Email alert
2015 Vol. 37, No. 9
Display Method:
2015, 37(9): 2033-2039.
doi: 10.11999/JEIT150031
Abstract:
For the robustness of visual object tracking, a new tracking algorithm based on multi-stage convolution filtering feature is proposed by introducing deep learning into visual tracking. The algorithm uses the Principal Component Analysis (PCA) eigenvectors obtained by stratified learning, to extract the deeper abstract expression of the original image by multi-stage convolutional filtering. Then the Bhattacharyya distance is used to evaluate the similarity among features. Finally, particle filter algorithm is combined to realize target tracking. The result shows that the feature obtained by multi-stage convolution filtering can express target better, the proposed algorithm has a better inflexibility to illumination, covering, rotation, and camera shake, and it exhibits very good robustness in video sequence with such characteristics.
For the robustness of visual object tracking, a new tracking algorithm based on multi-stage convolution filtering feature is proposed by introducing deep learning into visual tracking. The algorithm uses the Principal Component Analysis (PCA) eigenvectors obtained by stratified learning, to extract the deeper abstract expression of the original image by multi-stage convolutional filtering. Then the Bhattacharyya distance is used to evaluate the similarity among features. Finally, particle filter algorithm is combined to realize target tracking. The result shows that the feature obtained by multi-stage convolution filtering can express target better, the proposed algorithm has a better inflexibility to illumination, covering, rotation, and camera shake, and it exhibits very good robustness in video sequence with such characteristics.
2015, 37(9): 2040-2046.
doi: 10.11999/JEIT150086
Abstract:
In this paper, Dempster-Shafer (DS) theory and Dezert-Smarandache Theory (DSmT) are conducted thorough reasearch, and in order to obtain more accurate fusion results in the premise of needing less computation complexity, a fast DSmT-DS approximate reasoning method is proposed. This method is only fit for the case that there are only singleton focal elements with assignments in hyper-power set. The hyper-power set is splitted and mapped to a new hyper-power set which consists of the binary sets of the focal element and its complementary set to the assignments of the complementary sets are computed. Proportional Conflict Redistribution No.5 within Dezert-Smarandache framework (DSmT+PCR5) is applied to fuse the multi-source evidence in the binary sets of the new hyper-power set to get the fusion results of singleton focal elements. Then the assignments of singleton focal elements are obtained by normalization. Through the theoretical analysis, the conclusion is drawn that the fusion results of the mothod in this paper is between the results of DSmT+PCR5 and Dempsters combination rule based on DS model, and the fusion results of the method in this paper which is better than the rusults of Dempsters combination rule can be obtained in the premise of minimal computation complexity. Finally, by comparing the method in this paper with the existing methods from different views, the superiority of new one is testified well.
In this paper, Dempster-Shafer (DS) theory and Dezert-Smarandache Theory (DSmT) are conducted thorough reasearch, and in order to obtain more accurate fusion results in the premise of needing less computation complexity, a fast DSmT-DS approximate reasoning method is proposed. This method is only fit for the case that there are only singleton focal elements with assignments in hyper-power set. The hyper-power set is splitted and mapped to a new hyper-power set which consists of the binary sets of the focal element and its complementary set to the assignments of the complementary sets are computed. Proportional Conflict Redistribution No.5 within Dezert-Smarandache framework (DSmT+PCR5) is applied to fuse the multi-source evidence in the binary sets of the new hyper-power set to get the fusion results of singleton focal elements. Then the assignments of singleton focal elements are obtained by normalization. Through the theoretical analysis, the conclusion is drawn that the fusion results of the mothod in this paper is between the results of DSmT+PCR5 and Dempsters combination rule based on DS model, and the fusion results of the method in this paper which is better than the rusults of Dempsters combination rule can be obtained in the premise of minimal computation complexity. Finally, by comparing the method in this paper with the existing methods from different views, the superiority of new one is testified well.
2015, 37(9): 2047-2054.
doi: 10.11999/JEIT141473
Abstract:
The uneven color image can not be segmented successfully with the traditional C-V model, and the C-V model is sensitive to the initial contour and the location. The existing signed distance regularization term has disadvantages, such as the periodic oscillation and the local extremum. This paper proposes the target segmentation algorithm, which combines the local energy information with improved signed distance regularization term. Firstly, the global image information can be expanded to the HSV space, and each pixels and its statistical properties are analyzed with the local energy information within the neighborhood, which can effectively realize the uneven distribution of color image segmentation in less iteration. Secondly, the improved signed distance regularization term avoids re-initialization of level set function, improving the computational efficiency, and maintains stability in the level set function evolution process. Finally, the termination criterion of threshold evaluation method for the level set function evolution is defined, in order to make the curve accurately evolution to the target contour. The experimental results show that the proposed algorithm has higher segmentation accuracy and robust than other similar models.
The uneven color image can not be segmented successfully with the traditional C-V model, and the C-V model is sensitive to the initial contour and the location. The existing signed distance regularization term has disadvantages, such as the periodic oscillation and the local extremum. This paper proposes the target segmentation algorithm, which combines the local energy information with improved signed distance regularization term. Firstly, the global image information can be expanded to the HSV space, and each pixels and its statistical properties are analyzed with the local energy information within the neighborhood, which can effectively realize the uneven distribution of color image segmentation in less iteration. Secondly, the improved signed distance regularization term avoids re-initialization of level set function, improving the computational efficiency, and maintains stability in the level set function evolution process. Finally, the termination criterion of threshold evaluation method for the level set function evolution is defined, in order to make the curve accurately evolution to the target contour. The experimental results show that the proposed algorithm has higher segmentation accuracy and robust than other similar models.
2015, 37(9): 2055-2061.
doi: 10.11999/JEIT150173
Abstract:
To overcome the limitations of traditional image quality assessment methods, which are not well consistent with subjective human evaluation, a quality assessment algorithm of Weighting Sparse Fidelity (WSF) based on wavelet analysis is proposed. The arithmetic simulates nerve network of Human Vision System (HVS) as research point, the image is decomposed with wavelet into four-sub band images, which are divided into blocks at size of , then using Fast Independent Component Analysis training (FastICA) method to train the image blocks. Then, each image block sparse character matrix is extracted to calculate the sparse feature fidelity of the image and build the sparse fidelity quality evaluation model. On this basis, the image is divided into a plurality of interval according to the different details of the visual image information and a visual weight is set in each section, which can be consistent with subjective human evaluation. The experiment results on LIVE database show that the proposed method has a good evaluation of all kinds of distortion types and is highly consistent with human subjective evaluations. The proposed algorithm can effectively simulate the weighted visual cortex of the human visual system perception mechanisms, which compensates for deficiencies of existing image quality assessment methods.
To overcome the limitations of traditional image quality assessment methods, which are not well consistent with subjective human evaluation, a quality assessment algorithm of Weighting Sparse Fidelity (WSF) based on wavelet analysis is proposed. The arithmetic simulates nerve network of Human Vision System (HVS) as research point, the image is decomposed with wavelet into four-sub band images, which are divided into blocks at size of , then using Fast Independent Component Analysis training (FastICA) method to train the image blocks. Then, each image block sparse character matrix is extracted to calculate the sparse feature fidelity of the image and build the sparse fidelity quality evaluation model. On this basis, the image is divided into a plurality of interval according to the different details of the visual image information and a visual weight is set in each section, which can be consistent with subjective human evaluation. The experiment results on LIVE database show that the proposed method has a good evaluation of all kinds of distortion types and is highly consistent with human subjective evaluations. The proposed algorithm can effectively simulate the weighted visual cortex of the human visual system perception mechanisms, which compensates for deficiencies of existing image quality assessment methods.
