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2014 Vol. 36, No. 6
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2014, 36(6): 1271-1277.
doi: 10.3724/SP.J.1146.2013.01246
Abstract:
In order to construct a high complexity, secure and low cost image encryption algorithm, a class of chaos with Markov properties is studied and used to build the encryption algorithm. First, the key stream generator is designed by the Markov Chaos with changeable parameters and the improved spatiotemporal chaos. Then, a true uniform random number generator is used to disturb the original key of the algorithm, which can dynamically change the mixed matrix and the key stream. Finally, the diffusion function is built by two iterations of the round function which is composed of different kinds of additions in different groups to increase the complexity of decipher. The experiments indicate that the key stream possesses good statistical properties, and the characteristic of the original image is broken which makes the cipher image undistinguishable. The further analysis indicates that the proposed algorithm can resist some known attacks like differential attacks, and the proposed algorithm is more efficient than the existed algorithms based on super chaos. Additionally, the proposed algorithm is easy to realize and can satisfy the security and efficiency requirements, which indicates promising applications.
In order to construct a high complexity, secure and low cost image encryption algorithm, a class of chaos with Markov properties is studied and used to build the encryption algorithm. First, the key stream generator is designed by the Markov Chaos with changeable parameters and the improved spatiotemporal chaos. Then, a true uniform random number generator is used to disturb the original key of the algorithm, which can dynamically change the mixed matrix and the key stream. Finally, the diffusion function is built by two iterations of the round function which is composed of different kinds of additions in different groups to increase the complexity of decipher. The experiments indicate that the key stream possesses good statistical properties, and the characteristic of the original image is broken which makes the cipher image undistinguishable. The further analysis indicates that the proposed algorithm can resist some known attacks like differential attacks, and the proposed algorithm is more efficient than the existed algorithms based on super chaos. Additionally, the proposed algorithm is easy to realize and can satisfy the security and efficiency requirements, which indicates promising applications.
2014, 36(6): 1278-1284.
doi: 10.3724/SP.J.1146.2013.01528
Abstract:
To ensure the quality of watermarked image and improve the embedding capacity of watermarkings, an adaptive image reversible watermarking method based on interger transform is proposed in this paper, which defines a new generalized integer transform algorithm. Through the use of the method the image blocks of arbitrary sized are transformed, producing certain redundancy data that can be used for watermarking embedding. In addition, the parameter m used for integer transform is adaptively selected according to the variance of every image block, hence allowing for embedding more data bits into the smooth blocks while avoiding large distortion generated by complex ones, and thus the algorithm ensures a higher embedding capacity and better quality of watermarked image. Compared with similar algorithms, the experimental results show that the proposed method has larger maximal embedding capacity and taking Lena as a host image, the real payload can reach up to 2.36 bpp. The proposed integer transform algorithm is simple; through adaptively interger transforming and data embedding, the quality of watermarked image can be assured and the method offers a large real payload.
To ensure the quality of watermarked image and improve the embedding capacity of watermarkings, an adaptive image reversible watermarking method based on interger transform is proposed in this paper, which defines a new generalized integer transform algorithm. Through the use of the method the image blocks of arbitrary sized are transformed, producing certain redundancy data that can be used for watermarking embedding. In addition, the parameter m used for integer transform is adaptively selected according to the variance of every image block, hence allowing for embedding more data bits into the smooth blocks while avoiding large distortion generated by complex ones, and thus the algorithm ensures a higher embedding capacity and better quality of watermarked image. Compared with similar algorithms, the experimental results show that the proposed method has larger maximal embedding capacity and taking Lena as a host image, the real payload can reach up to 2.36 bpp. The proposed integer transform algorithm is simple; through adaptively interger transforming and data embedding, the quality of watermarked image can be assured and the method offers a large real payload.
2014, 36(6): 1285-1291.
doi: 10.3724/SP.J.1146.2013.01082
Abstract:
In order to extract accurately the image details, and improve the effect of image enhancement, an adaptive image enhancement algorithm with variable weighted matching based on morphological is proposed. With this method, extension omni-directional multi-scale structure element is constructed, which is used to decompose image of different scale details in different direction through top-hat translation. The proposed algorithm brokes the idea of that the detail weighted in each direction is taken average in traditional morphology method, and adjusts the weight of the different detail direction based on the dynamic characteristic analysis of the local gray level. In the image enhancement process, according to the structured feature of extracted details, the corresponding adaptive gain function is constructed to realize the image adaptive enhancement. The experimental results show that, the algorithm can highlight more effective image details than the traditional morphological method of image enhancement by using the autocorrelation of image, and can suppress the noise in some extent.
In order to extract accurately the image details, and improve the effect of image enhancement, an adaptive image enhancement algorithm with variable weighted matching based on morphological is proposed. With this method, extension omni-directional multi-scale structure element is constructed, which is used to decompose image of different scale details in different direction through top-hat translation. The proposed algorithm brokes the idea of that the detail weighted in each direction is taken average in traditional morphology method, and adjusts the weight of the different detail direction based on the dynamic characteristic analysis of the local gray level. In the image enhancement process, according to the structured feature of extracted details, the corresponding adaptive gain function is constructed to realize the image adaptive enhancement. The experimental results show that, the algorithm can highlight more effective image details than the traditional morphological method of image enhancement by using the autocorrelation of image, and can suppress the noise in some extent.
2014, 36(6): 1292-1298.
doi: 10.3724/SP.J.1146.2013.01220
Abstract:
In order to achieve smooth and continuous panorama stitching effect, an image fusion algorithm which based on contrast pyramid and combines color space conversion and Contourlet transform is proposed by considering the characteristics of panorama stitching. Firstly, luminance information of images is calculated using HSI transform. Then contrasting pyramid based Contourlet transform is used to decompose luminance information to get sub-band information of images. Finally, images are reconstructed by fusing different sub-bands information. Experimental results show that the proposed algorithm which leverages the contour features of Contourlet transform and the detail information of images could achieve good effects on panorama stitching fusion.
In order to achieve smooth and continuous panorama stitching effect, an image fusion algorithm which based on contrast pyramid and combines color space conversion and Contourlet transform is proposed by considering the characteristics of panorama stitching. Firstly, luminance information of images is calculated using HSI transform. Then contrasting pyramid based Contourlet transform is used to decompose luminance information to get sub-band information of images. Finally, images are reconstructed by fusing different sub-bands information. Experimental results show that the proposed algorithm which leverages the contour features of Contourlet transform and the detail information of images could achieve good effects on panorama stitching fusion.
2014, 36(6): 1299-1306.
doi: 10.3724/SP.J.1146.2013.00942
Abstract:
The main idea of Matching Pursuit (MP) is to get a local optimal solution by iteration, so as to gradually approach the original signal. To cope with the intersection of different atom sets, which may affect the classification performance of conventional MP methods, a new matching pursuit algorithm is proposed, which is suitable for supervised classification. The criterion for atoms selection consists of two parts. On one hand, by using the same atom set within the class, the intra-class structure of the similar signals is obtained for class-representation; on the other hand, by selecting the atom sets independently for every class, the discrimination ability for different classes could be further strengthened. The analysis on a toy example indicates that this scheme reduces the common factors between different classes and highlights the discrimination between signals, which may boost the performance of signal classification. Finally, the experiments on benchmark image databases and the measured radar emitter signals verify that the proposed algorithm achieves better robustness against noise and occlusion, compared with the convention MP-related methods.
