Method for improving the limit of detection in a data signal

DOEpatents

Synovec, R.E.; Yueng, E.S.

1989-10-17

Disclosed is a method for improving the limit of detection for a data set in which experimental noise is uncorrelated along a given abscissa and an analytical signal is correlated to the abscissa, the steps comprising collecting the data set, converting the data set into a data signal including an analytical portion and the experimental noise portion, designating and adjusting a baseline of the data signal to center the experimental noise numerically about a zero reference, and integrating the data signal preserving the corresponding information for each point of the data signal. The steps of the method produce an enhanced integrated data signal which improves the limit of detection of the data signal. 8 figs.

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis

PubMed Central

Leng, Yonggang; Fan, Shengbo

2018-01-01

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method. PMID:29693577

An evaluation of computer-aided disproportionality analysis for post-marketing signal detection.

PubMed

Lehman, H P; Chen, J; Gould, A L; Kassekert, R; Beninger, P R; Carney, R; Goldberg, M; Goss, M A; Kidos, K; Sharrar, R G; Shields, K; Sweet, A; Wiholm, B E; Honig, P K

2007-08-01

To understand the value of computer-aided disproportionality analysis (DA) in relation to current pharmacovigilance signal detection methods, four products were retrospectively evaluated by applying an empirical Bayes method to Merck's post-marketing safety database. Findings were compared with the prior detection of labeled post-marketing adverse events. Disproportionality ratios (empirical Bayes geometric mean lower 95% bounds for the posterior distribution (EBGM05)) were generated for product-event pairs. Overall (1993-2004 data, EBGM05> or =2, individual terms) results of signal detection using DA compared to standard methods were sensitivity, 31.1%; specificity, 95.3%; and positive predictive value, 19.9%. Using groupings of synonymous labeled terms, sensitivity improved (40.9%). More of the adverse events detected by both methods were detected earlier using DA and grouped (versus individual) terms. With 1939-2004 data, diagnostic properties were similar to those from 1993 to 2004. DA methods using Merck's safety database demonstrate sufficient sensitivity and specificity to be considered for use as an adjunct to conventional signal detection methods.

Analysis of digital communication signals and extraction of parameters

NASA Astrophysics Data System (ADS)

Al-Jowder, Anwar

1994-12-01

The signal classification performance of four types of electronics support measure (ESM) communications detection systems is compared from the standpoint of the unintended receiver (interceptor). Typical digital communication signals considered include binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), frequency shift keying (FSK), and on-off keying (OOK). The analysis emphasizes the use of available signal processing software. Detection methods compared include broadband energy detection, FFT-based narrowband energy detection, and two correlation methods which employ the fast Fourier transform (FFT). The correlation methods utilize modified time-frequency distributions, where one of these is based on the Wigner-Ville distribution (WVD). Gaussian white noise is added to the signal to simulate various signal-to-noise ratios (SNR's).

A deep learning approach for fetal QRS complex detection.

PubMed

Zhong, Wei; Liao, Lijuan; Guo, Xuemei; Wang, Guoli

2018-04-20

Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

PubMed Central

2018-01-01

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. PMID:29316731

Generation and coherent detection of QPSK signal using a novel method of digital signal processing

NASA Astrophysics Data System (ADS)

Zhao, Yuan; Hu, Bingliang; He, Zhen-An; Xie, Wenjia; Gao, Xiaohui

2018-02-01

We demonstrate an optical quadrature phase-shift keying (QPSK) signal transmitter and an optical receiver for demodulating optical QPSK signal with homodyne detection and digital signal processing (DSP). DSP on the homodyne detection scheme is employed without locking the phase of the local oscillator (LO). In this paper, we present an extracting one-dimensional array of down-sampling method for reducing unwanted samples of constellation diagram measurement. Such a novel scheme embodies the following major advantages over the other conventional optical QPSK signal detection methods. First, this homodyne detection scheme does not need strict requirement on LO in comparison with linear optical sampling, such as having a flat spectral density and phase over the spectral support of the source under test. Second, the LabVIEW software is directly used for recovering the QPSK signal constellation without employing complex DSP circuit. Third, this scheme is applicable to multilevel modulation formats such as M-ary PSK and quadrature amplitude modulation (QAM) or higher speed signals by making minor changes.

A Pulse Rate Detection Method for Mouse Application Based on Multi-PPG Sensors

PubMed Central

Chen, Wei-Hao

2017-01-01

Heart rate is an important physiological parameter for healthcare. Among measurement methods, photoplethysmography (PPG) is an easy and convenient method for pulse rate detection. However, as the PPG signal faces the challenge of motion artifacts and is constrained by the position chosen, the purpose of this paper is to implement a comfortable and easy-to-use multi-PPG sensor module combined with a stable and accurate real-time pulse rate detection method on a computer mouse. A weighted average method for multi-PPG sensors is used to adjust the weight of each signal channel in order to raise the accuracy and stability of the detected signal, therefore reducing the disturbance of noise under the environment of moving effectively and efficiently. According to the experiment results, the proposed method can increase the usability and probability of PPG signal detection on palms. PMID:28708112

Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department.

PubMed

Almenoff, June S; LaCroix, Karol K; Yuen, Nancy A; Fram, David; DuMouchel, William

2006-01-01

There is increasing interest in using disproportionality-based signal detection methods to support postmarketing safety surveillance activities. Two commonly used methods, empirical Bayes multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratio (PRR), perform differently with respect to the number and types of signals detected. The goal of this study was to compare and analyse the performance characteristics of these two methods, to understand why they differ and to consider the practical implications of these differences for a large, industry-based pharmacovigilance department. We compared the numbers and types of signals of disproportionate reporting (SDRs) obtained with MGPS and PRR using two postmarketing safety databases and a simulated database. We recorded signal counts and performed a qualitative comparison of the drug-event combinations signalled by the two methods as well as a sensitivity analysis to better understand how the thresholds commonly used for these methods impact their performance. PRR detected more SDRs than MGPS. We observed that MGPS is less subject to confounding by demographic factors because it employs stratification and is more stable than PRR when report counts are low. Simulation experiments performed using published empirical thresholds demonstrated that PRR detected false-positive signals at a rate of 1.1%, while MGPS did not detect any statistical false positives. In an attempt to separate the effect of choice of signal threshold from more fundamental methodological differences, we performed a series of experiments in which we modified the conventional threshold values for each method so that each method detected the same number of SDRs for the example drugs studied. This analysis, which provided quantitative examples of the relationship between the published thresholds for the two methods, demonstrates that the signalling criterion published for PRR has a higher signalling frequency than that published for MGPS. The performance differences between the PRR and MGPS methods are related to (i) greater confounding by demographic factors with PRR; (ii) a higher tendency of PRR to detect false-positive signals when the number of reports is small; and (iii) the conventional thresholds that have been adapted for each method. PRR tends to be more 'sensitive' and less 'specific' than MGPS. A high-specificity disproportionality method, when used in conjunction with medical triage and investigation of critical medical events, may provide an efficient and robust approach to applying quantitative methods in routine postmarketing pharmacovigilance.

Method and apparatus for automatically detecting patterns in digital point-ordered signals

DOEpatents

Brudnoy, David M.

1998-01-01

The present invention is a method and system for detecting a physical feature of a test piece by detecting a pattern in a signal representing data from inspection of the test piece. The pattern is detected by automated additive decomposition of a digital point-ordered signal which represents the data. The present invention can properly handle a non-periodic signal. A physical parameter of the test piece is measured. A digital point-ordered signal representative of the measured physical parameter is generated. The digital point-ordered signal is decomposed into a baseline signal, a background noise signal, and a peaks/troughs signal. The peaks/troughs from the peaks/troughs signal are located and peaks/troughs information indicating the physical feature of the test piece is output.

Method and apparatus for automatically detecting patterns in digital point-ordered signals

DOEpatents

Brudnoy, D.M.

1998-10-20

The present invention is a method and system for detecting a physical feature of a test piece by detecting a pattern in a signal representing data from inspection of the test piece. The pattern is detected by automated additive decomposition of a digital point-ordered signal which represents the data. The present invention can properly handle a non-periodic signal. A physical parameter of the test piece is measured. A digital point-ordered signal representative of the measured physical parameter is generated. The digital point-ordered signal is decomposed into a baseline signal, a background noise signal, and a peaks/troughs signal. The peaks/troughs from the peaks/troughs signal are located and peaks/troughs information indicating the physical feature of the test piece is output. 14 figs.

Detection of nuclear resonance signals: modification of the receiver operating characteristics using feedback.

PubMed

Blauch, A J; Schiano, J L; Ginsberg, M D

2000-06-01

The performance of a nuclear resonance detection system can be quantified using binary detection theory. Within this framework, signal averaging increases the probability of a correct detection and decreases the probability of a false alarm by reducing the variance of the noise in the average signal. In conjunction with signal averaging, we propose another method based on feedback control concepts that further improves detection performance. By maximizing the nuclear resonance signal amplitude, feedback raises the probability of correct detection. Furthermore, information generated by the feedback algorithm can be used to reduce the probability of false alarm. We discuss the advantages afforded by feedback that cannot be obtained using signal averaging. As an example, we show how this method is applicable to the detection of explosives using nuclear quadrupole resonance. Copyright 2000 Academic Press.

Ellipticity angle of electromagnetic signals and its use for non-energetic detection optimal by the Neumann-Pearson criterion

NASA Astrophysics Data System (ADS)

Gromov, V. A.; Sharygin, G. S.; Mironov, M. V.

2012-08-01

An interval method of radar signal detection and selection based on non-energetic polarization parameter - the ellipticity angle - is suggested. The examined method is optimal by the Neumann-Pearson criterion. The probability of correct detection for a preset probability of false alarm is calculated for different signal/noise ratios. Recommendations for optimization of the given method are provided.

Use of Multiscale Entropy to Facilitate Artifact Detection in Electroencephalographic Signals

PubMed Central

Mariani, Sara; Borges, Ana F. T.; Henriques, Teresa; Goldberger, Ary L.; Costa, Madalena D.

2016-01-01

Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. PMID:26738116

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord [Eau Claire, WI; Cornett, Frank N [Chippewa Falls, WI

2008-10-07

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in detected skew.

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord [Redwood Shores, CA; Cornett, Frank N [Chippewa Falls, WI

2011-10-04

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in detected skew.

[Absorption spectrum of Quasi-continuous laser modulation demodulation method].

PubMed

Shao, Xin; Liu, Fu-Gui; Du, Zhen-Hui; Wang, Wei

2014-05-01

A software phase-locked amplifier demodulation method is proposed in order to demodulate the second harmonic (2f) signal of quasi-continuous laser wavelength modulation spectroscopy (WMS) properly, based on the analysis of its signal characteristics. By judging the effectiveness of the measurement data, filter, phase-sensitive detection, digital filtering and other processing, the method can achieve the sensitive detection of quasi-continuous signal The method was verified by using carbon dioxide detection experiments. The WMS-2f signal obtained by the software phase-locked amplifier and the high-performance phase-locked amplifier (SR844) were compared simultaneously. The results show that the Allan variance of WMS-2f signal demodulated by the software phase-locked amplifier is one order of magnitude smaller than that demodulated by SR844, corresponding two order of magnitude lower of detection limit. And it is able to solve the unlocked problem caused by the small duty cycle of quasi-continuous modulation signal, with a small signal waveform distortion.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Label-free and ultrasensitive fluorescence detection of cocaine based on a strategy that utilizes DNA-templated silver nanoclusters and the nicking endonuclease-assisted signal amplification method.

PubMed

Zhang, Kai; Wang, Ke; Zhu, Xue; Zhang, Jue; Xu, Lan; Huang, Biao; Xie, Minhao

2014-01-07

A general and reliable strategy for the detection of cocaine was proposed utilizing DNA-templated silver nanoclusters as signal indicators and the nicking endonuclease-assisted signal amplification method. This strategy can detect cocaine specifically with a detection limit as low as 2 nM by using a small volume of 5 μL.

Multipath interference test method using synthesized chirped signal from directly modulated DFB-LD with digital-signal-processing technique.

PubMed

Aida, Kazuo; Sugie, Toshihiko

2011-12-12

We propose a method of testing transmission fiber lines and distributed amplifiers. Multipath interference (MPI) is detected as a beat spectrum between a multipath signal and a direct signal using a synthesized chirped test signal with lightwave frequencies of f(1) and f(2) periodically emitted from a distributed feedback laser diode (DFB-LD). This chirped test pulse is generated using a directly modulated DFB-LD with a drive signal calculated using a digital signal processing technique (DSP). A receiver consisting of a photodiode and an electrical spectrum analyzer (ESA) detects a baseband power spectrum peak appearing at the frequency of the test signal frequency deviation (f(1)-f(2)) as a beat spectrum of self-heterodyne detection. Multipath interference is converted from the spectrum peak power. This method improved the minimum detectable MPI to as low as -78 dB. We discuss the detailed design and performance of the proposed test method, including a DFB-LD drive signal calculation algorithm with DSP for synthesis of the chirped test signal and experiments on single-mode fibers with discrete reflections. © 2011 Optical Society of America

Variable threshold method for ECG R-peak detection.

PubMed

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

Method and apparatus for detecting timing errors in a system oscillator

DOEpatents

Gliebe, Ronald J.; Kramer, William R.

1993-01-01

A method of detecting timing errors in a system oscillator for an electronic device, such as a power supply, includes the step of comparing a system oscillator signal with a delayed generated signal and generating a signal representative of the timing error when the system oscillator signal is not identical to the delayed signal. An LED indicates to an operator that a timing error has occurred. A hardware circuit implements the above-identified method.

Chatter detection in milling process based on VMD and energy entropy

NASA Astrophysics Data System (ADS)

Liu, Changfu; Zhu, Lida; Ni, Chenbing

2018-05-01

This paper presents a novel approach to detect the milling chatter based on Variational Mode Decomposition (VMD) and energy entropy. VMD has already been employed in feature extraction from non-stationary signals. The parameters like number of modes (K) and the quadratic penalty (α) need to be selected empirically when raw signal is decomposed by VMD. Aimed at solving the problem how to select K and α, the automatic selection method of VMD's based on kurtosis is proposed in this paper. When chatter occurs in the milling process, energy will be absorbed to chatter frequency bands. To detect the chatter frequency bands automatically, the chatter detection method based on energy entropy is presented. The vibration signal containing chatter frequency is simulated and three groups of experiments which represent three cutting conditions are conducted. To verify the effectiveness of method presented by this paper, chatter feather extraction has been successfully employed on simulation signals and experimental signals. The simulation and experimental results show that the proposed method can effectively detect the chatter.

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Y., E-mail: thuzhangyu@foxmail.com; Huang, S. L., E-mail: huangsling@tsinghua.edu.cn; Wang, S.

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency formore » all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert–Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.« less

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals.

PubMed

Zhang, Y; Huang, S L; Wang, S; Zhao, W

2016-05-01

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency for all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert-Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.

Collaborative Wideband Compressed Signal Detection in Interplanetary Internet

NASA Astrophysics Data System (ADS)

Wang, Yulin; Zhang, Gengxin; Bian, Dongming; Gou, Liang; Zhang, Wei

2014-07-01

As the development of autonomous radio in deep space network, it is possible to actualize communication between explorers, aircrafts, rovers and satellites, e.g. from different countries, adopting different signal modes. The first mission to enforce the autonomous radio is to detect signals of the explorer autonomously without disturbing the original communication. This paper develops a collaborative wideband compressed signal detection approach for InterPlaNetary (IPN) Internet where there exist sparse active signals in the deep space environment. Compressed sensing (CS) can be utilized by exploiting the sparsity of IPN Internet communication signal, whose useful frequency support occupies only a small portion of an entirely wide spectrum. An estimate of the signal spectrum can be obtained by using reconstruction algorithms. Against deep space shadowing and channel fading, multiple satellites collaboratively sense and make a final decision according to certain fusion rule to gain spatial diversity. A couple of novel discrete cosine transform (DCT) and walsh-hadamard transform (WHT) based compressed spectrum detection methods are proposed which significantly improve the performance of spectrum recovery and signal detection. Finally, extensive simulation results are presented to show the effectiveness of our proposed collaborative scheme for signal detection in IPN Internet. Compared with the conventional discrete fourier transform (DFT) based method, our DCT and WHT based methods reduce computational complexity, decrease processing time, save energy and enhance probability of detection.

Acoustic enhancement for photo detecting devices

DOEpatents

Thundat, Thomas G; Senesac, Lawrence R; Van Neste, Charles W

2013-02-19

Provided are improvements to photo detecting devices and methods for enhancing the sensitivity of photo detecting devices. A photo detecting device generates an electronic signal in response to a received light pulse. An electro-mechanical acoustic resonator, electrically coupled to the photo detecting device, damps the electronic signal and increases the signal noise ratio (SNR) of the electronic signal. Increased photo detector standoff distances and sensitivities will result.

Optimal and adaptive methods of processing hydroacoustic signals (review)

NASA Astrophysics Data System (ADS)

Malyshkin, G. S.; Sidel'nikov, G. B.

2014-09-01

Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

Toward multimodal signal detection of adverse drug reactions.

PubMed

Harpaz, Rave; DuMouchel, William; Schuemie, Martijn; Bodenreider, Olivier; Friedman, Carol; Horvitz, Eric; Ripple, Anna; Sorbello, Alfred; White, Ryen W; Winnenburg, Rainer; Shah, Nigam H

2017-12-01

Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark. The results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals. Copyright © 2017 Elsevier Inc. All rights reserved.

Walsh transforms and signal detection

NASA Technical Reports Server (NTRS)

Welch, L. R.

1977-01-01

The detection of signals using Walsh power spectral estimates is analyzed. In addition, a generalization of this method of estimation is evaluated. The conclusion is that Walsh transforms are not suitable tools for the detection of weak signals in noise.

Detection the nonlinear ultrasonic signals based on modified Duffing equations

NASA Astrophysics Data System (ADS)

Zhang, Yuhua; Mao, Hanling; Mao, Hanying; Huang, Zhenfeng

The nonlinear ultrasonic signals, like second harmonic generation (SHG) signals, could reflect the nonlinearity of material induced by fatigue damage in nonlinear ultrasonic technique which are weak nonlinear signals and usually submerged by strong background noise. In this paper the modified Duffing equations are applied to detect the SHG signals relating to the fatigue damage of material. Due to the Duffing equation could only detect the signal with specific frequency and initial phase, firstly the frequency transformation is carried on the Duffing equation which could detect the signal with any frequency. Then the influence of initial phases of to-be-detected signal and reference signal on the detection result is studied in detail, four modified Duffing equations are proposed to detect actual engineering signals with any initial phase. The relationship between the response amplitude and the total driving force is applied to estimate the amplitude of weak periodic signal. The detection results show the modified Duffing equations could effectively detect the second harmonic in SHG signals. When the SHG signals include strong background noise, the noise doesn't change the motion state of Duffing equation and the second harmonic signal could be detected until the SNR of noisy SHG signals are -26.3, yet the frequency spectrum method could only identify when the SNR is greater than 0.5. When estimation the amplitude of second harmonic signal, the estimation error of Duffing equation is obviously less than the frequency spectrum analysis method under the same noise level, which illustrates the Duffing equation has the noise immune capacity. The presence of the second harmonic signal in nonlinear ultrasonic experiments could provide an insight about the early fatigue damage of engineering components.

Signal Amplification Technologies for the Detection of Nucleic Acids: from Cell-Free Analysis to Live-Cell Imaging.

PubMed

Fozooni, Tahereh; Ravan, Hadi; Sasan, Hosseinali

2017-12-01

Due to their unique properties, such as programmability, ligand-binding capability, and flexibility, nucleic acids can serve as analytes and/or recognition elements for biosensing. To improve the sensitivity of nucleic acid-based biosensing and hence the detection of a few copies of target molecule, different modern amplification methodologies, namely target-and-signal-based amplification strategies, have already been developed. These recent signal amplification technologies, which are capable of amplifying the signal intensity without changing the targets' copy number, have resulted in fast, reliable, and sensitive methods for nucleic acid detection. Working in cell-free settings, researchers have been able to optimize a variety of complex and quantitative methods suitable for deploying in live-cell conditions. In this study, a comprehensive review of the signal amplification technologies for the detection of nucleic acids is provided. We classify the signal amplification methodologies into enzymatic and non-enzymatic strategies with a primary focus on the methods that enable us to shift away from in vitro detecting to in vivo imaging. Finally, the future challenges and limitations of detection for cellular conditions are discussed.

System and method of reducing motion-induced noise in the optical detection of an ultrasound signal in a moving body of material

DOEpatents

Habeger, Jr., Charles C.; LaFond, Emmanuel F.; Brodeur, Pierre; Gerhardstein, Joseph P.

2002-01-01

The present invention provides a system and method to reduce motion-induced noise in the detection of ultrasonic signals in a moving sheet or body of material. An ultrasonic signal is generated in a sheet of material and a detection laser beam is moved along the surface of the material. By moving the detection laser in the same direction as the direction of movement of the sheet of material the amount of noise induced in the detection of the ultrasonic signal is reduced. The scanner is moved at approximately the same speed as the moving material. The system and method may be used for many applications, such in a paper making process or steel making process. The detection laser may be directed by a scanner. The movement of the scanner is synchronized with the anticipated arrival of the ultrasonic signal under the scanner. A photodetector may be used to determine when a ultrasonic pulse has been directed to the moving sheet of material so that the scanner may be synchronized the anticipated arrival of the ultrasonic signal.

Weak photoacoustic signal detection based on the differential duffing oscillator

NASA Astrophysics Data System (ADS)

Li, Chenjing; Xu, Xuemei; Ding, Yipeng; Yin, Linzi; Dou, Beibei

2018-04-01

In view of photoacoustic spectroscopy theory, the relationship between weak photoacoustic signal and gas concentration is described. The studies, on the principle of Duffing oscillator for identifying state transition as well as determining the threshold value, have proven the feasibility of applying the Duffing oscillator in weak signal detection. An improved differential Duffing oscillator is proposed to identify weak signals with any frequency and ameliorate the signal-to-noise ratio. The analytical methods and numerical experiments of the novel model are introduced in detail to confirm its superiority. Then the signal detection system of weak photoacoustic based on differential Duffing oscillator is constructed, it is the first time that the weak signal detection method with differential Duffing oscillator is applied triumphantly in photoacoustic spectroscopy gas monitoring technology.

New coherent laser communication detection scheme based on channel-switching method.

PubMed

Liu, Fuchuan; Sun, Jianfeng; Ma, Xiaoping; Hou, Peipei; Cai, Guangyu; Sun, Zhiwei; Lu, Zhiyong; Liu, Liren

2015-04-01

A new coherent laser communication detection scheme based on the channel-switching method is proposed. The detection front end of this scheme comprises a 90° optical hybrid and two balanced photodetectors which outputs the in-phase (I) channel and quadrature-phase (Q) channel signal current, respectively. With this method, the ultrahigh speed analog/digital transform of the signal of the I or Q channel is not required. The phase error between the signal and local lasers is obtained by simple analog circuit. Using the phase error signal, the signals of the I/Q channel are switched alternately. The principle of this detection scheme is presented. Moreover, the comparison of the sensitivity of this scheme with that of homodyne detection with an optical phase-locked loop is discussed. An experimental setup was constructed to verify the proposed detection scheme. The offline processing procedure and results are presented. This scheme could be realized through simple structure and has potential applications in cost-effective high-speed laser communication.

Method and apparatus for time dispersive spectroscopy

DOEpatents

Tarver, III, Edward E.; Siems, William F.

2003-06-17

Methods and apparatus are described for time dispersive spectroscopy. In particular, a modulated flow of ionized molecules of a sample are introduced into a drift region of an ion spectrometer. The ions are subsequently detected by an ion detector to produce an ion detection signal. The ion detection signal can be modulated to obtain a signal useful in assaying the chemical constituents of the sample.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

NASA Astrophysics Data System (ADS)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

PubMed

Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

2007-01-01

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

Multi-channel non-invasive fetal electrocardiography detection using wavelet decomposition

NASA Astrophysics Data System (ADS)

Almeida, Javier; Ruano, Josué; Corredor, Germán.; Romo-Bucheli, David; Navarro-Vargas, José Ricardo; Romero, Eduardo

2017-11-01

Non-invasive fetal electrocardiography (fECG) has attracted the medical community because of the importance of fetal monitoring. However, its implementation in clinical practice is challenging: the fetal signal has a low Signal- to-Noise-Ratio and several signal sources are present in the maternal abdominal electrocardiography (AECG). This paper presents a novel method to detect the fetal signal from a multi-channel maternal AECG. The method begins by applying filters and signal detrending the AECG signals. Afterwards, the maternal QRS complexes are identified and subtracted. The residual signals are used to detect the fetal QRS complex. Intervals of these signals are analyzed by using a wavelet decomposition. The resulting representation feds a previously trained Random Forest (RF) classifier that identifies signal intervals associated to fetal QRS complex. The method was evaluated on a public available dataset: the Physionet2013 challenge. A set of 50 maternal AECG records were used to train the RF classifier. The evaluation was carried out in signals intervals extracted from additional 25 maternal AECG. The proposed method yielded an 83:77% accuracy in the fetal QRS complex classification task.

A surface acoustic wave response detection method for passive wireless torque sensor

NASA Astrophysics Data System (ADS)

Fan, Yanping; Kong, Ping; Qi, Hongli; Liu, Hongye; Ji, Xiaojun

2018-01-01

This paper presents an effective surface acoustic wave (SAW) response detection method for the passive wireless SAW torque sensor to improve the measurement accuracy. An analysis was conducted on the relationship between the response energy-entropy and the bandwidth of SAW resonator (SAWR). A self-correlation method was modified to suppress the blurred white noise and highlight the attenuation characteristic of wireless SAW response. The SAW response was detected according to both the variation and the duration of energy-entropy ascension of an acquired RF signal. Numerical simulation results showed that the SAW response can be detected even when the signal-to-noise ratio (SNR) is 6dB. The proposed SAW response detection method was evaluated with several experiments at different conditions. The SAW response can be well distinguished from the sinusoidal signal and the noise. The performance of the SAW torque measurement system incorporating the detection method was tested. The obtained repeatability error was 0.23% and the linearity was 0.9934, indicating the validity of the detection method.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Signal analysis techniques for incipient failure detection in turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, T.

1985-01-01

Signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery were developed, implemented and evaluated. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques were implemented on a computer and applied to dynamic signals. A laboratory evaluation of the methods with respect to signal detection capability is described. Plans for further technique evaluation and data base development to characterize turbopump incipient failure modes from Space Shuttle main engine (SSME) hot firing measurements are outlined.

Pharmacovigilance data mining with methods based on false discovery rates: a comparative simulation study.

PubMed

Ahmed, I; Thiessard, F; Miremont-Salamé, G; Bégaud, B; Tubert-Bitter, P

2010-10-01

The early detection of adverse reactions caused by drugs that are already on the market is the prime concern of pharmacovigilance efforts; the methods in use for postmarketing surveillance are aimed at detecting signals pointing to potential safety concerns, on the basis of reports from health-care providers and from information available in various databases. Signal detection methods based on the estimation of false discovery rate (FDR) have recently been proposed. They address the limitation of arbitrary detection thresholds of the automatic methods in current use, including those last updated by the US Food and Drug Administration and the World Health Organization's Uppsala Monitoring Centre. We used two simulation procedures to compare the false-positive performances for three current methods: the reporting odds ratio (ROR), the information component (IC), the gamma Poisson shrinkage (GPS), and also for two FDR-based methods derived from the GPS model and Fisher's test. Large differences in FDR rates were associated with the signal-detection methods currently in use. These differences ranged from 0.01 to 12% in an analysis that was restricted to signals with at least three reports. The numbers of signals generated were also highly variable. Among fixed-size lists of signals, the FDR was lowered when the FDR-based approaches were used. Overall, the outcomes in both simulation studies suggest that improvement in effectiveness can be expected from use of the FDR-based GPS method.

