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2018 Vol. 40, No. 2
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2018, 40(2): 255-263.
doi: 10.11999/JEIT170497
Abstract:
This paper proposes a Hess (also known as Hessian) matrix-based multi-focus image fusion method. In this method, multi-scale Hess matrix is utilized to detect feature and background regions. On this basis, source images are split into two different parts, and different fusion strategies are applied to generating decision map respectively. By combining decision maps in different parts, an initial decision map is obtained, and then the initial decision map is refined with post-processing method. To improve the performance of the fusion method, a new focus measure is proposed based on multi-scale Hess matrix for both feature and background regions. Integral images are also introduced for fast computation to meet the real-time application requirements. Experimental results demonstrate that the proposed method is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics.
This paper proposes a Hess (also known as Hessian) matrix-based multi-focus image fusion method. In this method, multi-scale Hess matrix is utilized to detect feature and background regions. On this basis, source images are split into two different parts, and different fusion strategies are applied to generating decision map respectively. By combining decision maps in different parts, an initial decision map is obtained, and then the initial decision map is refined with post-processing method. To improve the performance of the fusion method, a new focus measure is proposed based on multi-scale Hess matrix for both feature and background regions. Integral images are also introduced for fast computation to meet the real-time application requirements. Experimental results demonstrate that the proposed method is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics.
2018, 40(2): 264-271.
doi: 10.11999/JEIT170381
Abstract:
Due to the influences of scattering of the light and interference of the noise, underwater image quality is always degraded severely. In order to remove the blur and suppress the noise, and improve the quality of underwater image, a novel underwater image restoration method based on the combination of dark-channel prior with sparse representation is proposed. This method adopts the dark-channel prior theory to calculate the dark-channel image at first, and then uses sparse representation to denoise and optimize the dark-channel image. Based on the improved dark-channel image, the more precise water transmissivity and light intensity can be achieved to compute the final restoration result, effectively eliminating the image blur as well as noise. The experimental results show that the proposed method can effectively improve the image factors, such as average gradient and entropy, so as to compensate the degraded image.
Due to the influences of scattering of the light and interference of the noise, underwater image quality is always degraded severely. In order to remove the blur and suppress the noise, and improve the quality of underwater image, a novel underwater image restoration method based on the combination of dark-channel prior with sparse representation is proposed. This method adopts the dark-channel prior theory to calculate the dark-channel image at first, and then uses sparse representation to denoise and optimize the dark-channel image. Based on the improved dark-channel image, the more precise water transmissivity and light intensity can be achieved to compute the final restoration result, effectively eliminating the image blur as well as noise. The experimental results show that the proposed method can effectively improve the image factors, such as average gradient and entropy, so as to compensate the degraded image.
2018, 40(2): 272-281.
doi: 10.11999/JEIT170473
Abstract:
The problem of tracking drift is produced easily by traditional sparse representation tracking methods in complex scene. To solve this problem, a novel tracking approach based on sparse optimization of local sensing is proposed. Firstly, the object area of the first frame is divided into non-overlapping uniform segmentation, and building the template set using global features and local features. Then, a local sensing correction method for constraining sparse optimization matching process is utilized to determine the optimal matching samples. Finally, a new method of occlusion decision is used to detect occlusion, and updating strategies are adopted according to different occlusion conditions, which makes the template sets more complete in the process of template update. The experiments compare with state-of-the-art tracking algorithms on 10 tracking test sequences of benchmark library. Experiment results indicate that the proposed method possesses characteristics of accurate tracking and strong adaptability in the conditions of partial occlusion, deformation, and complex background.
The problem of tracking drift is produced easily by traditional sparse representation tracking methods in complex scene. To solve this problem, a novel tracking approach based on sparse optimization of local sensing is proposed. Firstly, the object area of the first frame is divided into non-overlapping uniform segmentation, and building the template set using global features and local features. Then, a local sensing correction method for constraining sparse optimization matching process is utilized to determine the optimal matching samples. Finally, a new method of occlusion decision is used to detect occlusion, and updating strategies are adopted according to different occlusion conditions, which makes the template sets more complete in the process of template update. The experiments compare with state-of-the-art tracking algorithms on 10 tracking test sequences of benchmark library. Experiment results indicate that the proposed method possesses characteristics of accurate tracking and strong adaptability in the conditions of partial occlusion, deformation, and complex background.
2018, 40(2): 282-288.
doi: 10.11999/JEIT170496
Abstract:
To get the closed boundary of target object of given stereoscopic image, this paper proposes an improved algorithm with disparity-guided constraint for active contour extraction based on greedy snake model. The disparity relationship between the control points and the center of the object is used to design a new external force, which guides the original contour to real boundary. In addition, this method also uses the improved model iteratively with processed control points as the original input. Experiment results prove this method reduces the reliance on initial values of input contour, obtains accurate contour made up of uniform and dense points, which is more effective than the traditional greedy snake algorithm.
To get the closed boundary of target object of given stereoscopic image, this paper proposes an improved algorithm with disparity-guided constraint for active contour extraction based on greedy snake model. The disparity relationship between the control points and the center of the object is used to design a new external force, which guides the original contour to real boundary. In addition, this method also uses the improved model iteratively with processed control points as the original input. Experiment results prove this method reduces the reliance on initial values of input contour, obtains accurate contour made up of uniform and dense points, which is more effective than the traditional greedy snake algorithm.
2018, 40(2): 289-297.
doi: 10.11999/JEIT170521
Abstract:
With the development of smart phones and mobile internet, Quick Response (QR) codes are widely applied to mobile information interaction. However, the appearance of the standard QR code is similar to the noise signal. It is lack of visual aesthetics, easy to damage the overall aesthetic of the publicity materials so that the promotional effect will be affected. To solve this problem, this paper proposes a beautification approach for embedding a color image in a QR code. At first, the method processes the given color image with saliency detection and halftoning techniques to acquire the corresponding saliency image and halftone image. Then, the modules distribution of QR code is optimized by according to the halftone image. In order to improve the optimization efficiency, a saliency weighted random sampling algorithm is proposed. Finally, a binary search based color adjustment algorithm is proposed in color rendering. Experimental results show that the color QR code generated by the proposed method can be correctly decoded. At the same time, it improves the visual appearance, increases the visual aesthetics, and possesses more visual appeal.