The main idea of Matching Pursuit (MP) is to get a local optimal solution by iteration, so as to gradually approach the original signal. To cope with the intersection of different atom sets, which may affect the classification performance of conventional MP methods, a new matching pursuit algorithm is proposed, which is suitable for supervised classification. The criterion for atoms selection consists of two parts. On one hand, by using the same atom set within the class, the intra-class structure of the similar signals is obtained for class-representation; on the other hand, by selecting the atom sets independently for every class, the discrimination ability for different classes could be further strengthened. The analysis on a toy example indicates that this scheme reduces the common factors between different classes and highlights the discrimination between signals, which may boost the performance of signal classification. Finally, the experiments on benchmark image databases and the measured radar emitter signals verify that the proposed algorithm achieves better robustness against noise and occlusion, compared with the convention MP-related methods.
2014, 36(6): 1307-1311.
doi: 10.3724/SP.J.1146.2013.01099
Abstract:
To solve the model-fitting problem with different confidence levels of samples, a Neural-Network (NN)- based twice learning method is proposed. It is pointed out that the real model is a variation of experimental model. The neural network approximation to the mathematical expectation of real model, is believed to be the best network fusing the information of prior samples and real samples. In the first learning, neural network is trained using the prior samples only, and the error capacity intervals of the soft points, which are determined by the information of hard points, are calculated. Then, both prior samples and real samples are included in the training samples. The import-objective errors in the process of NN training are modified, using soft point error capacity intervals and hard point error-sensitivity coefficients. The expected network is generated by the second learning, with accurate fitting to the real samples and efficacious utilization of the prior samples. In contrast with Knowledge-Based Neural Networks (KBNN), this method is simpler and more amenable to manipulation with definite logical significance.
To solve the model-fitting problem with different confidence levels of samples, a Neural-Network (NN)- based twice learning method is proposed. It is pointed out that the real model is a variation of experimental model. The neural network approximation to the mathematical expectation of real model, is believed to be the best network fusing the information of prior samples and real samples. In the first learning, neural network is trained using the prior samples only, and the error capacity intervals of the soft points, which are determined by the information of hard points, are calculated. Then, both prior samples and real samples are included in the training samples. The import-objective errors in the process of NN training are modified, using soft point error capacity intervals and hard point error-sensitivity coefficients. The expected network is generated by the second learning, with accurate fitting to the real samples and efficacious utilization of the prior samples. In contrast with Knowledge-Based Neural Networks (KBNN), this method is simpler and more amenable to manipulation with definite logical significance.
2014, 36(6): 1312-1320.
doi: 10.3724/SP.J.1146.2013.00860
Abstract:
Latent tree-structured graphical models explore the latent relationships among variables by introducing hidden nodes, therefore they can better model the correlations among variables. In the learning process of tree-structured graphical models, the quantity of useful features extracted from observation data of variables reflects the models capability to model the deep relationships among variables. However, the excised algorithms learn the hidden tree only by the statics which are directly computed from observation data and ignore the different features among data. For the insufficiency of these algorithms in exploring the information, a new algorithm is proposed for learning the latent tree-structured graphical model based on fuzzy multi-features recursive-grouping. First, original observation data is transformed to multi-features by fuzzy membership functions and construct multi-dimensional fuzzy feature vectors. Then, the distance between each fuzzy feature vectors is computed and synthesized to get the fuzzy multi-features distance matrix of all variables. Finally, based on the distance matrix, the latent tree graphical model is constructed by the recursive-grouping algorithm. The proposed algorithm is applied to stock return data modeling and temperature data modeling, which demonstrate the effectiveness of the algorithm.
Latent tree-structured graphical models explore the latent relationships among variables by introducing hidden nodes, therefore they can better model the correlations among variables. In the learning process of tree-structured graphical models, the quantity of useful features extracted from observation data of variables reflects the models capability to model the deep relationships among variables. However, the excised algorithms learn the hidden tree only by the statics which are directly computed from observation data and ignore the different features among data. For the insufficiency of these algorithms in exploring the information, a new algorithm is proposed for learning the latent tree-structured graphical model based on fuzzy multi-features recursive-grouping. First, original observation data is transformed to multi-features by fuzzy membership functions and construct multi-dimensional fuzzy feature vectors. Then, the distance between each fuzzy feature vectors is computed and synthesized to get the fuzzy multi-features distance matrix of all variables. Finally, based on the distance matrix, the latent tree graphical model is constructed by the recursive-grouping algorithm. The proposed algorithm is applied to stock return data modeling and temperature data modeling, which demonstrate the effectiveness of the algorithm.
2014, 36(6): 1321-1326.
doi: 10.3724/SP.J.1146.2013.01244
Abstract:
There are few studies on the vehicle recognition methods based on grille regional characteristics both at home and abroad, and its classification efficiency and accuracy is low. Based on the characteristics parameters of structure, shape and texture, the vehicle grille recognition method of the improved C-Support Vector Classification (C-SVC) based on the optimal parameters searching algorithm is proposed in this paper, where the efficiency and the precision are controlled by the dual-angle constraint: on the one hand, based on the Mahalanobis distance and a-principle, and combining with the weighted judgment, the sample data is sorted and used to accelerate the training and testing speed of the Support Vector Machine (SVM) and to improve the algorithm generalization efficiency; on the other hand, in the process of setting kernel function parameter, the optimal parameter iterative searching algorithm based on priori knowledge is designed to improve the classification accuracy of the classifier. The experiment shows that the accuracy rate of vehicle grille recognition method is 97.53%, representing the advantages of higher accuracy and lower false detection rate. It is also proved that this method is able to optimize the classification efficiency and to meet the real-time requirements of recognition.
There are few studies on the vehicle recognition methods based on grille regional characteristics both at home and abroad, and its classification efficiency and accuracy is low. Based on the characteristics parameters of structure, shape and texture, the vehicle grille recognition method of the improved C-Support Vector Classification (C-SVC) based on the optimal parameters searching algorithm is proposed in this paper, where the efficiency and the precision are controlled by the dual-angle constraint: on the one hand, based on the Mahalanobis distance and a-principle, and combining with the weighted judgment, the sample data is sorted and used to accelerate the training and testing speed of the Support Vector Machine (SVM) and to improve the algorithm generalization efficiency; on the other hand, in the process of setting kernel function parameter, the optimal parameter iterative searching algorithm based on priori knowledge is designed to improve the classification accuracy of the classifier. The experiment shows that the accuracy rate of vehicle grille recognition method is 97.53%, representing the advantages of higher accuracy and lower false detection rate. It is also proved that this method is able to optimize the classification efficiency and to meet the real-time requirements of recognition.
2014, 36(6): 1327-1333.
doi: 10.3724/SP.J.1146.2013.01218
Abstract:
A new method called weighted Local Binary Pattern (LBP) with adaptive threshold is proposed in this paper to address the shortcomings of LBP and Center Symmetric Local Binary Pattern (CS-LBP), using unflexible threshold and non- discriminating respective sub-patches based on different textures. Firstly, the image is divided into several sub-images and LBP or CS-LBP texture histograms are extracted respectively from each sub-image based on the adaptive threshold. Then, the proposed algorithm adaptively weighted the LBP or CS-LBP histograms of sub-patches with information entropy as their basis and connected all histograms serially to create a final texture descriptor. Finally, the improved efficiency of the proposed algorithm is achieved by speeding up the computation of the average of an image. The experimental results by face databases show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification.
A new method called weighted Local Binary Pattern (LBP) with adaptive threshold is proposed in this paper to address the shortcomings of LBP and Center Symmetric Local Binary Pattern (CS-LBP), using unflexible threshold and non- discriminating respective sub-patches based on different textures. Firstly, the image is divided into several sub-images and LBP or CS-LBP texture histograms are extracted respectively from each sub-image based on the adaptive threshold. Then, the proposed algorithm adaptively weighted the LBP or CS-LBP histograms of sub-patches with information entropy as their basis and connected all histograms serially to create a final texture descriptor. Finally, the improved efficiency of the proposed algorithm is achieved by speeding up the computation of the average of an image. The experimental results by face databases show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification.
