Closed-Form Expressions of the Axial Step and Impulse Responses of a Parabolic Reflector Antenna

Electromagnetic pulses radiated by parabolic antennas and similar structures are needed in many applications like air and ground penetrating radar or high power microwaves (HPM) weapons. In this paper, an approach based on Skulkin and Turchin's work and on physical optics approximation in the time domain is developed to determine the radiated fields. Closed-form time-domain expressions of the electromagnetic step and impulse responses, along the axis of a classical parabolic reflector antenna, are derived. A closed-form expression of the impulse response duration is also given. The obtained E-field and H-field formula are valid along the axis, both near and far from the reflector. Using these closed-form expressions, the radiated fields are computed by means of a convolution product of the primary source excitation and the impulse response. Numerical results have been obtained in the case of a causal sine and a generalized Gaussian impulse excitations to illustrate some specific transients effects which occur with such an antenna     

Notes on Linear Systems and Impulse Responses

We show that the members of a certain important large family of multidimensional linear systems are completely characterized by their (suitably defined) impulse responses—which we show exist as certain limits. Not all linear shift-invariant systems are characterized by their impulse responses. Our results contribute to the reconstruction of an important part of the engineering theory of linear systems that had been based on the flawed concept of the Dirac impulse function.     

厅堂脉冲响应与人耳感知模型的研究

在分析厅堂脉冲响应的过程中引入了人耳听觉模型,在听觉生理学和心理学研究成果的基础上,建立了针对厅堂音质研究的人耳感知模型。该模型模拟了基底膜的频率选择特性,内毛细胞的半波整流特性以及神经纤维的时域累积特性等一系列人耳感知过程。通过对厅堂脉冲响应的实例分析发现,基于人耳感知模型的厅堂脉冲响应分析具有明显的优点。与现有的客观参量比较,该模型的计算结果与主观感觉之间存在更为紧密的联系。     

一种冲击噪声背景下稳健的目标方位估计方法

大气噪声、海杂波噪声和很多人为噪声的分布函数具有显著的拖尾特点,如果在这样的噪声环境下进行空间谱估计,还采用基于高斯噪声的模型,则会产生明显的性能退化。因此,针对这种冲击噪声条件下基于高斯噪声模型的目标检测方法性能退化的问题,提出了一种新的稳健的目标方位估计方法。在ROC-MNM算法基础上,通过采用共变矩阵对角加载的方法,提出了一种新的DROC-MNM算法。该方法可有效克服噪声对波束增益的影响,提高了目标的分辨力。仿真实验证明了所提出方法的有效性。     

Implementation of Digital Unbiased FIR Filters with Polynomial Impulse Responses

This paper addresses the design and implementation of digital unbiased finite impulse response (FIR) filters with polynomial impulse response functions. The transfer function, its fundamental properties, and a general block-diagram are discussed for the impulse response represented with the l-degree Taylor series expansion. As a particular results, we show a fundamental identity uniquely featured to such filters in the transform domain. For low-degree impulse responses, the transfer functions are found in simple closed forms and represented in compact block-diagrams. The magnitude and phase responses are also analyzed along with the group delays. A comparison with predictive FIR filters is given. As examples of applications, filtering of time errors of local clocks is discussed along with the low-pass filter design employing a cascade of the unbiased FIR filters.     

用瞬态电磁场测量目标的冲激响应和宽带雷达截面

突破传统的时域散射区域技术的限制，采用基于瞬态电磁测试系统的实验性冲激雷达，在实际空间对导体球和导体长椭球进行了瞬态磁脉冲响应测量。用修正最速下降法由实测瞬态电磁脉冲响应较发地反演了导体球和导体长椭球的冲激响应。提出了时域测量目标宽带雷达截面的瞬态响应法，并用此法测得了导体球、导体长椭球的雷达截面和谐振频率。     

试听室不同处双耳房间脉冲响应的相关性分析

现有的试听室大多在中心倾听位置附近设有多个座位,但是多人同时听音,由于人体散射的影响,对试听环境的最佳听音位置必然造成影响。在不同的座列方式下不同座位听音效果与最佳听音位置有何差异,这些问题都没有定量的分析和研究。分析了试听室不同听音位置处与中心倾听位置双耳房间脉冲响应的相关系数,以及1/3倍频程频谱幅度差异,发现不同的座位布放方式、不同声源方向均会引起不同位置处双耳房间脉冲响应函数的相关系数以及频谱发生明显改变,而这些数值的变化范围达到多大才能引起主观听感上的明显变化尚有待于进一步的研究验证。     