With the development of smart phones and mobile internet, Quick Response (QR) codes are widely applied to mobile information interaction. However, the appearance of the standard QR code is similar to the noise signal. It is lack of visual aesthetics, easy to damage the overall aesthetic of the publicity materials so that the promotional effect will be affected. To solve this problem, this paper proposes a beautification approach for embedding a color image in a QR code. At first, the method processes the given color image with saliency detection and halftoning techniques to acquire the corresponding saliency image and halftone image. Then, the modules distribution of QR code is optimized by according to the halftone image. In order to improve the optimization efficiency, a saliency weighted random sampling algorithm is proposed. Finally, a binary search based color adjustment algorithm is proposed in color rendering. Experimental results show that the color QR code generated by the proposed method can be correctly decoded. At the same time, it improves the visual appearance, increases the visual aesthetics, and possesses more visual appeal.
2018, 40(2): 298-305.
doi: 10.11999/JEIT170460
Abstract:
As a result of the existence of organisms and suspended particles under underwater conditions, images captured under water usually have low contrast, color distortion and loss of visibility. At the same time, due to the existence of the artificial light source, the underwater image usually has the non-uniform illumination. Traditional hazy-removal methods perform poorly under water. In order to take both absorption and scattering into consideration, a new underwater image formation model and restoration methods are proposed recently. However, these methods ignore the great impact of the red channel information and artificial light source. To solve this problem, a new approach is proposed for underwater image visibility restoration. Firstly, a threshold is set to determine whether to use the red channel information to estimate the dark channel, and a saturation indicator which is used to indicate the impact of artificial light source is utilized to calculate the scattering rate. Based on the red channel information anticipation and the saturation indicator, a new method is proposed to estimate the dark channel. Then, the transmission of each channel is estimated according to the attenuation coefficient ratio, which makes the proposed method more robust. Finally, the ambient light is obtained using the Shades of Gray algorithm, and the visibility restoration result is achieved based on a new underwater image formation model. Experimental results demonstrate that the proposed algorithm can significantly improve the contrast of the underwater image with more natural color and better visibility.
As a result of the existence of organisms and suspended particles under underwater conditions, images captured under water usually have low contrast, color distortion and loss of visibility. At the same time, due to the existence of the artificial light source, the underwater image usually has the non-uniform illumination. Traditional hazy-removal methods perform poorly under water. In order to take both absorption and scattering into consideration, a new underwater image formation model and restoration methods are proposed recently. However, these methods ignore the great impact of the red channel information and artificial light source. To solve this problem, a new approach is proposed for underwater image visibility restoration. Firstly, a threshold is set to determine whether to use the red channel information to estimate the dark channel, and a saturation indicator which is used to indicate the impact of artificial light source is utilized to calculate the scattering rate. Based on the red channel information anticipation and the saturation indicator, a new method is proposed to estimate the dark channel. Then, the transmission of each channel is estimated according to the attenuation coefficient ratio, which makes the proposed method more robust. Finally, the ambient light is obtained using the Shades of Gray algorithm, and the visibility restoration result is achieved based on a new underwater image formation model. Experimental results demonstrate that the proposed algorithm can significantly improve the contrast of the underwater image with more natural color and better visibility.
2018, 40(2): 306-313.
doi: 10.11999/JEIT170338
Abstract:
Fingerprint-based indoor localization technology is attracted extensive attention of researchers with the fusion of crowd-sensing and machine learning. However, existing approaches have the bottleneck of scalability and instantaneity caused by high radio map construction effort. Focusing on this issue, this paper proposes a novel and scalable lightweight radio map construction method, named FFIL. In the fingerprint construction phase, the whole indoor environment is divided into multi-loop to segment map rapidly and fingerprint data are obtained. In the fingerprint matching phase, the distance is calculated from Access Point (AP) to target firstly, and then the reference point is selected on the loop with most similar with the circle radius to match fingerprint data one by one. In the localization phase, contour-based clustering algorithm is used to improve the positioning accuracy. Abundant simulations and experiments are driven by real data show that FFIL can reduce the overhead of constructing radio fingerprint map and improve the positioning accuracy and the real-time performance of system simultaneously.
Fingerprint-based indoor localization technology is attracted extensive attention of researchers with the fusion of crowd-sensing and machine learning. However, existing approaches have the bottleneck of scalability and instantaneity caused by high radio map construction effort. Focusing on this issue, this paper proposes a novel and scalable lightweight radio map construction method, named FFIL. In the fingerprint construction phase, the whole indoor environment is divided into multi-loop to segment map rapidly and fingerprint data are obtained. In the fingerprint matching phase, the distance is calculated from Access Point (AP) to target firstly, and then the reference point is selected on the loop with most similar with the circle radius to match fingerprint data one by one. In the localization phase, contour-based clustering algorithm is used to improve the positioning accuracy. Abundant simulations and experiments are driven by real data show that FFIL can reduce the overhead of constructing radio fingerprint map and improve the positioning accuracy and the real-time performance of system simultaneously.
2018, 40(2): 314-322.
doi: 10.11999/JEIT170454
Abstract:
In order to balance between convergence and distribution in Multi-Objective Evolutionary Algorithms (MOEAs), a Many-Objective Evolutionary Algorithm based on Angle Penalized Distance (MaOEA-APD) is proposed. Firstly, considering the importance of convergence and diversity in the different stages of the evolutionary process, an angle penalized distance is constructed to dynamically balance between them. Then, the environmental selection based on removing the worse individual is designed to maintain the distribution and improve the convergence. Finally, the mating selection is designed based on the principle of the environmental selection. Both are complement and coordinated to each other for improving the evolutionary efficiency of the algorithm. Compared with three state-of-the-art many-objective evolutionary algorithms (MaOEAs), the experimental results on WFG test suite show that MaOEA-APD has more advantage than other algorithms in terms of the overall performance.
In order to balance between convergence and distribution in Multi-Objective Evolutionary Algorithms (MOEAs), a Many-Objective Evolutionary Algorithm based on Angle Penalized Distance (MaOEA-APD) is proposed. Firstly, considering the importance of convergence and diversity in the different stages of the evolutionary process, an angle penalized distance is constructed to dynamically balance between them. Then, the environmental selection based on removing the worse individual is designed to maintain the distribution and improve the convergence. Finally, the mating selection is designed based on the principle of the environmental selection. Both are complement and coordinated to each other for improving the evolutionary efficiency of the algorithm. Compared with three state-of-the-art many-objective evolutionary algorithms (MaOEAs), the experimental results on WFG test suite show that MaOEA-APD has more advantage than other algorithms in terms of the overall performance.
