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.

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.

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 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.

A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.

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.

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.

In this paper, a dedicated hardware design for motion estimation LSI of MPEG2 is presented. Combining our bits truncation adaptive pyramid (BTAP) algorithm with Window-MSPA architecture, the hardware cost is tremendously reduced without PSNR performance degradation for mean pyramid algorithm. The core of the test chip working at 83 MHz, performs a search range of 67 for image size of 1920 1152 and achieves video rate of 60 field/s. It can be used for HDTV purpose. The chip size is 4. 8 mm 4. 8 mm with 0. 5u 2-level metal CMOS technology. The result in this paper shows our promising future to realize one chip HDTV MPEG2 encoder.

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.

In our current work, we attempt to make an automatic diagnostic system of lung cancer based on the analysis of the sputum color images. In order to form general diagnostic rules, we have collected a database with thousands of sputum color images from normal and abnormal subjects. As a first step, in this paper, we present a segmentation method of sputum color images prepared by the Papanicalaou standard staining method. The segmentation is performed based on an energy function minimization using an unsupervised Hopfield neural network (HNN). This HNN have been used for the segmentation of magnetic resonance images (MRI). The results have been acceptable, however the method have some limitations due to the stuck of the network in an early local minimum because the energy landscape in general has more than one local minimum due to the nonconvex nature of the energy surface. To overcome this problem, we have suggested in our previous work some contributions. Similarly to the MRI images, the color images can be considered as multidimensional data as each pixel is represented by its three components in the RGB image planes. To the input of HNN we have applied the RGB components of several sputum images. However, the extreme variations in the gray-levels of the images and the relative contrast among nuclei due to unavoidable staining variations among individual cells, the cytoplasm folds and the debris cells, make the segmentation less accurate and impossible its automatization as the number of regions is difficult to be estimated in advance. On the other hand, the most important objective in processing cell clusters is the detection and accurate segmentation of the nuclei, because most quantitative procedures are based on measurements of nuclear features. For this reason, based on our collected database of sputum color images, we found an algorithm for NonSputum cell masking. Once these masked images are determined, they are given, with some of the RGB components of the raw image, to the input of HNN to make a crisp segmentation by assigning each pixel to label such as Background, Cytoplasm, and Nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

Image restoration using estimated parameters of image model and noise statistics is presented. The image is modeled as the output of a 2-D noncausal autoregressive (NCAR) model. The parameter estimation process is done by using the autocorrelation function and a biased term to a conventional least-squares (LS) method for the noncausal modeling. It is shown that the proposed method gives better results than the other parameter estimation methods which ignore the presence of the noise in the observation data. An appropriate image model selection process is also presented. A genetic algorithm (GA) for solving a multiobjective function with single constraint is discussed.

We show that characteristic functions of elements of self-similar tilings can be used as scaling functions of multiresolution analysis of L2(Rn). This multiresolution analysis is a generalization of a self-affine tiling multiresolution analysis using a characteristic function of element of self-affine tiling as a scaling function. We give a method of constructing a wavelet basis which realizes such an MRA.

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.

The VF (Variable-to-Fixed length) arithmetic coding method combines the advantage of an ordinary stream arithmetic code with the simplicity of a block code. One of the advantages of VF codes is that the transmission errors or channel errors do not propagate infinitely and are restricted to the block in question. In this paper, we propose a modified type of non-proper VF arithmetic coding method that defines an input alphabet subset according to both the number of codewords in the current codeword set and input symbol probability and that splits the codeword set completely for a newly defined alphabet subset when the codeword set becomes smaller by each splitting. The proposed coding method carrys out independence of each codeword and guarantees that there is no collision while there is a waste of codeword(s) in conventional AB-coding due to collision. We examine the performance of the proposed method and compare it with that of other VF codes in terms of compression ratio and algorithmic complexity.