2015, 37(9): 2062-2068.
doi: 10.11999/JEIT141641
Abstract:
The Structural SIMilarity (SSIM) algorithm of image quality assessment does not take into account the characteristics of multi-channel resolutions of human vision, it is also not consistent with subjective human evaluation for high level distortions. A Visual Salience Adaptive Pooling (VSAP) strategy of image quality assessment is proposed based on visual multi-scale and multi-orientation of log-Gabor transformation. Firstly, the visual characteristics of image on the high, medium, and low frequency are extracted by the log-Gabor transformation. Then the visual similarity scores based on visual scales and visual orientations of log-Gabor are calculated, accordingly, the visual distortion levels of image are calculated iteratively with the visual multi- resolution threshold. Finally, a strategy of image quality assessment is proposed with adaptive pooling similarity scores to distortion scores. The experimental results show that objective assessments of VSAP for different types of distortion hold higher correlation with subjective assessment. More importantly, the overall assessment performance of the Spearman Rank-Order Correlation Coefficient (SROCC), Correlation Coefficient (CC) and Root Mean Square Error (RMSE) for different levels of distortion is more consistent with subjective scores and superior to other methods.
The Structural SIMilarity (SSIM) algorithm of image quality assessment does not take into account the characteristics of multi-channel resolutions of human vision, it is also not consistent with subjective human evaluation for high level distortions. A Visual Salience Adaptive Pooling (VSAP) strategy of image quality assessment is proposed based on visual multi-scale and multi-orientation of log-Gabor transformation. Firstly, the visual characteristics of image on the high, medium, and low frequency are extracted by the log-Gabor transformation. Then the visual similarity scores based on visual scales and visual orientations of log-Gabor are calculated, accordingly, the visual distortion levels of image are calculated iteratively with the visual multi- resolution threshold. Finally, a strategy of image quality assessment is proposed with adaptive pooling similarity scores to distortion scores. The experimental results show that objective assessments of VSAP for different types of distortion hold higher correlation with subjective assessment. More importantly, the overall assessment performance of the Spearman Rank-Order Correlation Coefficient (SROCC), Correlation Coefficient (CC) and Root Mean Square Error (RMSE) for different levels of distortion is more consistent with subjective scores and superior to other methods.
2015, 37(9): 2069-2075.
doi: 10.11999/JEIT150003
Abstract:
Considering the problem that most of the existing skeleton calculation methods exhibit extreme sensitivity to the shape noise, a Bayes based algorithm for the skeleton pruning is proposed . The algorithm models the skeleton and growth process with Bayesian statistics framework. Based on the model, an iterative optimization is performed to prune the candidate branches. Due to the fact that the existing reconstruction error can not evaluate the simplicity of skeletons well, a new concept called Effective Rate is proposed to make quantitative analysis on the pruned skeleton with taking the simplicity into consideration. The experiments show that the proposed algorithm is robust to the shape noise and acts better in simplifying the skeleton structure and representing shape accurately.
Considering the problem that most of the existing skeleton calculation methods exhibit extreme sensitivity to the shape noise, a Bayes based algorithm for the skeleton pruning is proposed . The algorithm models the skeleton and growth process with Bayesian statistics framework. Based on the model, an iterative optimization is performed to prune the candidate branches. Due to the fact that the existing reconstruction error can not evaluate the simplicity of skeletons well, a new concept called Effective Rate is proposed to make quantitative analysis on the pruned skeleton with taking the simplicity into consideration. The experiments show that the proposed algorithm is robust to the shape noise and acts better in simplifying the skeleton structure and representing shape accurately.
2015, 37(9): 2076-2081.
doi: 10.11999/JEIT150165
Abstract:
The simple depth imaging device gains more and more attention because of its lower cost and easy- to-use property compared with traditional motion capture systems. However, this kind of devices lack the basic data condition of 3D motion reconstruction due to low resolution, occlusions, and mixing up of body parts. In this paper, a Dynamic Bayesian Network (DBN) model is proposed to describe the spatial and temporal characteristics of human body joints. The model is based on fusion of the parent-child characteristics of joints and multi-order Markov property of joint during motion. A golf swing capture and reconstruction system DBN-Motion (DBN-based Motion reconstruction system), is presented based on the DBN model and the similarity of swing with a simple depth imaging device, Kinect, as capturing device. The proposed system effectively solves the problem of occlusions and mixing up of body parts, and successfully captures and reconstructs golf swing in 3D space. Experimental results prove that the proposed system can achieve comparable reconstruction accuracy to the commercial optical motion caption system.
The simple depth imaging device gains more and more attention because of its lower cost and easy- to-use property compared with traditional motion capture systems. However, this kind of devices lack the basic data condition of 3D motion reconstruction due to low resolution, occlusions, and mixing up of body parts. In this paper, a Dynamic Bayesian Network (DBN) model is proposed to describe the spatial and temporal characteristics of human body joints. The model is based on fusion of the parent-child characteristics of joints and multi-order Markov property of joint during motion. A golf swing capture and reconstruction system DBN-Motion (DBN-based Motion reconstruction system), is presented based on the DBN model and the similarity of swing with a simple depth imaging device, Kinect, as capturing device. The proposed system effectively solves the problem of occlusions and mixing up of body parts, and successfully captures and reconstructs golf swing in 3D space. Experimental results prove that the proposed system can achieve comparable reconstruction accuracy to the commercial optical motion caption system.
2015, 37(9): 2082-2088.
doi: 10.11999/JEIT150074
Abstract:
The classical data driven Takagi-Sugeno-Kang (TSK) fuzzy system considers all the features of trained data, and faces a challenge that the interpretation is degenerated and the obtained fuzzy rule is complex when trained by high dimensional data. In this paper, a new fuzzy model, i.e., Fuzzy Subspace Clustering based zero-order L2-norm TSK Fuzzy System (FSC-0-L2-TSK-FS) is proposed to overcome this difficulty. The proposed fuzzy system not only reduces the feature spaces of the rule of antecedent, but also makes different rules implement the inference in different subspaces. The inference mechanism of the proposed fuzzy model training algorithm is very similar to the inference procedure of human. The experimental studies on the synthetic and real datasets prove that the interpretation of model constructed by the proposed method is enhanced when trained by high dimensional data and the generalization performance is better or comparative to several classical TSK fuzzy systems training methods.
The classical data driven Takagi-Sugeno-Kang (TSK) fuzzy system considers all the features of trained data, and faces a challenge that the interpretation is degenerated and the obtained fuzzy rule is complex when trained by high dimensional data. In this paper, a new fuzzy model, i.e., Fuzzy Subspace Clustering based zero-order L2-norm TSK Fuzzy System (FSC-0-L2-TSK-FS) is proposed to overcome this difficulty. The proposed fuzzy system not only reduces the feature spaces of the rule of antecedent, but also makes different rules implement the inference in different subspaces. The inference mechanism of the proposed fuzzy model training algorithm is very similar to the inference procedure of human. The experimental studies on the synthetic and real datasets prove that the interpretation of model constructed by the proposed method is enhanced when trained by high dimensional data and the generalization performance is better or comparative to several classical TSK fuzzy systems training methods.
