Table of Content

30 May 2022, Volume 40 Issue 3

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Communication Engineering

EDFA-Relaying Long-Haul Chaos Synchronization of Semiconductor Lasers Driven by a Common Signal

DONG Hongxia, GAO Hua, WANG Longsheng, YANG Yibiao, WANG Anbang

2022, 40(3):  361-371.  doi:10.3969/j.issn.0255-8297.2022.03.001

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This paper numerically studies the long-haul chaotic synchronization scheme of semiconductor lasers driven by a common signal based on erbium-doped fiber amplifier (EDFA) relay and periodic dispersion compensation. By optimizing the injection conditions of the driving light and the mismatch of the relaxation oscillation frequency between driving and response lasers, the response laser achieves synchronization with a synchronization coefficient of 0.98 in back-to-back situations. At the same time, the correlation between the driving and response lasers is reduced to 0.32, ensuring the security of the co-drive synchronization system. Furthermore, considering the damage factors such as fiber dispersion, nonlinear effects, and EDFA noise, the optimal conditions for fiber input power and single-span fiber length are numerically studied. It is expected that under the condition of dispersion compensation deviation of 5 ps/nm per 100 km fiber, a high-quality chaotic synchronization with a synchronization coefficient of 0.90 can be achieved after 700 km of fiber transmission. This research result has reference significance for long-distance chaotic laser carrier communication and key distribution.

Signal and Information Processing

Semi-automatic Extraction and Regularization of Buildings of Different Shapes from High-Resolution Remote Sensing Images

CUI Weihong, LI Jia, LIU Yu

2022, 40(3):  372-388.  doi:10.3969/j.issn.0255-8297.2022.03.002

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The current methods of interactive extraction of buildings from high-resolution remote sensing images mostly require complex user interaction and most of them only support extraction of buildings with right angles. In order to reduce interaction and achieve high-precision extraction of buildings in different shapes, this paper uses region grow, Gaussian mixture models (GMM), CannyLines edge detection and the max-flow/min-cut segmentation method based on multiple star constraints sequentially to obtain building patch, followed by regularization methods to get the building contours which are consistent with the actual building shapes. The average of F₁ is up to 0.9 in extraction experiments, and the experimental results also show the facility and strong robustness of the proposed method.

Tilt Rate Measurement of Power Tower Based on UAV LiDAR Point Cloud

LU Zhumao, GONG Hao, JIN Qiuheng, HU Qingwu, LI Jiayuan

2022, 40(3):  389-399.  doi:10.3969/j.issn.0255-8297.2022.03.003

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Power towers are the most basic equipment of the transmission line. Various weather or terrain conditions can cause damage to transmission lines such as wear, corrosion, strand breakage, resulting in deformation or tilting of the tower, which may cause serious accidents such as regional power outages if not repaired and inspected in time. Considering the difficulty, low efficiency and low accuracy of traditional measurement methods, an accurate measurement method of power tower tilt rate based on UAV LiDAR point cloud is proposed. This method uses the scattered 3D laser point cloud of power tower to calculate the tower centerline by fitting the tower body structure, so as to calculate the tilt rate. The measurement error sources and application conditions of this method are discussed. The application in transmission line detection proves the effectiveness and correctness of this method. To verify the effectiveness of the method, the paper selects 6 types of towers, 3 of each, 18 in total, to analyze the influence of point cloud density to the tilt rate measurement method, and calculate the accuracy of the method. The relative error of the calculation of the tilt rate is better than 0.7°, proves the validity and correctness of this method.

A Similar Image Retrieval Method Based on Nested Network Model

NI Cui, WANG Peng, ZHU Yuanting, ZHANG Dong

2022, 40(3):  400-410.  doi:10.3969/j.issn.0255-8297.2022.03.004

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A similar image retrieval method based on nested network model is proposed by improving the traditional dense network (DenseNet) which is a common deep-learning method. First, the proposed algorithm optimizes feature retrieving blocks by embedding squeeze-and-excitation network (SENet) into the original DenseNet and adjusting the structure of the DenseNet, so as to improve the accuracy of image retrieval. Second, the algorithm achieves the speed-up of image retrieval by setting proper weight for each feature channel of the image, thus suppressing the processing time of those invalid features. Experimental results show that the algorithm can strengthen the transmission of effective image features and improve the accuracy of image researching results effectively.

