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Songwei Wang
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China

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Journal article
Published: 24 May 2021 in Symmetry
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Brain science research often requires accurate localization and quantitative analysis of neuronal activity in different brain regions. The premise of related analysis is to determine the brain region of each site on the brain slice by referring to the Allen Reference Atlas (ARA), namely the regional localization of the brain slice. The image registration methodology can be used to solve the problem of regional localization. However, the conventional multi-modal image registration method is not satisfactory because of the complexity of modality between the brain slice and the ARA. Inspired by the idea that people can automatically ignore noise and establish correspondence based on key regions, we proposed a novel method known as the Joint Enhancement of Multimodal Information (JEMI) network, which is based on a symmetric encoder–decoder. In this way, the brain slice and the ARA are converted into a segmentation map with unified modality, which greatly reduces the difficulty of registration. Furthermore, combined with the diffeomorphic registration algorithm, the existing topological structure was preserved. The results indicate that, compared with the existing methods, the method proposed in this study can effectively overcome the influence of non-unified modal images and achieve accurate and rapid localization of the brain slice.

ACS Style

Songwei Wang; Yuhang Wang; Ke Niu; Qian Li; Xiaoping Rao; Hui Zhao; Liwei Chen; Li Shi. Regional Localization of Mouse Brain Slices Based on Unified Modal Transformation. Symmetry 2021, 13, 929 .

AMA Style

Songwei Wang, Yuhang Wang, Ke Niu, Qian Li, Xiaoping Rao, Hui Zhao, Liwei Chen, Li Shi. Regional Localization of Mouse Brain Slices Based on Unified Modal Transformation. Symmetry. 2021; 13 (6):929.

Chicago/Turabian Style

Songwei Wang; Yuhang Wang; Ke Niu; Qian Li; Xiaoping Rao; Hui Zhao; Liwei Chen; Li Shi. 2021. "Regional Localization of Mouse Brain Slices Based on Unified Modal Transformation." Symmetry 13, no. 6: 929.

Journal article
Published: 27 November 2020 in Symmetry
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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

ACS Style

Juncai Zhu; Zhizhong Wang; Songwei Wang; Shuli Chen. Moving Object Detection Based on Background Compensation and Deep Learning. Symmetry 2020, 12, 1965 .

AMA Style

Juncai Zhu, Zhizhong Wang, Songwei Wang, Shuli Chen. Moving Object Detection Based on Background Compensation and Deep Learning. Symmetry. 2020; 12 (12):1965.

Chicago/Turabian Style

Juncai Zhu; Zhizhong Wang; Songwei Wang; Shuli Chen. 2020. "Moving Object Detection Based on Background Compensation and Deep Learning." Symmetry 12, no. 12: 1965.

Journal article
Published: 09 November 2020 in Applied Sciences
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In order to maximize inventory benefits or minimize costs, reliability and cost of inventory control models need to be identified and analyzed. These importance measures are one important approach to recognize and evaluate system weaknesses. However, importance measures have fewer applications in inventory systems’ reliability. Considering the cost, this paper mainly discusses the reliability change of performance parameters with the importance measures in inventory systems. The calculation methods of differential importance and Birnbaum importance are studied in the inventory control model with shortages. By comparing the importance values of various parameters in the model, the optimization analysis of the inventory model can be used to identify the key parameters, so as to effectively reduce the total inventory cost. The importance order and the identification of key parameters are helpful to increase the operational efficiency of the inventory control and provide effective methods for improving the inventory management. Lastly, a case study with a shortage and limited inventory capacity is used to demonstrate the proposed model.

ACS Style

Liwei Chen; Meng Kou; Songwei Wang. On the Use of Importance Measures in the Reliability of Inventory Systems, Considering the Cost. Applied Sciences 2020, 10, 7942 .

AMA Style

Liwei Chen, Meng Kou, Songwei Wang. On the Use of Importance Measures in the Reliability of Inventory Systems, Considering the Cost. Applied Sciences. 2020; 10 (21):7942.

Chicago/Turabian Style

Liwei Chen; Meng Kou; Songwei Wang. 2020. "On the Use of Importance Measures in the Reliability of Inventory Systems, Considering the Cost." Applied Sciences 10, no. 21: 7942.

Article
Published: 16 May 2020 in Multimedia Tools and Applications
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Detecting small targets in large fields of view is a challenging task. Nowadays, many targets detection models based on the convolutional neural network (CNN) achieve excellent performance. However, these CNN-based detectors are inefficient when applied to tasks of real-time detection of small targets. This paper proposes a small-target detection model in large fields of view based on the tectofugal–thalamofugal–accessory optic system of birds. Within this model, first, we design an unsupervised saliency algorithm to generate saliency regions to suppress background information according to the visual information processing mechanism of the tectofugal pathway of birds. Second, we design a super-resolution (SR) analysis method to enlarge small targets and improve image resolution by the information processing mechanism of the accessory optic system of birds. Then, according to the information processing mechanism of the thalamofugal pathway, we propose a CNN-based method to detect small targets. We further test our model on two public datasets (the VEDAI dataset and DLR 3 K dataset), and the experimental results demonstrate that the proposed detection model outperforms the state-of-the-art methods on small-target detection.

ACS Style

Zhizhong Wang; Donghaisheng Liu; Yuehui Lei; Xiaoke Niu; Songwei Wang; Li Shi. Small target detection based on bird’s visual information processing mechanism. Multimedia Tools and Applications 2020, 79, 22083 -22105.

AMA Style

Zhizhong Wang, Donghaisheng Liu, Yuehui Lei, Xiaoke Niu, Songwei Wang, Li Shi. Small target detection based on bird’s visual information processing mechanism. Multimedia Tools and Applications. 2020; 79 (31-32):22083-22105.

Chicago/Turabian Style

Zhizhong Wang; Donghaisheng Liu; Yuehui Lei; Xiaoke Niu; Songwei Wang; Li Shi. 2020. "Small target detection based on bird’s visual information processing mechanism." Multimedia Tools and Applications 79, no. 31-32: 22083-22105.

Integrative systems
Published: 05 September 2018 in NeuroReport
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Reconstruction of visual input through a neuron response helps to understand the information processing mechanism of the visual system. This paper uses the amplitude and phase characteristics of the local field potential signal in the pigeon optic tectum area to reconstruct the visual input from the neuron response data by means of local information accumulation using a linear inverse filter and a back propagation neural network algorithm. The reconstructed results show that the correlation between three reconstructed images and their corresponding stimulus images (tree branches, birds, and eyeglasses) was 0.8461±0.1135 for optimal values of number of channels, response duration, time from stimulus onset, and frequency band. This method of reconstructing the natural image from the pigeon optic tectum area neuron response signal can be applied to coding mechanism analysis of brightness and structural information in the visual system and to feedback from implantable visual prostheses.

ACS Style

Zhizhong Wang; Xingyang Jiao; Songwei Wang; Xiaoke Niu; Li Shi. Natural image reconstruction on the basis of local field potential signals of pigeon optic tectum neurons. NeuroReport 2018, 29, 1092 -1098.

AMA Style

Zhizhong Wang, Xingyang Jiao, Songwei Wang, Xiaoke Niu, Li Shi. Natural image reconstruction on the basis of local field potential signals of pigeon optic tectum neurons. NeuroReport. 2018; 29 (13):1092-1098.

Chicago/Turabian Style

Zhizhong Wang; Xingyang Jiao; Songwei Wang; Xiaoke Niu; Li Shi. 2018. "Natural image reconstruction on the basis of local field potential signals of pigeon optic tectum neurons." NeuroReport 29, no. 13: 1092-1098.