2015, 37(9): 2089-2096.
doi: 10.11999/JEIT141478
Abstract:
In order to predict the pivotal visually attractive image regions more effectively, a novel saliency method using the phase congruency and the two-Dimensional Principal Component Analysis (2DPCA) is proposed in this paper. Firstly, the phase congruency is utilized to extract the most important feature points and the edge informations in the frequency domain, which is different from the conventional phase spectrum based methods. Then, after the quick shift superpixel based refinement, these features are incorporated with the local and global color contrast, to generate the low-level feature based saliency map. Then, the 2DPCA is adopted to extract the principal component vectors of image patches. The local and global distinctness between the different image patches in the principal component space are computed to get the pattern saliency map. Finally, these two complementary maps are integrated through the weighting strategy based on the spatial variance measure. The comparable experimental results on two benchmark eye tracking databases of the proposed method and 5 state-of-the-art methods show that the proposed method can predict eye fixation more accurately.
In order to predict the pivotal visually attractive image regions more effectively, a novel saliency method using the phase congruency and the two-Dimensional Principal Component Analysis (2DPCA) is proposed in this paper. Firstly, the phase congruency is utilized to extract the most important feature points and the edge informations in the frequency domain, which is different from the conventional phase spectrum based methods. Then, after the quick shift superpixel based refinement, these features are incorporated with the local and global color contrast, to generate the low-level feature based saliency map. Then, the 2DPCA is adopted to extract the principal component vectors of image patches. The local and global distinctness between the different image patches in the principal component space are computed to get the pattern saliency map. Finally, these two complementary maps are integrated through the weighting strategy based on the spatial variance measure. The comparable experimental results on two benchmark eye tracking databases of the proposed method and 5 state-of-the-art methods show that the proposed method can predict eye fixation more accurately.
2015, 37(9): 2097-2102.
doi: 10.11999/JEIT150033
Abstract:
Multi-scale decomposition based k-nearest-neighbor random search for fast image completion is presented. The image is decomposed using the bilateral filtering based down sampling. Starting from the coarsest level image, the most matching patch is searched using k-nearest-neighbor search algorithm based on the minimum heap for each coarse layer. The robust priority function is presented to determine the next patch that should be handled. The lower coarse layer is reconstructed using the bilateral filtering based up sampling after current coarse layer is repaired, so as to get the final result with iterative completion. Compared with related work, the presented algorithm preserves image details and edge information, and obtains higher completion quality. The completion results are evaluated utilizing the objective indictors. The experimental results show that presented method is effective, feasible, and the visual effect of the image completion is pleasing.
Multi-scale decomposition based k-nearest-neighbor random search for fast image completion is presented. The image is decomposed using the bilateral filtering based down sampling. Starting from the coarsest level image, the most matching patch is searched using k-nearest-neighbor search algorithm based on the minimum heap for each coarse layer. The robust priority function is presented to determine the next patch that should be handled. The lower coarse layer is reconstructed using the bilateral filtering based up sampling after current coarse layer is repaired, so as to get the final result with iterative completion. Compared with related work, the presented algorithm preserves image details and edge information, and obtains higher completion quality. The completion results are evaluated utilizing the objective indictors. The experimental results show that presented method is effective, feasible, and the visual effect of the image completion is pleasing.
2015, 37(9): 2103-2109.
doi: 10.11999/JEIT150019
Abstract:
Although segmentation-free Uyghur character document recognition can efficiently avoid character segmentation error, it does not work well on low-resource new-type samples. This paper suggests sharing stable character structure among different Uyghur fonts, and improves the efficiency of utilizing samples through Bootstrap. Experiments are made on new-type book samples, which contains only 1/5 training sample amount than the original. The average character recognition accuracy of the proposed method on test samples is 95.05%, and has 55.76%~63.84% recognition error rate relative decrease than the one of Maximum A Posteriori (MAP) method. Therefore, the proposed method can accomplish accurate Uyghur character model training under low data resource conditions.
Although segmentation-free Uyghur character document recognition can efficiently avoid character segmentation error, it does not work well on low-resource new-type samples. This paper suggests sharing stable character structure among different Uyghur fonts, and improves the efficiency of utilizing samples through Bootstrap. Experiments are made on new-type book samples, which contains only 1/5 training sample amount than the original. The average character recognition accuracy of the proposed method on test samples is 95.05%, and has 55.76%~63.84% recognition error rate relative decrease than the one of Maximum A Posteriori (MAP) method. Therefore, the proposed method can accomplish accurate Uyghur character model training under low data resource conditions.
2015, 37(9): 2110-2116.
doi: 10.11999/JEIT141651
Abstract:
Aiming the error calibration for the array channel uncertainty, a new fast algorithm named Simplified Multi-Stage Wiener Filter (SMSWF) based on the Multi-Stage Wiener Filter (MSWF) is proposed. The SMSWF takes the advantages of the DOA and the waveform of the cooperative source to estimate the gain and the phase factors, and it does not need to estimate the covariance matrix and the eigendecomposition operations. Compared with the eigendecomposition algorithm, the SMSWF has the same performance for estimating gain and phase factors while greatly reduce the complexity. The researches show that if a single source with a known waveform incidence on the array, the signal subspaces obtained by the SMSWF and one obtained by the eigendecomposition are equipollent, which demonstrate that the SMSWF is able to replace the eigendecomposition. The complexity of signal processing methods based on the eigendecomposition can greatly be reduced by replacing the eigendecomposition with the SMSWF. The extensive computer simulations and experiment in anechoice water tank show the superiori performance of the proposed algorithm.
Aiming the error calibration for the array channel uncertainty, a new fast algorithm named Simplified Multi-Stage Wiener Filter (SMSWF) based on the Multi-Stage Wiener Filter (MSWF) is proposed. The SMSWF takes the advantages of the DOA and the waveform of the cooperative source to estimate the gain and the phase factors, and it does not need to estimate the covariance matrix and the eigendecomposition operations. Compared with the eigendecomposition algorithm, the SMSWF has the same performance for estimating gain and phase factors while greatly reduce the complexity. The researches show that if a single source with a known waveform incidence on the array, the signal subspaces obtained by the SMSWF and one obtained by the eigendecomposition are equipollent, which demonstrate that the SMSWF is able to replace the eigendecomposition. The complexity of signal processing methods based on the eigendecomposition can greatly be reduced by replacing the eigendecomposition with the SMSWF. The extensive computer simulations and experiment in anechoice water tank show the superiori performance of the proposed algorithm.
2015, 37(9): 2117-2123.
doi: 10.11999/JEIT150139
Abstract:
According to research findings of speech acoustics, the timbre is applied to identify different types of targets. Since the information of timbre is indicated in the wave structure of time series, the feature of wave structure can be?extracted to classify various marine acoustic targets. The method of feature extraction based on wave structure is studied. The nine-dimension feature vector is constructed on the basis of signal statistical characteristics, including zero-crossing wavelength, peek-to-peek amplitude, zero-crossing-wavelength difference, wave train areas and so on. And the Support Vector Machine (SVM) is applied as a classifier for two kinds of marine acoustic target signals. The kernel function is set Radial Basis Function (RBF). The penalty?factor and parameter of RBF are properly selected by the method of combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO), which helps to obtain better recognition rates than the grid search method.
According to research findings of speech acoustics, the timbre is applied to identify different types of targets. Since the information of timbre is indicated in the wave structure of time series, the feature of wave structure can be?extracted to classify various marine acoustic targets. The method of feature extraction based on wave structure is studied. The nine-dimension feature vector is constructed on the basis of signal statistical characteristics, including zero-crossing wavelength, peek-to-peek amplitude, zero-crossing-wavelength difference, wave train areas and so on. And the Support Vector Machine (SVM) is applied as a classifier for two kinds of marine acoustic target signals. The kernel function is set Radial Basis Function (RBF). The penalty?factor and parameter of RBF are properly selected by the method of combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO), which helps to obtain better recognition rates than the grid search method.