2014, 36(6): 1334-1339.
doi: 10.3724/SP.J.1146.2013.01242
Abstract:
Most of spatial spectrum estimation methods fail when there is only one valid snapshot. To deal with this issue, a pseudo covariance matrix construction model utilizing array receiving signal is proposed. Theoretical analysis shows that the presented model, which consists in the existing methods, is more flexible and general. Combining with beam forming idea, a method based on weighted summarizing is proposed, which considers both the affections of signal-to-noise ratio and array freedom. Estimation performance can be enhanced in some degree. Theoretical analysis and simulation results verify the correctness and effectiveness of the proposed method and model.
Most of spatial spectrum estimation methods fail when there is only one valid snapshot. To deal with this issue, a pseudo covariance matrix construction model utilizing array receiving signal is proposed. Theoretical analysis shows that the presented model, which consists in the existing methods, is more flexible and general. Combining with beam forming idea, a method based on weighted summarizing is proposed, which considers both the affections of signal-to-noise ratio and array freedom. Estimation performance can be enhanced in some degree. Theoretical analysis and simulation results verify the correctness and effectiveness of the proposed method and model.
2014, 36(6): 1340-1347.
doi: 10.3724/SP.J.1146.2013.00798
Abstract:
De-noising is an important application field of the wavelet analysis. It has advantage over the traditional filtering methods for its well localized time and frequency property. A central issue in the signal de-nosing research is how to obtain a good balance between shrinking noise and preserving the signal singularity features. This paper presents a wavelet de-noising method based on an adaptive threshold function. By tuning the parameter of the threshold function, the noise wavelet coefficients are shrunk while the signal details are preserved as much as possible on the small scales of the wavelet transform, and on the other hand, the noise coefficients are removed to their maximum extent on a large scale. The simulation results of the blocks, bumps and signals corresponding to the sonar returns from underwater targets, demonstrate that the signal singularity features by adopting the proposed method are better preserved with significant advantage than the traditional threshold filtering method.
De-noising is an important application field of the wavelet analysis. It has advantage over the traditional filtering methods for its well localized time and frequency property. A central issue in the signal de-nosing research is how to obtain a good balance between shrinking noise and preserving the signal singularity features. This paper presents a wavelet de-noising method based on an adaptive threshold function. By tuning the parameter of the threshold function, the noise wavelet coefficients are shrunk while the signal details are preserved as much as possible on the small scales of the wavelet transform, and on the other hand, the noise coefficients are removed to their maximum extent on a large scale. The simulation results of the blocks, bumps and signals corresponding to the sonar returns from underwater targets, demonstrate that the signal singularity features by adopting the proposed method are better preserved with significant advantage than the traditional threshold filtering method.
2014, 36(6): 1348-1354.
doi: 10.3724/SP.J.1146.2013.01038
Abstract:
The update vector of Least Mean Square (LMS) algorithm is an estimation of the gradient vector, thus its convergence rate is limited by the method of steepest descent. Based on the discussion of basic LMS, a direction optimization method of LMS algorithm is proposed in order to get rid of this speed constraint. In the proposed method, the closest update vector to the Newton direction is chosen based on the analysis of the error signal. Based on the method, a Direction Optimization LMS (DOLMS) algorithm is proposed, and it is extended to the variable step-size DOLMS algorithm. The theoretical analysis and the simulation results show that the proposed method has higher speed of convergence and less computational complexity than traditional block LMS algorithm.
The update vector of Least Mean Square (LMS) algorithm is an estimation of the gradient vector, thus its convergence rate is limited by the method of steepest descent. Based on the discussion of basic LMS, a direction optimization method of LMS algorithm is proposed in order to get rid of this speed constraint. In the proposed method, the closest update vector to the Newton direction is chosen based on the analysis of the error signal. Based on the method, a Direction Optimization LMS (DOLMS) algorithm is proposed, and it is extended to the variable step-size DOLMS algorithm. The theoretical analysis and the simulation results show that the proposed method has higher speed of convergence and less computational complexity than traditional block LMS algorithm.
2014, 36(6): 1355-1361.
doi: 10.3724/SP.J.1146.2013.00629
Abstract:
The regularization parameter of sparse representation model is determined by the unknown noise and sparsity. Meanwhile, it can directly affect the performances of sparsity reconstruction. However, the optimization algorithm of sparsity representation issue, which is solved with parameter setting by expert reasoning, priori knowledge or experiments, can not set the parameter adaptively. In order to solve the issue, the sparsity Bayesian learning algorithm which can set the parameter adaptively without priori knowledge is proposed. Firstly, the parameters in the model is constructed with the probability. Secondly, on the basis of the framework of Bayesian learning, the issue of parameter setting and sparsity resolving is transformed to the convex optimization issue which is the addition of a series of mixture L1 normal and the weighted L2 normal. Finally, the parameter setting and sparsity resolving are achieved by the iterative optimization. Theoretical analysis and simulations show that the proposed algorithm is competitive and even better compared with other parameter non-adjusted automatically iterative reweighted algorithms when ideal parameter is known, and the reconstruction performance of the proposed algorithm is significantly better than the other algorithms when choosing the non-ideal parameters.
The regularization parameter of sparse representation model is determined by the unknown noise and sparsity. Meanwhile, it can directly affect the performances of sparsity reconstruction. However, the optimization algorithm of sparsity representation issue, which is solved with parameter setting by expert reasoning, priori knowledge or experiments, can not set the parameter adaptively. In order to solve the issue, the sparsity Bayesian learning algorithm which can set the parameter adaptively without priori knowledge is proposed. Firstly, the parameters in the model is constructed with the probability. Secondly, on the basis of the framework of Bayesian learning, the issue of parameter setting and sparsity resolving is transformed to the convex optimization issue which is the addition of a series of mixture L1 normal and the weighted L2 normal. Finally, the parameter setting and sparsity resolving are achieved by the iterative optimization. Theoretical analysis and simulations show that the proposed algorithm is competitive and even better compared with other parameter non-adjusted automatically iterative reweighted algorithms when ideal parameter is known, and the reconstruction performance of the proposed algorithm is significantly better than the other algorithms when choosing the non-ideal parameters.
2014, 36(6): 1362-1367.
doi: 10.3724/SP.J.1146.2013.01164
Abstract:
A weighted estimation method based on the Time Delay Difference (TDD) variance is proposed with regard to the problem of TDD estimation of unknown source. This method utilizes the TDD of the target radiation signal frequency unit and the TDD of the noise frequency unit with respective characteristics of being stable and random to weight TDD estimation results of each frequency unit, enhancing the TDD estimation results of signal frequency unit, and achieving the TDD estimation of unknown source. The simulation results show that, compared with the conventional cross-correlation method, the estimation performance of this method is improved 3 dB. The theoretical analysis and simulation results both show that the robustness of this method is better than the conventional cross-correlation method.
A weighted estimation method based on the Time Delay Difference (TDD) variance is proposed with regard to the problem of TDD estimation of unknown source. This method utilizes the TDD of the target radiation signal frequency unit and the TDD of the noise frequency unit with respective characteristics of being stable and random to weight TDD estimation results of each frequency unit, enhancing the TDD estimation results of signal frequency unit, and achieving the TDD estimation of unknown source. The simulation results show that, compared with the conventional cross-correlation method, the estimation performance of this method is improved 3 dB. The theoretical analysis and simulation results both show that the robustness of this method is better than the conventional cross-correlation method.
2014, 36(6): 1368-1373.
doi: 10.3724/SP.J.1146.2013.01198
Abstract:
This paper discusses the design of unimodular waveforms with low correlation sidelobes that is useful for MIMO radar. These waveforms can suppress range sidelobes masking and mutual interferences among different echo signals. First, according to the relationship between the aperiodic correlation sequences and the waveforms Power Spectral Density (PSD), the correlation porperty optimization is transformed into the PSD optimization. Then, based on the PSD approximation, the designed waveforms PSDs are approximated to ideal ones. Finally, under the algorithm framework of alternating projection, Fast Fourier Transform (FFT) are used to optimize the waveforms. The numerical simulations demonstrate that the proposed method can design waveforms with good correlations for MIMO radar and it is computationally efficient.
