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Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

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Counter-Unmanned Aerial Vehicle (c-UAV) systems are considered an emerging technology dedicated to address the critical issue of malicious UAV detection. Acquiring useful information from a multitude of data gathered using a topology of different sensors for UAV detection constitutes a problem with substantial importance. In this paper, we present a novel multimodal deep learning methodology to filter and combine data from a variety of unimodal approaches dedicated to UAV detection. Specifically, the aim of this work is to detect, and classify potential UAVs based on a fusion procedure of features from UAV detections provided by unimodal components. Actually, we propose a general fusion neural network framework in order to merge features extracted from unimodal modules and make deductions with increased accuracy. Our method is validated by thorough application to UAV detection and classification tasks. Our model approach achieves significant performance improvement over the unimodal detection results.

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.

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A programmable THz metamaterial, derived from the utilisation of a piezoelectric controlled microgripper as a split-ring resonator (SRR), is introduced in this paper. By applying the appropriate actuation voltage on the piezoelectric microelectromechanical systems (MEMS), a reconfigurable complex medium, offering enhanced bandwidth tunability, is attained. Several polarisation topologies are examined in order to clarify the interesting attributes of the metamaterial. Finally, thorough numerical investigations, via a robust finite element method (FEM), support the efficiency and reveal the advantages and applicability of the proposed device.