Adaptive scheme for elimination of broadband noise and impulsivedisturbances from AR and ARMA signals

The problem of elimination of measurement noise and impulsive disturbances from autoregressive and autoregressive moving average signals is considered. It is shown that the task of simultaneous detection/tracking/restoration can be stated as a nonlinear filtering problem and solved using the theory of extended Kalman filter. Numerical tests carried out for real audio signals corrupted by both real and artificially generated disturbances confirm very good properties of the proposed algorithm     

Analysis of low-amplitude signals of cardiac electrical activity

A non-invasive approach to estimation of the condition of cardiovascular system has been considered. This approach is based on detection of subtle manifestations of cardiac electrical activity using the high resolution technologies in electrocardiography. Combined methods of analysis of low-amplitude components of electrocardiosignals were developed. They are intended for detection of late potentials representing pathological cardiac electrical activity and also for the separation of fetal and maternal electrocardiosignals obtained during the abdominal recording. Combining of methods is based on the creation of eigensubspaces of signals and noise at different combinations of the decomposition of eigenvectors in basis, the principal component analysis, analysis of independent components, and the wavelet analysis. The conducted numerical examples show that the proposed methods make it possible to eliminate noises and interferences in recorded electrocardiograms, and also to detect the morphology changes of waves and complexes of cardiac cycles. This enables us to perform early diagnostics in the case of pathology.     

Identification of periodic signals with uncertain frequency

This paper presents a new algorithm to identify periodic signals with uncertain frequency. The approach is based on the behavior of an internal model in an error feedback system. As such, the signal is fed to a fictitious plant with a feedback controller. The feedback controller is based on a traditional controller in parallel with an internal model which identifies and cancels the periodic disturbances. Under ideal circumstances, the phase plot of the states of the internal model form an ellipse. The speed of rotation about this ellipse can be used to calculate the difference between the nominal frequency of the model and the true frequency of the periodic signal. An integral controller or a least-squares estimator can be used to drive this error to zero. Simulations demonstrate the validity of this approach with time-varying frequency, and the algorithm is then applied to some data collected from a spot welder that has been corrupted by a sinusoidal signal whose frequency is between 1 Hz and 5 KHz.     

A nonlinear adaptive method of elimination of power line interference in ECG signals

A new method of elimination of power line noise in electrocardiogram signals is presented. The proposed method employs, as its main building block, a recently developed signal processing algorithm capable of extracting a specified component of a signal and tracking its variations over time. Design considerations and performance of the proposed method are presented with the aid of computer simulations. Superior performance is observed in terms of effective elimination of noise under conditions of varying powerline interference frequency. The proposed method presents a simple and robust structure which complies with practical constraints involved in the problem such as low computational resource availability and low sampling frequency.     

A framework for the analysis of acoustical cardiac signals

Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.     

ShaLTeRR: A contribution to short and long-term redundancy reduction in digital signals

We suggest a novel time domain lossy compression method for digital signals. The method is composed of two main steps: Short-term redundancy reduction (STRR) and long-term redundancy reduction (LTRR). The STRR step uses a variant of the Douglas–Peucker line simplification algorithm. The LTRR step uses a combined block-sorting and curve simplification technique to reduce long-term redundancy. The proposed method is a general-purpose signals compression scheme, with more efficiency for quasi-periodic signals. In that respect, it has been applied to ECG signals selected from the MIT-BIH database and compared to existing recent time domain (CCSP), wavelet-based and TSVD methods. Results show that the proposed algorithm is in a comfortable competitive position with the most performant methods for moderate and near-lossless compression.     