2015, 37(9): 2124-2131.
doi: 10.11999/JEIT141618
Abstract:
The nonlinear stochastic resonance system has the ability to take advantage of background noise to enhance the weak signal among it. It provides the new approach to detect the weak signal embedded with heavy noise. This study proposes a new Varying Parameter Stochastic Resonance (VPSR) model. The model performs well in the detection of a time-varying signal with noise as well as the denoising and signal recovery. This study takes the determination coefficient and cross correlation coefficient as the criteria and analyzes the influence of different parameters variation on the system output. The simulation results indicate the model performs better in the time-varying signal recovery than the traditional one. The proposed method develops the area of time-varying signal detection with stochastic resonance which can be hoped to be widely used in the aperiodic signal processing, radar communication, etc. due to its superiority.
The nonlinear stochastic resonance system has the ability to take advantage of background noise to enhance the weak signal among it. It provides the new approach to detect the weak signal embedded with heavy noise. This study proposes a new Varying Parameter Stochastic Resonance (VPSR) model. The model performs well in the detection of a time-varying signal with noise as well as the denoising and signal recovery. This study takes the determination coefficient and cross correlation coefficient as the criteria and analyzes the influence of different parameters variation on the system output. The simulation results indicate the model performs better in the time-varying signal recovery than the traditional one. The proposed method develops the area of time-varying signal detection with stochastic resonance which can be hoped to be widely used in the aperiodic signal processing, radar communication, etc. due to its superiority.
2015, 37(9): 2132-2137.
doi: 10.11999/JEIT150204
Abstract:
The frequency pull-in process of traditional GPS receiver is time-consuming and its success rate is low under weak GPS signal environments. In order to solve this problem, FFT-based fine frequency estimation methods are presented. To overcome data bit phase transition, new input variables of FFT are constructed by using the squaring or differential algorithm. The experimental results show that the frequency detection probability is above 95% using differential algorithm when C/N0=21 dBHz if prior information of the navigation data is unknown. The frequency detection probability is above 95% using squaring algorithm when C/N0=19 dBHz if prior information of the navigation data is known.
The frequency pull-in process of traditional GPS receiver is time-consuming and its success rate is low under weak GPS signal environments. In order to solve this problem, FFT-based fine frequency estimation methods are presented. To overcome data bit phase transition, new input variables of FFT are constructed by using the squaring or differential algorithm. The experimental results show that the frequency detection probability is above 95% using differential algorithm when C/N0=21 dBHz if prior information of the navigation data is unknown. The frequency detection probability is above 95% using squaring algorithm when C/N0=19 dBHz if prior information of the navigation data is known.
2015, 37(9): 2138-2143.
doi: 10.11999/JEIT150116
Abstract:
Based on the analysis of the instantaneous phase characteristics of the echo Linear Frequency Modulated (LFM) signals respectively from single and multiple scatter point targets, an algorithm of the Doppler chirps estimation based on the Time-Chirp Distribution (TCD) of echo LFM signals from targets is proposed, in which the projection integral vertical to the chirp axis is adopted to enhance the Doppler chirps term of the TCD, but to suppress non-Doppler chirps cross term of the TCD and noise. Simultaneously, two-time search method is taken to obtain the maximum value of the TCDs projection integral to guarantee the estimation accuracy of the Doppler chirp and effectively reduce the amount of computation. The theoretical analysis shows that the time-consuming of calculation can be controlled to the minimum in the appropriate search step. Simulation results show the effectiveness of this method that it has higher estimation accuracy and good immunity for noise.
Based on the analysis of the instantaneous phase characteristics of the echo Linear Frequency Modulated (LFM) signals respectively from single and multiple scatter point targets, an algorithm of the Doppler chirps estimation based on the Time-Chirp Distribution (TCD) of echo LFM signals from targets is proposed, in which the projection integral vertical to the chirp axis is adopted to enhance the Doppler chirps term of the TCD, but to suppress non-Doppler chirps cross term of the TCD and noise. Simultaneously, two-time search method is taken to obtain the maximum value of the TCDs projection integral to guarantee the estimation accuracy of the Doppler chirp and effectively reduce the amount of computation. The theoretical analysis shows that the time-consuming of calculation can be controlled to the minimum in the appropriate search step. Simulation results show the effectiveness of this method that it has higher estimation accuracy and good immunity for noise.
2015, 37(9): 2144-2150.
doi: 10.11999/JEIT141659
Abstract:
A ship detection algorithm based on spectral residual transform is presented to detect ship in infrared remote sensing images. Firstly, the model parameters of spectral residual transform are designed according to the prior knowledge of ship and its natural backgrounds. Secondly, the spectral residual transform of sea infrared image is implemented. Thirdly, ship detection is done on the spectral residual transform image. Experimental results reveal that the new detection algorithm can remove large scale image interference and the image noise and improve the SCR of ship image. The detecting probability of the new algorithm is higher than other conventional methods.
A ship detection algorithm based on spectral residual transform is presented to detect ship in infrared remote sensing images. Firstly, the model parameters of spectral residual transform are designed according to the prior knowledge of ship and its natural backgrounds. Secondly, the spectral residual transform of sea infrared image is implemented. Thirdly, ship detection is done on the spectral residual transform image. Experimental results reveal that the new detection algorithm can remove large scale image interference and the image noise and improve the SCR of ship image. The detecting probability of the new algorithm is higher than other conventional methods.
2015, 37(9): 2151-2157.
doi: 10.11999/JEIT150125
Abstract:
Interferometric Inverse SAR (InISAR) is capable of acquiring three-dimensional image of the moving targets, which is much helpful to the target classification and identification. Meanwhile, multifunctional ISAR/InISAR system aims at maneuvering targets and only sparse aperture measurements are available for each target, which is a challenge to the conventional ISAR imaging algorithms. A joint sparsity-constraint InISAR 3-D imaging approaches is presented for maneuvering targets with sparse apertures. For a uniformly accelerated rotation target, the Doppler modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. Then the joint multi-channel InISAR imaging approach is converted into a joint sparse constraint optimization. And a modified Orthogonal Matching Pursuit (OMP) algorithm is employed to solve the optimization. The 3-D target geometry is followed by using obtaining 2-D images and estimated chirp parameters. Finally, the experiment using measured data is performed to confirm the effectiveness of the proposed method.
Interferometric Inverse SAR (InISAR) is capable of acquiring three-dimensional image of the moving targets, which is much helpful to the target classification and identification. Meanwhile, multifunctional ISAR/InISAR system aims at maneuvering targets and only sparse aperture measurements are available for each target, which is a challenge to the conventional ISAR imaging algorithms. A joint sparsity-constraint InISAR 3-D imaging approaches is presented for maneuvering targets with sparse apertures. For a uniformly accelerated rotation target, the Doppler modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. Then the joint multi-channel InISAR imaging approach is converted into a joint sparse constraint optimization. And a modified Orthogonal Matching Pursuit (OMP) algorithm is employed to solve the optimization. The 3-D target geometry is followed by using obtaining 2-D images and estimated chirp parameters. Finally, the experiment using measured data is performed to confirm the effectiveness of the proposed method.
2015, 37(9): 2158-2163.
doi: 10.11999/JEIT150108
Abstract:
The target detection performance of the locally optimum detector descends in the bad non-Gaussian clutter environment. To deal with this problem, a radar target detection method based on the fractional lower order locally optimum is proposed. First, the simplified locally optimum detector is obtained, then, based on the fractional lower order statistics theory, the fractional lower order correlation matrix expresses the clutter correlation and the fractional lower order quadratic form is proposed as the weight of the locally optimum detector to improve the radar target detection in a non-Gaussian correlated clutter background. Simulations and IPIX radar data results show that, the detection performance of the proposed method obviously outperforms the locally optimum detector in the non-Gaussian badly clutter environment for the weak target.
