This work presents the design of a rectenna topology by using a compact microstrip patch antenna based on a fractal model and with the rectifier circuit integrated into the same physical structure. This configuration presents a very reduced circuit area, which makes the proposed rectenna circuit suitable for harvesting and wireless power transfer applications where the size is critical. The proposed compact rectenna, implemented on a low‐cost FR‐4 substrate, can harvest RF power from 2.45 GHz (ISM band) with a efficiency of ∼62% when the input power harvested by the rectenna is +2 dBm. The suggested rectenna has been manufactured and experimentally characterised, showing a good agreement with the expected simulated results.
The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuouswave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.
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