Signal Extraction Method Research Articles

Separation of stimulus-selective and nonselective components in intrinsic optical signals

Optical imaging based on intrinsic optical signals has been widely used in studies on the functional organization of visual cortical areas. The presentation of a visual stimulus usually generates non-stimulus-selective global darkening and stimulus-selective localized spots superposed on it. It is critical to extract the stimulus-selective component for the investigation of the cortical functional organization. In this study, we propose a novel method based on a principal component analysis (PCA) of the spatial optical response patterns, and apply it to the separation of the global darkening in the cat visual cortex. The images submitted to PCA were those responding to the gratings with eight orientations from 0° to 180° in steps of 22.5°. The first principal component represents most of the common signals such as the global darkening and large blood vessels, and occupies more than 80% of the signal energy. By removing the first principal component, the stimulus-selective dark patches in the reconstructed images have higher contrasts and high orientation selectivity consistent with electrophysiological single-cell recording. The results suggest that the proposed method can be used to exclude the stimulus-nonselective component, which provides an alternative method for signal extraction. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The Impedance Calculation Methods Using Damped Sinusoidal Signal

There are large number of transient signals will be produced when the operation mode is changed. And the frequency of the transient signals distributed in wide range. The most of them are damped and oscillated. The impedance is a popular feature in power system, and it has been widely used in relay protection and fault location. So the impedance calculation based on the damped sinusoidal signal have been the important way to diagnosis the fault, locate the fault and so on. But the methods used now are based on the fundamental impedance calculation theory, lacking theoretical basis. In fact, the traditional methods for fundamental signal calculation are not appropriate for the quantitative analysis of damped sinusoidal signal. The paper analyzed the impedance calculation based on damped sinusoidal signal, combined the features of damped sinusoidal signals and the traditional impedance calculation method. The two typical signal analysis methods for damped sinusoidal signal extraction are used to calculate the accurate impedance based on the different extraction results. And the analysis laid a foundation for the impedance calculation using damped sinusoidal signals.

Design of signal extraction algorithms based on second order statistics exploiting beamforming techniques

Signal extraction methods are becoming increasingly popular due to lower computational demands and less restrictive requirements than source separation algorithms. Many existing signal extraction algorithms extract interesting signals based on some known features of the sources. However, immediate extraction of the desired signal is not guaranteed, leading to inefficient and ad hoc deflation techniques.We present a design strategy for efficient signal extraction algorithms. First, by incorporating some amount of prior information in the form of a guess of either the autocorrelation function or the mixing column of the desired source, immediate identification of the desired extraction filter is guaranteed. Second, for a parameterized mixing system new techniques for the design and evaluation of signal extraction algorithms have been developed. These techniques are used to ensure immediate extraction of the desired signal by exploiting knowledge on physical parameters.The design procedure is flexible in the use of a priori information and leads to extraction algorithms that are robust to noise, deal with incomplete prior information, and handle modeling errors. Furthermore, the extraction algorithms can be used to identify extraction filters with different objectives. The design procedure and the properties of the extraction algorithms are evaluated by examples and experiments.

Study on Signals Processing in State Detection of Tunnel Excavation Equipment's Cutter Head

In the period of real time mechanical analysis and life style detection to the excavation tunnel equipment's cutter head, it is a necessity for the signals sensed by detection unit to be pretreatment processed. It closely concerns with the accuracy and validity of whole mechanical analysis results. By analyzing the features of all possible interference, this paper gives a study on signals initial processing applying method of digital filter and blind signal extraction. Simulation results show that the proposed scheme is effective enough to the mechanical analysis task.

Event signature extraction and traffic modeling in WSNs

Motivated by earlier work on adaptive event forecasting, this paper proposes an iterative event signature extraction method for wireless sensor networks, and a probabilistic approach to model non i.i.d. (independent and identically distributed) aperiodic traffic. The model is validated on well described (collection included) real world measurements of various types, and it is then used to demonstrate the effectiveness of the proposed signature extraction method to support reliable event forecasting. Our scheme can continuously keep the event signature database low on artifacts, dynamically estimate the number of sequences and extract the signatures from noisy, overlapped events. The proposed solution is inspired by unsupervised competitive Hebbian learning used in self-organizing Kohonen maps. We evaluate the proposed solution analytically, but also empirically, by means of simulations.

Optimization of the Feedback and Control Module for Light Source of High-Speed Vision Inspection System

The situations of insufficient or too much exposure of lights often happen in high-speed vision inspection system. In order to improve the ability to adapt to environment, the feedback and control system is used. If we use the processing method of integral threshold value, the control of light source wont work. Besides, the former feedback system has the stoboflash problem, so optimization design is needed. A new feedback signal extraction method is put forward and the program of source control is optimized, and the adaptability of the diction system to the change of the environment light is improved, it works out the function of controlling the LED light source automatically and steadily under the effects of environment light and the problem of stroboflash.