2018, 40(2): 323-330.
doi: 10.11999/JEIT170357
Abstract:
Based on a large number of training samples to generate high confidence images, generative adversarial nets achieve good results, but the existing network of image generation in the training sample basis, the training parameters can not be used to generate images outside of training samples. In this paper, an improved generative adversarial nets model is proposed, and a reduction layer is added on the basis of the existing network, so that the test image can generate the corresponding high confidence image through the improved generative adversarial nets. The experimental results show that the improved generative adversarial nets parameters can be applied to the common samples outside the training set. At the same time, this paper improves the loss algorithm of the generated model, which greatly shortens the convergence time of the network.
Based on a large number of training samples to generate high confidence images, generative adversarial nets achieve good results, but the existing network of image generation in the training sample basis, the training parameters can not be used to generate images outside of training samples. In this paper, an improved generative adversarial nets model is proposed, and a reduction layer is added on the basis of the existing network, so that the test image can generate the corresponding high confidence image through the improved generative adversarial nets. The experimental results show that the improved generative adversarial nets parameters can be applied to the common samples outside the training set. At the same time, this paper improves the loss algorithm of the generated model, which greatly shortens the convergence time of the network.
2018, 40(2): 331-337.
doi: 10.11999/JEIT170501
Abstract:
For the image distortion of over-carved, this paper proposes a modified Seam Carving (SC) method based on image blocking. Images are segmented into protected and non-protected blocks according to the labelled averaged column summation energy vectors, and then each block is allocated the corresponding carving seams. Moreover, the cumulative energy map is optimized in order to reduce the possibility of the small significant regions to be cut off. This paper fused blocking with the SC method, and optimized the cumulative energy map, which can make a carving balance between the object and background parts. In the MSRA database, the proposed algorithm are compared with the SC method and its improved methods. The experimental results are evaluated on the Internet to test their subjective perceptions, which shows that the proposed method has a better subjective perception, and a general applicability for different images.
For the image distortion of over-carved, this paper proposes a modified Seam Carving (SC) method based on image blocking. Images are segmented into protected and non-protected blocks according to the labelled averaged column summation energy vectors, and then each block is allocated the corresponding carving seams. Moreover, the cumulative energy map is optimized in order to reduce the possibility of the small significant regions to be cut off. This paper fused blocking with the SC method, and optimized the cumulative energy map, which can make a carving balance between the object and background parts. In the MSRA database, the proposed algorithm are compared with the SC method and its improved methods. The experimental results are evaluated on the Internet to test their subjective perceptions, which shows that the proposed method has a better subjective perception, and a general applicability for different images.
2018, 40(2): 338-345.
doi: 10.11999/JEIT170416
Abstract:
To overcome the deficiency of static expression feature, which lacks time information and can not reflect the subtle changes of expression adequately, a dynamic expression recognition method is proposed for non-specific face: the dynamic expression recognition based on Dynamic Time Warping (DTW) and Active Appearance Model (AAM). Firstly, the method of DTW based on local gradient Dual Tree-Complex Wavelet Transform (DT-CWT) dominant direction pattern is used to warp expression sequence. Secondly, using AAM to locate 66 feature points of face image and track them. The changing feature of expression can be obtained by calculating the displacement of corresponding feature points in two adjacent expression sequences image. And using the feature points of neutral face to build the facial geometry model. The matching of facial geometry model can overcome the expression differences between various people. Finally, the nearest neighbor classifier is used for classification and recognition. The experimental results on CK+ database and HeFei University of Technology-Face Emotion (HFUT-FE) database show that the proposed algorithm has a high degree of accuracy.
To overcome the deficiency of static expression feature, which lacks time information and can not reflect the subtle changes of expression adequately, a dynamic expression recognition method is proposed for non-specific face: the dynamic expression recognition based on Dynamic Time Warping (DTW) and Active Appearance Model (AAM). Firstly, the method of DTW based on local gradient Dual Tree-Complex Wavelet Transform (DT-CWT) dominant direction pattern is used to warp expression sequence. Secondly, using AAM to locate 66 feature points of face image and track them. The changing feature of expression can be obtained by calculating the displacement of corresponding feature points in two adjacent expression sequences image. And using the feature points of neutral face to build the facial geometry model. The matching of facial geometry model can overcome the expression differences between various people. Finally, the nearest neighbor classifier is used for classification and recognition. The experimental results on CK+ database and HeFei University of Technology-Face Emotion (HFUT-FE) database show that the proposed algorithm has a high degree of accuracy.
2018, 40(2): 346-352.
doi: 10.11999/JEIT170282
Abstract:
Hand infrared trace images can not clearly reflect the original contact contour of hand, which belong to a special kind of infrared blurred images. Inspired by biological immune, an artificial targeting immunotherapy is proposed to extract the hand target. Firstly, according to the feature of temporal correlation, the innate immune recognition is designed to preliminary segmentation. Secondly, according to the immune presentation, a concentric circles template based on thermal diffusion is defined to extract features. Then adaptive immune recognition is applied to the fuzzy pixels set based on the obtained template features. Finally, for the detected finger valley and fingertips lesions, targeted therapy is implemented to keep the shape of the hand. The proposed algorithm is compared with watershed method, SOM network and recent research achievements. Experimental results show that the proposed algorithm exhibits better extraction performance, meanwhile the application time of thermal trace images is extended.
Hand infrared trace images can not clearly reflect the original contact contour of hand, which belong to a special kind of infrared blurred images. Inspired by biological immune, an artificial targeting immunotherapy is proposed to extract the hand target. Firstly, according to the feature of temporal correlation, the innate immune recognition is designed to preliminary segmentation. Secondly, according to the immune presentation, a concentric circles template based on thermal diffusion is defined to extract features. Then adaptive immune recognition is applied to the fuzzy pixels set based on the obtained template features. Finally, for the detected finger valley and fingertips lesions, targeted therapy is implemented to keep the shape of the hand. The proposed algorithm is compared with watershed method, SOM network and recent research achievements. Experimental results show that the proposed algorithm exhibits better extraction performance, meanwhile the application time of thermal trace images is extended.
2018, 40(2): 353-362.
doi: 10.11999/JEIT170238
Abstract:
For the problem of noninvasive continuous blood pressure algorithm with un-accuracy, a novel multi- parameter fusion algorithm based on BP neural network is proposed, according to the formation from electrocardiogram and photoplethysmograph of arterial blood pressure. The improved Pan Tompkins algorithm is used to extract the R peak of electrocardiogram, and difference-threshold algorithm is used to extract the features points of photo-plethysmograph, and the fifteen feature parameters relative to blood pressure are extracted and used to establish the model of blood pressure to estimate the beat-to-beat systolic blood pressure and diastolic blood pressure. The factor analysis method is used to analyze the weight of each parameter. The results show that the weight order is pulse transit time, time information, photoplethysmography area information, amplitude information and area ratio. The algorithm is tested in the TianTan Hospital, and the meansstandard difference of single measurement errors are respectively -1.576.12 mmHg and -0.624.82 mmHg, the means standard difference, D. of repeated measurement errors are respectively -2.125.10 mmHg and -2.524.41 mmHg, for systolic blood pressure and diastolic blood pressure. And the measurement accuracy for systolic blood pressure and diastolic blood pressure reaches Grade A of BHS standard and AAMI standard.
