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25 January 2024, Volume 45 Issue 1
Topics: Intelligent Communication and Network Technologies for Manned/Unmanned Cooperation Systems
Intelligent communication and networking key technologies for manned/unmanned cooperation: states-of-the-art and trends
Hao YIN, Jibo WEI, Haitao ZHAO, Jiao ZHANG, Haijun WANG, Baoquan REN
2024, 45(1):  1-17.  doi:10.11959/j.issn.1000-436x.2024037
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The intelligent communication and networking technologies for manned/unmanned cooperation was comprehensively surveyed.Firstly, the requirements on communication and networking were analyzed from the application scenarios of manned/unmanned cooperation.Then, in context of physical layer, link layer and network layer respectively, the key issues regarding channel modeling, waveform design, networking protocol and intelligent collaboration were analyzed.And the states-of-the-art in this research area and the characteristics of representative technologies were deeply studied.In the end, the possible development trends and promising technologies were prospected on the way to make the manned/unmanned cooperative communication and networking more intelligent, more efficient and more flexible.

Design and implementation of online learning assisted intelligent receiver
Lingjin KONG, Kai MEI, Xiaoran LIU, Jun XIONG, Haitao ZHAO, Jibo WEI
2024, 45(1):  18-30.  doi:10.11959/j.issn.1000-436x.2024012
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To address the issue of reliable communication under complicated scenarios, an online learning-assisted intelligent OFDM receiver was proposed.The variations of the channel environment could be precepted by the receiver, and the optimal parameters of the receiver under the current scenario were obtained by collecting data and training online.In the channel estimation module of the OFDM system, a performance comparator based on the mean square error of noisy channel samples was designed as the indicator of channel environment variations.To accelerate the online training progress, a lightweight neural network structure was applied.The proposed method was further implemented and verified based on universal software radio peripherals.The numerical simulation and over-the-air experimental results demonstrate that the proposed receiver can perceive and adapt to new environments effectively, and outperforms existing machine learning methods in terms of receiving performance and convergence rate with a limited number of pilots.

Algorithm for intelligent collaborative target search and trajectory planning of MAV/UAV
Zhuo LU, Qihui WU, Fuhui ZHOU
2024, 45(1):  31-40.  doi:10.11959/j.issn.1000-436x.2024003
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Based on the manned aerial vehicle (MAV) / unmanned aerial vehicle (UAV) intelligent cooperation platform, the search of multiple interfered signal sources with unknown locations and trajectory planning were studied.Considering the real-time and dynamic nature of the search process, a MAV/UAV intelligent collaborative target search and trajectory planning (MUICTSTP) algorithm based on multi-agent deep reinforcement learning (MADRL) was proposed.Each UAV made online decision on trajectory planning by sensing the received interference signal strength (RISS) values, and then transmitted the sensing information and decision-making actions to the MAV to obtain the global evaluation.The simulation results show that the proposed algorithm exhibits better performance in long-term RISS, collision, and other aspects compared to other algorithms, and the learning strategy is better.

Coordinated UAV-UGV trajectory planning based on load balancing in IoT data collection
Yuchao ZHU, Shaowei WANG
2024, 45(1):  41-53.  doi:10.11959/j.issn.1000-436x.2024005
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To improve the efficiency of large-scale Internet of things (IoT) data collection, a coordinated trajectory planning algorithm for multiple aerial and ground vehicles based on load balancing region partitioning was proposed, where unmanned aerial vehicles (UAVs) acting as aerial base stations were dispatched to gather data from IoT devices and unmanned ground vehicles (UGVs) acting as mobile battery swap stations were used to compensate for the shortage of UAV’s energy.Aiming at shortening the mission completion time, the optimization task was to minimize the longest mission time among a fleet of UAV-UGVs, which was formulated as a variant of min-max multi-depot vehicle routing problem and solved from the load-balancing perspective.Specifically, the IoT devices were assigned to the UAV-UGVs’ service zones by a load-balancing region partition algorithm, based on which the trajectory planning problem of multiple UAV and UGV was reduced to several independent route planning problems for each UAV-UGV pair.Then, a cooperative trajectory planning strategy was developed to optimize the route in each service zone.Numerical results validate that the proposed algorithm outperforms the compared algorithms in terms of mission completion time and balancing degree.