This paper discusses the design of unimodular waveforms with low correlation sidelobes that is useful for MIMO radar. These waveforms can suppress range sidelobes masking and mutual interferences among different echo signals. First, according to the relationship between the aperiodic correlation sequences and the waveforms Power Spectral Density (PSD), the correlation porperty optimization is transformed into the PSD optimization. Then, based on the PSD approximation, the designed waveforms PSDs are approximated to ideal ones. Finally, under the algorithm framework of alternating projection, Fast Fourier Transform (FFT) are used to optimize the waveforms. The numerical simulations demonstrate that the proposed method can design waveforms with good correlations for MIMO radar and it is computationally efficient.
2014, 36(6): 1374-1380.
doi: 10.3724/SP.J.1146.2013.01264
Abstract:
Sampling may cause some loss for coherent integration in time domain and the computation burdens of the common coherent integration algorithms are usually heavy. To resolve these issues, a fast algorithm realizing long-term coherent integration in fast-time frequency domain is proposed. The algorithm firstly utilizes non-uniform FFT to accomplish range walk correction and phase compensation in fast-time frequency domain, and then fulfills the integration via IFFT. The proposed algorithm can avoid loss entailed by sampling, and needs relatively less computation. The theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithm.
Sampling may cause some loss for coherent integration in time domain and the computation burdens of the common coherent integration algorithms are usually heavy. To resolve these issues, a fast algorithm realizing long-term coherent integration in fast-time frequency domain is proposed. The algorithm firstly utilizes non-uniform FFT to accomplish range walk correction and phase compensation in fast-time frequency domain, and then fulfills the integration via IFFT. The proposed algorithm can avoid loss entailed by sampling, and needs relatively less computation. The theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithm.
2014, 36(6): 1381-1388.
doi: 10.3724/SP.J.1146.2013.01147
Abstract:
Owing to the differences among scatterer distributions observed by wideband radars with different viewing angles, it is necessary to research the network radars imaging algorithm for asymmetrical spinning targets. By making use of the range profile series of spinning target obtained by two wideband radars at different locations, the scatterer association is accomplished based on the micro-motion feature invariability of asymmetrical spinning target. Then, the three-dimensional image, which can provide the real size of the target, is obtained. The simulation demonstrates the high precision of the proposed algorithm, and insensitivity to sheltering effects and RCS fluctuation of scatterers.
Owing to the differences among scatterer distributions observed by wideband radars with different viewing angles, it is necessary to research the network radars imaging algorithm for asymmetrical spinning targets. By making use of the range profile series of spinning target obtained by two wideband radars at different locations, the scatterer association is accomplished based on the micro-motion feature invariability of asymmetrical spinning target. Then, the three-dimensional image, which can provide the real size of the target, is obtained. The simulation demonstrates the high precision of the proposed algorithm, and insensitivity to sheltering effects and RCS fluctuation of scatterers.
2014, 36(6): 1389-1393.
doi: 10.3724/SP.J.1146.2013.01716
Abstract:
Estimating procession period based on ballistic missile targets Radar Cross Section (RCS) sequences is an important means of feature extraction and target identification. The RCS sequences of ballistic missile target are unstable random process when the target is in procession, and the conventional Fourier transform and correlation type method needs long observation time and high data rate to estimate the procession period, which are unacceptable to the limited radar resource. A novel method of estimating procession period based on RCS sequences is presented. The proposed method first fits the RCS sequences with trigonometric function of a certain frequency, then get a procession frequency that minimize the fitting errors of different frequencies trigonometric function. The proposed method estimate more accurately and needs fewer time resources than conventional ones, as is validated by the simulation results of RCS data.
Estimating procession period based on ballistic missile targets Radar Cross Section (RCS) sequences is an important means of feature extraction and target identification. The RCS sequences of ballistic missile target are unstable random process when the target is in procession, and the conventional Fourier transform and correlation type method needs long observation time and high data rate to estimate the procession period, which are unacceptable to the limited radar resource. A novel method of estimating procession period based on RCS sequences is presented. The proposed method first fits the RCS sequences with trigonometric function of a certain frequency, then get a procession frequency that minimize the fitting errors of different frequencies trigonometric function. The proposed method estimate more accurately and needs fewer time resources than conventional ones, as is validated by the simulation results of RCS data.
2014, 36(6): 1394-1399.
doi: 10.3724/SP.J.1146.2013.00702
Abstract:
Netted radar systems show great potential in improving the performance of radar detection, tracking and interference suppression. However, the systems suffer high auto-correlation and cross-correlations of transmitted waveforms. Meanwhile, they also have to face the congested spectrum environment, especially when some radars in the net working on High Frequency (HF) to Ultra High Frequency (UHF) band. To solve this issue, a new method for designing sparse frequency unimodular waveform with low range side lobes is proposed, which minimizes a new effective penalty function based on both requirements for the Power Spectrum Density (PSD) and Integrated Sidelobe Level (ISL). An iterative algorithm based on FFT and subspace decomposition is proposed. The numerical examples show that the proposed approach is efficient in computation and flexible in designing sparse frequency waveform with low auto-correlation and cross-correlations.
Netted radar systems show great potential in improving the performance of radar detection, tracking and interference suppression. However, the systems suffer high auto-correlation and cross-correlations of transmitted waveforms. Meanwhile, they also have to face the congested spectrum environment, especially when some radars in the net working on High Frequency (HF) to Ultra High Frequency (UHF) band. To solve this issue, a new method for designing sparse frequency unimodular waveform with low range side lobes is proposed, which minimizes a new effective penalty function based on both requirements for the Power Spectrum Density (PSD) and Integrated Sidelobe Level (ISL). An iterative algorithm based on FFT and subspace decomposition is proposed. The numerical examples show that the proposed approach is efficient in computation and flexible in designing sparse frequency waveform with low auto-correlation and cross-correlations.
2014, 36(6): 1400-1405.
doi: 10.3724/SP.J.1146.2013.01180
Abstract:
Based on the idea of the multiple beam method, the least squares?multi beam?method is developed to resolve the problem that there is no intersections between beams by the traditional means of implementation . The method is used to process the data obtained from the OS081H high frequency surface radar system and the results are compared with the data obtained from automatic meteorological station. It is showing that this method can eliminate the wind direction ambiguity. In addition, the results are compared with the data obtained by the maximum likelihood method and the effect of wind speed on the accuracy of inversion is discussed. The results show that the correlation and accuracy improves as wind speed increases.
Based on the idea of the multiple beam method, the least squares?multi beam?method is developed to resolve the problem that there is no intersections between beams by the traditional means of implementation . The method is used to process the data obtained from the OS081H high frequency surface radar system and the results are compared with the data obtained from automatic meteorological station. It is showing that this method can eliminate the wind direction ambiguity. In addition, the results are compared with the data obtained by the maximum likelihood method and the effect of wind speed on the accuracy of inversion is discussed. The results show that the correlation and accuracy improves as wind speed increases.
2014, 36(6): 1406-1412.
doi: 10.3724/SP.J.1146.2013.01132
Abstract:
In the hyperspectral compressive sensing reconstruction method, the exploitation of the prior information of the hyperspectral imagery can improve the reconstruction performance. As the existing methods have not taken into account the spectral structural redundancy information of hyperspectral imagery, a novel reconstruction method via spectrum structure similarity for hyperspectral compressive sensing is proposed in this paper. Structure images are proposed via spectrum structure similarity and a new regularizer is given based on structure images. It combines the new regularizer and other regularizers，so that the spatial redundancy, spectral statistical redundancy and spectral structural redundancy in hyperspectral imagery can all be exploited. In addition, an efficient solving algorithm based on variable-splitting is developed for the method. Experimental results show that the proposed method is able to reconstruct the hyperspectral imagery more efficiently than the current methods at the same measurement rates.
