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ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model Research Article

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2000511

Abstract: Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and . The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental results demonstrated an average identification rate of 97.71%, 99.41%, and 98.89% in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric can achieve a lower algorithm engineering effort and higher capacity for generalization.

Keywords： 心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法    

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Development and Application of Network Electronic Identity Management in Major Countries and Regions around the World

Hu Chuanping,Chen Bing and Fang Binxing、Zou Xiang

Strategic Study of CAE 2016, Volume 18, Issue 6,   Pages 99-103 doi: 10.15302/J-SSCAE-2016.06.020

Abstract:

This paper analyzes and sorts out the latest developments and typical applications of network electronic identity management in major countries and regions around the world, and discusses development trends in network identity management technology. It outlines network electronic identity management in China according to the 13th Five-Year Plan, including the development of ideas and constructive suggestions, the strengthening of network space identity management, and the construction of a network space identity management system. Such a system can help to regulate the behavior of Internet users, fight against network crime, build network power, safeguard national security and cyberspace sovereignty, and protect the privacy of network users; thus, it provides a reference for China’s network identity management development during the 13th Five-Year Plan.

Keywords： network electronic identity     identity management     digital signature    

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A Study on the Policies and Regulations of Network Electronic Identity Management

Zou Xiang,Hu Chuanping and Fang Binxing、Chen Bing

Strategic Study of CAE 2016, Volume 18, Issue 6,   Pages 23-27 doi: 10.15302/J-SSCAE-2016.06.005

Abstract:

This paper analyzes and sorts out the policies, laws, and regulations of network electronic identity management in major countries, regions in foreign countries, and China. It discusses the development of China's network identity management, and outlines the 13th Five-Year Plan's policies and regulations for network electronic identity management in China, including the development of ideas and constructive suggestions, the strengthening of network space identity management, and the construction of a network space identity management system.

Keywords： network electronic identity     identity management     policies and regulations    

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Perspectives of Individual-Worn Sensors Assessing Personal Environmental Exposure

Uwe Schlink, Maximilian Ueberham

Engineering 2021, Volume 7, Issue 3,   Pages 285-289 doi: 10.1016/j.eng.2020.07.023

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Efficient hierarchical identity based encryption scheme in the standard model over lattices Article

Feng-he WANG,Chun-xiao WANG,Zhen-hua LIU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 781-791 doi: 10.1631/FITEE.1500219

Abstract: Using lattice basis delegation in a fixed dimension, we propose an efficient lattice-based hierarchical identity based encryption (HIBE) scheme in the standard model whose public key size is only ( ) log bits and whose message-ciphertext expansion factor is only log , where is the maximum hierarchical depth and ( ) are public parameters. In our construction, a novel public key assignment rule is used to averagely assign one random and public matrix to two identity bits, which implies that d random public matrices are enough to build the proposed HIBE scheme in the standard model, compared with the case in which 2 such public matrices are needed in the scheme proposed at Crypto 2010 whose public key size is (2 ) log . To reduce the message-ciphertext expansion factor of the proposed scheme to log , the encryption algorithm of this scheme is built based on Gentry’s encryption scheme, by which m bits of plaintext are encrypted into m log q bits of ciphertext by a one time encryption operation. Hence, the presented scheme has some advantages with respect to not only the public key size but also the message-ciphertext expansion factor. Based on the hardness of the learning with errors problem, we demonstrate that the scheme is secure under selective identity and chosen plaintext attacks.

Keywords： Hierarchical identity based encryption scheme     Lattice-based cryptography     Standard model     Learning with errors problem     Gaussian    

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Novel efficient identity-based signature on lattices

Jiang-shan Chen, Yu-pu Hu, Hong-mei Liang, Wen Gao,JSChen@mnnu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 2,   Pages 141-286 doi: 10.1631/FITEE.1900318

Abstract: With the rapid development of electronic information technology, digital signature has become an indispensable part of our lives. Traditional public key certificate cryptosystems cannot overcome the limitations of certificate management. Identity-based cryptosystems can avoid the certificate management issues. The development of quantum computers has brought serious challenges to traditional cryptography. Post-quantum cryptography research is imperative. At present, almost all post-quantum (IBS) schemes are constructed using Gaussian sampling or trapdoor technologies. However, these two technologies have a great impact on computational efficiency. To overcome this problem, we construct an IBS scheme on s by employing Lyubashevsky’s signature scheme. Based on the shortest vector problem on s, our scheme does not use Gaussian sampling or trapdoor technologies. In the , it is proved that our scheme is strongly unforgeable against adaptive chosen messages and identity attacks. The security level of our scheme is strongly unforgeable, which is a higher level than the existential unforgeability of other schemes. Compared with other efficient schemes, our scheme has advantages in computation complexity and security.

