The Ka band has found applications in satellite, and radar communications. It is also expected that this band will be utilized for 5G applications. This paper presents single-and double-beam microstrip reflectarrays with single layer and compact size for Ka band communications at 28 GHz. Three different unit cells are investigated in this paper. Single-and double-beam reflectarrays are investigated. The reflectarrays are designed at 28 GHz with a physical size of 10k 9 10k. A pyramidal horn antenna is used for the feeding purpose. The focal-length-to-diameter (F/D) ratio is equal to one. Two different scenarios for single-beam reflectarrays are presented: one with a broadside direction and the other with a 10°tilt angle. The simulation results show that for the broadside single-beam scenario, it is possible to achieve a gain up to 28.5 dB, and a 1-dB gain-bandwidth up to 30.7%. On the other hand, the presented reflectarray for the single-beam design at 10°tilt angle gives a gain of about 26.4 dB, a side lobe level (SLL) of about-15.6 dB, and a 19.3% gain-bandwidth. For the double-beam reflectarray, four different designs at different angles of 5°, 10°, 15°, and 20°have been simulated and compared. Moreover, the simulation results on the double-beam reflectarray show that the double-beam design at 10°is better from the gain and SLL perspectives. Two prototypes for broadside single-beam reflectarrays have been fabricated and measured. The measurement results show a good match with the simulation results. Gain flatness is guaranteed for both the simulated and measured results over the band of interest.

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Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems.In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully-connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.

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