利用语音信号在离散余弦变换(DCT)域的近似稀疏性和量化压缩感知理论,文中提出一种基于量化压缩感知的语音压缩编码方案。编码端利用压缩感知技术,将语音信号投影成数据量大大减少的观测序列,然后对观测序列采用Lloyd-M ax量化得到量化...利用语音信号在离散余弦变换(DCT)域的近似稀疏性和量化压缩感知理论,文中提出一种基于量化压缩感知的语音压缩编码方案。编码端利用压缩感知技术,将语音信号投影成数据量大大减少的观测序列,然后对观测序列采用Lloyd-M ax量化得到量化后的观测样值;解码端直接利用量化后的观测样值,结合重构算法重构出原始语音信号的DCT系数,经过DCT反变换得到重构后的语音信号,并采用后置低通滤波器改善重构语音的听觉效果。该编码方案解码端不需要进行反量化,而是直接利用量化后的观测样值进行重构,有效降低了解码端的运算量及复杂度。仿真结果表明:采用量化迭代硬阈值(QIHT)算法重构效果优于迭代硬阈值算法(IHT),重构语音的信噪比能达到20 d B以上,MOS分达到3.26。展开更多
The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated...The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm.The deregularization maximized the energy of the solution,which was opposite to the Tikhonov regularization where the energy was minimized.However,combined with box-constraints,the deregularization forced the solution to be close to the binary set.It further exploited the box-constrained dichotomous coordinate descent algorithm and adapted it to the nonstationary iterative Tikhonov regularization to present an efficient detector.As a result,the worst-case and average complexity are reduced down as K2.8 and K2.5 floating point operation per second,respectively.The development improves the "efficient frontier" in multiuser detection,which is illustrated by simulation results.In addition,most operations in the detector are additions and bit-shifts.This makes the proposed technique attractive for fixed-point hardware implementation.展开更多
Non-uniform quantization for messages in Low-Density Parity-Check(LDPC)decoding canreduce implementation complexity and mitigate performance loss.But the distribution of messagesvaries in the iterative decoding.This l...Non-uniform quantization for messages in Low-Density Parity-Check(LDPC)decoding canreduce implementation complexity and mitigate performance loss.But the distribution of messagesvaries in the iterative decoding.This letter proposes a variable non-uniform quantized Belief Propaga-tion(BP)algorithm.The BP decoding is analyzed by density evolution with Gaussian approximation.Since the probability density of messages can be well approximated by Gaussian distribution,by theunbiased estimation of variance,the distribution of messages can be tracked during the iteration.Thusthe non-uniform quantization scheme can be optimized to minimize the distortion.Simulation resultsshow that the variable non-uniform quantization scheme can achieve better error rate performance andfaster decoding convergence than the conventional non-uniform quantization and uniform quantizationschemes.展开更多
In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is ...In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.展开更多
Iterative linear programming methods are proposed for optimum balanced animal diet in this paper. According to ＂wooden bucket theory＂ of the nutritional balance, each nutrient in the feeding standard has equal impor...Iterative linear programming methods are proposed for optimum balanced animal diet in this paper. According to ＂wooden bucket theory＂ of the nutritional balance, each nutrient in the feeding standard has equal importance. It＇s unreasonable to use common goal programming to attach different weighted value to different nutritional parameters. This paper introduces an effective algorithm to deal with this kind of problem. When the permitting cost of livestock ration is given, we can design a ration formula with linear program-this is the first round. Then, according to the differences between the permitting cost and the formula cost gained in the first round, adjust the feeding standard and the feeding raw materials, and conduct the second round of linear programming for ration formula. If there is still a very big difference between the formula cost and the permitting cost, the third round will be taken, and so on. In this iteration course the formula cost gradually approaches the permitting cost. It is the key that the feeding standard and feeding raw materials are modified in each round. This method ensured the nutritive equilibrium with the formulation of least-cost ration. This is an especially important method when the primary goal of the optimization tool is to improve economic and nutritive efficiency.展开更多
This study proposes an efficient indirect approach for general nonlinear dynamic optimization problems without path constraints. The approach incorporates the virtues both from indirect and direct methods: it solves t...This study proposes an efficient indirect approach for general nonlinear dynamic optimization problems without path constraints. The approach incorporates the virtues both from indirect and direct methods: it solves the optimality conditions like the traditional indirect methods do, but uses a discretization technique inspired from direct methods. Compared with other indirect approaches, the proposed approach has two main advantages: (1) the discretized optimization problem only employs unconstrained nonlinear programming (NLP) algorithms such as BFGS (Broyden-Fletcher-Goldfarb-Shanno), rather than constrained NLP algorithms, therefore the computational efficiency is increased; (2) the relationship between the number of the discretized time intervals and the integration error of the four-step Adams predictor-corrector algorithm is established, thus the minimal number of time intervals that under desired integration tolerance can be estimated. The classic batch reactor problem is tested and compared in detail with literature reports, and the results reveal the effectiveness of the proposed approach. Dealing with path constraints requires extra techniques, and will be studied in the second paper.展开更多
