#robotics #smartsensors #smartactuators #AI #artificialintelligence #machinelearning
#dataanalysis #systemdesign #bigdata #deeplearning #autonomoussystems #innovative #englishpostgraduateprogramme
As an undergraduate student of mechatronics or other related fields of study, the consecutive master's programme Artificial Intelligence for Smart Sensors and Actuators qualifies you as experts for the development and use of intelligent, technical systems of data processing, data analysis and automation. The knowledge transfer on study contents including artificial intelligence, machine learning, innovative sensors/actuators, additionally endows prospective students with the competence to work creatively in research and development departments.
Degree: Master of Engineering (M.Eng.)
Duration: 3 semesters (1.5 years)
Start: 1st October (winter semester) and 15th March (summer semester)
Application Period: 15th April - 15th July (winter semester) or 15th November - 15th January (summer semester)
Course language: English
Fees: No tuition fees, only €62 student services fee per semester
The command of intelligent sensor and actuator systems requires scientific-technical expert training tailored to current thematic challenges. Within three study semesters, these challenges are to be met by the structured and intensified knowledge transfer of the following topics:
The practical relevance of the course content is implemented through case studies conducted in collaboration with industrial experts.
The Technology Campus Cham (focusing on research & development in mechatronic systems) as well as the Digital Start-Up Centre (majoring in Digital Production) are directly located on Cham campus. They provide a specialised and application-oriented environment for highly innovative training concepts.
Artificial Intelligence (AI) describes a sub-discipline of computer science that deals with the research of "intelligent" problem-solving behaviour and the creation of "intelligent" computer systems. In a multitude of technical fields of application, AI-based systems are fed by sensor data and return process-influencing information to actuators. The interactions between information processing, the process as data source and data sink as well as the influences of the quality of the sensor data and the actuator interventions are equally decisive for the overall system function of the systems. In addition to the actual measuring principle for the respective process variable, a smart sensor also features signal pre-processing, monitoring algorithms for safeguarding the sensor function, connectivity (e.g. Bluetooth, WIFI, 5G) and, depending on the application, power supply functions. Smart actuators also supplement the actual control intervention in the technical process with extended signal processing and monitoring mechanisms as well as various communication methods. The resulting signal-processing system features additional "intelligent" properties that further enhance its performance.
After successfully graduating from this study course, you will have all the qualifications required to establish yourself as an expert in this transitioning professional world and to participate actively in its development.
In infrastructure projects, an essential requirement is to name the individual project documents according to a standardised procedure, e.g. according to VGB-S832. However, the documents created are not always named correctly according to the specifications. In addition, due to the large number of project documents, there are sometimes discrepancies between the document list and the documents actually created.
With the help of AI, in particular Natural Language Processing (NLP), the management of project documents is to be partially automated so that resources are freed up for other activities.
Aims of the project
Computer-assisted chatbots are technical dialogue systems based on natural speech recognition and are used to answer user queries automatically and without direct human intervention in real time.
Aims of the project
A student project group designed an extensive catalogue of questions about studying at the Cham campus. In cooperation with the respective university departments, the project group defined correct answer patterns for the questions asked and fed them into the system. The technical application recognises the user input, compares the predefined answer patterns and should, for example, help prospective students navigate better through the wealth of information on the website in the future.
With increasing traffic across the globe and the number of vehicles, traffic supervision has become increasingly complex. Artificial intelligence (AI) technology can reduce the complexity as well as increase the throughput.
Aims of the project
A project group in the AI master's programme developed an intelligent system that uses "computer vision" techniques and convolutional neural networks to detect the number, make and colour of vehicles in both directions of a lane and record them in the form of an Excel spreadsheet. Further development into a real-time system and the integration of infrared sensors for detection, whether during the day or at night, is currently being implemented in the form of further project and final work. The ultimate goal is to connect several of these units together and record the traffic in different parts of the city over time - keyword rush hour.
The project "Traffic Supervision System" was initiated in the Sensor Lab of Campus Cham. The objective was to build a "Watch Box" prototype (integrating AI Hardware with object detection software). This prototype can monitor traffic as well as vehicle features in real-time.
The project plan is to connect a centralized system through Cloud Direct Connect. The YOLOv5 object detection model is used for training of vehicle classification and identifying their corresponding manufacturer. A DeepSORT algorithm is used for tracking and counting.
The project "Vision Tracking" deals with the detection of the direction of gaze on a specific object with simultaneous object recognition. As a preliminary stage to this application, the determination of the direction of gaze and the recognition of the corresponding object was implemented with the help of a convolutional neural network.
Aims of the project
Where do our eyes wander to first when shopping for our daily needs, where do they linger the longest and what consequently appeals to us the most on the supermarket shelves? How should a supermarket, for example, optimally position or design its product range in order to appeal more to its customers?
The student is always focused on a pair of scissors, when the focus of vision and scissors are recognised by the neural network.
Overview of lectures and courses, SWS (Semesterwochenstunden = weekly hours/semester) and ECTS (European Credit Transfer and Accumulation System) in the postgraduate programme Artificial Intelligence for Smart Sensors and Actuators.
|Introduction to Artificial Intelligence||2||2|
|Machine Learning and Deep Learning||4||4|
|Microsystems and Physical Cross-Coupling||4||4|
|Data Acquisition and Control||2||2|
|Case Study Sensors and Actuators||4||6|
|Modell-Based Function Engineering||4||4|
|Case Study Embedded Control Solutions||4||6|
|Case Study Intelligent Systems||4||6|
|Algorithms of Autonomous Systems||4||4|
|Case Study Autonomous Systems||4||6|
|Compulsory Technical Elective (FWP)||4||4|