For the problem of noninvasive continuous blood pressure algorithm with un-accuracy, a novel multi- parameter fusion algorithm based on BP neural network is proposed, according to the formation from electrocardiogram and photoplethysmograph of arterial blood pressure. The improved Pan Tompkins algorithm is used to extract the R peak of electrocardiogram, and difference-threshold algorithm is used to extract the features points of photo-plethysmograph, and the fifteen feature parameters relative to blood pressure are extracted and used to establish the model of blood pressure to estimate the beat-to-beat systolic blood pressure and diastolic blood pressure. The factor analysis method is used to analyze the weight of each parameter. The results show that the weight order is pulse transit time, time information, photoplethysmography area information, amplitude information and area ratio. The algorithm is tested in the TianTan Hospital, and the meansstandard difference of single measurement errors are respectively -1.576.12 mmHg and -0.624.82 mmHg, the means standard difference, D. of repeated measurement errors are respectively -2.125.10 mmHg and -2.524.41 mmHg, for systolic blood pressure and diastolic blood pressure. And the measurement accuracy for systolic blood pressure and diastolic blood pressure reaches Grade A of BHS standard and AAMI standard.
2018, 40(2): 363-370.
doi: 10.11999/JEIT170484
Abstract:
In order to provide a recommended-path service for tourists with the shortest traveling time in the peak-season, the Time Optimal Traveling Salesman Problem (TOTSP) is further studied and the fit function is introduced into the fitness function of the hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm (SFLA-GA) to reflect the change of traffic over time, which is based on the classic and Symmetrical Traveling Salesman Problem (STSP). The experimental results show that compared with the random tour path, the tour path significantly saves the tour time which is obtained by the hybrid SFLA-GA. Compared with SFLA and hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA), the hybrid SFLA-GA has some advantages, such as less amount of calculation, fast speed of convergence, low dependency on initial population, good global superiority and so on. The hybrid SFLA-GA has stronger search capability and less search time in solving the TOTSP.
In order to provide a recommended-path service for tourists with the shortest traveling time in the peak-season, the Time Optimal Traveling Salesman Problem (TOTSP) is further studied and the fit function is introduced into the fitness function of the hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm (SFLA-GA) to reflect the change of traffic over time, which is based on the classic and Symmetrical Traveling Salesman Problem (STSP). The experimental results show that compared with the random tour path, the tour path significantly saves the tour time which is obtained by the hybrid SFLA-GA. Compared with SFLA and hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA), the hybrid SFLA-GA has some advantages, such as less amount of calculation, fast speed of convergence, low dependency on initial population, good global superiority and so on. The hybrid SFLA-GA has stronger search capability and less search time in solving the TOTSP.
2018, 40(2): 371-377.
doi: 10.11999/JEIT170365
Abstract:
The traditional Direct Position Determination (DPD) methods have localization accuracy decrease when locating distributed sources. DPD methods of the distributed source is proposed in this paper to overcome mentioned above shortcoming. Firstly, a DPD model of the distributed source is constructed. Then two new DPD methods based on Maximum likelihood criterion and multiple signal classification are proposed to locate the distributed sourceMaximum Likelihood estimation DPD method of the Distributed source (DML-DPD) and Generalized Subspace DPD method (GS-DPD). Finally, target position is estimated via multidimensional grid search. The simulations show that the proposed methods have higher localization accuracy than traditional DPD methods when locating the distributed source, and are close to CRLB under the low SNR condition. DML-DPD method has higher localization accuracy than GS-DPD method in the case of low SNR, while GS-DPD method has less computational complexity than DML-DPD method.
The traditional Direct Position Determination (DPD) methods have localization accuracy decrease when locating distributed sources. DPD methods of the distributed source is proposed in this paper to overcome mentioned above shortcoming. Firstly, a DPD model of the distributed source is constructed. Then two new DPD methods based on Maximum likelihood criterion and multiple signal classification are proposed to locate the distributed sourceMaximum Likelihood estimation DPD method of the Distributed source (DML-DPD) and Generalized Subspace DPD method (GS-DPD). Finally, target position is estimated via multidimensional grid search. The simulations show that the proposed methods have higher localization accuracy than traditional DPD methods when locating the distributed source, and are close to CRLB under the low SNR condition. DML-DPD method has higher localization accuracy than GS-DPD method in the case of low SNR, while GS-DPD method has less computational complexity than DML-DPD method.
2018, 40(2): 378-385.
doi: 10.11999/JEIT170366
Abstract:
To improve the convergence speed and noise suppression effects of blind equalizer under impulsive noise environment, a new dual-mode blind equalization algorithm based on Renyi entropy and fractional lower order statistics is presented. Renyi entropy and fractional lower order statistics are combined as cost functions to update the weight coefficients of the equalizer in this method, which can improve the convergence speed and enhance the ability of suppressing impulse noise. In addition, considering the robustness of system, a double-threshold based weighting decision method is proposed. By setting double thresholds and a nonlinear weighting function, the switching between two cost functions become smooth. Simulation experiments are carried out under different impulse noise and different channel conditions. The results show that the algorithm converges faster and suppresses impulse noise effectively at the same time.
To improve the convergence speed and noise suppression effects of blind equalizer under impulsive noise environment, a new dual-mode blind equalization algorithm based on Renyi entropy and fractional lower order statistics is presented. Renyi entropy and fractional lower order statistics are combined as cost functions to update the weight coefficients of the equalizer in this method, which can improve the convergence speed and enhance the ability of suppressing impulse noise. In addition, considering the robustness of system, a double-threshold based weighting decision method is proposed. By setting double thresholds and a nonlinear weighting function, the switching between two cost functions become smooth. Simulation experiments are carried out under different impulse noise and different channel conditions. The results show that the algorithm converges faster and suppresses impulse noise effectively at the same time.
