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作者： Jie Chen Xiaoman Liang Jian Zhang College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

With the developing of the social economy,the amount of currency circulation has increased and become more frequent,but the customer gathering is often occurred in the many bank's *** this paper,with some framework technology,Spring Boot and MyBatis,a WeChat public platform software is developed for customers who are available to book branch business,such as branch service reservation,bank business reservation,reservation information inquiry and so *** with WeChat public number and small program,the software offers humanized service and simple ***,it can meet the needs of users who can choose a reasonable time to go to the ideal branch to handle their business quickly,which not only saves users' time,but also improves the efficiency of the bank's system.

关键词： Reservation Branch WeChat public platform

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Design of a Real-Time and Reliable Multi-machine System

Design of a Real-Time and Reliable Multi-machine System

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作者： Jian Zhang Lang Li Qiuping Li Junxia Zhao Xiaoman Liang College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

An IoT(Internet of Things) system is a multi-machine system composed of a master controller and multiple slave controllers. In some automatic control systems such as petroleum and electric power, the main control uses UART(Universal Asynchronous Receiver/Transmitter) to communicate with the slave controller. However, the communication rate of the UART interface is limited and the delay time is long, which affects the system's response to external events. In order to improve the response speed of the system to external events, this paper designs a multi-machine system based on SPI(Serial Peripheral Interface) interface. First, we use the SPI interface of the microcontroller to build a communication network. The circuit structure of the system is simple, and the hardware resource consumption is low. Then, for the problem that the SPI interface cannot detect communication errors and there is no response mechanism, we designed a communication protocol to make up for these defects from the software level. Finally, we use STM32 F429 IGT6 as the master controller and STM32 F103 C8 T6 as the slave controller to build the experimental platform. The experimental results show that the system is safe and reliable, and can meet the requirements of real-time applications.

关键词： Real-Time Multi-Machine system SPI

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The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

arXiv

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arXiv 2022年

作者： Zhang, Ao Yu, Fan Huang, Kaixun Xie, Lei Wang, Longbiao Chng, Eng Siong Bu, Hui Zhang, Binbin Chen, Wei Xu, Xin School of Computer Science Northwestern Polytechnical University Xi’an China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Beijing Shell Shell Technology Co. Ltd Beijing China WeNet Open Source Community China Li Auto Inc. Beijing China

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce the associated dataset collected from a new-energy vehicle (NEV) covering a variety of cockpit acoustic conditions and linguistic contents. We then describe the track arrangement and the baseline system. Specifically, we set up two tracks in terms of allowed model/system size to investigate resource-constrained and -unconstrained setups, targeting to vehicle embedded as well as cloud ASR systems respectively. Finally we summarize the challenge results and provide the major observations from the submitted systems. © 2022, CC BY-SA.

关键词： Vehicles

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Power Analysis Attack on a Lightweight Block Cipher GIFT

Power Analysis Attack on a Lightweight Block Cipher GIFT

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GIFT is a new lightweight block cipher with smaller area and higher efficiency,which is very suitable for the Internet of Things(IoT) devices with constrained *** power analysis attack is an efficient method to extract the key from the cryptographic ***,it's not easy to reveal the key by means of power analysis attack,when the cipher is implemented by *** this article,we present the method of power analysis attack against ***,we implemented GIFT on FPGA using the SAKURA-G *** we explored the impact of point of interest(POI) on power analysis *** proposed the method of power analysis attack against the diffusion layer of *** experimental results show that the result of power analysis attack is affected by POI and the key can be recovered when POI is *** can reveal the key using correlation power analysis,when targeting the diffusion layer of GIFT.

关键词： Power Analysis Attack Lightweight Block Cipher GIFT SAKURA-G

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Gender Identification for Coloring Black and White Portrait with cGan

Gender Identification for Coloring Black and White Portrait ...

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作者： Qingyun Liu Mugang Lin Yaqi Sun Feng Zhang College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network(cGan). As a c Gan model essentially, a method named pix2 pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2 pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different genders while the ability of gender recognition isn't owned by pix2 pix in essence. Therefore, a gender recognition model is added to the network to produce more realistic and gender specific color portraits. The experiments show that our work has achieved better results than some other common methods.

关键词： Gender identification GANs Loss function Portrait style transfer

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Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

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2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

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Long Text Classification Based on BERT 5

Long Text Classification Based on BERT

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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging

arXiv

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arXiv 2023年

作者： Cai, Qingrui Zhu, Liuhong Zhou, Jianjun Qian, Chen Guo, Di Qu, Xiaobo National Integrated Circuit Industry Education Integration Innovation Platform School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361102 China Fudan University Xiamen361015 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Image super-resolution quality assessment: Structural fidelity versus statistical naturalness

arXiv

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arXiv 2021年

作者： Zhou, Wei Wang, Zhou Chen, Zhibo Dept. of Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assessment (IQA) methods that can not only evaluate and compare SISR algorithms, but also guide their future development. In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness. This allows us to observe the behaviors of different SISR algorithms as a tradeoff in the 2D space. Specifically, SISR methods are traditionally designed to achieve high structural fidelity but often sacrifice statistical naturalness, while recent generative adversarial network (GAN) based algorithms tend to create more natural-looking results but lose significantly on structural fidelity. Furthermore, such a 2D evaluation can be easily fused to a scalar quality prediction. Interestingly, we find that a simple linear combination of a straightforward local structural fidelity and a global statistical naturalness measures produce surprisingly accurate predictions of SISR image quality when tested using public subject-rated SISR image datasets. Code of the proposed SFSN model is publicly available at https://***/weizhou-geek/SFSN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

Semi-Supervised and Self-Supervised Collaborative Learning f...

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IEEE International Symposium on Biomedical Imaging

作者： Yousuf Babiker M. Osman Cheng Li Weijian Huang Nazik Elsayed Leslie Ying Hairong Zheng Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Faculty of Mathematical and Computer Sciences University of Gezira Wad Madani Sudan Department of Biomedical Engineering and Department of Electrical Engineering University at Buffalo The State University of New York New York USA Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by the Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.

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