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.

Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.

The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.

A method of determining emission limits was studied by using the amplitude probability distribution (APD) for low-probability pulsed electromagnetic disturbances due to discharge. The features of this method are 1) without using the previously reported relationship between APD and bit error rate, the limits are derived using the measured impact of a pulsed disturbance on various wireless communication systems having different bandwidths, and 2) disturbances caused by discharge with poor reproducibility are simulated by regularly repeated pulse-modulated sine waves to enable stable evaluation of the communication quality. APD-based limits are determined from the pulse repetition frequency of the simulated disturbance such that the block error rate (BLER) is less than a certain limit in wireless systems that are most sensitive to the pulsed disturbance. In the international standard CISPR 32 regulating electromagnetic disturbance, radiated disturbance due to discharge is excluded from the application of peak detection limits because of its low occurrence probability. In this paper we quantitatively determine appropriate criteria of the probability for the exclusion. Using the method, we measured the impact of low-probability pulsed interference on major wireless systems and found that GSM and Wi-Fi systems were the most sensitive. New APD-based limits were derived on the basis of these findings. The APD-based limits determined by the proposed method enable a valid evaluation of low-occurrence-probability pulsed disturbances without unconditionally excluding the measurement.

This paper proposes statistical analysis of phase-only correlation functions between two signals with stochastic phase-spectra following bivariate circular probability distributions based on directional statistics. We give general expressions for the expectation and variance of phase-only correlation functions in terms of joint characteristic functions of the bivariate circular probability density function. In particular, if we assume bivariate wrapped distributions for the phase-spectra, we obtain exactly the same results between in case of a bivariate linear distribution and its corresponding bivariate wrapped distribution.

This paper proposes statistical analysis of phase-only correlation functions with phase-spectrum differences following wrapped distributions. We first assume phase-spectrum differences between two signals to be random variables following a linear distribution. Next, based on directional statistics, we convert the linear distribution into a wrapped distribution by wrapping the linear distribution around the circumference of the unit circle. Finally, we derive general expressions of the expectation and variance of the POC functions with phase-spectrum differences following wrapped distributions. We obtain exactly the same expressions between a linear distribution and its corresponding wrapped distribution.

In this paper the amplitude probability distribution (APD) measurement method is applied to evaluate noise coupling to an antenna on an evaluation board that uses mixed RF and digital signals of an IC. We analytically investigate noise coupling path to the antenna where the correlation coefficient matches the APD curve of the evaluation board. Moreover, in order to verify the analysis results, the noise coupling path in the board is evaluated by measurements involving In-phase/Quadrature (I/Q) signals as well as electromagnetic simulations. As a result, we demonstrate that APD method is effective in evaluating a degree of noise coupling from an IC to multiple antennas on the board, and confirm that the intensity of noise coupling to each antenna is affected greatly by the board layout patterns.

This paper presents a method for evaluating the maximum bit error probability (BEP) of a digital communication system subjected to interference by measuring the amplitude probability distribution (APD) of the interfering noise. Necessary conditions for the BEP evaluation are clarified both for the APD measuring receiver and the communication receiver considered. A method of defining emission limits is presented in terms of APD so that the worst BEP of a communication system does not exceed a required permissible value. The methods provide a theoretical basis for a wide variety of applications such as emission requirements in compliance testing, dynamic spectrum allocations, characterization of an electromagnetic environment for introducing new radio systems, and evaluation of intra-system interference.

This paper proposes statistical analysis of phase-only correlation functions based on linear statistics and directional statistics. We derive the expectation and variance of the phase-only correlation functions assuming phase-spectrum differences of two input signals to be probability variables. We first assume linear probability distributions for the phase-spectrum differences. We next assume circular probability distributions for the phase-spectrum differences, considering phase-spectrum differences to be circular data. As a result, we can simply express the expectation and variance of phase-only correlation functions as linear and quadratic functions of circular variance of phase-spectrum differences, respectively.

The objective of our research is to build a statistical test that can evaluate different risks of a portfolio selection model with fuzzy data. The central points and radiuses of fuzzy numbers are used to determine the portfolio selection model, and we statistically evaluate the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk evaluation of the portfolio selection model with interval values. We conclude that the fuzzy statistical test enables us to evaluate a stable expected return and low risk investment with different choices for k, which indicates the risk level. The results of numerical examples show that our method is suitable for short-term investments.