In the hyperspectral compressive sensing reconstruction method, the exploitation of the prior information of the hyperspectral imagery can improve the reconstruction performance. As the existing methods have not taken into account the spectral structural redundancy information of hyperspectral imagery, a novel reconstruction method via spectrum structure similarity for hyperspectral compressive sensing is proposed in this paper. Structure images are proposed via spectrum structure similarity and a new regularizer is given based on structure images. It combines the new regularizer and other regularizers，so that the spatial redundancy, spectral statistical redundancy and spectral structural redundancy in hyperspectral imagery can all be exploited. In addition, an efficient solving algorithm based on variable-splitting is developed for the method. Experimental results show that the proposed method is able to reconstruct the hyperspectral imagery more efficiently than the current methods at the same measurement rates.
2014, 36(6): 1413-1418.
doi: 10.3724/SP.J.1146.2013.01270
Abstract:
Thinned rotating arrays of microwave/millimeter wave synthetic aperture imaging radiometer result in polar like visibility samplings where complex image reconstruction algorithms are in need and it is tough work to estimate important system parameters. To address this problem, a new method based on filtered back projection algorithm is proposed. In this approach, the system Point Spread Function (PSF) is obtained by 1-D Fourier-Bessel (Hankel) transforming from Dirac comb?function. Spatial resolution and alias-free of view affected by radial and angular sampling intervals are analyzed by decomposing PSF into main lobe and a series of ring lobes. A new formulation to estimate the radiometric sensitivity of thinned rotating arrays by filtered back projection algorithm is also proposed. The consistency between the numerical and measured point spread function indicates that the proposed model is accurate.
Thinned rotating arrays of microwave/millimeter wave synthetic aperture imaging radiometer result in polar like visibility samplings where complex image reconstruction algorithms are in need and it is tough work to estimate important system parameters. To address this problem, a new method based on filtered back projection algorithm is proposed. In this approach, the system Point Spread Function (PSF) is obtained by 1-D Fourier-Bessel (Hankel) transforming from Dirac comb?function. Spatial resolution and alias-free of view affected by radial and angular sampling intervals are analyzed by decomposing PSF into main lobe and a series of ring lobes. A new formulation to estimate the radiometric sensitivity of thinned rotating arrays by filtered back projection algorithm is also proposed. The consistency between the numerical and measured point spread function indicates that the proposed model is accurate.
2014, 36(6): 1419-1422.
doi: 10.3724/SP.J.1146.2013.01339
Abstract:
(1+uv)-cyclic codes over F2+uF2+vF2+uvF2 is defined, and the relations between(1+uv)- cyclic codes and cyclic codes is discussed. It is proved that the binary image on isometric Gray maphomof a (1+uv)-cyclic code of length n over R is a linear quasi-cyclic code of index 4 and of length 8n. Furthermore, some optimal binary linear quasi-cyclic codes are obtained.
(1+uv)-cyclic codes over F2+uF2+vF2+uvF2 is defined, and the relations between(1+uv)- cyclic codes and cyclic codes is discussed. It is proved that the binary image on isometric Gray maphomof a (1+uv)-cyclic code of length n over R is a linear quasi-cyclic code of index 4 and of length 8n. Furthermore, some optimal binary linear quasi-cyclic codes are obtained.
2014, 36(6): 1423-1427.
doi: 10.3724/SP.J.1146.2013.01324
Abstract:
In this paper, the multi-discrete logarithm problem is formally defined, and the necessary conditions of resistance to the quantum algorithm for the hidden subgroup problem are given. It is more difficult than the discrete logarithm problem. And the number field sieve for the discrete logarithm problem is not suitable for addressing it. Furthermore, the public-key cryptograph is designed against the problem, of which the key amount is small. This paper analyses the principles of parameter selection and proves the correctness of the decryption works. It is critical that different random integers are received to the encrypt different messages.
In this paper, the multi-discrete logarithm problem is formally defined, and the necessary conditions of resistance to the quantum algorithm for the hidden subgroup problem are given. It is more difficult than the discrete logarithm problem. And the number field sieve for the discrete logarithm problem is not suitable for addressing it. Furthermore, the public-key cryptograph is designed against the problem, of which the key amount is small. This paper analyses the principles of parameter selection and proves the correctness of the decryption works. It is critical that different random integers are received to the encrypt different messages.
2014, 36(6): 1428-1434.
doi: 10.3724/SP.J.1146.2013.01279
Abstract:
In Multiband Joint Detection (MJD) of wideband sensing, the most challenge is to set the optimal decision thresholds due to the non-convex nature of the problem. This paper proposes the Branch Reduce and Bound algorithm with Convex Relaxation (BRBCR) technique to optimize the problem which can be transformed into a Monotonic Optimization Problem (MOP). The performance of the proposed method is analyzed through computer simulations. Experiment results show that this method can significantly improve the system performance as compared with the conventional convex optimization method. The convergence speed of the proposed method is two orders of magnitude faster than the Polyblock Algorithm (PA) or the conventional Branch Reduce and Bound (BRB) algorithm. Even though the number of channels is 16 and the convergence precision is 10-6, this method can converge within 16 s. In addition, the proposed algorithm can also provide an important benchmark for evaluating the performance of other heuristic algorithms targeting with the same problem.
In Multiband Joint Detection (MJD) of wideband sensing, the most challenge is to set the optimal decision thresholds due to the non-convex nature of the problem. This paper proposes the Branch Reduce and Bound algorithm with Convex Relaxation (BRBCR) technique to optimize the problem which can be transformed into a Monotonic Optimization Problem (MOP). The performance of the proposed method is analyzed through computer simulations. Experiment results show that this method can significantly improve the system performance as compared with the conventional convex optimization method. The convergence speed of the proposed method is two orders of magnitude faster than the Polyblock Algorithm (PA) or the conventional Branch Reduce and Bound (BRB) algorithm. Even though the number of channels is 16 and the convergence precision is 10-6, this method can converge within 16 s. In addition, the proposed algorithm can also provide an important benchmark for evaluating the performance of other heuristic algorithms targeting with the same problem.
2014, 36(6): 1435-1440.
doi: 10.3724/SP.J.1146.2013.01187
Abstract:
In the context of the RF domain self-interference cancellation algorithms in the co-frequency and co-time full duplex system, the current research focuses mainly on the manually adjusting of self-interference parameters. To solve this problem, a RF domain adaptive self-interference cancellation is proposed. On the basis of the self-interference estimation construction within an in-phase and quadrature reference signal channels, the self-interference is reconstructed by searching the optimal weight vector with the method of gradient descending and cancelled at last. In addition, the convergence of the proposed algorithm is analyzed. The analysis and simulation show that the convergent speed is faster when the iterative step size is larger and the statistical time is shorter. The self-interference can decrease almost 100 dB adopting the RF domain adaptive self-interference cancellation algorithm proposed in this paper, when the statistical time is 100 symbol periods, the normalized iterative step is 0.3, the signal to noise ratio is 0 dB, and the interference to signal ratio is 80 dB.
In the context of the RF domain self-interference cancellation algorithms in the co-frequency and co-time full duplex system, the current research focuses mainly on the manually adjusting of self-interference parameters. To solve this problem, a RF domain adaptive self-interference cancellation is proposed. On the basis of the self-interference estimation construction within an in-phase and quadrature reference signal channels, the self-interference is reconstructed by searching the optimal weight vector with the method of gradient descending and cancelled at last. In addition, the convergence of the proposed algorithm is analyzed. The analysis and simulation show that the convergent speed is faster when the iterative step size is larger and the statistical time is shorter. The self-interference can decrease almost 100 dB adopting the RF domain adaptive self-interference cancellation algorithm proposed in this paper, when the statistical time is 100 symbol periods, the normalized iterative step is 0.3, the signal to noise ratio is 0 dB, and the interference to signal ratio is 80 dB.
