The microwave attenuation, which is the key factor for realizing very large bandwidths Ti:LiNbO3 optical modulators is fully studied and the causes and reduction techniques are discussed in detail. Practical realization of wide-band optical modulators with low microwave attenuation and low driving voltage is also discussed.

In this paper, we propose a new approach for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix (Gibbs random fields and cooccurrences related statistical model of texture analysis) to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the Topographic Structure described by the Surface-Shape Operator, which describe gray-level image patterns in terms of 3D shapes instead of intensity values. Then, we illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the MRSAR (Multiresolution Simultaneous Autoregressive) features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures: 112 categories, 2016 samples having various brightness in each category.

Recently, a great deal of effort has been devoted to the design problem of "constrained least squares M-D FIR filter" because a significant improvement of the squared error is expected by a slight relaxation of the minimax error condition. Unfortunately, no design method has been reported, which has some theoretical guarantee of the convergence to the optimal solution. In this paper, we propose a class of novel design methods of "constrained least squares M-D FIR filter. " The most remarkable feature is that all of the proposed methods have theoretical guarantees of convergences to the unique optimal solution under any consistent set of prescribed maximal error conditions. The proposed methods are based on "convex projection techniques" that computes the metric projection onto the intersection of multiple closed convex sets in real Hilbert space. Moreover, some of the proposed methods can still be applied even for the problem with any inconsistent set of maximal error conditions. These lead to the unique optimal solution over the set of all filters that attain the least sum of squared distances to all constraint sets.

Dense WDM techniques that exploit the enormous bandwidth of dispersion-shifted fibers (DSFs) while avoiding the impairments due to nonlinear effects are described. First, the nature of four-wave mixing (FWM), the dominant impairment factor in WDM transmission systems, is investigated using DSF installed in the field and laboratory experiments. This provides useful information for the practical design of WDM networks based on DSF. Second, practical techniques to reduce FWM impairment, unequal channel allocation and off-lambda-zero channel allocation (equal channel allocation in the novel 1580 nm band) along with gain-shifted erbium-doped fiber amplifiers for the 1570 to 1600 nm band, is described. Comparisons between off-lambda-zero and unequal channel allocation are provided in terms of the maximum transmission distance for various numbers of channels. Two schemes to immunize WDM systems against group velocity dispersion, span-by-span dispersion compensation and optical duobinary format, are presented. The combination of unequal channel allocation with off-lambda-zero channel allocation as well as the combination of two bands: the conventional 1550 nm band and the novel 1580 nm band are proven to be very useful in expanding the usable bandwidth of DSFs.

To improve speech coding quality, in particular, the long-term dependency prediction characteristics, we propose a new nonlinear predictor, i. e. , a fully connected recurrent neural network (FCRNN) where the hidden units have feedbacks not only from themselves but also from the output unit. The comparison of the capabilities of the FCRNN with conventional predictors shows that the former has less prediction error than the latter. We apply this FCRNN instead of the previously proposed recurrent neural networks in the code-excited predictive speech coding system (i. e. , CELP) and shows that our system (FCRNN) requires less bit rate/frame and improves the performance for speech coding.

The checkerboard effect is caused by the periodic time-variant property of multirate filters which consist of up-samplers and digital filters. Although the conditions for some one-dimensional (1D) multirate systems to avoid the checkerboard effect have been shown, the conditions for Multidimensional (MD) multirate systems have not been considered. In this paper, some theorems about the conditions for MD multirate filters without checkerboard effect are derived. In addition, we also consider MD multirate filter banks without checkerboard effect. Simulation examples show that the checkerboard effect can be avoided by using the proposed conditions.

In this paper, a two-dimensional (2-D) binary-valued (BV) lapped transform (LT) is proposed. The proposed LT has basis images which take only BV elements and satisfies the axial-symmetric (AS) property. In one dimension, there is no 2-point LT with the symmetric basis vectors, and the property is achieved only with the non-overlapping basis which the Hadamard transform (HT) has. Hence, in two dimension, there is no 22-point separable ASLT, and only 2-D HT can be the 22-point separable AS orthogonal transform. By taking non-separable BV basis images, this paper shows that a 22-point ASLT can be obtained. Since the proposed LT is similar to HT, it is referred to as the lapped Hadamard transform (LHT). LHT of larger size is shown to be provided with a tree structure. In addition, LHT is shown to be efficiently implemented by a lattice structure.

This paper discusses a design technique for multidimensional (M-D) multirate filters which cause no checkerboard distortion. In the first part of this paper, a necessary and sufficient condition for M-D multirate filters to be checkerboard-distortion-free is derived in the frequency domain. Then, in the second part, this result is applied to a scanning line conversion system for television signals. To confirm the effectiveness of the derived condition, band-limiting filters with and without considering the condition are designed, and the results by these filters are compared. A reducibility of the number of delay elements in such a system is also considered to derive efficient implementation.

Evaluating analytically computer architecture performance is mostly cheap and quick. However, existing analytical performance evaluation techniques usually have a difficult and time-consuming modeling process. Moreover, existing techniques do not support well the capability for finding the bottleneck and its cause of a target system being evaluated. To address the above problems and to enhance analytical performance evaluation technology, in this paper we propose a software tool that accepts system models described in a specification language, generating an executable program that performs the actual performance evaluation. The whole approach is built on a subsystem-oriented performance evaluation tool, which is, in turn, based on a formal subsystem-oriented performance evaluation technique and a subsystem specification language.

