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Site-specific weed management is the part of precision agriculture (PA) that tries to effectively control weed infestations with the least economical and environmental burdens. This can be achieved with the aid of ground-based or near-range sensors in combination with decision rules and precise application technologies. Near-range sensor technologies,(More)
Site-specific weed control techniques have gained interest in the precision farming community over the last years. Managing weeds on a subfield level requires measuring the varying density of weeds within a field. Decision models aid in the selection and adjustment of the treatments, depending on the weed infestation. The weed control can be done either(More)
The role of biological membranes as a target in biological radiation damage is still unclear. Recently much attention has been paid to the dynamic behaviour of the cell membrane. Maxwell displacement current technique (MDC) provides new possibility of conformation study of the membrane models. Oxidative stress can impair macromolecules in the cell on a(More)
The efficient analysis of electroencephalographic (EEG) data is a long standing problem in neuroscience, which has regained new interest due to the possibilities of multidimensional signal processing. We analyze event related multi-channel EEG recordings on the basis of the time-varying spectrum for each channel. It is a common approach to use wavelet(More)
Subspace-based high-resolution parameter estimation schemes are used in a variety of signal processing applications including radar, sonar, communications, medical imaging, and the estimation of the parameters of the dominant multipath components from MIMO channel sounder measurements. It is of great theoretical and practical interest to predict the(More)
Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as well as the management practice requires additional efforts. A new(More)
In this contribution we present a new analytical channel model for frequency selective, time variant MIMO systems. The model is based on a correlation tensor, which allows a natural description of multi–dimensional signals. By applying the Higher Order Singular Value Decomposition (HOSVD), we gain a better insight into the multi–dimensional eigenstructure(More)
BACKGROUND, AIM AND SCOPE European legislation stipulates that genetically modified organisms (GMO) have to be monitored to identify potential adverse environmental effects. A wealth of different types of monitoring data from various sources including existing environmental monitoring programmes is expected to accumulate. This requires an information system(More)
The identification of signal components in electroencephalographic (EEG) data originating from neural activities is a long standing problem in neuroscience. This area has regained new attention due to the possibilities of multi-dimensional signal processing. In this work we analyze measured visual-evoked potentials on the basis of the time-varying spectrum(More)