电容层析成像图像重构的三阶混合算法

 针对电容层析成像图像重构Tikhonov正则参数的选取问题,提出利用Morozov偏差原理确定正则参数,使其选取与初始数据的观测误差相匹配,并提出三阶收敛算法与双参数模型相结合的混合算法,使其选取更加快速、合理.数值实验表明:三阶混合算法优于典型的Newton方法;正则参数的选取影响重构图像的精度和速度;对于各种设定流型,本文提出的混合算法重构图像速度更快,重建图像质量更高.     

Generalized smoothing splines and the optimal discretization of the Wiener filter

We introduce an extended class of cardinal L/sup */L-splines, where L is a pseudo-differential operator satisfying some admissibility conditions. We show that the L/sup */L-spline signal interpolation problem is well posed and that its solution is the unique minimizer of the spline energy functional /spl par/Ls/spl par//sub L2//sup 2/, subject to the interpolation constraint. Next, we consider the corresponding regularized least squares estimation problem, which is more appropriate for dealing with noisy data. The criterion to be minimized is the sum of a quadratic data term, which forces the solution to be close to the input samples, and a "smoothness" term that privileges solutions with small spline energies. Here, too, we find that the optimal solution, among all possible functions, is a cardinal L/sup */L-spline. We show that this smoothing spline estimator has a stable representation in a B-spline-like basis and that its coefficients can be computed by digital filtering of the input signal. We describe an efficient recursive filtering algorithm that is applicable whenever the transfer function of L is rational (which corresponds to the case of exponential splines). We justify these algorithms statistically by establishing an equivalence between L/sup */L smoothing splines and the minimum mean square error (MMSE) estimation of a stationary signal corrupted by white Gaussian noise. In this model-based formulation, the optimum operator L is the whitening filter of the process, and the regularization parameter is proportional to the noise variance. Thus, the proposed formalism yields the optimal discretization of the classical Wiener filter, together with a fast recursive algorithm. It extends the standard Wiener solution by providing the optimal interpolation space. We also present a Bayesian interpretation of the algorithm.     

考虑亚像素配准误差的超分辨率图像复原

超分辨率图像复原作为第二代图像复原方向,已成为目前国际图像复原界的一个研究热点.一般来说,超分辨率图像复原是一个病态问题,可以结合图像的先验信息,使其成为良态的,这需要有效的规整化算法.但是,规整化参数的选择多数情况是通过经验确定的,且现有的一些计算规整化参数的方法又过于繁琐.本文讨论了亚像素配准误差引入的情况下噪声的统计模型,利用Miller规整的思想给出了简易可行的规整化参数计算方法.这种规整化参数计算方法能够自适应地根据配准误差和观测噪声局部调整由于配准误差导致的失真.仿真结果表明得到的规整化参数能使规整化算法有效收敛.     

Subband coding systems incorporating quantizer models

A new method for dealing with the effects of quantization in a subband system is proposed. It uses the "gain plus additive noise" linear model for the Lloyd-Max quantizer. Based on this, it is demonstrated how, by an appropriate choice of synthesis filters, one can cancel all signal-dependent errors at the output of the system. The only remaining error is random in nature and not correlated with the input signal. We therefore have a tradeoff between the error being only random or having signal-dependent components as well (since the error variances in both cases are comparable). As a result of having only a random error, it is possible to reduce this error using, for example, a noise removal technique. The result is then extended to the case where the input is a multidimensional signal, and arbitrary sampling lattices are used, as well as to the QMF (alias cancellation) case. To demonstrate the validity of the proposed approach, two types of experiments on images are carried out: In a toy example, it is shown that using noise removal could be beneficial. For a more realistic coding scheme, however, it is demonstrated that even in the case when the model is no longer valid (when some of the subbands are discarded), the output error is still much less correlated with the input signal as opposed to the commonly used subband system, while visually, the reconstructed images look very similar.     