Keywords： Identity-based signature     Lattice     Strong unforgeability     Random oracle model    

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Vented Individual Patient (VIP) Hoods for the Control of Infectious Airborne Diseases in Healthcare Facilities Review

J. Patel, F. McGain, T. Bhatelia, S. Wang, B. Sun, J. Monty, V. Pareek

Engineering 2022, Volume 15, Issue 8,   Pages 126-132 doi: 10.1016/j.eng.2020.12.024

Abstract:

By providing a means of separating the airborne emissions of patients from the air breathed by healthcare workers (HCWs), vented individual patient (VIP) hoods, a form of local exhaust ventilation (LEV), offer a new approach to reduce hospital-acquired infection (HAI). Results from recent studies have demonstrated that, for typical patient-emitted aerosols, VIP hoods provide protection at least equivalent to that of an N95 mask. Unlike a mask, hood performance can be easily monitored and HCWs can be alerted to failure by alarms. The appropriate use of these relatively simple devices could both reduce the reliance on personal protective equipment (PPE) for infection control and provide a low-cost and energy-efficient form of protection for hospitals and clinics. Although the development and deployment of VIP hoods has been accelerated by the coronavirus disease 2019 (COVID-19) pandemic, these devices are currently an immature technology. In this review, we describe the state of the art of VIP hoods and identify aspects in need of further development, both in terms of device design and the protocols associated with their use. The broader concept of individual patient hoods has the potential to be expanded beyond ventilation to the provision of clean conditions for individual patients and personalized control over other environmental factors such as temperature and humidity.

Keywords： COVID-19     Vented individual patient hood     Airborne transmission     Healthcare worker     Infectious disease    

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一种易用的实体识别消歧系统评测框架 Article

辉 陈,宝刚 魏,一鸣 李,Yong-huai LIU,文浩 朱

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 195-205 doi: 10.1631/FITEE.1500473

Abstract: 实体识别消歧是知识库扩充和信息抽取的重要技术之一。近些年该领域诞生了很多研究成果，提出了许多实体识别消歧系统。但由于缺乏对这些系统的完善评测对比，该领域依然处于良莠淆杂的状态。本文提出一个实体识别消歧系统的统一评测框架，用于公平地比较各个实体识别消歧系统的效果。该框架代码开源，可以采用新的系统、数据集、评测机制扩展。本文分析对比了几个公开的实体识别消歧系统，并总结出了一些有用的结论。

Keywords： 实体识别消歧；评测框架；信息抽取    

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A partition approach for robust gait recognition based on gait template fusion Research Articles

Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000377

Abstract: has significant potential for remote human identification, but it is easily influenced by identity-unrelated factors such as clothing, carrying conditions, and view angles. Many have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper, gait template fusion is proposed to improve the classical representative gait template (such as a ) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head, trunk, and leg regions) depending on the human body, and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for .

Keywords： 步态识别；分块算法；步态模板；步态分析；步态能量图；深度卷积神经网络；生物特征识别；模式识别    

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Surface Ship Target Recognition Research Based on SGA

Jiang Dingding,Xu Zhaolin,Li Kairui

Strategic Study of CAE 2004, Volume 6, Issue 8,   Pages 79-81

Abstract:

Surface ship recognition is an important contents of navy's aviation probe. This text discussed the principle, characteristics and calculating step of SGA, and applied this calculating way to surface ship recognition. The experiment results proved the scientificalness and the practical applicability of that methoded.

Keywords： SGA     target recognition     surface ship    

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Application of RFID in the Visual Logistics System

Wang Aiming,Mu Xiaoxi,Li Aihua

Strategic Study of CAE 2006, Volume 8, Issue 8,   Pages 65-68

Abstract:

The radio frequency identification (RFID) is a kind of new automatic identification technology. It has many characteristics such as high reliability, privacy , non-contact, convenient and swift. Applying RFID to visual logistics system, can gain actual requirement of guaranteed object and information about the type, amount and way of materials to supply and ensure the supply in the whole time, orientation and course. The paper introduces the RFID system and its principle, and brings about an application of RFID in visual logistics system, which is realized by the radio frequency labels stuck to the containers and equipments.

Keywords： visual logistics     radio frequency identification     visual system for the carrying materials    

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Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Strategic Study of CAE 2004, Volume 6, Issue 11,   Pages 33-37

Abstract:

Based on optimization of constrained nonlinear programming, an approach of clustering center and a fuzzy membership function of pattern classification are derived from an objective function of the constrained nonlinear programming. An unsupervised algorithm with recursive expression and a fuzzy central cluster neural network are suggested in this paper. The fuzzy central cluster neural network proposed here can realize crisp decision or fuzzy decision in pattern classification.

