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2017 Vol. 39, No. 1
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2017, 39(1): 1-8.
doi: 10.11999/JEIT160245
Abstract:
The optimal system design based on maximizing the Energy Efficiency (EE) is investigated for the multi- pair massive Multiple-Input Multiple-Output (MIMO) relaying system. By virtue of the law of large numbers, an analytical expression of the involved EE function is derived with respect to the transmit power at the users and the relay, and the antenna number of the relay, when the Maximum Ratio Combining Maximum Ratio Transmission (MRC/MRT) precoding is adopted at the relay. The existences of a unique globally optimal transmit power vector and a unique globally optimal antenna number at relay are demonstrated separately by exploring the properties of the EE function. In order to obtain the optimal transmit power vector, the original fractional optimization problem is first transformed into an equivalent subtractive form by using the properties of fractional programming. Then, a low-complexity iterative algorithm is developed and the closed-form solution is deduced. Regarding the optimal number of relay antennas, a closed-form solution is also achieved by use of the Lambert W function. Numerical simulations show that the proposed power optimization algorithm converges to a near optimal solution only with a few numbers of iterations and the provided closed-form solution to the optimal number of relay antennas is also accurate.
The optimal system design based on maximizing the Energy Efficiency (EE) is investigated for the multi- pair massive Multiple-Input Multiple-Output (MIMO) relaying system. By virtue of the law of large numbers, an analytical expression of the involved EE function is derived with respect to the transmit power at the users and the relay, and the antenna number of the relay, when the Maximum Ratio Combining Maximum Ratio Transmission (MRC/MRT) precoding is adopted at the relay. The existences of a unique globally optimal transmit power vector and a unique globally optimal antenna number at relay are demonstrated separately by exploring the properties of the EE function. In order to obtain the optimal transmit power vector, the original fractional optimization problem is first transformed into an equivalent subtractive form by using the properties of fractional programming. Then, a low-complexity iterative algorithm is developed and the closed-form solution is deduced. Regarding the optimal number of relay antennas, a closed-form solution is also achieved by use of the Lambert W function. Numerical simulations show that the proposed power optimization algorithm converges to a near optimal solution only with a few numbers of iterations and the provided closed-form solution to the optimal number of relay antennas is also accurate.
2017, 39(1): 9-15.
doi: 10.11999/JEIT160264
Abstract:
The cross-layer optimum scheme of Energy Efficiency (EE) for a multihop relay network with Chase- Combining based Hybrid Automatic Repeat reQuest (CC-HARQ) in Rayleigh fading channels is proposed. In order to maximize EE, a closed-form expression of Energy Efficiency in a multihop CC-HARQ system is derived, which is obtained via an average frame error rate model adopting a new log-domain linear threshold method, and then optimal frame length scheme and optimal transmission power allocation method are further designed, towards the frame length and transmission power, a joint optimization metric of those two parameters is considered. Simulation results verify the correctness and feasibility of the analytical solutions, meanwhile, simulation experiments of comparisons show that the proposed cross-layer optimization design is able to improve the EE performance of practical multihop networks.
The cross-layer optimum scheme of Energy Efficiency (EE) for a multihop relay network with Chase- Combining based Hybrid Automatic Repeat reQuest (CC-HARQ) in Rayleigh fading channels is proposed. In order to maximize EE, a closed-form expression of Energy Efficiency in a multihop CC-HARQ system is derived, which is obtained via an average frame error rate model adopting a new log-domain linear threshold method, and then optimal frame length scheme and optimal transmission power allocation method are further designed, towards the frame length and transmission power, a joint optimization metric of those two parameters is considered. Simulation results verify the correctness and feasibility of the analytical solutions, meanwhile, simulation experiments of comparisons show that the proposed cross-layer optimization design is able to improve the EE performance of practical multihop networks.
2017, 39(1): 16-23.
doi: 10.11999/JEIT160226
Abstract:
An energy-efficient resource allocation method is provided for the downlink massive Multiple-Input Multiple-Output (MIMO) Frequency Division Duplexing (FDD) system, which jointly evaluates the channel estimation stage and data transmission stage. The optimization problem is established based on the Energy Efficiency (EE) maximization by adjusting the pilot duration, pilot power and data power under the constraint of total transmit energy and spectral efficiency requirement. Since the analytical expression of the involved objective function is intractable, a closed-form expression is deduced using deterministic equivalent approximation technology. Based on this, the original non-convex fractional optimization problem is transformed into an equivalent problem in subtractive form by the means of fraction programming. Then, a lower bound of the transformed objective function is employed, which induces a relatively easy-to-solve equivalent problem. Finally, a three-layer iterative algorithm is developed. Numerical results validate the effectiveness and relatively fast convergence speed of the proposed algorithm.
An energy-efficient resource allocation method is provided for the downlink massive Multiple-Input Multiple-Output (MIMO) Frequency Division Duplexing (FDD) system, which jointly evaluates the channel estimation stage and data transmission stage. The optimization problem is established based on the Energy Efficiency (EE) maximization by adjusting the pilot duration, pilot power and data power under the constraint of total transmit energy and spectral efficiency requirement. Since the analytical expression of the involved objective function is intractable, a closed-form expression is deduced using deterministic equivalent approximation technology. Based on this, the original non-convex fractional optimization problem is transformed into an equivalent problem in subtractive form by the means of fraction programming. Then, a lower bound of the transformed objective function is employed, which induces a relatively easy-to-solve equivalent problem. Finally, a three-layer iterative algorithm is developed. Numerical results validate the effectiveness and relatively fast convergence speed of the proposed algorithm.
2017, 39(1): 24-30.
doi: 10.11999/JEIT160291
Abstract:
In-band full duplex is a key concept brought up in 5G, and digital Self-Interference (SI) cancellation has become an important field attracting much attention. SI channel estimation error introduced by nonlinear distortion leads to deleterious effect on the accurate estimation of distortion coefficient. This paper proposes a nonlinear SI cancellation algorithm based on an auxiliary symbol. The channel estimation error is mapped into cancellation residuals by performing SI cancellation for the designed auxiliary symbol, and then extracted to be an independent attributor for distortion coefficient estimation. A simplified implementation is proposed further for reducing the overhead of the algorithm. Simulation results show that the nonlinear SI component is suppressed to about -100 dBm with -5 dBm SI power received. In addition, the lower the received SI power is, the better the performance tends to be.
In-band full duplex is a key concept brought up in 5G, and digital Self-Interference (SI) cancellation has become an important field attracting much attention. SI channel estimation error introduced by nonlinear distortion leads to deleterious effect on the accurate estimation of distortion coefficient. This paper proposes a nonlinear SI cancellation algorithm based on an auxiliary symbol. The channel estimation error is mapped into cancellation residuals by performing SI cancellation for the designed auxiliary symbol, and then extracted to be an independent attributor for distortion coefficient estimation. A simplified implementation is proposed further for reducing the overhead of the algorithm. Simulation results show that the nonlinear SI component is suppressed to about -100 dBm with -5 dBm SI power received. In addition, the lower the received SI power is, the better the performance tends to be.
