Disturbance attenuation for Markov jump linear system over an additive white Gaussian noise channel

This paper studies the stabilisability and the performance of stochastic disturbance attenuation of a Markov jump linear system whose feedback channel is subject to an additive white Gaussian noise. First an inequality of differential entropy of random vectors under Markov switching is presented. Then by the concept of entropy power and the theory of information, a necessary condition to stabilise the system is obtained. This requires that the signal-to-noise ratio in the feedback channel is bigger than a specified value. Furthermore, to evaluate the performance of disturbance attenuation, a lower bound of the maximum fluctuation of the system state is presented.     

不等长信源的符号约束MAP联合译码算法

由于不等长编码信源（VLCs）所固有的易于误码扩散的弱点,传统的纠错编码并不能高效地解决其差错控制问题。提出了一种新的针对不等长编码信源的符号约束MAP联合译码算法,利用VLCs双树信源的构造方法,有效地抑制了VLCs的错误扩散;同时,通过将“符号约束”的思想应用于最大后验概率（MAP）译码算法,不仅充分利用了信源先验信息,而且极大地降低了误符号率。实验结果表明,在误符号率为1%时,对于在高斯加性白噪声（AWGN）信道传输情况下的JPEG信源,提出的算法比传统的“比特约束”MAP译码算法有将近0.8 dB的性能增益。     

Disturbance attenuation over a first-order moving average Gaussian noise channel

In this paper, the problem of disturbance attenuation has been studied for a linear time-invariant feedback control system with a first-order moving average Gaussian noise channel. By applying the concept of entropy power, a lower bound of signal-to-noise ratio has been achieved which is necessary for stabilisation of a system with the limited channel input power constraint. Moreover, the problem of minimising the influence of a stochastic disturbance on the output has also been investigated, and the controller design method has been obtained by using Youla parameterisation and H₂ theory. It is shown that the minimum variance of the system output depends not only on the disturbance variance, noise variance and unstable poles, but also on the non-minimum phase zeros and channel parameter. Finally, the effectiveness of the proposed results is illustrated by a numerical example.     

联合白噪声统计估计和卡尔曼滤波的MIMO信道辨识

提出了一种新的时间选择性衰落环境下MIMO信道辨识算法。为了提高信息传输效率,训练序列被直接叠加于信息序列之上。算法将信息符号输出、接收端AWGN和由于采用零中频接收技术而产生的直流偏移当做虚拟的观测噪声,其均值和自协方差均未知。通过联合的递推白噪声统计估计器和卡尔曼滤波器对时变信道进行跟踪,推导了一种计算简单的次优无偏时变白噪声统计估计器。以简单有效的方法抑制直流偏移对辨识精度的影响。仿真结果表明了算法具有良好的性能。     

高斯白噪声发生器的FPGA实现

分析了几种高斯白噪声发生器的实现途径,提出了一个基于Box-Muller算法级联中心极限定理实现高精度高斯白噪声发生器的方法,在FPGA中使用HDL语言实现。与现有的实现方法相比,本设计速度更快、占用的资源更少,且易于FPGA实现,可作为一个快速实用的误码率测试平台的一部分。     

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.     

Nonparametric estimation of signal amplitude in white Gaussian noise

We assume that a transmitted signal is of the form S(t)f(t), where f(t) is a known function vanishing at some points of the observation interval and S(t) is a function of a known smoothness class. The signal is transmitted over a communication channel with additive white Gaussian noise of small intensity ?. For this model, we construct an estimator for S(t) which is optimal with respect to the rate of convergence of the risk to zero as ? → 0.     

Attention-based neural joint source-channel coding of text for point to point and broadcast channel

In this work, we consider the transmissions of structured data such as text over a noisy channel and correlated texts over a broadcast channel. As the separate source-channel coding principle no longer holds in such scenarios, we propose a joint source-channel coding scheme which is based on deep learning architecture. In order to enhance the convergence speed, we adopt the bidirectional gated recurrent unit at the encoder. For the decoder, to improve the recovery quality, we propose the following two types of strategies: (1) After a unidirectional neural network based decoder is used, a generative adversarial network is applied to train the whole joint source-channel coding framework and pointwise mutual information is added to the objective function of beam search process; (2) Rather than using a unidirectional neural network-based decoder, we develop a bidirectional neural network based and bidirectional attention mechanism integrated decoder to utilize past and future information. Experiments under different types of channels show that our schemes are superior to the existing deep learning joint source-channel coding method and in the case of low bit budget, long sentence length and small channel signal to noise ratio, our models are significantly superior to those of separate source-channel coding. In addition, we extend the proposed unidirectional and bidirectional decoders to the broadcast channel. Additionally, to improve the performance of unidirectional decoding, we utilize not only the correlation between adjacent words in the same text but also the correlation between words in different languages with the same meaning in the beam search process.