关于冲激响应和冲激信号的思考

利用冲激信号和冲激响应推导线性时不变系统的卷积表示需要有平滑假定的支持,该假定的成立并不是线性时不变系统定义的必然结果。本文从频域考察了平滑假定的准确表述,系统的频率响应绝对可积是这一假定成立的前提。而在频域中定义冲激信号可以容易地验证冲激信号的性质,也可以方便地确定某个信号序列是否趋于冲激信号。     

基于加权特征值补偿的说话人识别

背景噪声的存在,使得说话人识别系统的训练环境和测试环境发生失配,导致系统性能发生急剧下降。本论文提出一种加权特征值补偿算法,把由噪声引起的使带噪语音信号特征值与纯净语音特征值发生偏差的部分去除,从而使进入识别器的特征值接近纯净语音的特征值。在特征值补偿过程中引入了信噪比加权的方法。实验表明,这种方法能够有效的提高说话人识别系统的性能。     

Automatic Feature Selection of Hardware Layout: A Step toward Robust Hardware Trojan Detection

Recently, the problem of hardware Trojan detection has gained a tangible significance in academia and industry. That problem, by its nature, is complex, time consuming and error prone due to design and fabrication outsourcing of hardware circuits to external untrusted foundries. Researchers have proposed different approaches, either destructive or non-destructive, to overcome that problem. The destructive approach depends on reverse engineering via decapsulation, delayering and layout identification. This paper presents a first trial of a new approach that can afford an automatic and robust solution for the step of layout identification. The proposed technique takes the underlying digital circuit as input, and automatically determines its basic features using Haar feature extractor. Based on that features, a decision tree is trained to act as a weak classifier, which is later boosted, by making use of AdaBoost learning algorithm, to produce a strong classifier in a chain of cascaded classifiers. Accordingly, a classification model is built up to provide an automatic hardware Trojan location and detection tool. To evaluate the proposed model, ISCAS89 benchmark dataset was used for training and testing. The hardware dataset has been altered deliberately to show different Trojan examples –namely, Trojan insertion, Trojan deletion and Trojan parametric- inside hardware circuits. By investigating the underlying experimental results, the capabilities of the proposed model are evaluated, and the evaluation shows that the approach can detect different hardware Trojan types in different circuit layouts, with high accuracy rate. The proposed approach is not only automatic, but also robust and promising.     

Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model

We introduce a novel algorithm for online estimation of Acoustic Impulse Responses (AIRs) which allows for fast convergence by exploiting prior knowledge about the fundamental structure of AIRs. The proposed method assumes that the variability of AIRs of an acoustic scene is confined to a low-dimensional manifold which is embedded in a high-dimensional space of possible AIR estimates. We discuss various approaches which exploit a training data set of AIRs, e.g., high-accuracy AIR estimates from the acoustic scene, to learn a local affine subspace approximation of the AIR manifold. The model is motivated by the idea of describing the generally nonlinear AIR manifold locally by tangential hyperplanes and its validity is verified for simulated data. Subsequently, we describe how the manifold assumption can be used to enhance online AIR estimates by projecting them onto an adaptively estimated subspace. Motivated by the assumption of manifolds being locally Euclidean, the parameters determining the adaptive subspace are learned from the nearest neighbor AIR training samples to the current AIR estimate. To assess the proximity of training data AIRs to the current AIR estimate, we introduce a probabilistic extension of the Euclidean distance which improves the performance for applications with non-white excitation signals. Furthermore, we describe how model imperfections can be tackled by a soft projection of the AIR estimates. The proposed algorithm exhibits significantly faster convergence properties in comparison to a high-performance state-of-the-art algorithm. Furthermore, we show an improved steady-state performance for speech-excited system identification scenarios suffering from high-level interfering noise and nonunique solutions.

     

A Robust ICA-Based Adaptive Filter Algorithm for System Identification

This paper proposes a new adaptive filter algorithm for system identification using independent component analysis. The additive noise is considered as an independent component to be separated from the noisy observation and is simultaneously estimated online. The proposed algorithm is derived by minimizing the mutual information between the estimated additive noise and the input signal. The local convergence conditions are also derived. The proposed algorithm can be directly applied to the acoustic echo canceller without any double-talk detector. Some simulations have been carried out to illustrate its effectiveness for synthetic and real speech signals.      