It becomes essential in practice to improve a processing rate and to divide an image into small segments adjusting a limited memory, because image filing systems handle large images up to A1 size. This paper proposes a new method of an automatic skew normalization, comprising a high-speed skew detection and a distortion-free dividing rotation. We have evaluated the proposed method from the viewpoints of the processing rate and the accuracy for typed documents. As results, the processing rate is 2. 9 times faster than that of a conventional method. A practical processing rate for A1 size documents can be achieved under the condition that the accuracy of a normalized angle is controlled within 0. 3 degrees. Especially, the rotation with dividing can have no error angle, even when the A1 size documents is divided into 200 segments, whereas the conventional method cause the error angle of 1. 68 degrees.

High- and low-reflection Bragg gratings with a flat-top spectral response free from ripples are proposed. Add/drop filters are created based on gratings photoinduced on planar waveguides by using the new design schemes. The measured spectral responses for the high and low reflection gratings are in good agreement with the calculated ones, and show the flat-top spectral responses.

This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.

Recent progress in neural network research has demonstrated the usefulness of neural networks in a variety of areas. In this work, its application in the spatial resolution improvement of a remotely sensed low resolution thermal infrared image using high spatial resolution of visible and near-infrared images from Landsat TM sensor is described. The same work is done by an algebraic method. The tests developed are explained and examples of the results obtained in each test are shown and compared with each other. The error analysis is also carried out. Future improvements of these methods are evaluated.

This paper is concerned with a concept called universality or completeness of sets of logic devices. Universality characterizes sets of logic devices which can be used for the construction of arbitrary logic circuits. The elemental universality proposed here is the most general condition of universality which covers logic devices with/without delay time and combinational/sequential circuits. The necessary and sufficient condition of elemental universality shows that nonlinearity and nonmonotonicity are essential conditions for the realization of various digital mechanisms.

In this paper, we give a solution to the problem of conflict-free access of various slices of data in parallel processor for image processing. Image processing operations require a memory system that permits parallel and conflict-free access of rows, columns, forward diagonals, backward diagonals, and blocks of two-dimensional image array for an arbitrary location. Linear skewing schemes are useful methods for those requirements, but these schemes require complex Euclidean division by prime number. On the contrary, nonlinear skewing schemes such as XOR-schemes have more advantages than the linear ones in address generation, but these schemes allow conflict-free access of some array slices in restricted region. In this paper, we propose a new XOR-scheme which allows conflict-free access of arbitrarily located various slices of data for image processing, with a two-fold the number of memory modules than that of processing elements. Further, we propose an efficient data alignment network which consists of log N + 2-stage multistage interconnection network utilizing Omega network.

A constant modulus adaptive array algorithm is derived using analysis and synthesis filter banks to permit adaptive digital beamforming for wideband signals. The properties of the CMA adaptive array using the filter banks are investigated. This array would be used to realize adaptive digital beamforming when this is difficult by means of ordinary (that is, non-subband) processing due to the limited speed of signal processor operations. As an actual application, we present a beamspace adaptive array structure that combines the analysis and synthesis filter banks with RF-domain multibeam array antennas, such as those utilizing optical signal processing.

The Heart Rate Variability (HRV) analysis has become vigorous these days. One reason for this is that the HRV analysis investigates the dynamics of the autonomic nervous system activities which control the HRV. The Integral Pulse Frequency Modulation (IPFM) model is a pulse generating mechanism model in the nervous system, that is one of the models which connects the HRV to the autonomic nervous system activities. The IPFM model is a single frequency component model; however, the real HRV has multiple frequency components. Moreover, there are refractory periods after generating action potentials are initiated. Nevertheless, the IPFM model does not consider refractory periods. In order to make sure of the accuracy and the effectiveness of the integral function (IF) method applied to the real data, we consider the absolute refractory periods and two frequency components. In this investigation, the simulated HRV was made with a single and double frequency component using the IPFM model with and without absolute refractory periods. The original generating function of the IPFM model was demodulated by using the instantaneous heart rate tachogram. The power of the instantaneous pulse rate per minute was analyzed by the direct FFT method, the IF FFT method without the absolute refractory periods, and the IF FFT method with the absolute refractory periods. It was concluded that the IF FFT method can demodulate the original generating function accurately.

The new technique for reducing the load latency is presented. This technique, named tunneling-load, utilizes the register specifier buffer in order to reduce the load latency without fetching the data cache speculatively, and thus eliminates the drawback of any load address prediction techniques. As a consequence of the trend toward increasing clock frequency, the internal cache is no longer able to fill the speed gap between the processor and the external memory, and the data cache latency degrades the processor performance. In order to hide this latency, several techniques predicting the load address have been proposed. These techniques carry out the speculative data cache fetching, which causes the explosion of the memory traffic and the pollution of the data cache. The tunneling-load solves these problems. We have evaluated the effects of the tunneling-load, and found that in an in-order-issue superscalar platform the instruction level parallelism is increased by approximately 10%.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

We introduce an image contour clustering method based on a multiscale image representation and its application to image compression. Multiscale gradient planes are obtained from the mean squared sum of 2D wavelet transform of an image. The decay on the multiscale gradient planes across scales depends on the Lipshitz exponent. Since the Lipshitz exponent indicates the spatial differentiability of an image, the multiscale gradient planes represent smoothness or sharpness around edges on image contours. We apply vector quatization to the multiscale gradient planes at contours, and cluster the contours in terms of represntative vectors in VQ. Since the multiscale gradient planes indicate the Lipshitz exponents, the image contours are clustered according to its gradients and Lipshitz exponents. Moreover, we present an image recovery algorithm to the multiscale gradient planes, and we achieve the skech-based image compression by the vector quantization on the multiscale gradient planes.