A Nonstationary Offered-Load Model for Packet Networks

Motivated by the desire to appropriately account for complex features of network traffic revealed in traffic measurements, such as heavy-tail probability distributions, long-range dependence, self similarity and nonstationarity, we propose a nonstationary offered-load model. Connections of multiple types arrive according to independent nonhomogeneous Poisson processes, and general bandwidth stochastic processes (not necessarily Markovian) describe the individual user bandwidth requirements at multiple links of a communication network during their connections. We obtain expressions for the moment generating function, mean and variance of the total required bandwidth of all customers on each link at any designated time. We justify Gaussian approximations by establishing a central limit theorem for the offered-load process. We also obtain a Gaussian approximation for the time-dependent buffer-content distribution in an infinite-capacity buffer with constant processing rate. The offered-load model can be used for predicting future bandwidth requirements; we then advocate exploiting information about the history of connections in progress.     

非平稳信号的一种ARMA模型参数估计法

本文采用一种经过特殊处理的时变自回归滑动平均（ARMA）模型对非平稳随机信号进行分析．将这种模型左边的时变参数假设为一组基时间函数的线性组合，右边时变参数简化为常数，并用反馈线性估计法进行参数估计。该方法的主要特点是简单，计算量小，占用存储空间少．并用仿真的方法对算法予以验证，可用于一些常用的非平稳随机信号的分析．     

An Empirical Model for Nonstationary Ricean Fading

Ricean fading is common in dense urban cellular networks and, as a mobile moves through that environment, the K-factor of the Ricean fading will change. This paper presents a statistical model for dense urban vehicular nonstationary Ricean fading, where the K-factor gradually changes due to movement through changing surroundings. This model is empirical and is based on K-factor fluctuations that are observed in dense urban cellular radio channel measurements. The K -factor is modeled using a random process with a distribution that is fit to the measured K-factor values. An autoregressive (AR) model is also used to ensure that the autocorrelation of the simulated K-factor process matches the empirical data. The nonstationary Ricean fading envelope that is generated using this model is verified by comparing it with the fading envelope that is observed in the measurements.     

一种非平稳网络延迟层析成像的方法

 网络层析成像将医学、地震学等领域成熟的层析成像理论应用于通信网络领域,它通过基于端到端测量来估计网络内部的行为,为目前在国际学术界备受关注的新技术之一.本文提出了一种基于递归多感知器网络(Recurrent Multilayer Perceptron :RMLP)延迟时变参数的追踪算法,该算法能够在没有任何先验信息条件下追踪非平稳网络链路平均延迟,估计链路延迟的概率密度分布.仿真实验也验证了以上两点,同时本文提出的算法比序贯蒙特卡洛(Sequential Monte Carlo:SMC)方法^[9]具有更小的估计误差.     

A Study of Electromyogram Based on Human-Computer Interface

In this paper, a new control system based on forearm electromyogram (EMG) is proposed for computer peripheral control and artificial prosthesis control. This control system intends to realize the commands of six pre-defined hand poses: up, down, left, right, yes, and no. In order to research the possibility of using a unified amplifier for both electroencephalogram (EEG) and EMG, the surface forearm EMG data is acquired by a 4-channel EEG measurement system. The Bayesian classifier is used to classify the power spectral density (PSD) of the signal. The experiment result verifies that this control system can supply a high command recognition rate (average 48%) even the EMG data is collected with an EEG system just with single electrode measurement.     

Automatic Decomposition of the Clinical Electromyogram

We describe a new, automatic signal-processing method (ADEMG) for extracting motor-unit action potentials (MUAP's) from the electromyographic interference pattern for clinical diagnostic purposes. The method employs digital filtering to select the spike components of the MUAP's from the background activity, identifies the spikes by template matching, averages the MUAP waveforms from the raw signal using the identified spikes as triggers, and measures their amplitudes, durations, rise rates, numbers of phases, and firing rates. Efficient new algorithms are used to align and compare spikes and to eliminate interference from the MUAP averages. In a typical 10-s signal recorded from the biceps brachii muscle using a needle electrode during a 20 percent-maximal isometric contraction, the method identifies 8-15 simultaneously active MUAP's and detects 30-70 percent of their occurrences. The analysis time is 90 s on a PDP-11/34A.     

A Study of Electromyogram Based on Human-Computer Interface

In this paper, a new control system based on forearm electromyogram （EMG） is proposed for computer peripheral control and artificial prosthesis control. This control system intends to realize the commands of six pre-defined hand poses： up, down, left, right, yes, and no. In order to research the possibility of using a unified amplifier for both electroencephalogram （EEG） and EMG, the surface forearm EMG data is acquired by a 4-channel EEG measurement system. The Bayesian classifier is used to classify the power spectral density （PSD） of the signal. The experiment result verifies that this control system can supply a high command recognition rate （average 48%） even the EMG data is collected with an EEG system just with single electrode measurement.     

一种新型的非平稳随机系统参数辨识算法

本文针对带输入端慢时变干扰非平稳动态系统，基于理论推导提出新型的两步最小二乘算法，并对算法性质进行了详细的理论分析，数值仿真结果表明该算法的有效性和算法的一些重要性质。     

一种非平稳网络链路丢包率层析成像方法

现有网络链路参数估计方法大都假设网络链路状态在测量周期内是平稳的,不能获得网络链路参数的时变特征。该文提出了一种非平稳的网络链路丢包率层析成像方法。假定在一个相对较小的时窗内,丢包率随时间变化的曲线可用一个k阶可导的函数来描述;用网络层析成像的方法求得这些函数的k阶泰勒展开式;然后根据各时窗内的逼近结果,用反比距离加权估计整个测量周期内链路的时变丢包率。NS2仿真验证了该方法能有效追踪链路丢包率的变化,且优于现有的网络链路丢包率层析成像方法。     

A Method for the Time-Varying Nonlinear Prediction of Complex Nonstationary Biomedical Signals

A method to perform time-varying (TV) nonlinear prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k -nearest neighbor local linear approximation to perform nonlinear prediction. The approach provides reasonable nonlinear prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated.     

