We present a simple technique for enhancing multi-modal images. The unsharp masking (UM) is at first nonlinearized to prevent halos around large edges. This edge-preserving UM is then extended to cross-sharpening of multi-modal images where a component image is sharpened with the aid of more clear edges in another component image.

An asynchronous version is proposed for the waveform relaxation-Newton method. A sufficient condition is given for the proposed algorithm to converge.

We propose a method for downsizing line pictures to generate pixel line arts. In our method, topological properties such as connectivity of lines and segments are preserved by allowing slight distortion in the form of objects in input images. When input line pictures are painted with colors, the number of colors is preserved by our method.

A method is presented for selecting items asked for new users to input their preference rates on those items in recommendation systems based on the collaborative filtering. Optimal item selection is formulated by an integer programming problem and we solve it by using a kind of the Hopfield-network-like scheme for interior point methods.

A simple adaptive scheme is proposed for controlling the length of time-steps for numerical simulation of Hopfield's networks.

A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.

This letter investigates the convergence property of first-available-task approach which is an asynchronous version of global-timestep Iterated Timing Analysis (ITA). This asynchronous iteration is proven to converge under the same condition as that for the convergence of the global-timestep ITA.

The multirate ITA for a linear circuit is proven to converge under a weaker condition on the capacitance matrix of the circuit or under a stronger condition on the conductance matrix than those for the global-timestep ITA to converge.

Sufficient conditions are given for the transient response of a class of MOS digital circuits composed of inverter-type logic gates, transfer gates and RC ladder networks to vary monotonically with the variations in the characteristics of circuit elements.

The alternative c-means algorithm has recently been presented by Wu and Yang for robust clustering of data. In this letter, we analyze the convergence of this algorithm by transforming it into an equivalent form with the Legendre transform. It is shown that this algorithm converges to a local optimal solution from any starting point.

Feedback of class memberships is incorporated into multimodal pattern classifiers and their unsupervised learning algorithm is presented. Classification decision at low levels is revised by the feedback information which also enables the reconstruction of patterns at low levels. The effects of the feedback are examined for the McGurk effect by using a simple model.

On the basis of a nonnegative and a monotonic property of the solution of a special class of differential equations, the transient responses of a class of MOS digital circuits are proven to have a monotone sensitivity with respect to some transistor parameters.

It is shown by the derivation of solution methods for an elementary optimization problem that the stochastic relaxation in image analysis, the Potts neural networks for combinatorial optimization and interior point methods for nonlinear programming have common formulation of their dynamics. This unification of these algorithms leads us to possibility for real time solution of these problems with common analog electronic circuits.

This paper investigates convergence properties of a circuit simulation technique called Waveform Relaxation (WR). A general formulation of a family of WR algorithms called a generalized WR is introduced. This formulation reduces to some hitherto introduced WR algorithms in particular cases. The following two sufficient conditions for the generalized WR method to converge locally are given: ) the capacitance matrix of the circuit is block strictly diagonally dominant and a time-steplength is sufficiently small; ) the conductance matrix of the circuit has the same property and a time-steplength is sufficiently large.

On the basis of inequality theorems for nonlinear differential equations, resistor-transfergate-capacitor ladder networks driven by an inverter-type logic gate are proven to have monotone sensitivities to perturbations in the inputs of the inverter-type gate, to those in the control signals of the transfer gates, and to the variations in transistors.

We propose an unsharp-masking technique which preserves the hue of colors in images. This method magnifies the contrast of colors and spatially sharpens textures in images. The contrast magnification ratio is adaptively controlled. We show by experiments that this method enhances the color tone of photographs while keeping their perceptual scene depth.

An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.

When a global-timestep discretization is used, the Iterated Timing Analysis (ITA) and the Waveform Relaxation (WR) converge under the same condition. On the other hand, the convergence of the multirate ITA needs a stronger condition than that for the mutirate WR.

We extend a graph spectral method for extracting clusters from graphs representing pairwise similarity between data to hypergraph data with hyperedges denoting higher order similarity between data. Our method is robust to noisy outlier data and the number of clusters can be easily determined. The unsupervised method extracts clusters sequentially in the order of the majority of clusters. We derive from the unsupervised algorithm a semi-supervised one which can extract any cluster irrespective of its majority. The performance of those methods is exemplified with synthetic toy data and real image data.

Hitherto obtained results on the qualitative behaviors of nonlinear dynamical systems are applied to MOS digital circuits, and a class of these circuits is shown to have some monotonic properties such as nonnegativity-preservation, isotonicity, and monotone convergence to a dc solution.