Iedereen heeft een groot aantal Embedded Systemen in zijn omgeving, maar weet niet dat ze er zijn. Embedded Sytemen doen hun nuttige werk namelijk "achter de schermen": door elektronica en software slim te combineren en te integreren in een apparaat of machine ontstaan nieuwe toepassingsmogelijkheden die eenvoudiger te bedienen zijn en die bovendien een stuk goedkoper op de markt gebracht kunnen worden dan met conventionele technieken. Dit heeft wel tot gevolg dat de embedded systemen zelf elk jaar complexer worden (om aan de buitenkant simpeler te worden). Het ontwerpen en bouwen van dit soort systemen is dus een stevige technische uitdaging. Een van de belangrijkste voorwaarden is dat de professionals van verschillende vakgebieden goed kunnen samenwerken, vooral over de grenzen van hun eigen vakgebied. Een andere voorwaarde is dat ontwerpers van alle intellectuele niveaus nauw met elkaar moeten samenwerken om het onderste uit de kan te kunnen halen. In de regio Zuidoost Nederland, in een brede strook rond de A67, is een groot aantal bedrijven in de maakindustrie van wereldklasse gevestigd. Het is de maatschappelijke taak van onderwijsinstellingen in het algemeen en Fonts Hogescholen in het bijzonder om voldoende studenten op hoog gekwalificeerd niveau op te leiden, zodat deze bedrijven op wereldniveau kunnen blijven concurreren. Daar wil het Lectoraat Architectuur van Embedded Systemen zo veel mogelijk aan bijdragen door relevant toegepast onderzoek uit te voeren.
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This paper addresses an approach to teaching embedded systems programming through a challenge-based competition involving robots. This pedagogical project distinguishes itself by incorporating international students from three international institutions through the Blended Intensive Program (BIP). The research findings indicate that this approach yields excellent results regarding student engagement and learning outcomes. The challenge-based program effectively promotes students' creative problem-solving abilities by combining theoretical instruction with hands-on experience in a competitive setting.
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The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
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Dit essay geeft een systeemvisie op het ontwikkelen van embedded software voor slimme systemen: (mobiele) robots en sensornetwerken.
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Studenten van Fontys Hogeschool ICT/Technische Informatica hebben vorig jaar hard gewerkt aan een minihovercraft. Die moest dienen als studieobject voor een aantal MKB'ers, die met een Raak Lightsubsidie meer informatie wilden inwinnen over het bouwen van toepassingen met embedded Linux en verschillende periferieën, waaronder WLan, USB-verbindingen, een afstandsbediening via een webservice-PDA-combinatie en een realtime aansturing van motoren.
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From the article: Abstract Over the last decades, philosophers and cognitive scientists have argued that the brain constitutes only one of several contributing factors to cognition, the other factors being the body and the world. This position we refer to as Embodied Embedded Cognition (EEC). The main purpose of this paper is to consider what EEC implies for the task interpretation of the control system. We argue that the traditional view of the control system as involved in planning and decision making based on beliefs about the world runs into the problem of computational intractability. EEC views the control system as relying heavily on the naturally evolved fit between organism and environment. A ‘lazy’ control structure could be ‘ignorantly successful’ in a ‘user friendly’ world, by facilitating the transitory creation of a flexible and integrated set of behavioral layers that are constitutive of ongoing behavior. We close by discussing the types of questions this could imply for empirical research in cognitive neuroscience and robotics.
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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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Computational thinking (CT) has become a necessity in many professional domains. As such, scholars argue that the acquisition of CT and application should be embedded in existing school subjects. Within the CT literature, a tax-onomy distinguishes CT practices in STEM education into four categories: data related, systems thinking, modeling & simulation and computational problem solving (CPSP). Practical applications of these different categories are still limited. This paper presents three examples in which edu-cators of science teachers integrate CT within STEM con-tent knowledge using the above mentioned taxonomy. The first example applies to CPSP and data practices, the sec-ond to CPSP exclusively, the final to systems thinking and modeling & simulation. The examples provide practical insight that makes the use of CT in STEM education more tangible for practitioners.
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In the wake of neo-liberal informed global trends to set performance standards and intensify accountability, the Dutch government aimed for ‘raising standards for basic skills’. While the implementation of literacy standards was hardly noticed, the introduction of numeracy standards caused a major backlash in secondary schools, which ended in a failed introduction of a high stakes test. How can these major differences be explained? Inspired by Foucault’s governmentality concept a theoretical framework is developed to allow for detailed empirical research on steering processes in complex systems in which many actors are involved in educational decision-making. A mixed-methods multiple embedded case-study was conducted comprising nine school boards and fifteen secondary schools. Analyses unveil processes of responsibilisation, normalisation and emerging dividing practices. Literacy standards reinforced responsibilities of Dutch language teachers; for numeracy, school leadership created entirely new roles and responsibilities for teachers. Literacy standards were incorporated in an already used instrument which made implementation both subtle and inevitable. For numeracy, schools distinguished students by risk of not passing the new test affirming the disciplinary nature of schools in the process. While little changed to address teachers main concerns about students’ literacy skills, the failed introduction of the numeracy test usurped most resources.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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