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2021 Vol.30 No.3

Published on 25 May 2021

Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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Rapid Path Extraction and Three-Dimensional Roaming of the Virtual Endonasal Endoscope
WANG Yudong, HAN Jing, PAN Junjun, et al.
2021, 30(3): 397-405.   doi: 10.1049/cje.2021.03.002
Abstract(2) PDF(0)
Abstract:
Compared with traditional endonasal endoscope, Virtual endonasal endoscope (VEE) is a computerized alternative solution with the advantages of being non-invasive, affordable, no perforation, low risk of infection, and high degree of sensitivity. A Central path extraction algorithm based on Pre-planning of the roaming area (CPE-PRA) is proposed to extract the central path of the nasal cavity model efficiently. And a B-spline curve fitting algorithm based on Relative center deviation (RCD) is introduced to fit a smooth curve to the extracted path, so that the virtual camera could have both a steady turning speed and a broad vision. A series of comparison tests were conducted to evaluate the effectiveness and efficiency of the proposed algorithms.
A CMA-ES-Based Adversarial Attack Against Black-Box Object Detectors
LYU Haoran, TAN Yu'an, XUE Yuan, et al.
2021, 30(3): 406-412.   doi: 10.1049/cje.2021.03.003
Abstract(0) PDF(0)
Abstract:
Object detection is one of the essential tasks of computer vision. Object detectors based on the deep neural network have been used more and more widely in safe-sensitive applications, like face recognition, video surveillance, autonomous driving, and other tasks. It has been proved that object detectors are vulnerable to adversarial attacks. We propose a novel black-box attack method, which can successfully attack regression-based and region-based object detectors. We introduce methods to reduce search dimensions, reduce the dimension of optimization problems and reduce the number of queries by using the Covariance matrix adaptation Evolution strategy (CMA-ES) as the primary method to generate adversarial examples. Our method only adds adversarial perturbations in the object box to achieve a precise attack. Our proposed attack can hide the specified object with an attack success rate of 86% and an average number of queries of 5, 124, and hide all objects with a success rate of 74% and an average number of queries of 6, 154. Our work illustrates the effectiveness of the CMA-ES method to generate adversarial examples and proves the vulnerability of the object detectors against the adversarial attacks.
Utility Preserved Facial Image De-identification Using Appearance Subspace Decomposition
LIU Chuanlu, WANG Yicheng, CHI Hehua, et al.
2021, 30(3): 413-418.   doi: 10.1049/cje.2021.03.004
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Abstract:
Automated human facial image deidentification is a much-needed technology for privacy-preserving social media and intelligent surveillance applications. We propose a novel utility preserved facial image de-identification to subtly tinker the appearance of facial images to achieve facial anonymity by creating “averaged identity faces”. This approach is able to preserve the utility of the facial images while achieving the goal of privacy protection. We explore a decomposition of an Active appearance model (AAM) face space by using subspace learning where the loss can be modeled as the difference between two trace ratio items, and each respectively models the level of discriminativeness on identity and utility. Finally, the face space is decomposed into subspaces that are respectively sensitive to face identity and face utility. For the subspace most relevant to face identity, a k-anonymity de-identification procedure is applied. To verify the performance of the proposed facial image de-identification approach, we evaluate the created “ averaged faces” using the extended Cohn-Kanade Dataset (CK+). The experimental results show that our proposed approach is satisfied to preserve the utility of the original image while defying face identity recognition.
Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier
GONG Chen, LIU Jiahui, NIU Yunyun
2021, 30(3): 419-425.   doi: 10.1049/cje.2021.03.005
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Abstract:
Automatic identification of intracranial electroencephalogram (iEEG) signals has become more and more important in the field of medical diagnostics. In this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of iEEG epileptic seizures. Four kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network (NN) during the identification process. Four kinds of classification tasks, namely Pre-ictal v Post-ictal (CD), Pre-ictal v Epileptic (CE), Post-ictal v Epileptic (DE), Pre-ictal v Post-ictal v Epileptic (CDE), are used to test the effect of our classification method. The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms. The effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.
Automatic Spectral Method of Mesh Segmentation Based on Fiedler Residual
LI Lingfei, WU Tieru
2021, 30(3): 426-436.   doi: 10.1049/cje.2020.11.001
Abstract(0) PDF(0)
Abstract:
In this paper, we propose a fully automatic mesh segmentation method, which divides meshes into sub-meshes recursively through spectral analysis. A common problem in the spectral analysis of geometric processing is how to choose the specific eigenvectors and the number of these vectors for analysis and processing. This method tackles this problem with only one eigenvector, i.e. Fiedler vector. In addition, using only one eigenvector drastically reduces the cost of computing. Different from the Fiedler vector commonly used in the bipartition of graphs and meshes, this method finds multiple parts in only one iteration, vastly reducing the number of iterations and thus the time of operation, because each iteration produces as many correct boundaries as possible, instead of only one. We have tested this method on many 3D models, the results of which suggest the proposed method performs better than many advanced methods of recent years.