Signal existence verification (SEV) for GPS low received power signal detection using the time-frequency approach.

PubMed

Jan, Shau-Shiun; Sun, Chih-Cheng

2010-01-01

The detection of low received power of global positioning system (GPS) signals in the signal acquisition process is an important issue for GPS applications. Improving the miss-detection problem of low received power signal is crucial, especially for urban or indoor environments. This paper proposes a signal existence verification (SEV) process to detect and subsequently verify low received power GPS signals. The SEV process is based on the time-frequency representation of GPS signal, and it can capture the characteristic of GPS signal in the time-frequency plane to enhance the GPS signal acquisition performance. Several simulations and experiments are conducted to show the effectiveness of the proposed method for low received power signal detection. The contribution of this work is that the SEV process is an additional scheme to assist the GPS signal acquisition process in low received power signal detection, without changing the original signal acquisition or tracking algorithms.

Mobile/android application for QRS detection using zero cross method

NASA Astrophysics Data System (ADS)

Rizqyawan, M. I.; Simbolon, A. I.; Suhendra, M. A.; Amri, M. F.; Kusumandari, D. E.

2018-03-01

In automatic ECG signal processing, one of the main topics of research is QRS complex detection. Detecting correct QRS complex or R peak is important since it is used to measure several other ECG metrics. One of the robust methods for QRS detection is Zero Cross method. This method uses an addition of high-frequency signal and zero crossing count to detect QRS complex which has a low-frequency oscillation. This paper presents an application of QRS detection using Zero Cross algorithm in the Android-based system. The performance of the algorithm in the mobile environment is measured. The result shows that this method is suitable for real-time QRS detection in a mobile application.

Testing local anisotropy using the method of smoothed residuals I — methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Appleby, Stephen; Shafieloo, Arman, E-mail: stephen.appleby@apctp.org, E-mail: arman@apctp.org

2014-03-01

We discuss some details regarding the method of smoothed residuals, which has recently been used to search for anisotropic signals in low-redshift distance measurements (Supernovae). In this short note we focus on some details regarding the implementation of the method, particularly the issue of effectively detecting signals in data that are inhomogeneously distributed on the sky. Using simulated data, we argue that the original method proposed in Colin et al. [1] will not detect spurious signals due to incomplete sky coverage, and that introducing additional Gaussian weighting to the statistic as in [2] can hinder its ability to detect amore » signal. Issues related to the width of the Gaussian smoothing are also discussed.« less

Method and system for detecting a failure or performance degradation in a dynamic system such as a flight vehicle

NASA Technical Reports Server (NTRS)

Miller, Robert H. (Inventor); Ribbens, William B. (Inventor)

2003-01-01

A method and system for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables and providing corresponding output signals in response to one or more system input signals are provided. The method includes calculating estimated gains of a filter and selecting an appropriate linear model for processing the output signals based on the input signals. The step of calculating utilizes one or more models of the dynamic system to obtain estimated signals. The method further includes calculating output error residuals based on the output signals and the estimated signals. The method also includes detecting one or more hypothesized failures or performance degradations of a component or subsystem of the dynamic system based on the error residuals. The step of calculating the estimated values is performed optimally with respect to one or more of: noise, uncertainty of parameters of the models and un-modeled dynamics of the dynamic system which may be a flight vehicle or financial market or modeled financial system.

Signal detection by means of orthogonal decomposition

NASA Astrophysics Data System (ADS)

Hajdu, C. F.; Dabóczi, T.; Péceli, G.; Zamantzas, C.

2018-03-01

Matched filtering is a well-known method frequently used in digital signal processing to detect the presence of a pattern in a signal. In this paper, we suggest a time variant matched filter, which, unlike a regular matched filter, maintains a given alignment between the input signal and the template carrying the pattern, and can be realized recursively. We introduce a method to synchronize the two signals for presence detection, usable in case direct synchronization between the signal generator and the receiver is not possible or not practical. We then propose a way of realizing and extending the same filter by modifying a recursive spectral observer, which gives rise to orthogonal filter channels and also leads to another way to synchronize the two signals.

In-situ fault detection apparatus and method for an encased energy storing device

DOEpatents

Hagen, Ronald A.; Comte, Christophe; Knudson, Orlin B.; Rosenthal, Brian; Rouillard, Jean

2000-01-01

An apparatus and method for detecting a breach in an electrically insulating surface of an electrically conductive power system enclosure within which a number of series connected energy storing devices are disposed. The energy storing devices disposed in the enclosure are connected to a series power connection. A detector is coupled to the series connection and detects a change of state in a test signal derived from the series connected energy storing devices. The detector detects a breach in the insulating layer of the enclosure by detecting a state change in the test signal from a nominal state to a non-nominal state. A voltage detector detects a state change of the test signals from a nominal state, represented by a voltage of a selected end energy storing device, to a non-nominal state, represented by a voltage that substantially exceeds the voltage of the selected opposing end energy storing device. Alternatively, the detector may comprise a signal generator that produces the test signal as a time-varying or modulated test signal and injects the test signal into the series connection. The detector detects the state change of the time-varying or modulated test signal from a nominal state, represented by a signal substantially equivalent to the test signal, to a non-nominal state, representative by an absence of the test signal.

Data acquisition and processing system and method for investigating sub-surface features of a rock formation

DOEpatents

Vu, Cung Khac; Nihei, Kurt; Johnson, Paul A; Guyer, Robert; Ten Cate, James A; Le Bas, Pierre-Yves; Larmat, Carene S

2015-01-27

A system and a method includes generating a first signal at a first frequency; and a second signal at a second frequency. Respective sources are positioned within the borehole and controllable such that the signals intersect in an intersection volume outside the borehole. A receiver detects a difference signal returning to the borehole generated by a non-linear mixing process within the intersection volume, and records the detected signal and stores the detected signal in a storage device and records measurement parameters including a position of the first acoustic source, a position of the second acoustic source, a position of the receiver, elevation angle and azimuth angle of the first acoustic signal and elevation angle and azimuth angle of the second acoustic signal.

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices.

PubMed

Choi, Yerim; Jeon, Yu-Mi; Wang, Lin; Kim, Kwanho

2017-08-23

The safety of children has always been an important issue, and several studies have been conducted to determine the stress state of a child to ensure the safety. Audio signals and biological signals including heart rate are known to be effective for stress state detection. However, collecting those data requires specialized equipment, which is not appropriate for the constant monitoring of children, and advanced data analysis is required for accurate detection. In this regard, we propose a stress state detection framework which utilizes both audio signal and heart rate collected from wearable devices, and adopted machine learning methods for the detection. Experiments using real-world data were conducted to compare detection performances across various machine learning methods and noise levels of audio signal. Adopting the proposed framework in the real-world will contribute to the enhancement of child safety.

Comparison of formant detection methods used in speech processing applications

NASA Astrophysics Data System (ADS)

Belean, Bogdan

2013-11-01

The paper describes time frequency representations of speech signal together with the formant significance in speech processing applications. Speech formants can be used in emotion recognition, sex discrimination or diagnosing different neurological diseases. Taking into account the various applications of formant detection in speech signal, two methods for detecting formants are presented. First, the poles resulted after a complex analysis of LPC coefficients are used for formants detection. The second approach uses the Kalman filter for formant prediction along the speech signal. Results are presented for both approaches on real life speech spectrograms. A comparison regarding the features of the proposed methods is also performed, in order to establish which method is more suitable in case of different speech processing applications.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

Effect of sample storage time on detection of hybridization signals in Checkerboard DNA-DNA hybridization.

PubMed

do Nascimento, Cássio; Muller, Katia; Sato, Sandra; Albuquerque Junior, Rubens Ferreira

2012-04-01

Long-term sample storage can affect the intensity of the hybridization signals provided by molecular diagnostic methods that use chemiluminescent detection. The aim of this study was to evaluate the effect of different storage times on the hybridization signals of 13 bacterial species detected by the Checkerboard DNA-DNA hybridization method using whole-genomic DNA probes. Ninety-six subgingival biofilm samples were collected from 36 healthy subjects, and the intensity of hybridization signals was evaluated at 4 different time periods: (1) immediately after collecting (n = 24) and (2) after storage at -20 °C for 6 months (n = 24), (3) for 12 months (n = 24), and (4) for 24 months (n = 24). The intensity of hybridization signals obtained from groups 1 and 2 were significantly higher than in the other groups (p < 0.001). No differences were found between groups 1 and 2 (p > 0.05). The Checkerboard DNA-DNA hybridization method was suitable to detect hybridization signals from all groups evaluated, and the intensity of signals decreased significantly after long periods of sample storage.

Detection of Ultrasonic Stress Waves in Structures Using 3D Shaped Optic Fiber Based on a Mach-Zehnder Interferometer.

PubMed

Lan, Chengming; Zhou, Wensong; Xie, Yawen

2018-04-16

This work proposes a 3D shaped optic fiber sensor for ultrasonic stress waves detection based on the principle of a Mach–Zehnder interferometer. This sensor can be used to receive acoustic emission signals in the passive damage detection methods and other types of ultrasonic signals propagating in the active damage detection methods, such as guided wave-based methods. The sensitivity of an ultrasonic fiber sensor based on the Mach–Zehnder interferometer mainly depends on the length of the sensing optical fiber; therefore, the proposed sensor achieves the maximum possible sensitivity by wrapping an optical fiber on a hollow cylinder with a base. The deformation of the optical fiber is produced by the displacement field of guided waves in the hollow cylinder. The sensor was first analyzed using the finite element method, which demonstrated its basic sensing capacity, and the simulation signals have the same characteristics in the frequency domain as the excitation signal. Subsequently, the primary investigations were conducted via a series of experiments. The sensor was used to detect guided wave signals excited by a piezoelectric wafer in an aluminum plate, and subsequently it was tested on a reinforced concrete beam, which produced acoustic emission signals via impact loading and crack extension when it was loaded to failure. The signals obtained from a piezoelectric acoustic emission sensor were used for comparison, and the results indicated that the proposed 3D fiber optic sensor can detect ultrasonic signals in the specific frequency response range.

Detection of Ultrasonic Stress Waves in Structures Using 3D Shaped Optic Fiber Based on a Mach–Zehnder Interferometer

PubMed Central

Xie, Yawen

2018-01-01

This work proposes a 3D shaped optic fiber sensor for ultrasonic stress waves detection based on the principle of a Mach–Zehnder interferometer. This sensor can be used to receive acoustic emission signals in the passive damage detection methods and other types of ultrasonic signals propagating in the active damage detection methods, such as guided wave-based methods. The sensitivity of an ultrasonic fiber sensor based on the Mach–Zehnder interferometer mainly depends on the length of the sensing optical fiber; therefore, the proposed sensor achieves the maximum possible sensitivity by wrapping an optical fiber on a hollow cylinder with a base. The deformation of the optical fiber is produced by the displacement field of guided waves in the hollow cylinder. The sensor was first analyzed using the finite element method, which demonstrated its basic sensing capacity, and the simulation signals have the same characteristics in the frequency domain as the excitation signal. Subsequently, the primary investigations were conducted via a series of experiments. The sensor was used to detect guided wave signals excited by a piezoelectric wafer in an aluminum plate, and subsequently it was tested on a reinforced concrete beam, which produced acoustic emission signals via impact loading and crack extension when it was loaded to failure. The signals obtained from a piezoelectric acoustic emission sensor were used for comparison, and the results indicated that the proposed 3D fiber optic sensor can detect ultrasonic signals in the specific frequency response range. PMID:29659540

A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Zhang, Xin; Wang, Yan; Shen, Yi; Makis, Viliam

2018-07-01

Acoustic emission (AE) technology is sensitive to subliminal rail defects, however strong wheel-rail contact rolling noise under high-speed condition has gravely impeded detecting of rail defects using traditional denoising methods. In this context, the paper develops an adaptive detection method for rail cracks, which combines multiresolution analysis with an improved adaptive line enhancer (ALE). To obtain elaborate multiresolution information of transient crack signals with low computational cost, lifting scheme-based undecimated wavelet packet transform is adopted. In order to feature the impulsive property of crack signals, a Shannon entropy-improved ALE is proposed as a signal enhancing approach, where Shannon entropy is introduced to improve the cost function. Then a rail defect detection plan based on the proposed method for high-speed condition is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.

Interference and deception detection technology of satellite navigation based on deep learning

NASA Astrophysics Data System (ADS)

Chen, Weiyi; Deng, Pingke; Qu, Yi; Zhang, Xiaoguang; Li, Yaping

2017-10-01

Satellite navigation system plays an important role in people's daily life and war. The strategic position of satellite navigation system is prominent, so it is very important to ensure that the satellite navigation system is not disturbed or destroyed. It is a critical means to detect the jamming signal to avoid the accident in a navigation system. At present, the detection technology of jamming signal in satellite navigation system is not intelligent , mainly relying on artificial decision and experience. For this issue, the paper proposes a method based on deep learning to monitor the interference source in a satellite navigation. By training the interference signal data, and extracting the features of the interference signal, the detection sys tem model is constructed. The simulation results show that, the detection accuracy of our detection system can reach nearly 70%. The method in our paper provides a new idea for the research on intelligent detection of interference and deception signal in a satellite navigation system.

[Research on Detection Method with Wearable Respiration Device Based on the Theory of Bio-impedance].

PubMed

Liu, Guangda; Wang, Xianzhong; Cai, Jing; Wang, Wei; Zha, Yutong

2016-12-01

Considering the importance of the human respiratory signal detection and based on the Cole-Cole bio-impedance model,we developed a wearable device for detecting human respiratory signal.The device can be used to analyze the impedance characteristics of human body at different frequencies based on the bio-impedance theory.The device is also based on the method of proportion measurement to design a high signal to noise ratio(SNR)circuit to get human respiratory signal.In order to obtain the waveform of the respiratory signal and the value of the respiration rate,we used the techniques of discrete Fourier transform(DFT)and dynamic difference threshold peak detection.Experiments showed that this system was valid,and we could see that it could accurately detect the waveform of respiration and the detection accuracy rate of respiratory wave peak point detection results was over 98%.So it can meet the needs of the actual breath test.

Determining dark matter properties with a XENONnT/LZ signal and LHC Run 3 monojet searches

NASA Astrophysics Data System (ADS)

Baum, Sebastian; Catena, Riccardo; Conrad, Jan; Freese, Katherine; Krauss, Martin B.

2018-04-01

We develop a method to forecast the outcome of the LHC Run 3 based on the hypothetical detection of O (100 ) signal events at XENONnT. Our method relies on a systematic classification of renormalizable single-mediator models for dark matter-quark interactions and is valid for dark matter candidates of spin less than or equal to one. Applying our method to simulated data, we find that at the end of the LHC Run 3 only two mutually exclusive scenarios would be compatible with the detection of O (100 ) signal events at XENONnT. In the first scenario, the energy distribution of the signal events is featureless, as for canonical spin-independent interactions. In this case, if a monojet signal is detected at the LHC, dark matter must have spin 1 /2 and interact with nucleons through a unique velocity-dependent operator. If a monojet signal is not detected, dark matter interacts with nucleons through canonical spin-independent interactions. In a second scenario, the spectral distribution of the signal events exhibits a bump at nonzero recoil energies. In this second case, a monojet signal can be detected at the LHC Run 3; dark matter must have spin 1 /2 and interact with nucleons through a unique momentum-dependent operator. We therefore conclude that the observation of O (100 ) signal events at XENONnT combined with the detection, or the lack of detection, of a monojet signal at the LHC Run 3 would significantly narrow the range of possible dark matter-nucleon interactions. As we argued above, it can also provide key information on the dark matter particle spin.

Faint Debris Detection by Particle Based Track-Before-Detect Method

NASA Astrophysics Data System (ADS)

Uetsuhara, M.; Ikoma, N.

2014-09-01

This study proposes a particle method to detect faint debris, which is hardly seen in single frame, from an image sequence based on the concept of track-before-detect (TBD). The most widely used detection method is detect-before-track (DBT), which firstly detects signals of targets from single frame by distinguishing difference of intensity between foreground and background then associate the signals for each target between frames. DBT is capable of tracking bright targets but limited. DBT is necessary to consider presence of false signals and is difficult to recover from false association. On the other hand, TBD methods try to track targets without explicitly detecting the signals followed by evaluation of goodness of each track and obtaining detection results. TBD has an advantage over DBT in detecting weak signals around background level in single frame. However, conventional TBD methods for debris detection apply brute-force search over candidate tracks then manually select true one from the candidates. To reduce those significant drawbacks of brute-force search and not-fully automated process, this study proposes a faint debris detection algorithm by a particle based TBD method consisting of sequential update of target state and heuristic search of initial state. The state consists of position, velocity direction and magnitude, and size of debris over the image at a single frame. The sequential update process is implemented by a particle filter (PF). PF is an optimal filtering technique that requires initial distribution of target state as a prior knowledge. An evolutional algorithm (EA) is utilized to search the initial distribution. The EA iteratively applies propagation and likelihood evaluation of particles for the same image sequences and resulting set of particles is used as an initial distribution of PF. This paper describes the algorithm of the proposed faint debris detection method. The algorithm demonstrates performance on image sequences acquired during observation campaigns dedicated to GEO breakup fragments, which would contain a sufficient number of faint debris images. The results indicate the proposed method is capable of tracking faint debris with moderate computational costs at operational level.

Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Remote NMR/MRI detection of laser polarized gases

DOEpatents

Pines, Alexander; Saxena, Sunil; Moule, Adam; Spence, Megan; Seeley, Juliette A.; Pierce, Kimberly L.; Han, Song-I; Granwehr, Josef

2006-06-13

An apparatus and method for remote NMR/MRI spectroscopy having an encoding coil with a sample chamber, a supply of signal carriers, preferably hyperpolarized xenon and a detector allowing the spatial and temporal separation of signal preparation and signal detection steps. This separation allows the physical conditions and methods of the encoding and detection steps to be optimized independently. The encoding of the carrier molecules may take place in a high or a low magnetic field and conventional NMR pulse sequences can be split between encoding and detection steps. In one embodiment, the detector is a high magnetic field NMR apparatus. In another embodiment, the detector is a superconducting quantum interference device. A further embodiment uses optical detection of Rb--Xe spin exchange. Another embodiment uses an optical magnetometer using non-linear Faraday rotation. Concentration of the signal carriers in the detector can greatly improve the signal to noise ratio.

Sensing Methods for Detecting Analog Television Signals

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Azizur; Song, Chunyi; Harada, Hiroshi

This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

A robust and versatile signal-on fluorescence sensing strategy based on SYBR Green I dye and graphene oxide

PubMed Central

Qiu, Huazhang; Wu, Namei; Zheng, Yanjie; Chen, Min; Weng, Shaohuang; Chen, Yuanzhong; Lin, Xinhua

2015-01-01

A robust and versatile signal-on fluorescence sensing strategy was developed to provide label-free detection of various target analytes. The strategy used SYBR Green I dye and graphene oxide as signal reporter and signal-to-background ratio enhancer, respectively. Multidrug resistance protein 1 (MDR1) gene and mercury ion (Hg2+) were selected as target analytes to investigate the generality of the method. The linear relationship and specificity of the detections showed that the sensitive and selective analyses of target analytes could be achieved by the proposed strategy with low detection limits of 0.5 and 2.2 nM for MDR1 gene and Hg2+, respectively. Moreover, the strategy was used to detect real samples. Analytical results of MDR1 gene in the serum indicated that the developed method is a promising alternative approach for real applications in complex systems. Furthermore, the recovery of the proposed method for Hg2+ detection was acceptable. Thus, the developed label-free signal-on fluorescence sensing strategy exhibited excellent universality, sensitivity, and handling convenience. PMID:25565810

Study of comparison between Ultra-high Frequency (UHF) method and ultrasonic method on PD detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Li, Li; Zhang, Jiwei; Li, Guang; Liu, Hongxia

2017-11-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. However, few studies have been conducted on comparison of this two methods. From the view point of safety, it is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. This paper presents study aimed at clarifying the effect of UHF method and ultrasonic method for partial discharge caused by free metal particles in GIS. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for UHF method and ultrasonic method. A new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of detection localization.

Exploiting vibrational resonance in weak-signal detection

NASA Astrophysics Data System (ADS)

Ren, Yuhao; Pan, Yan; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

2017-08-01

In this paper, we investigate the first exploitation of the vibrational resonance (VR) effect to detect weak signals in the presence of strong background noise. By injecting a series of sinusoidal interference signals of the same amplitude but with different frequencies into a generalized correlation detector, we show that the detection probability can be maximized at an appropriate interference amplitude. Based on a dual-Dirac probability density model, we compare the VR method with the stochastic resonance approach via adding dichotomous noise. The compared results indicate that the VR method can achieve a higher detection probability for a wider variety of noise distributions.

Exploiting vibrational resonance in weak-signal detection.

PubMed

Ren, Yuhao; Pan, Yan; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

2017-08-01

In this paper, we investigate the first exploitation of the vibrational resonance (VR) effect to detect weak signals in the presence of strong background noise. By injecting a series of sinusoidal interference signals of the same amplitude but with different frequencies into a generalized correlation detector, we show that the detection probability can be maximized at an appropriate interference amplitude. Based on a dual-Dirac probability density model, we compare the VR method with the stochastic resonance approach via adding dichotomous noise. The compared results indicate that the VR method can achieve a higher detection probability for a wider variety of noise distributions.

A Volterra series-based method for extracting target echoes in the seafloor mining environment.

PubMed

Zhao, Haiming; Ji, Yaqian; Hong, Yujiu; Hao, Qi; Ma, Liyong

2016-09-01

The purpose of this research was to evaluate the applicability of the Volterra adaptive method to predict the target echo of an ultrasonic signal in an underwater seafloor mining environment. There is growing interest in mining of seafloor minerals because they offer an alternative source of rare metals. Mining the minerals cause the seafloor sediments to be stirred up and suspended in sea water. In such an environment, the target signals used for seafloor mapping are unable to be detected because of the unavoidable presence of volume reverberation induced by the suspended sediments. The detection of target signals in reverberation is currently performed using a stochastic model (for example, the autoregressive (AR) model) based on the statistical characterisation of reverberation. However, we examined a new method of signal detection in volume reverberation based on the Volterra series by confirming that the reverberation is a chaotic signal and generated by a deterministic process. The advantage of this method over the stochastic model is that attributions of the specific physical process are considered in the signal detection problem. To test the Volterra series based method and its applicability to target signal detection in the volume reverberation environment derived from the seafloor mining process, we simulated the real-life conditions of seafloor mining in a water filled tank of dimensions of 5×3×1.8m. The bottom of the tank was covered with 10cm of an irregular sand layer under which 5cm of an irregular cobalt-rich crusts layer was placed. The bottom was interrogated by an acoustic wave generated as 16μs pulses of 500kHz frequency. This frequency is demonstrated to ensure a resolution on the order of one centimetre, which is adequate in exploration practice. Echo signals were collected with a data acquisition card (PCI 1714 UL, 12-bit). Detection of the target echo in these signals was performed by both the Volterra series based model and the AR model. The results obtained confirm that the Volterra series based method is more efficient in the detection of the signal in reverberation than the conventional AR model (the accuracy is 80% for the PIM-Volterra prediction model versus 40% for the AR model). Copyright © 2016 Elsevier B.V. All rights reserved.

Signal Detection Techniques for Diagnostic Monitoring of Space Shuttle Main Engine Turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Jong, Jen-Yi

1986-01-01

An investigation to develop, implement, and evaluate signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery is reviewed. A brief description of the Space Shuttle Main Engine (SSME) test/measurement program is presented. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques have been implemented on a computer and applied to dynamc signals. A laboratory evaluation of the methods with respect to signal detection capability is described. A unique coherence function (the hyper-coherence) was developed through the course of this investigation, which appears promising as a diagnostic tool. This technique and several other non-linear methods of signal analysis are presented and illustrated by application. Software for application of these techniques has been installed on the signal processing system at the NASA/MSFC Systems Dynamics Laboratory.

Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

PubMed Central

Brown, Jeffrey S.; Petronis, Kenneth R.; Bate, Andrew; Zhang, Fang; Dashevsky, Inna; Kulldorff, Martin; Avery, Taliser R.; Davis, Robert L.; Chan, K. Arnold; Andrade, Susan E.; Boudreau, Denise; Gunter, Margaret J.; Herrinton, Lisa; Pawloski, Pamala A.; Raebel, Marsha A.; Roblin, Douglas; Smith, David; Reynolds, Robert

2013-01-01

Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds. PMID:24300404

Methods to Estimate the Variance of Some Indices of the Signal Detection Theory: A Simulation Study

ERIC Educational Resources Information Center

Suero, Manuel; Privado, Jesús; Botella, Juan

2017-01-01

A simulation study is presented to evaluate and compare three methods to estimate the variance of the estimates of the parameters d and "C" of the signal detection theory (SDT). Several methods have been proposed to calculate the variance of their estimators, "d'" and "c." Those methods have been mostly assessed by…

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

NASA Astrophysics Data System (ADS)

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants.

PubMed

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Positron emission tomography wrist detector

DOEpatents

Schlyer, David J.; O'Connor, Paul; Woody, Craig; Junnarkar, Sachin Shrirang; Radeka, Veljko; Vaska, Paul; Pratte, Jean-Francois

2006-08-15

A method of serially transferring annihilation information in a compact positron emission tomography (PET) scanner includes generating a time signal representing a time-of-occurrence of an annihilation event, generating an address signal representing a channel detecting the annihilation event, and generating a channel signal including the time and address signals. The method also includes generating a composite signal including the channel signal and another similarly generated channel signal concerning another annihilation event. An apparatus that serially transfers annihilation information includes a time signal generator, address signal generator, channel signal generator, and composite signal generator. The time signal is asynchronous and the address signal is synchronous to a clock signal. A PET scanner includes a scintillation array, detection array, front-end array, and a serial encoder. The serial encoders include the time signal generator, address signal generator, channel signal generator, and composite signal generator.

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

Regularized non-stationary morphological reconstruction algorithm for weak signal detection in microseismic monitoring: methodology

NASA Astrophysics Data System (ADS)

Huang, Weilin; Wang, Runqiu; Chen, Yangkang

2018-05-01

Microseismic signal is typically weak compared with the strong background noise. In order to effectively detect the weak signal in microseismic data, we propose a mathematical morphology based approach. We decompose the initial data into several morphological multiscale components. For detection of weak signal, a non-stationary weighting operator is proposed and introduced into the process of reconstruction of data by morphological multiscale components. The non-stationary weighting operator can be obtained by solving an inversion problem. The regularized non-stationary method can be understood as a non-stationary matching filtering method, where the matching filter has the same size as the data to be filtered. In this paper, we provide detailed algorithmic descriptions and analysis. The detailed algorithm framework, parameter selection and computational issue for the regularized non-stationary morphological reconstruction (RNMR) method are presented. We validate the presented method through a comprehensive analysis through different data examples. We first test the proposed technique using a synthetic data set. Then the proposed technique is applied to a field project, where the signals induced from hydraulic fracturing are recorded by 12 three-component geophones in a monitoring well. The result demonstrates that the RNMR can improve the detectability of the weak microseismic signals. Using the processed data, the short-term-average over long-term average picking algorithm and Geiger's method are applied to obtain new locations of microseismic events. In addition, we show that the proposed RNMR method can be used not only in microseismic data but also in reflection seismic data to detect the weak signal. We also discussed the extension of RNMR from 1-D to 2-D or a higher dimensional version.

Study of New Method Combined Ultra-High Frequency (UHF) Method and Ultrasonic Method on PD Detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Zhang, Jiwei; Chen, Ning; Li, Xiaoqi; Gong, Xiaojing

2017-09-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. It is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. However, very few studies have been conducted on the method combined this two methods. From the view point of safety, a new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of fault localization. This paper presents study aimed at clarifying the effect of the new method combined UHF method and ultrasonic method. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for this new method combined UHF method and ultrasonic method.