Implantable Myoelectric Sensors (IMESs) for Intramuscular Electromyogram Recording

We have developed a multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES). The appeal of implanted sensors for myoelectric control is that electromyography (EMG) signals can be measured at their source providing relatively cross-talk-free signals that can be treated as independent control sites. An external telemetry controller receives telemetry sent over a transcutaneous magnetic link by the implanted electrodes. The same link provides power and commands to the implanted electrodes. Wireless telemetry of EMG signals from sensors implanted in the residual musculature eliminates the problems associated with percutaneous wires, such as infection, breakage, and marsupialization. Each implantable sensor consists of a custom-designed application-specified integrated circuit that is packaged into a biocompatible RF BION capsule from the Alfred E. Mann Foundation. Implants are designed for permanent long-term implantation with no servicing requirements. We have a fully operational system. The system has been tested in animals. Implants have been chronically implanted in the legs of three cats and are still completely operational four months after implantation.      

Electromyogram amplitude estimation with adaptive smoothing windowlength

Typical electromyogram (EMG) amplitude estimators use a fixed window length for smoothing the amplitude estimate. When the EMG amplitude is dynamic, previous research suggests that varying the smoothing length as a function of time may improve amplitude estimation. This paper develops optimal time-varying selection of the smoothing window length using a stochastic model of the EMG signal. Optimal selection is a function of the EMG amplitude and its derivatives. Simulation studies, in which EMG amplitude was changed randomly, found that the "best" adaptive filter performed as well as the "best" fixed-length filter. Experimental studies found the advantages of the adaptive processor to be situation dependent. Subjects used real-time EMG amplitude estimates to track a randomly-moving target. Perhaps due to task difficulty, no differences in adaptive versus fixed-length processors were observed when the target speed was fast. When the target speed was slow, the experimental results were consistent with the simulation predictions. When the target moved between two constant levels, the adaptive processor responded rapidly to the target level transitions and had low variance while the target dwelled on a level.     

Deep Learning-Based Symbol Detection for Time-Varying Nonstationary Channels

The highly dynamic channel(HDC) in an extremely dynamic environment mainly has fast timevarying nonstationary characteristics. In this article,we focus on the most difficult HDC case, where the channel coherence time is less than the symbol period.To this end, we propose a symbol detector based on a long short-term memory(LSTM) neural network.Taking the sampling sequence of each received symbol as the LSTM unit’s input data has the advantage of making full use of received information to obtain b...     

空间非平稳噪声下的DOA快速估计新算法

提出了空间非平稳噪声环境下DOA快速估计的新算法.通过利用估计的噪声相关矩阵对接受数据相关矩阵进行预处理.消除了空间非平稳噪声对方位估计的影响.利用线性运算来代替特征分解以求得噪声子空间.可以获得子空间算法的快速实现.计算机仿真结果证实了该算法的正确性和有效性.     

On-Line Subtraction of the Cardiac Activity from the Esophageal Electromyogram of the Diaphragm

A system is described whereby the integrated electrocardiogram is automatically subtracted from the total integrated electrical activity recorded in the esophagus. By this method, a measure of the electrical activity in the diaphragm is obtained, and the output from the respiratory center can be studied independently of mechanical alterations of chest wall and lungs.     

Nonstationary noise effects in the Abel inversion

A method for calculating the autocorrelation function of the output noise resulting from the Abel inversion of a wide-sense stationary random process is presented. An internal formula that expresses the autocorrelation in terms of the power spectral density of the input noise and explicitly as a function of radial position is derived. Asymptotic approximations for the variance are found for the case of bandlimited white input noise     

An Analysis of the Electromyogram by Fourier, Simulation and Experimental Techniques

The electromyogram of a single motor unit is studied by considering it as a time function defined by a convolution integral where a point process input passes through a filter whose impulse response is the shape of a single motor unit action potential. The interspike intervals are assumed to be normally distributed, independent random variables. Simulation is performed on a digital computer. The theoretical analysis shows that the absolute value of the ensemble average of the Fourier transform of the simulated EMG approaches the absolute value of the Fourier transform of the motor unit potential. This has been confirmed by simulation except at the very low end of the spectrum. These results are compared with the Fourier transforms of the recorded surface EMG data from human muscles.     

SAR Imaging Algorithm Filter for Based on Nonstationary FLGPR

This paper presents a fast nearfleld subsurface SAR imaging algorithm for Forward-looking ground penetrating radar （FLGPR）. By using nonstationary convolution filter, the spectrum of the refocused image can be reconstructed directly from the spectrum of backscattered signal from the target area. The target image compensation and target locating can be accomplished by the angle scanning method. The experimental results show the proposed method can achieve image refocusing and locating accurately for buried target. Also it has higher computational efficiency and azimuth resolution than the phaseshift migration approach and the Delay-and-sum （DAS） beamformlng approach. It can resolve the fast processing problem for FLGPR.     

Mathematical Analysis of Average Response Values for Nonstationary Data

Quantitative results are derived for the accuracy to be associated with average response computations on a wide class of nonstationary physical data as a function of underlying signal and noise properties and the sample size used by the average response computer. Input and output SNR's are defined in terms of these quantities. Statistical confidence bands are determined for both arbitrary probability distributions and for Gaussian probability distributions, which indicate how closely a measured sample mean value approximates the true mean value at any time.