A compact multiple input multiple output (MIMO) antenna operating at 2.45 GHz industrial scientific and medical band is presented for wearable devices. Open-end slotting is used to miniaturize the antenna dimensions. Inverted U-shaped ground stub is incorporated to reduce mutual coupling. On-body performance is analyzed on a three-layered equivalent tissue phantom model. The wide bandwidth of 300 MHz and port isolation of 30 dB are obtained from measured results. The antenna shows the efficiency of 40% and directivity of 4.56 dBi when placed at a gap of “s” = 4 mm from the body. Broadside radiation pattern and low specific absorption rate make the antenna suitable for on-body communication. Further, diversity performance is measured in terms of envelope correlation coefficient (ECC), diversity gain (DG), and channel capacity loss (CCL). The value of ECC is 0.025, DG is 9.98 dB, and CCL is 0.12 bits/s/Hz at 2.45 GHz. Antenna robustness is examined by bending the structure at different radii along the x-axis and y-axis. Performance of the proposed structure is reliable with structural deformation.

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Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications. In particular the need for automating the process of real-time food item identification, there is a huge surge of research so as to make smarter refrigerators. According to a survey by the Food and Agriculture Organization of the United Nations (FAO), it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself. Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage. But a major issue is the high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator. To address these issues, this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network (CNN) architectures. The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets. A total of eight test cases are considered with accuracy and training time as the performance metric. In the end, real-time testing results are also presented which validates the system's performance.