In his inaugural lecture "From Measuring the World to Sensor Data-Based Predictions" on 19 October 2021, Hubert Brückl provided insights into his field of research. As of 1 May 2019, he was appointed as a university professor according to § 98 UG 2002 at the University for Continuing Education Krems, where he became head of the Department for Integrated Sensor Systems at the Faculty of Education, Arts and Architecture.

The Department for Integrated Sensor Systems which is headed by Prof Hubert Brückl and located in Wiener Neustadt, broadened the scope of the University for Continuing Education Krems both in terms of subject matter and geography, said Rector Friedrich Faulhammer in his welcoming address at the inaugural lecture.

New findings through sensor data and models

To draw up an overview of the development of sensor technology, Brückl started with the first map of the world with isotherms, i.e. lines of equal temperature. The map was produced in 1823 based on measurement data from Alexander von Humboldt, who, equipped with portable sensors, a bus console consisting of a compass and a direction-finding instrument, carried out land surveys in Ecuador, among other places, with the aid of trigonometric calculations in spherical space developed by Carl Friedrich Gauss. Brückl used this historical example to illustrate the connection of sensor data with mathematics; today, more complex modeling is generally used.

More than the status quo: Predicting the future

The second example observes the world, too, not statically, but over the course of a year, to trace changes, specifically those regarding the CO2 dispersion. In this context, Brückl referred to the measurement of CO2 in the atmosphere. Carbon dioxide is known to play a decisive role in the greenhouse effect because it directs the solar radiation reflected from the earth back to the planet and thus contributes to global warming. The measurement fuses data from the Orbiting Carbon Observatory-2 satellite with data from more than 20 ground stations and those obtained in aircraft. This example illustrates how the merging of sensor data in sensor fusion, together with modeling, also enables predictions for the future.

Use in robotics

Sensor data is als used in robotics. MIT spinoff Boston Dynamics is probably known to the larger public for its yellow, four-legged autonomous robot Spot, which is the first commercial model of its kind. In this case, in order for robots to move autonomously, data from five cameras is analyzed, resulting in accurate sensing of the environment. The data from this cognitive sensor system is processed locally and rapidly with artificial intelligence, making the system autonomous. High-resolution real-time position determination in space already enables robots to perform complex motion sequences, as a video feed of two dancing bipedal robots showed.

The topic of movement also concerns humans; sensors record footfall and heart rate as well as blood pressure in the fitness area. Other vital signs will soon be recorded via sweat, including glucose and lactate.

Miniaturization, robotics, and AI

Miniaturization is a major factor in sensor development, as Brückl illustrated using the example of LIDAR (Light Imaging, Detection and Ranging), a sensor for optical distance measurement. Such shoebox-sized sensors deflect laser beams with a mechanical mirror and can now be integrated into a computer chip.

Brückl used a project from the Department for Integrated Sensor Systems to give insight into how artificial intelligence (AI) can be used in sensor systems: An AI trained with data can use ultrasound measurements to predict the failure of ball bearings within a month with almost 100 percent probability. The trick: To train the AI, in addition to measurement data from physically defective ball bearings, data obtained through simulations is also used, which is more easily accessible.


The development over the past decade, starting with digitization with the Internet of Things (IoT), leads through AI and Deep Learning to Cognification, where human abilities are emulated, for example in self-driving vehicles or robots. Research topics of the future include technical optimizations in sensitivity, miniaturization and energy efficiency, intelligent data processing with AI and machine learning, and so-called edge computing, where data is processed directly in the device, which increases speed and reduces costs. Brückl sees the solutions to the major challenges in the interaction between technology and society.

About the person

Hubert Brückl studied physics and received his PhD from the University of Regensburg in 1992. After two years as a postdoctoral researcher at the Technical University of Darmstadt, Brückl moved to Dresden as a group leader at the Institute for Solid State and Materials Research (IFW). In 1998, he accepted a position as a research associate at the Chair of Thin Films and Nanostructures at Bielefeld University, where he completed his habilitation. After a research period at Siemens, Brückl headed the Nano Systems business unit at the AIT Austrian Institute of Technology in Vienna between 2005 and 2012. Since 2016, Brückl has been head of the Department for Integrated Sensor Systems, which was established at the University for Continuing Education Krems in 2012.

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