The target detection performance of the locally optimum detector descends in the bad non-Gaussian clutter environment. To deal with this problem, a radar target detection method based on the fractional lower order locally optimum is proposed. First, the simplified locally optimum detector is obtained, then, based on the fractional lower order statistics theory, the fractional lower order correlation matrix expresses the clutter correlation and the fractional lower order quadratic form is proposed as the weight of the locally optimum detector to improve the radar target detection in a non-Gaussian correlated clutter background. Simulations and IPIX radar data results show that, the detection performance of the proposed method obviously outperforms the locally optimum detector in the non-Gaussian badly clutter environment for the weak target.
2015, 37(9): 2164-2170.
doi: 10.11999/JEIT141523
Abstract:
A new ESPRIT (Estimating Signal Via Rotational Invariance Techniques) algorithm is proposed to estimate the joint DOD (Direction Of Departure), DOA (Direction Of Arrival) and Doppler frequency based on the second extension of degree of freedom in the time and space domain under the conditions of non-uniform configurations of transmitter-receiver arrays and multiple delays for bistatic MIMO radar. Firstly, based on the special characteristic of direction vector in MIMO radar, the second extension of degree of freedom both in the time and space domains is attained by performing the twice row permutations on the received data and deleting the redundant items operations. Then, the ESPRIT algorithm is utilized to estimate the DOD, DOA, and Doppler frequency after performing the temporal-spatial smoothing window processing to the new data. The simulation shows that when the same number of real elements is used in time and space domain, the parameter estimation performance of the proposed algorithm is better than those of the quadrilinear decomposition and multi-dimension ESPRIT algorithms. Moreover, by using of the minimum redundancy configuration, the redundant information in the arrays decreases and hence the requirements of the array elements and the delay device are reduced, so it is more convenient to the practical application.
A new ESPRIT (Estimating Signal Via Rotational Invariance Techniques) algorithm is proposed to estimate the joint DOD (Direction Of Departure), DOA (Direction Of Arrival) and Doppler frequency based on the second extension of degree of freedom in the time and space domain under the conditions of non-uniform configurations of transmitter-receiver arrays and multiple delays for bistatic MIMO radar. Firstly, based on the special characteristic of direction vector in MIMO radar, the second extension of degree of freedom both in the time and space domains is attained by performing the twice row permutations on the received data and deleting the redundant items operations. Then, the ESPRIT algorithm is utilized to estimate the DOD, DOA, and Doppler frequency after performing the temporal-spatial smoothing window processing to the new data. The simulation shows that when the same number of real elements is used in time and space domain, the parameter estimation performance of the proposed algorithm is better than those of the quadrilinear decomposition and multi-dimension ESPRIT algorithms. Moreover, by using of the minimum redundancy configuration, the redundant information in the arrays decreases and hence the requirements of the array elements and the delay device are reduced, so it is more convenient to the practical application.
2015, 37(9): 2171-2176.
doi: 10.11999/JEIT141593
Abstract:
In order to solve the problem of large energy spectral density error of the constant modulus waveform synthesized in cognitive radar. A new waveform design algorithm based on iterative convex optimization is proposed. Firstly, in order to solve the problems of slow convergence speed and large error of energy spectral density, this algorithm transforms the waveform synthesis process into an optimization problem constrained by Peak-to-Average Power Ratio (PAPR). Secondly, Weighting Error Vector Magnitude (WEVM) is minimized to reduce stop-band power and suppress the interference and the clutter. Finally, the optimization problem is transformed into Second-Order Cone Programming (SOCP) problem. Simulation results verify the effectiveness of the proposed algorithm.
In order to solve the problem of large energy spectral density error of the constant modulus waveform synthesized in cognitive radar. A new waveform design algorithm based on iterative convex optimization is proposed. Firstly, in order to solve the problems of slow convergence speed and large error of energy spectral density, this algorithm transforms the waveform synthesis process into an optimization problem constrained by Peak-to-Average Power Ratio (PAPR). Secondly, Weighting Error Vector Magnitude (WEVM) is minimized to reduce stop-band power and suppress the interference and the clutter. Finally, the optimization problem is transformed into Second-Order Cone Programming (SOCP) problem. Simulation results verify the effectiveness of the proposed algorithm.
First-order Sea Clutter Spectrum Extraction Based on SNR Method for HF Hybrid Sky-surface Wave Radar
2015, 37(9): 2177-2182.
doi: 10.11999/JEIT150079
Abstract:
The characteristics of frequency shift and broadening of first-order sea clutter for High Frequency Hybrid Sky-Surface Wave Radar (HFHSSWR) make it harder to isolate the first-order sea clutter spectrum than that of High Frequency Surface Wave Radar (HFSWR). In this paper, the characteristics of first-order sea clutter for HFHSSWR are investigated, and based on its continuously distribution character on the range-Doppler spectrum, the Signal-to-Noise Ratio (SNR) method is used to isolate the first-order sea clutter spectrum for HFHSSWR. From the quantitative analysis of the frequency shift and broadening of the first-order clutter spectrum caused by the bistatic angle, the ionospheres conditions and the ocean current, the estimation method and the value ranges of three parameters are given, which are the center location, the width of first-order sea clutter spectrum and the spacing of the two first-order sea clutter peak. 2-D SNR method is used to solve the problem that the first-order spectrum boundary can not be determined accurately because of the low SNR. Finally, the proposed method is applied to both the simulated and the field data to verify its validity.
The characteristics of frequency shift and broadening of first-order sea clutter for High Frequency Hybrid Sky-Surface Wave Radar (HFHSSWR) make it harder to isolate the first-order sea clutter spectrum than that of High Frequency Surface Wave Radar (HFSWR). In this paper, the characteristics of first-order sea clutter for HFHSSWR are investigated, and based on its continuously distribution character on the range-Doppler spectrum, the Signal-to-Noise Ratio (SNR) method is used to isolate the first-order sea clutter spectrum for HFHSSWR. From the quantitative analysis of the frequency shift and broadening of the first-order clutter spectrum caused by the bistatic angle, the ionospheres conditions and the ocean current, the estimation method and the value ranges of three parameters are given, which are the center location, the width of first-order sea clutter spectrum and the spacing of the two first-order sea clutter peak. 2-D SNR method is used to solve the problem that the first-order spectrum boundary can not be determined accurately because of the low SNR. Finally, the proposed method is applied to both the simulated and the field data to verify its validity.
2015, 37(9): 2183-2190.
doi: 10.11999/JEIT141580
Abstract:
With the fast development of full-duplex technology in the same band, a novel hybrid scheme called Transmit Antenna Selection-receivever Artifical Noise (TAS-rAN), is proposed for lower complexity of beam- forming scheme and higher security of TAS in MISO wiretap channels. In this scheme, by using TAS protocol, the transmitter first selects a single antenna that maximizes the instantaneous Signal-to-Noise Ratio (SNR) at the full-duplex receiver. While the transmitter uses this antenna to transmit secrecy data, the full-duplex receiver sends Artificial Noise (AN) to confuse the potential eavesdropper. For the proposed protocol, Nakagami-m fading channels with different parameters for the main channel and the eavesdropper,s channel is considered, and a new closed-form expression for the exact secrecy outage probability is derived. The numerical simulation results demonstrate that the proposed TAS-rAN protocol is a robust secure system, and can offer higher secure performances than both existed single TAS-single and TAS-Alamouti schemes.