基于傅里叶变换和CELM的光纤传感信号的识别研究

为了对系统采集到的光纤振动数据进行更细致的特征刻画,文中采用傅里叶变换对数据进行特征提取。对特征提取后的数据,通过二分类任务决策树模型以及约束极速学习机(CELM)算法,进行挖掘机挖掘、人工挖掘、汽车行走、人员行走以及环境噪音总共5个类别进行识别。实验结果表明,与采用传统的短时能量/过零率特征及极速学习机(ELM)算法相比,该文采用傅里叶变换特征和CELM算法对光纤振动数据识别的分类正确率有显著提高。     

卫星通信干扰信号的识别研究

文章就几种常见的卫星干扰信号,对其进行MATLAB仿真实验,依据不同干扰信号的特点,从其时域、频域等方面分析各种干扰类型的分类依据,提取、构造各种干扰类型的有效识别参数,采用基于统计判决的模糊模式识别算法,进行仿真分析。实验表明,本文提取构造的参数得到了较高的识别率,为干扰信号的识别提供理论基础,具有重要意义。     

Identification of a group of internal signals of zero-memory nonlinear systems

The output of a zero-memory nonlinear system can often be considered as the algebraic sum of internal signals corresponding to the actions of separate components of the non-linearity described by a power series. A numerical procedure for determining these signals from recorded outputs is suggested, and their significance to the system identification is indicated.     

The contribution of motor unit pairs to the correlation functions computed from surface myoelectric signals

The contribution of motor unit action potential trains (MUAPT) of distinct motor units (MU) to the crosscorrelation function between myoelectric signals (MES) recorded at the skin surface is studied. In specific, the significance of the correlation between the firing activity of concurrently active MUs (which results in cross-terms in the overall correlation function) is compared to the representation obtained using the contributions of single MUs at each recording site (auto-terms). A model for the generation of surface MUAPs is combined with the generation of MU firing statistics in order to obtain surface MUAPTs. MU firing statistics are simulated to incorporate MU synchronization levels reported in the literature. Alternatively, experimental firing statistics are fed to the model generating the MUAPTs. The contribution of individual MU pairs to the global myoelectric signal correlation function is assessed. Results indicate that the cross-terms from different MUs decrease steadily contributing very little to the overall correlation for record lengths as short as 30 s. Thus, the error expected when computing the crosscorrelation function between two channels of MES as the superposition of the auto-terms contributed by single MUs (i.e., ignoring the cross-terms from different MUs) is shown to be very small.     

Estimating neural sources from each time-frequency component of magnetoencephalographic data

We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal-classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time-frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.     

Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources

We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained by the SSS method can be further decomposed by a simple operation into signals originating from deep and superficial sources within the brain. This is achieved by using a scheme that exploits the beamspace methodology that relies on a linear transformation that maximizes the power of the source space of interest. The efficiency and accuracy of the algorithm are demonstrated by experiments utilizing both simulated and real MEG data.     

Alignment methods for averaging of high-resolution cardiac signals:a comparative study of performance

Accurate signal estimation by means of coherent averaging techniques needs temporal alignment methods. A known low-pass filtering effect is yielded when alignment errors are present. This is very critical in the estimation of low-level high-frequency potentials in high-resolution ECG analysis. A comparative study of the performance of three alignment methods (the double-level method, a new time-delay estimation method based on normalized integrals, and matched filtering) is presented in this paper. A real signal and additive random noise for several signal-to-noise ratios (SNR's) are selected to make an ensemble of computer-simulated beats. The relation between the standard deviation of temporal misalignment versus SNR is discussed. A second study with real ECG signals is also presented. Several morphologies of QRS and P waves are tested. The results are in agreement with the computer simulation study. Nevertheless, the power spectrum of the noise process can affect the results. Matched filter estimation has been tested in the presence of power line interference (50 Hz), with poor results. An application of the three alignment methods as a function of the SNR is proposed. The new time-delay estimation method has been observed to be robust, even in the presence of nonwhite noise.     

