70 research outputs found
Parallel-in-Time Simulation of an Electrical Machine using MGRIT
We apply the multigrid-reduction-in-time (MGRIT) algorithm to an eddy current
simulation of a two-dimensional induction machine supplied by a
pulse-width-modulation signal. To resolve the fast-switching excitations, small
time steps are needed, such that parallelization in time becomes highly
relevant for reducing the simulation time. The MGRIT algorithm is well suited
for introducing time parallelism in the simulation of electrical machines using
existing application codes, as MGRIT is a non-intrusive approach that
essentially uses the same time integrator as a traditional time-stepping
algorithm. We investigate effects of spatial coarsening on MGRIT convergence
when applied to two numerical models of an induction machine, one with linear
material laws and a full nonlinear model. Parallel results demonstrate
significant speedup in the simulation time compared to sequential time
stepping, even for moderate numbers of processors.Comment: 14 page
Overlay Accuracy Limitations of Soft Stamp UV Nanoimprint Lithography and Circumvention Strategies for Device Applications
In this work multilevel pattering capabilities of Substrate Conformal Imprint
Lithography (SCIL) have been explored. A mix & match approach combining the
high throughput of nanoimprint lithography with the excellent overlay accuracy
of electron beam lithography (EBL) has been exploited to fabricate nanoscale
devices. An EBL system has also been utilized as a benchmarking tool to measure
both stamp distortions and alignment precision of this mix & match approach. By
aligning the EBL system to 20 mm x 20 mm and 8 mm x 8 mm cells to compensate
pattern distortions of order of over 6 inch wafer area, overlay
accuracy better than has been demonstrated. This result can
partially be attributed to the flexible SCIL stamp which compensates
deformations caused by the presence of particles which would otherwise
significantly reduce the alignment precision
Effiziente Gestaltung komplexer adaptiver Schätzalgorithmen durch objektorientierte Programmierung
Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat
UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed
Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits-A Case Study for Winter Wheat
UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.Peer reviewe
Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat
UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed
Surface modified silicon nanochannel for urea sensing
Silicon nanowires have been surface functionalized with the enzyme urease for
biosensor applications to detect and quantify urea concentration. The device is
nanofabricated from a silicon on insulator (SOI) wafer with a top down
lithography approach. The differential conductance of silicon nanowires can be
tuned for optimum performance using the source drain bias voltage, and is
sensitive to urea at low concentration. The experimental results show a linear
relationship between surface potential change and urea concentration in the
range of 0.1 to 0.68 mM. The sensitivity of our devices shows high
reproducibility with time and different measurement conditions. The nanowire
urea biosensor offers the possibility of high quality, reusable enzyme sensor
array integration with silicon based circuits.Comment: 5 pages, 6 figures, two-column format. Related papers can be found at
nano.bu.ed
- …