2014, 36(6): 1441-1447.
doi: 10.3724/SP.J.1146.2013.01321
Abstract:
Addressing the problem that the artificial noise method can be cracked by the eavesdropper with multiple antennas in wireless communication systems, a sufficient condition is proposed for secure physical layer transmission with finite alphabet inputs. Under this guideline, a signal-like artificial noise method is designed to ensure the system security transmission. Analysis reveals that the equivalent channel between the finite alphabet input and the eavesdroppers noise-free output is a Discrete Noisy Lossless Channel (DNLC). Since the reversibility of the input under a DNLC provides the necessary condition for eavesdropping, the eavesdropper can augment its antennas to successfully squeeze out the secure information, nullifying the systems secrecy mutual information. As a result, destroying the reversibility of the input signal becomes a sufficient condition for the secure physical layer transmission with finite alphabet inputs. The signal-like artificial noise method satisfies the sufficient condition, which can ensure the secure physical layer transmission. Simulation results demonstrate the efficacy of this method.
Addressing the problem that the artificial noise method can be cracked by the eavesdropper with multiple antennas in wireless communication systems, a sufficient condition is proposed for secure physical layer transmission with finite alphabet inputs. Under this guideline, a signal-like artificial noise method is designed to ensure the system security transmission. Analysis reveals that the equivalent channel between the finite alphabet input and the eavesdroppers noise-free output is a Discrete Noisy Lossless Channel (DNLC). Since the reversibility of the input under a DNLC provides the necessary condition for eavesdropping, the eavesdropper can augment its antennas to successfully squeeze out the secure information, nullifying the systems secrecy mutual information. As a result, destroying the reversibility of the input signal becomes a sufficient condition for the secure physical layer transmission with finite alphabet inputs. The signal-like artificial noise method satisfies the sufficient condition, which can ensure the secure physical layer transmission. Simulation results demonstrate the efficacy of this method.
2014, 36(6): 1448-1453.
doi: 10.3724/SP.J.1146.2013.01327
Abstract:
In the light of the fact that the performance of the existing joint channel estimation and OFDM signals detection methods are poor, a novel joint channel estimation and OFDM signals detection algorithm using total least square is therefore proposed. Firstly, the initial channel information is obtained by performing the pilots. Then the total least square is employed in the OFDM signals detection and channel estimation, and as such the effect of iterative model error can be effectively alleviated. The proposed algorithm is able to accelerate the rate of convergence, improve the accuracy of the channel estimation, and sequentially reduce the bit error rate of the OFDM system. Both the derived Cramer Bound (CRB) of the channel estimation and simulation results show that this algorithm is better than the existing joint channel estimation and OFDM signals detection methods as well.
In the light of the fact that the performance of the existing joint channel estimation and OFDM signals detection methods are poor, a novel joint channel estimation and OFDM signals detection algorithm using total least square is therefore proposed. Firstly, the initial channel information is obtained by performing the pilots. Then the total least square is employed in the OFDM signals detection and channel estimation, and as such the effect of iterative model error can be effectively alleviated. The proposed algorithm is able to accelerate the rate of convergence, improve the accuracy of the channel estimation, and sequentially reduce the bit error rate of the OFDM system. Both the derived Cramer Bound (CRB) of the channel estimation and simulation results show that this algorithm is better than the existing joint channel estimation and OFDM signals detection methods as well.
2014, 36(6): 1454-1459.
doi: 10.3724/SP.J.1146.2013.01201
Abstract:
A Physical-layer Network Coding (PNC) scheme based on Continuous Phase Frequency Shift Keying (CPFSK) modulation, i.e., CPFSK-PNC, for two-way relay channels is proposed. Compared with the current schemes of BPSK or QPSK for PNC, the CPFSK-PNC scheme, by exploitating the technical advantage of the CPFSK method, has higher power and spectral efficiency. The detection for the relay receiver in the CPFSK-PNC scheme over Rayleigh fading channels is investigated. Firstly, in the light of the memory property of the CPFSK signal, the detection method for PNC at the relay based on the Maximum-Likelihood (ML) criterion is designed. Secondly, the minimum Euclidean distance is analyzed and the tight lower bound for the average bit error rate at the relay is derived. And finally, the simulation results verify the theoretical asymptotic derivations.
A Physical-layer Network Coding (PNC) scheme based on Continuous Phase Frequency Shift Keying (CPFSK) modulation, i.e., CPFSK-PNC, for two-way relay channels is proposed. Compared with the current schemes of BPSK or QPSK for PNC, the CPFSK-PNC scheme, by exploitating the technical advantage of the CPFSK method, has higher power and spectral efficiency. The detection for the relay receiver in the CPFSK-PNC scheme over Rayleigh fading channels is investigated. Firstly, in the light of the memory property of the CPFSK signal, the detection method for PNC at the relay based on the Maximum-Likelihood (ML) criterion is designed. Secondly, the minimum Euclidean distance is analyzed and the tight lower bound for the average bit error rate at the relay is derived. And finally, the simulation results verify the theoretical asymptotic derivations.
2014, 36(6): 1460-1464.
doi: 10.3724/SP.J.1146.2013.01307
Abstract:
MultiUser Interference (MUI) caused by channel quantization error degrades the performance of the limited feedback-based multiuser Multiple-Input Multiple-Output (MIMO) systems. Antenna combining techniques can effectively improve the system performance with the additional dimension of freedom. In this paper, a linear antenna combiner is proposed for the feedback overhead allocation strategy which is proved to be the optimal scheme. First, the closed-form lower bound of each users expected post-combining Signal-to-Interference- plus-Noise Ratio (SINR) is derived. Then, using this bound expression, the proposed combiner is obtained which aims to maximize the expected post-combining SINR. Monte Carlo simulations show that the proposed combiner achieves better performance compared with the existing antenna combining algorithms.
MultiUser Interference (MUI) caused by channel quantization error degrades the performance of the limited feedback-based multiuser Multiple-Input Multiple-Output (MIMO) systems. Antenna combining techniques can effectively improve the system performance with the additional dimension of freedom. In this paper, a linear antenna combiner is proposed for the feedback overhead allocation strategy which is proved to be the optimal scheme. First, the closed-form lower bound of each users expected post-combining Signal-to-Interference- plus-Noise Ratio (SINR) is derived. Then, using this bound expression, the proposed combiner is obtained which aims to maximize the expected post-combining SINR. Monte Carlo simulations show that the proposed combiner achieves better performance compared with the existing antenna combining algorithms.
2014, 36(6): 1465-1471.
doi: 10.3724/SP.J.1146.2013.01027
Abstract:
The main problems of the application of the Turbo equalizer in underwater acoustic communication are long time spread of channel and the multi-array processing. The union algorithm of time reversal and Markov Chain Monte Carlo (MCMC) equalization is proposed. Time reversal compresses the long time spread by combining multi-array signal, then the whitening filter is adopted to the solution of the noise model mismatch, at last the MCMC equalizer under optimal Maximum A Posteriori (MAP) criterion realizes the soft-in soft-out equalizer with the channel information obtained by channel estimation of soft iteration. The simulation based on the real experimental condition is conducted for the error model of truncated channel estimation. Simulation results denote that, this algorithm gets 1.6 dB Bit Error Rate (BER) performance gain, and 67% complexity loss over adaptive Turbo equalization. In the real experiment conducted in a lake, result of data processing denotes that the union algorithm of time reversal and MCMC equalizer have a superior performance over the algorithm of multichannel adaptive Turbo equalizer.
