ABSTRACT The features of memristive-coupled neural networks have been studied extensively in the continuous field. However, the particularities of the discrete domain are rarely mentioned. This paper constructs a discrete memristor with sine-type conductance and applies the discrete memristor to coupling the Rulkov neuron maps for the first time. The properties of the proposed memristive-coupled bi-neuron Rulkov map and multi-neuron Rulkov neural network are probed. In order to better characterize the discrete system, many numerical simulation methods are used. Such as the normalized mean synchronization error, bifurcation diagrams, phase portraits, spatiotemporal patterns and so on. Numerical studies have shown that in discrete memristor-coupled neural networks, both parameters and coupling factors affect the dynamics of the system, resulting in complex and interesting behavioural changes.

Rulkov neural network coupled with discrete memristors

ABSTRACTABSTRACTThe features of memristive-coupled neural networks have been studied extensively in the continuous field. However, the particularities of the discrete domain are rarely mentioned. This paper constructs a discrete memristor with sine-type conductance and applies the discrete memristor to coupling the Rulkov neuron maps for the first time. The properties of the proposed memristive-coupled bi-neuron Rulkov map and multi-neuron Rulkov neural network are probed. In order to better characterize the discrete system, many numerical simulation methods are used. Such as the normalized mean synchronization error, bifurcation diagrams, phase portraits, spatiotemporal patterns and so on. Numerical studies have shown that in discrete memristor-coupled neural networks, both parameters and coupling factors affect the dynamics of the system, resulting in complex and interesting behavioural changes.KEYWORDS: Discrete memristorRulkov mapsynchronizationinitial boostingchimera state AcknowledgmentsThe authors are very thankful to the editors and anonymous reviewers for their careful reading, constructive comments and fruitful suggestions to improve this article.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the National Natural Science Foundation of China [No. 61971185]. 

In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning. The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID), which are known to have high installation and maintenance costs. According to our experiments, our two-stage methodology achieves impressive results on real-world scenarios, i.e., 100% accuracy in the counting stage and 99.7% recognition rate in the identification one. Moreover, the system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes. The results achieved were surprising considering that the proposed system requires low processing power (i.e., it can run in low-end setups) and that we used a relatively small number of images to train our Convolutional Neural Network (CNN) for character recognition. The proposed method is registered, under number BR512020000808-9, with the National Institute of Industrial Property (Brazil).

Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning

In this work we present an efficient solution for counting train wagons and recognizing their numbers using deep learning computer vision models. The proposed method is a good alternative for radio-frequency identification (RFID) method in terms of low cost and ease of use. Our system shows 99% accuracy in real-world scenarios, including corrupted wagon numbers and night shooting conditions. At the same time, the proposed method is capable to process video-stream in real-time speed without GPU-acceleration.

Real-time Train Wagon Counting and Number Recognition Algorithm

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Identification of Rolling Stock of Railways Based on Multi-Projection Image Processing Methods.

In this paper, the recognition of freight train's text information is studied. The text has the characteristics of discontinuous strokes, large interval between strokes and serious corrosion by environmental factors in this application scenario. The traditional template matching or geometric feature extraction cannot achieve a good recognition effect. Instead, the convolutional neural network (CNN) which is trained by a large number of image samples obtained by previous image segmentation is selected for recognition. The image segmentation processing use the Suzuki algorithm for the original image contour extraction to determine the text area. The result of contour extraction is projected horizontally using the edge information of the text area. The traversal template that combines with fixed aspect ratio of the text segmented the text image to a single text image.

Convolutional Neural Network for Freight Train Information Recognition

As a new type of nonvolatile device, the memristor has become one of the most promising technologies for designing a new generation of high-density memory. In this paper, a 4-bit high-density nonvolatile memory based on a memristor is designed and applied to image storage. Firstly, a memristor cluster structure consisting of a transistor and four memristors is designed. Furthermore, the memristor cluster is used as a memory cell in the crossbar array structure to realize the memory design. In addition, when the designed non-volatile memory is applied to gray scale image storage, only two memory cells are needed for the storage of one pixel. Through the Pspice circuit simulation, the results show that compared with the state-of-the-art technology, the memory designed in this paper has better storage density and read–write speed. When it is applied to image storage, it achieves the effect of no distortion and fast storage.

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Meiqi Jiang

Papers

Convolutional Neural Network for Freight Train Information Recognition

Memristive Cluster Based Compact High-Density Nonvolatile Memory Design and Application for Image Storage