Point Cloud Object Extraction Based on Gaussian Kernel Function and Exponential Function Clustering

CHEN Xijiang, AN Qing, BAN Ya, WANG Dexin, LI Kun, LIU Haipeng

2022, 40(3):  411-422.  doi:10.3969/j.issn.0255-8297.2022.03.005

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In view of problems of repeatability of cluster center and disability of conducting point cloud clustering, a point cloud clustering method of clustering center homogenization combining Gaussian kernel and exponential function is proposed to optimize homogenization distribution of cluster centers and achieve the homogeneous clustering of point cloud. Firstly, local density is determined according to the Gaussian kernel function and density exponential function, and distance parameters are determined according to the size of local density. Then cluster centers are determined according to the product of the local density and distance parameters, and the proximity of the cluster centers is eliminated, so that the cluster centers are more evenly distributed in the entire data set. Finally, the distances between the data point and the cluster centers are used to determine the cluster attribution of each data, and the neighboring clusters are combined to achieve the extraction of the point cloud target. This algorithm is compared with the clustering function based on density peak (CFDP), K-means clustering algorithm, DBSCAN (density-based spatial clustering of applications with noise) algorithm, and the advantages of clustering algorithm in this paper are confirmed. Compared with the DPC algorithm and the deep learning method, the accuracy of objects point cloud extraction with different resolutions is 96.7%. The proposed method is prior than the other two methods in terms of computational efficiency and precision.

Infrared Image Fusion Enhancement Algorithm Based on MSR and AMSR

LIU Shuo, QU Chongxiao, ZHU Zhongke, ZHANG Fujun, FAN Changjun

2022, 40(3):  423-433.  doi:10.3969/j.issn.0255-8297.2022.03.006

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There are some problems related to infrared images, such as low contrast, poor definition, low signal to noise ratio and blurred edge information for infrared sensors. In this paper, an infrared fusion enhancement algorithm and the improvement of the algorithm are proposed based on the combination of multi-scale retinex (MSR) and adaptive multi-scale retinex (AMSR). Experimental results show that the proposed algorithms can improve the contrast and definition of infrared images. And the algorithms are better than single scale retinex (SSR), multi-scale retinex (MSR), adaptive multi-scale retinex (AMSR) in subjective visual effects and objective evaluation criteria.

Water Level Detection Algorithm Based on Computer Vision

SUN Weiya, WANG Da, XU Shuai, WANG Jingye, MA Zhanyu

2022, 40(3):  434-447.  doi:10.3969/j.issn.0255-8297.2022.03.007

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In view of the problems of high uncertainty and high cost in existing water level meters, this paper presents a set of water level monitoring system based on computer vision to gain high accurate, real-time, robust intelligent water level monitoring. First, preprocessing and edge detection of captured images are carried out to find out the position of the water gauge to be read, and the calibration of the water gauge is carried out by using affine transformation algorithm. Second, the keyword in the water ruler area is positioned and processed by filtering method. Then the edge information is projected to find water surface. Finally, the height of water surface is calculated according to the results of keyword processing and edge information. Experimental verification and field deployment show that the complete water level detection scheme proposed in this paper has both theoretical and practical value in the field of water level detection and water gauge reading based on computer vision.

Lightweight Image Semantic Segmentation Network Based on Parity Cross Convolution

LI Fengyong, YE Bin, QIN Chuan

2022, 40(3):  448-456.  doi:10.3969/j.issn.0255-8297.2022.03.008

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The multi-step down-sampling convolution in traditional lightweight image semantic segmentation networks easily causes a thorn-like distribution in receptive fields. This will introduce systematic deviations in pixel utilization and finally affect the improvement of segmentation accuracy. Regarding this problem, a down-sampling module of parity cross convolution is designed for traditional lightweight image semantic segmentation networks. In the scheme, an even-convolution module is added before the strided odd-numbered convolution module, in order to alleviate the negative effects caused by spur distribution. Accordingly, the deviation of pixel utilization at different spatial positions in the segmentation network can be eliminated effectively, and the improvement of pixel segmentation accuracy can be finally achieved. Experimental results demonstrate that through the comparison of seven different lightweight image semantic segmentation networks, the proposed model can obviously eliminate the thorn-like distribution and improve the accuracy of the segmentation network. Also, the proposed model performs excellent adaptability to different lightweight networks.

Reversible Data Hiding in Encrypted Image Using Parametric Binary Tree Labeling

QIU Yingqiang, CHEN Xin, YANG Yuyan, ZENG Huanqiang, QIAN Zhenxing

2022, 40(3):  457-469.  doi:10.3969/j.issn.0255-8297.2022.03.009

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To achieve high embedding capacity, an improved reversible data hiding method based on parametric binary tree labeling is proposed for encrypted images. The proposed method includes the following three independent stages:image owner, data hider and authorized receivers. In the first stage, image owner encrypts the original image in a special way of modulating randomly and scrambling image blocks but reserving the pixels' correlation within image blocks. After the encrypted image is uploaded onto cloud servers, data hider first chooses a reference pixel of each image block by adoptive way, and predicts the rest pixels according to the correlation information. Then the data hider can embed large amounts of additional data into the encrypted image by using parametric binary tree labeling in the second stage. In the last stage, authorized receivers can extract the embedded data, or recover the original image losslessly. Experimental results show that the improved method can not only achieve high embedding capacity, but also be applicable in medical, cloud services, military and other fields.

