Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilitie...Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization,the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar （SAR） multi-angle collaborative detection,in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two,three and four independent SAR systems. Besides,detection performances with varying K and N are compared and analyzed.展开更多
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
Fountain codes are considered to be a promising coding technique in underwater acoustic communication（UAC） which is challenged with the unique propagation features of the underwater acoustic channel and the harsh ma...Fountain codes are considered to be a promising coding technique in underwater acoustic communication（UAC） which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform（LT） codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage（IC） of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced,high coding overhead is required and decoding delay is increased.These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First,a doping and non-uniform selecting（DNS） encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation（OFG-BP） decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.展开更多
An effective Luby transform （LT） encoding algorithm based on short cycle elimination is proposed to improve decoding probabilities of short length LT codes. By searching the generator ma- trix, some special encoded ...An effective Luby transform （LT） encoding algorithm based on short cycle elimination is proposed to improve decoding probabilities of short length LT codes. By searching the generator ma- trix, some special encoded symbols are generated by the encoder to effectively break the short cycles that have negative effect on the performance of LT codes. Analysis and numerical results show that by employing the proposed algorithm, the encoding complexity decreases and the decoding probabili- ties improve both in binary erasure channels （BECs） and additive white gauss noise （AWGN） chan- nels.展开更多
A novel technique for calibrating crucial parameters of chassis components is proposed,which utilizes the machine vision metrology to measure 3D coordinates of the center of a component's hole for assembling in th...A novel technique for calibrating crucial parameters of chassis components is proposed,which utilizes the machine vision metrology to measure 3D coordinates of the center of a component's hole for assembling in the 3D world coordinate system.In the measurement,encoding marks with special patterns will be assembled on the chassis component associated with cross drone and staff gauge located near the chassis.The geometry and coordinates of the cross drone consist of two planes orthogonal to each other and the staff gauge is in 3D space with high precision.A few images are taken by a highresolution camera in different orientations and perspectives.The 3D coordinates of 5 key points on the encoding marks will be calculated by the machine vision technique and those of the center of the holes to be calibrated will be calculated by the deduced algorithm in this paper.Experimental results show that the algorithm and the technique can satisfy the precision requirement when the components are assembled,and the average measurement precision provided by the algorithm is 0.0174 mm.展开更多
Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm f...Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm for optimizing pilot assignment in a multi-user massive multiple input multiple output(MIMO)system.Results show improvement on existing results especially in the case of strong user estimation rates.展开更多
A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by...A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by a method named interpolation method,so that we can get an ideal pulse compression result of the target,and then use the adjusted ideal pulse compression side-lobe to cut the actual pulse compression result,so as to achieve the remarkable performance of side-lobe suppression for large targets,and let the adjacent small targets appear.The computer simulations by MATLAB with this method analyze the effect of side-lobe suppression in an ideal or noisy environment.It is proved that this method can effectively solve the problem due to the side-lobe of pseudo-random coding being too high,and can enhance the radar＇s multi-target detection ability.展开更多
To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of t...To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.展开更多
基金New Century Program for Excellent Talents of Minis-try of Education of China (NECT-06-0166)The Eleventh Five-year Scientific and Technological Development Plan of National Defense Pre-study Foundation (A2120060006)
文摘Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization,the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar （SAR） multi-angle collaborative detection,in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two,three and four independent SAR systems. Besides,detection performances with varying K and N are compared and analyzed.
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
基金supported by the National Natural Science Foundation of China(61371099)the Fundamental Research Funds for the Central Universities of China(HEUCF150812/150810)
文摘Fountain codes are considered to be a promising coding technique in underwater acoustic communication（UAC） which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform（LT） codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage（IC） of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced,high coding overhead is required and decoding delay is increased.These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First,a doping and non-uniform selecting（DNS） encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation（OFG-BP） decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.
基金Supported by China Mobile Research Institute and China National S&T Major Project(2010ZX03003-003)
文摘An effective Luby transform （LT） encoding algorithm based on short cycle elimination is proposed to improve decoding probabilities of short length LT codes. By searching the generator ma- trix, some special encoded symbols are generated by the encoder to effectively break the short cycles that have negative effect on the performance of LT codes. Analysis and numerical results show that by employing the proposed algorithm, the encoding complexity decreases and the decoding probabili- ties improve both in binary erasure channels （BECs） and additive white gauss noise （AWGN） chan- nels.
基金supported by the National Natural Science Foundation of China (Nos.60808020 and 61078041)the Tianjin Research Program of Application Foundation and Advanced Technology (No.10JCYBJC07200)
文摘A novel technique for calibrating crucial parameters of chassis components is proposed,which utilizes the machine vision metrology to measure 3D coordinates of the center of a component's hole for assembling in the 3D world coordinate system.In the measurement,encoding marks with special patterns will be assembled on the chassis component associated with cross drone and staff gauge located near the chassis.The geometry and coordinates of the cross drone consist of two planes orthogonal to each other and the staff gauge is in 3D space with high precision.A few images are taken by a highresolution camera in different orientations and perspectives.The 3D coordinates of 5 key points on the encoding marks will be calculated by the machine vision technique and those of the center of the holes to be calibrated will be calculated by the deduced algorithm in this paper.Experimental results show that the algorithm and the technique can satisfy the precision requirement when the components are assembled,and the average measurement precision provided by the algorithm is 0.0174 mm.
文摘Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm for optimizing pilot assignment in a multi-user massive multiple input multiple output(MIMO)system.Results show improvement on existing results especially in the case of strong user estimation rates.
文摘A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by a method named interpolation method,so that we can get an ideal pulse compression result of the target,and then use the adjusted ideal pulse compression side-lobe to cut the actual pulse compression result,so as to achieve the remarkable performance of side-lobe suppression for large targets,and let the adjacent small targets appear.The computer simulations by MATLAB with this method analyze the effect of side-lobe suppression in an ideal or noisy environment.It is proved that this method can effectively solve the problem due to the side-lobe of pseudo-random coding being too high,and can enhance the radar＇s multi-target detection ability.
文摘To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.