The main problems of the application of the Turbo equalizer in underwater acoustic communication are long time spread of channel and the multi-array processing. The union algorithm of time reversal and Markov Chain Monte Carlo (MCMC) equalization is proposed. Time reversal compresses the long time spread by combining multi-array signal, then the whitening filter is adopted to the solution of the noise model mismatch, at last the MCMC equalizer under optimal Maximum A Posteriori (MAP) criterion realizes the soft-in soft-out equalizer with the channel information obtained by channel estimation of soft iteration. The simulation based on the real experimental condition is conducted for the error model of truncated channel estimation. Simulation results denote that, this algorithm gets 1.6 dB Bit Error Rate (BER) performance gain, and 67% complexity loss over adaptive Turbo equalization. In the real experiment conducted in a lake, result of data processing denotes that the union algorithm of time reversal and MCMC equalizer have a superior performance over the algorithm of multichannel adaptive Turbo equalizer.
2014, 36(6): 1472-1477.
doi: 10.3724/SP.J.1146.2013.01337
Abstract:
For addressing the two focus issues under research , security authentication protocol and the multi-tag anti-collision algorithm in the field of Radio Frequency IDentification (RFID), a Code Division Multiple Access (CDMA)-based anti-collision algorithm of the RFID authentication protocol is presented in this paper. The authentication protocol supports dynamic updates of the key and resists database synchronization attacks by using flag mechanism to select spare key. Meanwhile, by combining with CDMA and by retransmitting random number to select spreading code, the authentication protocol solves recognition of tags due to data collisions during the multi-tag identification by one-time retransmission. Firstly, the process of the authentication protocol and an anti-collision theory is described. Secondly, the SVO logic is used to prove correctness of the protocol in theory. Finally, numerical analysis of the system throughput applying the protocol shows that its throughput efficiency is higher than the traditional one.
For addressing the two focus issues under research , security authentication protocol and the multi-tag anti-collision algorithm in the field of Radio Frequency IDentification (RFID), a Code Division Multiple Access (CDMA)-based anti-collision algorithm of the RFID authentication protocol is presented in this paper. The authentication protocol supports dynamic updates of the key and resists database synchronization attacks by using flag mechanism to select spare key. Meanwhile, by combining with CDMA and by retransmitting random number to select spreading code, the authentication protocol solves recognition of tags due to data collisions during the multi-tag identification by one-time retransmission. Firstly, the process of the authentication protocol and an anti-collision theory is described. Secondly, the SVO logic is used to prove correctness of the protocol in theory. Finally, numerical analysis of the system throughput applying the protocol shows that its throughput efficiency is higher than the traditional one.
2014, 36(6): 1478-1484.
doi: 10.3724/SP.J.1146.2013.01163
Abstract:
Energy efficiency and balance of sensor nodes in processing top-k queries can prolong the lifetime of wireless sensor networks. In this paper, an Energy-efficient and Balanced query Sampling Top-k algorithm named EBSTopk(,) is proposed, which is based on the sampling techniques and the spatial correlations among sensor nodes. First, the sensor network is partitioned into several regions. Next, the linear regression prediction model and Gaussian prediction model are constructed based on the spatial correlations of pairwise sensor nodes. Then, the criteria of high spatial correlation is established due to the given relative error bound and the confidence level 1-. Finally, according to the predicting models and criteria above, two energy balanced algorithms named EBSTopk(,)-LR and EBSTopk(,)-MG are proposed, which are based on iterative random sampling technique. Experimental results show that, the proposed EBSTopk(,) algorithms not only reduce the global energy consumption in wireless sensor networks, but also achieve balanced energy consumption among all sensor nodes after continuous processing top-k queries.
Energy efficiency and balance of sensor nodes in processing top-k queries can prolong the lifetime of wireless sensor networks. In this paper, an Energy-efficient and Balanced query Sampling Top-k algorithm named EBSTopk(,) is proposed, which is based on the sampling techniques and the spatial correlations among sensor nodes. First, the sensor network is partitioned into several regions. Next, the linear regression prediction model and Gaussian prediction model are constructed based on the spatial correlations of pairwise sensor nodes. Then, the criteria of high spatial correlation is established due to the given relative error bound and the confidence level 1-. Finally, according to the predicting models and criteria above, two energy balanced algorithms named EBSTopk(,)-LR and EBSTopk(,)-MG are proposed, which are based on iterative random sampling technique. Experimental results show that, the proposed EBSTopk(,) algorithms not only reduce the global energy consumption in wireless sensor networks, but also achieve balanced energy consumption among all sensor nodes after continuous processing top-k queries.
2014, 36(6): 1485-1491.
doi: 10.3724/SP.J.1146.2013.01225
Abstract:
This paper presents an effective solution for personalized video recommendation based on the weight increment and similar aggregation user behavior analysis algorithm. The method is implemented in three steps: first, the user behavior is analyzed using the RFM (Recentness, Frequency, Monetary amount) model, users with the same behavior are classified as a group; second, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users, and by using the VSM model for similarity calculation, the user similarity aggregation is realized; finally, the whole process of personalized video recommendation is completed by means of collaborative filtering. The proposed method can automatically collects user behavioral data and avoids direct video big data processing. In addition, the video recommend dynamically changes with the change of user behavior. The experiment results show that, the presented effective and stable, and the method achieves significantly increasement in precision and recall comparing with the single recommendation method.
This paper presents an effective solution for personalized video recommendation based on the weight increment and similar aggregation user behavior analysis algorithm. The method is implemented in three steps: first, the user behavior is analyzed using the RFM (Recentness, Frequency, Monetary amount) model, users with the same behavior are classified as a group; second, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users, and by using the VSM model for similarity calculation, the user similarity aggregation is realized; finally, the whole process of personalized video recommendation is completed by means of collaborative filtering. The proposed method can automatically collects user behavioral data and avoids direct video big data processing. In addition, the video recommend dynamically changes with the change of user behavior. The experiment results show that, the presented effective and stable, and the method achieves significantly increasement in precision and recall comparing with the single recommendation method.
2014, 36(6): 1492-1497.
doi: 10.3724/SP.J.1146.2013.01171
Abstract:
Localization of sensor nodes is an important issue in Wireless Sensor Networks (WSNs), and positioning of the mobile nodes is one of the difficulties. To deal with this issue, a strong self-adaptive Localization Algorithm based on Gray Prediction model for mobile nodes (GPLA) is proposed. On the background of Monte Carlo Localization Algoritm, gray prediction model is used in GPLA, which can accurate sampling area is used to predict nodes motion situation. In filtering process, estimated distance is taken to improve the validity of the sample particles. Finally, restrictive linear crossover is used to generate new particles, which can accelerate the sampling process, reduce the times of sampling and heighten the efficiency of GPLA. Simulation results show that the algorithm has excellent performance and strong self-adaptivity in different communication radius, anchor node, sample size, and other conditions.
Localization of sensor nodes is an important issue in Wireless Sensor Networks (WSNs), and positioning of the mobile nodes is one of the difficulties. To deal with this issue, a strong self-adaptive Localization Algorithm based on Gray Prediction model for mobile nodes (GPLA) is proposed. On the background of Monte Carlo Localization Algoritm, gray prediction model is used in GPLA, which can accurate sampling area is used to predict nodes motion situation. In filtering process, estimated distance is taken to improve the validity of the sample particles. Finally, restrictive linear crossover is used to generate new particles, which can accelerate the sampling process, reduce the times of sampling and heighten the efficiency of GPLA. Simulation results show that the algorithm has excellent performance and strong self-adaptivity in different communication radius, anchor node, sample size, and other conditions.
