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3Additions 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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This report is the final report for the FPGA accelerated PID controller, part of the Distributed Control Systems project. This project runs within the Lectoraat Robotics and High Tech Mechatronics of Fontys Hogeschool Engineering Eindhoven. The Lectoraat has the goal to develop applicable knowledge to support education and industry. This knowledge is acquired with projects run in conjunction with the industry. The report will go into detail for the software designed for this project, not the hardware design. This report is intended for follow up students working on the Distributed Control Systems project. Within this report the assumption is made that the reader is at least familiar with the terms EtherCAT, FPGA, Linux and PID controllers. However for each part a small basic introduction is included. For readers looking for the accomplishments in this project, the results are in chapter six. Following are short descriptions of the chapters in this report. The first chapter will give a short introduction to the project. It talks about why the project was conceived, where the project was done and what the expected end result is. The second chapter, the problem definition, talks about how the project has been defined, what is included and what is not and how the customer expects the final product to function and look like. The third chapter details the methodology used during this project. All the research preformed for this project will be described in the forth chapter. This chapter goes into the research into the Xilinx Zynq 7000 chip, Beckhoff's EtherCAT system, how the Serial Peripheral Interface works and how a PID controller functions. Following in chapter five the design is expanded upon. First the toolchain for building for the Zynq chip is explained. This is followed by and explanation of the different software parts that have been designed. Finally chapters six and seven provide the results and the conclusions and recommendations for this project.
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The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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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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When datasets are collected and accessed legitimately, they must still be used appropriately according to policies, guidelines, rules, laws, and/or the (current) preferences of data subjects. Any inconsistency between the data collection and data usage processes can conflict with many principles of privacy like the transparency principle, no secondary use principle, or intended purpose usage principle. In this contribution we show how the usage control for the inner join operation in vertically separated relational datasets can be characterized as pre and post obligations of the Usage Control (UCON) model. This type of obligations is defined not only by the state of the UCON object (i.e., a dataset) itself, but also with respect to the state of another dataset. Such dependency on two datasets/objects provides a new insight in UCON obligation constructs when applied to the join operation. We describe also a mechanism to realize the identified obligation in a database management system.
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Systems Engineering (SE) is a methodical approach to the development of New (high-tech) products. In 2012 a group of Dutch universities of applied Sciences and companies started a so-called RAAK MBK project to make the Tools of SE more accessible for SME’s. At the same time these tools can be used for multidisciplinary students projects.
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Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
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In the fall of 1999, an international integrated product development pilot project based on collaborative engineering was started with team members in two international teams from the United States, The Netherlands and Germany. Team members interacted using various Internet capabilities, including, but not limited to, ICQ (means: I SEEK YOU, an internet feature which immediately detects when somebody comes "on line"), web phones, file servers, chat rooms and Email along with video conferencing. For this study a control group with all members located in the USA only also worked on the same project.
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