Discrimination of neutrons and gamma rays in plastic scintillator based on pulse-coupled neural network

Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamma rays due to their unique penetration characteristic;thus,the development of n-γdiscrimination methods is especially crucial.In the present study,a novel n-γdiscrimination method is proposed that implements a pulse-coupled neural network for n-γdiscrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γdiscrimination.The proposed method was compared to three other discrimination methods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49%improvement in FoM-value compared to the charge comparison method,a72.80%improvement compared to the BPNN,a 66.24%improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent antinoise effect and the ability to process the dynamic information contained in a pulse signal.

Linear Pulse-Coupled Oscillators Model¬—A New Approach for Time Synchronization in Wireless Sensor Networks

Mutual synchronization is a ubiquitous phenomenon that exists in various natural systems. The individual participants in this process can be modeled as oscillators, which interact by discrete pulses. In this paper, we analyze the synchronization condition of two- and multi-oscillators system, and propose a linear pulse-coupled oscillators model. We prove that the proposed model can achieve synchronization for almost all conditions. Numerical simulations are also included to investigate how different model parameters affect the synchronization. We also discuss the implementation of the model as a new approach for time synchronization in wireless sensor networks.

Attractors with controllable basin sizes from cooperation of contracting and expanding dynamics in pulse-coupled oscillators

Unstable attractors are a novel type of attractor with local unstable dynamics, but with positive measures of basins.Here, we introduce local contracting dynamics by slightly modifying the function which mediates the interactions among the oscillators. Thus, the property of unstable attractors can be controlled through the cooperation of expanding and contracting dynamics. We demonstrate that one certain type of unstable attractor is successfully controlled through this simple modification. Specifically, the staying time for unstable attractors can be prolonged, and we can even turn the unstable attractors into stable attractors with predictable basin sizes. As an application, we demonstrate how to realize the switching dynamics that is only sensitive to the finite size perturbations.

Discrimination of neutron and gamma ray using the ladder gradient method and analysis of filter adaptability

This study proposes a ladder gradient method for neutron and gamma-ray discrimination.The proposed method exhibited state-of-the-art performance with low time consumption,which incorporates two parts:information extraction and discrimination factor calculation.A quasi-continuous spiking cortical model was proposed to extract information from the radiation pulse signals,thus generating an ignition map corresponding to each pulse signal.The ignition map can be used to calculate the discrimination factor.A ladder gradient calculation was introduced to obtain a discrimination factor with low computational complexity.The proposed method was compared with five other discrimination methods to evaluate its robustness and efficacy.Furthermore,the filter adaptability of the pulse-coupled neural network and ladder gradient methods was investigated.Possible reasons for adapting the conditions with different discrimination methods and filters were analyzed.Experiments were conducted in 20 filtering situations with 11 types of filters to determine the most suitable filters for discrimination methods.The experimental results revealed that the three most adaptive filters of the pulse-coupled neural networks and ladder gradient methods are the wavelet,elliptic,and median filters and the elliptic,moving average,and wavelet filters,respectively.

Research on image segmentation of weld seam of oil derrick welded by arc welding robot

This paper puts forward a new method of PCNN （pulse-coupled neural networks ） image segmentation, in which the binary matrix of the ignition frequency matrix is employed, for the first time, to act as the final result of image segmentation. It gives the principles of PCNN parameter selection under the guidance of this process. The new method reduces the dependence of PCNN on parameters, improves the effect of image segmentation, and produces good results after being applied to image recognition of weld seam of oil derrick welded by arc welding robot.

A new approach for automated image segmentation based on simplified PCNN

Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.

Image segmentation of embryonic plant cell using pulse-coupled neural networks

Traditional image segmentation algorithms exhibit weak performance for plant cells which have complex structure. On the other hand, pulse-coupled neural network (PCNN) based on Eckhorn’s model of the cat visual cortex should be suitable to the segmentation of plant cell image. But the present theories cannot explain the relationship between the parameters of PCNN mathematical model and the effect of segmentation. Satisfactory results usually require time-consuming selection of experimental parameters. Mean-while, in a proper, selected parametric model, the number of iteration determines the segmented effect evaluated by visual judgment, which decreases the efficiency of image segmentation. To avoid these flaws, this note proposes a new PCNN algorithm for automatically segmenting plant embryonic cell image based on the maximum entropy principle. The algorithm produces a desirable result. In addition, a model with proper parameters can automatically determine the number of iteration, avoid visual judgment,

Output-threshold coupled neural network for solving the shortest path problems

This paper presents a coupled neural network, called output-threshold coupled neural network (OTCNN), which can mimic the autowaves in the present pulsed coupled neural networks (PCNNs), by the construction of mutual coupling between neuron outputs and the threshold of a neuron. Based on its autowaves, this paper presents a method for finding the shortest path in shortest time with OTCNNs. The method presented here features much fewer neurons needed, simplicity of the structure of the neurons and the networks, and large scale of parallel computation. It is shown that OTCNN is very effective in finding the shortest paths from a single start node to multiple destination nodes for asymmetric weighted graph, with a number of iterations proportional only to the length of the shortest paths, but independent of the complexity of the graph and the total number of existing paths in the graph. Finally, examples for finding the shortest path are presented.

Segment-based coding of color images

Based on the idea of second generation image coding, a novel scheme for coding still images is pre- sented.At first, an image was partitioned with a pulse-coupled neural network; and then an improved chain code and the 2D discrete cosine transform was adopted to encode the shape and the color of its edges respectively.To code its smooth and texture regions, an improved zero-trees strategy based on the 2nd generation wavelet was chosen.After that, the zero-tree chart was selected to rearrange quantified coefficients.And finally some regulations were given according to psychology of various users.Experiments under noiseless channels demonstrate that the proposed method performs better than those of the current one, such as JPEG, CMP, EZW and JPEG2000.