Signal processing for non-destructive testing of railway tracks

NASA Astrophysics Data System (ADS)

Heckel, Thomas; Casperson, Ralf; Rühe, Sven; Mook, Gerhard

2018-04-01

Increased speed, heavier loads, altered material and modern drive systems result in an increasing number of rail flaws. The appearance of these flaws also changes continually due to the rapid change in damage mechanisms of modern rolling stock. Hence, interpretation has become difficult when evaluating non-destructive rail testing results. Due to the changed interplay between detection methods and flaws, the recorded signals may result in unclassified types of rail flaws. Methods for automatic rail inspection (according to defect detection and classification) undergo continual development. Signal processing is a key technology to master the challenge of classification and maintain resolution and detection quality, independent of operation speed. The basic ideas of signal processing, based on the Glassy-Rail-Diagram for classification purposes, are presented herein. Examples for the detection of damages caused by rolling contact fatigue also are given, and synergetic effects of combined evaluation of diverse inspection methods are shown.

Finger wear detection for production line battery tester

DOEpatents

Depiante, Eduardo V.

1997-01-01

A method for detecting wear in a battery tester probe. The method includes providing a battery tester unit having at least one tester finger, generating a tester signal using the tester fingers and battery tester unit with the signal characteristic of the electrochemical condition of the battery and the tester finger, applying wavelet transformation to the tester signal including computing a mother wavelet to produce finger wear indicator signals, analyzing the signals to create a finger wear index, comparing the wear index for the tester finger with the index for a new tester finger and generating a tester finger signal change signal to indicate achieving a threshold wear change.

Recovering a redshift-extended varying speed of light signal from galaxy surveys

NASA Astrophysics Data System (ADS)

Salzano, Vincenzo

2017-04-01

We investigate a new method to recover (if any) a possible varying speed of light (VSL) signal from cosmological data. It comes as an upgrade by Salzano, Dąbrowski, and Lazkoz [Phys. Rev. Lett.114, 101304 (2015), 10.1103/PhysRevLett.114.101304; Phys. Rev. D 93, 063521 (2016), 10.1103/PhysRevD.93.063521], where it was argued that such a signal could be detected at a single redshift location only. Here, we show how it is possible to extract information on a VSL signal on an extended redshift range. We use mock cosmological data from future galaxy surveys (BOSS, DESI, WFirst-2.4 and SKA): the sound horizon at decoupling imprinted in the clustering of galaxies (baryon acoustic oscillations) as an angular diameter distance, and the expansion rate derived from those galaxies recognized as cosmic chronometers. We find that, given the forecast sensitivities of such surveys, a ˜1 % VSL signal can be detected at 3 σ confidence level in the redshift interval z ∈[0. ,1.55 ]. Smaller signals (˜0.1 % ) will be hardly detected (even if some lower possibility for a 1 σ detection is still possible). Finally, we discuss the degeneration between a VSL signal and a non-null spatial curvature; we show that, given present bounds on curvature, any signal, if detected, can be attributed to a VSL signal with a very high confidence. On the other hand, our method turns out to be useful even in the classical scenario of a constant speed of light: in this case, the signal we reconstruct can be totally ascribed to spatial curvature and, thus, we might have a method to detect a 0.01-order curvature in the same redshift range with a very high confidence.

Doppler Radar Vital Signs Detection Method Based on Higher Order Cyclostationary.

PubMed

Yu, Zhibin; Zhao, Duo; Zhang, Zhiqiang

2017-12-26

Due to the non-contact nature, using Doppler radar sensors to detect vital signs such as heart and respiration rates of a human subject is getting more and more attention. However, the related detection-method research meets lots of challenges due to electromagnetic interferences, clutter and random motion interferences. In this paper, a novel third-order cyclic cummulant (TOCC) detection method, which is insensitive to Gaussian interference and non-cyclic signals, is proposed to investigate the heart and respiration rate based on continuous wave Doppler radars. The k -th order cyclostationary properties of the radar signal with hidden periodicities and random motions are analyzed. The third-order cyclostationary detection theory of the heart and respiration rate is studied. Experimental results show that the third-order cyclostationary approach has better estimation accuracy for detecting the vital signs from the received radar signal under low SNR, strong clutter noise and random motion interferences.

Eventogram: A Visual Representation of Main Events in Biomedical Signals.

PubMed

Elgendi, Mohamed

2016-09-22

Biomedical signals carry valuable physiological information and many researchers have difficulty interpreting and analyzing long-term, one-dimensional, quasi-periodic biomedical signals. Traditionally, biomedical signals are analyzed and visualized using periodogram, spectrogram, and wavelet methods. However, these methods do not offer an informative visualization of main events within the processed signal. This paper attempts to provide an event-related framework to overcome the drawbacks of the traditional visualization methods and describe the main events within the biomedical signal in terms of duration and morphology. Electrocardiogram and photoplethysmogram signals are used in the analysis to demonstrate the differences between the traditional visualization methods, and their performance is compared against the proposed method, referred to as the " eventogram " in this paper. The proposed method is based on two event-related moving averages that visualizes the main time-domain events in the processed biomedical signals. The traditional visualization methods were unable to find dominant events in processed signals while the eventogram was able to visualize dominant events in signals in terms of duration and morphology. Moreover, eventogram -based detection algorithms succeeded with detecting main events in different biomedical signals with a sensitivity and positive predictivity >95%. The output of the eventogram captured unique patterns and signatures of physiological events, which could be used to visualize and identify abnormal waveforms in any quasi-periodic signal.

Supervised segmentation of microelectrode recording artifacts using power spectral density.

PubMed

Bakstein, Eduard; Schneider, Jakub; Sieger, Tomas; Novak, Daniel; Wild, Jiri; Jech, Robert

2015-08-01

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

[A research in speech endpoint detection based on boxes-coupling generalization dimension].

PubMed

Wang, Zimei; Yang, Cuirong; Wu, Wei; Fan, Yingle

2008-06-01

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.

[Fluorescent signal detection of chromatographic chip by algorithms of pyramid connection and Gaussian mixture model].

PubMed

Hu, Beibei; Zhang, Xueqing; Chen, Haopeng; Cui, Daxiang

2011-03-01

We proposed a new algorithm for automatic identification of fluorescent signal. Based on the features of chromatographic chips, mathematic morphology in RGB color space was used to filter and enhance the images, pyramid connection was used to segment the areas of fluorescent signal, and then the method of Gaussian Mixture Model was used to detect the fluorescent signal. Finally we calculated the average fluorescent intensity in obtained fluorescent areas. Our results show that the algorithm has a good efficacy to segment the fluorescent areas, can detect the fluorescent signal quickly and accurately, and finally realize the quantitative detection of fluorescent signal in chromatographic chip.

Force sensor

DOEpatents

Grahn, A.R.

1993-05-11

A force sensor and related method for determining force components is described. The force sensor includes a deformable medium having a contact surface against which a force can be applied, a signal generator for generating signals that travel through the deformable medium to the contact surface, a signal receptor for receiving the signal reflected from the contact surface, a generation controller, a reception controller, and a force determination apparatus. The signal generator has one or more signal generation regions for generating the signals. The generation controller selects and activates the signal generation regions. The signal receptor has one or more signal reception regions for receiving signals and for generating detections signals in response thereto. The reception controller selects signal reception regions and detects the detection signals. The force determination apparatus measures signal transit time by timing activation and detection and, optionally, determines force components for selected cross-field intersections. The timer which times by activation and detection can be any means for measuring signal transit time. A cross-field intersection is defined by the overlap of a signal generation region and a signal reception region.

Force sensor

DOEpatents

Grahn, Allen R.

1993-01-01

A force sensor and related method for determining force components. The force sensor includes a deformable medium having a contact surface against which a force can be applied, a signal generator for generating signals that travel through the deformable medium to the contact surface, a signal receptor for receiving the signal reflected from the contact surface, a generation controller, a reception controller, and a force determination apparatus. The signal generator has one or more signal generation regions for generating the signals. The generation controller selects and activates the signal generation regions. The signal receptor has one or more signal reception regions for receiving signals and for generating detections signals in response thereto. The reception controller selects signal reception regions and detects the detection signals. The force determination apparatus measures signal transit time by timing activation and detection and, optionally, determines force components for selected cross-field intersections. The timer which times by activation and detection can be any means for measuring signal transit time. A cross-field intersection is defined by the overlap of a signal generation region and a signal reception region.

Real-time method and apparatus for measuring the temperature of a fluorescing phosphor

DOEpatents

Britton, Jr., Charles L.; Beshears, David L.; Simpson, Marc L.; Cates, Michael R.; Allison, Steve W.

1999-01-01

A method for determining the temperature of a fluorescing phosphor is provided, together with an apparatus for performing the method. The apparatus includes a photodetector for detecting light emitted by a phosphor irradiated with an excitation pulse and for converting the detected light into an electrical signal. The apparatus further includes a differentiator for differentiating the electrical signal and a zero-crossing discrimination circuit that outputs a pulse signal having a pulse width corresponding to the time period between the start of the excitation pulse and the time when the differentiated electrical signal reaches zero. The width of the output pulse signal is proportional to the decay-time constant of the phosphor.

[Detection of Heart Rate of Fetal ECG Based on STFT and BSS].

PubMed

Wang, Xu; Cai, Kun

2016-01-01

Changes in heart rate of fetal is function regulating performance of the circulatory system and the central nervous system, it is significant to detect heart rate of fetus in perinatal fetal. This paper puts forward the fetal heart rate detection method based on short time Fourier transform and blind source separation. First of all, the mixed ECG signal was preprocessed, and then the wavelet transform technique was used to separate the fetal ECG signal with noise from mixed ECG signal, after that, the short-time Fourier transform and the blind separation were carried on it, and then calculated the correlation coefficient of it, Finally, An independent component that it has strongest correlation with the original signal was selected to make FECG peak detection and calculated the fetal instantaneous heart rate. The experimental results show that the method can improve the detection rate of the FECG peak (R), and it has high accuracy in fixing peak(R) location in the case of low signal-noise ratio.

System and method for determination of the reflection wavelength of multiple low-reflectivity bragg gratings in a sensing optical fiber

NASA Technical Reports Server (NTRS)

Moore, Jason P. (Inventor)

2009-01-01

A system and method for determining a reflection wavelength of multiple Bragg gratings in a sensing optical fiber comprise: (1) a source laser; (2) an optical detector configured to detect a reflected signal from the sensing optical fiber; (3) a plurality of frequency generators configured to generate a signal having a frequency corresponding to an interferometer frequency of a different one of the plurality of Bragg gratings; (4) a plurality of demodulation elements, each demodulation element configured to combine the signal produced by a different one of the plurality of frequency generators with the detected signal from the sensing optical fiber; (5) a plurality of peak detectors, each peak detector configured to detect a peak of the combined signal from a different one of the demodulation elements; and (6) a laser wavenumber detection element configured to determine a wavenumber of the laser when any of the peak detectors detects a peak.

How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation.

PubMed

Scholkmann, F; Spichtig, S; Muehlemann, T; Wolf, M

2010-05-01

Near-infrared imaging (NIRI) is a neuroimaging technique which enables us to non-invasively measure hemodynamic changes in the human brain. Since the technique is very sensitive, the movement of a subject can cause movement artifacts (MAs), which affect the signal quality and results to a high degree. No general method is yet available to reduce these MAs effectively. The aim was to develop a new MA reduction method. A method based on moving standard deviation and spline interpolation was developed. It enables the semi-automatic detection and reduction of MAs in the data. It was validated using simulated and real NIRI signals. The results show that a significant reduction of MAs and an increase in signal quality are achieved. The effectiveness and usability of the method is demonstrated by the improved detection of evoked hemodynamic responses. The present method can not only be used in the postprocessing of NIRI signals but also for other kinds of data containing artifacts, for example ECG or EEG signals.

Method and apparatus for remote sensing of molecular species at nanoscale utilizing a reverse photoacoustic effect

DOEpatents

Su, Ming [Oviedo, FL; Thundat, Thomas G [Knoxville, TN; Hedden, David [Lenoir City, TN

2010-02-23

A method and apparatus for identifying a sample, involves illuminating the sample with light of varying wavelengths, transmitting an acoustic signal against the sample from one portion and receiving a resulting acoustic signal on another portion, detecting a change of phase in the acoustic signal corresponding to the light of varying wavelengths, and analyzing the change of phase in the acoustic signal for the varying wavelengths of illumination to identify the sample. The apparatus has a controlled source for illuminating the sample with light of varying wavelengths, a transmitter for transmitting an acoustic wave, a receiver for receiving the acoustic wave and converting the acoustic wave to an electronic signal, and an electronic circuit for detecting a change of phase in the acoustic wave corresponding to respective ones of the varying wavelengths and outputting the change of phase for the varying wavelengths to allow identification of the sample. The method and apparatus can be used to detect chemical composition or visual features. A transmission mode and a reflection mode of operation are disclosed. The method and apparatus can be applied at nanoscale to detect molecules in a biological sample.

Balanced detection for self-mixing interferometry to improve signal-to-noise ratio

NASA Astrophysics Data System (ADS)

Zhao, Changming; Norgia, Michele; Li, Kun

2018-01-01

We apply balanced detection to self-mixing interferometry for displacement and vibration measurement, using two photodiodes for implementing a differential acquisition. The method is based on the phase opposition of the self-mixing signal measured between the two laser diode facet outputs. The balanced signal obtained by enlarging the self-mixing signal, also by canceling of the common-due noises mainly due to disturbances on laser supply and transimpedance amplifier. Experimental results demonstrate the signal-to-noise ratio significantly improves, with almost twice signals enhancement and more than half noise decreasing. This method allows for more robust, longer-distance measurement systems, especially using fringe-counting.

Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

Heart rate detection from an electronic weighing scale.

PubMed

González-Landaeta, R; Casas, O; Pallàs-Areny, R

2007-01-01

We propose a novel technique for heart rate detection on a subject that stands on a common electronic weighing scale. The detection relies on sensing force variations related to the blood acceleration in the aorta, works even if wearing footwear, and does not require any sensors attached to the body. We have applied our method to three different weighing scales, and estimated whether their sensitivity and frequency response suited heart rate detection. Scale sensitivities were from 490 nV/V/N to 1670 nV/V/N, all had an underdamped transient response and their dynamic gain error was below 19% at 10 Hz, which are acceptable values for heart rate estimation. We also designed a pulse detection system based on off-the-shelf integrated circuits, whose gain was about 70x10(3) and able to sense force variations about 240 mN. The signal-to-noise ratio (SNR) of the main peaks of the pulse signal detected was higher than 48 dB, which is large enough to estimate the heart rate by simple signal processing methods. To validate the method, the ECG and the force signal were simultaneously recorded on 12 volunteers. The maximal error obtained from heart rates determined from these two signals was +/-0.6 beats/minute.

Fiber fault location utilizing traffic signal in optical network.

PubMed

Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

2013-10-07

We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.

Finger wear detection for production line battery tester

DOEpatents

Depiante, E.V.

1997-11-18

A method is described for detecting wear in a battery tester probe. The method includes providing a battery tester unit having at least one tester finger, generating a tester signal using the tester fingers and battery tester unit with the signal characteristic of the electrochemical condition of the battery and the tester finger, applying wavelet transformation to the tester signal including computing a mother wavelet to produce finger wear indicator signals, analyzing the signals to create a finger wear index, comparing the wear index for the tester finger with the index for a new tester finger and generating a tester finger signal change signal to indicate achieving a threshold wear change. 9 figs.

Detection of delamination defects in CFRP materials using ultrasonic signal processing.

PubMed

Benammar, Abdessalem; Drai, Redouane; Guessoum, Abderrezak

2008-12-01

In this paper, signal processing techniques are tested for their ability to resolve echoes associated with delaminations in carbon fiber-reinforced polymer multi-layered composite materials (CFRP) detected by ultrasonic methods. These methods include split spectrum processing (SSP) and the expectation-maximization (EM) algorithm. A simulation study on defect detection was performed, and results were validated experimentally on CFRP with and without delamination defects taken from aircraft. Comparison of the methods for their ability to resolve echoes are made.

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

[Application of recombinase polymerase amplification in the detection of Pseudomonas aeruginosa].

PubMed

Jin, X J; Gong, Y L; Yang, L; Mo, B H; Peng, Y Z; He, P; Zhao, J N; Li, X L

2018-04-20

Objective: To establish an optimized method of recombinase polymerase amplification (RPA) to rapidly detect Pseudomonas aeruginosa in clinic. Methods: (1) The DNA templates of one standard Pseudomonas aeruginosa strain was extracted and detected by polymerase chain reaction (PCR), real-time fluorescence quantitative PCR and RPA. Time of sample loading, time of amplification, and time of detection of the three methods were recorded. (2) One standard Pseudomonas aeruginosa strain was diluted in 7 concentrations of 1×10(7,) 1×10(6,) 1×10(5,) 1×10(4,) 1×10(3,) 1×10(2,) and 1×10(1) colony forming unit (CFU)/mL after recovery and cultivation. The DNA templates of Pseudomonas aeruginosa and negative control strain Pseudomonas putida were extracted and detected by PCR, real-time fluorescence quantitative PCR, and RPA separately. The sensitivity of the three methods in detecting Pseudomonas aeruginosa was analyzed. (3) The DNA templates of one standard Pseudomonas aeruginosa strain and four negative control strains ( Staphylococcus aureus, Acinetobacter baumanii, Candida albicans, and Pseudomonas putida ) were extracted separately, and then they were detected by PCR, real-time fluorescence quantitative PCR, and RPA. The specificity of the three methods in detecting Pseudomonas aeruginosa was analyzed. (4) The DNA templates of 28 clinical strains of Pseudomonas aeruginosa preserved in glycerin, 1 clinical strain of which was taken by cotton swab, and negative control strain Pseudomonas putida were extracted separately, and then they were detected by RPA. Positive amplification signals of the clinical strains were observed, and the detection rate was calculated. All experiments were repeated for 3 times. Sensitivity results were analyzed by GraphPad Prism 5.01 statistical software. Results: (1) The loading time of RPA, PCR, and real-time fluorescence quantitative PCR for detecting Pseudomonas aeruginosa were all 20 minutes. In PCR, time of amplification was 98 minutes, time of gel detection was 20 minutes, and the total time was 138 minutes. In real-time fluorescence quantitative PCR, amplification and detection could be completed simultaneously, which took 90 minutes, and the total time was 110 minutes. In RPA, amplification and detection could also be completed simultaneously, which took 15 minutes, and the total time was 35 minutes. (2) Pseudomonas putida did not show positive amplification signals or gel positive results in any of the three detection methods. The detection limit of Pseudomonas aeruginosa in real-time fluorescence quantitative PCR and PCR was 1×10(1) CFU/mL, and that of Pseudomonas aeruginosa in RPA was 1×10(2) CFU/mL. In RPA and real-time fluorescence quantitative PCR, the higher the concentration of Pseudomonas aeruginosa, the shorter threshold time and smaller the number of cycles, namely shorter time for detecting the positive amplified signal. In real-time fluorescence quantitative PCR, all positive amplification signal could be detected when the concentration of Pseudomonas aeruginosa was 1×10(1)-1×10(7) CFU/mL. In RPA, the detection rate of positive amplification signal was 0 when the concentration of Pseudomonas aeruginosa was 1×10(1) CFU/mL, while the detection rate of positive amplification signal was 67% when the concentration of Pseudomonas aeruginosa was 1×10(2) CFU/mL, and the detection rate of positive amplification signal was 100% when the concentration of Pseudomonas aeruginosa was 1×10(3)-1×10(7) CFU/mL. (3) In RPA, PCR, and real-time fluorescence quantitative PCR, Pseudomonas aeruginosa showed positive amplification signals and gel positive results, but there were no positive amplification signals or gel positive results in four negative control strains of Acinetobacter baumannii, Staphylococcus aureus, Candida albicans, and Pseudomonas putida . (4) In RPA, 28 clinical strains of Pseudomonas aeruginosa preserved in glycerin and 1 clinical strain of Pseudomonas aeruginosa taken by cotton swab showed positive amplification signals, while Pseudomonas putida did not show positive amplification signal. The detection rate of positive amplification signal of 29 clinical strains of Pseudomonas aeruginosa in RPA was 100%. Conclusions: The established optimized RPA technology for fast detection of Pseudomonas aeruginosa requires shorter time, with high sensitivity and specificity. It was of great value in fast detection of Pseudomonas aeruginosa infection in clinic.

Apparatus and method for noninvasive particle detection using doppler spectroscopy

DOEpatents

Sinha, Dipen N.

2016-05-31

An apparatus and method for noninvasively detecting the presence of solid particulate matter suspended in a fluid flowing through a pipe or an oil and gas wellbore are described. Fluid flowing through a conduit containing the particulate solids is exposed to a fixed frequency (>1 MHz) of ultrasonic vibrations from a transducer attached to the outside of the pipe. The returning Doppler frequency shifted signal derived from the scattering of sound from the moving solid particles is detected by an adjacent transducer. The transmitted signal and the Doppler signal are combined to provide sensitive particulate detection. The magnitude of the signal and the Doppler frequency shift are used to determine the particle size distribution and the velocity of the particles. Measurement of the phase shift between the applied frequency and the detected Doppler shifted may be used to determine the direction of motion of the particles.

Comparison of two drug safety signals in a pharmacovigilance data mining framework.

PubMed

Tubert-Bitter, Pascale; Bégaud, Bernard; Ahmed, Ismaïl

2016-04-01

Since adverse drug reactions are a major public health concern, early detection of drug safety signals has become a top priority for regulatory agencies and the pharmaceutical industry. Quantitative methods for analyzing spontaneous reporting material recorded in pharmacovigilance databases through data mining have been proposed in the last decades and are increasingly used to flag potential safety problems. While automated data mining is motivated by the usually huge size of pharmacovigilance databases, it does not systematically produce relevant alerts. Moreover, each detected signal requires appropriate assessment that may involve investigation of the whole therapeutic class. The goal of this article is to provide a methodology for comparing two detected signals. It is nested within the automated surveillance framework as (1) no extra information is required and (2) no simple inference on the actual risks can be extrapolated from spontaneous reporting data. We designed our methodology on the basis of two classical methods used for automated signal detection: the Bayesian Gamma Poisson Shrinker and the frequentist Proportional Reporting Ratio. A simulation study was conducted to assess the performances of both proposed methods. The latter were used to compare cardiovascular signals for two HIV treatments from the French pharmacovigilance database. © The Author(s) 2012.

System and method for assaying a radionuclide

DOEpatents

Cadieux, James R; King, III, George S; Fugate, Glenn A

2014-12-23

A system for assaying a radionuclide includes a liquid scintillation detector, an analyzer connected to the liquid scintillation detector, and a delay circuit connected to the analyzer. A gamma detector and a multi-channel analyzer are connected to the delay circuit and the gamma detector. The multi-channel analyzer produces a signal reflective of the radionuclide in the sample. A method for assaying a radionuclide includes selecting a sample, detecting alpha or beta emissions from the sample with a liquid scintillation detector, producing a first signal reflective of the alpha or beta emissions, and delaying the first signal a predetermined time. The method further includes detecting gamma emissions from the sample, producing a second signal reflective of the gamma emissions, and combining the delayed first signal with the second signal to produce a third signal reflective of the radionuclide.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Methods and Apparatus for Detecting Defects in an Object of Interest

NASA Technical Reports Server (NTRS)

Hartman, John K. (Inventor); Pearson, Lee H (Inventor)

2017-01-01

A method for detecting defects in an object of interest comprises applying an ultrasonic signal including a tone burst having a predetermined frequency and number of cycles into an object of interest, receiving a return signal reflected from the object of interest, and processing the return signal to detect defects in at least one inner material. The object may have an outer material and the at least one inner material that have different acoustic impedances. An ultrasonic sensor system includes an ultrasonic sensor configured to generate an ultrasonic signal having a tone burst at a predetermined frequency corresponding to a resonant frequency of an outer material of an object of interest.

Mover Position Detection for PMTLM Based on Linear Hall Sensors through EKF Processing

PubMed Central

Yan, Leyang; Zhang, Hui; Ye, Peiqing

2017-01-01

Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creating errors in mover position detection. To filter out the third-order harmonics, a signal processing method based on the extended Kalman filter (EKF) is presented. The limitation of conventional processing method is first analyzed, and then EKF is adopted to detect the mover position. In the EKF model, the amplitude of the fundamental component and the percentage of the harmonic component are taken as state variables, and they can be estimated based solely on the measured sensor signals. Then, the harmonic component can be calculated and eliminated. The proposed method has the advantages of faster convergence, better stability and higher accuracy. Finally, experimental results validate the effectiveness and superiority of the proposed method. PMID:28383505

Fault detection of gearbox using time-frequency method

NASA Astrophysics Data System (ADS)

Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

2017-04-01

This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

Highly sensitive chemiluminescent point mutation detection by circular strand-displacement amplification reaction.

PubMed

Shi, Chao; Ge, Yujie; Gu, Hongxi; Ma, Cuiping

2011-08-15

Single nucleotide polymorphism (SNP) genotyping is attracting extensive attentions owing to its direct connections with human diseases including cancers. Here, we have developed a highly sensitive chemiluminescence biosensor based on circular strand-displacement amplification and the separation by magnetic beads reducing the background signal for point mutation detection at room temperature. This method took advantage of both the T4 DNA ligase recognizing single-base mismatch with high selectivity and the strand-displacement reaction of polymerase to perform signal amplification. The detection limit of this method was 1.3 × 10(-16)M, which showed better sensitivity than that of most of those reported detection methods of SNP. Additionally, the magnetic beads as carrier of immobility was not only to reduce the background signal, but also may have potential apply in high through-put screening of SNP detection in human genome. Copyright © 2011 Elsevier B.V. All rights reserved.

Commonality of drug-associated adverse events detected by 4 commonly used data mining algorithms.

PubMed

Sakaeda, Toshiyuki; Kadoyama, Kaori; Minami, Keiko; Okuno, Yasushi

2014-01-01

Data mining algorithms have been developed for the quantitative detection of drug-associated adverse events (signals) from a large database on spontaneously reported adverse events. In the present study, the commonality of signals detected by 4 commonly used data mining algorithms was examined. A total of 2,231,029 reports were retrieved from the public release of the US Food and Drug Administration Adverse Event Reporting System database between 2004 and 2009. The deletion of duplicated submissions and revision of arbitrary drug names resulted in a reduction in the number of reports to 1,644,220. Associations with adverse events were analyzed for 16 unrelated drugs, using the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM). All EBGM-based signals were included in the PRR-based signals as well as IC- or ROR-based ones, and PRR- and IC-based signals were included in ROR-based ones. The PRR scores of PRR-based signals were significantly larger for 15 of 16 drugs when adverse events were also detected as signals by the EBGM method, as were the IC scores of IC-based signals for all drugs; however, no such effect was observed in the ROR scores of ROR-based signals. The EBGM method was the most conservative among the 4 methods examined, which suggested its better suitability for pharmacoepidemiological studies. Further examinations should be performed on the reproducibility of clinical observations, especially for EBGM-based signals.

Method of Fault Detection and Rerouting

NASA Technical Reports Server (NTRS)

Gibson, Tracy L. (Inventor); Medelius, Pedro J. (Inventor); Lewis, Mark E. (Inventor)

2013-01-01

A system and method for detecting damage in an electrical wire, including delivering at least one test electrical signal to an outer electrically conductive material in a continuous or non-continuous layer covering an electrically insulative material layer that covers an electrically conductive wire core. Detecting the test electrical signals in the outer conductive material layer to obtain data that is processed to identify damage in the outer electrically conductive material layer.

An R-peak detection method that uses an SVD filter and a search back system.

