An adaptive waveform coding algorithm and its application in speech coding

This paper proposes a novel waveform coding algorithm based on the forward adaptive technique with the goal to provide the overreaching of the signal to quantization noise ratio achievable by the coding solution designed according to G.711 standard. The novel algorithm performs frame-by-frame analysis of the input signal, according to which one of the two compandors, the restricted or the unrestricted one, is selected for the particular frame procession. The basic concept of the proposed algorithm is to enable a more preferable selection of the restricted compandor than the unrestricted one, since, in such a manner, an increase of the signal to quantization noise ratio can be provided. Since both the theoretical and the simulation results, which are obtained for the assumed input speech signal, indicate the performance improvement over the G.711 standard along with approximately 1 bit/sample compression, one can expect that the proposed algorithm will be effective in coding of signals, that as well as speech signals follow Laplacian distribution and have the time varying characteristics.     

A Uniform White-Noise Model for Fixed-Point Roundoff Errors in Digital Systems

Fixed-point roundoff noise in digital implementation of linear systems arises due to overflow, quantization of coefficients and input signals, and arithmetical errors. In uniform white-noise models, the last two types of roundoff errors are regarded as uniformly distributed independent random vectors on cubes of suitable size. For input signal quantization errors, the heuristic model is justified by a quantization theorem, which cannot be directly applied to arithmetical errors due to the complicated input-dependence of errors. The complete uniform white-noise model is shown to be valid in the sense of weak convergence of probabilistic measures as the lattice step tends to zero if the matrices of realization of the system in the state space satisfy certain nonresonance conditions and the finite-dimensional distributions of the input signal are absolutely continuous.     

Efficient bit-rate scalability for weighted squared error optimization in audio coding

We propose two quantization techniques for improving the bit-rate scalability of compression systems that optimize a weighted squared error (WSE) distortion metric. We show that quantization of the base-layer reconstruction error using entropy-coded scalar quantizers is suboptimal for the WSE metric. By considering the compandor representation of the quantizer, we demonstrate that asymptotic (high resolution) optimal scalability in the operational rate-distortion sense is achievable by quantizing the reconstruction error in the compandor's companded domain. We then fundamentally extend this work to the low-rate case by the use of enhancement-layer quantization which is conditional on the base-layer information. In the practically important case that the source is well modeled as a Laplacian process, we show that such conditional coding is implementable by only two distinct switchable quantizers. Conditional coding leads to substantial improvement over the companded scalable quantization scheme introduced in the first part, which itself significantly outperforms standard techniques. Simulation results are presented for synthetic memoryless Laplacian sources with /spl mu/-law companding, and for real-world audio signals in conjunction with MPEG AAC. Using the objective noise-mask ratio (NMR) metric, the proposed approaches were found to result in bit-rate savings of a factor of 2 to 3 when implemented within the scalable MPEG AAC. Moreover, the four-layer scalable coder consisting of 16-kb/s layers achieves performance close to that of the 64-kb/s nonscalable coder on the standard test database of 44.1-kHz audio.     

Dither在语音信号量化系统中的应用

在多媒体信号处理中 ,由于量化误差是输入信号的函数 ,使得语音信号产生信号失真和噪声调制。引入具有适当统计特性的dither(随机扰动 ) ,会使语音误差听起来象是白噪声。在适当压缩了数据量的同时 ,有效地提高了语音质量。文中对该方法进行了分析和总结 ,并用计算机仿真结果说明该方法的良好性能。     

Analysis of switched quantizer based on the quadratic spline functions

In this paper, an approximation of the optimal compressor function using the quadratic spline functions with 2L?=?8 segments is described. Since the quadratic spline with 2L?=?8 segments provides better approximation of the optimal compression function than quadratic spline with 2L?=?4 segments, capitalizing on the benefits of the obtained spline approximation, quantizer designing process is firstly performed for the so assumed number of segments and the Laplacian source of a unit variance. Then, to enhance the usability of the proposed model, the switched quantization technique is applied and a beneficial analysis is derived, providing insight in the robustness of the proposed quantizer performances with respect to the mismatch in designed for and applied to variances. Reached quality has been compared to another model from the literature, and it has been shown that the proposed model outperforms the previous model by almost 1.3?dB.     

单片机小瓶质量检测系统的数据采集方法

在工业检测中，往往对采集装置的动态范围有一定的要求，以能处理大小各异的输入信号。如果对不同的信号用不同的放大器进行相应的放大或衰减，往往增加了硬件的开销。本文提出了一种以单片机为控制器的采集系统，该系统采用一路程控放大、缓冲及滤波电路，既适应了较宽的电压输入范围，又保证了对小信号的采样精度。该系统用于玻璃瓶的质量检测，已取得良好效果。     

