A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-...A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.展开更多
A two-dimensional （2-D） polynomial regression model is set up to approximate the time-frequency response of slowly time-varying orthogonal frequency-division multiplexing （OFDM） systems. With this model the estima...A two-dimensional （2-D） polynomial regression model is set up to approximate the time-frequency response of slowly time-varying orthogonal frequency-division multiplexing （OFDM） systems. With this model the estimation of the OFDM time-frequency response is turned into the optimization of some time-invariant model parameters. A new algorithm based on the expectation-maximization （EM） method is proposed to obtain the maximum-likelihood （ML） estimation of the polynomial model parameters over the 2-D observed data. At the same time, in order to reduce the complexity and avoid the computation instability, a novel recursive approach （RPEMTO） is given to calculate the values of the parameters. It is further shown that this 2-D polynomial EM-based algorithm for time-varying OFDM （PEMTO） can be simplified mathematically to handle the one-dimensional sequential estimation. Simulations illustrate that the proposed algorithms achieve a lower bit error rate （BER） than other blind algorithms.展开更多
An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the ...An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.展开更多
A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absol...A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.展开更多
The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is...The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is proposed for the differential unitary space-time modulation （DUSTM） system. In the first step, the data sequence is estimated by conventional unitary space-time demodulation （DUSTD） and differentially encoded again to produce an initial estimate of the transmitted symbol stream. In the second step, the initial estimate of the symbol stream is utilized to initialize an expectation maximization （EM）-based iterative detector. In each iteration, the most recent detected symbol stream is employed to estimate the channel, which is then used to implement coherent sequence detection to refine the symbol stream. Simulation results show that the proposed detection scheme performs much better than the conventional DUSTD after several iterations.展开更多
The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this a...The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.展开更多
A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one （SSR1） update with the limited memory and nonmonotone line search technique. In this algorithm, th...A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one （SSR1） update with the limited memory and nonmonotone line search technique. In this algorithm, the descent search direction is generated by inverse limited memory SSR1 update, thus simplifying the computation. Numerical comparison of the algorithm and the famous limited memory BFGS algorithm is given. Comparison results indicate that the new algorithm can process a kind of large-scale unconstrained optimization problems.展开更多
基金Supported by the National Natural Science Foundation of China(60505004,60773061)~~
文摘A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.
基金The National Natural Science Foundation of China(No60472026)
文摘A two-dimensional （2-D） polynomial regression model is set up to approximate the time-frequency response of slowly time-varying orthogonal frequency-division multiplexing （OFDM） systems. With this model the estimation of the OFDM time-frequency response is turned into the optimization of some time-invariant model parameters. A new algorithm based on the expectation-maximization （EM） method is proposed to obtain the maximum-likelihood （ML） estimation of the polynomial model parameters over the 2-D observed data. At the same time, in order to reduce the complexity and avoid the computation instability, a novel recursive approach （RPEMTO） is given to calculate the values of the parameters. It is further shown that this 2-D polynomial EM-based algorithm for time-varying OFDM （PEMTO） can be simplified mathematically to handle the one-dimensional sequential estimation. Simulations illustrate that the proposed algorithms achieve a lower bit error rate （BER） than other blind algorithms.
基金The National Natural Science Foundation of China(No.61105048,60972165)the Doctoral Fund of Ministry of Education of China(No.20110092120034)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK2010240)the Technology Foundation for Selected Overseas Chinese Scholar,Ministry of Human Resources and Social Security of China(No.6722000008)the Open Fund of Jiangsu Province Key Laboratory for Remote Measuring and Control(No.YCCK201005)
文摘An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.
基金The US National Science Foundation (No. CMMI-0408390,CMMI-0644552,BCS-0527508)the National Natural Science Foundation of China (No. 51010044,U1134206)+2 种基金the Fok YingTong Education Foundation (No. 114024)the Natural Science Foundation of Jiangsu Province (No. BK2009015)the Postdoctoral Science Foundation of Jiangsu Province (No. 0901005C)
文摘A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.
基金The National Natural Science Foundation of China(No60572072,60496311)the National High Technology Research and Development Program of China (863Program) (No2006AA01Z264)+1 种基金the National Basic Research Program of China (973Program) (No2007CB310603)the PhD Programs Foundation of Ministry of Educa-tion of China (No20060286016)
文摘The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is proposed for the differential unitary space-time modulation （DUSTM） system. In the first step, the data sequence is estimated by conventional unitary space-time demodulation （DUSTD） and differentially encoded again to produce an initial estimate of the transmitted symbol stream. In the second step, the initial estimate of the symbol stream is utilized to initialize an expectation maximization （EM）-based iterative detector. In each iteration, the most recent detected symbol stream is employed to estimate the channel, which is then used to implement coherent sequence detection to refine the symbol stream. Simulation results show that the proposed detection scheme performs much better than the conventional DUSTD after several iterations.
基金National 863 Foundation of China(No.2006AA09A102-10)National Natural Science Foundation of China(No.40874056)NCET Fund
文摘The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.
基金the National Natural Science Foundation of China(10471062)the Natural Science Foundation of Jiangsu Province(BK2006184)~~
文摘A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one （SSR1） update with the limited memory and nonmonotone line search technique. In this algorithm, the descent search direction is generated by inverse limited memory SSR1 update, thus simplifying the computation. Numerical comparison of the algorithm and the famous limited memory BFGS algorithm is given. Comparison results indicate that the new algorithm can process a kind of large-scale unconstrained optimization problems.