2018, 40(2): 386-393.
doi: 10.11999/JEIT170424
Abstract:
To solve the problem of data loss in the field of Fiber Bragg Grating (FBG) sensing, a signal repaired method based on compressed sensing with improved reconstruction algorithm is proposed. According to the characteristics of signal, the suitable observation matrix and sparse dictionary are selected to repair the damaged spectral signal. An adaptive threshold function, which is used to match the characteristics of signal, is proposed in the reconstruction algorithm, and the criterion of threshold rationality is added. The relationship between the recovery precision of signal and sensing accuracy of fiber Bragg grating is analyzed, and the repairing effects are validated by peak-detected error of reconstructed signal. Simulation results show that the average relative error is10-6 when 30% of the data is lost. The root mean square error is 0.0707, which is 0.0232~0.1159 lower than the contrast algorithms. The peak-detected error is lower than the others. Besides, the average running time of the system is much lower than the compared algorithms. All the results show that the proposed algorithm can well achieve the recovery of missing data, so as to improve the measurement precision of fiber Bragg grating sensor.
To solve the problem of data loss in the field of Fiber Bragg Grating (FBG) sensing, a signal repaired method based on compressed sensing with improved reconstruction algorithm is proposed. According to the characteristics of signal, the suitable observation matrix and sparse dictionary are selected to repair the damaged spectral signal. An adaptive threshold function, which is used to match the characteristics of signal, is proposed in the reconstruction algorithm, and the criterion of threshold rationality is added. The relationship between the recovery precision of signal and sensing accuracy of fiber Bragg grating is analyzed, and the repairing effects are validated by peak-detected error of reconstructed signal. Simulation results show that the average relative error is10-6 when 30% of the data is lost. The root mean square error is 0.0707, which is 0.0232~0.1159 lower than the contrast algorithms. The peak-detected error is lower than the others. Besides, the average running time of the system is much lower than the compared algorithms. All the results show that the proposed algorithm can well achieve the recovery of missing data, so as to improve the measurement precision of fiber Bragg grating sensor.
2018, 40(2): 394-399.
doi: 10.11999/JEIT170552
Abstract:
For the generator polynomial blind recognition method of binary pseudo-random sequences, it is necessary to know the polynomials order in advance, the algorithm with poor fault tolerance and high complexity. In this paper, the analysis matrix is first constructed according to the estimated polynomials order of the intercepted sequence. Then the method of Galoisian column Gaussian elimination is used to identify the order of the polynomial of the intercept sequence. Finally, the equation set is constructed according to the polynomials order. In order to reduce the complexity of the algorithm, the polynomials that satisfy the equations in the finite polynomial library are the generator polynomials of the intercepted sequences. The simulation results show that the proposed method can distinguish the m sequence, the Gold sequence, or other binary pseudorandom sequences, and effectively identify their own generating polynomials, and has good fault tolerance.
For the generator polynomial blind recognition method of binary pseudo-random sequences, it is necessary to know the polynomials order in advance, the algorithm with poor fault tolerance and high complexity. In this paper, the analysis matrix is first constructed according to the estimated polynomials order of the intercepted sequence. Then the method of Galoisian column Gaussian elimination is used to identify the order of the polynomial of the intercept sequence. Finally, the equation set is constructed according to the polynomials order. In order to reduce the complexity of the algorithm, the polynomials that satisfy the equations in the finite polynomial library are the generator polynomials of the intercepted sequences. The simulation results show that the proposed method can distinguish the m sequence, the Gold sequence, or other binary pseudorandom sequences, and effectively identify their own generating polynomials, and has good fault tolerance.
2018, 40(2): 400-407.
doi: 10.11999/JEIT170356
Abstract:
In order to achieve the justification of whether a frame structure detection exists or not under the condition that protocol is unknown, an algorithm for frame structure detection in physical layer based on multi-fractal spectrum is proposed. Firstly, the relationship between bias and occurrence probability of 0 and 1 bit is defined. Through analyzing the generation principle of channel code, scrambler, frame sequence and calculating the occurrence probability of 0 and 1 bit, the conclusion is that the bias of synchronization word is larger than the others. Then, due to the above conclusion and the character that bias distribution can be described by multi-fractal spectrum, the width of multi-fractal spectrum of split sequences is calculated. At last, frame structure can be detected by observing distribution of the width of multi-fractal spectrum of split sequences. Simulation results show the proposed algorithm is effective, and it has value in engineering application.
In order to achieve the justification of whether a frame structure detection exists or not under the condition that protocol is unknown, an algorithm for frame structure detection in physical layer based on multi-fractal spectrum is proposed. Firstly, the relationship between bias and occurrence probability of 0 and 1 bit is defined. Through analyzing the generation principle of channel code, scrambler, frame sequence and calculating the occurrence probability of 0 and 1 bit, the conclusion is that the bias of synchronization word is larger than the others. Then, due to the above conclusion and the character that bias distribution can be described by multi-fractal spectrum, the width of multi-fractal spectrum of split sequences is calculated. At last, frame structure can be detected by observing distribution of the width of multi-fractal spectrum of split sequences. Simulation results show the proposed algorithm is effective, and it has value in engineering application.
2018, 40(2): 408-415.
doi: 10.11999/JEIT170577
Abstract:
This paper proposes a new OFDM symbol duration based cyclostationary spectrum sensing method. The method first estimates the cyclic autocorrelation function from every received OFDM symbol during its symbol period, then constructs the test statistic and the threshold by using multivariate statistical analysis, and finally gets the decision result by comparing the test statistic with the threshold. The method is nonparametric so that it is immune from noise uncertainty. Simulation results show that the method can significantly reduce the complexity at the cost of a little performance loss, compared with conventional cyclostationary spectrum sensing method. Moreover, this paper further proposes a multiple antenna based nonparametric linear weighted combination scheme. Simulation results also show that the performance of the proposed combination scheme is almost the same as that of conventional cyclostationary spectrum sensing method while the proposed combination scheme has the advantage of complexity by optimizing the nonparametric weights reasonably.
This paper proposes a new OFDM symbol duration based cyclostationary spectrum sensing method. The method first estimates the cyclic autocorrelation function from every received OFDM symbol during its symbol period, then constructs the test statistic and the threshold by using multivariate statistical analysis, and finally gets the decision result by comparing the test statistic with the threshold. The method is nonparametric so that it is immune from noise uncertainty. Simulation results show that the method can significantly reduce the complexity at the cost of a little performance loss, compared with conventional cyclostationary spectrum sensing method. Moreover, this paper further proposes a multiple antenna based nonparametric linear weighted combination scheme. Simulation results also show that the performance of the proposed combination scheme is almost the same as that of conventional cyclostationary spectrum sensing method while the proposed combination scheme has the advantage of complexity by optimizing the nonparametric weights reasonably.