Identification of cardiac rhythm features by mathematical analysis of vector fields

Automated techniques for locating cardiac arrhythmia features are limited, and cardiologists generally rely on isochronal maps to infer patterns in the cardiac activation sequence during an ablation procedure. Velocity vector mapping has been proposed as an alternative method to study cardiac activation in both clinical and research environments. In addition to the visual cues that vector maps can provide, vector fields can be analyzed using mathematical operators such as the divergence and curl. In the current study, conduction features were extracted from velocity vector fields computed from cardiac mapping data. The divergence was used to locate ectopic foci and wavefront collisions, and the curl to identify central obstacles in reentrant circuits. Both operators were applied to simulated rhythms created from a two-dimensional cellular automaton model, to measured data from an in situ experimental canine model, and to complex three-dimensional human cardiac mapping data sets. Analysis of simulated vector fields indicated that the divergence is useful in identifying ectopic foci, with a relatively small number of vectors and with errors of up to 30 degrees in the angle measurements. The curl was useful for identifying central obstacles in reentrant circuits, and the number of velocity vectors needed increased as the rhythm became more complex. The divergence was able to accurately identify canine in situ pacing sites, areas of breakthrough activation, and wavefront collisions. In data from human arrhythmias, the divergence reliably estimated origins of electrical activity and wavefront collisions, but the curl was less reliable at locating central obstacles in reentrant circuits, possibly due to the retrospective nature of data collection. The results indicate that the curl and divergence operators applied to velocity vector maps have the potential to add valuable information in cardiac mapping and can be used to supplement human pattern recognition.     

Influence of head tissue conductivity in forward and inverse magnetoencephalographic simulations using realistic head models

The influence of head tissue conductivity on magnetoencephalography (MEG) was investigated by comparing the normal component of the magnetic field calculated at 61 detectors and the localization accuracy of realistic head finite element method (FEM) models using dipolar sources and containing altered scalp, skull, cerebrospinal fluid, gray, and white matter conductivities to the results obtained using a FEM realistic head model with the same dipolar sources but containing published baseline conductivity values. In the models containing altered conductivity values, the tissue conductivity values were varied, one at a time, between 10% and 200% of their baseline values, and then varied simultaneously. Although changes in conductivity values for a single tissue layer often altered the calculated magnetic field and source localization accuracy only slightly, varying multiple conductivity layers simultaneously caused significant discrepancies in calculated results. The conductivity of scalp, and to a lesser extent that of white and gray matter, appears especially influential in determining the magnetic field. Comparing the results obtained from models containing the baseline conductivity values to the results obtained using other published conductivity values suggests that inaccuracies can occur depending upon which tissue conductivity values are employed. We show the importance of accurate head tissue conductivities for MEG source localization in human brain, especially for deep dipole sources or when an accuracy greater than 1.4 cm is needed.     

缆桥信号与CATV信号相互影响的研究

缆桥接入系统实现在有线电视同轴电缆上进行高速数据接入,通过CB Switch和CB Modem将高速数据调制到CATV同轴电缆的空闲信道上,实现电信网、有线电视网和互联网在同轴电缆上的共用。根据实测结果对低频缆桥HomePNA信号（12MHz~28MHz）和高频缆桥MoCA信号（860MHz~1.5GHz）与CATV信号相互间的干扰影响进行分析研究。     

Integration of signals/systems and electromagnetics courses through the design of a communication system for a cardiac pacemaker

The integration of multiple concepts and courses through laboratory design projects are part of a new experimental program at the University of Utah, supported by the National Science Foundation, the College of Engineering, and the University of Utah. One such project is the design of an FSK communication system for a cardiac pacemaker, which combines labs from the junior-level "signals and systems" courses and the junior-level electromagnetics course, which are both taught in the same semester. The electromagnetic and the signal and system labs are briefly described in this paper.     

Identification of cubic systems using higher order moments ofi.i.d. signals

A simple method for estimating the Volterra kernels of cubic systems with a zero-mean i.i.d. input is presented. This method significantly reduces the computational complexity of Volterra kernel estimation compared to the non-i.i.d. and non-Gaussian input case     

电磁干扰及其抑制技术

介绍高速电路设计中电磁干扰问题及其抑制技术。