Quality-Aware Compressed Sensing for Distributed Precision Agriculture Systems

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dc.contributor.advisorProf. Dr. Nils Aschenbruckger
dc.creatorHänel, Thomas-
dc.description.abstractAround 2006 the signal processing community was thrilled when the concept of Compressed Sensing was brought forward. While according to the long-established Nyquist-Shannon theorem, the sampling rate for signals must be at least twice as high as the highest relevant frequency in the signal, with Compressed Sensing lower average sampling rates become suitable. As a more general toolkit, Compressed Sensing allows for merging sensing and compression in a single step. In other words, only a tiny amount of data needs to be sensed but a huge amount of information can be reconstructed from this data. While Compressed Sensing already has been successfully applied in some areas, mainly in medical scanning where it helps to reduce the exposure to radiation, adaptation to new application areas is relatively slow. We identify two causes that slow down the adaption and contribute to overcoming these obstacles: firstly, the adaption of Compressed Sensing requires interdisciplinary thinking even more than for many other new technologies: Compressed Sensing itself comes from the field of signal processing, the application adds a second field. The automatized processing of the data always touches the field of computer science and lastly, designing Compressed Sensing solutions often requires a modification or redesign of measurement hardware, touching the fields of electronics and sometimes mechanics. We address this issue by supplying a more structured approach for designing Compressed Sensing solutions. Secondly, Compressed Sensing usually performs a lossy compression on real world data. Not knowing the quality of the solution limits its usability. We address this issue by supplying a list of potential metrics for assessing the quality of the solution and evaluate their performance for various datasets. Along the way, we develop two Compressed Sensing solutions in the application area of Precision Agriculture: The first is Compressive Field Estimate (CFE), a method for improving the remote estimate of a scalar field based on limited data supplied by a moving probe such as a combine harvester. The second is Multi- to Hyperspectral Sensor Network (M2HSN), a wireless sensor network that records light spectra at mediocre spectral resolution and allows for increasing the spectral resolution. For the M2HSN, we discuss different designs of the sensor nodes and different approaches for increasing the resolution. Those are simulatively evaluated on different datasets and in a real-world prototype.eng
dc.subjectWireless Sensor Networkseng
dc.subjectCompressed Sensingeng
dc.subjectPrecision Agricultureeng
dc.subject.ddc004 - Informatikger
dc.titleQuality-Aware Compressed Sensing for Distributed Precision Agriculture Systemseng
dc.typeDissertation oder Habilitation [doctoralThesis]-
thesis.typeDissertation [thesis.doctoral]-
dc.contributor.refereeProf. Dr. Ulf Kulauger
Enthalten in den Sammlungen:FB06 - E-Dissertationen

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