Buchbeiträge

Though the ever-increasing availability of digital data in the context of NDE 4.0 is mostly considered a blessing, it can turn to a curse quite rapidly: managing large amounts of data puts a burden on the sensor devices in terms of sampling and transmission, the networks, as well as the server infrastructure in terms of storing, maintaining, and accessing the data. Yet, NDE data can be highly redundant so the storage of massive amounts of data may indeed be wasteful. This is the main reason why focusing on relevant data as early as possible in the NDE process is highly advocated in the context of NDE 4.0. This chapter introduces Compressed Sensing as a potential approach to put this vision to practice. Compressed Sensing theory has shown that sampling signals with sampling rates that are significantly below the Shannon-Nyquist rate is possible without loss of information, provided that prior knowledge about the signals to be acquired is available. In fact, we may sample as low as the actual information rate if our prior knowledge is sufficiently accurate. In the NDE 4.0 context, prior knowledge can stem from the known inspection task and geometry but it can also include previous recordings of the same piece (such as in Structural Health Monitoring), information stored in the digital product memory along the products’ life cycle, or predictions generated through the products’ digital twins. In addition to data reduction, reconstruction algorithms developed in the Compressed Sensing community can be applied for enhanced processing of NDE data, providing added value in terms of accuracy or reliability. The chapter introduces Compressed Sensing basics and gives some concrete examples of its application in the NDE 4.0 context, in particular for ultrasound.

This chapter is the perfect introduction to get an overview of THz channel sounder technologies. Additionally, all relevant state of the art and references for the field of THz channel sounding are summarized. The aim of the THz sounder chapter is to create a basic understanding of measurement setups and challenges for the measurement of the electromagnetic wave propagation in the THz range. All necessary principles, from generating the transmit signal over different mixing principles to the THz band and the data acquisition, are compact summarized.

Current high-throughput plant phenotyping pipelines are mainly focused on quantitative assessment of macroscopic parameters. Such morphological or physiological parameters measured on entire plants or major plant parts are not well adapted to the accurate description of plant-pathogen interactions because plant pathogens are microorganisms causing only microscale changes in their hosts or non-hosts during the initial stages on infection, which often decide about susceptibility or resistance. This makes the use of microscopic phenomics techniques unavoidable. However, the high-throughput requirements of modern phenomics screens represent a considerable challenge to the available microscopic approaches and underlying instruments used to characterize plant-pathogen interactions. To meet this challenge we have developed a platform that combines high-throughput DNA cloning, single cell transformation protocols, and automated microscopy and phenotyping that we called “microphenomics”. It was used to address the function of genes in nonhost- and race-nonspecific host resistance of barley interacting with the powdery mildew fungus Blumeria graminis. More than 1,300 genes derived from plant or fungal genomes were tested by silencing and approximately 100 of them had a significant effect on the resistance or susceptibility to the pathogen. The chapter gives an overview on the current status of this microphenomics platform for very early and early stages of plant-pathogen interactions.