2018, 40(2): 416-420.
doi: 10.11999/JEIT170498
Abstract:
Minimum Mean Square Error (MMSE) algorithm is near-optimal for uplink massive MIMO systems, but it involves high-complexity matrix inversion. Recently, the proposed detection algorithm based on Neumann series approximation reduces the complexity with some performance losses. In order to reduce the complexity while approaching the performance of MMSE algorithm, the Neumann series approximation based on two-diagonal matrix decomposition is proposed in this paper, that is, the large matrix is decomposed into the sum of the two elements of the main diagonal and the hollow matrix. The theoretical analysis and simulation results show that the detection performance of the proposed algorithm is close to the MMSE detection algorithm while its computational complexity is reduced from O(K3)toO(K2), where K is the number of users.
Minimum Mean Square Error (MMSE) algorithm is near-optimal for uplink massive MIMO systems, but it involves high-complexity matrix inversion. Recently, the proposed detection algorithm based on Neumann series approximation reduces the complexity with some performance losses. In order to reduce the complexity while approaching the performance of MMSE algorithm, the Neumann series approximation based on two-diagonal matrix decomposition is proposed in this paper, that is, the large matrix is decomposed into the sum of the two elements of the main diagonal and the hollow matrix. The theoretical analysis and simulation results show that the detection performance of the proposed algorithm is close to the MMSE detection algorithm while its computational complexity is reduced from O(K3)toO(K2), where K is the number of users.
2018, 40(2): 421-429.
doi: 10.11999/JEIT170300
Abstract:
Most of the existing vertical handover algorithms are only based on user centric or network centric, which do not fully consider the common impact of the two sides on the handover decision. In order to solve this problem, a two-sided matching model based vertical handover algorithm in heterogeneous wireless networks is proposed. The handover performance is improved from the following aspects: first, two evaluation models of user centered and network centered are respectively proposed based on quality of service revenue and the blocking rate. And the ranking value of the two sides is evaluated further. Then, based on the ranking value, a one-to-many two-sided matching model is used to model the two-sided matching behavior of users and networks. In this way, users can access suitable networks. Simulation results show that the proposed evaluation models are in accordance with the practical network scenario, and the proposed algorithm can better balance the demand of users and networks.
Most of the existing vertical handover algorithms are only based on user centric or network centric, which do not fully consider the common impact of the two sides on the handover decision. In order to solve this problem, a two-sided matching model based vertical handover algorithm in heterogeneous wireless networks is proposed. The handover performance is improved from the following aspects: first, two evaluation models of user centered and network centered are respectively proposed based on quality of service revenue and the blocking rate. And the ranking value of the two sides is evaluated further. Then, based on the ranking value, a one-to-many two-sided matching model is used to model the two-sided matching behavior of users and networks. In this way, users can access suitable networks. Simulation results show that the proposed evaluation models are in accordance with the practical network scenario, and the proposed algorithm can better balance the demand of users and networks.
2018, 40(2): 430-437.
doi: 10.11999/JEIT170422
Abstract:
Mixed Operation based Ciphers (MOC) attract cryptographers owing to their high security and high efficiency on both software and hardware platforms. As a basic principle of cryptosystem design, completeness refers to that every output bit contains the information of every input bit. This paper presents a universal algorithm of completeness analysis against MOC. Based on the algorithm, a method of constructing impossible differential distinguishers utilizing completeness is proposed. This method constructs heavy weight impossible differential distinguishers directly with high efficiency. The method can provide theory and technology direction for the construction of impossible differential distinguishers. Then, this paper analysis SIMON and SPECK with this method and introduces all the longest impossible differential distinguishers of SIMON currently public and new impossible differential distinguishers of SPECK.
Mixed Operation based Ciphers (MOC) attract cryptographers owing to their high security and high efficiency on both software and hardware platforms. As a basic principle of cryptosystem design, completeness refers to that every output bit contains the information of every input bit. This paper presents a universal algorithm of completeness analysis against MOC. Based on the algorithm, a method of constructing impossible differential distinguishers utilizing completeness is proposed. This method constructs heavy weight impossible differential distinguishers directly with high efficiency. The method can provide theory and technology direction for the construction of impossible differential distinguishers. Then, this paper analysis SIMON and SPECK with this method and introduces all the longest impossible differential distinguishers of SIMON currently public and new impossible differential distinguishers of SPECK.
2018, 40(2): 438-446.
doi: 10.11999/JEIT170489
Abstract:
In order to overcome the defects such as contour emerging induced by the cipher phase information mainly concentrated in the pure phase mask and difficult to resist the amplitude phase retrieval attack in current optical image encryption technology, the optical image encryption algorithm based on coherent superposition and equal modulus vector decomposition is proposed in this paper. Firstly, the optical image is normalized, and the initial value of Logistic map is generated using the pixel characteristics of plaintext, and the random phase mask is outputted by iterative the logistic map. Then the image is modulated by random phase function, and the modulated image is processed based on Fourier transform to output the Fourier spectrum. Then, the Fourier spectrum is decomposed equal module to obtain two masks. Each mask is transformed based on Fourier mechanism with different fractional order. Finally, a one-way coding scheme is designed based on phase - amplitude truncation coding technique to get the amplitude and phase information of each Fourier spectrum, which regards the phase part as coding cipher, and the amplitude information as the decryption key. The input plaintext is transformed into 4 different information of phases and amplitudes using the equal modulus vector decomposition technique to solve effectively the problem of contour representation. The experimental results show that the proposed algorithm has higher security for effectively solving the contour appearance problem compared with current image encryption schemes based on the interference theory.
In order to overcome the defects such as contour emerging induced by the cipher phase information mainly concentrated in the pure phase mask and difficult to resist the amplitude phase retrieval attack in current optical image encryption technology, the optical image encryption algorithm based on coherent superposition and equal modulus vector decomposition is proposed in this paper. Firstly, the optical image is normalized, and the initial value of Logistic map is generated using the pixel characteristics of plaintext, and the random phase mask is outputted by iterative the logistic map. Then the image is modulated by random phase function, and the modulated image is processed based on Fourier transform to output the Fourier spectrum. Then, the Fourier spectrum is decomposed equal module to obtain two masks. Each mask is transformed based on Fourier mechanism with different fractional order. Finally, a one-way coding scheme is designed based on phase - amplitude truncation coding technique to get the amplitude and phase information of each Fourier spectrum, which regards the phase part as coding cipher, and the amplitude information as the decryption key. The input plaintext is transformed into 4 different information of phases and amplitudes using the equal modulus vector decomposition technique to solve effectively the problem of contour representation. The experimental results show that the proposed algorithm has higher security for effectively solving the contour appearance problem compared with current image encryption schemes based on the interference theory.