2017, 39(1): 31-37.
doi: 10.11999/JEIT160268
Abstract:
The predetermined target channel has high risk of being unavailable in the proactive-decision spectrum handoff. To solve this problem, an optimum target channel selection algorithm based on hybrid spectrum handoff is proposed. This algorithm coordinates the advantages of both the proactive-decision and reactive-decision spectrum handoffs. With the metric of minimizing the cumulative handoff delay, the impacts of secondary users multiple interruptions, primary users arrival rates and imperfect sensing on secondary users cumulative handoff delay are included and the detailed analysis and derivation of the secondary users cumulative handoff delay are conducted under imperfect sensing. Compared with proactive-decision and reactive-decision target channel selection schemes, the simulation results demonstrate that the performance of the proposed algorithm is especially excellent with frequent channel states variations or heavy traffic loads.
The predetermined target channel has high risk of being unavailable in the proactive-decision spectrum handoff. To solve this problem, an optimum target channel selection algorithm based on hybrid spectrum handoff is proposed. This algorithm coordinates the advantages of both the proactive-decision and reactive-decision spectrum handoffs. With the metric of minimizing the cumulative handoff delay, the impacts of secondary users multiple interruptions, primary users arrival rates and imperfect sensing on secondary users cumulative handoff delay are included and the detailed analysis and derivation of the secondary users cumulative handoff delay are conducted under imperfect sensing. Compared with proactive-decision and reactive-decision target channel selection schemes, the simulation results demonstrate that the performance of the proposed algorithm is especially excellent with frequent channel states variations or heavy traffic loads.
2017, 39(1): 38-44.
doi: 10.11999/JEIT160285
Abstract:
This paper investigates a resource allocation scheme in heterogeneous wireless small cell networks with imperfect Channel State Information (CSI). In this work, the math expression for the stochastic dynamic uncertainty in CSI is proposed for model analysis and the robust Stackelberg game model with various interference power constraints is established firstly. Then, the Stackelberg game Equilibrium (SE) is obtained and analyzed. Lastly, an improved hierarchical Q-learning algorithm is also given to search the Stackelberg equilibrium strategies of macro-cell base station and small-cell base station. Both theoretical analysis and simulation results verify the proposed scheme can effectively restrain declining revenue due to incomplete CSI and the proposed algorithms can improves the convergence rate, especially applicable to the fast varying communication environment.
This paper investigates a resource allocation scheme in heterogeneous wireless small cell networks with imperfect Channel State Information (CSI). In this work, the math expression for the stochastic dynamic uncertainty in CSI is proposed for model analysis and the robust Stackelberg game model with various interference power constraints is established firstly. Then, the Stackelberg game Equilibrium (SE) is obtained and analyzed. Lastly, an improved hierarchical Q-learning algorithm is also given to search the Stackelberg equilibrium strategies of macro-cell base station and small-cell base station. Both theoretical analysis and simulation results verify the proposed scheme can effectively restrain declining revenue due to incomplete CSI and the proposed algorithms can improves the convergence rate, especially applicable to the fast varying communication environment.
2017, 39(1): 45-50.
doi: 10.11999/JEIT160246
Abstract:
In Underwater Wireless Sensor Network (UWSN) the accuracy of target localization suffers from invalid anchors. To reduce the impact, an improved cross-bearing localization method is proposed based on the Parzen window. In this method, the probability of target location is estimated by the Parzen window according to the distribution characteristics of all intersection points, and the target location is selected as the point corresponding to the maximum value of probability. Because of the nonlinear and multi-peak features of the probability distribution, the standard particle swarm optimization method is adopted to solve the problem. Simulations indicate that the proposed method avoids effectively the influence of the invalid anchors on the performance of localization, and has better accuracy and robustness compared with other cross-bearing localization methods in the complex underwater environment.
In Underwater Wireless Sensor Network (UWSN) the accuracy of target localization suffers from invalid anchors. To reduce the impact, an improved cross-bearing localization method is proposed based on the Parzen window. In this method, the probability of target location is estimated by the Parzen window according to the distribution characteristics of all intersection points, and the target location is selected as the point corresponding to the maximum value of probability. Because of the nonlinear and multi-peak features of the probability distribution, the standard particle swarm optimization method is adopted to solve the problem. Simulations indicate that the proposed method avoids effectively the influence of the invalid anchors on the performance of localization, and has better accuracy and robustness compared with other cross-bearing localization methods in the complex underwater environment.
2017, 39(1): 51-57.
doi: 10.11999/JEIT160382
Abstract:
Since in wireless sensor networks, the joint of new nodes or the death of old nodes lead to a dynamic topology, this paper studies one completely distributed Second-Order Consensus Time Synchronization (SOCTS) algorithm. The clock feature of each node is modeled into a second order state equation, and the local virtual time is broadcasted according to the pseudo synchronous cycle, Moreover, the synchronization control input is constructed according to the disagreement on local virtual time among neighboring nodes. By virtue of the matrix transformation, the network time synchronization issue is turned into the stability issue of some transformed system, and the convergence and convergence condition for the SOCTS algorithm are analyzed theoretically. Moreover, the factors that influence the convergence rate of the SOCTS algorithm are investigated. Finally, the effectiveness of the proposed method is verified by numerical simulations.
Since in wireless sensor networks, the joint of new nodes or the death of old nodes lead to a dynamic topology, this paper studies one completely distributed Second-Order Consensus Time Synchronization (SOCTS) algorithm. The clock feature of each node is modeled into a second order state equation, and the local virtual time is broadcasted according to the pseudo synchronous cycle, Moreover, the synchronization control input is constructed according to the disagreement on local virtual time among neighboring nodes. By virtue of the matrix transformation, the network time synchronization issue is turned into the stability issue of some transformed system, and the convergence and convergence condition for the SOCTS algorithm are analyzed theoretically. Moreover, the factors that influence the convergence rate of the SOCTS algorithm are investigated. Finally, the effectiveness of the proposed method is verified by numerical simulations.
2017, 39(1): 58-65.
doi: 10.11999/JEIT160254
Abstract:
Adaptive steganography ignores the interactive impact introduced by the embedding operation during the embedding operation. Considering the cross impact of the embedding operation, an adaptive k steganography is put forward based on the dynamic distortion cost updating strategy Modification Degree Strategy (MDS). First, the analysis is conducted to prove the optimizing modification of the central pixel under the condition of the neighborhoods modifications. Then the MDS of updating the distortion cost is presented to adjust the distortion cost according to the modification of neighborhood. Finally, the steganography scheme is proposed using the MDS. The experimental result illustrates that the UNIWARD-MDS (Pentary Version) has a better performance than S-UNIWARD (Pentary Version) at the embedding rate 0.5~1.0 bpp when resisting the steganalysis SRM. Meanwhile the UNIWARD-MDS (Pentary Version) is better than the S-UNIWARD (Pentary Version) at the embedding rate 0.05~1.0 bpp when resisting the maxSRMd2 detection. The HILL-MDS and UNIWARD-MDS (Ternary Version) perform better than the corresponding schemes HILL and S-UNIWARD (Ternary Version).