压缩感知的量化率失真分析

压缩感知理论表明稀疏信号能由少量的随机测量值恢复,从信息理论的角度来看,随机测量值能否有效表示稀疏信号仍是一个值得探讨的问题。针对压缩感知测量值的量化,将率失真理论作为工具研究压缩测量值的量化带来的平均失真度,包括均匀量化和非均匀量化两种情况,并进一步得到由量化测量值重构信号的率失真性能极限。理论分析和实验结果表明,相对于信号的自适应编码随机观测过程会引起较大的失真,但是压缩感知能利用信号的稀疏度来减小量化后的重构失真,这说明量化压缩感知适用于低稀疏度的信号。     

The use of clipped input information in multidimensional cross-correlation for estimating Wiener-like kernels of non-linear systems

While the measurement of Wiener-like kernels by multidimensional input-output cross-correlation is a well-known non-parametric approach to non-linear system identification, we propose here a simplifying kernel estimation scheme; rather than using the white-noise signal that is actually applied to stimulate the system, the present method uses a clipped information (that is, two- or three-level quantization) of the continuous-level test input for computing the cross-correlation. This greatly reduces the computational requirement without disturbing the generality of actual test input which may be gaussian. The statistical variance of the kernel estimation is discussed in comparison with other algorithms. Certain non-statistical errors may be incurred using this approach, but are thought to be minor in most applications. A special emphasis is given to the problem of choosing optimal procedural parameters for ternary quantization in the case of white gaussian input.     

Principled exploitation of behavioural coupling

In robot building, attention tends to focus on internal signal processing and the way desired motor signals are generated. But equally important is the selection and configuration of the robot's sensory resources. The degree to which sensory input informs the process of output generation directly impacts internal complexity since the more informative input signals are, the less internal processing is required. In the extreme case, input signals may identify motor signals exactly and internal processing may then be eliminated completely. The paper presents an information theoretic model for measuring this tradeoff being input information and internal processing complexity, and establishes the conditions under which it is feasible to utilize the processing-free strategy of ‘direct connection’. The model is then used for purposes of analysing five robots, including two commercial products (Sony AIBO and WowWee's Robosapiens). The principled exploitation of very informative sensory input is then shown to be effective in an unexpectedly wide range of situations.     

Distributed bias-compensated normalized least-mean squares algorithms with noisy input

In this paper, we study the problem of distributed normalized least-mean squares (NLMS) estimation over multi-agent networks, where all nodes collaborate to estimate a common parameter of interest. We consider the situations that all nodes in the network are corrupted by both input and output noise. This yields into biased estimates by the distributed NLMS algorithms. In our analysis, we take all the noise into consideration and prove that the bias is dependent on the input noise variance. Therefore, we propose a bias compensation method to remove the noise-induced bias from the estimated results. In our development, we first assume that the variances of the input noise are known a priori and develop a series of distributed-based bias-compensated NLMS (BCNLMS) methods. Under various practical scenarios, the input noise variance is usually unknown a priori, therefore it is necessary to first estimate for its value before bias removal. Thus, we develop a real-time estimation method for the input noise variance, which overcomes the unknown property of this noise. Moreover, we perform some main analysis results of the proposed distributed BCNLMS algorithms. Furthermore, we illustrate the performance of the proposed distributed bias compensation method via graphical simulation results.     

Networked control systems for multi-input plants based on polar logarithmic quantization

This paper investigates the use of polar logarithmic quantization (PLQ) for multi-input systems, and the corresponding design issues for the underlying networked control system (NCS). It is shown that the PLQ induces sector bounded nonlinear uncertainties in multiplicative and relative forms for vector-valued analog signals, similar to those in the scalar case. For the two-input NCS, optimal quantization is obtained through minimization of the quantization error that is quantified explicitly. The results are extended to more than two-input NCSs with an upper bound derived for the quantization error. Feedback stabilization and control of the NCS are also investigated under the PLQ at the plant input under state feedback. The coarsest quantization density (CQD) is studied and obtained. Results in this paper are illustrated by a numerical example.     

Perceptual coding of narrow-band audio signals at low rates

This paper describes a coding paradigm using coding tools based on the characteristics of the human hearing system so as to accommodate a wide range of narrow-band audio inputs without annoying artifacts at low rates (down to 8 kb/s). The narrow-band perceptual audio coder (NPAC) employs a variety of algorithms to account for the perceptually irrelevant parts of the input signal in addition to statistical redundancies. The new algorithms used in the NPAC coder include a perceptual error measure in training the codebooks and selecting the best codewords which takes into account the audible parts of the quantization noise, a perception-based bit-allocation algorithm and a new predictive scheme to vector quantize the scale factors. The NPAC coder delivers acceptable quality without annoying artifacts for most narrow-band audio signals at around 1 bit/sample. Informal subjective tests have shown that the NPAC coder outperforms a commercial low-rate music coder operating at 8 kb/s.     

