This paper proposes an accurate and efficient method to calculate probability distributions of pulse-shaped complex signals. We show that the distribution over the in-phase and quadrature-phase (I/Q) complex plane is obtained by a recursive probability mass function of the accumulator for a pulse-shaping filter. In contrast to existing analytical methods, the proposed method provides complex-plane distributions in addition to instantaneous power distributions. Since digital signal processing generally deals with complex amplitude rather than power, the complex-plane distributions are more useful when considering digital signal processing. In addition, our approach is free from the derivation of signal-dependent functions. This fact results in its easy application to arbitrary constellations and pulse-shaping filters like Monte Carlo simulations. Since the proposed method works without numerical integrals and calculations of transcendental functions, the accuracy degradation caused by floating-point arithmetic is inherently reduced. Even though our method is faster than Monte Carlo simulations, the obtained distributions are more accurate. These features of the proposed method realize a novel framework for evaluating the characteristics of pulse-shaped signals, leading to new modulation, predistortion and peak-to-average power ratio (PAPR) reduction schemes.

With the widespread use of indoor positioning technology, the need for high-precision positioning services is rising; nevertheless, there are several challenges, such as the difficulty of simulating the distribution of interior location data and the enormous inaccuracy of probability computation. As a result, this paper proposes three different neural network model comparisons for indoor location based on WiFi fingerprint - indoor location algorithm based on improved back propagation neural network model, RSSI indoor location algorithm based on neural network angle change, and RSSI indoor location algorithm based on depth neural network angle change - to raise accurately predict indoor location coordinates. Changing the action range of the activation function in the standard back-propagation neural network model achieves the goal of accurately predicting location coordinates. The revised back-propagation neural network model has strong stability and enhances indoor positioning accuracy based on experimental comparisons of loss rate (loss), accuracy rate (acc), and cumulative distribution function (CDF).

This paper proposes to pre-compute approximate normal distribution functions and store them in textures such that real-time applications can process complex specular surfaces simply by sampling the textures. The proposed method is compatible with the GPU pipeline-based algorithms, and rendering is completed at real time. The experimental results show that the features of complex specular surfaces, such as the glinty appearance of leather and metallic flakes, are successfully reproduced.

In this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.

In this paper, a new skin detection method using pixel color and image regional information, intended for objectionable image filtering is proposed. The method consists of three stages: skin detection, feature extraction and image classification. Skin detection is implemented in two steps. First, a Sinc function, fitted to skin color distribution in the Cb-Cr chrominance plane is used for detecting pixels with skin color properties. Next, to benefit regional information, based on the theory of color image reproduction, it's shown that the scattering of skin pixels in the RGB color space can be approximated by an exponential function. This function is incorporated to extract the final accurate skin map of the image. As objectionable image features, new shape and direction features, along with area feature are extracted. Finally, a Multi-Layer Perceptron trained with the best set of input features is used for filtering images. Experimental results on a dataset of 1600 images illustrate that the regional method improves the pixel-based skin detection rate by 10%. The final classification result with 94.12% accuracy showed better results when compared to other methods.

The statistical static timing analysis has been studied intensively in the last decade so as to deal with the process variability, and various techniques to represent distributions of timing information, such as a gate delay, a signal arrival time, and a slack, have been proposed. Among them, the Gaussian mixture model is distinguished from the others in that it can handle various correlations, non-Gaussian distributions, and slew distributions easily. However, the previous algorithm of computing the statistical maximum for Gaussian mixture models, which is one of key operations in the statistical static timing analysis, has a defect such that it produces a distribution similar to Gaussian in a certain case, although the correct distribution is far from Gaussian. In this paper, we propose a new algorithm for statistical maximum (minimum) operation for Gaussian mixture models. It takes the cumulative distribution function curve into consideration so as to compute accurate criticalities (probabilities of timing violation), which is important for detecting delay faults and circuit optimization with the use of statistical approaches. We also show some experimental results to evaluate the performance of the proposed method.

We formulated the excitation rate of VUV and emitted visible light from rare gas on PDP by using the Boltzmann equation with electron-atom collision integral term and obtained the excitation rate as the function of Temperature and Mass. This form of excitation rate was firstly derived in PDP area. In addition we showed the Pressure dependence of intensity ratio of Ne/VUV as the application of our excitation rate formulae.