PubMed

Jung, Woo-Hyuk; Lee, Sang-Goog

2012-12-01

In this paper, we present a method for detecting the R-peak of an ECG signal by using an singular value decomposition (SVD) filter and a search back system. The ECG signal was detected in two phases: the pre-processing phase and the decision phase. The pre-processing phase consisted of the stages for the SVD filter, Butterworth High Pass Filter (HPF), moving average (MA), and squaring, whereas the decision phase consisted of a single stage that detected the R-peak. In the pre-processing phase, the SVD filter removed noise while the Butterworth HPF eliminated baseline wander. The MA removed the remaining noise of the signal that had gone through the SVD filter to make the signal smooth, and squaring played a role in strengthening the signal. In the decision phase, the threshold was used to set the interval before detecting the R-peak. When the latest R-R interval (RRI), suggested by Hamilton et al., was greater than 150% of the previous RRI, the method of detecting the R-peak in such an interval was modified to be 150% or greater than the smallest interval of the two most latest RRIs. When the modified search back system was used, the error rate of the peak detection decreased to 0.29%, compared to 1.34% when the modified search back system was not used. Consequently, the sensitivity was 99.47%, the positive predictivity was 99.47%, and the detection error was 1.05%. Furthermore, the quality of the signal in data with a substantial amount of noise was improved, and thus, the R-peak was detected effectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Methods and systems for low frequency seismic and infrasound detection of geo-pressure transition zones

DOEpatents

Shook, G. Michael; LeRoy, Samuel D.; Benzing, William M.

2006-07-18

Methods for determining the existence and characteristics of a gradational pressurized zone within a subterranean formation are disclosed. One embodiment involves employing an attenuation relationship between a seismic response signal and increasing wavelet wavelength, which relationship may be used to detect a gradational pressurized zone and/or determine characteristics thereof. In another embodiment, a method for analyzing data contained within a response signal for signal characteristics that may change in relation to the distance between an input signal source and the gradational pressurized zone is disclosed. In a further embodiment, the relationship between response signal wavelet frequency and comparative amplitude may be used to estimate an optimal wavelet wavelength or range of wavelengths used for data processing or input signal selection. Systems for seismic exploration and data analysis for practicing the above-mentioned method embodiments are also disclosed.

Selectivity enhancement in photoacoustic gas analysis via phase-sensitive detection at high modulation frequency

NASA Technical Reports Server (NTRS)

Kosterev, Anatoliy (Inventor)

2010-01-01

A method for detecting a target fluid in a fluid sample comprising a first fluid and the target fluid using photoacoustic spectroscopy (PAS), comprises a) providing a light source configured to introduce an optical signal having at least one wavelength into the fluid sample; b) modulating the optical signal at a desired modulation frequency such that the optical signal generates an acoustic signal in the fluid sample; c) measuring the acoustic signal in a resonant acoustic detector; and d) using the phase of the acoustic signal to detect the presence of the target fluid.

A novel approach for automatic visualization and activation detection of evoked potentials induced by epidural spinal cord stimulation in individuals with spinal cord injury.

PubMed

Mesbah, Samineh; Angeli, Claudia A; Keynton, Robert S; El-Baz, Ayman; Harkema, Susan J

2017-01-01

Voluntary movements and the standing of spinal cord injured patients have been facilitated using lumbosacral spinal cord epidural stimulation (scES). Identifying the appropriate stimulation parameters (intensity, frequency and anode/cathode assignment) is an arduous task and requires extensive mapping of the spinal cord using evoked potentials. Effective visualization and detection of muscle evoked potentials induced by scES from the recorded electromyography (EMG) signals is critical to identify the optimal configurations and the effects of specific scES parameters on muscle activation. The purpose of this work was to develop a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effects across a high number of scES parameters. This new method is designed to automate the current process for performing this task, which has been accomplished manually by data analysts through observation of raw EMG signals, a process that is laborious and time-consuming as well as prone to human errors. The proposed method provides a fast and accurate five-step algorithms framework for activation detection and visualization of the results including: conversion of the EMG signal into its 2-D representation by overlaying the located signal building blocks; de-noising the 2-D image by applying the Generalized Gaussian Markov Random Field technique; detection of the occurrence of evoked potentials using a statistically optimal decision method through the comparison of the probability density functions of each segment to the background noise utilizing log-likelihood ratio; feature extraction of detected motor units such as peak-to-peak amplitude, latency, integrated EMG and Min-max time intervals; and finally visualization of the outputs as Colormap images. In comparing the automatic method vs. manual detection on 700 EMG signals from five individuals, the new approach decreased the processing time from several hours to less than 15 seconds for each set of data, and demonstrated an average accuracy of 98.28% based on the combined false positive and false negative error rates. The sensitivity of this method to the signal-to-noise ratio (SNR) was tested using simulated EMG signals and compared to two existing methods, where the novel technique showed much lower sensitivity to the SNR.

A novel approach for automatic visualization and activation detection of evoked potentials induced by epidural spinal cord stimulation in individuals with spinal cord injury

PubMed Central

Mesbah, Samineh; Angeli, Claudia A.; Keynton, Robert S.; Harkema, Susan J.

2017-01-01

Voluntary movements and the standing of spinal cord injured patients have been facilitated using lumbosacral spinal cord epidural stimulation (scES). Identifying the appropriate stimulation parameters (intensity, frequency and anode/cathode assignment) is an arduous task and requires extensive mapping of the spinal cord using evoked potentials. Effective visualization and detection of muscle evoked potentials induced by scES from the recorded electromyography (EMG) signals is critical to identify the optimal configurations and the effects of specific scES parameters on muscle activation. The purpose of this work was to develop a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effects across a high number of scES parameters. This new method is designed to automate the current process for performing this task, which has been accomplished manually by data analysts through observation of raw EMG signals, a process that is laborious and time-consuming as well as prone to human errors. The proposed method provides a fast and accurate five-step algorithms framework for activation detection and visualization of the results including: conversion of the EMG signal into its 2-D representation by overlaying the located signal building blocks; de-noising the 2-D image by applying the Generalized Gaussian Markov Random Field technique; detection of the occurrence of evoked potentials using a statistically optimal decision method through the comparison of the probability density functions of each segment to the background noise utilizing log-likelihood ratio; feature extraction of detected motor units such as peak-to-peak amplitude, latency, integrated EMG and Min-max time intervals; and finally visualization of the outputs as Colormap images. In comparing the automatic method vs. manual detection on 700 EMG signals from five individuals, the new approach decreased the processing time from several hours to less than 15 seconds for each set of data, and demonstrated an average accuracy of 98.28% based on the combined false positive and false negative error rates. The sensitivity of this method to the signal-to-noise ratio (SNR) was tested using simulated EMG signals and compared to two existing methods, where the novel technique showed much lower sensitivity to the SNR. PMID:29020054

Signal Detection and Monitoring Based on Longitudinal Healthcare Data

PubMed Central

Suling, Marc; Pigeot, Iris

2012-01-01

Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudinal data. This paper gives a broad overview of the signal detection process and introduces some types of data sources typically used. The most commonly applied signal detection algorithms are presented, covering simple frequentistic methods like the proportional reporting rate or the reporting odds ratio, more advanced Bayesian techniques for spontaneous and longitudinal data, e.g., the Bayesian Confidence Propagation Neural Network or the Multi-item Gamma-Poisson Shrinker and methods developed for longitudinal data only, like the IC temporal pattern detection. Additionally, the problem of adjustment for underlying confounding is discussed and the most common strategies to automatically identify false-positive signals are addressed. A drug monitoring technique based on Wald’s sequential probability ratio test is presented. For each method, a real-life application is given, and a wide set of literature for further reading is referenced. PMID:24300373

Real-time method and apparatus for measuring the decay-time constant of a fluorescing phosphor

DOEpatents

Britton, Jr., Charles L.; Beshears, David L.; Simpson, Marc L.; Cates, Michael R.; Allison, Steve W.

1999-01-01

A method for determining the decay-time constant of a fluorescing phosphor is provided, together with an apparatus for performing the method. The apparatus includes a photodetector for detecting light emitted by a phosphor irradiated with an excitation pulse and for converting the detected light into an electrical signal. The apparatus further includes a differentiator for differentiating the electrical signal and a zero-crossing discrimination circuit that outputs a pulse signal having a pulse width corresponding to the time period between the start of the excitation pulse and the time when the differentiated electrical signal reaches zero. The width of the output pulse signal is proportional to the decay-time constant of the phosphor.

Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection

NASA Astrophysics Data System (ADS)

López, Cristian; Zhong, Wei; Lu, Siliang; Cong, Feiyun; Cortese, Ignacio

2017-12-01

Vibration signals are widely used for bearing fault detection and diagnosis. When signals are acquired in the field, usually, the faulty periodic signal is weak and is concealed by noise. Various de-noising methods have been developed to extract the target signal from the raw signal. Stochastic resonance (SR) is a technique that changed the traditional denoising process, in which the weak periodic fault signal can be identified by adding an expression, the potential, to the raw signal and solving a differential equation problem. However, current SR methods have some deficiencies such us limited filtering performance, low frequency input signal and sequential search for optimum parameters. Consequently, in this study, we explore the application of SR based on the FitzHug-Nagumo (FHN) potential in rolling bearing vibration signals. Besides, we improve the search of the SR optimum parameters by the use of particle swarm optimization (PSO). The effectiveness of the proposed method is verified by using both simulated and real bearing data sets.

Comparison of optomagnetic and AC susceptibility readouts in a magnetic nanoparticle agglutination assay for detection of C-reactive protein.

PubMed

Fock, Jeppe; Parmvi, Mattias; Strömberg, Mattias; Svedlindh, Peter; Donolato, Marco; Hansen, Mikkel Fougt

2017-02-15

There is an increasing need to develop biosensor methods that are highly sensitive and that can be combined with low-cost consumables. The use of magnetic nanoparticles (MNPs) is attractive because their detection is compatible with low-cost disposables and because application of a magnetic field can be used to accelerate assay kinetics. We present the first study and comparison of the performance of magnetic susceptibility measurements and a newly proposed optomagnetic method. For the comparison we use the C-reactive protein (CRP) induced agglutination of identical samples of 100nm MNPs conjugated with CRP antibodies. Both methods detect agglutination as a shift to lower frequencies in measurements of the dynamics in response to an applied oscillating magnetic field. The magnetic susceptibility method probes the magnetic response whereas the optomagnetic technique probes the modulation of laser light transmitted through the sample. The two techniques provided highly correlated results upon agglutination when they measure the decrease of the signal from the individual MNPs (turn-off detection strategy), whereas the techniques provided different results, strongly depending on the read-out frequency, when detecting the signal due to MNP agglomerates (turn-on detection strategy). These observations are considered to be caused by differences in the volume-dependence of the magnetic and optical signals from agglomerates. The highest signal from agglomerates was found in the optomagnetic signal at low frequencies. Copyright © 2016 Elsevier B.V. All rights reserved.

Label-free detection of circulating melanoma cells by in vivo photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Wang, Xiaoling; Yang, Ping; Liu, Rongrong; Niu, Zhenyu; Suo, Yuanzhen; He, Hao; Gao, Wenyuan; Tang, Shuo; Wei, Xunbin

2016-03-01

Melanoma is a malignant tumor of melanocytes. Melanoma cells have high light absorption due to melanin highly contained in melanoma cells. This property is employed for the detection of circulating melanoma cell by in vivo photoacoustic flow cytometry (PAFC), which is based on photoacoustic effect. Compared to in vivo flow cytometry based on fluorescence, PAFC can employ high melanin content of melanoma cells as endogenous biomarkers to detect circulating melanoma cells in vivo. We have developed in vitro experiments to prove the ability of PAFC system of detecting photoacoustic signals from melanoma cells. For in vivo experiments, we have constructed a model of melanoma tumor bearing mice by inoculating highly metastatic murine melanoma cancer cells, B16F10 with subcutaneous injection. PA signals are detected in the blood vessels of mouse ears in vivo. The raw signal detected from target cells often contains some noise caused by electronic devices, such as background noise and thermal noise. We choose the Wavelet denoising method to effectively distinguish the target signal from background noise. Processing in time domain and frequency domain would be combined to analyze the signal after denoising. This algorithm contains time domain filter and frequency transformation. The frequency spectrum image of the signal contains distinctive features that can be used to analyze the property of target cells or particles. The processing methods have a great potential for analyzing signals accurately and rapidly. By counting circulating melanoma cells termly, we obtain the number variation of circulating melanoma cells as melanoma metastasized. Those results show that PAFC is a noninvasive and label-free method to detect melanoma metastases in blood or lymph circulation.

New method for enhanced efficiency in detection of gravitational waves from supernovae using coherent network of detectors

NASA Astrophysics Data System (ADS)

Mukherjee, S.; Salazar, L.; Mittelstaedt, J.; Valdez, O.

2017-11-01

Supernovae in our universe are potential sources of gravitational waves (GW) that could be detected in a network of GW detectors like LIGO and Virgo. Core-collapse supernovae are rare, but the associated gravitational radiation is likely to carry profuse information about the underlying processes driving the supernovae. Calculations based on analytic models predict GW energies within the detection range of the Advanced LIGO detectors, out to tens of Mpc for certain types of signals e.g. coalescing binary neutron stars. For supernovae however, the corresponding distances are much less. Thus, methods that can improve the sensitivity of searches for GW signals from supernovae are desirable, especially in the advanced detector era. Several methods have been proposed based on various likelihood-based regulators that work on data from a network of detectors to detect burst-like signals (as is the case for signals from supernovae) from potential GW sources. To address this problem, we have developed an analysis pipeline based on a method of noise reduction known as the harmonic regeneration noise reduction (HRNR) algorithm. To demonstrate the method, sixteen supernova waveforms from the Murphy et al. 2009 catalog have been used in presence of LIGO science data. A comparative analysis is presented to show detection statistics for a standard network analysis as commonly used in GW pipelines and the same by implementing the new method in conjunction with the network. The result shows significant improvement in detection statistics.

Detection method based on Kalman filter for high speed rail defect AE signal on wheel-rail rolling rig

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Shen, Yi; Wang, Yan; Zhang, Xin

2018-01-01

Nondestructive test (NDT) of rails has been carried out intermittently in traditional approaches, which highly restricts the detection efficiency under rapid development of high speed railway nowadays. It is necessary to put forward a dynamic rail defect detection method for rail health monitoring. Acoustic emission (AE) as a practical real-time detection technology takes advantage of dynamic AE signal emitted from plastic deformation of material. Detection capacities of AE on rail defects have been verified due to its sensitivity and dynamic merits. Whereas the application under normal train service circumstance has been impeded by synchronous background noises, which are directly linked to the wheel speed. In this paper, surveys on a wheel-rail rolling rig are performed to investigate defect AE signals with varying speed. A dynamic denoising method based on Kalman filter is proposed and its detection effectiveness and flexibility are demonstrated by theory and computational results. Moreover, after comparative analysis of modelling precision at different speeds, it is predicted that the method is also applicable for high speed condition beyond experiments.

Wavelet Representation of the Corneal Pulse for Detecting Ocular Dicrotism

PubMed Central

Melcer, Tomasz; Danielewska, Monika E.; Iskander, D. Robert

2015-01-01

Purpose To develop a reliable and powerful method for detecting the ocular dicrotism from non-invasively acquired signals of corneal pulse without the knowledge of the underlying cardiopulmonary information present in signals of ocular blood pulse and the electrical heart activity. Methods Retrospective data from a study on glaucomatous and age-related changes in corneal pulsation [PLOS ONE 9(7),(2014):e102814] involving 261 subjects was used. Continuous wavelet representation of the signal derivative of the corneal pulse was considered with a complex Gaussian derivative function chosen as mother wavelet. Gray-level Co-occurrence Matrix has been applied to the image (heat-maps) of CWT to yield a set of parameters that can be used to devise the ocular dicrotic pulse detection schemes based on the Conditional Inference Tree and the Random Forest models. The detection scheme was first tested on synthetic signals resembling those of a dicrotic and a non-dicrotic ocular pulse before being used on all 261 real recordings. Results A detection scheme based on a single feature of the Continuous Wavelet Transform of the corneal pulse signal resulted in a low detection rate. Conglomeration of a set of features based on measures of texture (homogeneity, correlation, energy, and contrast) resulted in a high detection rate reaching 93%. Conclusion It is possible to reliably detect a dicrotic ocular pulse from the signals of corneal pulsation without the need of acquiring additional signals related to heart activity, which was the previous state-of-the-art. The proposed scheme can be applied to other non-stationary biomedical signals related to ocular dynamics. PMID:25906236

Cluster signal-to-noise analysis for evaluation of the information content in an image.

PubMed

Weerawanich, Warangkana; Shimizu, Mayumi; Takeshita, Yohei; Okamura, Kazutoshi; Yoshida, Shoko; Yoshiura, Kazunori

2018-01-01

(1) To develop an observer-free method of analysing image quality related to the observer performance in the detection task and (2) to analyse observer behaviour patterns in the detection of small mass changes in cone-beam CT images. 13 observers detected holes in a Teflon phantom in cone-beam CT images. Using the same images, we developed a new method, cluster signal-to-noise analysis, to detect the holes by applying various cut-off values using ImageJ and reconstructing cluster signal-to-noise curves. We then evaluated the correlation between cluster signal-to-noise analysis and the observer performance test. We measured the background noise in each image to evaluate the relationship with false positive rates (FPRs) of the observers. Correlations between mean FPRs and intra- and interobserver variations were also evaluated. Moreover, we calculated true positive rates (TPRs) and accuracies from background noise and evaluated their correlations with TPRs from observers. Cluster signal-to-noise curves were derived in cluster signal-to-noise analysis. They yield the detection of signals (true holes) related to noise (false holes). This method correlated highly with the observer performance test (R 2 = 0.9296). In noisy images, increasing background noise resulted in higher FPRs and larger intra- and interobserver variations. TPRs and accuracies calculated from background noise had high correlation with actual TPRs from observers; R 2 was 0.9244 and 0.9338, respectively. Cluster signal-to-noise analysis can simulate the detection performance of observers and thus replace the observer performance test in the evaluation of image quality. Erroneous decision-making increased with increasing background noise.

[Analysis and experimental verification of sensitivity and SNR of laser warning receiver].

PubMed

Zhang, Ji-Long; Wang, Ming; Tian, Er-Ming; Li, Xiao; Wang, Zhi-Bin; Zhang, Yue

2009-01-01

In order to countermeasure increasingly serious threat from hostile laser in modern war, it is urgent to do research on laser warning technology and system, and the sensitivity and signal to noise ratio (SNR) are two important performance parameters in laser warning system. In the present paper, based on the signal statistical detection theory, a method for calculation of the sensitivity and SNR in coherent detection laser warning receiver (LWR) has been proposed. Firstly, the probabilities of the laser signal and receiver noise were analyzed. Secondly, based on the threshold detection theory and Neyman-Pearson criteria, the signal current equation was established by introducing detection probability factor and false alarm rate factor, then, the mathematical expressions of sensitivity and SNR were deduced. Finally, by using method, the sensitivity and SNR of the sinusoidal grating laser warning receiver developed by our group were analyzed, and the theoretic calculation and experimental results indicate that the SNR analysis method is feasible, and can be used in performance analysis of LWR.

Remote detection of electronic devices

DOEpatents

Judd, Stephen L [Los Alamos, NM; Fortgang, Clifford M [Los Alamos, NM; Guenther, David C [Los Alamos, NM

2012-09-25

An apparatus and method for detecting solid-state electronic devices are described. Non-linear junction detection techniques are combined with spread-spectrum encoding and cross correlation to increase the range and sensitivity of the non-linear junction detection and to permit the determination of the distances of the detected electronics. Nonlinear elements are detected by transmitting a signal at a chosen frequency and detecting higher harmonic signals that are returned from responding devices.

The stratospheric QBO signal in the NCEP reanalysis, 1958-2001

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Gallego, David; Peña-Ortiz, Cristina; Gimeno, Luis; Garcia-Herrera, Ricardo; Hernandez, Emiliano; Calvo, Natalia

2003-07-01

The spatiotemporal evolution of the zonal wind in the stratosphere is analyzed based on the use of the NCEP reanalysis (1958-2001). MultiTaper Method-Singular Value Decomposition (MTM-SVD), a frequency-domain analysis method, is applied to isolate significant spatially-coherent variability with narrowband oscillatory character. A quasibiennial oscillation is detected as the most intense coherent signal in the stratosphere, the signal being less intense in the lower levels. There is a clear downward propagation of the signal with time at low latitudes, not evident at mid and high latitudes. There are differences in the behavior of the signal over both hemispheres, being much weaker over the SH. In the NH an anomaly in the zonal wind field, in phase with the equatorial signal, is detected at approximately 60°N. Two different areas at subtropical latitudes are detected to be characterized by wind anomalies opposed to that of the equator.

Applying cognitive acuity theory to the development and scoring of situational judgment tests.

PubMed

Leeds, J Peter

2017-11-09

The theory of cognitive acuity (TCA) treats the response options within items as signals to be detected and uses psychophysical methods to estimate the respondents' sensitivity to these signals. Such a framework offers new methods to construct and score situational judgment tests (SJT). Leeds (2012) defined cognitive acuity as the capacity to discern correctness and distinguish between correctness differences among simultaneously presented situation-specific response options. In this study, SJT response options were paired in order to offer the respondent a two-option choice. The contrast in correctness valence between the two options determined the magnitude of signal emission, with larger signals portending a higher probability of detection. A logarithmic relation was found between correctness valence contrast (signal stimulus) and its detectability (sensation response). Respondent sensitivity to such signals was measured and found to be related to the criterion variables. The linkage between psychophysics and elemental psychometrics may offer new directions for measurement theory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blaut, Arkadiusz; Babak, Stanislav; Krolak, Andrzej

We present data analysis methods used in the detection and estimation of parameters of gravitational-wave signals from the white dwarf binaries in the mock LISA data challenge. Our main focus is on the analysis of challenge 3.1, where the gravitational-wave signals from more than 6x10{sup 7} Galactic binaries were added to the simulated Gaussian instrumental noise. The majority of the signals at low frequencies are not resolved individually. The confusion between the signals is strongly reduced at frequencies above 5 mHz. Our basic data analysis procedure is the maximum likelihood detection method. We filter the data through the template bankmore » at the first step of the search, then we refine parameters using the Nelder-Mead algorithm, we remove the strongest signal found and we repeat the procedure. We detect reliably and estimate parameters accurately of more than ten thousand signals from white dwarf binaries.« less

Accurate derivation of heart rate variability signal for detection of sleep disordered breathing in children.

PubMed

Chatlapalli, S; Nazeran, H; Melarkod, V; Krishnam, R; Estrada, E; Pamula, Y; Cabrera, S

2004-01-01

The electrocardiogram (ECG) signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Accurate determination of the QRS complex, in particular, reliable detection of the R wave peak, is essential in computer based ECG analysis. ECG data from Physionet's Sleep-Apnea database were used to develop, test, and validate a robust heart rate variability (HRV) signal derivation algorithm. The HRV signal was derived from pre-processed ECG signals by developing an enhanced Hilbert transform (EHT) algorithm with built-in missing beat detection capability for reliable QRS detection. The performance of the EHT algorithm was then compared against that of a popular Hilbert transform-based (HT) QRS detection algorithm. Autoregressive (AR) modeling of the HRV power spectrum for both EHT- and HT-derived HRV signals was achieved and different parameters from their power spectra as well as approximate entropy were derived for comparison. Poincare plots were then used as a visualization tool to highlight the detection of the missing beats in the EHT method After validation of the EHT algorithm on ECG data from the Physionet, the algorithm was further tested and validated on a dataset obtained from children undergoing polysomnography for detection of sleep disordered breathing (SDB). Sensitive measures of accurate HRV signals were then derived to be used in detecting and diagnosing sleep disordered breathing in children. All signal processing algorithms were implemented in MATLAB. We present a description of the EHT algorithm and analyze pilot data for eight children undergoing nocturnal polysomnography. The pilot data demonstrated that the EHT method provides an accurate way of deriving the HRV signal and plays an important role in extraction of reliable measures to distinguish between periods of normal and sleep disordered breathing (SDB) in children.

Dual-process theory and signal-detection theory of recognition memory.

PubMed

Wixted, John T

2007-01-01

Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know procedure, and both methods are now widely used in the neuroscience literature to identify the brain correlates of recollection and familiarity. However, in recent years, a substantial literature has accumulated directly contrasting the signal-detection model against the threshold/detection model, and that literature is almost unanimous in its endorsement of signal-detection theory. A dual-process version of signal-detection theory implies that individual recognition decisions are not process pure, and it suggests new ways to investigate the brain correlates of recognition memory. ((c) 2007 APA, all rights reserved).

Electronic system for floor surface type detection in robotics applications

NASA Astrophysics Data System (ADS)

Tarapata, Grzegorz; Paczesny, Daniel; Tarasiuk, Łukasz

2016-11-01

The paper reports a recognizing method base on ultrasonic transducers utilized for the surface types detection. Ultra-sonic signal is transmitted toward the examined substrate, then reflected and scattered signal goes back to another ultra-sonic receiver. Thee measuring signal is generated by a piezo-electric transducer located at specified distance from the tested substrate. The detector is a second piezo-electric transducer located next to the transmitter. Depending on thee type of substrate which is exposed by an ultrasonic wave, the signal is partially absorbed inn the material, diffused and reflected towards the receiver. To measure the level of received signal, the dedicated electronic circuit was design and implemented in the presented systems. Such system was designed too recognize two types of floor surface: solid (like concrete, ceramic stiles, wood) and soft (carpets, floor coverings). The method will be applied in electronic detection system dedicated to autonomous cleaning robots due to selection of appropriate cleaning method. This work presents the concept of ultrasonic signals utilization, the design of both the measurement system and the measuring stand and as well number of wide tests results which validates correctness of applied ultrasonic method.

Enhanced speed in fluorescence imaging using beat frequency multiplexing

NASA Astrophysics Data System (ADS)

Mikami, Hideharu; Kobayashi, Hirofumi; Wang, Yisen; Hamad, Syed; Ozeki, Yasuyuki; Goda, Keisuke

2016-03-01

Fluorescence imaging using radiofrequency-tagged emission (FIRE) is an emerging technique that enables higher imaging speed (namely, temporal resolution) in fluorescence microscopy compared to conventional fluorescence imaging techniques such as confocal microscopy and wide-field microscopy. It works based on the principle that it uses multiple intensity-modulated fields in an interferometric setup as excitation fields and applies frequency-division multiplexing to fluorescence signals. Unfortunately, despite its high potential, FIRE has limited imaging speed due to two practical limitations: signal bandwidth and signal detection efficiency. The signal bandwidth is limited by that of an acousto-optic deflector (AOD) employed in the setup, which is typically 100-200 MHz for the spectral range of fluorescence excitation (400-600 nm). The signal detection efficiency is limited by poor spatial mode-matching between two interfering fields to produce a modulated excitation field. Here we present a method to overcome these limitations and thus to achieve higher imaging speed than the prior version of FIRE. Our method achieves an increase in signal bandwidth by a factor of two and nearly optimal mode matching, which enables the imaging speed limited by the lifetime of the target fluorophore rather than the imaging system itself. The higher bandwidth and better signal detection efficiency work synergistically because higher bandwidth requires higher signal levels to avoid the contribution of shot noise and amplifier noise to the fluorescence signal. Due to its unprecedentedly high-speed performance, our method has a wide variety of applications in cancer detection, drug discovery, and regenerative medicine.

Studying Overt Word Reading and Speech Production with Event-Related fMRI: A Method for Detecting, Assessing, and Correcting Articulation-Induced Signal Changes and for Measuring Onset Time and Duration of Articulation

ERIC Educational Resources Information Center

Huang, Jie; Francis, Andrea P.; Carr, Thomas H.

2008-01-01

A quantitative method is introduced for detecting and correcting artifactual signal changes in BOLD time series data arising from the magnetic field warping caused by motion of the articulatory apparatus when speaking aloud, with extensions to detection of subvocal articulatory activity during silent reading. Whole-head images allow the large,…

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements.