Causality adversarial attack generation algorithm for intelligent unmanned communication system
Shuwen YU, Wei XU, Jiacheng YAO
2024, 45(1):  54-62.  doi:10.11959/j.issn.1000-436x.2024036
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A causality adversarial attack generation algorithm was proposed in response to the causality issue of gradient-based adversarial attack generation algorithms in practical communication system.The sequential input-output features and temporal memory capability of long short-term memory networks were utilized to extract the temporal correlation of communication signals while satisfying practical causality constraints, and enhance the adversarial attack performance against unmanned communication systems.Simulation results demonstrate that the proposed algorithm outperforms existing causality adversarial attack algorithms, such as universal adversarial perturbation, under identical conditions.

Papers
Unsupervised dimensionality reduction method for multivariate time series based on global and local scatter
Zhengxin LI, Gang HU, Fengming ZHANG, Xiaofeng ZHANG, Yongmei ZHAO
2024, 45(1):  63-76.  doi:10.11959/j.issn.1000-436x.2024008
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To solve the problem that the traditional dimensionality reduction methods cannot be directly applied to multivariate time series, and for the existing approaches, it is difficult to ensure the effectiveness of dimensionality reduction while significantly reducing the dimension, an unsupervised dimensionality reduction method of multivariate time series based on global and local scatter was proposed.Firstly, a feature series extraction method was proposed to extract the upper triangular elements of the co-variance matrix of each multivariate time series and combine them into a feature sequence.Then, based on the idea of “minimum local scatter and maximum global scatter,” an unsupervised dimensionality reduction model was presented, which preserved the global information as much as possible while maintaining the local nearest neighbor relationship.Using the feature sequence as the input, the sum of the neighborhood variances of all sample points was minimized, and the variance of all the neighborhood centroids were maximized.The projection matrix obtained by solving the proposed model could be used to perform the dimensionality reduction.Finally, the proposed method was evaluated with experiments on 20 public data sets.The results show that the proposed method can significantly reduce the dimension of multivariate time series, while ensuring the effectiveness of dimensionality reduction.

Risk assessment of autonomous vehicle based on six-dimensional semantic space
Yanan CHEN, Ang LI, Dan WU
2024, 45(1):  77-93.  doi:10.11959/j.issn.1000-436x.2024001
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To address the problems of inadequate extraction of risk elements and low robustness of risk scenario assessment in autonomous vehicles, a risk assessment framework based on six-dimensional semantic space was proposed, which included risk element extraction based on six-dimensional semantic space and risk scenario assessment based on knowledge graph.Formerly, the semantic space was constructed with RGB and IR data mapped, and rich features were extracted using inter-modal correlations for explicit and potential risk elements.Subsequently, risk elements were distilled into a knowledge graph by semantic role annotation and entity fusion, and an inference method was designed by combining node completion and risk level function for accurate risk assessment.Simulations show that the proposed method surpasses current MSMatch and iSQRT-COV-Net in accuracy, false/missed alarm rate, and processing time.

Volume-hiding encrypted multi-map scheme based on hierarchical structure
Jing CHEN, Hanxing LI, Kun HE, Meng JIA, Yuqing LI, Ruiying DU
2024, 45(1):  94-105.  doi:10.11959/j.issn.1000-436x.2024002
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The volume-hiding encrypted multi-map (EMM) scheme is able to prevent attackers from inferring plaintext through the volume leakage, but existing solutions have problems with large storage and search computational costs.Based on the characteristic that the volume of the searched data often follows Zipf’s law, a hierarchical volume-hiding EMM scheme was proposed.Compared to the plain scheme that filled all keys’ volumes equally, converting the filling of the overall data into the filling of multiple sub-data blocks could reduce storage costs, and also achieved constant level complexity query overhead.Security analysis shows that the proposed scheme can achieve hidden matching volume without loss of query results.Simulation results show that compared with the most efficient scheme XorMM, the proposed scheme can increase the storage overhead by 10%, reduce the search computing overhead by 90%, and significantly improve the search efficiency.

