Implementation of a cusp-like for real-time digital pulse shaper in nuclear spectrometry

Pulse shaping,which improves signal-to-noise ratio excellently,has been extensively used in nuclear signal processing.This paper presents a cusp-like pulse-shaping technique developed through the recursive difference equation in time domain.It can be implemented in field programmable gate array hardware system.Another flat-topped cusp-like shaper is developed to optimize the time constant of pulse shaping and reduce the influence of ballistic deficit.The methods of both baseline restoration and pile-up rejection are described.The ^(137)Cs energy spectra measured with the digital cusp-like shaper are 6.6% energy resolution,while those by traditional analog pulse shaper are 7.2% energy resolution,under the same conditions.This technique offers flexibility,too,in adjusting the pulse shaper parameters.

A novel approach to predict green density by high-velocity compaction based on the materials informatics method

High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm^3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.

GAM:A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks

In smart phones,vehicles and wearable devices,GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real world.Given a set of weighted points and a rectangle r in the space,a maximizing range sum(MaxRS)query is to find the position of r,so as to maximize the total weight of the points covered by r(i.e.,the range sum).It has a wide spectrum of applications in spatial crowdsourcing,facility location and traffic monitoring.Most of the existing research focuses on the Euclidean space;however,in real life,the user’s moving route is constrained by the road network,and the existing MaxRS query algorithms in the road network are inefficient.In this paper,we propose a novel GPU-accelerated algorithm,namely,GAM,to tackle MaxRS queries in road networks in two phases efficiently.In phase 1,we partition the entire road network into many small cells by a grid and theoretically prove the correctness of parallel query results by grid shifting,and then we propose an effective multi-grained pruning technique,by which the majority of cells can be pruned without further checking.In phase 2,we design a GPU-friendly storage structure,cell-based road network(CRN),and a two-level parallel framework to compute the final result in the remaining cells.Finally,we conduct extensive experiments on two real-world road networks,and the experimental results demonstrate that GAM is on average one order faster than state-of-the-art competitors,and the maximum speedup can achieve about 55 times.