     

Adaptive filtering of a random signal in Gaussian white noise

We consider the problem of estimating an infinite-dimensional vector θ observed in Gaussian white noise. Under the condition that components of the vector have a Gaussian prior distribution that depends on an unknown parameter β, we construct an adaptive estimator with respect to β. The proposed method of estimation is based on the empirical Bayes approach.     

On the reliability exponent of the additive exponential noise channel

We study the reliability exponent of the additive exponential noise channel (AENC) in the absence as well as in the presence of noiseless feedback. For rates above the critical rate, the fixed-transmission-time reliability exponent of the AENC is completely determined, while below the critical rate an expurgated exponent is obtained. Using a fixed-block-length code ensemble (with block length denoting the number of recorded departures), we obtain a lower bound on the random-transmission-time reliability exponent of the AENC. Finally, with a variable-block-length code ensemble, a lower bound on the random-transmission-time zero-error capacity of the AENC with noiseless feedback is obtained.     

高斯白噪声对神经元映射模型动力学的影响*

研究了噪声引发的神经元相干共振现象。基于神经元二维映射模型,用高斯白噪声模拟生物神经系统中的环境噪声,通过数字仿真研究了噪声对神经元动力学特性的影响。研究发现,噪声可以提高静态神经元的可兴奋性,诱导其产生动作电位,当噪声达到某一适中强度时,动作电位的有序性可以达到最佳,产生相干共振现象。研究了系统参数的选取对神经元共振特性的影响,发现控制参数越接近分岔点,相干共振性越好。结果表明合适强度的噪声可以促进神经元的信息传递。     

The response of grandstands driven by filtered Gaussian white noise processes

This paper presents a semi-analytical estimate of the response of a grandstand occupied by an active crowd and by a passive crowd. Filtered Gaussian white noise processes are used to approximate the loading terms representing an active crowd. Lumped biodynamic models with a single degree of freedom are included to reflect passive spectators occupying the structure. The response is described in terms of the first two moments, employing the Itô formula and the state augmentation method for the stationary time domain solution. The quality of the approximation is compared on the basis of three examples of varying complexity using Monte Carlo simulation based on a synthetic generator available in the literature. For comparative purposes, there is also a brief review of frequency domain estimates.     

Dynamic behavior of periodic potential system driven by cross-correlated non-Gaussian noise and Gaussian white noise

The dynamic behavior of the periodic potential system driven by the cross-correlated non-Gaussian noise and Gaussian white noise is studied in this article. According to path integral method and unified color noise approximation, the periodic potential system is transformed into a stochastic equivalent Stratonovich stochastic differential equation. Then the Fokker–Planck equation and the expression of the steady-state probability density are derived. The fourth-order Runge–Kutta algorithm is used to calculate the times response of the system. Meanwhile, the probability density function (PDF) of the first-passage time (FPT) is simulated, and the mean first-passage time (MFPT) is obtained by averaging these values. Finally, the influence of noise parameters on MFPT and PDF of FPT is analyzed.     

Two approximation methods to synthesize the power spectrum of fractional Gaussian noise

The simplest models with long-range dependence (LRD) are self-similar processes. Self-similar processes have been formally considered for modeling packet traffic in communication networks. The fractional Gaussian noise (FGN) is a proper example of exactly self-similar processes. Several numeric approximation methods are considered and reviewed, two methods are found that are able to provide a better accuracy and less running time than previous approximation methods for synthesizing the power spectrum of FGN. The first method is based on a second-order approximation. It is demonstrated that a parabolic curve can be indirectly used to approximate the power spectrum of FGN. The second method is based on cubic splines. Despite the fact that splines cannot be used directly to approximate the power spectrum of FGN, they can, however, considerably simplify the calculations while maintaining high accuracy. Both of the methods proposed can be used to estimate the Hurst parameter using Whittle's estimator. Additionally, they can be used on synthesis of LRD sequences.     