In this paper, the expectation maximization (EM) algorithm is used for unsupervised classification of polarimetric synthetic aperture radar (SAR) images. The EM algorithm provides an estimate of the parameters of the underlying probability distribution functions (pdf's) for each class. The feature vector is 9-dimensional, consisting of the six magnitudes and three angles of the elements of a coherency matrix. Each of the elements of the feature vector is assigned a specific parametric pdf. In this work, all the features are supposed to be statistically independent. Then we present a two-stage unsupervised clustering procedure. The EM algorithm is first run for a few iterations to obtain an initial partition of, for example, four clusters. A randomly selected sample of, for example, 2% pixels of the polarimetric SAR image may be used for unsupervised training. In the second stage, the EM algorithm may be run again to reclassify the first stage clusters into smaller sub-clusters. Each cluster from the first stage will be processed separately in the second stage. This approach makes further classification possible as shown in the results. The training cost is also reduced as the number of feature vector in a specific cluster is much smaller than the whole image.

A procedure for estimating radiated power of radio transmitter is proposed based on a statistical property of field intensity time variation distribution in a reverberation chamber. When random varying multipath waves produced by stirrers in a reverberation chamber are received together with a direct wave, the resulting mixed waves are regarded as a kind of multipath waves. Theoretical and experimental results are reported regarding a procedure for estimating radiated power from the 63.2% value of CDF (Cumulative Distribution Function) of an envelope of multipath waves.

This study was designed to evaluate localized muscular fatigue induced during mouse operation in a VDT task. Ten male undergraduates from 19 to 23 years old participated in the experiment. The subject performed a pointing task with a PC mouse for about 4 hours. The EMG measurements and psychological rating of fatigue were conducted before the experimental task and after each 30-minutes block during the experimental task. The changes in the Mean Power Frequency (MPF) and Percentage Maximum Voluntary Contraction (%MVC)-shift for the constant cumulative probability in the Amplitude Probability Distribution Function (APDF) with time were explored. The correspondence between the index (MPF or APDF) and the subjective rating of localized muscular fatigue was also examined. The performance was nearly constant across all blocks. The psychological rating of fatigue tended to increase with time. The MPF tended to increase with time, although the main effect of block (time) was not statistically significant. The %MVC-shift tended to increase with time. The correspondence with the perceived sensation of localized muscular fatigue was higher when using the %MVC-shift than when using the MPF. Based on the results, the effectiveness of the indexes used for evaluating localized muscular fatigue was discussed. The %MVC-shift obtained from the APDF was found to be a sensitive index of localized muscular fatigue and corresponded well with the subjective rating of localized muscular fatigue.

For several years, more and more people are joining the Internet and various kind of packets (so called transaction-, block-, and stream-types) have been transmitted in the same network, so that poor network conditions cause loss of the stream-type data packets, such as voices, which request smaller transmission delay time than others. We consider a switching node (router) in a network as an N-series M/G/1-type queueing model and have mainly evaluated the fluctuation of packet delay time and end-to-end delay time, using the two moments matching method with initial value, then define the delay jitter D of a network which consists of jointed N switching nodes. It is clarified that this network is not suitable for voice packets transmission media without measures.

Bidirectional reflection distribution functions (BRDFs) of commercially pure titanium sheets with three different kinds of surface morphology were measured. Those experimental BRDFs were analyzed by using Phong's reflection model. Topographic measurements of the specimens' surfaces were performed with using a stylus-method. An explicit microfacet model based on topographic data was proposed. With using the explicit microfacet model and geometrical optics the calculated BRDFs were obtained and then compared with the experimental BRDFs. Both of them were in a good agreement. Through this comparison physical meanings of Phong's reflection model were discussed. We concluded that with using the explicit microfacet model it will be possible to calculate the BRDF of the materials' surface in arbitrary illumination conditions and that this modeling will be useful to develop new aesthetic surface appearance in material industries, computer graphics, architectural design and surface science.

This paper describes a cumulative distribution function (CDF) of edge direction for detecting road lanes. Based on the assumptions that there are no abrupt changes in the direction and location of road lanes and that the intensity of lane boundaries differs from that of the background, the CDF is formulated, which accumulates the edge magnitude for edge directions. The CDF has distinctive peak points at the vicinity of lane directions due to the directional and the positional continuities of a lane. To obtain lane-related information, we construct a scatter diagram by collecting edge pixels, of which the direction corresponds to the peak point of the CDF, then perform the principal axis-based line fitting for the scatter diagram. Because noises can cause many similar features appear or disappear in an image, to prevent false alarms or miss detection, a recursive estimator of the CDF was introduced, and also a scene understanding index (SUI) was formulated by the statistical parameters of the CDF. The proposed algorithm has been implemented in real time on video data obtained from a test vehicle driven on a typical highway.