Research on distributed network intrusion detection system for IoT based on honeyfarm
Hao WU, Jiajia HAO, Yunlong LU
2024, 45(1):  106-118.  doi:10.11959/j.issn.1000-436x.2024020
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To solve the problems that the network intrusion detection system in the Internet of things couldn’t identify new attacks and has limited flexibility, a network intrusion detection system based on honeyfarm was proposed, which could effectively identify abnormal traffic and have continuous learning ability.Firstly, considering the characteristics of the convolutional block attention module, an abnormal traffic detection model was developed, focusing on both channel and spatial dimensions, to enhance the model’s recognition abilities.Secondly, a model training scheme utilizing federated learning was employed to enhance the model’s generalization capabilities.Finally, the abnormal traffic detection model at the edge nodes was continuously updated and iterated based on the honeyfarm, so as to improve the system’s accuracy in recognizing new attack traffic.The experimental results demonstrate that the proposed system not only effectively detects abnormal behavior in network traffic, but also continually enhances performance in detecting abnormal traffic.

Time-varying channel estimation in reconfigurable intelligent surface assisted communication system
Kai SHAO, Ben LU, Guangyu WANG
2024, 45(1):  119-128.  doi:10.11959/j.issn.1000-436x.2024028
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Aiming at the key problems need to be solved, such as cascade channel sparse representation, time-varying channel parameter tracking and signal reconstruction, for time-varying cascade channels estimation of reconfigurable intelligent surface (RIS) assisted communication system, a Khatri-Rao and hierarchical Bayesian Kalman filter (KR-HBKF) algorithm was proposed.Firstly, the Khatri-Rao product and Kronecker product transformations were used to obtain the sparse representation of RIS cascaded channels based on the sparse characteristics of channels, thus the RIS cascaded channel estimation problem was transformed into a low-dimensional sparse signal recovery problem.Then, according to the state evolution model of RIS cascaded channel, the time correlation parameter was introduced into the prediction model of HBKF algorithm, and the improved HBKF was applied to solve the problem of time-varying channel parameter tracking and signal reconstruction for completing the time-varying cascaded channels estimation.The sparsity and time correlation of the channel were comprehensively considered in the KR-HBKF algorithm, thus better estimation accuracy could be obtained with small pilot overhead.Compared with the traditional compressed sensing algorithm, the simulation results show that the proposed algorithm has about 5 dB estimated performance improvement, and better robustness performance under different time-varying channel conditions.

Binary phase-coded radar waveform optimization based on improved hybrid quantum genetic algorithm
Yu ZHANG, Jing ZHAO, Yanguo JIA, Xiumin SHEN
2024, 45(1):  129-140.  doi:10.11959/j.issn.1000-436x.2024022
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To overcome the problem of single search focus and limited application scope of existing algorithm, a binary phase-coded radar waveform optimization (RWO) based on improved hybrid quantum genetic algorithm (IHQGA) was proposed.IHQGA adopted a novel self-adaptive rotation angle strategy, which dynamically adjusted the rotation angle based on evolutionary process and cosine similarity.The convergence speed, global search capability, and solution quality were improved.Simulation results demonstrate that compared with genetic algorithms, basic quantum genetic algorithms, and hybrid quantum genetic algorithms, IHQGA performs better in terms of solution quality and resource consumption for six benchmark functions that include single-peak, multi-peak, and non-convex optimization problems.Additionally, for binary phase-coded RWO, which verifies the feasibility and effectiveness of IHQGA in WO.