A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy
WANG Hongbo, YANG Fan, TIAN Kena, et al.
2021, 30(3): 437-443.   doi: 10.1049/cje.2021.03.006
Abstract(0) PDF(0)
Abstract:
In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a Many-objective evolutionary algorithm based on a spatial division and angle-culling strategy (MaOEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed MaOEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of MaOEA-SDAC in solving high-dimensional optimization problems has been verified.
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Differential Fault Attack on Camellia
ZHOU Yongbin, WU Wenling, XU Nannan, FENG Dengguo
2009, 18(1): 13-19.  
[Abstract](604) [PDF 423KB](7)
Abstract:
Camellia is the final winner of 128-bit blockcipher in NESSIE project, and is also certified as the international IETF standard cipher for SSL/TLS cipher suites.In this study, we present an effcient differential fault attack on Camellia. Ideally, by using our techniques, on average, the complete key of Camellia-128 is recovered with64 faulty ciphertexts while the full keys of Camellia-192and Camellia-256 are retrieved with 96 faulty ciphertexts.Our attack is applicable to generic block ciphers with overall Fiestel structure using a SPN round function.All theseattacks have been successfully put into experimental simulations on a personal computer.
Face Liveness Detection Based on the Improved CNN with Context and Texture Information
GAO Chenqiang, LI Xindou, ZHOU Fengshun, MU Song
2019, 28(6): 1092-1098.   doi: 10.1049/cje.2019.07.012
[Abstract](62) [PDF 3162KB](14)
Abstract:
Face liveness detection, as a key module of real face recognition systems, is to distinguish a fake face from a real one. In this paper, we propose an improved Convolutional neural network (CNN) architecture with two bypass connections to simultaneously utilize low-level detailed information and high-level semantic information. Considering the importance of the texture information for describing face images, texture features are also adopted under the conventional recognition framework of Support vector machine (SVM). The improved CNN and the texture feature based SVM are fused. Context information which is usually neglected by existing methods is well utilized in this paper. Two widely used datasets are used to test the proposed method. Extensive experiments show that our method outperforms the state-of-the-art methods.
An Ultra Low Steady-State Current Power-on- Reset Circuit in 65nm CMOS Technology
SHAN Weiwei, WANG Xuexiang, LIU Xinning, SUN Huafang
2014, 23(4): 678-681.  
[Abstract](214) [PDF 832KB](6)
Abstract:
A novel Power-on-reset (POR) circuit is proposed with ultra-low steady-state current consumption. A band-gap voltage comparator is used to generate a stable pull-up voltage. To eliminate the large current consumptions of the analog part, a power switch is adopted to cut the supply of band-gap voltage comparator, which gained ultra-low current consumption in steady-state after the POR rest process completed. The state of POR circuit is maintained through a state latch circuit. The whole circuit was designed and implemented in 65nm CMOS technology with an active area of 120μm*160μm. Experimental results show that it has a steady pull-up voltage of 0.69V and a brown-out voltage of 0.49V under a 1.2V supply voltage rising from 0V, plus its steady-state current is only 9nA. The proposed circuit is suitable to be integrated in system on chip to provide a reliable POR signal.
Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing
CHENG Hongbing, RONG Chunming, TAN Zhenghua, ZENG Qingkai
2012, 21(2): 254-259.  
[Abstract](979) [PDF 273KB](5)
Abstract:
Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.
A Global K-modes Algorithm for Clustering Categorical Data
BAI Tian, C.A. Kulikowski, GONG Leiguang, YANG Bin, HUANG Lan, ZHOU Chunguang
2012, 21(3): 460-465.  
[Abstract](415) [PDF 334KB](9)
Abstract:
In this paper, a new Global k-modes (GKM) algorithm is proposed for clustering categorical data. The new method randomly selects a sufficiently large number of initial modes to account for the global distribution of the data set, and then progressively eliminates the redundant modes using an iterative optimization process with an elimination criterion function. Systematic experiments were carried out with data from the UCI Machine learning repository. The results and a comparative evaluation show a high performance and consistency of the proposed method, which achieves significant improvement compared to other well-known k-modes-type algorithms in terms of clustering accuracy.
Large Spaceborne Deployable Antennas (LSDAs)-A Comprehensive Summary
DUAN Baoyan
2020, 29(1): 1-15.   doi: 10.1049/cje.2019.09.001
[Abstract](318) [PDF 4261KB](86)
Abstract:
This paper provides a survey of research activities of Large spaceborne deployable antennas (LSDAs) in the past, present and future. Firstly, three main kinds of spaceborne antennas, such as solid reflector, inflatable reflector and mesh reflector, are issued by showing the strengths and weaknesses. Secondly, a detailed research situation of LSDAs with mesh is discussed, for majority of the in-orbit large diameter and high frequency antennas are made in this type of structures. Thirdly, new conception of antenna is proposed as it does have both advantages of large aperture (high gain) and high precision (high frequency). Fourthly, the design theory and approach of LSDAs are concerned. It includes thermal-electromechanical multidisciplinary optimization, shaped beam design technique, performance testing technology and evaluation method, passive intermodulation of mesh, and application of new materials. Finally, the ultra large spaceborne deployable antennas of the next generation are presented, such as the deployable frame and inflatable reflector antennas, space-assembled ultra large antennas, smart array antennas and so on.

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