PubMed

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K; Cai, Chang; Nagarajan, Srikantan S

2018-06-01

Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K.; Cai, Chang; Nagarajan, Srikantan S.

2018-06-01

Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Systems for low frequency seismic and infrasound detection of geo-pressure transition zones

DOEpatents

Shook, G. Michael; LeRoy, Samuel D.; Benzing, William M.

2007-10-16

Methods for determining the existence and characteristics of a gradational pressurized zone within a subterranean formation are disclosed. One embodiment involves employing an attenuation relationship between a seismic response signal and increasing wavelet wavelength, which relationship may be used to detect a gradational pressurized zone and/or determine characteristics thereof. In another embodiment, a method for analyzing data contained within a response signal for signal characteristics that may change in relation to the distance between an input signal source and the gradational pressurized zone is disclosed. In a further embodiment, the relationship between response signal wavelet frequency and comparative amplitude may be used to estimate an optimal wavelet wavelength or range of wavelengths used for data processing or input signal selection. Systems for seismic exploration and data analysis for practicing the above-mentioned method embodiments are also disclosed.

DeepSig: deep learning improves signal peptide detection in proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2018-05-15

The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. pierluigi.martelli@unibo.it. Supplementary data are available at Bioinformatics online.

Method for detecting gas turbine engine flashback

DOEpatents

Singh, Kapil Kumar; Varatharajan, Balachandar; Kraemer, Gilbert Otto; Yilmaz, Ertan; Lacy, Benjamin Paul

2012-09-04

A method for monitoring and controlling a gas turbine, comprises predicting frequencies of combustion dynamics in a combustor using operating conditions of a gas turbine, receiving a signal from a sensor that is indicative of combustion dynamics in the combustor, and detecting a flashback if a frequency of the received signal does not correspond to the predicted frequencies.

P wave detection in ECG signals using an extended Kalman filter: an evaluation in different arrhythmia contexts.

PubMed

Rahimpour, M; Mohammadzadeh Asl, B

2016-07-01

Monitoring atrial activity via P waves, is an important feature of the arrhythmia detection procedure. The aim of this paper is to present an algorithm for P wave detection in normal and some abnormal records by improving existing methods in the field of signal processing. In contrast to the classical approaches, which are completely blind to signal dynamics, our proposed method uses the extended Kalman filter, EKF25, to estimate the state variables of the equations modeling the dynamic of an ECG signal. This method is a modified version of the nonlinear dynamical model previously introduced for a generation of synthetic ECG signals and fiducial point extraction in normal ones. It is capable of estimating the separate types of activity of the heart with reasonable accuracy and performs well in the presence of morphological variations in the waveforms and ectopic beats. The MIT-BIH Arrhythmia and QT databases have been used to evaluate the performance of the proposed method. The results show that this method has Se  =  98.38% and Pr  =  96.74% in the overall records (considering normal and abnormal rhythms).

Measurement and classification of heart and lung sounds by using LabView for educational use.

PubMed

Altrabsheh, B

2010-01-01

This study presents the design, development and implementation of a simple low-cost method of phonocardiography signal detection. Human heart and lung signals are detected by using a simple microphone through a personal computer; the signals are recorded and analysed using LabView software. Amplitude and frequency analyses are carried out for various phonocardiography pathological cases. Methods for automatic classification of normal and abnormal heart sounds, murmurs and lung sounds are presented. Various cases of heart and lung sound measurement are recorded and analysed. The measurements can be saved for further analysis. The method in this study can be used by doctors as a detection tool aid and may be useful for teaching purposes at medical and nursing schools.

Bilinear Time-frequency Analysis for Lamb Wave Signal Detected by Electromagnetic Acoustic Transducer

NASA Astrophysics Data System (ADS)

Sun, Wenxiu; Liu, Guoqiang; Xia, Hui; Xia, Zhengwu

2018-03-01

Accurate acquisition of the detection signal travel time plays a very important role in cross-hole tomography. The experimental platform of aluminum plate under the perpendicular magnetic field is established and the bilinear time-frequency analysis methods, Wigner-Ville Distribution (WVD) and the pseudo-Wigner-Ville distribution (PWVD), are applied to analyse the Lamb wave signals detected by electromagnetic acoustic transducer (EMAT). By extracting the same frequency component of the time-frequency spectrum as the excitation frequency, the travel time information can be obtained. In comparison with traditional linear time-frequency analysis method such as short-time Fourier transform (STFT), the bilinear time-frequency analysis method PWVD is more appropriate in extracting travel time and recognizing patterns of Lamb wave.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Systems and methods of monitoring acoustic pressure to detect a flame condition in a gas turbine

DOEpatents

Ziminsky, Willy Steve [Simpsonville, SC; Krull, Anthony Wayne [Anderson, SC; Healy, Timothy Andrew , Yilmaz, Ertan

2011-05-17

A method may detect a flashback condition in a fuel nozzle of a combustor. The method may include obtaining a current acoustic pressure signal from the combustor, analyzing the current acoustic pressure signal to determine current operating frequency information for the combustor, and indicating that the flashback condition exists based at least in part on the current operating frequency information.

Noise suppression for the differential detection in nuclear magnetic resonance gyroscope

NASA Astrophysics Data System (ADS)

Yang, Dan; Zhou, Binquan; Chen, LinLin; Jia, YuChen; Lu, QiLin

2017-10-01

The nuclear magnetic resonance gyroscope is based on spin-exchange optical pumping of noble gases to detect and measure the angular velocity of the carrier, but it would be challenging to measure the precession signal of noble gas nuclei directly. To solve the problem, the primary detection method utilizes alkali atoms, the precession of nuclear magnetization modulates the alkali atoms at the Larmor frequency of nuclei, relatively speaking, and it is easier to detect the precession signal of alkali atoms. The precession frequency of alkali atoms is detected by the rotation angle of linearly polarized probe light; and differential detection method is commonly used in NMRG in order to detect the linearly polarized light rotation angle. Thus, the detection accuracy of differential detection system will affect the sensitivity of the NMRG. For the purpose of further improvement of the sensitivity level of the NMRG, this paper focuses on the aspects of signal detection, and aims to do an error analysis as well as an experimental research of the linearly light rotation angle detection. Through the theoretical analysis and the experimental illustration, we found that the extinction ratio σ2 and DC bias are the factors that will produce detective noise in the differential detection method.

Synchrosqueezing an effective method for analyzing Doppler radar physiological signals.

PubMed

Yavari, Ehsan; Rahman, Ashikur; Jia Xu; Mandic, Danilo P; Boric-Lubecke, Olga

2016-08-01

Doppler radar can monitor vital sign wirelessly. Respiratory and heart rate have time-varying behavior. Capturing the rate variability provides crucial physiological information. However, the common time-frequency methods fail to detect key information. We investigate Synchrosqueezing method to extract oscillatory components of the signal with time varying spectrum. Simulation and experimental result shows the potential of the proposed method for analyzing signals with complex time-frequency behavior like physiological signals. Respiration and heart signals and their components are extracted with higher resolution and without any pre-filtering and signal conditioning.

Reliably detectable flaw size for NDE methods that use calibration

NASA Astrophysics Data System (ADS)

Koshti, Ajay M.

2017-04-01

Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-1823 and associated mh18232 POD software gives most common methods of POD analysis. In this paper, POD analysis is applied to an NDE method, such as eddy current testing, where calibration is used. NDE calibration standards have known size artificial flaws such as electro-discharge machined (EDM) notches and flat bottom hole (FBH) reflectors which are used to set instrument sensitivity for detection of real flaws. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. Therefore, it is important to correlate signal responses from real flaws with signal responses form artificial flaws used in calibration process to determine reliably detectable flaw size.

Reliably Detectable Flaw Size for NDE Methods that Use Calibration

NASA Technical Reports Server (NTRS)

Koshti, Ajay M.

2017-01-01

Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-1823 and associated mh1823 POD software gives most common methods of POD analysis. In this paper, POD analysis is applied to an NDE method, such as eddy current testing, where calibration is used. NDE calibration standards have known size artificial flaws such as electro-discharge machined (EDM) notches and flat bottom hole (FBH) reflectors which are used to set instrument sensitivity for detection of real flaws. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. Therefore, it is important to correlate signal responses from real flaws with signal responses form artificial flaws used in calibration process to determine reliably detectable flaw size.

Signal-on fluorescence biosensor for microRNA-21 detection based on DNA strand displacement reaction and Mg2+-dependent DNAzyme cleavage.

PubMed

Yin, Huan-Shun; Li, Bing-Chen; Zhou, Yun-Lei; Wang, Hai-Yan; Wang, Ming-Hui; Ai, Shi-Yun

2017-10-15

MicroRNAs have been involved into many biological processes and are regarded as disease biomarkers. Simple, rapid, sensitive and selective method for microRNA detection is crucial for early diagnosis and therapy of diseases. In this work, sensitive fluorescence assay was developed for microRNA-21 detection based on DNA polymerase induced strand displacement amplification reaction, Mg 2+ -dependent DNAzyme catalysis reaction, and magnetic separation. In the presence of target microRNA-21, amounts of trigger DNA could be produced with DNA polymerase induced strand displacement amplification reaction, and the trigger DNA could be further hybridized with signal DNA, which was labeled with biotin and AMCA dye. After introduction of Mg 2+ , trigger DNA could form DNAzyme to cleave signal DNA. After magnetic separation, the DNA fragment with AMCA dye could give fluorescence signal, which was related to microRNA-21 concentration. Based on the two efficient signal amplifications, the developed method showed high detection sensitivity with low detection limit of 0.27fM (3σ). In addition, this fluorescence strategy also possessed excellent detection specificity, and could be applied to analyze microRNA-21 expression level in serum of cancer patient. According to the obtained results, the developed fluorescence method might be a promising detection platform for microRNA-21 quantitative analysis in biomedical research and clinical diagnosis. Copyright © 2017 Elsevier B.V. All rights reserved.

Overview of MPLNET Version 3 Cloud Detection

NASA Technical Reports Server (NTRS)

Lewis, Jasper R.; Campbell, James; Welton, Ellsworth J.; Stewart, Sebastian A.; Haftings, Phillip

2016-01-01

The National Aeronautics and Space Administration Micro Pulse Lidar Network, version 3, cloud detection algorithm is described and differences relative to the previous version are highlighted. Clouds are identified from normalized level 1 signal profiles using two complementary methods. The first method considers vertical signal derivatives for detecting low-level clouds. The second method, which detects high-level clouds like cirrus, is based on signal uncertainties necessitated by the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multitemporal averaging scheme is used to improve cloud detection under conditions of a weak signal-to-noise ratio. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, Maryland) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5 km, above MSL), which increase in occurrence by over 5%. There is also an increase in the detection of multilayered cloud profiles from 9% to 19%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which the cirrus cloud optical depth could be reliably estimated occurs between 0.5 and 0.8. A comparison using collocated CALIPSO measurements at the Goddard Space Flight Center and Singapore Micro Pulse Lidar Network (MPLNET) sites indicates improvements in cloud occurrence frequencies and layer heights.

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

NASA Technical Reports Server (NTRS)

Thompson, David R.; Majid, Walid A.; Reed, Colorado J.; Wagstaff, Kiri L.

2011-01-01

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

The technology on noise reduction of the APD detection circuit

NASA Astrophysics Data System (ADS)

Wu, Xue-ying; Zheng, Yong-chao; Cui, Jian-yong

2013-09-01

The laser pulse detection is widely used in the field of laser range finders, laser communications, laser radar, laser Identification Friend or Foe, et al, for the laser pulse detection has the advantage of high accuracy, high sensitivity and strong anti-interference. The avalanche photodiodes (APD) has the advantage of high quantum efficiency, high response speed and huge gain. The APD is particularly suitable for weak signal detection. The technology that APD acts as the photodetector for weak signal reception and amplification is widely used in laser pulse detection. The APD will convert the laser signal to weak electrical signal. The weak signal is amplified, processed and exported by the circuit. In the circuit design, the optimal signal detection is one key point in photoelectric detection system. The issue discusses how to reduce the noise of the photoelectric signal detection circuit and how to improve the signal-to-noise ratio, related analysis and practice included. The essay analyzes the mathematical model of the signal-to-noise ratio for photoelectric conversion and the noise of the APD photoelectric detection system. By analysis the bandwidth of the detection system is determined, and the circuit devices are selected that match the APD. In the circuit design separated devices with low noise are combined with integrated operational amplifier for the purpose of noise reduction. The methods can effectively suppress the noise, and improve the detection sensitivity.

NMR and MRI apparatus and method

DOEpatents

Clarke, John; Kelso, Nathan; Lee, SeungKyun; Moessle, Michael; Myers, Whittier; McDermott, Robert; ten Haken, Bernard; Pines, Alexander; Trabesinger, Andreas

2007-03-06

Nuclear magnetic resonance (NMR) signals are detected in microtesla fields. Prepolarization in millitesla fields is followed by detection with an untuned dc superconducting quantum interference device (SQUID) magnetometer. Because the sensitivity of the SQUID is frequency independent, both signal-to-noise ratio (SNR) and spectral resolution are enhanced by detecting the NMR signal in extremely low magnetic fields, where the NMR lines become very narrow even for grossly inhomogeneous measurement fields. Additional signal to noise benefits are obtained by use of a low noise polarization coil, comprising litz wire or superconducting materials. MRI in ultralow magnetic field is based on the NMR at ultralow fields. Gradient magnetic fields are applied, and images are constructed from the detected NMR signals.

Design of nuclease-based target recycling signal amplification in aptasensors.

PubMed

Yan, Mengmeng; Bai, Wenhui; Zhu, Chao; Huang, Yafei; Yan, Jiao; Chen, Ailiang

2016-03-15

Compared with conventional antibody-based immunoassay methods, aptasensors based on nucleic acid aptamer have made at least two significant breakthroughs. One is that aptamers are more easily used for developing various simple and rapid homogeneous detection methods by "sample in signal out" without multi-step washing. The other is that aptamers are more easily employed for developing highly sensitive detection methods by using various nucleic acid-based signal amplification approaches. As many substances playing regulatory roles in physiology or pathology exist at an extremely low concentration and many chemical contaminants occur in trace amounts in food or environment, aptasensors for signal amplification contribute greatly to detection of such targets. Among the signal amplification approaches in highly sensitive aptasensors, the nuclease-based target recycling signal amplification has recently become a research focus because it shows easy design, simple operation, and rapid reaction and can be easily developed for homogenous assay. In this review, we summarized recent advances in the development of various nuclease-based target recycling signal amplification with the aim to provide a general guide for the design of aptamer-based ultrasensitive biosensing assays. Copyright © 2015 Elsevier B.V. All rights reserved.

Detecting special nuclear materials in containers using high-energy gamma rays emitted by fission products

DOEpatents

Norman, Eric B.; Prussin, Stanley G.

2007-10-02

A method and a system for detecting the presence of special nuclear materials in a container. The system and its method include irradiating the container with an energetic beam, so as to induce a fission in the special nuclear materials, detecting the gamma rays that are emitted from the fission products formed by the fission, to produce a detector signal, comparing the detector signal with a threshold value to form a comparison, and detecting the presence of the special nuclear materials using the comparison.

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

PubMed Central

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-01-01

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets. PMID:27801795

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

PubMed

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-10-27

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

A Survey on Pharmacovigilance Activities in ASEAN and Selected Non-ASEAN Countries, and the Use of Quantitative Signal Detection Algorithms.

PubMed

Chan, Cheng Leng; Ang, Pei San; Li, Shu Chuen

2017-06-01

Most Countries have pharmacovigilance (PV) systems in place to monitor the safe use of health products. The process involves the detection and assessment of safety issues from various sources of information, communicating the risk to stakeholders and taking other relevant risk minimization measures. This study aimed to assess the PV status in Association of Southeast Asian Nation (ASEAN) countries, sources for postmarket safety monitoring, methods used for signal detection and the need for a quantitative signal detection algorithm (QSDA). Comparisons were conducted with centres outside ASEAN. A questionnaire was sent to all PV centres in ASEAN countries, as well as seven other countries, from November 2015 to June 2016. The questionnaire was designed to collect information on the status of PV, with a focus on the use of a QSDA. Data were collected from nine ASEAN countries and seven other countries. PV activities were conducted in all these countries, which were at different stages of development. In terms of adverse drug reaction (ADR) reports, the average number received per year ranged from 3 to 50,000 reports for ASEAN countries and from 7000 to 1,103,200 for non-ASEAN countries. Thirty-three percent of ASEAN countries utilized statistical methods to help detect signals from ADR reports compared with 100% in the other non-ASEAN countries. Eighty percent agreed that the development of a QSDA would help in drug signal detection. The main limitation identified was the lack of knowledge and/or lack of resources. Spontaneous ADR reports from healthcare professionals remains the most frequently used source for safety monitoring. The traditional method of case-by-case review of ADR reports prevailed for signal detection in ASEAN countries. As the reports continue to grow, the development of a QSDA would be useful in helping detect safety signals.

Augmented Reality for Real-Time Detection and Interpretation of Colorimetric Signals Generated by Paper-Based Biosensors.

PubMed

Russell, Steven M; Doménech-Sánchez, Antonio; de la Rica, Roberto

2017-06-23

Colorimetric tests are becoming increasingly popular in point-of-need analyses due to the possibility of detecting the signal with the naked eye, which eliminates the utilization of bulky and costly instruments only available in laboratories. However, colorimetric tests may be interpreted incorrectly by nonspecialists due to disparities in color perception or a lack of training. Here we solve this issue with a method that not only detects colorimetric signals but also interprets them so that the test outcome is understandable for anyone. It consists of an augmented reality (AR) app that uses a camera to detect the colored signals generated by a nanoparticle-based immunoassay, and that yields a warning symbol or message when the concentration of analyte is higher than a certain threshold. The proposed method detected the model analyte mouse IgG with a limit of detection of 0.3 μg mL -1 , which was comparable to the limit of detection afforded by classical densitometry performed with a nonportable device. When adapted to the detection of E. coli, the app always yielded a "hazard" warning symbol when the concentration of E. coli in the sample was above the infective dose (10 6 cfu mL -1 or higher). The proposed method could help nonspecialists make a decision about drinking from a potentially contaminated water source by yielding an unambiguous message that is easily understood by anyone. The widespread availability of smartphones along with the inexpensive paper test that requires no enzymes to generate the signal makes the proposed assay promising for analyses in remote locations and developing countries.

Sensitive SERS detection of lead ions via DNAzyme based quadratic signal amplification.

PubMed

Tian, Aihua; Liu, Yu; Gao, Jian

2017-08-15

Highly sensitive detection of Pb 2+ is very necessary for water quality control, clinical toxicology, and industrial monitoring. In this work, a simple and novel DNAzyme-based SERS quadratic amplification method is developed for the detection of Pb 2+ . This strategy possesses some remarkable features compared to the conventional DNAzyme-based SERS methods, which are as follows: (i) Coupled DNAzyme-activated hybridization chain reaction (HCR) with bio barcodes; a quadratic amplification method is designed using the unique catalytic selectivity of DNAzyme. The SERS signal is significantly amplified. This method is rapid with a detection time of 2h. (ii) The problem of high background induced by excess bio barcodes is circumvented by using magnetic beads (MBs) as the carrier of signal-output products, and this sensing system is simple in design and can easily be carried out by simple mixing and incubation. Given the unique and attractive characteristics, a simple and universal strategy is designed to accomplish sensitive detection of Pb 2+ . The detection limit of Pb 2+ via SERS detection is 70 fM, with the linear range from 1.0×10 -13 M to 1.0×10 -7 M. The method can be further extended to the quantitative detection of a variety of targets by replacing the lead-responsive DNAzyme with other functional DNA. Copyright © 2017 Elsevier B.V. All rights reserved.

Techniques of EMG signal analysis: detection, processing, classification and applications

PubMed Central

Hussain, M.S.; Mohd-Yasin, F.

2006-01-01

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. PMID:16799694

Leak detection in gas pipeline by acoustic and signal processing - A review

NASA Astrophysics Data System (ADS)

Adnan, N. F.; Ghazali, M. F.; Amin, M. M.; Hamat, A. M. A.

2015-12-01

The pipeline system is the most important part in media transport in order to deliver fluid to another station. The weak maintenance and poor safety will contribute to financial losses in term of fluid waste and environmental impacts. There are many classifications of techniques to make it easier to show their specific method and application. This paper's discussion about gas leak detection in pipeline system using acoustic method will be presented in this paper. The wave propagation in the pipeline is a key parameter in acoustic method when the leak occurs and the pressure balance of the pipe will generated by the friction between wall in the pipe. The signal processing is used to decompose the raw signal and show in time- frequency. Findings based on the acoustic method can be used for comparative study in the future. Acoustic signal and HHT is the best method to detect leak in gas pipelines. More experiments and simulation need to be carried out to get the fast result of leaking and estimation of their location.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

NASA Astrophysics Data System (ADS)

Horstmann, T.; Harrington, R. M.; Cochran, E. S.

2013-09-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low-amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross-correlation technique, followed by a Self-Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being "semiautomated". We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal-to-noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal-to-noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Method and device for identifying different species of honeybees

DOEpatents

Kerr, Howard T.; Buchanan, Michael E.; Valentine, Kenneth H.

1989-01-01

A method and device have been provided for distinguishing Africanized honeybees from European honeybees. The method is based on the discovery of a distinct difference in the acoustical signatures of these two species of honeybees in flight. The European honeybee signature has a fundamental power peak in the 210 to 240 Hz range while the Africanized honeybee signature has a fundamental power peak in the 260 to 290 Hz range. The acoustic signal produced by honeybees is analyzed by means of a detecting device to quickly determine the honeybee species through the detection of the presence of frequencies in one of these distinct ranges. The device includes a microphone for acoustical signal detection which feeds the detected signal into a frequency analyzer which is designed to detect the presence of either of the known fundamental wingbeat frequencies unique to the acoustical signatures of these species as an indication of the identity of the species and indicate the species identity on a readout device.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

System and method for detection of dispersed broadband signals

DOEpatents

Qian, S.; Dunham, M.E.

1999-06-08

A system and method for detecting the presence of dispersed broadband signals in real time are disclosed. The present invention utilizes a bank of matched filters for detecting the received dispersed broadband signals. Each matched filter uses a respective robust time template that has been designed to approximate the dispersed broadband signals of interest, and each time template varies across a spectrum of possible dispersed broadband signal time templates. The received dispersed broadband signal x(t) is received by each of the matched filters, and if one or more matches occurs, then the received data is determined to have signal data of interest. This signal data can then be analyzed and/or transmitted to Earth for analysis, as desired. The system and method of the present invention will prove extremely useful in many fields, including satellite communications, plasma physics, and interstellar research. The varying time templates used in the bank of matched filters are determined as follows. The robust time domain template is assumed to take the form w(t)=A(t)cos[l brace]2[phi](t)[r brace]. Since the instantaneous frequency f(t) is known to be equal to the derivative of the phase [phi](t), the trajectory of a joint time-frequency representation of x(t) is used as an approximation of [phi][prime](t). 10 figs.

System and method for detection of dispersed broadband signals

DOEpatents

Qian, Shie; Dunham, Mark E.

1999-06-08

A system and method for detecting the presence of dispersed broadband signals in real time. The present invention utilizes a bank of matched filters for detecting the received dispersed broadband signals. Each matched filter uses a respective robust time template that has been designed to approximate the dispersed broadband signals of interest, and each time template varies across a spectrum of possible dispersed broadband signal time templates. The received dispersed broadband signal x(t) is received by each of the matched filters, and if one or more matches occurs, then the received data is determined to have signal data of interest. This signal data can then be analyzed and/or transmitted to Earth for analysis, as desired. The system and method of the present invention will prove extremely useful in many fields, including satellite communications, plasma physics, and interstellar research. The varying time templates used in the bank of matched filters are determined as follows. The robust time domain template is assumed to take the form w(t)=A(t)cos{2.phi.(t)}. Since the instantaneous frequency f(t) is known to be equal to the derivative of the phase .phi.(t), the trajectory of a joint time-frequency representation of x(t) is used as an approximation of .phi.'(t).

A nonlinear merging method of analog and photon signals for CO2 detection in lower altitudes using differential absorption lidar

NASA Astrophysics Data System (ADS)

Qi, Zhong; Zhang, Teng; Han, Ge; Li, Dongcang; Ma, Xin; Gong, Wei

2017-04-01

The current acquisition system of a lidar detects return signals in two modes (i.e., analog and photon counting); resulting in the lower (below 1500 m) and upper (higher than 1100 m) atmospheric parameters need analog and photon counting signal to retrieve, respectively. Hence, a lidar cannot obtain a continuous column of the concentrations of atmospheric components. For carbon cycle studies, the range-resolved concentration of atmospheric CO2 in the lower troposphere (below 1500 m) is one of the most significant parameters that should be determined. This study proposes a novel gluing method that merges the CO2 signal detected by ground-based DIAL in the lower troposphere. Through simulation experiments, the best uniform approximation polynomial theorem is utilized to determine the transformation coefficient to correlate signals from the different modes perfectly. The experimental results (both simulation experiments and actual measurement of signals) show that the proposed method is suitable and feasible for merging data in the region below 1500 m. Hence, the photon-counting signals whose SNRs are higher than those of the analog signals can be used to retrieve atmospheric parameters at an increased near range, facilitating atmospheric soundings using ground-based lidar in various fields.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

PubMed Central

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Estimation of neural energy in microelectrode signals

NASA Astrophysics Data System (ADS)

Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

2004-09-01

We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

Research on the frequency hopping bistatic sonar system

NASA Astrophysics Data System (ADS)

Liang, Guo-long; Zhang, Yao; Zhang, Guang-pu; Liu, Kai

2011-10-01

A new model for bistatic sonar system is established, in which frequency hopping (FH) signals are used for targets detection according to some rules. This model can decrease the time between adjacent signals and obtain more information in a unit time. The receiving system will receive and process the signals of different frequency respectively, according the FH pattern, for detecting and locating targets. This method can helps yield more stable and accurate outputs, using the characteristic of the FH signals, increase the ability of anti-detection and anti partial-band jamming.

Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact detection-reduction approach. Part I: Motion and noise artifact detection.

PubMed

Chong, Jo Woon; Dao, Duy K; Salehizadeh, S M A; McManus, David D; Darling, Chad E; Chon, Ki H; Mendelson, Yitzhak

2014-11-01

Motion and noise artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion artifact detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA detection methods to our proposed algorithm. All compared detection algorithms were evaluated in terms of motion artifact detection accuracy, heart rate (HR) error, and oxygen saturation (SpO2) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO2 errors (2.3 bpm and 2.7%) were noted when the artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code.

Passive Fetal Heart Monitoring System

NASA Technical Reports Server (NTRS)

Zuckerwar, Allan J. (Inventor); Mowrey, Dennis L. (Inventor)

2003-01-01

A fetal heart monitoring system and method for detecting and processing acoustic fetal heart signals transmitted by different signal transmission modes. One signal transmission mode, the direct contact mode, occurs in a first frequency band when the fetus is in direct contact with the maternal abdominal wall. Another signal transmission mode, the fluid propagation mode, occurs in a second frequency band when the fetus is in a recessed position with no direct contact with the maternal abdominal wall. The second frequency band is relatively higher than the first frequency band. The fetal heart monitoring system and method detect and process acoustic fetal heart signals that are in the first frequency band and in the second frequency band.

Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data.

PubMed

Zhao, Yifan; Billings, Steve A; Wei, Hualiang; Sarrigiannis, Ptolemaios G

2012-11-01

This paper introduces an error reduction ratio-causality (ERR-causality) test that can be used to detect and track causal relationships between two signals. In comparison to the traditional Granger method, one significant advantage of the new ERR-causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Numerical examples are provided to illustrate the effectiveness of the new method together with the determination of the causality between electroencephalograph signals from different cortical sites for patients during an epileptic seizure.