2018, 40(2): 447-454.
doi: 10.11999/JEIT170465
Abstract:
As for energy hole issue in Wireless Sensor Network (WSN), an energy-balanced dynamic hierarchical data forwarding strategy is proposed. According to the node usable energy, relative position and the energy consumption of cluster head in different area, the unequal hierarchical model is constructed. Furthermore, in order to realize energy efficiency of inter-cluster multi-hop communication, both node energy cost in the phase of inner-cluster communication and node relationship are considered for relay node selection, then the packet compression algorithm, a way to further reduce the forwarding packet, is performed during the process of multi-hop inner-cluster packet forwarding. The numerical results demonstrate that the proposed mechanism can balance the network load effectively, prolong the network lifetime, and improve the performance of network packet forwarding.
As for energy hole issue in Wireless Sensor Network (WSN), an energy-balanced dynamic hierarchical data forwarding strategy is proposed. According to the node usable energy, relative position and the energy consumption of cluster head in different area, the unequal hierarchical model is constructed. Furthermore, in order to realize energy efficiency of inter-cluster multi-hop communication, both node energy cost in the phase of inner-cluster communication and node relationship are considered for relay node selection, then the packet compression algorithm, a way to further reduce the forwarding packet, is performed during the process of multi-hop inner-cluster packet forwarding. The numerical results demonstrate that the proposed mechanism can balance the network load effectively, prolong the network lifetime, and improve the performance of network packet forwarding.
2018, 40(2): 455-461.
doi: 10.11999/JEIT170464
Abstract:
With the expansion of Software Defined Network (SDN), the decoupling of control layer and data layer brings new problems such as controller deployment. In this paper, a Multi-Controller Deployment Algorithm Based on Load Balance (MCDALB) in SDN is proposed. Firstly, the number, K, of controllers is determined based on network topology and its load. Secondly, according to the limitation of controller capacity, a multi-controller load balance algorithm with approximate ratio of 2 is proposed, which divides the network into K control regions. Lastly, the position of the controller in each region is selected, according to the minimum sum of all switch-to-controller distances in the region. In order to verify the performance of the proposed algorithm, the actual network topologies are applied. As to compare with the AL and WL algorithms, simulation results show that the proposed algorithm not only balances the controllers load, with an approximation ratio of 2, but also meets the maximum gap of network delay not more than 0.65 ms.
With the expansion of Software Defined Network (SDN), the decoupling of control layer and data layer brings new problems such as controller deployment. In this paper, a Multi-Controller Deployment Algorithm Based on Load Balance (MCDALB) in SDN is proposed. Firstly, the number, K, of controllers is determined based on network topology and its load. Secondly, according to the limitation of controller capacity, a multi-controller load balance algorithm with approximate ratio of 2 is proposed, which divides the network into K control regions. Lastly, the position of the controller in each region is selected, according to the minimum sum of all switch-to-controller distances in the region. In order to verify the performance of the proposed algorithm, the actual network topologies are applied. As to compare with the AL and WL algorithms, simulation results show that the proposed algorithm not only balances the controllers load, with an approximation ratio of 2, but also meets the maximum gap of network delay not more than 0.65 ms.
2018, 40(2): 462-469.
doi: 10.11999/JEIT170490
Abstract:
For spaceborne GNSS-R, Delay-Doppler Map (DDM) shape is less sensitive to wind speed, resulting in poor retrieval accuracy measured by fitting theoretical DDM to the measured one. To solve this problem, a method directly linking the individual DDM power measurement to wind speed is developed to obtain wind speed. Scattered power is normalized by correction factor and its simplified form based on Zavorotny-Voronovich (Z-V) model, respectively. Empirical geophysical model functions linking both normalized scattered power and its simplified form to ocean surface wind speed are derived using spaceborne data on UK TechDemoSat-1 (TDS-1) together with in-situ Advanced Scatterometer (ASCAT) wind measurements. The Root-Mean-Square Error (RMSE) of wind speeds retrieved utilizing normalized scattered power is 2.11 m/s at 0~20 m/s. Simplified normalized scattered power by which wind speed accuracy retrieved is the same level with normalized scattered power is more suitable for real-time processing on-board.
For spaceborne GNSS-R, Delay-Doppler Map (DDM) shape is less sensitive to wind speed, resulting in poor retrieval accuracy measured by fitting theoretical DDM to the measured one. To solve this problem, a method directly linking the individual DDM power measurement to wind speed is developed to obtain wind speed. Scattered power is normalized by correction factor and its simplified form based on Zavorotny-Voronovich (Z-V) model, respectively. Empirical geophysical model functions linking both normalized scattered power and its simplified form to ocean surface wind speed are derived using spaceborne data on UK TechDemoSat-1 (TDS-1) together with in-situ Advanced Scatterometer (ASCAT) wind measurements. The Root-Mean-Square Error (RMSE) of wind speeds retrieved utilizing normalized scattered power is 2.11 m/s at 0~20 m/s. Simplified normalized scattered power by which wind speed accuracy retrieved is the same level with normalized scattered power is more suitable for real-time processing on-board.
2018, 40(2): 470-478.
doi: 10.11999/JEIT170439
Abstract:
The regular false targets which are generated by the intermittent sampling repeater jamming can be easily recognized. To overcome this disadvantage, an inter-pulse periodical shift-frequency jamming method is proposed against Synthetic Aperture Radar (SAR). Combining the intermittent sampling jamming method with Doppler shift-frequency jamming method, the dense false targets can be produced by sectionalized modulation. Firstly, theoretical analysis shows that the two dimensional dense false targets with suppression effect can be produced ahead of the designated location of the jammer, and the main false target is deviated from the jammer in the azimuth. Moreover, the effect of the jamming and the factors are discussed. Subsequently, the model based on obtaining the jamming parameters is established and the energy compensation coefficient of false targets is also given. Finally, the validity is preliminarily verified by simulation experiments.
The regular false targets which are generated by the intermittent sampling repeater jamming can be easily recognized. To overcome this disadvantage, an inter-pulse periodical shift-frequency jamming method is proposed against Synthetic Aperture Radar (SAR). Combining the intermittent sampling jamming method with Doppler shift-frequency jamming method, the dense false targets can be produced by sectionalized modulation. Firstly, theoretical analysis shows that the two dimensional dense false targets with suppression effect can be produced ahead of the designated location of the jammer, and the main false target is deviated from the jammer in the azimuth. Moreover, the effect of the jamming and the factors are discussed. Subsequently, the model based on obtaining the jamming parameters is established and the energy compensation coefficient of false targets is also given. Finally, the validity is preliminarily verified by simulation experiments.