Social Behavior Information Hiding Based on Time Interval

SHI Wuhai, WANG Zichi, WU Hanzhou, ZHANG Xinpeng

2022, 40(3):  470-476.  doi:10.3969/j.issn.0255-8297.2022.03.010

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This paper proposes a time interval-based social behavior information hiding algorithm. First, both sender and receiver store the user information of the social relationship shared by both parties; then, the sender binds the key, and hides secret information during the time interval between social interactions such as "liking"; finally, the receiver extracts secret information by periodically acquiring the behavioral information of co-shared social users. Experiment is conducted by taking the popular QQ social platform as research object, and experimental results show that the method can significantly improve the capacity of secret information transmission with a low time cost. The proposed method is not subject to any particular social platforms, and therefore has good applicability.

Electric Network Frequency Modeling for Multimedia Forensics

WANG Qingyi, HUA Guang, ZHANG Haijian

2022, 40(3):  477-492.  doi:10.3969/j.issn.0255-8297.2022.03.011

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Electric network frequency (ENF) modeling, which aims to use mathematical models to describe its random fluctuation properties and establish feature extraction, statistical analysis, and trajectory prediction capabilities, has been an important research subtopic in ENF-based digital forensics. In this paper, we first illustrate the limitations of the existing autoregressive (AR) model and then conduct a comprehensive statistical analysis based on practically recorded ENF data from Central China Grid. Specifically, we apply the autoregressive integrated moving average model (ARIMA) and two Markov chain based models respectively in ENF modeling for solving corresponding model parameters. Through the comparative analysis, we reveal that the ARMA(2,4) model is theoretically the best choice for ENF modeling, whereas with the consideration of the frequency resolution limitation in practical situations, the Markov chain model is more suitable to model the estimated ENF from a testing file.

Computer Science and Applications

Federated Ensemble Algorithm Based on Deep Neural Network

LUO Changyin, CHEN Xuebin, SONG Shangwen, ZHANG Shufen, LIU Zhiyu

2022, 40(3):  493-510.  doi:10.3969/j.issn.0255-8297.2022.03.012

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Federated learning is a research hotspot in the field of multi-source privacy data protection. It has advantage that its framework can train a common model that is satisfactory to many parties when the data is not local, but it hardly integrates local model parameters and cannot make full use of multiple sources under safe conditions. Aiming to the problem, this article proposes a deep learning-based federated integration algorithm,which applies deep learning and integrated learning to the framework of federated learning. Under the framework of the proposed federated learning, the parameters of local modes are optimized, accordingly the accuracy of the local model is improved. Besides, by applying variety of integration algorithms to integrate local model parameters, the improvement of model accuracy with taking into account the security of multi-source data simultaneously can be achieved. Experimental comparisons with traditional multi-source data processing technology are demonstrated, and show that the accuracies of the proposed training model on the mnist, digits, letter, and wine data sets are increased by 1%, 8%, -1%, 1%, respectively. So that the proposed algorithm could guarantee the improvement of accuracy and the security of multi-source data and models at the same time, and this has important practical application value.

Alternative Multipath Payment Scheme of Blockchain Payment Channel Networks

LIU Ya, DU Haizhou

2022, 40(3):  511-527.  doi:10.3969/j.issn.0255-8297.2022.03.013

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The off-chain payment channel network has problems such as the inability of user nodes to choose the transfer method according to actual needs, and the low fault tolerance rate caused by only using a single path to initiate transactions in a single routing. This paper proposes an alternative multipath payment scheme (AMPS) based on the off-chain payment channel network. This scheme provides user nodes with three forwarding options according to actual needs, i.e., the shortest distance priority, the lowest handling fees priority and the comprehensive measurement of the distance and handling fees. In AMPS, we improve the open shortest path priority protocol and construct multiple paths to forward payments in parallel according to different priorities selected by the nodes. Compared with previous schemes under a single route, the scheme AMPS significantly improves the transfer payment success rate. At the same time, the receiver nodes can actively release other paths once immediately the transfer is successful, which improves the transfer payment performance of the whole network.

PKI Model Based on Dual Blockchain

WANG Cheng, ZHENG Hong, HUANG Jianhua, QIAN Shihui

2022, 40(3):  528-538.  doi:10.3969/j.issn.0255-8297.2022.03.014

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Public key infrastructure (PKI) system provides very important security services for the implementation of e-commerce, e-government and office automation. This paper builds a certificate authority (CA) based on blockchain and smart contract and proposes a dual blockchain-based distributed PKI model. As used in medical application scenarios, the PKI model can manage certificates and patient information with blockchain and smart contract technology, realize the privacy protection and access control of patient information, and solve the problems of single point of failure, multi-CA mutual trust, insecure certificate issuance, certificate transparency and rapid verification in traditional PKI. Security analysis and experiments show that the proposed model can solve a variety of problems existing in traditional and existing single-chain PKI models, effectively protect patient privacy, and significantly improve the efficiency of certificate issuance and validation.