Improved signal recovery for flow cytometry based on ‘spatially modulated emission’

NASA Astrophysics Data System (ADS)

Quint, S.; Wittek, J.; Spang, P.; Levanon, N.; Walther, T.; Baßler, M.

2017-09-01

Recently, the technique of ‘spatially modulated emission’ has been introduced (Baßler et al 2008 US Patent 0080181827A1; Kiesel et al 2009 Appl. Phys. Lett. 94 041107; Kiesel et al 2011 Cytometry A 79A 317-24) improving the signal-to-noise ratio (SNR) for detecting bio-particles in the field of flow cytometry. Based on this concept, we developed two advanced signal processing methods which further enhance the SNR and selectivity for cell detection. The improvements are achieved by adapting digital filtering methods from RADAR technology and mainly address inherent offset elimination, increased signal dynamics and moreover reduction of erroneous detections due to processing artifacts. We present a comprehensive theory on SNR gain and provide experimental results of our concepts.

Method and apparatus for clockless analog-to-digital conversion and peak detection

DOEpatents

DeGeronimo, Gianluigi

2007-03-06

An apparatus and method for analog-to-digital conversion and peak detection includes at least one stage, which includes a first switch, second switch, current source or capacitor, and discriminator. The discriminator changes state in response to a current or charge associated with the input signal exceeding a threshold, thereby indicating whether the current or charge associated with the input signal is greater than the threshold. The input signal includes a peak or a charge, and the converter includes a peak or charge detect mode in which a state of the switch is retained in response to a decrease in the current or charge associated with the input signal. The state of the switch represents at least a portion of a value of the peak or of the charge.

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Eddy current testing for blade edge micro cracks of aircraft engine

NASA Astrophysics Data System (ADS)

Zhang, Wei-min; Xu, Min-dong; Gao, Xuan-yi; Jin, Xin; Qin, Feng

2017-10-01

Based on the problems of low detection efficiency in the micro cracks detection of aircraft engine blades, a differential excitation eddy current testing system was designed and developed. The function and the working principle of the system were described, the problems which contained the manufacture method of simulated cracks, signal generating, signal processing and the signal display method were described. The detection test was carried out by taking a certain model aircraft engine blade with simulated cracks as a tested specimen. The test data was processed by digital low-pass filter in the computer and the crack signals of time domain display and Lissajous figure display were acquired. By comparing the test results, it is verified that Lissajous figure display shows better performance compared to time domain display when the crack angle is small. The test results show that the eddy current testing system designed in this paper is feasible to detect the micro cracks on the aeroengine blade and can effectively improve the detection efficiency of micro cracks in the practical detection work.

A new strategy for in vivo spectral editing. Application to GABA editing using selective homonuclear polarization transfer spectroscopy

NASA Astrophysics Data System (ADS)

Shen, Jun; Yang, Jehoon; Choi, In-Young; Li, Shizhe Steve; Chen, Zhengguang

2004-10-01

A novel single-shot in vivo spectral editing method is proposed in which the signal to be detected, is regenerated anew from the thermal equilibrium magnetization of a source to which it is J-coupled. The thermal equilibrium magnetization of the signal to be detected together with those of overlapping signals are suppressed by single-shot gradient dephasing prior to the signal regeneration process. Application of this new strategy to in vivo GABA editing using selective homonuclear polarization transfer allows complete suppression of overlapping creatine and glutathione while detecting the GABA-4 methylene resonance at 3.02 ppm with an editing yield similar to that of conventional editing methods. The NAA methyl group at 2.02 ppm was simultaneously detected and can be used as an internal navigator echo for correcting the zero order phase and frequency shifts and as an internal reference for concentration. This new method has been demonstrated for robust in vivo GABA editing in the rat brain and for study of GABA synthesis after acute vigabatrin administration.

Proposal and Implementation of a Robust Sensing Method for DVB-T Signal

NASA Astrophysics Data System (ADS)

Song, Chunyi; Rahman, Mohammad Azizur; Harada, Hiroshi

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120dBm over an 8MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120dBm over an 8MHz channel with the sensing time equals to 0.1 second.

Detection of plumes at Redoubt and Etna volcanoes using the GPS SNR method

NASA Astrophysics Data System (ADS)

Larson, Kristine M.; Palo, Scott; Roesler, Carolyn; Mattia, Mario; Bruno, Valentina; Coltelli, Mauro; Fee, David

2017-09-01

Detection and characterization of volcanic eruptions is important both for public health and aircraft safety. A variety of ground sensors are used to monitor volcanic eruptions. Data from these ground sensors are subsequently incorporated into models that predict the movement of ash. Here a method to detect volcanic plumes using GPS signals is described. Rather than carrier phase data used by geodesists, the method takes advantage of attenuations in signal to noise ratio (SNR) data. Two datasets are evaluated: the 2009 Redoubt Volcano eruptions and the 2013/2015 eruptions at Mt. Etna. SNR-based eruption durations are compared with previously published seismic, infrasonic, and radar studies at Redoubt Volcano. SNR-based plume detections from Mt. Etna are compared with L-band radar and tremor observations. To place these SNR observations from Redoubt and Etna in context, a model of the propagation of GPS signals through both water/water vapor and tephra is developed. Neither water nor fine ash particles will produce the observed attenuation of GPS signals, while scattering caused by particles > 1 cm in diameter potentially could.

Essential Limitations of the Standard THz TDS Method for Substance Detection and Identification and a Way of Overcoming Them

PubMed Central

Trofimov, Vyacheslav A.; Varentsova, Svetlana A.

2016-01-01

Low efficiency of the standard THz TDS method of the detection and identification of substances based on a comparison of the spectrum for the signal under investigation with a standard signal spectrum is demonstrated using the physical experiments conducted under real conditions with a thick paper bag as well as with Si-based semiconductors under laboratory conditions. In fact, standard THz spectroscopy leads to false detection of hazardous substances in neutral samples, which do not contain them. This disadvantage of the THz TDS method can be overcome by using time-dependent THz pulse spectrum analysis. For a quality assessment of the standard substance spectral features presence in the signal under analysis, one may use time-dependent integral correlation criteria. PMID:27070617

Essential Limitations of the Standard THz TDS Method for Substance Detection and Identification and a Way of Overcoming Them.

PubMed

Trofimov, Vyacheslav A; Varentsova, Svetlana A

2016-04-08

Low efficiency of the standard THz TDS method of the detection and identification of substances based on a comparison of the spectrum for the signal under investigation with a standard signal spectrum is demonstrated using the physical experiments conducted under real conditions with a thick paper bag as well as with Si-based semiconductors under laboratory conditions. In fact, standard THz spectroscopy leads to false detection of hazardous substances in neutral samples, which do not contain them. This disadvantage of the THz TDS method can be overcome by using time-dependent THz pulse spectrum analysis. For a quality assessment of the standard substance spectral features presence in the signal under analysis, one may use time-dependent integral correlation criteria.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Espinosa-Paredes, Gilberto; Prieto-Guerrero, Alfonso; Nunez-Carrera, Alejandro

This paper introduces a wavelet-based method to analyze instability events in a boiling water reactor (BWR) during transient phenomena. The methodology to analyze BWR signals includes the following: (a) the short-time Fourier transform (STFT) analysis, (b) decomposition using the continuous wavelet transform (CWT), and (c) application of multiresolution analysis (MRA) using discrete wavelet transform (DWT). STFT analysis permits the study, in time, of the spectral content of analyzed signals. The CWT provides information about ruptures, discontinuities, and fractal behavior. To detect these important features in the signal, a mother wavelet has to be chosen and applied at several scales tomore » obtain optimum results. MRA allows fast implementation of the DWT. Features like important frequencies, discontinuities, and transients can be detected with analysis at different levels of detail coefficients. The STFT was used to provide a comparison between a classic method and the wavelet-based method. The damping ratio, which is an important stability parameter, was calculated as a function of time. The transient behavior can be detected by analyzing the maximum contained in detail coefficients at different levels in the signal decomposition. This method allows analysis of both stationary signals and highly nonstationary signals in the timescale plane. This methodology has been tested with the benchmark power instability event of Laguna Verde nuclear power plant (NPP) Unit 1, which is a BWR-5 NPP.« less

Signal processing techniques for damage detection with piezoelectric wafer active sensors and embedded ultrasonic structural radar

NASA Astrophysics Data System (ADS)

Yu, Lingyu; Bao, Jingjing; Giurgiutiu, Victor

2004-07-01

Embedded ultrasonic structural radar (EUSR) algorithm is developed for using piezoelectric wafer active sensor (PWAS) array to detect defects within a large area of a thin-plate specimen. Signal processing techniques are used to extract the time of flight of the wave packages, and thereby to determine the location of the defects with the EUSR algorithm. In our research, the transient tone-burst wave propagation signals are generated and collected by the embedded PWAS. Then, with signal processing, the frequency contents of the signals and the time of flight of individual frequencies are determined. This paper starts with an introduction of embedded ultrasonic structural radar algorithm. Then we will describe the signal processing methods used to extract the time of flight of the wave packages. The signal processing methods being used include the wavelet denoising, the cross correlation, and Hilbert transform. Though hardware device can provide averaging function to eliminate the noise coming from the signal collection process, wavelet denoising is included to ensure better signal quality for the application in real severe environment. For better recognition of time of flight, cross correlation method is used. Hilbert transform is applied to the signals after cross correlation in order to extract the envelope of the signals. Signal processing and EUSR are both implemented by developing a graphical user-friendly interface program in LabView. We conclude with a description of our vision for applying EUSR signal analysis to structural health monitoring and embedded nondestructive evaluation. To this end, we envisage an automatic damage detection application utilizing embedded PWAS, EUSR, and advanced signal processing.

Wavelength modulation spectroscopy--digital detection of gas absorption harmonics based on Fourier analysis.

PubMed

Mei, Liang; Svanberg, Sune

2015-03-20

This work presents a detailed study of the theoretical aspects of the Fourier analysis method, which has been utilized for gas absorption harmonic detection in wavelength modulation spectroscopy (WMS). The lock-in detection of the harmonic signal is accomplished by studying the phase term of the inverse Fourier transform of the Fourier spectrum that corresponds to the harmonic signal. The mathematics and the corresponding simulation results are given for each procedure when applying the Fourier analysis method. The present work provides a detailed view of the WMS technique when applying the Fourier analysis method.

Development of an inverse distance weighted active infrared stealth scheme using the repulsive particle swarm optimization algorithm.

PubMed

Han, Kuk-Il; Kim, Do-Hwi; Choi, Jun-Hyuk; Kim, Tae-Kuk

2018-04-20

Treatments for detection by infrared (IR) signals are higher than for other signals such as radar or sonar because an object detected by the IR sensor cannot easily recognize its detection status. Recently, research for actively reducing IR signal has been conducted to control the IR signal by adjusting the surface temperature of the object. In this paper, we propose an active IR stealth algorithm to synchronize IR signals from the object and the background around the object. The proposed method includes the repulsive particle swarm optimization statistical optimization algorithm to estimate the IR stealth surface temperature, which will result in a synchronization between the IR signals from the object and the surrounding background by setting the inverse distance weighted contrast radiant intensity (CRI) equal to zero. We tested the IR stealth performance in mid wavelength infrared (MWIR) and long wavelength infrared (LWIR) bands for a test plate located at three different positions on a forest scene to verify the proposed method. Our results show that the inverse distance weighted active IR stealth technique proposed in this study is proved to be an effective method for reducing the contrast radiant intensity between the object and background up to 32% as compared to the previous method using the CRI determined as the simple signal difference between the object and the background.

Evaluation and Uncertainty of a New Method to Detect Suspected Nuclear and WMD Activity: Project Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurzeja, R.; Werth, D.; Buckley, R.

The Atmospheric Technology Group at SRNL developed a new method to detect signals from Weapons of Mass Destruction (WMD) activities in a time series of chemical measurements at a downwind location. This method was tested with radioxenon measured in Russia and Japan after the 2013 underground test in North Korea. This LDRD calculated the uncertainty in the method with the measured data and also for a case with the signal reduced to 1/10 its measured value. The research showed that the uncertainty in the calculated probability of origin from the NK test site was small enough to confirm the test.more » The method was also wellbehaved for small signal strengths.« less

Isothermal amplification detection of nucleic acids by a double-nicked beacon.

PubMed

Shi, Chao; Zhou, Meiling; Pan, Mei; Zhong, Guilin; Ma, Cuiping

2016-03-01

Isothermal and rapid amplification detection of nucleic acids is an important technology in environmental monitoring, foodborne pathogen detection, and point-of-care clinical diagnostics. Here we have developed a novel method of isothermal signal amplification for single-stranded DNA (ssDNA) detection. The ssDNA target could be used as an initiator, coupled with a double-nicked molecular beacon, to originate amplification cycles, achieving cascade signal amplification. In addition, the method showed good specificity and strong anti-jamming capability. Overall, it is a one-pot and isothermal strand displacement amplification method without the requirement of a stepwise procedure, which greatly simplifies the experimental procedure and decreases the probability of contamination of samples. With its advantages, the method would be very useful to detect nucleic acids in point-of-care or field use. Copyright © 2015 Elsevier Inc. All rights reserved.

A new method for detecting signal regions in ordered sequences of real numbers, and application to viral genomic data.

PubMed

Gog, Julia R; Lever, Andrew M L; Skittrall, Jordan P

2018-01-01

We present a fast, robust and parsimonious approach to detecting signals in an ordered sequence of numbers. Our motivation is in seeking a suitable method to take a sequence of scores corresponding to properties of positions in virus genomes, and find outlying regions of low scores. Suitable statistical methods without using complex models or making many assumptions are surprisingly lacking. We resolve this by developing a method that detects regions of low score within sequences of real numbers. The method makes no assumptions a priori about the length of such a region; it gives the explicit location of the region and scores it statistically. It does not use detailed mechanistic models so the method is fast and will be useful in a wide range of applications. We present our approach in detail, and test it on simulated sequences. We show that it is robust to a wide range of signal morphologies, and that it is able to capture multiple signals in the same sequence. Finally we apply it to viral genomic data to identify regions of evolutionary conservation within influenza and rotavirus.

A threshold-based approach for muscle contraction detection from surface EMG signals

NASA Astrophysics Data System (ADS)

Morantes, Gaudi; Fernández, Gerardo; Altuve, Miguel

2013-11-01

Surface electromyographic (SEMG) signals are commonly used as control signals in prosthetic and orthotic devices. Super cial electrodes are placed on the skin of the subject to acquire its muscular activity through this signal. The muscle contraction episode is then in charge of activating and deactivating these devices. Nevertheless, there is no gold standard" to detect muscle contraction, leading to delayed responses and false and missed detections. This fact motivated us to propose a new approach that compares a smoothed version of the SEMG signal with a xed threshold, in order to detect muscle contraction episodes. After preprocessing the SEMG signal, the smoothed version is obtained using a moving average lter, where three di erent window lengths has been evaluated. The detector was tuned by maximizing sensitivity and speci city and evaluated using SEMG signals obtained from the anterior tibial and gastrocnemius muscles, taken during the walking of ve subjects. Compared with traditional detection methods, we obtain a reduction of 3 ms in the detection delay, an increase of 8% in sensitivity but a decrease of 15% in speci city. Future work is directed to the inclusion of a temporal threshold (a double-threshold approach) to minimize false detections and reduce detection delays.

Eddy Current Rail Inspection Using AC Bridge Techniques.

PubMed

Liu, Ze; Koffman, Andrew D; Waltrip, Bryan C; Wang, Yicheng

2013-01-01

AC bridge techniques commonly used for precision impedance measurements have been adapted to develop an eddy current sensor for rail defect detection. By using two detection coils instead of just one as in a conventional sensor, we can balance out the large baseline signals corresponding to a normal rail. We have significantly enhanced the detection sensitivity of the eddy current method by detecting and demodulating the differential signal of the two coils induced by rail defects, using a digital lock-in amplifier algorithm. We have also explored compensating for the lift-off effect of the eddy current sensor due to vibrations by using the summing signal of the detection coils to measure the lift-off distance. The dominant component of the summing signal is a constant resulting from direct coupling from the excitation coil, which can be experimentally determined. The remainder of the summing signal, which decreases as the lift-off distance increases, is induced by the secondary eddy current. This dependence on the lift-off distance is used to calibrate the differential signal, allowing for a more accurate characterization of the defects. Simulated experiments on a sample rail have been performed using a computer controlled X-Y moving table with the X-axis mimicking the train's motion and the Y-axis mimicking the train's vibrational bumping. Experimental results demonstrate the effectiveness of the new detection method.

Modulation and detection of single neuron activity using spin transfer nano-oscillators

NASA Astrophysics Data System (ADS)

Algarin, Jose Miguel; Ramaswamy, Bharath; Venuti, Lucy; Swierzbinski, Matthew; Villar, Pablo; Chen, Yu-Jin; Krivorotov, Ilya; Weinberg, Irving N.; Herberholz, Jens; Araneda, Ricardo; Shapiro, Benjamin; Waks, Edo

2017-09-01

The brain is a complex network of interconnected circuits that exchange electrical signals with each other. These electrical signals provide insight on how neural circuits code information, and give rise to sensations, thoughts, emotions and actions. Currents methods to detect and modulate these electrical signals use implanted electrodes or optical fields with light sensitive dyes in the brain. These techniques require complex surgeries or suffer low resolution. In this talk we explore a new method to both image and stimulate single neurons using spintronics. We propose using a Spin Transfer Nano-Oscillators (STNOs) as a nanoscale sensor that converts neuronal action potentials to microwave field oscillations that can be detected wirelessly by magnetic induction. We will describe our recent proof-of-concept demonstration of both detection and wireless modulation of neuronal activity using STNOs. For detection we use electrodes to connect a STNO to a lateral giant crayfish neuron. When we stimulate the neuron, the STNO responds to the neuronal activity with a corresponding microwave signal. For modulation, we stimulate the STNOs wirelessly using an inductively coupled solenoid. The STNO rectifies the induced microwave signal to produce a direct voltage. This direct voltage from the STNO, when applied in the vicinity of a mammalian neuron, changes the frequency of electrical signals produced by the neuron.

Radar signal pre-processing to suppress surface bounce and multipath

DOEpatents

Paglieroni, David W; Mast, Jeffrey E; Beer, N. Reginald

2013-12-31

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes that return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Guided wave imaging of oblique reflecting interfaces in pipes using common-source synthetic focusing

NASA Astrophysics Data System (ADS)

Sun, Zeqing; Sun, Anyu; Ju, Bing-Feng

2018-04-01

Cross-mode-family mode conversion and secondary reflection of guided waves in pipes complicate the processing of guided waves signals, and can cause false detection. In this paper, filters operating in the spectral domain of wavenumber, circumferential order and frequency are designed to suppress the signal components of unwanted mode-family and unwanted traveling direction. Common-source synthetic focusing is used to reconstruct defect images from the guided wave signals. Simulations of the reflections from linear oblique defects and a semicircle defect are separately implemented. Defect images, which are reconstructed from the simulation results under different excitation conditions, are comparatively studied in terms of axial resolution, reflection amplitude, detectable oblique angle and so on. Further, the proposed method is experimentally validated by detecting linear cracks with various oblique angles (10-40°). The proposed method relies on the guided wave signals that are captured during 2-D scanning of a cylindrical area on the pipe. The redundancy of the signals is analyzed to reduce the time-consumption of the scanning process and to enhance the practicability of the proposed method.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring.

PubMed

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-05-21

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

PubMed Central

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-01-01

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. PMID:29883411

Detection of BCG bacteria using a magnetoresistive biosensor: A step towards a fully electronic platform for tuberculosis point-of-care detection.

PubMed

Barroso, Teresa G; Martins, Rui C; Fernandes, Elisabete; Cardoso, Susana; Rivas, José; Freitas, Paulo P

2018-02-15

Tuberculosis is one of the major public health concerns. This highly contagious disease affects more than 10.4 million people, being a leading cause of morbidity by infection. Tuberculosis is diagnosed at the point-of-care by the Ziehl-Neelsen sputum smear microscopy test. Ziehl-Neelsen is laborious, prone to human error and infection risk, with a limit of detection of 10 4 cells/mL. In resource-poor nations, a more practical test, with lower detection limit, is paramount. This work uses a magnetoresistive biosensor to detect BCG bacteria for tuberculosis diagnosis. Herein we report: i) nanoparticle assembly method and specificity for tuberculosis detection; ii) demonstration of proportionality between BCG cell concentration and magnetoresistive voltage signal; iii) application of multiplicative signal correction for systematic effects removal; iv) investigation of calibration effectiveness using chemometrics methods; and v) comparison with state-of-the-art point-of-care tuberculosis biosensors. Results present a clear correspondence between voltage signal and cell concentration. Multiplicative signal correction removes baseline shifts within and between biochip sensors, allowing accurate and precise voltage signal between different biochips. The corrected signal was used for multivariate regression models, which significantly decreased the calibration standard error from 0.50 to 0.03log 10 (cells/mL). Results show that Ziehl-Neelsen detection limits and below are achievable with the magnetoresistive biochip, when pre-processing and chemometrics are used. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of Low-Energy Microwaves Technology (Termatrac) for Detecting Western Drywood Termite in a Simulated Drywall System.

PubMed

Taravati, Siavash

2018-05-28

Detecting drywood termites in structures is very challenging. Microwaves technology (Termatrac T3i) is a nondestructive method for detecting drywood termites in structures. Termatrac device and its mobile application provide a bar as well as a line graph when detecting insect movements, but interpreting these graphs is very subjective. In this paper, Termatrac's output signal is quantified using a new method to study the effect of wall layers, wood type, and termite density on signal strength measured as area under curve in a simulated drywall system in laboratory. Two experiments were conducted on Termatrac T3i at its maximum sensitivity (Gain: 10). In experiment I, HEXBUG Nano was used as a source of movement/vibration and two wood types were used in which the wall layers significantly predicted signal strength, but wood type did not. In experiment II, two different densities of live western drywood termites, Incisitermes minor (Hagen) (Isoptera: Kalotermitidae), were used to study the effect of termite density on signal strength. Interestingly, termite density did not significantly predict signal strength. The maximum reliable wood depth for detecting termites was 5 cm. Microwaves produced by Termatrac also showed good penetration into drywall and produced detectable signals even on a single drywood termite which confirms manufacturer's claim. Suggestions on using and improving microwaves technology for detecting termites is provided which can potentially be applied to other types of insects and noninsect animals.

Detecting special nuclear materials in suspect containers using high-energy gamma rays emitted by fission products

DOEpatents

Norman, Eric B [Oakland, CA; Prussin, Stanley G [Kensington, CA

2009-05-05

A method and a system for detecting the presence of special nuclear materials in a suspect container. The system and its method include irradiating the suspect container with a beam of neutrons, so as to induce a thermal fission in a portion of the special nuclear materials, detecting the gamma rays that are emitted from the fission products formed by the thermal fission, to produce a detector signal, comparing the detector signal with a threshold value to form a comparison, and detecting the presence of the special nuclear materials using the comparison.

Detecting special nuclear materials in suspect containers using high-energy gamma rays emitted by fission products

DOEpatents

Norman, Eric B [Oakland, CA; Prussin, Stanley G [Kensington, CA

2009-01-27

A method and a system for detecting the presence of special nuclear materials in a suspect container. The system and its method include irradiating the suspect container with a beam of neutrons, so as to induce a thermal fission in a portion of the special nuclear materials, detecting the gamma rays that are emitted from the fission products formed by the thermal fission, to produce a detector signal, comparing the detector signal with a threshold value to form a comparison, and detecting the presence of the special nuclear materials using the comparison.

Detecting special nuclear materials in suspect containers using high-energy gamma rays emitted by fission products

DOEpatents

Norman, Eric B [Oakland, CA; Prussin, Stanley G [Kensington, CA

2009-01-06

A method and a system for detecting the presence of special nuclear materials in a suspect container. The system and its method include irradiating the suspect container with a beam of neutrons, so as to induce a thermal fission in a portion of the special nuclear materials, detecting the gamma rays that are emitted from the fission products formed by the thermal fission, to produce a detector signal, comparing the detector signal with a threshold value to form a comparison, and detecting the presence of the special nuclear materials using the comparison.

Quaternion-valued single-phase model for three-phase power system

NASA Astrophysics Data System (ADS)

Gou, Xiaoming; Liu, Zhiwen; Liu, Wei; Xu, Yougen; Wang, Jiabin

2018-03-01

In this work, a quaternion-valued model is proposed in lieu of the Clarke's α, β transformation to convert three-phase quantities to a hypercomplex single-phase signal. The concatenated signal can be used for harmonic distortion detection in three-phase power systems. In particular, the proposed model maps all the harmonic frequencies into frequencies in the quaternion domain, while the Clarke's transformation-based methods will fail to detect the zero sequence voltages. Based on the quaternion-valued model, the Fourier transform, the minimum variance distortionless response (MVDR) algorithm and the multiple signal classification (MUSIC) algorithm are presented as examples to detect harmonic distortion. Simulations are provided to demonstrate the potentials of this new modeling method.

Improving the signal analysis for in vivo photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Niu, Zhenyu; Yang, Ping; Wei, Dan; Tang, Shuo; Wei, Xunbin

2015-03-01

At early stage of cancer, a small number of circulating tumor cells (CTCs) appear in the blood circulation. Thus, early detection of malignant circulating tumor cells has great significance for timely treatment to reduce the cancer death rate. We have developed an in vivo photoacoustic flow cytometry (PAFC) to monitor the metastatic process of CTCs and record the signals from target cells. Information of target cells which is helpful to the early therapy would be obtained through analyzing and processing the signals. The raw signal detected from target cells often contains some noise caused by electronic devices, such as background noise and thermal noise. We choose the Wavelet denoising method to effectively distinguish the target signal from background noise. Processing in time domain and frequency domain would be combined to analyze the signal after denoising. This algorithm contains time domain filter and frequency transformation. The frequency spectrum image of the signal contains distinctive features that can be used to analyze the property of target cells or particles. The PAFC technique can detect signals from circulating tumor cells or other particles. The processing methods have a great potential for analyzing signals accurately and rapidly.

Baseline-free damage detection in composite plates based on the reciprocity principle

NASA Astrophysics Data System (ADS)

Huang, Liping; Zeng, Liang; Lin, Jing

2018-01-01

Lamb wave based damage detection techniques have been widely used in composite structures. In particular, these techniques usually rely on reference signals, which are significantly influenced by the operational and environmental conditions. To solve this issue, this paper presents a baseline-free damage inspection method based on the reciprocity principle. If a localized nonlinear scatterer exists along the wave path, the reciprocity breaks down. Through estimating the loss of reciprocity, the delamination could be detected. A reciprocity index (RI), which compares the discrepancy between the signal received in transducer B when emitting from transducer A and the signal received in A when the same source is located in B, is established to quantitatively analyze the reciprocity. Experimental results show that the RI value of a damaged path is much higher than that of a healthy path. In addition, the effects of the parameters of excitation signal (i.e., central frequency and bandwidth) and the position of delamination on the RI value are discussed. Furthermore, a RI based probabilistic imaging algorithm is proposed for detecting delamination damage of composite plates without reference signals. Finally, the effectiveness of this baseline-free damage detection method is validated by an experimental example.

Hetero-enzyme-based two-round signal amplification strategy for trace detection of aflatoxin B1 using an electrochemical aptasensor.

