Quantification of dark-field effects in a single-shot grid-based x-ray imaging

Dark-field x-ray imaging (DFXI) is a technology that can obtain information related to the small-angle x-ray scattering of a sample. In this paper, we report on the quantification of the dark-field effects by measuring the real space correlation function of scattering samples in a single-shot grid-based x-ray imaging setup that enables a simple approach to DFXI. The experimental measurements of the dark-field effects in our imaging setup were in good agreement with the theoretical quantification over the entire range of test conditions, thus verifying its effectiveness for single-shot grid-based DFXI. Consequently, we were able to clearly understand the associated particle-scale selectivity, which can help us determine suitable applications for single-shot grid-based x-ray DFXI.

An enhanced spatial smoothing algorithm for coherent signals DOA estimation

PurposeThe existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.Design/methodology/approachIn this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.FindingsTheoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.Originality/valueThe proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

Visibility Nowcasting For UAS Operations using Deep Learning

<p>Unmanned Aircraft System (UAS) operations have spread rapidly worldwide performing a variety of military and civilian applications. The ability and performance of UAS to carry out these applications are strongly affected by poor weather conditions. Fog is one of the critical issues that threaten the safety of UAS missions by altering visibility. Therefore, the mission planning based on accurate visibility nowcasts prior to Beyond Visual Line Of Sight (BVLOS) UAS missions will be mandatory to ensure safer UAS operations.</p><p>Two types of models are generally considered for visibility nowcasting: physics-based or data-driven models. However, physics-based visibility forecasts remain expensive and difficult to use operationally. Recently, with the increase of the number of available historical data, data-driven models, especially those using deep learning approaches in particular, have attracted increasing attention in weather forecasting and have proven themselves as a powerful prediction tool.</p><p>This study aims at developing a Visibility Nowcasting System (VNS) that improves the performance and the capability of nowcasting the visibility using deep learning over the U.S.. To that end,  a deep neural network, called an encoder-decoder convolutional neural network (CNN), is used to demonstrate specifically how basic NWP fields such as temperature, wind speed, relative humidity, etc. and visibility from surface observations can provide accurate visibility nowcasts. The VNS will be then tested in different geographical environments where UAS flights are deployed (for example, over North Dakota) since it can learn the time and space correlation according to the historical data.</p><p>To train the network, we created a labeled data set from available METAR reports and hourly reanalysis data from the High-Resolution Rapid Refresh (HRRR) model. This dataset will be also used to test the CNN and evaluate their nowcasting performance. The model will be then evaluated in operational use cases and compared to other available visibility observations during fog events.</p><p> </p>

Robust and Secure Data Fusion Algorithm Based on Intelligent Sensing in Wireless Sensor Networks

Presently, the wireless sensor network (WSN) plays an important role in smart farming. However, due to the limitation of wireless sensor network resources, the time and space correlation of data acquisition is strong. In order to reduce the number of nodes participating in data compression, the robust and secure data fusion algorithm based on intelligent sensing is proposed. The algorithm can divide the whole network into many clusters. In order to maintain energy balance of nodes in the cluster, the probability of each node in each cluster participating in each round of data collection is computed according to the residual energy of the node. On the sink node, the number of sampling rounds of joint reconstruction of collected data is designated according to the application requirements and reconstruction accuracy requirements, and the number of nodes participating in is further reduced. The simulation results show that the number of nodes participating in the data collection of the proposed scheme in this paper is lower than that of the ordinary intelligent sensing LEACH data acquisition scheme. Meanwhile, the proposed scheme can dramatically extend the network lifetime. This paper provides an insight into various needs of WSN used in agriculture and challenges involved in the deployment of WSN.

Automated orientation of water molecules in neutron crystallographic structures of proteins

The structure and function of proteins are strongly affected by the surrounding solvent water, for example through hydrogen bonds and the hydrophobic effect. These interactions depend not only on the position, but also on the orientation, of the water molecules around the protein. Therefore, it is often vital to know the detailed orientations of the surrounding ordered water molecules. Such information can be obtained by neutron crystallography. However, it is tedious and time-consuming to determine the correct orientation of every water molecule in a structure (there are typically several hundred of them), which is presently performed by manual evaluation. Here, a method has been developed that reliably automates the orientation of a water molecules in a simple and relatively fast way. Firstly, a quantitative quality measure, the real-space correlation coefficient, was selected, together with a threshold that allows the identification of water molecules that are oriented. Secondly, the refinement procedure was optimized by varying the refinement method and parameters, thus finding settings that yielded the best results in terms of time and performance. It turned out to be favourable to employ only the neutron data and a fixed protein structure when reorienting the water molecules. Thirdly, a method has been developed that identifies and reorients inadequately oriented water molecules systematically and automatically. The method has been tested on three proteins, galectin-3C, rubredoxin and inorganic pyrophosphatase, and it is shown that it yields improved orientations of the water molecules for all three proteins in a shorter time than manual model building. It also led to an increased number of hydrogen bonds involving water molecules for all proteins.

Extensions of the worm-like-chain model to tethered active filaments under tension

Intracellular elastic filaments such as microtubules are subject to thermal Brownian noise and active noise generated by molecular motors that convert chemical energy into mechanical work. Similarly, polymers in living fluids such as bacterial suspensions and swarms suffer bending deformations as they interact with single bacteria or with cell clusters. Often these filaments perform mechanical functions and interact with their networked environment through cross-links, or have other similar constraints placed on them. Here we examine the mechanical properties - under tension - of such constrained active filaments under canonical boundary conditions motivated by experiments. Fluctuations in the filament shape are a consequence of two types of random forces - thermal Brownian forces, and activity derived forces with specified time and space correlation functions. We derive force-extension relationships and expressions for the mean square deflections for tethered filaments under various boundary conditions including hinged and clamped constraints. The expressions for hinged-hinged boundary conditions are reminiscent of the worm-like-chain model and feature effective bending moduli and mode-dependent non-thermodynamic effective temperatures controlled by the imposed force and by the activity. Our results provide methods to estimate the activity by measurements of the force-extension relation of the filaments or their mean-square deflections which can be routinely performed using optical traps, tethered particle experiments, or other single molecule techniques.