Adaptive steganography ignores the interactive impact introduced by the embedding operation during the embedding operation. Considering the cross impact of the embedding operation, an adaptive k steganography is put forward based on the dynamic distortion cost updating strategy Modification Degree Strategy (MDS). First, the analysis is conducted to prove the optimizing modification of the central pixel under the condition of the neighborhoods modifications. Then the MDS of updating the distortion cost is presented to adjust the distortion cost according to the modification of neighborhood. Finally, the steganography scheme is proposed using the MDS. The experimental result illustrates that the UNIWARD-MDS (Pentary Version) has a better performance than S-UNIWARD (Pentary Version) at the embedding rate 0.5~1.0 bpp when resisting the steganalysis SRM. Meanwhile the UNIWARD-MDS (Pentary Version) is better than the S-UNIWARD (Pentary Version) at the embedding rate 0.05~1.0 bpp when resisting the maxSRMd2 detection. The HILL-MDS and UNIWARD-MDS (Ternary Version) perform better than the corresponding schemes HILL and S-UNIWARD (Ternary Version).
2017, 39(1): 66-74.
doi: 10.11999/JEIT160555
Abstract:
Since social applications involve various types of information related to the user privacy, events of privacy leakage occur frequently along with their popular applications and few studies are available on the privacy leakage detection for social applications. With the combination of the characteristics of the Android system as well as the exploitation of the taint tracking technology and Xposed framework, a privacy leakage detection tool named X-Decaf (Xposed-based-detecting-cache-file) is proposed, which is oriented to social applications on Android platform. It suspects the leakage paths within the applications and detects the privacy datas cache files. This paper also presents a suggestion for the evaluation of the privacy leakage. Evaluation results of 50 kinds of Android social applications show that many vulnerabilities of user privacy leakage exist in the social applications on Android platform.
Since social applications involve various types of information related to the user privacy, events of privacy leakage occur frequently along with their popular applications and few studies are available on the privacy leakage detection for social applications. With the combination of the characteristics of the Android system as well as the exploitation of the taint tracking technology and Xposed framework, a privacy leakage detection tool named X-Decaf (Xposed-based-detecting-cache-file) is proposed, which is oriented to social applications on Android platform. It suspects the leakage paths within the applications and detects the privacy datas cache files. This paper also presents a suggestion for the evaluation of the privacy leakage. Evaluation results of 50 kinds of Android social applications show that many vulnerabilities of user privacy leakage exist in the social applications on Android platform.
2017, 39(1): 75-81.
doi: 10.11999/JEIT160139
Abstract:
A new companding transform based on-law companding schemes is proposed for the reduction of Peak-to-Average Power Ratio (PAPR) of Prolate Spheroidal Wave Function (PSWF) orthogonal modulation signal, which causes serious degradation in performance when a nonlinear Power Amplifier (PA) is used. According to the input signal, the method adjusts the compression parameters automatically, which can guarantee the average signal power constant before and after compression, compression signal peak. Both the mathematical deduction and simulation results show that the proposed method can effectively reduce PAPR of PSWF orthogonal modulation signal, and effectively improve the power spectrum density of the signal and the BER performance of system under AWGN channel. The PAPR of compressed modulation signal decrease about 2.1 dB in comparison to the original modulation signal, when the parameter=1 and Complementary Cumulative Distribution Function CCDF=10-4.
A new companding transform based on-law companding schemes is proposed for the reduction of Peak-to-Average Power Ratio (PAPR) of Prolate Spheroidal Wave Function (PSWF) orthogonal modulation signal, which causes serious degradation in performance when a nonlinear Power Amplifier (PA) is used. According to the input signal, the method adjusts the compression parameters automatically, which can guarantee the average signal power constant before and after compression, compression signal peak. Both the mathematical deduction and simulation results show that the proposed method can effectively reduce PAPR of PSWF orthogonal modulation signal, and effectively improve the power spectrum density of the signal and the BER performance of system under AWGN channel. The PAPR of compressed modulation signal decrease about 2.1 dB in comparison to the original modulation signal, when the parameter=1 and Complementary Cumulative Distribution Function CCDF=10-4.
2017, 39(1): 82-87.
doi: 10.11999/JEIT160261
Abstract:
In a certain observation time duration, the instantaneous frequency of motion target with large acceleration is ambiguous. This case is usually met in flexible-motion target with high velocity. If target has micro-motion, it will cause micro-Doppler modulation which adds in the ambiguous Doppler frequency. In order to extract micro-motion feature of large acceleration target, a parameter estimation method is proposed. Through the ambiguity resolution of Doppler frequency and the estimation and compensation of bulk motion of target, micro-Doppler extraction is achieved. And then, micro-motion period is estimated. Analysis based on simulation and measured data show that the method is suit for micro-motion parameter estimation of large acceleration flexible-motion target.
In a certain observation time duration, the instantaneous frequency of motion target with large acceleration is ambiguous. This case is usually met in flexible-motion target with high velocity. If target has micro-motion, it will cause micro-Doppler modulation which adds in the ambiguous Doppler frequency. In order to extract micro-motion feature of large acceleration target, a parameter estimation method is proposed. Through the ambiguity resolution of Doppler frequency and the estimation and compensation of bulk motion of target, micro-Doppler extraction is achieved. And then, micro-motion period is estimated. Analysis based on simulation and measured data show that the method is suit for micro-motion parameter estimation of large acceleration flexible-motion target.
2017, 39(1): 88-94.
doi: 10.11999/JEIT160207
Abstract:
In a multistatic radar system, perfect registration is unavailable in practice even after a registration process. In this paper, a target detection problem for a distributed Multiple-Input Multiple-Output (MIMO) radar with registration errors is considered. To estimate target positions by weather using a knowing a priori information of registration errors or not, a Maximum A Posteriori Generalized Likelihood Ratio Test (MAP-GLRT) detector and a Maximum Likelihood GLRT (ML-GLRT) detector are proposed. The MAP-GLRT detector outperforms the ML-GLRT detector due to the prior information. The two proposed algorithms have better detection performance over the conventional detection fusion algorithm with registration errors. Simulation results verify the effectiveness of the proposed detection algorithms.
In a multistatic radar system, perfect registration is unavailable in practice even after a registration process. In this paper, a target detection problem for a distributed Multiple-Input Multiple-Output (MIMO) radar with registration errors is considered. To estimate target positions by weather using a knowing a priori information of registration errors or not, a Maximum A Posteriori Generalized Likelihood Ratio Test (MAP-GLRT) detector and a Maximum Likelihood GLRT (ML-GLRT) detector are proposed. The MAP-GLRT detector outperforms the ML-GLRT detector due to the prior information. The two proposed algorithms have better detection performance over the conventional detection fusion algorithm with registration errors. Simulation results verify the effectiveness of the proposed detection algorithms.