Research on a Method of Fault Signal Extraction Based on Improved Algorithm

Considering the difficulty of diagnosis signal de-noising and feature extraction problems, according to the characteristics of periodicity and shock attenuation respond of mechanical fault vibration signals, a method of improved sequential decomposition algorithm is proposed, it transforms an initial time series into a group of two-dimensional time series, prominent time series partial information, time series decomposition is reversible, can be used for filtering and feature extraction of time signal. Through the simulation and experiments, the validity of method for highlighting partial feature information of the signal is verified, helping to extract weak fault information in strong background noise environment.

Micro-motion Signature Extraction Method for Wideband Radar Based on Complex Image OMP Decomposition

Micro-motion Signature Extraction Method for Wideband Radar Based on Complex Image OMP Decomposition

A Novel Positioning and Orientation System Based on Three-Axis Magnetic Coils

The positioning and orientation system consists of three-axis generating coils and three-axis sensor coils in quasi-static magnetic field. The three-axis generating coils are fixed orthogonally and excited by the alternating current (AC) signals with different frequencies. They create the magnetic field that is equivalent to that generated by three orthogonal magnetic dipoles. Using the amplitude and phase information of the sensing signals in the sensor coils, the position and orientation parameters of the sensor coils can be computed by using an appropriate algorithm. In this paper, a novel algorithm is proposed to determine the position of the sensor coil object by some equations directly, so that its position and orientation parameters can be calculated much easier and faster. Based on this method, a system with support circuitry is designed with some special signal acquisition and sampling methods. Especially, a signal extraction (function fitting) method is proposed to pick up the coupling AC signal magnitude of the sensor coils, which simplifies the hardware circuitry and improves the signal acquisition accuracy. The simulation and real experimental results show that the system works satisfactorily with good accuracy.

SOM기반 특징 신호 추출 기법을 이용한 불균형 주기 신호의 이상 탐지

본 연구는 공정신호가 불균형 데이터인 경우 이상 탐지 알고리즘의 성능 개선을 위한 특징 신호 추출 기법을 제안한다. 불균형 데이터란 범주 구분 문제에서 하나의 범주의 속하는 데이터의 비율이 다른 범주의 데이터에 비해 크게 차이나 이상 탐지성능이 크게 저하되는 경우를 의미한다. 공정이 운영되는 경우 얻을 수 있는 이상 신호의 수는 정상 신호에 비해 매우 적기에 이러한 문제를 해결하여 이상 탐지 기법을 적용하는 것은 매우 중요하다. 불균형 문제 해결을 위해 SOM(Self-Organizing Map) 알고리즘을 이용하여 각 노드에 대응되는 가중치를 특징 신호로 간주하여 정상 데이터와 이상 데이터의 비율을 맞춘다. 특징 신호 데이터 집단의 이상 탐지를 위해 클래스 분류 기법인 kNN(k-Nearest Neighbor)과 SVM(Support Vector Machine)을 적용하여 이를 공정 신호 이상탐지를 위해 주로 사용하는 Hotelling's <TEX>$T^2$</TEX> 관리도와 성능을 비교한다. 반도체 공정에서 발생한다고 알려진 공정 신호를 모사하여 신호 알고리즘 성능의 우수성을 검증한다. In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's <TEX>$T^2$</TEX> Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

Semantics-Based Malware Behavior Signature Extraction and Detection Method

This paper proposes a semantic-based approach to malware behavioral signature extraction and detection. This approach extracts critical malware behaviors as well as dependencies among these behaviors, integrating instruction-level taint analysis and behavior-level semantics analysis. Then, it acquires anti-interference malware behavior signatures using anti-obfuscation engine to identify semantic irrelevance and semantically equivalence. Further, a prototype system based on this signature extraction and detection approach is developed and evaluated by multiple malware samples. Experimental results have demonstrated that the malware signatures extracted show good ability to anti obfuscation and the detection based on theses signatures could recognize malware variants effectively.

A new method for blind extraction of cyclostationary signals

This paper illustrates the significance of the purity of cyclostationary signals and presents a new method for blind extraction of these signals. The advantage of the method can not only extract the cyclostationary signals from low order to high order in turn but also perform algorithm without knowing cyclic frequencies of the extracted signals. The effectiveness of the proposed method is finally demonstrated by computer simulations and experiments.

Experimental study on leak detection and location for gas pipeline based on acoustic method

The leak of gas pipelines can be detected and located by the acoustic method. The technologies of recognizing and extracting wave characteristics are summarized in details in this paper, which is to distinguish leaking and disturbing signals from time and frequency domain. A high-pressure and long distance leak test loop is designed and established by similarity analysis with field transmission pipelines. The acoustic signals collected by sensors are de-noised by wavelet transform to eliminate the background noises, and time-frequency analysis is used to analyze the characteristics of frequency domain. The conclusion can be drawn that most acoustic signals are concentrated on the ranges of 0–100 Hz. The acoustic signal recognition and extraction methods are verified and compared with others and it proves that the disturbing signals can be efficiently removed by the analysis of time and frequency domain, while the new characteristics of the accumulative value difference, mean value difference and peak value difference of signals in adjacent intervals can detect the leak effectively and decrease the false alarm rate significantly. The formula for leak location is modified with consideration of the influences of temperature and pressure. The positioning accuracy can be significantly improved with relative error between 0.01% and 1.37%.