With the fast development of full-duplex technology in the same band, a novel hybrid scheme called Transmit Antenna Selection-receivever Artifical Noise (TAS-rAN), is proposed for lower complexity of beam- forming scheme and higher security of TAS in MISO wiretap channels. In this scheme, by using TAS protocol, the transmitter first selects a single antenna that maximizes the instantaneous Signal-to-Noise Ratio (SNR) at the full-duplex receiver. While the transmitter uses this antenna to transmit secrecy data, the full-duplex receiver sends Artificial Noise (AN) to confuse the potential eavesdropper. For the proposed protocol, Nakagami-m fading channels with different parameters for the main channel and the eavesdropper,s channel is considered, and a new closed-form expression for the exact secrecy outage probability is derived. The numerical simulation results demonstrate that the proposed TAS-rAN protocol is a robust secure system, and can offer higher secure performances than both existed single TAS-single and TAS-Alamouti schemes.
2015, 37(9): 2191-2197.
doi: 10.11999/JEIT141579
Abstract:
This paper investigates the relay transmission system in the presence of multiple collusion single-antenna eavesdroppers. A physical layer security scheme employing nullspace Artificial Noise (AN) and Amplify-and-Forward (AF) relay beamforming is designed. In the case that channel state information can not be accurately obtained, the weighted matrice of relay beamforming and the nullspace AN covariance are jointed optimized based on the Semi-Definite Programs (SDP), which can effectively reduce the amount of the information likely to be obtained by the multiple collusion eavesdroppers and significantly improve the security capacity of the system. It is an effective physical security transmission scheme with good robustness. Simulation results verify that the scheme has good performance.
This paper investigates the relay transmission system in the presence of multiple collusion single-antenna eavesdroppers. A physical layer security scheme employing nullspace Artificial Noise (AN) and Amplify-and-Forward (AF) relay beamforming is designed. In the case that channel state information can not be accurately obtained, the weighted matrice of relay beamforming and the nullspace AN covariance are jointed optimized based on the Semi-Definite Programs (SDP), which can effectively reduce the amount of the information likely to be obtained by the multiple collusion eavesdroppers and significantly improve the security capacity of the system. It is an effective physical security transmission scheme with good robustness. Simulation results verify that the scheme has good performance.
2015, 37(9): 2198-2203.
doi: 10.11999/JEIT150088
Abstract:
An energy-efficient resource allocation scheme is proposed for multi-user massive MIMO mobile communication uplink system. A mathematical formulation of optimization issue is provided with the objective of maximizing system energy efficiency lower bound under the data rate of user and tolerable interference level constraint, meanwhile the Base Station (BS) uses a Maximum-Ratio Combining (MRC) receiver. By transforming the originally fractional optimization problem into an equivalent subtractive form using the properties of fractional programming, then convex optimization is adopted to maximize the energy efficiency. Specifically, both the numbers of antenna arrays at the BS and the transmit data rate at the user are adjusted. Simulation results show that the energy-efficiency difference between the proposed algorithm and the exhaustive algorithm is less than 9%, at the same time, the performance of spectral-efficiency of the proposed algorithm is very well and the complexity is significantly reduced.
An energy-efficient resource allocation scheme is proposed for multi-user massive MIMO mobile communication uplink system. A mathematical formulation of optimization issue is provided with the objective of maximizing system energy efficiency lower bound under the data rate of user and tolerable interference level constraint, meanwhile the Base Station (BS) uses a Maximum-Ratio Combining (MRC) receiver. By transforming the originally fractional optimization problem into an equivalent subtractive form using the properties of fractional programming, then convex optimization is adopted to maximize the energy efficiency. Specifically, both the numbers of antenna arrays at the BS and the transmit data rate at the user are adjusted. Simulation results show that the energy-efficiency difference between the proposed algorithm and the exhaustive algorithm is less than 9%, at the same time, the performance of spectral-efficiency of the proposed algorithm is very well and the complexity is significantly reduced.
2015, 37(9): 2204-2210.
doi: 10.11999/JFIT141621
Abstract:
An Improved Random Incomplete Coloring (IRIC) algorithm is proposed based on Random Incomplete Coloring (RIC) to mitigate inter-Wireless Body Area Network (WBAN) interference. The proposed IRIC algorithm can realize high resource spatial reuse by controlling colored nodes to participate in next round of coloring, and the fairness is also guaranteed by restricting the gap of assigned color amounts between adjacent nodes. Simulation results show that the proposed IRIC algorithm can further improve the system throughput and resource spatial reuse.
An Improved Random Incomplete Coloring (IRIC) algorithm is proposed based on Random Incomplete Coloring (RIC) to mitigate inter-Wireless Body Area Network (WBAN) interference. The proposed IRIC algorithm can realize high resource spatial reuse by controlling colored nodes to participate in next round of coloring, and the fairness is also guaranteed by restricting the gap of assigned color amounts between adjacent nodes. Simulation results show that the proposed IRIC algorithm can further improve the system throughput and resource spatial reuse.
2015, 37(9): 2211-2217.
doi: 10.11999/JEIT141395
Abstract:
Co-channel attack may cause data blocking and distortion in ZigBee networks. To resolve the issues, a model to detect co-channel attack is proposed. According to the Gauss distributed characteristics of the signal spectrum and the influence of co-channel attack on transform-domain amplitude, the proposed model can perceive whether the co-channel attack is existent in ZigBee network. On this basis, a scheme to detect and restrain co-channel attack is provided by embedding the free band channel-hop mechanism and self-adaption algorithm based on variable backoff cycle and access probability. Finally, experimental results indicate that the proposed model and scheme can inhibit the co-channel attack?efficiently.
Co-channel attack may cause data blocking and distortion in ZigBee networks. To resolve the issues, a model to detect co-channel attack is proposed. According to the Gauss distributed characteristics of the signal spectrum and the influence of co-channel attack on transform-domain amplitude, the proposed model can perceive whether the co-channel attack is existent in ZigBee network. On this basis, a scheme to detect and restrain co-channel attack is provided by embedding the free band channel-hop mechanism and self-adaption algorithm based on variable backoff cycle and access probability. Finally, experimental results indicate that the proposed model and scheme can inhibit the co-channel attack?efficiently.
2015, 37(9): 2218-2224.
doi: 10.11999/JEIT150023
Abstract:
Aiming at the characteristics of uncertainty and fuzziness of the network traffic attribute, an Intuitionistic Fuzzy Reasoning Theory (IFRT) is introduced to the anomaly detection field. A method of IFRT detection based on the inclusion degree is proposed. Firstly, the membership and non-membership functions of attributes in anomaly detection are designed. Secondly, the intensity similarity measure method based on the inclusion degree is presented and the rules library is generated. And then, the FMP rules of the IFRT are presented. Finally, an anomaly detection based on the IFRT is constructed. The validity is checked by experiment on the standard detection dataset KDD99, compared with other traditional theory, the IFRT anomaly detection method performs better than others.
Aiming at the characteristics of uncertainty and fuzziness of the network traffic attribute, an Intuitionistic Fuzzy Reasoning Theory (IFRT) is introduced to the anomaly detection field. A method of IFRT detection based on the inclusion degree is proposed. Firstly, the membership and non-membership functions of attributes in anomaly detection are designed. Secondly, the intensity similarity measure method based on the inclusion degree is presented and the rules library is generated. And then, the FMP rules of the IFRT are presented. Finally, an anomaly detection based on the IFRT is constructed. The validity is checked by experiment on the standard detection dataset KDD99, compared with other traditional theory, the IFRT anomaly detection method performs better than others.