2017, 39(1): 95-102.
doi: 10.11999/JEIT160222
Abstract:
While the use of differential SAR tomography based on Compressive Sensing (CS) makes it possible to reconstruct the four-dimensional information of an observed scene, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, a method using differential SAR tomography based on Khatri-Rao Subspace and Block Compressive Sensing (KRS-BCS) is proposed. Using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix, the proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao Subspace, and then the KRS-BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CS methods, the proposed KRS-BCS method not only maintains the high resolution characteristics of CS methods, but also has higher reconstruction accuracy and better performance. Simulations, ENVISAT-ASAR data and ground-based GPS data verify the effectiveness of the proposed method.
While the use of differential SAR tomography based on Compressive Sensing (CS) makes it possible to reconstruct the four-dimensional information of an observed scene, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, a method using differential SAR tomography based on Khatri-Rao Subspace and Block Compressive Sensing (KRS-BCS) is proposed. Using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix, the proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao Subspace, and then the KRS-BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CS methods, the proposed KRS-BCS method not only maintains the high resolution characteristics of CS methods, but also has higher reconstruction accuracy and better performance. Simulations, ENVISAT-ASAR data and ground-based GPS data verify the effectiveness of the proposed method.
2017, 39(1): 103-109.
doi: 10.11999/JEIT160204
Abstract:
Considering the shortcoming of detection method based on polarimetric contrast enhanced with single polarimetric scattering mechanism, a PolSAR detection method based on multiple polarimetric mechanisms called Dirichlet Process mixture of Latent Variable SVM (DPLVSVM) is proposed. By assembling a set of local polarimetric detectors that based on single polarimetric scattering mechanism, a global multiple polarimetric scattering mechanisms detector is obtained. With a fully Bayes treatment, DPLVSVM learns the clustering and the local detectors jointly. Taking the advantage of Bayes nonparametric, DPLVSVM handles the model selection problem flexibly. Further, in order to reduce the redundancy of polarimetric feature and improve the model generalization, a model with feature selection, Sparsity-Promoting Dirichlet Process mixture of Latent Variable SVM (SPDPLVSVM), is proposed. Thanks to the conjugate property, the parameters in both of models can be inferred efficiently via the Gibbs sampler. Finally, the proposed models on RADARSAR-2 dataset is implemented to validate their effectiveness.
Considering the shortcoming of detection method based on polarimetric contrast enhanced with single polarimetric scattering mechanism, a PolSAR detection method based on multiple polarimetric mechanisms called Dirichlet Process mixture of Latent Variable SVM (DPLVSVM) is proposed. By assembling a set of local polarimetric detectors that based on single polarimetric scattering mechanism, a global multiple polarimetric scattering mechanisms detector is obtained. With a fully Bayes treatment, DPLVSVM learns the clustering and the local detectors jointly. Taking the advantage of Bayes nonparametric, DPLVSVM handles the model selection problem flexibly. Further, in order to reduce the redundancy of polarimetric feature and improve the model generalization, a model with feature selection, Sparsity-Promoting Dirichlet Process mixture of Latent Variable SVM (SPDPLVSVM), is proposed. Thanks to the conjugate property, the parameters in both of models can be inferred efficiently via the Gibbs sampler. Finally, the proposed models on RADARSAR-2 dataset is implemented to validate their effectiveness.
2017, 39(1): 110-116.
doi: 10.11999/JEIT160134
Abstract:
The traditional radar system can form an angle-dependent beam for target tracking, which is independent on the range of target and as a result can not make the transmit energy focus on the targets position. For this problem, a novel hybrid phased-MIMO radar with non-monotone increasing frequency offset for target tracking is proposed based on the combination of the Frequency Diverse Array (FDA) with MIMO radar. With the non-monotone increasing frequency offset, this new method can form a transmit beampattern in two dimensions of range and angle, and cancel the periodicity of basic FDA beampattern in range domain as well as decouple the beampattern in range and angle dimensions. With the help of the decoupled beampattern, a two dimensional point beam can be formed to track target. With the advantages of the hybrid phased-MIMO radars transmit gain and waveform diversity, the tracking performance can be enforced. Finally, the target tracking accuracy of the proposed method is analysed and the performance of anti-jamming is proved by simulations results.
The traditional radar system can form an angle-dependent beam for target tracking, which is independent on the range of target and as a result can not make the transmit energy focus on the targets position. For this problem, a novel hybrid phased-MIMO radar with non-monotone increasing frequency offset for target tracking is proposed based on the combination of the Frequency Diverse Array (FDA) with MIMO radar. With the non-monotone increasing frequency offset, this new method can form a transmit beampattern in two dimensions of range and angle, and cancel the periodicity of basic FDA beampattern in range domain as well as decouple the beampattern in range and angle dimensions. With the help of the decoupled beampattern, a two dimensional point beam can be formed to track target. With the advantages of the hybrid phased-MIMO radars transmit gain and waveform diversity, the tracking performance can be enforced. Finally, the target tracking accuracy of the proposed method is analysed and the performance of anti-jamming is proved by simulations results.
2017, 39(1): 117-123.
doi: 10.11999/JEIT160384
Abstract:
A novel unified cascade constrained interactive multi-model Kalman filter is put forward. The filter is composed of two cascade connected filters, a standard interactive-multiple-model and a unified constrained filter. The latter is effective for everyone in model set of controlled plant and refines the estimation of the former using smoothly constraint Kalman algorithm. Numerical simulation and flying experiments are made for maneuvering target tracking and lower estimated error and covariance are achieved by the unified cascade constrained interactive multi-model Kalman filter compared with conventional interactive multi-model filter. The added computation cost is reasonable and acceptable. The paper is valuable reference for maneuvering target tracking and interactive multi-model filter.
A novel unified cascade constrained interactive multi-model Kalman filter is put forward. The filter is composed of two cascade connected filters, a standard interactive-multiple-model and a unified constrained filter. The latter is effective for everyone in model set of controlled plant and refines the estimation of the former using smoothly constraint Kalman algorithm. Numerical simulation and flying experiments are made for maneuvering target tracking and lower estimated error and covariance are achieved by the unified cascade constrained interactive multi-model Kalman filter compared with conventional interactive multi-model filter. The added computation cost is reasonable and acceptable. The paper is valuable reference for maneuvering target tracking and interactive multi-model filter.
2017, 39(1): 124-128.
doi: 10.11999/JEIT160203
Abstract:
Based on optimized geometry structure of tapered slot antenna, an end-fire printed antenna is designed with rectangular-grooved and periodic structure. Its effects on the radiation pattern of antenna is studied and simulated. From 1 GHz to 3.5 GHz, those structures improve the end-fire characteristics of the antenna obviously. Finally, pairs of antennas are constructed and measured. The measured results show that VSWR is smaller than 2 and the gain is approximately equal to 7 dBi from 1 GHz to 3.5 GHz. The measured transfer characteristics results show that the antenna achieves a stable group delay and a low late-time ring. The antenna can be applied to the impulse radar.