PubMed

Zheng, Wanli; Teng, Jun; Cheng, Lin; Ye, Yingwang; Pan, Daodong; Wu, Jingjing; Xue, Feng; Liu, Guodong; Chen, Wei

2016-06-15

An electrochemical aptasensor for trace detection of aflatoxin B1 (AFB1) was developed by using an aptamer as the recognition unit while adopting the telomerase and EXO III based two-round signal amplification strategy as the signal enhancement units. The telomerase amplification was used to elongate the ssDNA probes on the surface of gold nanoparticles, by which the signal response range of the signal-off model electrochemical aptasensor could be correspondingly enlarged. Then, the EXO III amplification was used to hydrolyze the 3'-end of the dsDNA after the recognition of target AFB1, which caused the release of bounded AFB1 into the sensing system, where it participated in the next recognition-sensing cycle. With this two-round signal amplified electrochemical aptasensor, target AFB1 was successfully measured at trace concentrations with excellent detection limit of 0.6*10(-4)ppt and satisfied specificity due to the excellent affinity of the aptamer against AFB1. Based on this designed two-round signal amplification strategy, both the sensing range and detection limit were greatly improved. This proposed ultrasensitive electrochemical aptasensor method was also validated by comparison with the classic instrumental methods. Importantly, this hetero-enzyme based two-round signal amplified electrochemical aptasensor offers a great promising protocol for ultrasensitive detection of AFB1 and other mycotoxins by replacing the core recognition sequence of the aptamer. Copyright © 2016 Elsevier B.V. All rights reserved.

A hybrid method based on Band Pass Filter and Correlation Algorithm to improve debris sensor capacity

NASA Astrophysics Data System (ADS)

Hong, Wei; Wang, Shaoping; Liu, Haokuo; Tomovic, Mileta M.; Chao, Zhang

2017-01-01

The inductive debris detection is an effective method for monitoring mechanical wear, and could be used to prevent serious accidents. However, debris detection during early phase of mechanical wear, when small debris (<100 um) is generated, requires that the sensor has high sensitivity with respect to background noise. In order to detect smaller debris by existing sensors, this paper presents a hybrid method which combines Band Pass Filter and Correlation Algorithm to improve sensor signal-to-noise ratio (SNR). The simulation results indicate that the SNR will be improved at least 2.67 times after signal processing. In other words, this method ensures debris identification when the sensor's SNR is bigger than -3 dB. Thus, smaller debris will be detected in the same SNR. Finally, effectiveness of the proposed method is experimentally validated.

Heartbeat detection in multimodal physiological signals using signal quality assessment based on sample entropy.

PubMed

Singh, Omkar; Sunkaria, Ramesh Kumar

2017-12-01

This paper presents a novel technique to identify heartbeats in multimodal data using electrocardiogram (ECG) and arterial blood pressure (ABP) signals. Multiple physiological signals such as ECG, ABP, and Respiration are often recorded in parallel from the activity of heart. These signals generally possess related information as they are generated by the same physical system. The ECG and ABP correspond to the same phenomenon of contraction and relaxation activity of heart. Multiple signals acquired from various sensors are generally processed independently, thus discarding the information from other measurements. In the estimation of heart rate and heart rate variability, the R peaks are generally identified from ECG signal. Efficient detection of R-peaks in electrocardiogram (ECG) is a key component in the estimation of clinically relevant parameters from ECG. However, when the signal is severely affected by undesired artifacts, this becomes a challenging task. Sometimes in clinical environment, other physiological signals reflecting the cardiac activity such as ABP signal are also acquired simultaneously. Under the availability of such multimodal signals, the accuracy of R peak detection methods can be improved using sensor-fusion techniques. In the proposed method, the sample entropy (SampEn) is used as a metric for assessing the noise content in the physiological signal and the R peaks in ECG and the systolic peaks in ABP signals are fused together to enhance the efficiency of heartbeat detection. The proposed method was evaluated on the 100 records from the computing in cardiology challenge 2014 training data set. The performance parameters are: sensitivity (Se) and positive predictivity (PPV). The unimodal R peaks detector achieved: Se gross = 99.40%, PPV gross = 99.29%, Se average = 99.37%, PPV average = 99.29%. Similarly unimodal BP delineator achieved Se gross = 99.93%, PPV gross = 99.99%, Se average = 99.93%, PPV average = 99.99% whereas, the proposed multimodal beat detector achieved: Se gross = 99.65%, PPV gross = 99.91%, Se average = 99.68%, PPV average = 99.91%.

Develop guidelines for new vehicle detectors at high-speed signalized intersections : project summary.

DOT National Transportation Integrated Search

2017-01-01

The traditional vehicle detection method that has been used by the Texas Department of Transportation (TxDOT) on high-speed signalized intersection approaches for many years involved multiple detection points, with inductive loops being the early fav...

Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method.

PubMed

Galletto Pregliasco, A; Collin, A; Guéguen, A; Metten, M A; Aboab, J; Deschamps, R; Gout, O; Duron, L; Sadik, J C; Savatovsky, J; Lecler, A

2018-06-07

MR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging. This prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient. There were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4, P < .001). The coregistration-fusion method detected significantly more patients with at least 1 new high-signal T2 lesion (59% versus 46%, P = .02) and was associated with significantly faster overall reading time (86 seconds faster, P < .001) and higher reader confidence (91% versus 40%, P < 1 × 10 -4 ). Inter- and intraobserver agreement was excellent for counting new high-signal T2 lesions. Our study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care. © 2018 by American Journal of Neuroradiology.

Optical Johnson noise thermometry

DOEpatents

Shepard, Robert L.; Blalock, Theron V.; Roberts, Michael J.; Maxey, Lonnie C.

1992-01-01

Method and device for direct, non-contact temperature measure of a body. A laser beam is reflected from the surface of the body and detected along with the Planck radiation. The detected signal is analyzed using signal correlation technique to generate an output signal proportional to the Johnson noise introduced into the reflected laser beam as a direct measure of the absolute temperature of the body.

A comparison of earthquake backprojection imaging methods for dense local arrays

NASA Astrophysics Data System (ADS)

Beskardes, G. D.; Hole, J. A.; Wang, K.; Michaelides, M.; Wu, Q.; Chapman, M. C.; Davenport, K. K.; Brown, L. D.; Quiros, D. A.

2018-03-01

Backprojection imaging has recently become a practical method for local earthquake detection and location due to the deployment of densely sampled, continuously recorded, local seismograph arrays. While backprojection sometimes utilizes the full seismic waveform, the waveforms are often pre-processed and simplified to overcome imaging challenges. Real data issues include aliased station spacing, inadequate array aperture, inaccurate velocity model, low signal-to-noise ratio, large noise bursts and varying waveform polarity. We compare the performance of backprojection with four previously used data pre-processing methods: raw waveform, envelope, short-term averaging/long-term averaging and kurtosis. Our primary goal is to detect and locate events smaller than noise by stacking prior to detection to improve the signal-to-noise ratio. The objective is to identify an optimized strategy for automated imaging that is robust in the presence of real-data issues, has the lowest signal-to-noise thresholds for detection and for location, has the best spatial resolution of the source images, preserves magnitude, and considers computational cost. Imaging method performance is assessed using a real aftershock data set recorded by the dense AIDA array following the 2011 Virginia earthquake. Our comparisons show that raw-waveform backprojection provides the best spatial resolution, preserves magnitude and boosts signal to detect events smaller than noise, but is most sensitive to velocity error, polarity error and noise bursts. On the other hand, the other methods avoid polarity error and reduce sensitivity to velocity error, but sacrifice spatial resolution and cannot effectively reduce noise by stacking. Of these, only kurtosis is insensitive to large noise bursts while being as efficient as the raw-waveform method to lower the detection threshold; however, it does not preserve the magnitude information. For automatic detection and location of events in a large data set, we therefore recommend backprojecting kurtosis waveforms, followed by a second pass on the detected events using noise-filtered raw waveforms to achieve the best of all criteria.

Sensitive detection of unlabeled oligonucleotides using a paired surface plasma waves biosensor.

PubMed

Li, Ying-Chang; Chiou, Chiuan-Chian; Luo, Ji-Dung; Chen, Wei-Ju; Su, Li-Chen; Chang, Ying-Feng; Chang, Yu-Sun; Lai, Chao-Sung; Lee, Cheng-Chung; Chou, Chien

2012-05-15

Detection of unlabeled oligonucleotides using surface plasmon resonance (SPR) is difficult because of the oligonucleotides' relatively lower molecular weight compared with proteins. In this paper, we describe a method for detecting unlabeled oligonucleotides at low concentration using a paired surface plasma waves biosensor (PSPWB). The biosensor uses a sensor chip with an immobilized probe to detect a target oligonucleotide via sequence-specific hybridization. PSPWB measures the demodulated amplitude of the heterodyne signal in real time. In the meantime, the ratio of the amplitudes between the detected output signal and reference can reduce the excess noise from the laser intensity fluctuation. Also, the common-path propagation of p and s waves cancels the common phase noise induced by temperature variation. Thus, a high signal-to-noise ratio (SNR) of the heterodyne signal is detected. The sequence specificity of oligonucleotide hybridization ensures that the platform is precisely discriminating between target and non-target oligonucleotides. Under optimized experimental conditions, the detected heterodyne signal increases linearly with the logarithm of the concentration of target oligonucleotide over the range 0.5-500 pM. The detection limit is 0.5 pM in this experiment. In addition, the non-target oligonucleotide at concentrations of 10 pM and 10nM generated signals only slightly higher than background, indicating the high selectivity and specificity of this method. Different length of perfectly matched oligonucleotide targets at 10-mer, 15-mer and 20-mer were identified at the concentration of 150 pM. Copyright © 2012 Elsevier B.V. All rights reserved.

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

NASA Astrophysics Data System (ADS)

Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

2017-12-01

Detection of landslide occurrences and measurement of their dynamics properties during run-out is a high research priority but a logistical and technical challenge. Seismology has started to help in several important ways. Taking advantage of the densification of global, regional and local networks of broadband seismic stations, recent advances now permit the seismic detection and location of landslides in near-real-time. This seismic detection could potentially greatly increase the spatio-temporal resolution at which we study landslides triggering, which is critical to better understand the influence of external forcings such as rainfalls and earthquakes. However, detecting automatically seismic signals generated by landslides still represents a challenge, especially for events with small mass. The low signal-to-noise ratio classically observed for landslide-generated seismic signals and the difficulty to discriminate these signals from those generated by regional earthquakes or anthropogenic and natural noises are some of the obstacles that have to be circumvented. We present a new method for automatically constructing instrumental landslide catalogues from continuous seismic data. We developed a robust and versatile solution, which can be implemented in any context where a seismic detection of landslides or other mass movements is relevant. The method is based on a spectral detection of the seismic signals and the identification of the sources with a Random Forest machine learning algorithm. The spectral detection allows detecting signals with low signal-to-noise ratio, while the Random Forest algorithm achieve a high rate of positive identification of the seismic signals generated by landslides and other seismic sources. The processing chain is implemented to work in a High Performance Computers centre which permits to explore years of continuous seismic data rapidly. We present here the preliminary results of the application of this processing chain for years of continuous seismic record by the Alaskan permanent seismic network and Hi-Climb trans-Himalayan seismic network. The processing chain we developed also opens the possibility for a near-real time seismic detection of landslides, in association with remote-sensing automated detection from Sentinel 2 images for example.

Optimized velocity distributions for direct dark matter detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibarra, Alejandro; Rappelt, Andreas, E-mail: ibarra@tum.de, E-mail: andreas.rappelt@tum.de

We present a method to calculate, without making assumptions about the local dark matter velocity distribution, the maximal and minimal number of signal events in a direct detection experiment given a set of constraints from other direct detection experiments and/or neutrino telescopes. The method also allows to determine the velocity distribution that optimizes the signal rates. We illustrate our method with three concrete applications: i) to derive a halo-independent upper limit on the cross section from a set of null results, ii) to confront in a halo-independent way a detection claim to a set of null results and iii) tomore » assess, in a halo-independent manner, the prospects for detection in a future experiment given a set of current null results.« less

Distance-based microfluidic quantitative detection methods for point-of-care testing.

PubMed

Tian, Tian; Li, Jiuxing; Song, Yanling; Zhou, Leiji; Zhu, Zhi; Yang, Chaoyong James

2016-04-07

Equipment-free devices with quantitative readout are of great significance to point-of-care testing (POCT), which provides real-time readout to users and is especially important in low-resource settings. Among various equipment-free approaches, distance-based visual quantitative detection methods rely on reading the visual signal length for corresponding target concentrations, thus eliminating the need for sophisticated instruments. The distance-based methods are low-cost, user-friendly and can be integrated into portable analytical devices. Moreover, such methods enable quantitative detection of various targets by the naked eye. In this review, we first introduce the concept and history of distance-based visual quantitative detection methods. Then, we summarize the main methods for translation of molecular signals to distance-based readout and discuss different microfluidic platforms (glass, PDMS, paper and thread) in terms of applications in biomedical diagnostics, food safety monitoring, and environmental analysis. Finally, the potential and future perspectives are discussed.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

NASA Astrophysics Data System (ADS)

Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

2018-04-01

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

Reversible interactions with para-hydrogen enhance NMR sensitivity by polarization transfer.

PubMed

Adams, Ralph W; Aguilar, Juan A; Atkinson, Kevin D; Cowley, Michael J; Elliott, Paul I P; Duckett, Simon B; Green, Gary G R; Khazal, Iman G; López-Serrano, Joaquín; Williamson, David C

2009-03-27

The sensitivity of both nuclear magnetic resonance spectroscopy and magnetic resonance imaging is very low because the detected signal strength depends on the small population difference between spin states even in high magnetic fields. Hyperpolarization methods can be used to increase this difference and thereby enhance signal strength. This has been achieved previously by incorporating the molecular spin singlet para-hydrogen into hydrogenation reaction products. We show here that a metal complex can facilitate the reversible interaction of para-hydrogen with a suitable organic substrate such that up to an 800-fold increase in proton, carbon, and nitrogen signal strengths are seen for the substrate without its hydrogenation. These polarized signals can be selectively detected when combined with methods that suppress background signals.

Narrowband signal detection in the SETI field test

NASA Technical Reports Server (NTRS)

Cullers, D. Kent; Deans, Stanley R.

1986-01-01

Various methods for detecting narrow-band signals are evaluated. The characteristics of synchronized and unsynchronized pulses are examined. Synchronous, square law, regular pulse, and the general form detections are discussed. The CW, single pulse, synchronous, and four pulse detections are analyzed in terms of false alarm rate and threshold relative to average noise power. Techniques for saving memory and retaining sensitivity are described. Consideration is given to nondrifting CW detection, asynchronous pulse detection, interpolative and extrapolative pulse detectors, and finite and infinite pulses.

Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

PubMed Central

Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Sanchez, Javier; Revie, Crawford W.

2013-01-01

Background Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. Methods This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. Results The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. Conclusion The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes. PMID:24349216

Detecting weak position fluctuations from encoder signal using singular spectrum analysis.

PubMed

Xu, Xiaoqiang; Zhao, Ming; Lin, Jing

2017-11-01

Mechanical fault or defect will cause some weak fluctuations to the position signal. Detection of such fluctuations via encoders can help determine the health condition and performance of the machine, and offer a promising alternative to the vibration-based monitoring scheme. However, besides the interested fluctuations, encoder signal also contains a large trend and some measurement noise. In applications, the trend is normally several orders larger than the concerned fluctuations in magnitude, which makes it difficult to detect the weak fluctuations without signal distortion. In addition, the fluctuations can be complicated and amplitude modulated under non-stationary working condition. To overcome this issue, singular spectrum analysis (SSA) is proposed for detecting weak position fluctuations from encoder signal in this paper. It enables complicated encode signal to be reduced into several interpretable components including a trend, a set of periodic fluctuations and noise. A numerical simulation is given to demonstrate the performance of the method, it shows that SSA outperforms empirical mode decomposition (EMD) in terms of capability and accuracy. Moreover, linear encoder signals from a CNC machine tool are analyzed to determine the magnitudes and sources of fluctuations during feed motion. The proposed method is proven to be feasible and reliable for machinery condition monitoring. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

[Detection of protein-protein interactions by FRET and BRET methods].

PubMed

Matoulková, E; Vojtěšek, B

2014-01-01

Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

System and method for ultrafast optical signal detecting via a synchronously coupled anamorphic light pulse encoded laterally

DOEpatents

Heebner, John E [Livermore, CA

2010-08-03

In one general embodiment, a method for ultrafast optical signal detecting is provided. In operation, a first optical input signal is propagated through a first wave guiding layer of a waveguide. Additionally, a second optical input signal is propagated through a second wave guiding layer of the waveguide. Furthermore, an optical control signal is applied to a top of the waveguide, the optical control signal being oriented diagonally relative to the top of the waveguide such that the application is used to influence at least a portion of the first optical input signal propagating through the first wave guiding layer of the waveguide. In addition, the first and the second optical input signals output from the waveguide are combined. Further, the combined optical signals output from the waveguide are detected. In another general embodiment, a system for ultrafast optical signal recording is provided comprising a waveguide including a plurality of wave guiding layers, an optical control source positioned to propagate an optical control signal towards the waveguide in a diagonal orientation relative to a top of the waveguide, at least one optical input source positioned to input an optical input signal into at least a first and a second wave guiding layer of the waveguide, and a detector for detecting at least one interference pattern output from the waveguide, where at least one of the interference patterns results from a combination of the optical input signals input into the first and the second wave guiding layer. Furthermore, propagation of the optical control signal is used to influence at least a portion of the optical input signal propagating through the first wave guiding layer of the waveguide.

Comparative evaluation of Oxoid Signal and BACTEC radiometric blood culture systems for the detection of bacteremia and fungemia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weinstein, M.P.; Mirrett, S.; Reller, L.B.

1988-05-01

The Oxoid Signal blood culture system is a newly described, innovative method for visually detecting growth of microorganisms. We did 5,999 paired comparisons of equal volumes (10 ml) of blood in the Oxoid Signal and BACTEC radiometric blood culture systems at two university hospitals that use identical methods of obtaining and processing specimens. Overall, more microorganisms were detected in the BACTEC system (P less than 0.001), in particular, streptococci (P less than 0.01), fungi (P less than 0.001), and nonfermentative gram-negative rods, especially Acinetobacter species (P less than 0.001). Trends favoring the BACTEC system for detection of Pseudomonas aeruginosa, Haemophilusmore » species, and Neisseria species were noted. There were no differences in the yield of staphylococci, members of the family Enterobacteriaceae, and anaerobic bacteria. When both systems detected sepsis, the BACTEC did so earlier (P less than 0.001). This advantage was most notable at 24 h (70% of BACTEC positives detected versus 48% of Oxoid positives). The proportion of positives detected after 48 h, however, was similar (BACTEC, 84%; Oxoid, 78%). Revisions in the Oxoid Signal system itself or in the processing of Oxoid bottles appear to be necessary to improve its performance in detecting certain microorganism groups, especially fungi.« less

Coherent Detection of High-Rate Optical PPM Signals

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor; Fernandez, Michela Munoz

2006-01-01

A method of coherent detection of high-rate pulse-position modulation (PPM) on a received laser beam has been conceived as a means of reducing the deleterious effects of noise and atmospheric turbulence in free-space optical communication using focal-plane detector array technologies. In comparison with a receiver based on direct detection of the intensity modulation of a PPM signal, a receiver based on the present method of coherent detection performs well at much higher background levels. In principle, the coherent-detection receiver can exhibit quantum-limited performance despite atmospheric turbulence. The key components of such a receiver include standard receiver optics, a laser that serves as a local oscillator, a focal-plane array of photodetectors, and a signal-processing and data-acquisition assembly needed to sample the focal-plane fields and reconstruct the pulsed signal prior to detection. The received PPM-modulated laser beam and the local-oscillator beam are focused onto the photodetector array, where they are mixed in the detection process. The two lasers are of the same or nearly the same frequency. If the two lasers are of different frequencies, then the coherent detection process is characterized as heterodyne and, using traditional heterodyne-detection terminology, the difference between the two laser frequencies is denoted the intermediate frequency (IF). If the two laser beams are of the same frequency and remain aligned in phase, then the coherent detection process is characterized as homodyne (essentially, heterodyne detection at zero IF). As a result of the inherent squaring operation of each photodetector, the output current includes an IF component that contains the signal modulation. The amplitude of the IF component is proportional to the product of the local-oscillator signal amplitude and the PPM signal amplitude. Hence, by using a sufficiently strong local-oscillator signal, one can make the PPM-modulated IF signal strong enough to overcome thermal noise in the receiver circuits: this is what makes it possible to achieve near-quantum-limited detection in the presence of strong background. Following quantum-limited coherent detection, the outputs of the individual photodetectors are automatically aligned in phase by use of one or more adaptive array compensation algorithms [e.g., the least-mean-square (LMS) algorithm]. Then the outputs are combined and the resulting signal is processed to extract the high-rate information, as though the PPM signal were received by a single photodetector. In a continuing series of experiments to test this method (see Fig. 1), the local oscillator has a wavelength of 1,064 nm, and another laser is used as a signal transmitter at a slightly different wavelength to establish an IF of about 6 MHz. There are 16 photodetectors in a 4 4 focal-plane array; the detector outputs are digitized at a sampling rate of 25 MHz, and the signals in digital form are combined by use of the LMS algorithm. Convergence of the adaptive combining algorithm in the presence of simulated atmospheric turbulence for optical PPM signals has already been demonstrated in the laboratory; the combined output is shown in Fig. 2(a), and Fig. 2(b) shows the behavior of the phase of the combining weights as a function of time (or samples). We observe that the phase of the weights has a sawtooth shape due to the continuously changing phase in the down-converted output, which is not exactly at zero frequency. Detailed performance analysis of this coherent free-space optical communication system in the presence of simulated atmospheric turbulence is currently under way.

Solution-grown crystals for neutron radiation detectors, and methods of solution growth

DOEpatents

Zaitseva, Natalia P; Hull, Giulia; Cherepy, Nerine J; Payne, Stephen A; Stoeffl, Wolfgang

2012-06-26

A method according to one embodiment includes growing an organic crystal from solution, the organic crystal exhibiting a signal response signature for neutrons from a radioactive source. A system according to one embodiment includes an organic crystal having physical characteristics of formation from solution, the organic crystal exhibiting a signal response signature for neutrons from a radioactive source; and a photodetector for detecting the signal response of the organic crystal. A method according to another embodiment includes growing an organic crystal from solution, the organic crystal being large enough to exhibit a detectable signal response signature for neutrons from a radioactive source. An organic crystal according to another embodiment includes an organic crystal having physical characteristics of formation from solution, the organic crystal exhibiting a signal response signature for neutrons from a radioactive source, wherein the organic crystal has a length of greater than about 1 mm in one dimension.

Signal Detection of Imipenem Compared to Other Drugs from Korea Adverse Event Reporting System Database

PubMed Central

Park, Kyounghoon; Soukavong, Mick; Kim, Jungmee; Kwon, Kyoung-eun; Jin, Xue-mei; Lee, Joongyub; Yang, Bo Ram

2017-01-01

Purpose To detect signals of adverse drug events after imipenem treatment using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD). Materials and Methods We performed data mining using KIDS-KD, which was constructed using spontaneously reported adverse event (AE) reports between December 1988 and June 2014. We detected signals calculated the proportional reporting ratio, reporting odds ratio, and information component of imipenem. We defined a signal as any AE that satisfied all three indices. The signals were compared with drug labels of nine countries. Results There were 807582 spontaneous AEs reports in the KIDS-KD. Among those, the number of antibiotics related AEs was 192510; 3382 reports were associated with imipenem. The most common imipenem-associated AE was the drug eruption; 353 times. We calculated the signal by comparing with all other antibiotics and drugs; 58 and 53 signals satisfied the three methods. We compared the drug labelling information of nine countries, including the USA, the UK, Japan, Italy, Switzerland, Germany, France, Canada, and South Korea, and discovered that the following signals were currently not included in drug labels: hypokalemia, cardiac arrest, cardiac failure, Parkinson's syndrome, myocardial infarction, and prostate enlargement. Hypokalemia was an additional signal compared with all other antibiotics, and the other signals were not different compared with all other antibiotics and all other drugs. Conclusion We detected new signals that were not listed on the drug labels of nine countries. However, further pharmacoepidemiologic research is needed to evaluate the causality of these signals. PMID:28332362

Supercontinuum Fourier transform spectrometry with balanced detection on a single photodiode

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goncharov, Vasily V.; Hall, Gregory E., E-mail: gehall@bnl.gov

We demonstrate a method of combining a supercontinuum light source with a commercial Fourier transform spectrometer, using a novel approach to dual-beam balanced detection, implemented with phase-sensitive detection on a single light detector. A 40 dB reduction in the relative intensity noise is achieved for broadband light, analogous to conventional balanced detection methods using two matched photodetectors. Unlike conventional balanced detection, however, this method exploits the time structure of the broadband source to interleave signal and reference pulse trains in the time domain, recording the broadband differential signal at the fundamental pulse repetition frequency of the supercontinuum. The method ismore » capable of real-time correction for instability in the supercontinuum spectral structure over a broad range of wavelengths and is compatible with commercially designed spectrometers. A proof-of-principle experimental setup is demonstrated for weak absorption in the 1500-1600 nm region.« less

Locomotive track detection for underground

NASA Astrophysics Data System (ADS)

Ma, Zhonglei; Lang, Wenhui; Li, Xiaoming; Wei, Xing

2017-08-01

In order to improve the PC-based track detection system, this paper proposes a method to detect linear track for underground locomotive based on DSP + FPGA. Firstly, the analog signal outputted from the camera is sampled by A / D chip. Then the collected digital signal is preprocessed by FPGA. Secondly, the output signal of FPGA is transmitted to DSP via EMIF port. Subsequently, the adaptive threshold edge detection, polar angle and radius constrain based Hough transform are implemented by DSP. Lastly, the detected track information is transmitted to host computer through Ethernet interface. The experimental results show that the system can not only meet the requirements of real-time detection, but also has good robustness.

A highly sensitive SPRi biosensing strategy for simultaneous detection of multiplex miRNAs based on strand displacement amplification and AuNP signal enhancement.

PubMed

Wei, Xiaotong; Duan, Xiaolei; Zhou, Xiaoyan; Wu, Jiangling; Xu, Hongbing; Min, Xun; Ding, Shijia

2018-06-07

Herein, a dual channel surface plasmon resonance imaging (SPRi) biosensor has been developed for the simultaneous and highly sensitive detection of multiplex miRNAs based on strand displacement amplification (SDA) and DNA-functionalized AuNP signal enhancement. In the presence of target miRNAs (miR-21 or miR-192), the miRNAs could specifically hybridize with the corresponding hairpin probes (H) and initiate the SDA, resulting in massive triggers. Subsequently, the two parts of the released triggers could hybridize with capture probes (CP) and DNA-functionalized AuNPs, assembling DNA sandwiches with great mass on the chip surface. A significantly amplified SPR signal readout was achieved. This established biosensing method was capable of simultaneously detecting multiplex miRNAs with a limit of detection down to 0.15 pM for miR-21 and 0.22 pM for miR-192. This method exhibited good specificity and acceptable reproducibility. Moreover, the developed method was applied to the determination of target miRNAs in a complex matrix. Thus, this developed SPRi biosensing method may present a potential alternative tool for miRNA detection in biomedical research and clinical diagnosis.

Amplitude-phase cross talk as a deterioration factor of signal-to-noise ratio in phase-detection noise-cancellation technique for spectral pump/probe measurements and compensation of the amplitude-phase cross talk

NASA Astrophysics Data System (ADS)

Seto, Keisuke; Tarumi, Takashi; Tokunaga, Eiji

2018-06-01

Noise cancellation of the light source is an important method to enhance the signal-to-noise ratio (SNR) and facilitate high-speed detection in pump/probe measurements. We developed a method to eliminate the noise for the multichannel spectral pump/probe measurements with a spectral dispersion of a white probe pulse light. In this method, the sample-induced intensity modulation is converted to the phase modulation of the pulse repetition irrespective of the intensity noise of the light source. The SNR is enhanced through the phase detection of the observed signal with the signal synchronized to the pulse repetition serving as the phase reference (synchronized signal). However, the shot-noise limited performance is not achieved with an intense probe light. In this work, we demonstrate that the performance limitation below the shot noise limit is caused by the amplitude-phase cross talk. It converts the amplitude noise into the phase noise and is caused by the space-charge effect in the photodetector, the reverse bias voltage drop across the load impedance, and the phase detection circuit. The phase delay occurs with an intense light at a PIN photodiode, whereas the phase is advanced in an avalanche photodiode. Although the amplitude distortion characteristics also reduce the performance, the distortion effect is equivalent to the amplitude-phase cross talk. We also propose possible ways to compensate the cross talk effect by using the phase modulation of the synchronized signal for the phase detection based on the instantaneous amplitude.

