Table of Content

31 January 2019, Volume 37 Issue 1

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Adaptive Joint Power Control Algorithm Based on Compensation Factor for Device-to-Device Communication

ZHAI Xu-ping, GONG Lei, ZHANG Nan

2019, 37(1):  1-11.  doi:10.3969/j.issn.0255-8297.2019.01.001

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An adaptive joint power control algorithm is proposed to improve the path loss compensation factor and the performance of power control in the cellular system incorporating device to device(D2D)communications. The added amount of the path loss compensation factor could be calculated according to the distance between D2D users to base station and cellular users, then the path loss compensation factor matrix of D2D users and cellular users could be used to improve the performance of power control. In order to improve the co-channel interference furtherly, a joint closed loop power control mode is adopted for D2D users and cellular users. Compared with traditional power control algorithms, simulation results show that the signal to interference plus noise ratio (SINR) of the system could be improved better by the proposed algorithm, and the number of users of the proposed algorithm whose throughput is more than 1000 kbit/s is 1.7 times of the traditional power control algorithm.

Estimation of Broadband Communication Transmitter Fingerprints Based on B-Spline Neural Network

YUAN Hong-lin, LU Xiao-dan, XU Chen

2019, 37(1):  12-23.  doi:10.3969/j.issn.0255-8297.2019.01.002

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A method for the identity authentication of orthogonal frequency division multiplexing (OFDM) communication devices with the received signal is proposed. The IQ imbalance and nonlinearity of the transmitter are estimated as the transmitter fingerprints. Firstly, the multipath channel impulse response is estimated according to the conjugate symmetric pilot. Secondly, the channel impulse response estimation, the conjugate antisymmetric pilot, and the linear approximation of the nonlinear power amplifier are used to estimate the IQ imbalance parameter combination of the transmitter. Then, the B-Spline neural network model coefficients of the nonlinearity of the transmitter are estimated. Finally, the similarity factor of the nonlinear model coefficient estimation is extracted, which constructs the feature vector of the transmitter fingerprint with the estimated IQ imbalance parameter combination. The feature vector is used to recognize or confirm the identity of the communication devices. Theoretical derivation and numerical experiments show that the proposed method can be applied to the physical layer high-intensity authentication and anti-counterfeiting of OFDM communication devices.

Speaker Recognition Based on Combination of MFCC and GFCC Feature Parameters

ZHOU Ping, SHEN Hao, ZHENG Kai-peng

2019, 37(1):  24-32.  doi:10.3969/j.issn.0255-8297.2019.01.003

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Aiming at the issue that single feature parameter of speaker recognition has the shortcoming of low representation ability, a set of mixture feature parameters is formed by combining the single poor anti-noise Mel frequency cepstral coefficients (MFCC) with more robust Gammatone frequency cepstral coefficients (GFCC) and their dynamic differential in this paper. Since the high dimension of the mixture feature parameters, the relationships of each dimension of different feature parameters and recognition results is studied, where dimensionality reduction on high dimensional features is implemented by discarding the dimensions with low contribution ratio. After that, the combination of feature parameters was applied to the speaker recognition system based on Gaussian mixture model. Experimental results show that the combination of parameters can better describe the speakers' feature and have better anti-noise capability.

Identifying Image Authenticity Based on CFA Inconsistency of Interpolation Characteristics

SU Wen-xuan, FANG Zhen

2019, 37(1):  33-40.  doi:10.3969/j.issn.0255-8297.2019.01.004

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Single-sensor digital cameras generally acquire the missing color components by color filter array (CFA) interpolation. In this work, CFA interpolation characteristics are exploited to identify image forgery. Using the differences in frequency spectrum between interpolated images and ideal full color ones, the interpolation characteristics are described by block spectral change and chrominance artifacts features. The feature difference between test images and their re-interpolated version are computed as forensic features. Finally, support vector machine (SVM) is exploited to classify the authentic and tampered images using the block-wise inconsistency of forensic features. Experimental results verify effectiveness of the proposed method and its robustness against JPEG compression.

Steganalysis of JPEG Images Based on Reducing Between-Class Scatter

WANG Ran, NIU Shao-zhang, PING Xi-jian, ZHANG Tao, SANG Xiao-dan

2019, 37(1):  41-50.  doi:10.3969/j.issn.0255-8297.2019.01.005

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Compared with the process of embedding, image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger within-class scatter distances and smaller between-class scatter distances. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are classified according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close complexity evaluation function to build a classifier. The theoretical analysis and experimental results can demonstrate the validity of the proposed framework.

