An image stabilization algorithm is presented that is specifically oriented toward video-surveillance applications. The proposed approach is based on a novel motion-compensation method that is an adaptation of a well-known image-stabilization algorithm for visualization in video-surveillance applications. In particular, the illustrated methods take into account the specificity of typical video-surveillance applications, where objects moving in a scene often cover a large part of an image thus causing the failure of classic image-stabilization techniques. Evaluation methods for image stabilization algorithms are discussed.

Introducing a data-driven Self-Awareness (SA) module in Cognitive Radio (CR) can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum in order to make the CR learn wrong behaviours and take mistaken actions. A basic SA module includes the ability to learn generative models and detect abnormalities inside the radio spectrum. In this work, we propose and implement Artificial Intelligence (AI)-based Abnormality Detection techniques at the physical (PHY)-layer in CR enabled by learning Generative Models. Specifically, two real-world practical applications related to different data dimensionality and sampling rates are presented. The first application implements the Conditional Generative Adversarial Network (C-GAN) investigated on generalized state vectors extracted from spectrum representation samples to study the dynamic behaviour of the wideband signal. While the second application is based on learning a Dynamic Bayesian Network (DBN) model from a generalized state vector which contains sub-bands information extracted from the radio spectrum. Results show that both of the proposed methods are capable of detecting abnormal signals in the spectrum and pave the road towards Self-Aware radio.

A novel integrated multi-camera video-sensor (panoramic scene analysis – PSA) system is proposed for surveillance applications. In the proposed set-up, an omnidirectional imaging device is used in conjunction with a pan tilt zoom (PTZ) camera leading to an innovative kind of sensor that is able to automatically track at a higher zoom level any moving object within the guarded area. In particular, the catadioptric sensor is calibrated and used in order to track every single moving object within its 360 degree field of view. Omnidirectional image portions are eventually rectified and pan, tilt and zoom parameters of the moving camera are automatically adjusted by the system in order to track detected objects. In addition a co-operative strategy was developed for the selection of the object to be tracked by the PTZ sensor in the case of multiple targets.

The integration of Cognitive Radio (CR) with Unmanned Aerial Vehicles (UAVs) is an effective step towards relieving the spectrum scarcity and empowering the UAV with a high degree of intelligence. The dynamic nature of CR and the dominant line-of-sight links of UAVs poses serious security challenges and make the CR-UAV prone to a variety of attacks as malicious jamming. Joint jammer detection and automatic jammer classification is a powerful approach against the physical layer threats by identifying multiple jammers attacking the network that realize a crucial stage towards efficient interference management. This paper proposes a novel method for joint detection and automatic classification of multiple jammers attacking with different modulation schemes. The method is based on learning a representation of the radio environment encoded in a Generalized Dynamic Bayesian Network (GDBN) whilst multiple GDBN models represent various jamming signals under different modulation schemes. The CR-UAV performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best-fit model that explains the detected jammer and recognize the modulation scheme accordingly. The simulated results demonstrate that the proposed GDBN-based method outperforms Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE) in terms of classification accuracy and achieves a higher degree of explainability of its own decisions by interpreting causes and effects at hierarchical levels under the Bayesian learning and reasoning processes.

Increasing interest in unmanned aerial vehicles (UAVs), commonly referred to as drones, has occurred in recent years. Search and rescue scenarios where humans in emergency situations need to be quickly found in difficult-toaccess areas constitute an important field of application for this technology. Drones have already been used by humanitarian organizations in countries such as Haiti and the Philippines to map areas after a natural disaster using high-resolution embedded cameras, as documented in a recent United Nations report [1]. Although research efforts have focused mostly on developing video-based solutions for this task [2], UAV-embedded audio-based localization has received relatively less attention [3]?[7]. However, UAVs equipped with a microphone array could be of critical help to localize people in emergency situations, especially when video sensors are limited by a lack of visual feedback due to bad lighting conditions (such as at night or in fog) or obstacles limiting the field of view (Figure 1).