Impossible statistical fault analysis of the PRIDE lightweight cryptosystem
Wei LI, Wenqian SUN, Dawu GU, Ailin ZHANG, Yunhua WEN
2024, 45(1):  141-151.  doi:10.11959/j.issn.1000-436x.2024019
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To analyze the implementation security of the PRIDE lightweight cryptosystem proposed at CRYPTO in 2014, a novel method of impossible statistical fault analysis on the ciphertext-only attack assumption was proposed.Furthermore, new distinguishers were designed, such as the Chi-square goodness-of-fit test-Hamming weight, and Chi-square goodness-of-fit test-maximum likelihood estimation.The proposed method had a random nibble-oriented fault model, and combined the statistical distribution states with the impossible relationship.On the difference among the intermediate states before and after the fault injections, at least 432 faults were required to recover the 128 bit secret key of PRIDE with a reliability of at least 99%.The experimental analysis demonstrates that the proposed method can not only reduce injected faults and latency, but also increase the accuracy.The results provide a vital reference for exploring the implementation security of lightweight cryptosystems.

Adaptive-clustered image-block hierarchical DCT hybrid digital-analog wireless transmission method
Shu TANG, Peng YANG, Xianzhong XIE, Guangyi ZHOU, Jiaqing LI, Yu ZHAO
2024, 45(1):  152-166.  doi:10.11959/j.issn.1000-436x.2024015
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In order to achieve high quality image transmission, a novel hybrid digital-analog (HDA) wireless transmission method was proposed.Firstly, the image residual layer was constructed by subtracting the downsampled and upsampled images from the original image respectively.Secondly, an adaptive-clustered block-level discrete cosine transform (DCT) algorithm was proposed to efficiently remove the correlation between image pixels in the residual layer.Finally, an image block level-based unequal power allocation strategy for further boosting the quality of received images was proposed.Extensive experiments results demonstrate that the proposed method is significantly superior to existing successful hybrid digital analog image wireless transmission methods in both qualitative evaluation and quantitative measurement.

Blockchain-based and verifiable multidimensional data aggregation and sharing scheme for smart grid
Jianwei CHEN, Shuyu WANG, Meiping ZHANG, Zhenping ZHANG
2024, 45(1):  167-179.  doi:10.11959/j.issn.1000-436x.2024010
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Aiming at the problem that how to support lightweight multi-dimensional data aggregation, achieve double-end integrity verification of multi-dimensional data in the overall process of the system, and deal with the centralization of cloud servers, a blockchain-based and verifiable multidimensional data aggregation and sharing scheme for smart grid was proposed.Firstly, in order to meet the demand for fine-grained analysis of power data in smart grid, privacy-preserving multidimensional data aggregation was achieved by using masked values and Horner’s rule.On this basis, for the data integrity verification problem of the existing data aggregation scheme in the sharing of cloud storage data with the third party, a new signature algorithm was designed by borrowing the RSA-based multiplicative homomorphic commitment scheme and homomorphic hash function homomorphism, which enabled the cloud server to verify the integrity of aggregated data and be used for integrity verification in the data sharing phase, i.e., it achieved double-end verifiability of cloud storage data and was resistant to internal attacks.Meanwhile, an aggregated data sharing architecture based on federated chain multichain was proposed to effectively avoid centralisation problems such as single-machine processing bottleneck and vulnerability to attacks.The theoretical analysis proves the security of the scheme.Performance experiments show that the proposed scheme has lower computation and communication costs compared with existing schemes.

Electromagnetic signal fast adversarial attack method based on Jacobian saliency map
Jian ZHANG, Xia ZHOU, Yiran ZHANG, Zicong WANG
2024, 45(1):  180-193.  doi:10.11959/j.issn.1000-436x.2024021
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In order to generate high-quality electromagnetic signal countermeasure examples, a fast Jacobian saliency map attack (FJSMA) method was proposed.The Jacobian matrix of the attack target class was calculated and feature saliency maps based on the matrix were generated, then the most salient feature points were iteratively selected and perturbations in their neighborhood were continuously added while introducing a single point perturbation constraint, finally adversarial examples were generated.Experimental results show that, compared with Jacobian saliency map attack method, FJSMA improves the generation speed by about 10 times while maintaining the same high attack success rate, and improves the similarity by more than 11%, and compared with other gradient-based methods, the attack success rate is improved by more than 20%, and the similarity is improved by 20% to 30%.