An Almost Linear Relation Between the Step Size Behavior and the Input Signal Intensity in Robust Adaptive Quantization

A considerable amount of information regarding the behavior and performance of robust adaptive quantizers is contained in the functionC(sigma)which denotes the dependence of the central (or typical) step size,C, on the intensity, σ, of the input random variables. The graphical representation ofC(sigma)is called the load curve. It is quite remarkable that plots of logC(sigma)are found to be almost linearly dependent on log σ. One of our contributions is to show that this linearity follows from an unusual, but quite effective, approximation to the Gaussian and Laplacian distributions of the input random variables. The central result is a quite explicit expression for the functionC(sigma), and thus for the load curve, derived from the aforementioned approximation:log C(sigma) = S log sigma + T. The quantitiesSandTare explicitly given in terms of the various fixed parameters of the system. The expression forSprovides insight into the tradeoffs between the dynamic range of the step size, the robustness in the presence of channel errors and the ability to track changes in the signal intensity σ.     

Robust System for Indoor Localisation and Identification for the Health Care Environment

In this study, an improved version of the low-frequency indoor location system, with a larger detection range and more durable antenna laminate, is presented. The basic system uses quad antennas, placed under the floor surface, to locate tags with 125-kHz radio signals. The improvements were achieved with a one-layer laminate construction and transmitter electronics that can feed larger currents to the antennas. The measured tag detection height was 2 m, which is adequate for location applications. The low-frequency signal was not affected by normal objects. The tag location reliability of 96.3% was verified with a practical test.     

Speaker Modeling Using Emotional Speech for More Robust Speaker Identification

Journal of Communications Technology and Electronics - Automatic identity recognition in fast, reliable and non-intrusive way is one of the most challenging topics in digital world of today. A...     

改进变步长LMS算法在系统辨识中的应用及性能分析

提出一种改进的自适应变步长最小均方（LMS）算法,该算法利用e^4（n）和遗忘因子λ（n）共同调整步长,同时具有在初始阶段和未知系统时变阶段自适应步长增大而稳态时步长变小的特点,更好地解决了稳态误差与收敛时间之间的矛盾。将该算法应用到系统辨识中,与一般的变步长算法相比,改进算法具有更快的参数辨识速度和更小的稳态误差,同时还具有很好地跟踪多时变系统的能力。     

A Universal Impulse Noise Filter with an Impulse Detector and Nonlocal Means

In this paper, we propose an efficient filter for universal impulse noise removal. Operation is carried out in two stages: impulse detection followed by filtering. For detection, a robust local image statistic, called the extremum compression rank-order absolute difference (ECROAD), is designed to detect impulse noise in an image. For filtering, a universal impulse noise filter is proposed by combining the ECROAD statistic with the nonlocal means (NLM). The inherited switching behavior will preserve image details by selecting possible “noise pixels” for processing. Meanwhile, the joint impulsive weight is able to avoid the effect of impulsive components in restoring candidates. Simulation results show that the proposed filter produces excellent results and outperforms most existing filters for different impulse noise models.     

Orthonormal Vector Fitting: A Robust Macromodeling Tool for Rational Approximation of Frequency Domain Responses

Vector Fitting is widely accepted as a robust macromodeling tool for approximating frequency domain responses of complex physical structures. In this paper, the Orthonormal Vector Fitting technique is presented, which uses orthonormal rational functions to improve the numerical stability of the method. This reduces the numerical sensitivity of the system equations to the choice of starting poles significantly and limits the overall macromodeling time     

Impulse invariance and multiple-order poles

The impulse-invariant method of IIR digital filter design from a given analog filter is useful both in filter design and especially in discrete-time simulation of continuous-time systems. The article clarifies aspects of impulse invariance and, in particular, the handling of multiple-order poles. The user is currently faced with incorrect formulas and Matlab software. The problems are discussed, and a method valid for any order poles is presented via a numerical example using original Matlab code     

噪声背景下基于多模板矢量量化的与文本无关的话者辩识

在话者辨识系统的实际应用中,导致系统识别率下降的根本原因是噪声的影响,它使得测试与训练条件不一致.本文针对实际环境中常见的加性背景噪声,提出了利用加入不同类型、不同信噪比噪声的含噪语音进行训练说话人的模型,每个说话人具有多个模板.实验结果表明,这种方法能够有效的提高系统的鲁棒性.文中还讨论了距离加权方法在话者辨识中的应用.