A novel approach for fast traffic classification for the high speed networks is proposed, which bases on the protocol behavior statistical features. The packet size and a new parameter named "Estimated Protocol Processing Time" are collected from the real data flows. Then a set of joint probability distributions is obtained to describe the protocol behaviors and classify the traffic. Comparing the parameters of an unknown flow with the pre-obtained joint distributions, we can judge which application protocol the unknown flow belongs to. Distinct from other methods based on traditional inter-arrival time, we use the "Estimated Protocol Processing Time" to reduce the location dependence and time dependence and obtain better results than traditional traffic classification method. Since there is no need for character string searching and parallel feature for hardware implementation with pipeline-mode data processing, the proposed approach can be easily deployed in the hardware for real-time classification in the high speed networks.

This paper shows experimental results of packet error rates (PERs) in wireless-LAN mounted printed circuit boards and gives a discussion on a mechanism of electromagnetic noise coupling that affects the PER. We utilized the amplitude probability distribution to investigate the noise coupling channel. We measured the magnetic near-field distribution to obtain information about noise sources. Based on measurement results, we also performed parallel plate resonance analysis to find out electromagnetic interference antennas. We confirmed that noise radiates from a power supply system of a digital circuit and its coupling to a receiving antenna causes an increase of the PER.

Dynamic Voltage Scaling (DVS) is a well-known low-power design technique, which adjusts the clock speed and supply voltage dynamically to reduce the energy consumption of real-time systems. Previous studies considered the probabilistic distribution of tasks' workloads to assist DVS in task scheduling. These studies use probability information for intra-task frequency scheduling but do not sufficiently explore the opportunities for the system workload to save more energy. This paper presents a novel DVS algorithm for periodic real-time tasks based on the analysis of the system workload to reduce its power consumption. This algorithm takes full advantage of the probabilistic distribution characteristics of the system workload under priority-driven scheduling such as Earliest-Deadline-First (EDF). Experimental results show that the proposed algorithm reduces processor idle time and spends more busy time in lower-power speeds. The measurement indicates that compared to the relative DVS algorithms, this algorithm saves energy by at least 30% while delivering statistical performance guarantees.

In this paper we investigate the reliability of general type shared protection systems i.e. M for N (M:N) that can typically be applied to various telecommunication network devices. We focus on the reliability that is perceived by an end user of one of N units. We assume that any failed unit is instantly replaced by one of the M units (if available). We describe the effectiveness of such a protection system in a quantitative manner. The mathematical analysis gives the closed-form solution of the availability, the recursive computing algorithm of the MTTFF (Mean Time to First Failure) and the MTTF (Mean Time to Failure) perceived by an arbitrary end user. We also show that, under a certain condition, the probability distribution of TTFF (Time to First Failure) can be approximated by a simple exponential distribution. The analysis provides useful information for the analysis and the design of not only the telecommunication network devices but also other general shared protection systems that are subject to service level agreements (SLA) involving user-perceived reliability measures.

To estimate the impact of electromagnetic disturbances on multi-carrier wireless systems, a method for converting an amplitude probability distribution (APD) of disturbance measured at a frequency to be valid for another frequency is presented. The conversion uses two parameters, the receiver noise power of the APD measuring equipment and a scale factor that can be estimated from a measured disturbance spectrum. The method is based on the assumption that the difference in measurement frequency affects only the relative scale of the probability distribution of band-limited disturbance amplitude, and is applicable to disturbances of practically importance such as 1) continuous or pulse-modulated wideband Gaussian noise, 2) disturbance with a much narrower bandwidth than receiver bandwidth B, and 3) repetitive short pulses with similar waveforms with an interval much longer than 1/B. The validity of the proposed method is examined by measurements of actual disturbances.

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.

In evaluating the teletraffic of mobile communication networks, it is important to model the motion of terminals. In the previous migration model, mobility characteristics of terminals, such as cell dwell time, have been expressed by a single probability distribution. In this paper, we discuss the modeling of the cell dwell time of terminals in each cell. Using measured data we show that cell dwell time differs from cell to cell and follows log-normal distributions rather than conventional exponential distributions.

We have recently developed a programmable composite noise generator (P-CNG) which can easily control noise parameters such as average power, time-based amplitude probability distribution (APD), crossing rate distribution, occurrence frequency distribution and burst duration. Two applications of the P-CNG are demonstrated to show its usefulness. For the first application, Middleton's Class A noise is simulated. A method of setting parameters for Class A noise is demonstrated. The APD of P-CNG output is in good agreement with that of true Class A noise. In the second application, the P-CNG is used for subjective evaluation test (opinion test) of TV picture degradation. Five simple composite noise models with two kinds of APD are used. Other parameters such as average power are kept constant. Experimental results show that the envelope and APD of composite noises do not greatly influence the subjective evaluation. Finally the capabilities of the P-CNG are shown.