文摘利用语音信号在离散余弦变换(DCT)域的近似稀疏性和量化压缩感知理论,文中提出一种基于量化压缩感知的语音压缩编码方案。编码端利用压缩感知技术,将语音信号投影成数据量大大减少的观测序列,然后对观测序列采用Lloyd-M ax量化得到量化后的观测样值;解码端直接利用量化后的观测样值,结合重构算法重构出原始语音信号的DCT系数,经过DCT反变换得到重构后的语音信号,并采用后置低通滤波器改善重构语音的听觉效果。该编码方案解码端不需要进行反量化,而是直接利用量化后的观测样值进行重构,有效降低了解码端的运算量及复杂度。仿真结果表明:采用量化迭代硬阈值(QIHT)算法重构效果优于迭代硬阈值算法(IHT),重构语音的信噪比能达到20 d B以上,MOS分达到3.26。
文摘The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm.The deregularization maximized the energy of the solution,which was opposite to the Tikhonov regularization where the energy was minimized.However,combined with box-constraints,the deregularization forced the solution to be close to the binary set.It further exploited the box-constrained dichotomous coordinate descent algorithm and adapted it to the nonstationary iterative Tikhonov regularization to present an efficient detector.As a result,the worst-case and average complexity are reduced down as K2.8 and K2.5 floating point operation per second,respectively.The development improves the "efficient frontier" in multiuser detection,which is illustrated by simulation results.In addition,most operations in the detector are additions and bit-shifts.This makes the proposed technique attractive for fixed-point hardware implementation.
基金the Aerospace Technology Support Foun-dation of China(No.J04-2005040).
文摘Non-uniform quantization for messages in Low-Density Parity-Check(LDPC)decoding canreduce implementation complexity and mitigate performance loss.But the distribution of messagesvaries in the iterative decoding.This letter proposes a variable non-uniform quantized Belief Propaga-tion(BP)algorithm.The BP decoding is analyzed by density evolution with Gaussian approximation.Since the probability density of messages can be well approximated by Gaussian distribution,by theunbiased estimation of variance,the distribution of messages can be tracked during the iteration.Thusthe non-uniform quantization scheme can be optimized to minimize the distortion.Simulation resultsshow that the variable non-uniform quantization scheme can achieve better error rate performance andfaster decoding convergence than the conventional non-uniform quantization and uniform quantizationschemes.
基金Project supported by the National Natural Science Foundation of China(Grant No.60504033)
文摘In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.
文摘Iterative linear programming methods are proposed for optimum balanced animal diet in this paper. According to ＂wooden bucket theory＂ of the nutritional balance, each nutrient in the feeding standard has equal importance. It＇s unreasonable to use common goal programming to attach different weighted value to different nutritional parameters. This paper introduces an effective algorithm to deal with this kind of problem. When the permitting cost of livestock ration is given, we can design a ration formula with linear program-this is the first round. Then, according to the differences between the permitting cost and the formula cost gained in the first round, adjust the feeding standard and the feeding raw materials, and conduct the second round of linear programming for ration formula. If there is still a very big difference between the formula cost and the permitting cost, the third round will be taken, and so on. In this iteration course the formula cost gradually approaches the permitting cost. It is the key that the feeding standard and feeding raw materials are modified in each round. This method ensured the nutritive equilibrium with the formulation of least-cost ration. This is an especially important method when the primary goal of the optimization tool is to improve economic and nutritive efficiency.
基金Supported by the National Natural Science Foundation of China (U1162130)the National High Technology Research and Development Program of China (2006AA05Z226)the Outstanding Youth Science Foundation,Zhejiang Province (R4100133)
文摘This study proposes an efficient indirect approach for general nonlinear dynamic optimization problems without path constraints. The approach incorporates the virtues both from indirect and direct methods: it solves the optimality conditions like the traditional indirect methods do, but uses a discretization technique inspired from direct methods. Compared with other indirect approaches, the proposed approach has two main advantages: (1) the discretized optimization problem only employs unconstrained nonlinear programming (NLP) algorithms such as BFGS (Broyden-Fletcher-Goldfarb-Shanno), rather than constrained NLP algorithms, therefore the computational efficiency is increased; (2) the relationship between the number of the discretized time intervals and the integration error of the four-step Adams predictor-corrector algorithm is established, thus the minimal number of time intervals that under desired integration tolerance can be estimated. The classic batch reactor problem is tested and compared in detail with literature reports, and the results reveal the effectiveness of the proposed approach. Dealing with path constraints requires extra techniques, and will be studied in the second paper.