2015, 37(9): 2225-2231.
doi: 10.11999/JEIT150205
Abstract:
Key information leakage is one of the most serious problems in Intercloud service, to solve this problem, a scheme of user key revocation on attribute-based ring signatures is proposed. Focused on user ciphertext access in Intercloud, the mechanism of ciphertext matrixes mapping without attribute leakage is discussed, multi-authority can extend attribute sets for generation key, then full user attributes can not be acquired by Cloud Service Providers (CSP), thus overhead on attribute storage is reduced. In addition, user signature verification revocation based on revocable ring and monotone span programs is designed, which constitutes ring of CSPs, authorities and users. Receiving CSP can define ciphertext access structure, users can access ciphertext through source CSP verifying, and authorities can remove decryption key from attribute-lost users without affecting any other users. The mechanism of collusion resistance with integrating attributes on the basis of Ciphertext-Policy Attribute Base Encryption (CP-ABE) and monotone span programs is discussed, with which user attribute confidentiality can be protected from leakage. Finally, to prove the effectiviness of the proposed model, the performance analysis of communication cost and computational efficiency are verified.
Key information leakage is one of the most serious problems in Intercloud service, to solve this problem, a scheme of user key revocation on attribute-based ring signatures is proposed. Focused on user ciphertext access in Intercloud, the mechanism of ciphertext matrixes mapping without attribute leakage is discussed, multi-authority can extend attribute sets for generation key, then full user attributes can not be acquired by Cloud Service Providers (CSP), thus overhead on attribute storage is reduced. In addition, user signature verification revocation based on revocable ring and monotone span programs is designed, which constitutes ring of CSPs, authorities and users. Receiving CSP can define ciphertext access structure, users can access ciphertext through source CSP verifying, and authorities can remove decryption key from attribute-lost users without affecting any other users. The mechanism of collusion resistance with integrating attributes on the basis of Ciphertext-Policy Attribute Base Encryption (CP-ABE) and monotone span programs is discussed, with which user attribute confidentiality can be protected from leakage. Finally, to prove the effectiviness of the proposed model, the performance analysis of communication cost and computational efficiency are verified.
2015, 37(9): 2232-2237.
doi: 10.11999/JEIT150058
Abstract:
Regarding the uncertainty introduced by load balancing when determining which end-to-end path is measured and that the classical Boolean model is not well developed for the scenario of multiple congestion links, this paper bases on the identification of end-to-end probing paths and proposes an enlarged state space based congestion link identification algorithm. Firstly, the mapping relationship between the probing flows and the measured paths is obtained after performing adaptive clustering on the probing flows with their delay correlation measures. Secondly, with multiple thresholds, it is able to assign a path with a different congestion state according to its different loss rate levels. Lastly, the issue of the congestion link identification is modeled as a constrained optimization problem, and is solved with Enlarged State Space based Congestion Link Identification (ESSCLI) algorithm. The simulation results demonstrate that ESSCLI can achieve a better detection rate of the congestion link in various network scenarios compared with existing algorithms.
Regarding the uncertainty introduced by load balancing when determining which end-to-end path is measured and that the classical Boolean model is not well developed for the scenario of multiple congestion links, this paper bases on the identification of end-to-end probing paths and proposes an enlarged state space based congestion link identification algorithm. Firstly, the mapping relationship between the probing flows and the measured paths is obtained after performing adaptive clustering on the probing flows with their delay correlation measures. Secondly, with multiple thresholds, it is able to assign a path with a different congestion state according to its different loss rate levels. Lastly, the issue of the congestion link identification is modeled as a constrained optimization problem, and is solved with Enlarged State Space based Congestion Link Identification (ESSCLI) algorithm. The simulation results demonstrate that ESSCLI can achieve a better detection rate of the congestion link in various network scenarios compared with existing algorithms.
2015, 37(9): 2238-2245.
doi: 10.11999/JEIT141649
Abstract:
There are many bipartite networks composed of two types of nodes in the real world, studying the community structure of them is helpful to understand the complex network from a new point of view. Non- negative matrix factorization can overcome the limitation of the two-mode structure of bipartite networks, but it is also subject to several problems such as slow convergence and large computation. In this paper, a novel algorithm using graph regularized-based non-negative matrix factorization is presented for community detection in bipartite networks. It respectively introduces the internal connecting information of two-kinds of nodes into the Non- negative Matrix Tri-Factorization (NMTF) model as the graph regularizations. Moreover, this paper divides NMTF into two sub problems of minimizing the approximation error, and presents an alternative iterative algorithm to update the factor matrices, thus the iterations of matrix factorization can be simplified and accelerated. Through the experiments on both computer-generated and real-world networks, the results and analysis show that the proposed method has superior performances than the typical community algorithms in terms of the accuracy and stability, and can effectively discover the meaningful community structures in bipartite networks.
There are many bipartite networks composed of two types of nodes in the real world, studying the community structure of them is helpful to understand the complex network from a new point of view. Non- negative matrix factorization can overcome the limitation of the two-mode structure of bipartite networks, but it is also subject to several problems such as slow convergence and large computation. In this paper, a novel algorithm using graph regularized-based non-negative matrix factorization is presented for community detection in bipartite networks. It respectively introduces the internal connecting information of two-kinds of nodes into the Non- negative Matrix Tri-Factorization (NMTF) model as the graph regularizations. Moreover, this paper divides NMTF into two sub problems of minimizing the approximation error, and presents an alternative iterative algorithm to update the factor matrices, thus the iterations of matrix factorization can be simplified and accelerated. Through the experiments on both computer-generated and real-world networks, the results and analysis show that the proposed method has superior performances than the typical community algorithms in terms of the accuracy and stability, and can effectively discover the meaningful community structures in bipartite networks.
2015, 37(9): 2246-2253.
doi: 10.11999/JEIT141626
Abstract:
Constructing hierarchical topology is an effective way to prolong network lifetime. The topology control process is divided into sensing layer composed by cluster members and planar data forwarding layer composed by cluster heads, while the mathematical models of energy consumption based on wireless signal irregularity and stability of cluster are proposed. Further, a Wireless Signal Irregularity Based hierarchical Topology Control (WSIBTC) algorithm is proposed, which divides the monitoring region into several sub-regions based on the average effective transmission range. The cluster heads are elected based on the stability of cluster and the location of sensor nodes in the clusters, and a planar topology is formed by cluster heads to prolong the network lifetime. Analysis and simulation results show that the proposed WSIBTC algorithm improves greatly the lifetime of the network.
Constructing hierarchical topology is an effective way to prolong network lifetime. The topology control process is divided into sensing layer composed by cluster members and planar data forwarding layer composed by cluster heads, while the mathematical models of energy consumption based on wireless signal irregularity and stability of cluster are proposed. Further, a Wireless Signal Irregularity Based hierarchical Topology Control (WSIBTC) algorithm is proposed, which divides the monitoring region into several sub-regions based on the average effective transmission range. The cluster heads are elected based on the stability of cluster and the location of sensor nodes in the clusters, and a planar topology is formed by cluster heads to prolong the network lifetime. Analysis and simulation results show that the proposed WSIBTC algorithm improves greatly the lifetime of the network.