Based on optimized geometry structure of tapered slot antenna, an end-fire printed antenna is designed with rectangular-grooved and periodic structure. Its effects on the radiation pattern of antenna is studied and simulated. From 1 GHz to 3.5 GHz, those structures improve the end-fire characteristics of the antenna obviously. Finally, pairs of antennas are constructed and measured. The measured results show that VSWR is smaller than 2 and the gain is approximately equal to 7 dBi from 1 GHz to 3.5 GHz. The measured transfer characteristics results show that the antenna achieves a stable group delay and a low late-time ring. The antenna can be applied to the impulse radar.
2017, 39(1): 129-137.
doi: 10.11999/JEIT160201
Abstract:
Theoretical analysis and engineering experience of SAR imaging shows that radar platforms motion error will affect the quality of the image if its amplitude is greater than sub wavelength. Compared with traditional SAR working in microwave frequency band, TeraHertz SAR (THz-SAR) works in a shorter wavelength as the TeraHertz band, the control and measure accuracy of radar platforms motion should be micron dimension, but the current technology can not meet the requirements. A novel motion compensation algorithm for THz-SAR imaging based on echo data is proposed in this paper. The attitude information from the inertial measurement unit is used to calibrate the migration error caused by the motion. Firstly, an obvious point like object is found in the coarse focusing image and the optimal position of this point is estimated by combining the antenna pattern and the maximum echos amplitude. Then the ideal echo of this point object is generated using the above estimated position and the phase error caused by the motion error of the platform is extracted by comparing the actual echo and the ideal echo. The extracted phase error is used to compensate the motion error of platform. The SAR system with center frequency 0.2 THz is used to carry out the outdoor vehicle experiment. Two dimensional high resolutions of SAR images of the corner reflectors and the metal strips are achieved. The validity of the proposed motion compensation algorithm is proved by experimental results.
Theoretical analysis and engineering experience of SAR imaging shows that radar platforms motion error will affect the quality of the image if its amplitude is greater than sub wavelength. Compared with traditional SAR working in microwave frequency band, TeraHertz SAR (THz-SAR) works in a shorter wavelength as the TeraHertz band, the control and measure accuracy of radar platforms motion should be micron dimension, but the current technology can not meet the requirements. A novel motion compensation algorithm for THz-SAR imaging based on echo data is proposed in this paper. The attitude information from the inertial measurement unit is used to calibrate the migration error caused by the motion. Firstly, an obvious point like object is found in the coarse focusing image and the optimal position of this point is estimated by combining the antenna pattern and the maximum echos amplitude. Then the ideal echo of this point object is generated using the above estimated position and the phase error caused by the motion error of the platform is extracted by comparing the actual echo and the ideal echo. The extracted phase error is used to compensate the motion error of platform. The SAR system with center frequency 0.2 THz is used to carry out the outdoor vehicle experiment. Two dimensional high resolutions of SAR images of the corner reflectors and the metal strips are achieved. The validity of the proposed motion compensation algorithm is proved by experimental results.
2017, 39(1): 138-143.
doi: 10.11999/JEIT160118
Abstract:
As an effective category of deception jamming, repeater jamming generates range false targets appearing dispersedly by modulating and retransmitting intercepted radar signals. However, distributed multiple-radar system will reject the false targets overstep a space resolution cell automatically in spatial registration. Therefore, it is necessary to discuss the ability of repeater jamming on multiple-radar system. When the distance between different stations is not far, this paper derives the effective jamming condition. When the jammer locates in the far field, it is theoretically proved that the false targets generating by the same delays can always deceive the multiple-radar system. When the jammer locates in the near field, it can also deceive the multiple-radar system by adjusting the time delay. The effective range of time delay is derived. The obtained conclusion is a good guidance for both the jamming and anti-jamming of multiple-radar systems.
As an effective category of deception jamming, repeater jamming generates range false targets appearing dispersedly by modulating and retransmitting intercepted radar signals. However, distributed multiple-radar system will reject the false targets overstep a space resolution cell automatically in spatial registration. Therefore, it is necessary to discuss the ability of repeater jamming on multiple-radar system. When the distance between different stations is not far, this paper derives the effective jamming condition. When the jammer locates in the far field, it is theoretically proved that the false targets generating by the same delays can always deceive the multiple-radar system. When the jammer locates in the near field, it can also deceive the multiple-radar system by adjusting the time delay. The effective range of time delay is derived. The obtained conclusion is a good guidance for both the jamming and anti-jamming of multiple-radar systems.
2017, 39(1): 144-149.
doi: 10.11999/JEIT160248
Abstract:
To resolve the problem of low efficiency which traditional multi-threshold Otsu existing in searching of optimal thresholds on the brute-force method, the thresholds properties of multi-threshold Otsu are analyzed, and the mathematical correspondence is proved between a set of optimal thresholds and the means of various categories. A new algorithm is proposed to calculate the optimal thresholds and a new model of searching thresholds is also built according to the properties of thresholds of multi-threshold Otsu. The algorithm searches for a set of optimal thresholds that satisfy the correspondence between the thresholds and the means of various categories segmented by them, so the optimal thresholds of Otsu can be determined. The algorithm reduces the search range effectively and optimizes the calculation of means and variances using lookup table. Experimental results show that the segmentation speed of the algorithm is greatly improved compared with the traditional multi-threshold Otsu method, and the algorithm can not only improve the computation speed, but also overcome the shortcomings of randomness and contingency of thresholds compared with other fast multi-threshold Otsu algorithm, and the results are strictly in line with the principle of multi-threshold Otsu.
To resolve the problem of low efficiency which traditional multi-threshold Otsu existing in searching of optimal thresholds on the brute-force method, the thresholds properties of multi-threshold Otsu are analyzed, and the mathematical correspondence is proved between a set of optimal thresholds and the means of various categories. A new algorithm is proposed to calculate the optimal thresholds and a new model of searching thresholds is also built according to the properties of thresholds of multi-threshold Otsu. The algorithm searches for a set of optimal thresholds that satisfy the correspondence between the thresholds and the means of various categories segmented by them, so the optimal thresholds of Otsu can be determined. The algorithm reduces the search range effectively and optimizes the calculation of means and variances using lookup table. Experimental results show that the segmentation speed of the algorithm is greatly improved compared with the traditional multi-threshold Otsu method, and the algorithm can not only improve the computation speed, but also overcome the shortcomings of randomness and contingency of thresholds compared with other fast multi-threshold Otsu algorithm, and the results are strictly in line with the principle of multi-threshold Otsu.
