In the processing of measured data, the number of operations of the algorithm for picking out outlier data in batches is very large. A large number of linear or nonlinear equations based on the parameter model built a...In the processing of measured data, the number of operations of the algorithm for picking out outlier data in batches is very large. A large number of linear or nonlinear equations based on the parameter model built according to the characteristics of measuring equipment and measured object are to be sovied. This paper presents the criterion of picking out outlier data point by point and a parallel algorithm for picking out outlier data in batches, with regard to large-scale linear regression model. The scalability for the parallel algorithm is analyzed, and the results for the algorithm on a group of computers are given. High speed-up is obtained.展开更多
In this paper, a 2D discrete W transform is turned to another 2D discrete transform.The kernel of the resulting transform is separable, thus it can be computed by the wellknown row-column algorithm. Therefore, a fast ...In this paper, a 2D discrete W transform is turned to another 2D discrete transform.The kernel of the resulting transform is separable, thus it can be computed by the wellknown row-column algorithm. Therefore, a fast algorithm is obtained for 2D DWT witharbitrary length. Methods are also given in the paper for computing 2D cyclic convolutions,2D skew-cyclic convolutions and 2D generalized discrete Fourier transforms by 2D discreteW transform. Furthermore, running time of the algorithms on a kind of micro computeris given.展开更多
This paper presents the Cp, criterion and a parallel algorithm with regard to the large-scale linear regression model. The scalability for the algorithm is analyzed. And the results for the algorithm on a group of com...This paper presents the Cp, criterion and a parallel algorithm with regard to the large-scale linear regression model. The scalability for the algorithm is analyzed. And the results for the algorithm on a group of computers are given. The quite good speed-up ratio is obtained.展开更多
In this paper, the Multi-dimensional Polynomial Transform is used to convert the Multi-dimensional W Transform (MDDWT) into a series of one-dimensional W transform (DWT). Thus, a new polynomial transform algorithms fo...In this paper, the Multi-dimensional Polynomial Transform is used to convert the Multi-dimensional W Transform (MDDWT) into a series of one-dimensional W transform (DWT). Thus, a new polynomial transform algorithms for MDDWT is obtained. The algorithm needs no complex number operations and is simple in structure. The number of multiplications for computing a r-d DWT is only times that of the common used row-column method. The number of additions is also reduced considerablely. Running time of the algorithm on micro-computers is given and is compared with the common used row-column method.展开更多
文摘In the processing of measured data, the number of operations of the algorithm for picking out outlier data in batches is very large. A large number of linear or nonlinear equations based on the parameter model built according to the characteristics of measuring equipment and measured object are to be sovied. This paper presents the criterion of picking out outlier data point by point and a parallel algorithm for picking out outlier data in batches, with regard to large-scale linear regression model. The scalability for the parallel algorithm is analyzed, and the results for the algorithm on a group of computers are given. High speed-up is obtained.
文摘In this paper, a 2D discrete W transform is turned to another 2D discrete transform.The kernel of the resulting transform is separable, thus it can be computed by the wellknown row-column algorithm. Therefore, a fast algorithm is obtained for 2D DWT witharbitrary length. Methods are also given in the paper for computing 2D cyclic convolutions,2D skew-cyclic convolutions and 2D generalized discrete Fourier transforms by 2D discreteW transform. Furthermore, running time of the algorithms on a kind of micro computeris given.
文摘This paper presents the Cp, criterion and a parallel algorithm with regard to the large-scale linear regression model. The scalability for the algorithm is analyzed. And the results for the algorithm on a group of computers are given. The quite good speed-up ratio is obtained.
文摘In this paper, the Multi-dimensional Polynomial Transform is used to convert the Multi-dimensional W Transform (MDDWT) into a series of one-dimensional W transform (DWT). Thus, a new polynomial transform algorithms for MDDWT is obtained. The algorithm needs no complex number operations and is simple in structure. The number of multiplications for computing a r-d DWT is only times that of the common used row-column method. The number of additions is also reduced considerablely. Running time of the algorithm on micro-computers is given and is compared with the common used row-column method.