加性噪声下网络化多输入离散系统均方可镇定

本文研究了静态信噪比约束下多输入离散系统均方可镇定问题,其中信道资源固定且不可任意分配,信道被建模为加性高斯白噪声.主要目的是借用优化序理论讨论系统可镇定时各子信道的信道容量与系统拓扑熵的关系.本文基本思想是从供需平衡角度讨论可镇定性.具体地,对于通信资源,每个系统输入被视为需求方,而信道被视为供应方.信道的供应资源由其各自的均方容量刻画,网络化系统均方可镇定要求通信资源供求平衡.因信道资源不可配置,人们可以通过调节需求方(一定的传输机制)来满足供给方要求.给出了网络化系统均方可镇定时的一个充分条件和一个必要条件.最后数值算例验证所得结论.     

高斯中继信道反馈下马尔可夫跳变线性系统的可镇定性

研究利用高斯中继信道实现反馈信息传输情形下马尔可夫跳变线性系统的均方可镇定性问题.首先针对一般类型的高斯信道,从信息论角度给出上述马尔可夫跳变线性系统镇定所需要的必要条件;然后分别针对半双工串联中继信道以及非正交网络信道这两种特殊类型的高斯信道,在考虑传输信号存在平均功率约束的情况下,给出马尔可夫跳变线性系统的均方可镇定性与上述信道参量之间的内在联系;最后通过数值仿真例子验证了上述理论结果.     

Probabilistic solutions of some multi-degree-of-freedom nonlinear stochastic dynamical systems excited by filtered Gaussian white noise

In this paper, the state-space-split method is extended for the dimension reduction of some high-dimensional Fokker–Planck–Kolmogorov equations or the nonlinear stochastic dynamical systems in high dimensions subject to external excitation which is the filtered Gaussian white noise governed by the second order stochastic differential equation. The selection of sub state variables and then the dimension-reduction procedure for a class of nonlinear stochastic dynamical systems is given when the external excitation is the filtered Gaussian white noise. The stretched Euler–Bernoulli beam with hinge support at two ends, point-spring supports, and excited by uniformly distributed load being filtered Gaussian white noise governed by the second-order stochastic differential equation is analyzed and numerical results are presented. The results obtained with the presented procedure are compared with those obtained with the Monte Carlo simulation and equivalent linearization method to show the effectiveness and advantage of the state-space-split method and exponential polynomial closure method in analyzing the stationary probabilistic solutions of the multi-degree-of-freedom nonlinear stochastic dynamical systems excited by filtered Gaussian white noise.     

Asymptotic properties of estimates for unknown parameters of almost periodic functions under Gaussian noise

Sufficient conditions of strong consistency are established. Asymptotic distributions of estimates for unknown parameters of an almost periodic function are found in a nonlinear regression model with continuous time and random noise. The noise is assumed to be a weakly dependent Gaussian stationary process.     

Intelligibility enhancement of HMM-generated speech in additive noise by modifying Mel cepstral coefficients to increase the glimpse proportion

This paper describes speech intelligibility enhancement for Hidden Markov Model (HMM) generated synthetic speech in noise. We present a method for modifying the Mel cepstral coefficients generated by statistical parametric models that have been trained on plain speech. We update these coefficients such that the glimpse proportion – an objective measure of the intelligibility of speech in noise – increases, while keeping the speech energy fixed. An acoustic analysis reveals that the modified speech is boosted in the region 1–4 kHz, particularly for vowels, nasals and approximants. Results from listening tests employing speech-shaped noise show that the modified speech is as intelligible as a synthetic voice trained on plain speech whose duration, Mel cepstral coefficients and excitation signal parameters have been adapted to Lombard speech from the same speaker. Our proposed method does not require these additional recordings of Lombard speech. In the presence of a competing talker, both modification and adaptation of spectral coefficients give more modest gains.     

Analyzing speech of patients with vocal polyps based on channel parameters and fuzzy logic systems

In this paper, the spectrum map of speech samples is used to identify patients with vocal polypus. Comparing the spectrum map with impulse response in wireless communication channels, statistical characteristic root-mean square-delay spread (RMSD) and standard deviation (SD) are employed to describe the speech frequency domain characteristics. Fuzzy logic system (FLS) is used to make polypus patients diagnosis. RMSD and SD are used as two antecedents and fuzzy rules are designed based on the data we collected from polypus patients and normal people. Based on the real data, we demonstrate that the FLS could be used in polypus patients’ diagnosis with very low probability of miss detection and 0% false alarm rate.