2017, 39(1): 150-158.
doi: 10.11999/JEIT160176
Abstract:
Word similarity measurement plays an important role in machine learning, information retrieval and many other fields. Regarding the concept primitive symbol system of Hierarchical network of concepts theory as semantic resource and comparing commonness with difference, a multi-dimensional computational method for similarity is proposed which considers the hierarchy, netted nature, comparability and duality, attached feature and quintuple information of the system. Weight strategy is introduced for node depth and distance measurement to increase the discrimination of node level. Experiment on manual scoring test set shows that the computed similarities are consistent with human judgments. The proposed method achieves 0.812, 0.786, and 0.775 in compatibility degree, correlation coefficient, and ordinal pair conformity respectively. Meanwhile, the result of correlation test further proofs that the computed similarities and humans scores are significantly correlated.
Word similarity measurement plays an important role in machine learning, information retrieval and many other fields. Regarding the concept primitive symbol system of Hierarchical network of concepts theory as semantic resource and comparing commonness with difference, a multi-dimensional computational method for similarity is proposed which considers the hierarchy, netted nature, comparability and duality, attached feature and quintuple information of the system. Weight strategy is introduced for node depth and distance measurement to increase the discrimination of node level. Experiment on manual scoring test set shows that the computed similarities are consistent with human judgments. The proposed method achieves 0.812, 0.786, and 0.775 in compatibility degree, correlation coefficient, and ordinal pair conformity respectively. Meanwhile, the result of correlation test further proofs that the computed similarities and humans scores are significantly correlated.
2017, 39(1): 159-166.
doi: 10.11999/JEIT160247
Abstract:
Algorithm of motif discovery for multiple attributes uncertain data stream is proposed on the basis of MEME (Multiple Expectation-maximization for Motif Elicitation), which consults the thought of sequential pattern discovery in bioinformatics to solve the problem of frequent pattern discovery for multiple attributes uncertain data stream. A new method for update calculation of uncertain sliding window is designed based on mixed type model, SAX (Symbolic Aggregate approXimation) symbolic strategy is improved, and similarity analysis method for multiple attributes motifs under different sliding windows is put forward. The proposed algorithm is verified to be correct functionally by a set of uncertain data stream in the wireless sensor network of air and missile defense. Its accuracy is measured through planting different number of motifs. Furthermore, comparison with previous algorithm with tuples valid probability set to 1 shows that the proposed algorithm can discover frequent pattern for multiple attributes uncertain data stream precisely.
Algorithm of motif discovery for multiple attributes uncertain data stream is proposed on the basis of MEME (Multiple Expectation-maximization for Motif Elicitation), which consults the thought of sequential pattern discovery in bioinformatics to solve the problem of frequent pattern discovery for multiple attributes uncertain data stream. A new method for update calculation of uncertain sliding window is designed based on mixed type model, SAX (Symbolic Aggregate approXimation) symbolic strategy is improved, and similarity analysis method for multiple attributes motifs under different sliding windows is put forward. The proposed algorithm is verified to be correct functionally by a set of uncertain data stream in the wireless sensor network of air and missile defense. Its accuracy is measured through planting different number of motifs. Furthermore, comparison with previous algorithm with tuples valid probability set to 1 shows that the proposed algorithm can discover frequent pattern for multiple attributes uncertain data stream precisely.
2017, 39(1): 167-175.
doi: 10.11999/JEIT160241
Abstract:
To apply unsupervised feature learning to emotional semantic analysis for images in small sample size situations, convolutional sparse autoencoder based self-taught learning for domain adaption is adopted for affective classification of a small amount of labeled abstract images. To visually compare the results of feature learning on different domains, an average gradient criterion based method is further proposed for the sorting of weights learned by sparse autoencoders. Image patches are first randomly collected from a large number of unlabeled images in the source domain and local features are learned using a sparse autoencoder. Then the weight matrices corresponding to different features are sorted according to the minimal average gradient of each matrix in three color channels. Global feature activations of labeled images in the target domain are finally obtained by a convolutional neural network including a pooling layer and sent into a logistic regression model for affective classification. Experimental results show that self-taught learning based domain adaption can provide training data for the application of unsupervised feature learning in target domains with limited samples. Sparse autoencoder based feature learning across different domains can produce better identification effect than low-level visual features in emotional semantic analysis of a limited number of abstract images.
To apply unsupervised feature learning to emotional semantic analysis for images in small sample size situations, convolutional sparse autoencoder based self-taught learning for domain adaption is adopted for affective classification of a small amount of labeled abstract images. To visually compare the results of feature learning on different domains, an average gradient criterion based method is further proposed for the sorting of weights learned by sparse autoencoders. Image patches are first randomly collected from a large number of unlabeled images in the source domain and local features are learned using a sparse autoencoder. Then the weight matrices corresponding to different features are sorted according to the minimal average gradient of each matrix in three color channels. Global feature activations of labeled images in the target domain are finally obtained by a convolutional neural network including a pooling layer and sent into a logistic regression model for affective classification. Experimental results show that self-taught learning based domain adaption can provide training data for the application of unsupervised feature learning in target domains with limited samples. Sparse autoencoder based feature learning across different domains can produce better identification effect than low-level visual features in emotional semantic analysis of a limited number of abstract images.
2017, 39(1): 176-182.
doi: 10.11999/JEIT160193
Abstract:
A breast coordinate system based on physiological features is developed for multi-view analysis in mammograms. It is constructed according to the locations of nipple, pectoral muscle and the fitted breast boundary. The breast regions in mammograms are mapped into a parameter frame because of the coordinate system. Experiments are implemented on data set of Breast Cancer of Peking University Peoples Hospital. The performance of locating the physiological features and matching the regions of interest is evaluated. Results show that the proposed coordinate system could achieve favorable performance and facilitate the multi-view analysis in mammograms.
A breast coordinate system based on physiological features is developed for multi-view analysis in mammograms. It is constructed according to the locations of nipple, pectoral muscle and the fitted breast boundary. The breast regions in mammograms are mapped into a parameter frame because of the coordinate system. Experiments are implemented on data set of Breast Cancer of Peking University Peoples Hospital. The performance of locating the physiological features and matching the regions of interest is evaluated. Results show that the proposed coordinate system could achieve favorable performance and facilitate the multi-view analysis in mammograms.
2017, 39(1): 183-190.
doi: 10.11999/JEIT160120
Abstract:
A sound event recognition method based on optimized Orthogonal Matching Pursuit (OMP) is proposed for decreasing the influence of sound event recognition on various environments. Firstly, OMP is used for sparse decomposition and reconstruction of sound signal to decrease the influence of noise and reserve the main body of sound signal, where Particle Swarm Optimization (PSO) is adopted to accelerate the best atom searching in the process of sparse decomposition. Then, an optimized composited feature of Mel-Frequency Cepstral Coefficients (MFCCs), time-frequency OMP feature, and PITCH feature is extracted from reconstructed signal. Finally, Random Forests (RF) classifier is employed to recognize 40 classes of sound events in different environments and Signal-to-Noise Rates (SNRs). The experiment result shows that the proposed method can effectively recognize sound events in various environments.
