In this paper, a methodology based on machine learning for fault detection in continuous processes is presented. It aims to monitor fully distributed scenarios, such as the Tennessee Eastman Process, selected as the use case of this work, where sensors are distributed throughout an industrial plant. A hybrid feature selection approach based on filters and wrappers, called Hybrid Fisher Wrapper method, is proposed to select the most representative sensors to get the highest detection quality for fault identification. The proposed methodology provides a complete design space of solutions differing in the sensing effort, the processing complexity, and the obtained detection quality. It constitutes an alternative to the typical scheme in Industry 4.0, where multiple distributed sensor systems collect and send data to a centralised cloud. Differently, the proposed technique follows a distributed approach, in which processing can be done eventually close to the sensors where data is generated, i.e., at the edge of the Internet of Things. This approach overcomes the bandwidth, privacy, and latency limitations that centralised approaches may suffer. The experimental results show that the proposed methodology provides Tennessee Eastman Process fault detection solutions with state-of-the-art detection quality figures. In terms of latency, solutions obtained outperform in 37.5 times the implementation with the highest detection quality, using 1.99 times fewer features, on average. Also, the scalability of the framework provides a design space where the optimal implementation can be chosen according to the application needs.

Object detection is an essential technology for surveillance systems, particularly in areas with a high risk of accidents such as railway level crossings. To prevent future collisions, the system must detect and track any object that passes through the monitored area with high accuracy, and this process must be performed fulfilling real-time specifications. In this work, an edge IoT HW platform implementation capable of detecting and tracking objects in a railway level crossing scenario is proposed. The response of the system has to be calculated and sent from the proposed IoT platform to the train, so as to trigger a warning action to avoid a possible collision. The system uses a low-resolution 3D 16-channel LIDAR as a sensor that provides an accurate point cloud map with a large amount of data. The element used to process the information is a custom embedded edge platform with low computing resources and low-power consumption. This processing element is located as close as possible to the sensor, where data is generated to improve latency, privacy, and avoid bandwidth limitations, compared to performing processing in the cloud. Additionally, lightweight object detection and tracking algorithm is proposed in this work to process a large amount of information provided by the LIDAR, allowing to reach real-time specifications. The proposed method is validated quantitatively by carrying out implementation on a car road, emulating a railway level crossing.

The main research motivation of this paper is the fight against gender-based violence and achieving gender equality from a technological perspective. The solution proposed in this work goes beyond currently existing panic buttons, needing to be manually operated by the victims under difficult circumstances. Instead, Bindi, our end-to-end autonomous multimodal system, relies on artificial intelligence methods to automatically identify violent situations, based on detecting fear-related emotions, and trigger a protection protocol, if necessary. To this end, Bindi integrates modern state-of-the-art technologies, such as the Internet of Bodies, affective computing, and cyber-physical systems, leveraging i) affective IoT with auditory and physiological commercial off-the-shelf smart sensors embedded in wearable devices, ii) hierarchical multisensorial information fusion, and iii) the edge-fog-cloud IoT architecture. This solution is evaluated using our own dataset named WEMAC, a very recently collected and freely-available collection of data comprising the auditory and physiological responses of 47 women to several emotions elicited by using a virtual reality environment. On this basis, this work provides an analysis of multimodal late fusion strategies to combine the physiological and speech data processing pipelines to identify the best intelligence engine strategy for Bindi. In particular, the best data fusion strategy reports an overall fear classification accuracy of 63.61% for a subject-independent approach. Both a power consumption study and an audio data processing pipeline to detect violent acoustic events complement this analysis. This research is intended as an initial multimodal baseline that facilitates further work with real-life elicited fear in women.