2015, 37(9): 2254-2259.
doi: 10.11999/JEIT150077
Abstract:
The results of the integral equation method on the low-frequency ground-wave attenuation factor are inconsistent with other classic algorithms for the homogeneous/inhomogeneous smooth-path models. To solve this problem, a spherical correction factor and an elevation converting technique are introduced to the integral equation method. With these efforts, the results of the improved method are more consistent with other algorithms under the smooth-path conditions. Then, it is used for a real irregular-path model, and the calculated results are compared with the measured ones. It is shown that the proposed method is more suitable for the occasions both on the ground and in the air as the propagation path is long, irregular, and curved.
The results of the integral equation method on the low-frequency ground-wave attenuation factor are inconsistent with other classic algorithms for the homogeneous/inhomogeneous smooth-path models. To solve this problem, a spherical correction factor and an elevation converting technique are introduced to the integral equation method. With these efforts, the results of the improved method are more consistent with other algorithms under the smooth-path conditions. Then, it is used for a real irregular-path model, and the calculated results are compared with the measured ones. It is shown that the proposed method is more suitable for the occasions both on the ground and in the air as the propagation path is long, irregular, and curved.
2015, 37(9): 2260-2264.
doi: 10.11999/JEIT150192
Abstract:
In this paper, the effect of distributed loss loading and guiding center radius modifying on the stability of a TE11 mode Gyro-Traveling Wave Tube (Gyro-TWT) is studied by multimode steady-state method. The result shows that the output power of the backward oscillation mode keeps weaken till zero as the conductance of the lossy material reduces, while the output power of the working mode grows significantly. As guiding center radius increases, loss loading needed to suppress oscillation completely is weaker, which makes heat easier to dissipate. Besides, the increment of guiding center radius also makes the output characteristic less sensitive to conductance variation.
In this paper, the effect of distributed loss loading and guiding center radius modifying on the stability of a TE11 mode Gyro-Traveling Wave Tube (Gyro-TWT) is studied by multimode steady-state method. The result shows that the output power of the backward oscillation mode keeps weaken till zero as the conductance of the lossy material reduces, while the output power of the working mode grows significantly. As guiding center radius increases, loss loading needed to suppress oscillation completely is weaker, which makes heat easier to dissipate. Besides, the increment of guiding center radius also makes the output characteristic less sensitive to conductance variation.
2015, 37(9): 2265-2271.
doi: 10.11999/JEIT150095
Abstract:
As digital microfluidic biochips are widely used in biochemical field, it highly demands the chip reliability and manufacturing costs. Online testing is an important method to ensure the normal work of the digital microfluidic biochips. In this paper, an online parallel testing scheme is proposed based on improved max-min ant colony algorithm for pin-constrained digital microfluidic biochips. The scheme uses pseudo-random-proportional rules, establishes a taboo judgment strategy, and changes the pheromone trail persistence adaptively to realize the online parallel testing of pin-constrained digital microfluidic biochips. The experiment results show that the proposed method can be used for both offline and online testing, and compared with the offline and online testing of the single droplet, the proposed method can effectively reduce the test time and improve the efficiency.
As digital microfluidic biochips are widely used in biochemical field, it highly demands the chip reliability and manufacturing costs. Online testing is an important method to ensure the normal work of the digital microfluidic biochips. In this paper, an online parallel testing scheme is proposed based on improved max-min ant colony algorithm for pin-constrained digital microfluidic biochips. The scheme uses pseudo-random-proportional rules, establishes a taboo judgment strategy, and changes the pheromone trail persistence adaptively to realize the online parallel testing of pin-constrained digital microfluidic biochips. The experiment results show that the proposed method can be used for both offline and online testing, and compared with the offline and online testing of the single droplet, the proposed method can effectively reduce the test time and improve the efficiency.
2015, 37(9): 2272-2276.
doi: 10.11999/JEIT150167
Abstract:
Based on Iterative Scattering Process (ISP) algorithm, the scattering characteristics of cylinder array are analyzed. The relationship between incident field and scattering filed can be evolved from the cylindrical harmonic functions under surface boundary condition of perfect conductor. With the previous iterative scattering fields among cylinders being the incident field next iteration, the coefficient relationship of scattering fields among cylinder array is derived. After analyzing the scattering fields of two or four cylinders within different iterations, it can be obtained that the calculated result of ISP algorithm keeps high accuracy with only three iterations. The results compared with numerical methods show that ISP algorithm and Method of Moments (MoM) agree well, whereas ISP algorithm has an observably faster calculated pace.
Based on Iterative Scattering Process (ISP) algorithm, the scattering characteristics of cylinder array are analyzed. The relationship between incident field and scattering filed can be evolved from the cylindrical harmonic functions under surface boundary condition of perfect conductor. With the previous iterative scattering fields among cylinders being the incident field next iteration, the coefficient relationship of scattering fields among cylinder array is derived. After analyzing the scattering fields of two or four cylinders within different iterations, it can be obtained that the calculated result of ISP algorithm keeps high accuracy with only three iterations. The results compared with numerical methods show that ISP algorithm and Method of Moments (MoM) agree well, whereas ISP algorithm has an observably faster calculated pace.
2015, 37(9): 2277-2281.
doi: 10.11999/JEIT141566
Abstract:
A controllable tri-band-notched UltraWide Band (UWB) antenna is presented to avoid the interference of services working in the UWB band such as WiMax, WLAN, and X-band satellite system applications. The tri-band-notched characteristics are achieved by etching slots on the radiating patch and the ground plane, and adding a parasitic ring unit on the back of substrate. The proposed antenna operates on the UWB (3.1~10.6 GHz) efficiently, and it can inhibit interference from three different kinds of narrow band communication systems. A switch is added on the parasitic ring unit in order to realize the conversion between dual band-notched and tri-band-notched functions. Simulated and measured results show that the proposed antenna provides excellent band-notched function on the rejectband and has nearly omnidirectional radiation patterns on the passband.
A controllable tri-band-notched UltraWide Band (UWB) antenna is presented to avoid the interference of services working in the UWB band such as WiMax, WLAN, and X-band satellite system applications. The tri-band-notched characteristics are achieved by etching slots on the radiating patch and the ground plane, and adding a parasitic ring unit on the back of substrate. The proposed antenna operates on the UWB (3.1~10.6 GHz) efficiently, and it can inhibit interference from three different kinds of narrow band communication systems. A switch is added on the parasitic ring unit in order to realize the conversion between dual band-notched and tri-band-notched functions. Simulated and measured results show that the proposed antenna provides excellent band-notched function on the rejectband and has nearly omnidirectional radiation patterns on the passband.
2015, 37(9): 2282-2286.
doi: 10.11999/JEIT150105
Abstract:
In order to improve the Q (Quality factor) value and SNR (Signal to Noise Ratio) and reduce the driving voltage, chip-level vacuum package of Micro-Electro-Mechanical Systems (MEMS) based resonant miniature electric field sensor is realized. By way of a novel fusion bonding process with nanogetter added, the package cap is successfully bonded with the base substrate in very low pressure. The experimental results show that the Q-value of the sensor increases 500 times to 30727.4 and the driving voltage reduces to 100 mV +60 mVp-p which decreases to 1/200 and 1/16 respectively compared to air pressure.
In order to improve the Q (Quality factor) value and SNR (Signal to Noise Ratio) and reduce the driving voltage, chip-level vacuum package of Micro-Electro-Mechanical Systems (MEMS) based resonant miniature electric field sensor is realized. By way of a novel fusion bonding process with nanogetter added, the package cap is successfully bonded with the base substrate in very low pressure. The experimental results show that the Q-value of the sensor increases 500 times to 30727.4 and the driving voltage reduces to 100 mV +60 mVp-p which decreases to 1/200 and 1/16 respectively compared to air pressure.