Keywords： fuzzy sets     central cluster     pattern recognition     neural network    

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Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

Lu Shuang,Zhang Zida,Li Meng

Strategic Study of CAE 2004, Volume 6, Issue 2,   Pages 56-60

Abstract:

Radial basis function neural network is a type of three — layer feedforward network. It has many good properties, such as powerful ability for function approximation, classification and learning rapidly. In this paper, in the light of the merit of radial basis function neural network and on the basis of the feature analysis of vibration signal of rolling bearing, AR model is presented by using time series method. Radial basis function neural networks is established based on AR model parameters. In the light of the theory of radial basis function neural networks, fault pattern of rolling bearing is recognized correspondingly. Theory and experiment show that the recognition of fault pattern of rolling bearing based on radial basis function neural networks theory is available and its precision is high.

Keywords： rolling bearing     vibration signal     AR model     RBF neural networks     pattern recognition    

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Binary neural networks for speech recognition Regular Papers

Yan-min QIAN, Xu XIANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 701-715 doi: 10.1631/FITEE.1800469

Abstract:

Recently, deep neural networks (DNNs) significantly outperform Gaussian mixture models in acoustic modeling for speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue, structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for speech recognition are developed to reduce the computational cost during the inference stage. A fast implementation of binary matrix multiplication is introduced. On modern central processing unit (CPU) and graphics processing unit (GPU) architectures, a 5–7 times speedup compared with full precision floatingpoint matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks (CNNs) can restrict the word error rate (WER) degradation to within 15.0%, compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.

Keywords： Speech recognition     Binary neural networks     Binary matrix multiplication     Knowledge distillation     Population count    

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Analysis of Disruptive Technology Identification Methods in Foreign Countries

Wang An,Sun Zongtan,Shen Yanbo and Xu Yuan

Strategic Study of CAE 2017, Volume 19, Issue 5,   Pages 79-84 doi: 10.15302/J-SSCAE-2017.05.014

Abstract:

Disruptive technologies, which have groundbreaking effects on existing traditional or mainstream technologies, have great potential applications, and are actively recognized and nurtured by all countries. This article summarizes the standard reports on technology identification research released by foreign government agencies, think tanks, intelligence agencies, consulting firms, universities, and patent analysis institutions. It then analyzes and evaluates the disruptive technology identification methods in order to provide reference for the corresponding disruptive technology identification methods in China.

Keywords： disruptive technologies     identification     methods    

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Title Author Date Type Operation

ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Journal Article

Development and Application of Network Electronic Identity Management in Major Countries and Regions around the World

Hu Chuanping,Chen Bing and Fang Binxing、Zou Xiang

Journal Article

A Study on the Policies and Regulations of Network Electronic Identity Management

Zou Xiang,Hu Chuanping and Fang Binxing、Chen Bing

Journal Article

Perspectives of Individual-Worn Sensors Assessing Personal Environmental Exposure

Uwe Schlink, Maximilian Ueberham

Journal Article

Efficient hierarchical identity based encryption scheme in the standard model over lattices

Feng-he WANG,Chun-xiao WANG,Zhen-hua LIU

Journal Article

Novel efficient identity-based signature on lattices

Jiang-shan Chen, Yu-pu Hu, Hong-mei Liang, Wen Gao,JSChen@mnnu.edu.cn

Journal Article

Vented Individual Patient (VIP) Hoods for the Control of Infectious Airborne Diseases in Healthcare Facilities

J. Patel, F. McGain, T. Bhatelia, S. Wang, B. Sun, J. Monty, V. Pareek

Journal Article

一种易用的实体识别消歧系统评测框架

辉 陈,宝刚 魏,一鸣 李,Yong-huai LIU,文浩 朱

Journal Article

A partition approach for robust gait recognition based on gait template fusion

Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn

Journal Article

Surface Ship Target Recognition Research Based on SGA

Jiang Dingding,Xu Zhaolin,Li Kairui

Journal Article

Application of RFID in the Visual Logistics System

Wang Aiming,Mu Xiaoxi,Li Aihua

Journal Article

Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Journal Article

Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

Lu Shuang,Zhang Zida,Li Meng

Journal Article

Binary neural networks for speech recognition

Yan-min QIAN, Xu XIANG

Journal Article

Analysis of Disruptive Technology Identification Methods in Foreign Countries

Wang An,Sun Zongtan,Shen Yanbo and Xu Yuan

Journal Article