, reverse transcription-polymerase chain reaction (RT-PCR) and antigens tests require long acquisition times, and often provides unreliable results for COVID-19 virus detection, the piezo-based sensors coupled with MEMS have demonstrated a significant role in robust viral detection. In this work, we have designed and simulated a piezoelectric MEMS-based biosensor integrated into a wearable face mask for early detection of the SARS-CoV-2 virus droplets. We systematically investigated the influence of virus droplets in changing the applied stress on the cantilever receptor pit with change in mass when viruses (pathogens) from airborne coughing droplets-nuclei binds with coated antibodies on the sensor's cantilever layer with receptor pit thereby generating electric potential. Additionally, Bio-MEMS sensor results have manifested that it has the ability to detect a single size particle of 1 virion with a diameter ≥100 nm and mass of 1fg in a single cough containing droplet nuclei of radius 0.05μm in a less amount of time. Additionally, we empirically set electrical potential as thresholds parameter for our wearable biosensor embedded in the face mask for public monitoring to detect contagious virus particle droplets. Furthermore, this study presented the prospective use of MEMS-based sensing method to identify and detect other biological (bacteria and toxins) analytes. © 2021 IEEE.

Super-resolution using Deep Networks for Chest X-Ray Images

Super-resolution imaging is extensively deliberated in medical imaging modalities nowadays, there being a wide panic on the effect of COVID-19 virus impression. Generally, spatial resolutions of CXR are insufficient due to the constraints such as image acquisition time, hardware limits and physical limits. It is a clinically challenging task to recover the high resolution CXR images. A significant concern in CXR imaging is X-Ray contrast disparity and the demand to attain high quality images with adequate structural and imaging details. To address these problems, we propose an effective deep network for the super-resolution reconstruction method to recover high-resolution CXR images while retaining diagnostic capabilities. Specifically, the reinforcement subnetwork is hosted to generate sharp and informative qualitative features. The quantitative and qualitative assessments found that the proposed model based on the evaluation index improves the CXR super-resolution. In addition, the PSNR index of the proposed model has 0.30 higher than that of the SRCNN network. © 2021 IEEE.