2014, 36(6): 1498-1503.
doi: 10.3724/SP.J.1146.2013.01128
Abstract:
For large scale underwater sensor networks, the characteristics of underwater acoustic channel are investigated. A static beacon node based clock synchronization algorithm is presented for the nodes inside the beacon nodes coverage area, and a dynamic node assisted clock synchronization algorithm is designed for the nodes outside the beacon nodes coverage area. These methods reduce the effect of node mobility. A layering clock synchronization mechanism based on the feature of underwater sound velocity is proposed, which solves the clock synchronization problem of large scale underwater sensor networks. The simulation results show that, the proposed synchronization method is obviously better than existing algorithms.
For large scale underwater sensor networks, the characteristics of underwater acoustic channel are investigated. A static beacon node based clock synchronization algorithm is presented for the nodes inside the beacon nodes coverage area, and a dynamic node assisted clock synchronization algorithm is designed for the nodes outside the beacon nodes coverage area. These methods reduce the effect of node mobility. A layering clock synchronization mechanism based on the feature of underwater sound velocity is proposed, which solves the clock synchronization problem of large scale underwater sensor networks. The simulation results show that, the proposed synchronization method is obviously better than existing algorithms.
2014, 36(6): 1504-1508.
doi: 10.3724/SP.J.1146.2013.01296
Abstract:
With the new electronic devices are increasingly used by airborne avionics equipment, Single Event Upset (SEU) fault has become a major hazard on aviation safety. Because of the randomness of SEU fault, the SEU fault occurs at any moments. Firstly, a multi-clock control is introduced to construct an SEU fault injection testing system. Secondly, the system simulates multi-time point of failure with real situations caused by single event upset effects. For sequential circuits constructed by SRAM-based FPGA, the influence of SEU is studied by the system and the failure data and failure rate of the undertest module is counted online. Two kinds of FPGA-based fault-tolerant circuit are tested by this system. Comparing with the traditional Triple Modular Redundancy (TMR) technology, the anti-SEU performance of the proposed multi-clock edge TMR reinforcement technology is improved about 1.86-fold. The experiment results verify that the proposed multi-clock SEU fault injection testing system is a quick, low-cost and highly accurate test for the single-event upsets fault, and demonstrate the effectiveness of the proposed SEU reinforcement technology.
With the new electronic devices are increasingly used by airborne avionics equipment, Single Event Upset (SEU) fault has become a major hazard on aviation safety. Because of the randomness of SEU fault, the SEU fault occurs at any moments. Firstly, a multi-clock control is introduced to construct an SEU fault injection testing system. Secondly, the system simulates multi-time point of failure with real situations caused by single event upset effects. For sequential circuits constructed by SRAM-based FPGA, the influence of SEU is studied by the system and the failure data and failure rate of the undertest module is counted online. Two kinds of FPGA-based fault-tolerant circuit are tested by this system. Comparing with the traditional Triple Modular Redundancy (TMR) technology, the anti-SEU performance of the proposed multi-clock edge TMR reinforcement technology is improved about 1.86-fold. The experiment results verify that the proposed multi-clock SEU fault injection testing system is a quick, low-cost and highly accurate test for the single-event upsets fault, and demonstrate the effectiveness of the proposed SEU reinforcement technology.
2014, 36(6): 1509-1514.
doi: 10.3724/SP.J.1146.2013.01005
Abstract:
The effect of Digital BaseBand Converter (DBBC) sub-channel delay on bandwidth synthesis precision is analyzed under the condition of the consistent receiving system channel delay in order to improve the accuracy of bandwidth synthesis. Through a theoretical derivation, it is for the first time to discover that the sub-channel delay could induce phase step phenomenon among the different sub-channels in the group delay measurement of a single station. And in the delay difference measurement of different stations, the phase step phenomenon could occur when the frequency differences of different stations corresponding sub-channel Local Oscillator (LO) are different. The phase step could deteriorate the precision of bandwidth synthesis. The impact domain of the sub-channel delay is discussed for different DBBCs, suggesting that the phase step can be eliminated by a sub-channel delay compensation. The simulation results show that the sub-channel delay compensation can effectively remove the phase step and the precision of bandwidth synthesis can be improved by at least one order of magnitude or more.
The effect of Digital BaseBand Converter (DBBC) sub-channel delay on bandwidth synthesis precision is analyzed under the condition of the consistent receiving system channel delay in order to improve the accuracy of bandwidth synthesis. Through a theoretical derivation, it is for the first time to discover that the sub-channel delay could induce phase step phenomenon among the different sub-channels in the group delay measurement of a single station. And in the delay difference measurement of different stations, the phase step phenomenon could occur when the frequency differences of different stations corresponding sub-channel Local Oscillator (LO) are different. The phase step could deteriorate the precision of bandwidth synthesis. The impact domain of the sub-channel delay is discussed for different DBBCs, suggesting that the phase step can be eliminated by a sub-channel delay compensation. The simulation results show that the sub-channel delay compensation can effectively remove the phase step and the precision of bandwidth synthesis can be improved by at least one order of magnitude or more.
2014, 36(6): 1515-1519.
doi: 10.3724/SP.J.1146.2013.01214
Abstract:
A novel planar beam steering parasitic array of planar printed dipoles is proposed, which can be used in the mobile terminals to improve communication quality and system capacity. The main beam can be controlled by the reactive loads on the parasitic elements. The antenna prototype is designed using the software based on the full wave analysis and the loaded reactance values are optimized by the Differential Evolutionary (DE) algorithm. The measured results show that the main beam can be steered from -34~38 in the xoy plane. At every main radiation direction, the gain at the center frequency is from 3.6~4.9 dBi and the bandwidth of the reflection coefficient less than -10 dB is 330 MHz, which is from 5.63~5.96 GHz. The proposed antenna has good beam steering performance and the optimum design method is accurate and efficient.
A novel planar beam steering parasitic array of planar printed dipoles is proposed, which can be used in the mobile terminals to improve communication quality and system capacity. The main beam can be controlled by the reactive loads on the parasitic elements. The antenna prototype is designed using the software based on the full wave analysis and the loaded reactance values are optimized by the Differential Evolutionary (DE) algorithm. The measured results show that the main beam can be steered from -34~38 in the xoy plane. At every main radiation direction, the gain at the center frequency is from 3.6~4.9 dBi and the bandwidth of the reflection coefficient less than -10 dB is 330 MHz, which is from 5.63~5.96 GHz. The proposed antenna has good beam steering performance and the optimum design method is accurate and efficient.
2014, 36(6): 1520-1524.
doi: 10.3724/SP.J.1146.2013.01239
Abstract:
By utilizing the balanceable difference weight of high order Data Dependent Operations (DDO) and the high probability differentials of SPN structures, two 5-round related-key differentials of Eagle-128 are constructed. A full round related-key rectangle distinguisher of Eagle-128 is constructed by connecting two 5-round related-key differentials, and a related-key rectangle attack is proposed on the cipher to recover 64 bit of the master key. The corresponding data complexity is about 281.5 related-key chosen-plain-text, the computation complexity is about 2106.7 encryptions of the cipher, and the storage complexity is about 250 Byte of storage space. The success rate of the attack is about 0.954. The analysis results show that the practical length of Eagle-128s master key is 192 bit.
By utilizing the balanceable difference weight of high order Data Dependent Operations (DDO) and the high probability differentials of SPN structures, two 5-round related-key differentials of Eagle-128 are constructed. A full round related-key rectangle distinguisher of Eagle-128 is constructed by connecting two 5-round related-key differentials, and a related-key rectangle attack is proposed on the cipher to recover 64 bit of the master key. The corresponding data complexity is about 281.5 related-key chosen-plain-text, the computation complexity is about 2106.7 encryptions of the cipher, and the storage complexity is about 250 Byte of storage space. The success rate of the attack is about 0.954. The analysis results show that the practical length of Eagle-128s master key is 192 bit.