A sound event recognition method based on optimized Orthogonal Matching Pursuit (OMP) is proposed for decreasing the influence of sound event recognition on various environments. Firstly, OMP is used for sparse decomposition and reconstruction of sound signal to decrease the influence of noise and reserve the main body of sound signal, where Particle Swarm Optimization (PSO) is adopted to accelerate the best atom searching in the process of sparse decomposition. Then, an optimized composited feature of Mel-Frequency Cepstral Coefficients (MFCCs), time-frequency OMP feature, and PITCH feature is extracted from reconstructed signal. Finally, Random Forests (RF) classifier is employed to recognize 40 classes of sound events in different environments and Signal-to-Noise Rates (SNRs). The experiment result shows that the proposed method can effectively recognize sound events in various environments.
2017, 39(1): 191-197.
doi: 10.11999/JEIT160253
Abstract:
Human activity recognition based on accelerometer embedded in smartphones is wildly applied to inertial positioning, personalized recommendation, daily exercise estimating and other fields. The low recognition rate which caused by varying phone displacement is a crucial problem which needs to solve. To improve the recognition rate when the phones displacement is unfixed, a hierarchical classification-based activity recognition method is proposed. The activity recognition process is divided into multiple layers in this method, and each layer contains a classifier. For training each layers classifier, it runs the feature selection algorithm first, and the classifier is trained based on the selected features. Then, the trained classifier is used to classify the training set, and each samples classification confidence is calculated. Finally, samples whose confidence is lower than the hierarchical threshold are selected as the next layers training set. This process continues until each activitys sample number is less than the predefined pruning threshold. When an unlabeled sample comes, the first layer is used to classify this sample. If the classification confidence is higher than the hierarchical threshold, the recognition is over. Otherwise, the next layer will repeat this process until all the layers are traversed. The experiment collects activity data, and simulates the activity recognition. The simulation show that compared with the current methods, this method may improve the recognition rate from 85.2% to 89.2%.
Human activity recognition based on accelerometer embedded in smartphones is wildly applied to inertial positioning, personalized recommendation, daily exercise estimating and other fields. The low recognition rate which caused by varying phone displacement is a crucial problem which needs to solve. To improve the recognition rate when the phones displacement is unfixed, a hierarchical classification-based activity recognition method is proposed. The activity recognition process is divided into multiple layers in this method, and each layer contains a classifier. For training each layers classifier, it runs the feature selection algorithm first, and the classifier is trained based on the selected features. Then, the trained classifier is used to classify the training set, and each samples classification confidence is calculated. Finally, samples whose confidence is lower than the hierarchical threshold are selected as the next layers training set. This process continues until each activitys sample number is less than the predefined pruning threshold. When an unlabeled sample comes, the first layer is used to classify this sample. If the classification confidence is higher than the hierarchical threshold, the recognition is over. Otherwise, the next layer will repeat this process until all the layers are traversed. The experiment collects activity data, and simulates the activity recognition. The simulation show that compared with the current methods, this method may improve the recognition rate from 85.2% to 89.2%.
2017, 39(1): 198-205.
doi: 10.11999/JEIT160211
Abstract:
In order to solve the total-path-shortest Multiple Traveling Salesman Problem (MTSP), an improved grouping genetic algorithm is proposed. This algorithm employs a new encoding scheme called ordered grouping encoding, which makes the adjusted individuals corresponding one by one to valid solutions of MTSP. According to the features of the encoding scheme, a fast crossover operator is constructed for the sake of reducing the running time of the algorithm. For enhancing its local search ability, the algorithm combines the greedy algorithm and the 2-opt algorithm to design a new local search operator. The comparison of results shows that the proposed algorithm can solve MTSP effectively and has an excellent search performance no matter in computing efficiency or convergence precision.
In order to solve the total-path-shortest Multiple Traveling Salesman Problem (MTSP), an improved grouping genetic algorithm is proposed. This algorithm employs a new encoding scheme called ordered grouping encoding, which makes the adjusted individuals corresponding one by one to valid solutions of MTSP. According to the features of the encoding scheme, a fast crossover operator is constructed for the sake of reducing the running time of the algorithm. For enhancing its local search ability, the algorithm combines the greedy algorithm and the 2-opt algorithm to design a new local search operator. The comparison of results shows that the proposed algorithm can solve MTSP effectively and has an excellent search performance no matter in computing efficiency or convergence precision.
2017, 39(1): 206-212.
doi: 10.11999/JEIT160213
Abstract:
Reconfigurable cipher stream architecture is a newly proposed architecture for cipher processing, but poor Very Large Instruction Word (VLIW) code density and huge Kernel level code cubage are always serious problems on this architecture. Through analyzing the characteristics of a series of cryptographic algorithms on Stream based Reconfiguable Clustered block Cipher Processing Array (S-RCCPA) architecture, a reconfigurable VLIW dynamically technology is proposed, and the corresponding Kernel level instruction set and hardware circuit structure are designed. The experiments demonstrate that this technology can reduce VLIW width, thus improve the instruction density of VLIW effectively. Meanwhile, it can reduce about 33% of the Kernel volume, and depress the microcode store capacity from 96 kB to 64 kB. Thus it can also reduce the whole area and power consumption of chip respectively.
Reconfigurable cipher stream architecture is a newly proposed architecture for cipher processing, but poor Very Large Instruction Word (VLIW) code density and huge Kernel level code cubage are always serious problems on this architecture. Through analyzing the characteristics of a series of cryptographic algorithms on Stream based Reconfiguable Clustered block Cipher Processing Array (S-RCCPA) architecture, a reconfigurable VLIW dynamically technology is proposed, and the corresponding Kernel level instruction set and hardware circuit structure are designed. The experiments demonstrate that this technology can reduce VLIW width, thus improve the instruction density of VLIW effectively. Meanwhile, it can reduce about 33% of the Kernel volume, and depress the microcode store capacity from 96 kB to 64 kB. Thus it can also reduce the whole area and power consumption of chip respectively.
2017, 39(1): 213-220.
doi: 10.11999/JEIT160283
Abstract:
In order to decrease energy consumption of digital circuits by reducing the supply voltage, an Adaptive Voltage Scaling (AVS) for DC-DC converter based on Pulse Skip Modulation (PSM) is proposed. The AVS technique can scale supply voltage adaptively by probing and tracking the Critical Path Replica (CPR) delay time. To improve the output voltage ripple and efficiency of converter especially in light load, the PSM with Adaptive ratio duty (APSM) also is used. The experimental results show that the output voltage is well regulated from 0.6~ 1.5 V when the operation frequency of load varies within the range of 30~150 MHz. The maximum energy saving of 83% is obtained with the proposed converter compared to the traditional fixed voltage.
In order to decrease energy consumption of digital circuits by reducing the supply voltage, an Adaptive Voltage Scaling (AVS) for DC-DC converter based on Pulse Skip Modulation (PSM) is proposed. The AVS technique can scale supply voltage adaptively by probing and tracking the Critical Path Replica (CPR) delay time. To improve the output voltage ripple and efficiency of converter especially in light load, the PSM with Adaptive ratio duty (APSM) also is used. The experimental results show that the output voltage is well regulated from 0.6~ 1.5 V when the operation frequency of load varies within the range of 30~150 MHz. The maximum energy saving of 83% is obtained with the proposed converter compared to the traditional fixed voltage.
