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Autonomous driving is an area that has been growing in recent years. However, cars are unprepared to cooperate with others nearby, wasting resources and computational power. Thus, cooperative autonomous driving reveals its importance in the future. In this work-in-progress paper, we define, implement and test an architecture for a simulation environment where cooperative autonomous driving protocols can be tested. Additionally, a Manoeuvre Negotiation Protocol is implemented. This protocol will make an existing autonomous driving (AD) stack more resilient in real driving scenarios, improving its robustness and safety. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to expand, data security has become increasingly important for ensuring privacy and safety, especially given the sensitive and, sometimes, critical nature of the data handled by IoT devices. There exist hardware-based trusted execution en-vironments used to protect data, but they are not compatible with low-cost devices that lack hardware-assisted security features. The research in this paper presents software-based protection and encryption mechanisms explicitly designed for embedded devices. The proposed architecture consists of two parts: the Agent, which is designed to work with low-cost, low-end devices without requiring modifications to the underlying hardware, and the Computing Module, which is designed for slightly more computationally powerful devices. The Computing Module enables devices to write data in protected memory and continuously verifies its integrity to provide protection. Additionally, it utilizes the Agents located on the device to safeguard device applications against attacks by requesting the Agent to generate an application code signature and validating it. The proposed solution is an alternative data security approach for low-cost IoT devices without compromising performance or functionality. Our work underscores the importance of developing secure and cost-effective solutions for protecting data in the context of IoT. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks used in aquatic environments for continuous monitoring are typically subject to physical or environmental factors that create anomalies in collected data. A possible approach to identify and correct these anomalies, hence to improve the quality of data, is to use artificial neural networks, as done by the previously proposed ANNODE (Artificial Neural Network-based Outlier Detection) framework [1]. In this paper we propose ANNODE+, which extends the ANNODE framework by detecting missing data in addition to outliers. We also describe the design and implementation of ANNODE+, implemented in Python to exploit readily available machine learning (ML) tools and libraries, also allowing online processing of incoming measurements. To evaluate the ANNODE+ capabilities, we used a dataset from a sensor deployment in Seixal's bay, Portugal. This dataset includes measurements of water level, temperature and salinity. We observed that our implementation of ANNODE+ performed as intended, being able to detect injected anomalies and successfully correcting them. [ABSTRACT FROM AUTHOR]

Background: We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism.Methods: We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines.Results: The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p < 0.02).Conclusions: The observed pattern of results show that Self and Third Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts. [ABSTRACT FROM AUTHOR]

Nowadays, Human-Computer Interaction (HCI) systems e.g. keyboard, mouse and touch screen are frequently used by anyone. However, those ways to interact with computers may be not suitable for disabled persons. Brain-Computer Interface (BCI) is an HCI system which can be used as an alternative for these persons. This approach allows operating a computer using only the brain signals, for instance, imagine a situation where a participant only needs to think about an action in order to make it happen: to move, to select an object, to shift gaze, to control the movements of their (virtual) body, or to design the environment itself, by “thought” alone. In this paper we propose a BCI framework to process brain signals resulting from imagination processes that is used together with serious games to teach or improve autonomy of physically handicapped people. Two simple scenarios based on grasp and eye-gaze imagination games were developed to test the framework. Games consist on choosing the right object to put on the recycling bin or to choose a piece of a puzzle to fit the other one in a white board. Through the proposed approach we are able to discriminate the direction intended by the player. Our results show that we achieve about 80% of accuracy when a block of 30 seconds of imagination is considered. [ABSTRACT FROM AUTHOR]

Nowadays public buildings are changing constantly, often people have to take different routes to reach known destinations. At the same time, new services and places are made available to attract more people to the shopping center. This dynamic environment is usually signalled and labelled with visual marks and signs which are not appropriated for blind persons. Therefore, blind users are unintentionally deprived of a full participation in the society. With the purpose of equalize the access to services and spaces among all persons, this work proposes an innovative indoor navigation and information system for public buildings, namely shopping centers, based on existing technologies not used for this purpose. Intending to allow a comfortable and helpful aid on blind persons trips to the shopping center, this proposal system relies on users smartphone and wireless sensors deployed in the environment. [ABSTRACT FROM AUTHOR]

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings. [ABSTRACT FROM AUTHOR]

There have been both research and commercial advances on applyingWireless Sensor and ActuatorNetworks (WSN) in industrial premises. These have cost advantages related to avoiding some cabled deployments. A possible architecture involves a Networked Control System (NCS) with many small WSN subnetworks, cabled nodes and computer servers (e.g., servers, control stations). In those systems individual sensor nodes can be programmed, as opposed to cabled analog systems. We investigate approaches for networked-wide configuration, where all nodes--cabled or WSN sensors--can be configured with simplicity from a single interface, instead of hand-coding or complex configurations of individual nodes. We propose an architecture and approach for configuration and operation. Previous related proposals on middleware involving WSNs suffer from two major limitations: they either program within an individual WSN or configure operation outside WSNs, wrapping data coming from WSN. They do not allow configuring WSN and non-WSN nodes for operation from a single interface. We discuss the architecture and propose the NCSWSN configuration and operation approach. We are applying this system in an industrial testbed, therefore we test the approach and also show user interfaces and results from the deployment. [ABSTRACT FROM AUTHOR]