A packet transmission delay is an important quality characteristic for various applications including real-time and data applications. In particular, it is necessary to investigate not only a whole distribution of the packet transmission delay, but also the tail part of the distribution, in order to detect the packet loss. In this paper, we analyze the characteristics of the tail part of packet delay distributions by statistical analytic approach. Our analytic results show that the Pareto distribution is most appropriate in 95-99.9% region of the cumulative distribution of packet transmission delays. Based on our statistical analysis, we next propose an adaptive playout control algorithm, which is suitable to real-time applications. Numerical examples show that our algorithm provides the stable packet loss ratio independently on traffic fluctuations.

An improved pulse shaper is proposed which is able to control both the spatial and temporal profile of femtosecond light pulses. Our pulse shaper exploits the spatio-temporal coupling effect seen in pulse shapers. Its properties are numerically analyzed by application of the Wigner distribution function. We confirm that the spatio-temporal output pulse track dictates the differentiation of the phase mask; that the degree of spatio-temporal coupling is determined by the focal length ratio of the lenses in the pulse shaper; and that space to spatial-frequency chirp results from misalignment of lenses.

With the progress of LSI technology, the electronic device size is presently scaling down to the nano-meter region. In such an ultrasmall device, it is indispensable to take quantum mechanical effects into account in device modeling. In this paper, we first review the approaches to the quantum mechanical modeling of carrier transport in ultrasmall semiconductor devices. Then, we propose a novel quantum device model based upon a direct solution of the Boltzmann equation for multi-dimensional practical use. In this model, the quantum effects are represented in terms of quantum mechanically corrected potential in the classical Boltzmann equation.

The Bidirectional Reflectance Distribution Function (BRDF) is an intrinsic measurement of directional properties of the earth's surface. However, the estimation of the BRDF requires many remote sensing measurements of a given surface target from different viewing angles. In addition, a good atmospheric correction scheme is a prerequisite for such an attempt. The airborne POLDER sensor measures successively reflected radiation by terrestrial surfaces in a framed image form at different viewing angles during a single airplane pass, like taking snap-shot pictures. A specially improved atmospheric correction algorithm which is applicable to a framed image data by POLDER sensor is presented. The observed reflectance images taken successively by the airborne POLDER at slightly different viewing angles are converted to a series of surface albedo images by applying our atmospheric correction algorithm. Then, the BRDFs for three surface covers, namely, "River Water," "Forest," and "Rice Field," are estimated by using successive albedo images. It is found that the BRDF for "River Water" follows Lambert law at both 550nm and 850nm. It is also found that the BRDFs for "Forest" and "Rice Field" follow Lambert's law at 550nm, but they follow an anisotropic reflection law at 850nm and fitting parameters for their BRDFs are presented.

We model a road network as a directed graph G(V,E) with a source s and a sink t, where each edge e has a positive length l(e) and each vertex v has a distribution function αv with respect to the traffic entering and leaving v. This paper proposes a polynomial time algorithm for evaluating the importance of each edge e E whicn is defined to be the traffic f(e) passing through e in order to assign the required traffic Fst(0) from s to t along only shortest s-t paths in accordance with the distribution function αv at each vertex v.

The wave distribution functions (WDFs) have been reconstructed by means of the maximum entropy inversion to the observed spectral matrix composed of the auto- and cross-power spectra among the three field components (Bx, By and Ez) in which the exactly right-handed circular polarization is taken in the integration kernels. The purpose of this paper is to investigate the properties of wave distribution functions reconstructed for wave sources whose central polarization is somewhat deviated from right-handed circular and to study (1) the WDF's by using the right-handed circular polarization in the kernels, (2) the effect of larger deviations for the polarization of elementary plane waves consituting the wave source, (3) the WDF's based on the elliptical polarization kernels and (4) the effect of limiting the number of eigenvalues. It is then found that changing the polarization model in the integration kernels would be helpful in finding out the polarization of the actually observed signals.