Feature Selection in Precise Crop Classification Using Remote Sensing Data Based on Improved SEaTH Algorithm

YANG Hui-wen, FANG Jun-yong, ZHAO Dong

2019, 37(1):  51-63.  doi:10.3969/j.issn.0255-8297.2019.01.006

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In this paper, we take full advantage of visible and infrared remote sensing images with high spatial resolution to improve classification processing efficiency. With these aims, we propose a method of feature bands selection combined with image entropy based on SEparability and THresholds (SEaTH) method. The proposed method can solve the problem that SEaTH takes no feature information into account. We apply random forest (RF) classifier in the selected features to finely classify crops of our experimental site. The proposed method is compared with those from the modified adaptive band selection (MABS) and density peaks clustering feature selection based on image entropy (IE-DPC). The comparisons illustrate that the proposed method can improve classification accuracy effectively and decrease the number of features. Meanwhile, our method can analyze quantitatively how the selected features affect identification of various land-covers.

Discriminant Subspace and Multi-window Fusion RX Algorithm for Hyperspectral Image Anomaly Detection

MA Chun-xiao, HUANG Yuan-cheng, HU Rong-ming, ZHANG Chun-sen

2019, 37(1):  64-72.  doi:10.3969/j.issn.0255-8297.2019.01.007

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Disturbed by heterogeneous background and noise, the direct application of traditional RX anomaly detection algorithm for hyperspectral image often results in high false alarms. In order to solve this problem, an improved RX algorithm based on discriminant subspace combined with multi-window fusion is proposed. Firstly, the discriminant features of dominant clustering samples are extracted. Secondly, the orthogonality subspace projection which is built by dominant feature vectors is used to obtain the maximum separation of the background and the target information, achieving the suppression of the background. Then multiple RX with different local window size are applied for the anomaly target enhanced data. Finally, the multi-window RX results are added together. The performance on testing methods is evaluated by AUC. The AUC statistical values of the NUANCE and HYDICE hyperspectral data anomaly detection experiments show that the multi-window fusion algorithm outperforms the classical global and local RX algorithms in detection performance, and it has a stronger inhibition on the background and noise, the detected abnormal target is more accurate, which proves the effectivity and feasibility of the proposed algorithm.

Spatial-Temporal Evolution and Influencing Factors of Environmental Efficiency of Construction Land in China

XU Hui-min, HU Shou-geng, LI Xi

2019, 37(1):  73-86.  doi:10.3969/j.issn.0255-8297.2019.01.008

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In this paper, data envelopment analysis (DEA) model of super efficiency and Malmquist index are used to measure the environmental efficiency of construction land in China from 2006 to 2016, analyze its spatial and temporal evolution characteristics, and analyze the impact of various factors on the environmental efficiency of inter-provincial construction land quantitatively through Tobit model of panel fixation effect. The following research results are obtained:1) The overall environmental efficiency of construction land in China is low, among which the eastern and northern coastal economic zones perform the highest average efficiency. 2) From the perspective of the change trend of the environmental efficiency of construction land in various regions, Beijing, Shanghai, Jiangsu and Shaanxi have witnessed rapid growth, among which technological progress is the main driving force. 3) The industrial structure and the intensity of scientific and technological research and development have significant positive impacts on the environmental efficiency of construction land, whereas the relationship between the degree of economic development and the environmental efficiency of construction land shows an U-shape. The research results could provide a helpful basis for controlling the intensity and allocation of regional construction land in China in future.

On Spatial Simulation of Secondary and Tertiary Industrial GDP in Northern Slope Tianshan Mountains Urban Agglomeration Based on Night Light Data

AZIGULI Heni, GAO Qian, ALIMUJIANG Kasimu

2019, 37(1):  87-98.  doi:10.3969/j.issn.0255-8297.2019.01.009

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For the demand of small scale resources and environment of research on spatial model of social and economic data,a spatial simulation is conducted on the secondary and tertiary industry gross domestic product (GDP) of the 10 major cities and towns in the city cluster on northern slope of Tianshan Mountains. By using correlation and regression analysis methods, we quantitatively analyze the relations between DMSP/OLS (defense meteorological satellite program/operational line scan system) night light data and the urban agglomeration census data of northern slope of Tianshan Mountain in 2002 and 2012. And we build a 1 km GDP density map of the urban agglomeration of northern slope of Tiashan Mountain. The simulated spatial GDP distribution consistent precisely with the distribution of industry level of the region. First, it is indicated that the night light exponent of the two periods has obvious dependency on secondary and tertiary industries. The correlation coefficients are 0.75 and 0.83 for secondary industry, and 0.86 and 0.87 for tertiary industry. Second, it is seen that high value areas mainly gather in the line of Urumqi-Changji-Shihezi-Kuytun-Karamay. The interior GDP density of each county or city radiates decreasingly from the center to surroundings. The GDP density of suburban and rural areas are significantly lower than that of established districts. Third, the GDP distribution based on DMSP/OLS night light data simulation is reliable. The relative errors between GDP spatial simulations and statistical values of secondary industry in 2002 and 2012 are only 0.58% and 0.01% respectively. The comparison of GDP spatial simulations of the two periods reflects the dynamic change of the economic development of the urban agglomeration of northern slope of Tianshan Mountain, which is of practical value for more accurate prediction of the regional economic trend.

