Research on restoration method of nuclear pulse current signal of semiconductor detector based on artificial neural networks

The charge pulse generated by semiconductor detector caused by nuclear event carries nuclide and nuclear reaction information, but the amplified charge pulse amplitude is obviously weak and the noise is so large. Aiming at the difficulty of obtaining the charge signal pulse generated by the detector, a method for recovering the nuclear pulse current signal of semiconductor detector is proposed. Pulse recovery is divided into two parts: pulse shape recovery and pulse amplitude recovery. Point at the pulse shape, a shape recognition network of nuclear pulse current signal based on deep learning is proposed. For pulse amplitude,it can be obtained by Mexican straw hat wavelet forming algorithm. This algorithm can eliminate the baseline fluctuation caused by pulse stacking. The proposed shape recognition network of nuclear pulse current signal is composed of classifier and regressor. The classifier is used to judge whether the data contains a complete rising edge. The data containing the complete rising edge is sent to the regressor for prediction, so as to obtain the parameters related to the current pulse shape. The precision, recall and F-Measure of the classifier in classifying the test set are 98.88%, 98.05% and 98.33%, respectively. The average absolute error of the regressor in predicting the parameters related to the current pulse shape is about 9 ns. The experimental results show that the proposed method can recover the shape and amplitude of the current signal.

Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration.

DeepUMQA: Ultrafast Shape Recognition-based Protein Model Quality Assessment using Deep Learning

MotivationProtein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for describing residue-level topological information. Design features that can further reflect residue-level topology when combined with deep learning methods are therefore crucial to improve the performance of model quality assessment.ResultsWe developed a deep-learning method, DeepUMQA, based on Ultrafast Shape Recognition (USR) for the residue-level single-model quality assessment. In the framework of the deep residual neural network, the residue-level USR feature was introduced to describe the topological relationship between the residue and overall structure by calculating the first moment of a set of residue distance sets and then combined with 1D, 2D, and voxelization features to assess the quality of the model. Experimental results on test datasets of CASP13, CASP14, and CAMEO show that USR could complement the voxelization feature to comprehensively characterize residue structure information and significantly improve the model assessment accuracy. DeepUMQA outperformed the state-of-the-art single-model quality assessment methods, including ProQ2, ProQ3, ProQ3D, Ornate, VoroMQA, and DeepAccNet.AvailabilityThe source code and executable are freely available at https://github.com/iobio-zjut/[email protected]