2018, 40(2): 479-485.
doi: 10.11999/JEIT170295
Abstract:
The traditional detectors of colocated MIMO radar can detect a target without training data. However, the detection performance is poor with low number of waveform sampling. In this paper, two detectors, based on the Generalized Likelihood Ratio Test (GLRT) and Wald test criteria, are given by utilizing the persymmetric structure of the noise covariance matrix of colocated MIMO radar. In addition, the statistical distributions of the proposed detectors are analyzed and the analytical expressions of the probabilities of false alarm and detection are given. Simulation results show that the proposed detectors achieve good performance when the number of waveform sampling is low, and verify the theoretical results.
The traditional detectors of colocated MIMO radar can detect a target without training data. However, the detection performance is poor with low number of waveform sampling. In this paper, two detectors, based on the Generalized Likelihood Ratio Test (GLRT) and Wald test criteria, are given by utilizing the persymmetric structure of the noise covariance matrix of colocated MIMO radar. In addition, the statistical distributions of the proposed detectors are analyzed and the analytical expressions of the probabilities of false alarm and detection are given. Simulation results show that the proposed detectors achieve good performance when the number of waveform sampling is low, and verify the theoretical results.
2018, 40(2): 486-492.
doi: 10.11999/JEIT170217
Abstract:
Focus on the problem of phase ambiguity and the issue that it is impossible to estimate the unambiguous angles of multiple sources with the same frequency and time of arrival. An approach for multiple sources parameters estimation with rotating interferometer using virtual circular array processing is proposed in this paper. Firstly, the virtual circular array data is constructed by taking conjugate multiplication of the two channel data received by the rotating interferometer. Then, the virtue linear array data is obtained by employing beamspace transformation, which performs mapping from element-space to beamspace domain. Finally, the unambiguous angles of the multiple emitters are achieved in beamspace domain. Compared with conventional rotating interferometer methods, the proposed method can deal with the problem of unambiguous Direction Of Arrival (DOA) estimation of multiple emitters with only two receiving channels. The validity of the proposed method is verified by the simulation results.
Focus on the problem of phase ambiguity and the issue that it is impossible to estimate the unambiguous angles of multiple sources with the same frequency and time of arrival. An approach for multiple sources parameters estimation with rotating interferometer using virtual circular array processing is proposed in this paper. Firstly, the virtual circular array data is constructed by taking conjugate multiplication of the two channel data received by the rotating interferometer. Then, the virtue linear array data is obtained by employing beamspace transformation, which performs mapping from element-space to beamspace domain. Finally, the unambiguous angles of the multiple emitters are achieved in beamspace domain. Compared with conventional rotating interferometer methods, the proposed method can deal with the problem of unambiguous Direction Of Arrival (DOA) estimation of multiple emitters with only two receiving channels. The validity of the proposed method is verified by the simulation results.
2018, 40(2): 493-499.
doi: 10.11999/JEIT170455
Abstract:
Considering the problem of mismatched impedance of millimeter wave antenna, an algorithm based on Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameters and the pheromone guidance mechanism of the ant colony algorithm is used to obtain the patch length, width and feed position optimized by the particle swarm algorithm. The Fuzzy Decision-Making Comprehensive Evaluation (FD-MCE) model is used to solve the ground slotted position of the millimeter wave antenna to realize the bandwidth expansion . For the designed and simulated 28.0 GHz center frequency, the results show that the proposed method can effectively and quickly realize the impedance matching of millimeter wave antenna, and the resonant frequency is exactly the same as the center frequency. When the slot area is not greater than the total area of 30% of the ground, the bandwidth can be extended about 33%, and the return loss characteristics are also significantly improved. The proposed algorithm has the advantages of low computational complexity and fast convergence.
Considering the problem of mismatched impedance of millimeter wave antenna, an algorithm based on Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameters and the pheromone guidance mechanism of the ant colony algorithm is used to obtain the patch length, width and feed position optimized by the particle swarm algorithm. The Fuzzy Decision-Making Comprehensive Evaluation (FD-MCE) model is used to solve the ground slotted position of the millimeter wave antenna to realize the bandwidth expansion . For the designed and simulated 28.0 GHz center frequency, the results show that the proposed method can effectively and quickly realize the impedance matching of millimeter wave antenna, and the resonant frequency is exactly the same as the center frequency. When the slot area is not greater than the total area of 30% of the ground, the bandwidth can be extended about 33%, and the return loss characteristics are also significantly improved. The proposed algorithm has the advantages of low computational complexity and fast convergence.
2018, 40(2): 500-508.
doi: 10.11999/JEIT170419
Abstract:
Aggregate signcryption can not only reduce the cost of the verification of ciphertexts, but also ensure the confidentiality and authentication. Analyzed Liu et al s CertificateLess Aggregate SignCryption (CLASC) scheme with Const Pairings, it is found that type II adversary, who is the malicious key generator center, could forge the ciphertexts. It means that Liu et als scheme does not satisfy the indistinguishability under the adaptive chosen ciphertext attacks and unforgeability under the adaptive chosen message attacks. In order to improve the security level and verification efficiency of CLASC scheme, in this paper, the internal secure model of CLASC is defined and a concrete CLASC scheme with this property is presented. As the new scheme only needs 3 bilinear pairingis, it is more efficient than existing CLASC schemes. Based on the assumption of computational Diffie-Hellman, in the random oracle model and the internal security mode of CLASC, the new schems is proved to satisfy the confidentiality, unforgeability and public verification.
Aggregate signcryption can not only reduce the cost of the verification of ciphertexts, but also ensure the confidentiality and authentication. Analyzed Liu et al s CertificateLess Aggregate SignCryption (CLASC) scheme with Const Pairings, it is found that type II adversary, who is the malicious key generator center, could forge the ciphertexts. It means that Liu et als scheme does not satisfy the indistinguishability under the adaptive chosen ciphertext attacks and unforgeability under the adaptive chosen message attacks. In order to improve the security level and verification efficiency of CLASC scheme, in this paper, the internal secure model of CLASC is defined and a concrete CLASC scheme with this property is presented. As the new scheme only needs 3 bilinear pairingis, it is more efficient than existing CLASC schemes. Based on the assumption of computational Diffie-Hellman, in the random oracle model and the internal security mode of CLASC, the new schems is proved to satisfy the confidentiality, unforgeability and public verification.