2017, 39(1): 221-230.
doi: 10.11999/JEIT160886
Abstract:
In this paper, a new domino extending-contracting operation, called 334 extending-contracting operation, is put forward, on the basis of which, it is proposed to construct a particular kind of graphs, i.e., 334-type maximal planar graphs, and proved that all those graphs are tree-type and 2-chromatic cycle-unchanged colored and every 334-type maximal planar graphs of order4k has exactly2k-1 2-chromatic cycled-unchanged colorings and2k-2 tree-colorings. Additionally, it is proved that an infinite family of purely tree-colored graphs can be generated by implementing a series of 334 extending-wheel operations, and conjectured that if a maximal planar graph Gis purely tree-colored (purely cycle-colored or impure-colored), then the graph obtained by implementing one 334 extending-wheel (contracting-wheel) operation on G is still purely tree-colored (purely cycle-colored or impure-colored).
In this paper, a new domino extending-contracting operation, called 334 extending-contracting operation, is put forward, on the basis of which, it is proposed to construct a particular kind of graphs, i.e., 334-type maximal planar graphs, and proved that all those graphs are tree-type and 2-chromatic cycle-unchanged colored and every 334-type maximal planar graphs of order4k has exactly2k-1 2-chromatic cycled-unchanged colorings and2k-2 tree-colorings. Additionally, it is proved that an infinite family of purely tree-colored graphs can be generated by implementing a series of 334 extending-wheel operations, and conjectured that if a maximal planar graph Gis purely tree-colored (purely cycle-colored or impure-colored), then the graph obtained by implementing one 334 extending-wheel (contracting-wheel) operation on G is still purely tree-colored (purely cycle-colored or impure-colored).
2017, 39(1): 231-239.
doi: 10.11999/JEIT160692
Abstract:
Spoof Surface Plasmon Polariton (SSPP), which possesses extraordinary ability of sub-wavelength- scaled field confinement, can be realized by an ultrathin corrugated metallic strip at microwave frequencies. Advantages of SSPP such as the high confinement, low loss, and controllable dispersion properties are analyzed in this paper. SSPP waveguide, a novel high-performance transmission line, is studied for its great potentials in modern integrated circuits. A series of reported applications for microwave circuits/devices are reviewed. In the end, future development of this technique is discussed.
Spoof Surface Plasmon Polariton (SSPP), which possesses extraordinary ability of sub-wavelength- scaled field confinement, can be realized by an ultrathin corrugated metallic strip at microwave frequencies. Advantages of SSPP such as the high confinement, low loss, and controllable dispersion properties are analyzed in this paper. SSPP waveguide, a novel high-performance transmission line, is studied for its great potentials in modern integrated circuits. A series of reported applications for microwave circuits/devices are reviewed. In the end, future development of this technique is discussed.
2017, 39(1): 240-244.
doi: 10.11999/JEIT160197
Abstract:
Binarization for degraded document images is a difficult point in image processing. This paper presents a new binarization method for the degraded document images by analyzing the differences of image grayscale contrast in different areas. Firstly, theory of quadtree is used to divide areas adaptively. Secondly, various contrast enhancements are selected to adjust local grayscale contrast for different contrast areas. Lastly, the frequency of gray value is utilized to calculate threshold. The proposed algorithm is tested on random shooting degraded images and datasets of Document Image Binarization COntest (DIBCO). Compared with other four classical algorithms, the binaried images using the proposed algorithm gain the highest F-measure and PSNR (Peak Signal-to-Noise Ratio).
Binarization for degraded document images is a difficult point in image processing. This paper presents a new binarization method for the degraded document images by analyzing the differences of image grayscale contrast in different areas. Firstly, theory of quadtree is used to divide areas adaptively. Secondly, various contrast enhancements are selected to adjust local grayscale contrast for different contrast areas. Lastly, the frequency of gray value is utilized to calculate threshold. The proposed algorithm is tested on random shooting degraded images and datasets of Document Image Binarization COntest (DIBCO). Compared with other four classical algorithms, the binaried images using the proposed algorithm gain the highest F-measure and PSNR (Peak Signal-to-Noise Ratio).
2017, 39(1): 245-249.
doi: 10.11999/JEIT160225
Abstract:
In order to quickly and automatically analyze the soft error sensitivity for microprocessors, a soft error sensitivity analysis method using FPGA-based fault injection is proposed. The fault and fault-free microprocessors on a FPGA are board run simultaneously. Moreover, a fault injection controller, a fault classification module and a fault list module are also implemented on the hardware. The method inherits the parallelism of the FPGA and achieves a fast and automatical fault injection for all storage bits. Further, using a PIC16F54 microprocessor as experimental subject, approximate 300, 000 soft errors are injected into the microprocessors to analyze its soft error sensitivity. In order to demonstrate the sensitivity evaluation efficiency of the method, the quite sensitive storage cells are hardened and the sensitivity is analyzed again. Compared to the simulation approach, experimental results show that the proposed technique achieves four orders of magnitude speedup.
In order to quickly and automatically analyze the soft error sensitivity for microprocessors, a soft error sensitivity analysis method using FPGA-based fault injection is proposed. The fault and fault-free microprocessors on a FPGA are board run simultaneously. Moreover, a fault injection controller, a fault classification module and a fault list module are also implemented on the hardware. The method inherits the parallelism of the FPGA and achieves a fast and automatical fault injection for all storage bits. Further, using a PIC16F54 microprocessor as experimental subject, approximate 300, 000 soft errors are injected into the microprocessors to analyze its soft error sensitivity. In order to demonstrate the sensitivity evaluation efficiency of the method, the quite sensitive storage cells are hardened and the sensitivity is analyzed again. Compared to the simulation approach, experimental results show that the proposed technique achieves four orders of magnitude speedup.
2017, 39(1): 250-254.
doi: 10.11999/JEIT160186
Abstract:
Anti-collision algorithm is a key technique to improve identification efficiency in Radio Frequency IDentification (RFID) system. For this problem of the efficient identification and the large amount of data transmission, a group-based anti-collision algorithm is proposed. With the improved binary tree search algorithm combining, the tags in each group are identified by reader in turn, which can reduce the amount of data communication effectively. The simulation results show that, compared with several other algorithms, the proposed algorithm has the advantage of efficient identification and a small amount of data exchange.
Anti-collision algorithm is a key technique to improve identification efficiency in Radio Frequency IDentification (RFID) system. For this problem of the efficient identification and the large amount of data transmission, a group-based anti-collision algorithm is proposed. With the improved binary tree search algorithm combining, the tags in each group are identified by reader in turn, which can reduce the amount of data communication effectively. The simulation results show that, compared with several other algorithms, the proposed algorithm has the advantage of efficient identification and a small amount of data exchange.
