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With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods.Encryption and optimization techniques for ensuring long−lasting and secure communications, as well as for power management, are discussed.Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra-reliable and low−latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications.

Abstract-This paper presents a one-wavelength loop antenna fed by an inductively coupled loop for on-body applications. An equivalent circuit for the inductively coupled loop antenna is proposed to synthesize the antenna system with a microchip. The designed tag is printed on a PVC substrate and placed close to a four-layer stratified elliptical cylinder human model. The card-type tag measures 85.5 × 54 × 0.76 mm 3 and is suitable for use on a student ID card for a broad range of applications. The impedance bandwidth of the inductively coupled loop tag antenna is 60 MHz (880-940 MHz, 6.6%), which covers the operating UHF bands in U.S. and Taiwan. The measured reading distance ranges from 2.7 to 5.7 meters when placed at different positions on the chest of a human body in the open site.

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Epilepsy is one of the most common neurological disorders, which is characterized by unpredictable brain seizure. About 30% of the patients are not even aware that they have epilepsy and many have to undergo surgeries to relieve the pain. Therefore, developing a robust brain-computer interface for seizure prediction can help epileptic patients significantly. In this paper, we propose a hybrid CNN-SVM model for better epileptic seizure prediction. A convolutional neural network (CNN) consists of a multilayer structure, which can be adapted and modified according to the requirement of different applications. A support vector machine is a discriminative classifier which can be described by a separating optimal hyperplane used for categorizing new samples. The combination of CNN and SVM is found to provide an effective way for epileptic prediction. Furthermore, the resulting model is made autonomous using edge computing services and is shown to be a viable seizure prediction method. The results can be beneficial in real-life support of epilepsy patients.

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