复信号去噪的正则化方法

针对复信号,提出了一种新的基于正则化的去噪方法。在借鉴Tikhonov正则化参数选择方法的基础上,给出了最优正则化参数的自动选择方法。与目前大多数仅在实数域的去噪方法不同,它可直接对复信号进行处理。仿真计算结果表明,该方法有较好的去噪能力,同时能够有效地保护信号中的重要特征。     

正交调制过程的误差分析

在正交调制的实现过程中有四路输入信号，每路输入信号都不可避免的引入误差。本文主要讨论引入的线性误差对已调信号的影响，并提出了改善方法。     

What are the analytical conditions for which a blind equalizer will loose the convergence state?

In this paper, I propose for the noiseless, real and two independent quadrature carrier case some approximated conditions on the step-size parameter, on the equalizer’s tap length and on the channel power, related to the nature of the chosen equalizer and input signal statistics, for which a blind equalizer will not converge anymore. These conditions are valid for type of blind equalizers where the error that is fed into the adaptive mechanism that updates the equalizer’s taps can be expressed as a polynomial function of the equalized output of order three like in Godard’s algorithm. Since the channel power is measurable or can be calculated if the channel coefficients are given, there is no need anymore to carry out any simulation with various step-size parameters and equalizer’s tap length for a given equalization method and input signal statistics in order to find the maximum step-size parameter for which the equalizer still converges.     

深度神经网络在无源定位中的应用研究

针对无源定位中噪声统计特性不准确和对多源信息的综合利用,提出一种利用深度神经网络（DNN）的无源定位方法,该方法将训练集数据输入到深度神经网络中进行学习训练,利用随机失活这一正则化方法提高了模型的泛化能力,对模型的超参数选择进行二维搜索,最终得到深度神经网络模型的最优参数设置。将其和传统的无源定位方程解算方法以及单层神经网络模型进行对比,仿真结果表明提出的方法能有效降低噪声对无源定位的精度影响,增强了系统鲁棒性,同时也证明了深度神经网络对多源信息的综合利用能力。     

Adaptive Input Design for Identification of Output Error Model with Constrained Output

Optimal input design for system identification is an area of intensive modern research. This paper considers the identification of output error (OE) model, for the case of constrained output variance. The constraint plays a very important role in the process industry, in the reduction of degradation of product quality. In this paper, it is shown, in the form of a theorem, that the optimal input signal, with constrained output, is achieved by a minimum variance controller together with a stochastic reference. The key problem is that the optimal input depends on the system parameters to be identified. In order to overcome this problem, a two-stage adaptive procedure is proposed: obtaining an initial model using PRBS as input signal; application of adaptive minimum variance controller together with the stochastic variable reference, in order to generate input signals for system identification. Theoretical results are illustrated by simulations.     

Amplifier linearization using compact microstrip resonant cell-theory and experiment

This paper presents a novel technique for reducing the intermodulation distortions (IMDs) in power amplifiers. In this method, both second- and third-harmonic components generated by the transistor are reflected back simultaneously by the compact microstrip resonant cell (CMRC) at the input port with proper phases to mix with the fundamental signal for the reduction of IMDs. A rigorous mathematical analysis on the effectiveness of multiharmonic reflections has been formulated and derived using the Volterra series. Moreover, the delay mismatch factor of the proposed method is analytically studied and the result shows that a better tolerance to the delay error can be achieved by using CMRC circuitry. Standard two-tone test measurements reveal 32- and 22-dB reductions for the third-order IMD and fifth-order IMD, respectively, without affecting the fundamental signal at 2.45 GHz. Meanwhile, the proposed approach gives a peak power added efficiency of 53% with 11.5 dB transducer gain and 15 dBm output power for a single-stage SiGe bipolar junction transistor. The adjacent channel power ratio (ACPR) is -55dBc for a data rate of 384-kb/s quadrature phase shift keyed modulated signal with 2-MHz spanning bandwidth, and this ACPR is maintained for a broad range of output power level.     

Adaptive regularized constrained least squares image restoration

In noisy environments, a constrained least-squares (CLS) approach is presented to restore images blurred by a Gaussian impulse response, where instead of choosing a global regularization parameter, each point in the signal has its own associated regularization parameter. These parameters are found by constraining the weighted standard deviation of the wavelet transform coefficients on the finest scale of the inverse signal by a function r which is a local measure of the intensity variations around each point of the blurred and noisy observed signal. Border ringing in the inverse solution is proposed decreased by manipulating its wavelet transform coefficients on the finest scales close to the borders. If the noise in the inverse solution is significant, wavelet transform techniques are also applied to denoise the solution. Examples are given for images, and the results are shown to outperform the optimum constrained least-squares solution using a global regularization parameter, both visually and in the mean squared error sense.     

