A nonlocally weighted soft-constrained natural gradient iterative method is introduced for robust blind separation in reverberant environment. The nonlocal weighting of the iterations promotes stability and convergence of the algorithm for long demixing filters. The scaling degree of freedom is controlled by soft-constraints built into the auxiliary difference equations. The small divisor problem of iterations in silence durations of speech is resolved. Computations on synthetic speech mixtures based on measured binaural room impulse responses show that the algorithm achieves higher signal-to-inteference ratio improvement than existing method (natural gradient time domain algorithm) in an office size room with reverberation time over 0.5 second.

This paper deals with the merging problem, i.e. to approximate two adjacent Bézier curves by a single Bézier curve. A novel approach for approximate merging is introduced in the paper by using the constrained optimization method. The basic idea of this method is to find conditions for the precise merging of Bézier curves first, and then compute the constrained optimization solution by moving the control points. “Discrete” coefficient norm in L2 sense and “squared difference integral” norm are used in our method. Continuity at the endpoints of curves are considered in the merging process, and approximate merging with points constraints are also discussed. Further, it is shown that the degree elevation of original Bézier curves will reduce the merging error.

Automatic reconstruction of 3D objects from 2D orthographic views has been a major research issue in CAD/CAM. In this paper, two accelerating techniques to improve the eÆciency of reconstruction are presented. First, some pseudo elements are removed by depth and topology information as soon as the wire-frame is constructed, which reduces the searching space. Second, the proposed algorithm does not establish all possible surfaces in the process of generating 3D faces. The surfaces and edge loops are generated by using the relationship between the boundaries of 3D faces and their projections. This avoids the growth in combinational complexity of previous methods that have to check all possible pairs of 3D candidate edges.

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We study sparse blind source separation (BSS) for a class of positive and partially overlapped signals. The signals are only allowed to have nonoverlapping at certain locations, while they could overlap with each other elsewhere. For nonnegative data, a novel approach has been proposed by Naanaa and Nuzillard (NN) assuming that nonoverlapping exists for each source signal at some location of acquisition variable. However, the NN method introduces errors (spurious peaks) in the output when their nonoverlapping condition is not satisfied. To resolve this problem and improve robustness of separation, postprocessing techniques are developed in two aspects. One is to detect coherent and uncertain components from NN outputs by using multiple mixture data, then removing the uncertain portion to enhance signals. The other is to find better estimation of mixing matrix by leveraging reliable source peak