In this paper, we present a hierarchical technique for simultaneous pin assignment and global routing during floorplanning based on the minimum cost maximum integer flow algorithm with several heuristic cost functions. Furthermore, our algorithm handles feedthrough pins and equi-potential pins taking into account global routes. Our algorithm allows various user specified constraints such as pre-specified pin positions, wiring paths, wiring widths and critical nets. Experimental results including Xerox floorplanning benchmark have shown the effectiveness of the heuristics.

Adaptive estimation of nonstationary sinusoidal signals or quasi-periodic signals in additive noise is of essential importance in many diverse engineering fields, such as communications, biomedical engineering, power systems, pitch detection in transcription and so forth. So far, Kalman filtering based techniques, recursive least square (RLS), simplified RLS (SRLS) and LMS algorithms, for examples, have been developed for this purpose. This work presents in detail a performance analysis for the SRLS algorithm proposed recently in the literature, which is used to estimate an enhanced sinusoid. Its dynamic and tracking properties, noise and lag misadjustments are developed and discussed. It is found that the SRLS estimator is biased, and its misadjustments are functions of not only the noise variance but also, unpleasantly, of the signal parameters. Simulations demonstrate the validity of the analysis. Application of the SRLS to a real-life piano sound is also given to peek at its effectiveness.

The effectiveness of periodic dispersion compensation on single-channel 40 Gbit/s soliton transmission system was experimentally investigated. This technique requires just the dispersion compensation fibers and wideband optical filters in the transmission line, which has no difficulty to be used in the practical system. By using polarization-division-multiplexing together with periodic dispersion compensation, single-channel 40 Gbit/s transmission over 4700 km was demonstrated. Single-polarization 40 Gbit/s transmission experiments, which are more suitable for system implementation and compatible with WDM were also conducted. We investigated the transmission characteristics and pulse dynamics in different dispersion maps and in the optimized dispersion map, single-channel, single-polarization 40 Gbit/s transmission over 6300 km was successfully demonstrated.

This paper describes the wavelength division multiplexing technology for the long-haul optical communication system, especially for the undersea cable system. At first, the present WDM technology for the undersea cable system is reviewed briefly. After that, some experiments using compensation of the dispersion slope of the transmission fiber are discussed as future technical options of undersea systems with over 100 Gbit/s capacity.

We studied 2 types of polarization insensitive semiconductor optical amplifier (SOA) gates for use in wavelength division multiplexing (WDM) applications: 1) a low operation current SOA gate with a small and square bulk active region but without spot-size converters and 2) a multi channel SOA gate array with tapered waveguide spot-size converters (SS-SOA) on both sides. The low operation current SOA gate provided a very low current for fiber-to-fiber loss-less operation (5. 4-7. 0 mA) and a high extinction ratio (>30 dB) over a wide wavelength range (1530-1580 nm). For multi channel array assembling, the SS is indispensable. The 4-channel SS-SOA gate array was assembled on a planar lightwave circuit (PLC) platform for the first time. The gain characteristics of each channel were very similar and a low fiber-to-fiber loss-less current of 33 mA and a high extinction ratio of nearly 40 dB were achieved in all channels. The polarization dependence was less than 1 dB. Using the fully packaged 4-channel hybrid gate array module (a 4 channel SS-SOA on PLC platform), an ultra-wide-band (1530-1600 nm) high speed wavelength selector was successfully demonstrated. Both rise- and fall-times were less than 1 ns, which makes the wavelength selector suitable for high-speed optical packet switching. Electrical and optical interference between channels were negligible.

The temperature dependence of central wavelength of optical filters is a serious problem for the dense WDM systems. This dependence is owing to the temperature dependence of optical path-length of the waveguide. In this study, we realized a temperature independent silica-based optical filter at 1. 55 µm wavelength using an athermal waveguide, in which optical pathlength is independent of temperature. First, we designed a silica-based athermal waveguide, and next we designed and fabricated a ring resonator using the athermal waveguide. As a result, we successfully decreased the temperature dependence of central wavelength to less than 4 10 -4 nm/K, which is 3% and 0. 3% of corresponding values of conventional silica-based and semiconductor waveguide filters, respectively.