Image restoration using deep learning

We propose a new image restoration method that reduces noise and blur in degraded images. In contrast to many state of the art methods, our method does not rely on intensive iterative approaches, instead it uses a pre-trained convolutional neural network

3D reconstruction of maize plants in the phenoVision system

In order to efficiently study the impact of environmental changes, or the differences between various genotypes, large numbers of plants need to be measured. At the VIB, a system named \emph{PhenoVision} was built to automatically image plants during their growth. This system is used to evaluate the impact of drought on different maize genotypes. To this end, we require 3D reconstructions of the maize plants, which we obtain from voxel carving

GPU-based maize plant analysis: accelerating CNN segmentation and voxel carving

PHENOVISION is a high-throughput plant phenotyping system for crop plants in greenhouse conditions. A conveyor belt transports plants between automated irrigation stations and imaging cabins. The aim is to phenotype maize varieties grown under different conditions. To this end we model the plants in 3D and automate the measuring of the plants

Machine learning for maize plant segmentation

High-throughput plant phenotyping platforms produce immense volumes of image data. Here, a binary segmentation of maize colour images is required for 3D reconstruction of plant structure and measurement of growth traits. To this end, we employ a convolutional neural network (CNN) to perform this segmentation successfully

From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery

Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "reward" over time, such as the maximum property value discovered so far. While the materials science community achieved much progress in developing property models that predict well on average with respect to the training distribution, this form of in-distribution performance measurement is not directly coupled with the discovery reward. This is because an iterative discovery process has a shifting reward distribution that is over-proportionally determined by the model performance for exceptional materials. We demonstrate this problem using the example of bulk modulus maximization among double perovskite oxides. We find that the in-distribution predictive performance suggests random forests as superior to Gaussian process regression, while the results are inverse in terms of the discovery rewards. We argue that the lack of proper performance estimation methods from pre-computed data collections is a fundamental problem for improving data-driven materials discovery, and we propose a novel such estimator that, in contrast to na\"ive reward estimation, successfully predicts Gaussian processes with the "expected improvement" acquisition function as the best out of four options in our demonstrational study for double perovskites. Importantly, it does so without requiring the over thousand ab initio computations that were needed to confirm this prediction.Comment: Simplified notatio

Robust plane-based calibration for linear cameras

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Large scale IRAM 30m CO-observations in the giant molecular cloud complex W43

We aim to give a full description of the distribution and location of dense molecular clouds in the giant molecular cloud complex W43. It has previously been identified as one of the most massive star-forming regions in our Galaxy. To trace the moderately dense molecular clouds in the W43 region, we initiated an IRAM 30m large program, named W43-HERO, covering a large dynamic range of scales (from 0.3 to 140 pc). We obtained on-the-fly-maps in 13CO (2-1) and C18O (2-1) with a high spectral resolution of 0.1 km/s and a spatial resolution of 12". These maps cover an area of ~1.5 square degrees and include the two main clouds of W43, as well as the lower density gas surrounding them. A comparison with Galactic models and previous distance calculations confirms the location of W43 near the tangential point of the Scutum arm at a distance from the Sun of approximately 6 kpc. The resulting intensity cubes of the observed region are separated into sub-cubes, centered on single clouds which are then analyzed in detail. The optical depth, excitation temperature, and H2 column density maps are derived out of the 13CO and C18O data. These results are then compared with those derived from Herschel dust maps. The mass of a typical cloud is several 10^4 solar masses while the total mass in the dense molecular gas (>100 cm^-3) in W43 is found to be about 1.9e6 solar masses. Probability distribution functions obtained from column density maps derived from molecular line data and Herschel imaging show a log-normal distribution for low column densities and a power-law tail for high densities. A flatter slope for the molecular line data PDF may imply that those selectively show the gravitationally collapsing gas

The Active CryoCubeSat Project: Testing and Preliminary Results

The Center for Space Engineering at Utah State University and NASA’s Jet Propulsion Laboratory have jointly developed an active thermal control technology to better manage thermal loads and enable cryogenic instrumentation for CubeSats. The Active CryoCubeSat (ACCS) project utilizes a two-stage active thermal control architecture with the first stage consisting of a single-phase mechanically pumped fluid loop, which circulates coolant between a cold plate rejection heat exchanger and a deployed radiator. The second stage relies upon a miniature tactical cryocooler, which provides sub 110 K thermal management. This research details the experimental setup for a groundbased prototype demo which was tested in an appropriate, and relevant thermal vacuum environment. The preliminary results, which include the input power required by the system, rejection and environmental temperatures and the total thermal dissipation capabilities of the ACCS system, are presented along with a basic analysis and a discussion of the results