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Bachelor-Fachprojekte SummerSemester 2021

Bring Your Idea into Reality: Design and Implement Your Own Embedded Systems (English)

    • News: Please refer to: https://daes.cs.tu-dortmund.de/teaching/courses/ss2021/fachprojekt/ for the latest updates!

    • Explanation of SS2020: Due to the corona crisis, TU Dortmund suggests to host the lectures in the summer semester 2020 completely online unless there is any further notification in the future. The organization team of the Fachproject discussed possible forms to host this course online. However, this is basically impossible even if the university opens in May. After brainstorming, we have decided to bring your idea to online projects. We will design some small projects which can be completely carried out online without any specialized hardware requirement. We will give a wide range of project topics that range from embedded machine learning, real-time operating systems, hardware/software codesign (in simulation), and cyber-physical systems (in simulation). Some of them can be further continued as Bachelor thesis topics if you would like. We will first host a seminar meeting to introduce the topics that are offered from our side. You are then requested to join one of the projects designed by us or form a team of 3 people with your own ideas. If you do not like this transformation and would not like to participate in the new format of this Fachproject, please kindly let me know. I will help you de-register from this course.

    • Decision SS2021: Due to the unknown future of SS2021, we decided to provide an online version of Fachprojekt in the upcoming summer semester 2021 as well. We provided 3 sub-topics which are related to embedded systems, and each student can make a decision to join one of these sub-topics.

    • Final Presentation date: TBA.
    • Modul

      Design of Embedded Systems

    • Organization

      • We have in total 18 students, and each group can have at most 3 students.

      • In the first meeting, supervisors will give brief presentations to introduce the topics. Each group can select one of these topics and work on it.

      • In the end, each group have to give a talk to present what they have done and submit a report.

    • Supervisors

      Junjie Shi (E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it )

      Mikail Yayla (E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it )

      Prof. Dr. Jian-Jia Chen (E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it )

    • Topics 

      • Automatic power profiling of Applications on NVM

        • Up to two groups

        • Supervised by Christian Hakert

        • The idea here is that the students develop a toolframe on the MSP430FRxxx to execute applications on the board and measure the power consumption with the integrated sensor meanwhile. The application the offers tuning knobs (relocatable memory, clock speed, memory layout for the dedicated ML algorthm, ...) which can be adjusted and the power consumption shall be re-evaluated. The ultimate target then is to automatically find the optimal parameter configuration (either by explorative profiling and offline optimization or by online optimization).

      • Design Your Own CPU

        • Up to two groups

        • Supervised by Mikail Yayla

        • In this project, the students first learn the basics of VHDL to build a single cycle MIPS processor that can execute a few selected instructions (lw, sw, addi, beq, etc.). Then, the students are expected to either implement extensions for this processor or design a completely new processor from scratch.

      • Deploy Machine Learning Applications on A Swarm

        • Up to two groups

        • Supervised by Junjie Shi

        • In this project, the students first establish a simulation based platform, i.e., Paparazzi UAV (detailed can be found in https://wiki.paparazziuav.org/wiki/Main_Page). Afterwards, a distributed embedded system is designed, i.e., a swarm consists of several drones. Each drone can be considered as an embedded system. Then, an ensemble learning algoritm is implemented, e.g., detect an item using several images from different points of view that are obtained by different drones. In the end, the perforamnce of the application on the system is evaluated.  

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