Additions to the book "Systems Design and Engineering" by Bonnema et.al. Subjects were chosen based on the Systems Engineering needs for Small and Medium Enterprises, as researched in the SESAME project. The
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Eating rate is a basic determinant of appetite regulation, as people who eat more slowly feel sated earlier and eat less. Without assistance, eating rate is difficult to modify due to its automatic nature. In the current study, participants used an augmented fork that aimed to decelerate their rate of eating. A total of 114 participants were randomly assigned to the Feedback Condition (FC), in which they received vibrotactile feedback from their fork when eating too fast (i.e., taking more than one bite per 10 s), or a Non-Feedback Condition (NFC). Participants in the FC took fewer bites per minute than did those in the NFC. Participants in the FC also had a higher success ratio, indicating that they had significantly more bites outside the designated time interval of 10 s than did participants in the NFC. A slower eating rate, however, did not lead to a significant reduction in the amount of food consumed or level of satiation.These findings indicate that real-time vibrotactile feedback delivered through an augmented fork is capable of reducing eating rate, but there is no evidence from this study that this reduction in eating rate is translated into an increase in satiation or reduction in food consumption. Overall, this study shows that real-time vibrotactile feedback may be a viable tool in interventions that aim to reduce eating rate. The long-term effectiveness of this form of feedback on satiation and food consumption, however, awaits further investigation.
We present the Brigade renderer: an efficient system that uses the path tracing algorithm to produce images for real-time games. We describe the architecture of the Brigade renderer, and provide implementation details. We describe two games that have been created using Brigade.
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The utilization of drones in various industries, such as agriculture, infrastructure inspection, and surveillance, has significantly increased in recent years. However, navigating low-altitude environments poses a challenge due to potential collisions with “unseen” obstacles like power lines and poles, leading to safety concerns and equipment damage. Traditional obstacle avoidance systems often struggle with detecting thin and transparent obstacles, making them ill-suited for scenarios involving power lines, which are essential yet difficult to perceive visually. Together with partners that are active in logistics and safety and security domains, this project proposal aims at conducting feasibility study on advanced obstacle detection and avoidance system for low-flying drones. To that end, the main research question is, “How can AI-enabled, robust and module invisible obstacle avoidance technology can be developed for low-flying drones? During this feasibility study, cutting-edge sensor technologies, such as LiDAR, radar, camera and advanced machine learning algorithms will be investigated to what extent they can be used be to accurately detect “Not easily seen” obstacles in real-time. The successful conclusion of this project will lead to a bigger project that aims to contribute to the advancement of drone safety and operational capabilities in low-altitude environments, opening new possibilities for applications in industries where low-flying drones and obstacle avoidance are critical.
Real-Time Cyber-Physical Systems (RT-CPS) zijn onmisbaar in onze samenleving, van medische apparatuur tot autonome voertuigen. De betrouwbaarheid en robuustheid van deze systemen zijn echter cruciaal, fouten kunnen immers grote gevolgen hebben. Dit project beoogt de betrouwbaarheid van RT-CPS te vergroten door middel van een modulaire hardware-architectuur en geavanceerde validatie- en verificatiemethoden (V&V). In samenwerking met praktijkpartners, waaronder het Wilhelmina Kinderziekenhuis, wordt een proof-of-concept demonstrator ontwikkeld in een praktijkgerichte casus. De modulaire hardware-architectuur maakt RT-CPS flexibeler, toekomstbestendig en breed toepasbaar. De geavanceerde V&V-methoden borgen de betrouwbaarheid van de systemen en helpen MKB-bedrijven bij de ontwikkeling van hun eigen RT-CPS-applicaties. Naast de directe voordelen voor de betrokken partners, draagt dit project bij aan een bredere maatschappelijke impact. De verhoogde betrouwbaarheid van RT-CPS kan leiden tot verbeterde veiligheid en efficiëntie in diverse sectoren. Een krachtige samenwerking tussen kennisinstituten, praktijkpartners en het MKB is de sleutel tot succes. Dit project bundelt expertise en praktijkkennis om Nederland een leidende positie te laten innemen op het gebied van betrouwbare RT-CPS. In dit 1-jarig verkennend project zal de Hogeschool van Arnhem en Nijmegen samenwerken met Gemini Embedded Technology, Wilhelmina Kinderziekenhuis, het grootbedrijf Capgemini en de Universiteit Utrecht.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
