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Umut Onus, Embedded Software Engineer

“With the know-how at IMMS, not only I can build AI models that can help shape the future of maintenance and prognostics, but also I can contribute to the optimality and availability of such models for embedded devices.”

“Ever since I was involved with electrical engineering, I have had a keen interest in signal processing and telecommunications. During my bachelor thesis, I have worked on a testbed for antenna arrays using microcontrollers. That was my motivation to take a step into higher education at Ilmenau TU for the Master of Science program on Communications and Signal Processing.

My master thesis was on the topic of automated radio-signal characterisation for low-altitude drone-to-mobile radio channels using artificial intelligence (AI) assisted methods.

Such a topic drew my interest due to the use of drones and widely used mobile phones in rescue operations after disaster scenarios. Throughout the time, I built interest in AI and its application in the signal processing field. With the advances in machine learning and artificial intelligence approaches, we can automise labour-intensive tasks using mathematical models which will greatly impact future industrial technology. During my Master’s degree at Ilmenau TU, I took the great chance to work as a student assistant on a signal processing project with the IMMS System Design team, and I could get to know more about the embedded system perspective of electrical engineering. A great opportunity to research merging signal processing and AI on embedded systems was presented to me by IMMS.

I am now working as a research associate in the field of signal processing and AI for its optimal deployment on resource-constraint embedded devices. My target is AI-assisted optimal machine health status prognostics in Industry 4.0 domain applications, such as machine tool health condition estimation and remaining lifetime predictions. My research interests include machine signal representation in unique and compressed ways in time/frequency/space domains, ranking, and selection of built signal features for their optimality on target embedded platforms. To this end, the required AI-processing chain (from signal acquisition to model deployment) is investigated and build optimal solutions for industrial processes at hand.

With the know-how at IMMS, not only I can build AI models that can help shape the future of maintenance and prognostics, but also I can contribute to the optimality and availability of such models for embedded devices. With such optimal embedded solutions, we can diagnose machinery conditions with affordable embedded platforms on reduced carbon footprints.

Thanks to colleagues that are highly competent in their fields, working towards my goals at IMMS is very convenient. Professional communication is uncomplicated and clear. Despite having a long lockdown and a home office period, it is still comfortable to work with other colleagues through integrated digital platforms. I could not stress enough my appreciation for everyone that contributed to this healthy working environment.”

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All publicationsUmut Onus

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Lead application

Adaptive edge AI systems for industrial application

We are researching solutions for adaptive edge AI systems to make AI possible on low-consumption embedded systems in industry and to network them in real time.

Service for R&D

Development of embedded systems

We develop embedded systems for you as complete solutions consisting of sensors and actuators, signal processing and communications technology as well as open-loop and closed-loop controls.

Core topic

Embedded AI

The numerous existing AI algorithms and methods for high-performance computing are unsuitable for embedded systems. We are researching to optimise AI algorithms and methods so that they can be used on embedded systems.

Research field

Smart distributed measurement and test systems

Integrated sensor ICs are the heart of sensor and measurement systems like wireless sensors, stationary or handheld devices. We are researching solutions for ever more powerful sensors with more intrinsic intelligence and task allocation in the network.