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Object recognition is a quite critical task that involves the identification and potential localization of a target inside a monitored area. Target detection usually relies on material, shape and size identification in order to infer higher level information, and it can be employed in many different frameworks and applications. In this connection, we carry out a performance evaluation campaign on WiFi Channel State Information (CSI)-based object recognition techniques used to automatically identify material, category and specific objects among daily life items. The main contribution of this work is to provide a thorough comparison of the performance of different machine learning algorithms on recognition using a real-life experimental testbed. The performance study shows that the Random Forest (RF) classifier proves very accurate in terms of correct target recognition, independently of the considered task. The employed deep learning algorithm, Long Short-Term Memory (LSTM), is similarly able to attain very good results. In particular, accuracy values reach 94.8% in material identification, 97.1% in category differentiation and 98.1% in object recognition tasks.

Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities.

The Internet of Things (IoT) framework is moving the research community to provide smart systems and solutions aimed at revolutionizing medical sciences and healthcare. Given the extreme diffusion of Alzheimer’s disease (AD) and Parkinson’s disease (PD), the demand for a solution to early detect neurological symptoms of such diseases strongly arose. According to the medical literature, such early detection can be obtained through the correlation between PD and AD and some external symptoms: the Essential Tremor (ET) and the number of Eye Blinks (EBs). In this paper, which can be considered as an extended version of [1], we present a prototype of wearable smart glasses able to detect the presence of ET of the head and to count the number of EBs at the same time, in a transparent way with respect to the final user. Numerical results demonstrate the reliability of the proposed approach: the proposed algorithms are able to i) correctly recognize the ET with an overall accuracy above 97% and ii) count the number of EBs with an overall error around 9%.