Design and Characterization of Artificial Haircell Sensor for Flow Sensing With Ultrahigh Velocity and Angular Sensitivity

We report the development of an artificial hair cell (AHC) sensor with design inspired by biological hair cells. The sensor consists of a silicon cantilever beam with a high-aspect-ratio cilium attached at the distal end. Sensing is based on silicon piezoresistive strain gauge at the base of the cantilever. The cilium is made of photodefinable SU-8 epoxy and can be up to 700-mum tall. In this paper, we focus on flow-sensing applications. We have characterized the performance of the AHC sensor both in water and in air. For underwater applications, we have characterized the sensor under two flow conditions: steady-state laminar flow (dc flow) and oscillatory flow (ac flow). The detection limit of the sensor under ac flow in water is experimentally established to be below 1 mm/s. A best case angular resolution of 2.16deg is also achieved for the sensor's yaw response in air.     

A deep learning approach for classification of COVID and pneumonia using DenseNet-201

In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.