Discarding data may help in system identification

We present results concerning the parameter estimates obtained by prediction error methods in the case of input that are insufficiently rich. Such input signals are typical of industrial measurements where occasional stepwise reference changes occur. As is intuitively obvious, the data located around the input signal discontinuities carry most of the useful information. Using singular value decomposition (SVD) techniques, we show that in noise undermodeling situations, the remaining data may introduce large bias on the model parameters with a possible increase of their total mean square error. A data selection criterion is then proposed to discard such poorly informative data to increase the accuracy of the transfer function estimate. The system discussed in particular is a SISO ARMAX system     

Least square identification of alias components of linear periodically time-varying systems and optimal training signal design

A least-squares (LS) method for identifying alias components of discrete linear periodically time-varying (LPTV) systems is proposed.The authors apply a periodic input signal to a finite impulse response (FIR)--LPTV system and measure the noise-contaminated output.The output of this LPTV system has the same period as the input when the period of the input signal is amultiple of the period of the LPTV system.The authors show that the input and the output can be related by using the discrete Fourier transform. In the frequency domain, an LS method can be used to identify the alias components. A lower bound on the mean square error (MSE) of the estimated alias components is given for FIR--LPTV systems.The optimal training signal achieving this lower MSE bound is designed subsequently. The algorithm is extended to the identification of infinite impulse response (IIR)--LPTV systems as well. Simulation results show the accuracy of the estimation and the efficiency of the optimal training signal design.     

A Bayesian recovery technique with Toeplitz matrix for compressive spectrum sensing in cognitive radio networks

Compressive sensing has been proposed as a low‐cost solution for dynamic wideband spectrum sensing in cognitive radio networks. It aims to accelerate the acquisition process and minimize the hardware cost. It consists of directly acquiring a sparse signal in its compressed form that includes the maximum information using a minimum number of measurements and then recovering the original signal at the receiver. Over the last decade, a number of compressive sensing techniques have been proposed to enable scanning the wideband radio spectrum at or below the Nyquist rate. However, these techniques suffer from uncertainty due to random measurements, which degrades their performances. To enhance the compressive sensing efficiency, reduce the level of randomness, and handle uncertainty, signal sampling requires a fast, structured, and robust sampling matrix; and signal recovery requires an accurate and efficient reconstruction algorithm. In this paper, we proposed a method that addresses the previously mentioned problems by exploiting the Bayesian model strengths and the Toeplitz matrix structure. The proposed method was implemented and extensively tested. The simulation results were analyzed and compared to those of the 2 techniques: basis pursuit and orthogonal matching pursuit algorithms with Toeplitz and random matrix. To evaluate the efficiency of the proposed method, several metrics were used, namely, sampling time, sparsity, required number of measurements, recovery time, processing time, recovery error, signal‐to‐noise ratio, and mean square error. The results demonstrate the superiority of our proposed method over the 2 other techniques in speed, robustness, recovery success, and handling uncertainty.     

Low complexity ordered successive interference cancelation detection algorithm for uplink MIMO SC-FDMA system

In mobile communication, the most exploratory technology of fifth generation is massive multiple input multiple output (MIMO). The minimum mean square error and zero forcing based linear detectors are used in multiuser detection for MIMO single-carrier frequency division multiple access (SC-FDMA). When the received signal is detected and regularization sequence is joined in the equalization of spectral null amplification, these schemes experience an error performance and the signal detection assesses an inversion of a matrix computation that grows into complexity. Ordered successive interference cancelation (OSIC) detection is considered for MIMO SC-FDMA, which uses a posteriori information to eradicate these problems in a realistic environment. To cancel the interference, sorting is preferred based on signal-to-noise ratio and log-likelihood ratio. The distinctiveness of the methodology is to predict the symbol with the lowest error probability. The proposed work is compared with the existing methods, and simulation results prove that the defined algorithm outperforms conventional detection methods and accomplishes better performance with lower complication.     

