This paper presents a non-intrusive hybrid fault detection approach that combines hardware and software techniques to detect transient faults in microprocessors. Such faults have a major influence in microprocessor-based systems, affecting both data and control flow. In order to protect the system, an application-oriented hardware module is automatically generated and reconfigured on the system during runtime. When combined with fault tolerance techniques based on software, this solution offers full system protection against transient faults. A fault injection campaign is performed using a MIPS microprocessor executing a set of applications. HW/SW implementation in a reprogrammable platform shows smaller memory area and execution time overhead when compared to related works. Fault injection results show the efficiency of this method by detecting 100% of faults.

Rapid and accurate reconnaissance in the event of radiological and nuclear (RN) incidents or attacks is vital to launch an appropriate response. This need is made stronger by the increasing threat of RN attacks on soft targets and critical infrastructure in densely populated areas. In such an event, even small radioactive sources can cause major disruption to the general population. In this work, we present a real-time radiological source localization method based on an optimization problem considering a background and radiation model. Supported by extensive real-world experiments, we show that an airborne system using this method is capable for reliably locating category 3–4 radioactive sources according to IAEA safety standards in real time from altitudes up to 150 m. A sensor bundle including a LiDAR sensor, a Gamma probe as well as a communication module was mounted on a UAV that served as a carrier platform. The method was evaluated on a comprehensive set of test flights, including 28 flight scenarios over 316 min using three different radiation sources. All additional gamma sources were correctly detected, multiple sources were detected if they were sufficiently separated from each other, with the distance between the true source position and the estimated source averaging 17.1 m. We also discuss the limitations of the system in terms of detection limit and source separation.

Wide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%.

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.

In this paper we describe the concept and ongoing work of the research project MOBILIZE, which addresses the operational safety and security of large railway systems to prevent sabotage and vandalism. Examples of such acts are manipulation of system components, intentional placement of objects on the tracks, theft of copper cables or damage to property such as graffiti on parked trains. The prevention of personal injuries resulting from crossing tracks or climbing on parked wagons and thereby getting too close to or even touching the overhead lines is also an important aspect. A permanent installation of video surveillance systems for the entire railway infrastructure is not feasible and, what is more, state-of-the-art video surveillance alone is currently not up to the challenges of monitoring very large areas completely. Therefore, MOBILIZE focuses on the development of a reliable portable system with multi-sensor modalities. In case of increased incidents in a specific region, the system can be deployed quickly and easily. The development of such a system raises questions that represent the main scientific challenges to be explored within MOBILIZE: which combination of sensor technologies is the most suitable to reduce false alarm rates to a minimum in practical operation, legal issues such as the changing regulations regarding the usage of drones, usability for the operator, integration into the operational procedures of the railway operators as well as future economic exploitation of the MOBILIZE project. The current paper focuses on the work done on ground-based visual sensors as well as their fusion with other sensors employed within MOBILIZE, and an assessment of their social impact.