A New Method of Time-frequency Synthesis of Harmonic Signal Extraction from Chaotic Background

The separation of chaos and signal is an important problem of chaos signal processing. In recent years, the time-frequency analysis method is more and more mature. It can extract the time-domain character and frequency-domain character at the meantime. Time-frequency method can mainly carry out the problem of extraction from continuous chaos system background; achieve separation between chaos and signal according to different time-frequency character of chaos signal, noise signal and harmonic signal. So it can get useful signal from chaotic background. This paper first introduced the basic theory of time-frequency methods. Use the wavelet method and empirical mode decomposition method to analyze the extraction performance of harmonic signal from chaos background according to the different noise situation. After compare the wavelet method and empirical mode decomposition method, we summarize a new complementary synthesis method of harmonic signal extraction combine the wavelet threshold and empirical mode decomposition according to the experiments and simulation. Computer simulation verified that the methods have high availability.

Adventitious Sounds Identification and Extraction Using Temporal&amp;#x2013;Spectral Dominance-Based Features

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of 92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.

Bio-Potential Signal Extraction from Multi-Channel Paper Recorded Charts

Problem Statement: Almost all of the modern biomedical equipments that record the biopotential actions have digital output but the paper chart records are a must. The volume of these records is significant and increasing rapidly. Keeping a bio-signal chart of a patient make it easy for quick assessment but still create problems in the essence of data storage, archiving , data interchange and communications. Approach: As a solution to all these problems is to convert these paper records to digital form. In this study, a method for bio-potential signal extraction from single or multi-channel paper recorded charts using image processing techniques is developed. Results: After scanning the paper charts and converting them into images using a commercial scanner, the developed algorithm is applied to eliminate the background of the scanned paper chart from any recording device single channel or multi-channel using binary neighborhood morphological operations then converting the extracted waveform image into quantized values representing the waveform recorded paper chart. The extracted signal is then filtered to remove the high frequency effects that result from the morphological operations. A correlation and frequency analysis procedure is then conducted to verify the result against known sampled waveform. Conclusion: A chart paper conversion to digital values that represent only the values for the biopotential waveform and eliminating the other irrelevant information has been achieved. These resulted in a less space occupation of patient records and make it easy for data further processing and manipulations.

Maximum likelihood estimation for dynamic factor models with missing data

This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to evaluate the likelihood function and to produce optimal factor estimates in a computationally efficient way when missing data is present. The implementation details of our methods for signal extraction and maximum likelihood estimation are discussed. The computational gains of the new devices are presented based on simulated data sets with varying numbers of missing entries.

Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach

ABSTRACTWe present and apply singular spectrum analysis (SSA), a relatively new, non‐parametric and data‐driven method for signal extraction (trends, seasonal and business cycle components) and forecasting of UK tourism income. Our results show that SSA slightly outperforms SARIMA and time‐varying‐parameter state space models in terms of root mean square error, mean absolute error and mean absolute percentage error forecasting criteria. Copyright © 2011 John Wiley &amp; Sons, Ltd.

A study of making clear body‐conducted speech using differential acceleration

AbstractAn existing problem for speech recognition is that the recognition rate tends to decrease because of the noise. Body‐conducted speech offers a robust signal extraction method to extract speech signals from noisy environments, particularly because body‐conducted speech is a propagated sound and not easily influenced by noise. However, when body‐conducted speech is extracted with an accelerator, the typical frequency component of 2 kHz or more decreases in comparison with normal speech. Therefore, much research has been carried out investigating the techniques for improving the sound quality of body‐conducted speech. Particularly, conventional methods require normal speech to estimate clear speech. Here, we propose a technique that combines differential acceleration and noise reduction to estimate a clear signal using only body‐conducted speech. By comparing spectrogram differences with each method, we found that the Wiener filtering method was suitable for eliminating noise with differential acceleration. Thereafter, improvements in recognition rates were obtained using retrieval signals although the decoder has an acoustic model made by speech parameters. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Performance comparison of genetic algorithm and principal component analysis methods for ECG signal extraction

Electrocardiogram (ECG) signal analysis is a technique to diagnose the cardiac diseases. But, the desired electrocardiogram signals are often corrupted by baseline interference, power line interference and electromyogram. Here, a method is proposed to extract ECG from noisy signals based on Singular Value Decomposition (SVD) and Genetic Algorithm. The advantage of this method compared to conventional methods like adaptive filtering, neural networks is that, it does not require any prior knowledge of the signals. It is found that the signal to noise ratio improvement is nearly double when compared to neural network methods.