Comparison of Radiation Intensity and Estimation of Electric Power Consumption between DMSP/OLS and VIIRS Nighttime Light Images

QIU Ya-qian, XUE Xiang-yu, HAN Wen-jun, CHEN Xiao-ling, LI Xi

2019, 37(1):  99-111.  doi:10.3969/j.issn.0255-8297.2019.01.010

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As the acquisition time of nighttime light image of defense meteorological satellite program/operational linescan system (DMSP/OLS) and visible infrared imaging radiometer suite (VⅡRS) is different, nighttime lights are different at different times in the same area. In view of the difference, the ratio of two kinds of NTL mean values is taken as the measurement index to create light difference index (LDI). In this paper, we explore the consistency and difference of the NTL at different time in the same area, and analyze the causes of the differences. Meanwhile, the capabilities of DMSP/OLS and VⅡRS are compared for predicting electricity power consumption (EPC) at provincial level in China. The results show that NTL behaves some time-consistency and time-difference. There is a good correlation between two kinds of NTL images, and the higher nighttime light mean value of DMSP/OLS, the smaller the light difference. The difference is related to the regional economic development. Undeveloped areas are more likely to have great differences. In the forecast of electricity power consumption (EPC), VⅡRS is more relevant to EPC and superior in estimating EPC than DMSP/OLS.

An Improved Project Pursuit Risk Assessment Function Model

QIAN Long-xia, ZHANG Ren, WANG Hong-rui, HOU Tai-ping

2019, 37(1):  112-125.  doi:10.3969/j.issn.0255-8297.2019.01.011

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A model of project pursuit risk assessment is developed based on information entropy theory for dealing with the weakness of current project pursuit model. First, range normalization is performed to remove dimensional effect and keep the same trend for indicators. Second, the entropy of the projection value, which cannot describe sequence variations under some situations quantitatively, is applied to express the projection index function, and the optimal projection direction is estimated by the maximum entropy principle for dimension reduction. Finally, an S type risk assessment model is built and it characterized from the aspects of monotonicity, boundness, and continuous and smooth change. Two experiments about flood risk and marine environmental risk have been made. The results show that our model can improve the result of the project pursuit by between 7.0% and 8.8% for the flood risk, and have an evident improvement by between 27.1% and 53.9% for the marine environmental risk.

Multi-population Evolutionary Algorithm Based on Dynamic Area Division

CHEN Hao, XU Chun-lei, LI Ming, ZHANG Cong-xuan

2019, 37(1):  126-136.  doi:10.3969/j.issn.0255-8297.2019.01.012

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Aiming at the problem that solution space cannot be divided accurately in multi-population evolutionary algorithms, a cloud model is used to estimate the optimization problem in the process of evolution. According to the difference between the cloud estimation and the original problem, the solution space can be partitioned dynamically. We build several sub-populations by using clustering algorithm, and adopt heterogeneous evolutionary strategy to sub-populations. The validity of area division is analyzed, and it is proved that the method can reduce the searching space. Experimental results show that the proposed partition strategy can not only reduce the difficulty of the optimization problem, but also improve the effectiveness and feasibility of the algorithm.

Nonlinear PI Cascade Attitude Control with Prescribed Performance for a Quadrotor UAV

CHEN Long-sheng, NING Xiao-ming

2019, 37(1):  137-150.  doi:10.3969/j.issn.0255-8297.2019.01.013

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In this paper, a prescribed performance nonlinear PI cascade (PPN-CPI) attitude tracking control scheme is proposed for a quadrotor unmanned aerial vehicle (QUAV) with unknown external disturbances and unknown nonlinearities based on known initial conditions. By dividing the quadrotor attitude system into two subsystems, i.e., attitude angles and angular velocities, a cascade controller designed with consideration of the system internal causality is adopted to tackle underactuated constraints effectively. Next, a simple nonlinear function is established by using the error transformation theory and Taylor polynomials, and two nonlinear PI controllers are developed to handle the unknown nonlinearities, unknown external disturbances, and the singular value problem for attitude angles and angular velocities subsystems with the feasibility proved by theoretical analysis, respectively. The proposed controller inherits the advantages of traditional PID with better adaptability and robustness, and flexibility in parameter adjustment as well. Simulation studies results demonstrate the effectiveness and superiority of the proposed attitude tracking control scheme.