Correspondences
Concrete constructions of 2-resilient rotation symmetric Boolean functions with 7 variables
Jiao DU, Lin LI, Ziwei ZHAO, Yueyue LI, Tianyin WANG
2024, 45(1):  194-200.  doi:10.11959/j.issn.1000-436x.2024007
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Based on the properties of the 2-tuples distribution matrix of the rotation symmetric orbits, all 7-variable 2-resilient rotation symmetric Boolean functions were constructed concretely.The results show that there are only 280 nonlinear 2-resilient rotation symmetric Boolean functions over F 2 7 .Furthermore, there are at least 280 nonlinear 2-resilient rotation symmetric Boolean functions over F 2 7k for any odd number k.

Shuffled differential privacy protection method for K-Modes clustering data collection and publication
Weijin JIANG, Yilin CHEN, Yuqing HAN, Yuting WU, Wei ZHOU, Haijuan WANG
2024, 45(1):  201-213.  doi:10.11959/j.issn.1000-436x.2024004
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Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.

A Review of Threat Discovery and Forensic Analysis Based on System-level Provenance Graphs
LENG Tao , , CAI lijun lijun, YU Aimin , , ZHU ziyuan , M A Jian ganggang, LI Caofei , , NIU Ruicheng , , MENG Dan ,
Online First: 2022-07-25
Construction of De Bruijn Sequences from Whole LFSRs with 4 Cycles
ZHOU Cong wei, HU Bin, GUAN Jie
Online First: 2022-07-25
Improved Meet-in -the-Middle Attack on ReducedReduced-Round KiasuKiasu-BC Cipher
LI Manman , CHEN Shaozhen ,
Online First: 2022-07-25
A Review of Threat Discovery and Forensic Analysis Based on System-level Provenance Graphs
LENG T Tao , , CAI lijun lijun, YU Aimin , , ZHU ziyuan , M A Jian ganggang, LI Caofei , , NIU Ruicheng , , MENG Dan ,
Online First: 2022-06-25
Multi-level recommendation framework for local differential privacy algorithms
WANG Hanyi, LI Xiaoguang, BI Wenqing, CHEN Yahong, LI Fenghua, NIU Ben
10.11959/j.issn.1000?436x.2020029
Online First: 2022-06-25
New Dimension in Orbital Angular Momentum Transmission
Online First: 2022-06-25
Joint QoS prediction for Web services based on deep fusion of features
LIU Jianxun , , DING Linghang , , KANG Guosheng , , CAO Buqing , , XIAO Yong ,
Online First: 2022-06-25
6G-Oriented cross-modal signal reconstruction technology
LI Ang, CHEN Jianxin, WEI Xin, ZHOU Liang,
Online First: 2022-06-25
Multi-level recommendation framework for local differential privacy algorithms
WANG Hanyi, LI Xiaoguang, BI Wenqing, CHEN Yahong, LI Fenghua, NIU Ben
10.11959/j.issn.1000?436x.2020029
Online First: 2022-06-24
Joint QoS Prediction for Web Services based on Deep Fusion of Features
Online First: 2022-06-24
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Copyright Information
Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications
Editor-in-Chief: Zhang Ping
Associate Editor-in-Chief:
Zhang Yanchuan, Ma Jianfeng, Yang Zhen, Shen Lianfeng, Tao Xiaofeng, Liu Hualu
Editorial Director: Wu Nada, Zhao Li
Address: F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
Post: 100079
Tel: 010-53933889、53878169、
53859522、010-53878236
Email: xuebao@ptpress.com.cn
Email: txxb@bjxintong.com.cn
ISSN 1000-436X
CN 11-2102/TN
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