Robust adaptive output feedback control for uncertain nonlinear systems with quantized input

In this paper, we study the input quantization problem for a class of uncertain nonlinear systems. The quantizer adopted belongs to a class of sector‐bounded quantizers, which basically include all the currently available static quantizers. Different from the existing results, the quantized input signal, rather than the input signal itself, is used to design the state observers, which guarantees that the state estimation errors will eventually converge to zero. Because the resulting system may be discontinuous and non‐smooth, the existence of the solution in the classical sense is not guaranteed. To cope with this problem, we utilize the non‐smooth analysis techniques and consider the Filippov solutions. A robust way based on the sector bound property of the quantizers is used to handle the quantization errors such that certain restrictive conditions in the existing results are removed and the problem of output feedback control with input signal quantized by logarithmic (or hysteresis) quantizers is solved for the first time. The designed controller guarantees that all the closed‐loop signals are globally bounded and the tracking error exponentially converges towards a small region around zero, which is adjustable. Copyright © 2016 John Wiley & Sons, Ltd.     

Identification for control: Optimal input intended to identify a minimum variance controller

It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure.     

The general design of asymptotic unrestricted polar quantizers with square cells

The aim of this paper is the presentation of the designing of asymptotic unrestricted polar quantizers with square cells, since it is known that square cells give minimal moment of inertia, which leads to minimization of distortion. Until now, polar quantizers with square cells have been designed only for the optimal companding function and their performances have been analyzed only for the stationary variance. In this paper, the design is done in a general manner, i.e. it is valid for any companding function and performances are analyzed in the wide range of variances. After that, this general design is applied for the logarithmic μ-law companding function. It is important that expressions for the numbers of magnitude and phase levels are obtained in the closed form, which simplifies the design. It is shown that the proposed polar quantizer has better performances (more than 3 dB higher the maximal and the average SQNR (signal-to-quantization noise ratio)) than the corresponding scalar quantizer with μ-law. Simulation is performed, and it is shown that theoretical and simulation results are matched very well. It is shown that the proposed polar quantizer can be used both for stationary and non-stationary signals, choosing the appropriate value of μ. This quantizer can be widely used, for all signals with Gaussian distribution.     

基于核密度估计的故障诊断信号非均匀量化方法

在工业物联网的某些故障诊断场景中,由于缺少电信网络覆盖,采集信号通过远距离无线电（Long Range Radio,LoRa）技术实现无线可靠回传,但其较低的传输速率会限制故障诊断的精度。针对LoRa窄带宽的技术限制,提出了采样频率和量化分辨率固定条件下的时域信号非均匀量化方案。首先,通过建立基于核密度估计（Kernel Density Estimation,KDE）的非参数拟合模型,重点研究了带宽受限场景中合适的核函数类型和带宽确定准则,拟合生成传感信号幅度的概率密度函数（Probability Density Function,PDF）。其次,以PDF为输入,以最小化量化噪声为目标函数,通过非线性规划,输出最佳的一组非均匀量化电平值。其特点在于针对出现频次最高的时域幅度,采用更小的量化间隔,实现量化噪声的最小化。最后,以轴流风机状态检测为例进行了实验,结果表明,基座松动和轴承故障对量化电平的影响更大。随着量化分辨率的增加,KDE量化逐渐趋近均匀量化,相较于高斯量化的优势逐渐缩小。因此,提出的KDE量化方案适合窄带宽条件下的非均匀量化,可提高信道利用率,并在传输带宽和量化噪声之间取得折中。     

Input design for structured nonlinear system identification

This paper is concerned with the input design problem for a class of structured nonlinear models. This class contains models described by an interconnection of known linear dynamic systems and unknown static nonlinearities. Many widely used model structures are included in this class. The model class considered naturally accommodates a priori knowledge in terms of signal interconnections. Under certain structural conditions, the identification problem for this model class reduces to standard least squares. We treat the input design problem in this situation.An expression for the expected estimate variance is derived. A method for synthesizing an informative input sequence that minimizes an upper bound on this variance is developed. This reduces to a convex optimization problem. Features of the solution include parameterization of the expected estimate variance by the input distribution, and a graph-based method for input generation.     

Invertibility of switched linear systems

We address the invertibility problem for switched systems, which is the problem of recovering the switching signal and the input uniquely given an output and an initial state. In the context of hybrid systems, this corresponds to recovering the discrete state and the input from partial measurements of the continuous state. In solving the invertibility problem, we introduce the concept of singular pairs for two systems. We give a necessary and sufficient condition for a switched system to be invertible, which says that the individual subsystems should be invertible and there should be no singular pairs. When the individual subsystems are invertible, we present an algorithm for finding switching signals and inputs that generate a given output in a finite interval when there is a finite number of such switching signals and inputs. Detailed examples are included.     

基于MAP估计双树复小波的电能质量扰动信号去噪方法

针对电能质量信号的去噪,提出了一种基于MAP估计的双树复小波电能质量扰动信号的去噪方法。首先对带噪信号进行相关性预处理,然后通过MAP方法对双树复小波分解不同层次的细节系数估计噪声方差和信号方差,并计算各层阀值从而得到去噪方案,针对带噪的电压跌落等扰动信号进行仿真,并与传统实小波去噪进行了信噪比和突变点信息保留能力的比较。仿真结果表明,所提算法速度快,去噪效果理想,且易于实现,实用性强,有良好的发展前景。