A joint-optimization NSAF algorithm based on the first-order Markov model

Recently, the normalized subband adaptive filter (NSAF) algorithm has attracted much attention for handling colored input signals. Based on the first-order Markov model of the optimal weight vector, this paper provides some insights for the convergence of the standard NSAF. Following these insights, both the step size and the regularization parameter in the NSAF are jointly optimized by minimizing the mean-square deviation. The resulting joint-optimization step size and regularization parameter algorithm achieves a good tradeoff between fast convergence rate and low steady-state error. Simulation results in the context of acoustic echo cancelation demonstrate good features of the proposed algorithm.     

Effects of experimental and modeling errors on electrocardiographic inverse formulations

The inverse problem of electrocardiology aims to reconstruct the electrical activity occurring within the heart using information obtained noninvasively on the body surface. Potentials obtained on the torso surface can be used as input for the inverse problem and an electrical image of the heart obtained. There are a number of different inverse algorithms currently used to produce electrical images of the heart. The relative performances of these inverse algorithms at this stage is largely unknown. Although there have been many simulation studies investigating the accuracy of each of these algorithms, to date, there has been no comprehensive study which compares a wide variety of inverse methods. By performing a detailed simulation study, we compare the performances of epicardial potential [Tikhonov, Truncated singular value decomposition (TSVD), and Greensite] and myocardial activation-based (critical point) inverse simulations along with different methods of choosing the appropriate level of regularization (optimal, L-curve, composite residual and smoothing operator, zero-crossing) to apply to each of these inverse methods. We also examine the effects of a variety of signal error, material property error, geometric error and a combination of these errors on each of the electrocardiographic inverse algorithms. Results from the simulation study show that the activation-based method is able to produce solutions which are more accurate and stable than potential-based methods especially in the presence of correlated errors such as geometric uncertainty. In general, the Greensite-Tikhonov method produced the most realistic potential-based solutions while the zero-crossing and L-curve were the preferred method for determining the regularization parameter. The presence of signal or material property error has little effect on the inverse solutions when compared with the large errors which resulted from the presence of any geometric error. In the presence of combined Gaussian and correlated errors representing conditions which may be encountered in an experimental or clinical environment, there was less variability between potential-based solutions produced by each of the inverse algorithms.     

采用萨尼亚克环的外腔半导体激光器无调制稳频技术

提出了一种基于相位差偏置萨尼亚克环的新型外腔半导体激光（ECDL）无调制稳频方法,采用偏振分束器（PBS）作为萨尼亚克环的输入及输出端,并利用1/4波片在环内沿相反方向传播的偏振方向互相垂直的两束光之间引入/2的相位差（）,萨尼亚克环的输出光经过起偏器可以分解得到由Rb的饱和吸收峰引起的色散相移,通过这种方法可以得到适合稳频的误差信号。相比现有的利用全内反射引入相位差（sin=0.64）的方法,色散信号放大系数sin的值可达到理论最大值,有效地提高了误差信号的强度,这种方法简单、稳健,且在原子物理实验等方面具有潜在应用。     

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.     

CEP: Correlated Error Propagation for Hierarchical Soft Error Analysis

Due to the continuous technology scaling, soft error becomes a major reliability issue at nanoscale technologies. Single or multiple event transients at low levels can result in multiple correlated bit flips at logic or higher abstraction levels. Addressing this correlation is essential for accurate low-level soft error rate estimation, and more importantly, for the cross-level error abstraction, e.g. from bit errors at logic level to word errors at register-transfer level. This paper proposes a novel error estimation method to take into consideration both signal and error correlations. It unifies the treatment of error-free signals and erroneous signals, so that the computation of error probabilities and correlations can be carried out using techniques for signal probabilities and correlations calculation. The proposed method not only reports accurate error probabilities when internal gates are impaired by soft errors, but also gives quantification of the error correlations in their propagation process. This feature enables our method to be a versatile technique used in high-level error estimation. The experimental results validate our proposed technique showing that compared with Monte-Carlo simulation, it is 5 orders of magnitude faster, while the average inaccuracy of error probability estimation is only 0.02.