[A new method of distinguishing weak and overlapping signals of proton magnetic resonance spectroscopy].

PubMed

Jiang, Gang; Quan, Hong; Wang, Cheng; Gong, Qiyong

2012-12-01

In this paper, a new method of combining translation invariant (TI) and wavelet-threshold (WT) algorithm to distinguish weak and overlapping signals of proton magnetic resonance spectroscopy (1H-MRS) is presented. First, the 1H-MRS spectrum signal is transformed into wavelet domain and then its wavelet coefficients are obtained. Then, the TI method and WT method are applied to detect the weak signals overlapped by the strong ones. Through the analysis of the simulation data, we can see that both frequency and amplitude information of small-signals can be obtained accurately by the algorithm, and through the combination with the method of signal fitting, quantitative calculation of the area under weak signals peaks can be realized.

A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data.

PubMed

Cai, Yi; Du, Jingcheng; Huang, Jing; Ellenberg, Susan S; Hennessy, Sean; Tao, Cui; Chen, Yong

2017-07-05

To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals. Reports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations. We applied the proposed method to detect safety signals of FLU3, an influenza vaccine containing three flu strains, in the VAERS database. We showed that it had high statistical power to detect the variation in reporting rates across years. The identified vaccine-event combinations with significant different reporting rates over years suggested potential safety issues due to changes in vaccines which require further investigation. We developed a statistical model to detect safety signals arising from heterogeneity of reporting rates of a given vaccine-event combinations across reporting years. This method detects variation in reporting rates over years with high power. The temporal trend of reporting rate across years may reveal the impact of vaccine update on occurrence of adverse events and provide evidence for further investigations.

Refining historical limits method to improve disease cluster detection, New York City, New York, USA.

PubMed

Levin-Rector, Alison; Wilson, Elisha L; Fine, Annie D; Greene, Sharon K

2015-02-01

Since the early 2000s, the Bureau of Communicable Disease of the New York City Department of Health and Mental Hygiene has analyzed reportable infectious disease data weekly by using the historical limits method to detect unusual clusters that could represent outbreaks. This method typically produced too many signals for each to be investigated with available resources while possibly failing to signal during true disease outbreaks. We made method refinements that improved the consistency of case inclusion criteria and accounted for data lags and trends and aberrations in historical data. During a 12-week period in 2013, we prospectively assessed these refinements using actual surveillance data. The refined method yielded 74 signals, a 45% decrease from what the original method would have produced. Fewer and less biased signals included a true citywide increase in legionellosis and a localized campylobacteriosis cluster subsequently linked to live-poultry markets. Future evaluations using simulated data could complement this descriptive assessment.

Physics-based signal processing algorithms for micromachined cantilever arrays

DOEpatents

Candy, James V; Clague, David S; Lee, Christopher L; Rudd, Robert E; Burnham, Alan K; Tringe, Joseph W

2013-11-19

A method of using physics-based signal processing algorithms for micromachined cantilever arrays. The methods utilize deflection of a micromachined cantilever that represents the chemical, biological, or physical element being detected. One embodiment of the method comprises the steps of modeling the deflection of the micromachined cantilever producing a deflection model, sensing the deflection of the micromachined cantilever and producing a signal representing the deflection, and comparing the signal representing the deflection with the deflection model.

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing.

PubMed

Li, Jun; Lin, Qiu-Hua; Kang, Chun-Yu; Wang, Kai; Yang, Xiu-Ting

2018-03-18

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets.

The practical application of signal detection theory to image quality assessment in x-ray image intensifier-TV fluoroscopy.

PubMed

Marshall, N W

2001-06-01

This paper applies a published version of signal detection theory to x-ray image intensifier fluoroscopy data and compares the results with more conventional subjective image quality measures. An eight-bit digital framestore was used to acquire temporally contiguous frames of fluoroscopy data from which the modulation transfer function (MTF(u)) and noise power spectrum were established. These parameters were then combined to give detective quantum efficiency (DQE(u)) and used in conjunction with signal detection theory to calculate contrast-detail performance. DQE(u) was found to lie between 0.1 and 0.5 for a range of fluoroscopy systems. Two separate image quality experiments were then performed in order to assess the correspondence between the objective and subjective methods. First, image quality for a given fluoroscopy system was studied as a function of doserate using objective parameters and a standard subjective contrast-detail method. Following this, the two approaches were used to assess three different fluoroscopy units. Agreement between objective and subjective methods was good; doserate changes were modelled correctly while both methods ranked the three systems consistently.

"Utilizing" signal detection theory.

PubMed

Lynn, Spencer K; Barrett, Lisa Feldman

2014-09-01

What do inferring what a person is thinking or feeling, judging a defendant's guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows signal detection theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to signal detection theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A "utilized" signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. © The Author(s) 2014.

“UTILIZING” SIGNAL DETECTION THEORY

PubMed Central

Lynn, Spencer K.; Barrett, Lisa Feldman

2014-01-01

What do inferring what a person is thinking or feeling, deciding to report a symptom to your doctor, judging a defendant’s guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, which engender different appropriate responses), and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial we show how, by incorporating the economic concept of utility, signal detection theory serves as a model of optimal decision making, beyond its common use as an analytic method. This utility approach to signal detection theory highlights potentially enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (a functional relationship between bias and sensitivity). A “utilized” signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. PMID:25097061

Method of detecting system function by measuring frequency response

DOEpatents

Morrison, John L.; Morrison, William H.

2008-07-01

Real time battery impedance spectrum is acquired using one time record, Compensated Synchronous Detection (CSD). This parallel method enables battery diagnostics. The excitation current to a test battery is a sum of equal amplitude sin waves of a few frequencies spread over range of interest. The time profile of this signal has duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known, synchronous detection processes the time record and each component, both magnitude and phase, is obtained. For compensation, the components, except the one of interest, are reassembled in the time domain. The resulting signal is subtracted from the original signal and the component of interest is synchronously detected. This process is repeated for each component.

Method of Detecting System Function by Measuring Frequency Response

NASA Technical Reports Server (NTRS)

Morrison, John L. (Inventor); Morrison, William H. (Inventor)

2008-01-01

Real time battery impedance spectrum is acquired using one time record, Compensated Synchronous Detection (CSD). This parallel method enables battery diagnostics. The excitation current to a test battery is a sum of equal amplitude sin waves of a few frequencies spread over range of interest. The time profile of this signal has duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known, synchronous detection processes the time record and each component, both magnitude and phase, is obtained. For compensation, the components, except the one of interest, are reassembled in the time domain. The resulting signal is subtracted from the original signal and the component of interest is synchronously detected. This process is repeated for each component.

Gravitational wave spectroscopy of binary neutron star merger remnants with mode stacking

NASA Astrophysics Data System (ADS)

Yang, Huan; Paschalidis, Vasileios; Yagi, Kent; Lehner, Luis; Pretorius, Frans; Yunes, Nicolás

2018-01-01

A binary neutron star coalescence event has recently been observed for the first time in gravitational waves, and many more detections are expected once current ground-based detectors begin operating at design sensitivity. As in the case of binary black holes, gravitational waves generated by binary neutron stars consist of inspiral, merger, and postmerger components. Detecting the latter is important because it encodes information about the nuclear equation of state in a regime that cannot be probed prior to merger. The postmerger signal, however, can only be expected to be measurable by current detectors for events closer than roughly ten megaparsecs, which given merger rate estimates implies a low probability of observation within the expected lifetime of these detectors. We carry out Monte Carlo simulations showing that the dominant postmerger signal (the ℓ=m =2 mode) from individual binary neutron star mergers may not have a good chance of observation even with the most sensitive future ground-based gravitational wave detectors proposed so far (the Einstein Telescope and Cosmic Explorer, for certain equations of state, assuming a full year of operation, the latest merger rates, and a detection threshold corresponding to a signal-to-noise ratio of 5). For this reason, we propose two methods that stack the postmerger signal from multiple binary neutron star observations to boost the postmerger detection probability. The first method follows a commonly used practice of multiplying the Bayes factors of individual events. The second method relies on an assumption that the mode phase can be determined from the inspiral waveform, so that coherent mode stacking of the data from different events becomes possible. We find that both methods significantly improve the chances of detecting the dominant postmerger signal, making a detection very likely after a year of observation with Cosmic Explorer for certain equations of state. We also show that in terms of detection, coherent stacking is more efficient in accumulating confidence for the presence of postmerger oscillations in a signal than the first method. Moreover, assuming the postmerger signal is detected with Cosmic Explorer via stacking, we estimate through a Fisher analysis that the peak frequency can be measured to a statistical error of ˜4 - 20 Hz for certain equations of state. Such an error corresponds to a neutron star radius measurement to within ˜15 - 56 m , a fractional relative error ˜4 %, suggesting that systematic errors from theoretical modeling (≳100 m ) may dominate the error budget.

A Robust Apnea Period Detection Method in Changing Sleep Posture by Average Mutual Information of Heartbeat and Respiration

NASA Astrophysics Data System (ADS)

Kurihara, Yosuke; Watanabe, Kajiro; Kobayashi, Kazuyuki; Tanaka, Tanaka

Sleep disorders disturb the recovery from mental and physical fatigues, one of the functions of the sleep. The majority of those who with the disorders are suffering from Sleep Apnea Syndrome (SAS). Continuous Hypoxia during sleep due to SAS cause Circulatory Disturbances, such as hypertension and ischemic heart disease, and Malfunction of Autonomic Nervous System, and other severe complications, often times bringing the suffers to death. In order to prevent these from happening, it is important to detect the SAS in its early stage by monitoring the daily respirations during sleep, and to provide appropriate treatments at medical institutions. In this paper, the Pneumatic Method to detect the Apnea period during sleep is proposed. Pneumatic method can measure heartbeat and respiration signal. Respiration signal can be considered as noise against heartbeat signal, and the decrease in the respiration signal due to Apnea increases the Average Mutual Information of heartbeat. The result of scaling analysis of the average mutual information is defined as threshold to detect the apnea period. The root mean square error between the lengths of Apnea measured by Strain Gauge using for reference and those measured by using the proposed method was 3.1 seconds. And, error of the number of apnea times judged by doctor and proposal method in OSAS patients was 3.3 times.

Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods.

PubMed

Alves, Natasha; Chau, Tom

2010-04-01

Knowledge of muscle activity timing is critical to many clinical applications, such as the assessment of muscle coordination and the prescription of muscle-activated switches for individuals with disabilities. In this study, we introduce a continuous wavelet transform (CWT) algorithm for the detection of muscle activity via mechanomyogram (MMG) signals. CWT coefficients of the MMG signal were compared to scale-specific thresholds derived from the baseline signal to estimate the timing of muscle activity. Test signals were recorded from the flexor carpi radialis muscles of 15 able-bodied participants as they squeezed and released a hand dynamometer. Using the dynamometer signal as a reference, the proposed CWT detection algorithm was compared against a global-threshold CWT detector as well as amplitude-based event detection for sensitivity and specificity to voluntary contractions. The scale-specific CWT-based algorithm exhibited superior detection performance over the other detectors. CWT detection also showed good muscle selectivity during hand movement, particularly when a given muscle was the primary facilitator of the contraction. This may suggest that, during contraction, the compound MMG signal has a recurring morphological pattern that is not prevalent in the baseline signal. The ability of CWT analysis to be implemented in real time makes it a candidate for muscle-activity detection in clinical applications.

A new s-adenosylhomocysteine hydrolase-linked method for adenosine detection based on DNA-templated fluorescent Cu/Ag nanoclusters.

PubMed

Ahn, Jun Ki; Kim, Hyo Yong; Baek, Songyi; Park, Hyun Gyu

2017-07-15

We herein describe a novel fluorescent method for the rapid and selective detection of adenosine by utilizing DNA-templated Cu/Ag nanoclusters (NCs) and employing s-adenosylhomocysteine hydrolase (SAHH). SAHH is allowed to promote hydrolysis reaction of s-adenosylhomocysteine (SAH) and consequently produces homocysteine, which would quench the fluorescence signal from DNA-templated Cu/Ag nanoclusters employed as a signaling probe in this study. On the other hand, adenosine significantly inhibits the hydrolysis reaction and prevent the formation of homocysteine. Consequently, highly enhanced fluorescence signal from DNA-Cu/Ag NCs is retained, which could be used to identify the presence of adenosine. By employing this design principle, adenosine was sensitively detected down to 19nM with high specificity over other adenosine analogs such as AMP, ADP, ATP, cAMP, guanosine, cytidine, and urine. Finally, the diagnostic capability of this method was successfully verified by reliably detecting adenosine present in a real human serum sample. Copyright © 2016 Elsevier B.V. All rights reserved.

An Amplitude-Based Estimation Method for International Space Station (ISS) Leak Detection and Localization Using Acoustic Sensor Networks

NASA Technical Reports Server (NTRS)

Tian, Jialin; Madaras, Eric I.

2009-01-01

The development of a robust and efficient leak detection and localization system within a space station environment presents a unique challenge. A plausible approach includes the implementation of an acoustic sensor network system that can successfully detect the presence of a leak and determine the location of the leak source. Traditional acoustic detection and localization schemes rely on the phase and amplitude information collected by the sensor array system. Furthermore, the acoustic source signals are assumed to be airborne and far-field. Likewise, there are similar applications in sonar. In solids, there are specialized methods for locating events that are used in geology and in acoustic emission testing that involve sensor arrays and depend on a discernable phase front to the received signal. These methods are ineffective if applied to a sensor detection system within the space station environment. In the case of acoustic signal location, there are significant baffling and structural impediments to the sound path and the source could be in the near-field of a sensor in this particular setting.

Population density estimated from locations of individuals on a passive detector array

USGS Publications Warehouse

Efford, Murray G.; Dawson, Deanna K.; Borchers, David L.

2009-01-01

The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture–recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ("proximity" detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small.

Object detection with a multistatic array using singular value decomposition

DOEpatents

Hallquist, Aaron T.; Chambers, David H.

2014-07-01

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across a surface and that travels down the surface. The detection system converts the return signals from a time domain to a frequency domain, resulting in frequency return signals. The detection system then performs a singular value decomposition for each frequency to identify singular values for each frequency. The detection system then detects the presence of a subsurface object based on a comparison of the identified singular values to expected singular values when no subsurface object is present.

Noise-tolerant instantaneous heart rate and R-peak detection using short-term autocorrelation for wearable healthcare systems.

PubMed

Fujii, Takahide; Nakano, Masanao; Yamashita, Ken; Konishi, Toshihiro; Izumi, Shintaro; Kawaguchi, Hiroshi; Yoshimoto, Masahiko

2013-01-01

This paper describes a robust method of Instantaneous Heart Rate (IHR) and R-peak detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the R-wave interval. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable bio-signal monitoring systems, noise increases the incidence of misdetection and false detection of R-peaks. To prevent incorrect detection, we introduce a short-term autocorrelation (STAC) technique and a small-window autocorrelation (SWAC) technique, which leverages the similarity of QRS complex waveforms. Simulation results show that the proposed method improves the noise tolerance of R-peak detection.

Development of gait segmentation methods for wearable foot pressure sensors.

PubMed

Crea, S; De Rossi, S M M; Donati, M; Reberšek, P; Novak, D; Vitiello, N; Lenzi, T; Podobnik, J; Munih, M; Carrozza, M C

2012-01-01

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

Color-coded automated signal intensity curves for detection and characterization of breast lesions: preliminary evaluation of a new software package for integrated magnetic resonance-based breast imaging.

PubMed

Pediconi, Federica; Catalano, Carlo; Venditti, Fiammetta; Ercolani, Mauro; Carotenuto, Luigi; Padula, Simona; Moriconi, Enrica; Roselli, Antonella; Giacomelli, Laura; Kirchin, Miles A; Passariello, Roberto

2005-07-01

The objective of this study was to evaluate the value of a color-coded automated signal intensity curve software package for contrast-enhanced magnetic resonance mammography (CE-MRM) in patients with suspected breast cancer. Thirty-six women with suspected breast cancer based on mammographic and sonographic examinations were preoperatively evaluated on CE-MRM. CE-MRM was performed on a 1.5-T magnet using a 2D Flash dynamic T1-weighted sequence. A dosage of 0.1 mmol/kg of Gd-BOPTA was administered at a flow rate of 2 mL/s followed by 10 mL of saline. Images were analyzed with the new software package and separately with a standard display method. Statistical comparison was performed of the confidence for lesion detection and characterization with the 2 methods and of the diagnostic accuracy for characterization compared with histopathologic findings. At pathology, 54 malignant lesions and 14 benign lesions were evaluated. All 68 (100%) lesions were detected with both methods and good correlation with histopathologic specimens was obtained. Confidence for both detection and characterization was significantly (P < or = 0.025) better with the color-coded method, although no difference (P > 0.05) between the methods was noted in terms of the sensitivity, specificity, and overall accuracy for lesion characterization. Excellent agreement between the 2 methods was noted for both the determination of lesion size (kappa = 0.77) and determination of SI/T curves (kappa = 0.85). The novel color-coded signal intensity curve software allows lesions to be visualized as false color maps that correspond to conventional signal intensity time curves. Detection and characterization of breast lesions with this method is quick and easily interpretable.

Circuitry, systems and methods for detecting magnetic fields

DOEpatents

Kotter, Dale K [Shelley, ID; Spencer, David F [Idaho Falls, ID; Roybal, Lyle G [Idaho Falls, ID; Rohrbaugh, David T [Idaho Falls, ID

2010-09-14

Circuitry for detecting magnetic fields includes a first magnetoresistive sensor and a second magnetoresistive sensor configured to form a gradiometer. The circuitry includes a digital signal processor and a first feedback loop coupled between the first magnetoresistive sensor and the digital signal processor. A second feedback loop which is discrete from the first feedback loop is coupled between the second magnetoresistive sensor and the digital signal processor.

Transmission and full-band coherent detection of polarization-multiplexed all-optical Nyquist signals generated by Sinc-shaped Nyquist pulses

PubMed Central

Zhang, Junwen; Yu, Jianjun; Chi, Nan

2015-01-01

All optical method is considered as a promising technique for high symbol rate Nyquist signal generation, which has attracted a lot of research interests for high spectral-efficiency and high-capacity optical communication system. In this paper, we extend our previous work and report the fully experimental demonstration of polarization-division multiplexed (PDM) all-optical Nyquist signal generation based on Sinc-shaped Nyquist pulse with advanced modulation formats, fiber-transmission and single-receiver full-band coherent detection. Using this scheme, we have successfully demonstrated the generation, fiber transmission and single-receiver full-band coherent detection of all-optical Nyquist PDM-QPSK and PDM-16QAM signals up to 125-GBaud. 1-Tb/s single-carrier PDM-16QAM signal generation and full-band coherent detection is realized, which shows the advantage and feasibility of the single-carrier all-optical Nyquist signals. PMID:26323238

Detailed Vibration Analysis of Pinion Gear with Time-Frequency Methods

NASA Technical Reports Server (NTRS)

Mosher, Marianne; Pryor, Anna H.; Lewicki, David G.

2003-01-01

In this paper, the authors show a detailed analysis of the vibration signal from the destructive testing of a spiral bevel gear and pinion pair containing seeded faults. The vibration signal is analyzed in the time domain, frequency domain and with four time-frequency transforms: the Short Time Frequency Transform (STFT), the Wigner-Ville Distribution with the Choi-Williams kernel (WV-CW), the Continuous Wavelet' Transform (CWT) and the Discrete Wavelet Transform (DWT). Vibration data of bevel gear tooth fatigue cracks, under a variety of operating load levels and damage conditions, are analyzed using these methods. A new metric for automatic anomaly detection is developed and can be produced from any systematic numerical representation of the vibration signals. This new metric reveals indications of gear damage with all of the time-frequency transforms, as well as time and frequency representations, on this data set. Analysis with the CWT detects changes in the signal at low torque levels not found with the other transforms. The WV-CW and CWT use considerably more resources than the STFT and the DWT. More testing of the new metric is needed to determine its value for automatic anomaly detection and to develop fault detection methods for the metric.

Detection of interference phase by digital computation of quadrature signals in homodyne laser interferometry.

PubMed

Rerucha, Simon; Buchta, Zdenek; Sarbort, Martin; Lazar, Josef; Cip, Ondrej

2012-10-19

We have proposed an approach to the interference phase extraction in the homodyne laser interferometry. The method employs a series of computational steps to reconstruct the signals for quadrature detection from an interference signal from a non-polarising interferometer sampled by a simple photodetector. The complexity trade-off is the use of laser beam with frequency modulation capability. It is analytically derived and its validity and performance is experimentally verified. The method has proven to be a feasible alternative for the traditional homodyne detection since it performs with comparable accuracy, especially where the optical setup complexity is principal issue and the modulation of laser beam is not a heavy burden (e.g., in multi-axis sensor or laser diode based systems).

Method for curing polymers using variable-frequency microwave heating

DOEpatents

Lauf, R.J.; Bible, D.W.; Paulauskas, F.L.

1998-02-24

A method for curing polymers incorporating a variable frequency microwave furnace system designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity is disclosed. By varying the frequency of the microwave signal, non-uniformities within the cavity are minimized, thereby achieving a more uniform cure throughout the workpiece. A directional coupler is provided for detecting the direction of a signal and further directing the signal depending on the detected direction. A first power meter is provided for measuring the power delivered to the microwave furnace. A second power meter detects the magnitude of reflected power. The furnace cavity may be adapted to be used to cure materials defining a continuous sheet or which require compressive forces during curing. 15 figs.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

PubMed

Azami, Hamed; Escudero, Javier

2016-05-01

Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the formulation of PE. The AAPE algorithm can be used in almost every irregularity-based application in various signal and image processing fields. We also made freely available the Matlab code of the AAPE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Built-in-test by signature inspection (bitsi)

DOEpatents

Bergeson, Gary C.; Morneau, Richard A.

1991-01-01

A system and method for fault detection for electronic circuits. A stimulus generator sends a signal to the input of the circuit under test. Signature inspection logic compares the resultant signal from test nodes on the circuit to an expected signal. If the signals do not match, the signature inspection logic sends a signal to the control logic for indication of fault detection in the circuit. A data input multiplexer between the test nodes of the circuit under test and the signature inspection logic can provide for identification of the specific node at fault by the signature inspection logic. Control logic responsive to the signature inspection logic conveys information about fault detection for use in determining the condition of the circuit. When used in conjunction with a system test controller, the built-in test by signature inspection system and method can be used to poll a plurality of circuits automatically and continuous for faults and record the results of such polling in the system test controller.

SiPM electro-optical detection system noise suppression method

NASA Astrophysics Data System (ADS)

Bi, Xiangli; Yang, Suhui; Hu, Tao; Song, Yiheng

2014-11-01

In this paper, the single photon detection principle of Silicon Photomultipliers (SiPM) device is introduced. The main noise factors that infect the sensitivity of the electro-optical detection system are analyzed, including background light noise, detector dark noise, preamplifier noise and signal light noise etc. The Optical, electrical and thermodynamic methods are used to suppress the SiPM electro-optical detection system noise, which improved the response sensitivity of the detector. Using SiPM optoelectronic detector with a even high sensitivity, together with small field large aperture optical system, high cutoff narrow bandwidth filters, low-noise operational amplifier circuit, the modular design of functional circuit, semiconductor refrigeration technology, greatly improved the sensitivity of optical detection system, reduced system noise and achieved long-range detection of weak laser radiation signal. Theoretical analysis and experimental results show that the proposed methods are reasonable and efficient.

Deep neural network-based bandwidth enhancement of photoacoustic data.

PubMed

Gutta, Sreedevi; Kadimesetty, Venkata Suryanarayana; Kalva, Sandeep Kumar; Pramanik, Manojit; Ganapathy, Sriram; Yalavarthy, Phaneendra K

2017-11-01

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

A parametric symmetry breaking transducer

NASA Astrophysics Data System (ADS)

Eichler, Alexander; Heugel, Toni L.; Leuch, Anina; Degen, Christian L.; Chitra, R.; Zilberberg, Oded

2018-06-01

Force detectors rely on resonators to transduce forces into a readable signal. Usually, these resonators operate in the linear regime and their signal appears amidst a competing background comprising thermal or quantum fluctuations as well as readout noise. Here, we demonstrate a parametric symmetry breaking transduction method that leads to a robust nonlinear force detection in the presence of noise. The force signal is encoded in the frequency at which the system jumps between two phase states which are inherently protected against phase noise. Consequently, the transduction effectively decouples from readout noise channels. For a controlled demonstration of the method, we experiment with a macroscopic doubly clamped string. Our method provides a promising paradigm for high-precision force detection.

Bearing failure detection of micro wind turbine via power spectral density analysis for stator current signals spectrum

NASA Astrophysics Data System (ADS)

Mahmood, Faleh H.; Kadhim, Hussein T.; Resen, Ali K.; Shaban, Auday H.

2018-05-01

The failure such as air gap weirdness, rubbing, and scrapping between stator and rotor generator arise unavoidably and may cause extremely terrible results for a wind turbine. Therefore, we should pay more attention to detect and identify its cause-bearing failure in wind turbine to improve the operational reliability. The current paper tends to use of power spectral density analysis method of detecting internal race and external race bearing failure in micro wind turbine by estimation stator current signal of the generator. The failure detector method shows that it is well suited and effective for bearing failure detection.

Signal-on electrochemiluminescence biosensor for microRNA-319a detection based on two-stage isothermal strand-displacement polymerase reaction.

PubMed

Wang, Minghui; Zhou, Yunlei; Yin, Huanshun; Jiang, Wenjing; Wang, Haiyan; Ai, Shiyun

2018-06-01

MicroRNAs play crucial role in regulating gene expression in organism, thus it is very necessary to exploit an efficient method for the sensitive and specific detection of microRNA. Herein, a signal-on electrochemiluminescence biosensor was fabricated for microRNA-319a detection based on two-stage isothermal strand-displacement polymerase reaction (ISDPR). In the presence of target microRNA, amounts of trigger DNA could be generated by the first ISDPR. Then, the trigger DNA and the primer hybridized simultaneously with the hairpin probe to open the stem of the probe, and then the ECL signal will be emitted. In the presence of phi29 DNA polymerase and dNTPs, the trigger DNA could be displaced to initiate a new cycle which was the second ISDPR. Due to the two-stage amplification, this method presented excellent detection sensitivity with a low detection limit of 0.14 fM. Moreover, the applicability of the developed method was demonstrated by detecting the change of microRNA-319a content in the leaves of rice seedlings after the rice seeds were incubated with chemical mutagen of ethyl methanesulfonate. Copyright © 2018 Elsevier B.V. All rights reserved.

On the detection of other planetary systems by astrometric techniques

NASA Technical Reports Server (NTRS)

Black, D. C.; Scargle, J. D.

1982-01-01

A quantitative method for astrometrically detecting perturbations induced in a star's motion by the presence of a planetary object is described. A periodogram is defined, wherein signals observed from a star show exactly periodic variations, which can be extracted from observational data using purely statistical methods. A detection threshold is defined for the frequency of occurrence of some detectable signal, e.g., the Nyquist frequency. Possible effects of a stellar orbital eccentricity and multiple companions are discussed, noting that assumption of a circular orbit assures the spectral purity of the signal described. The periodogram technique was applied to 12 yr of astrometric data from the U.S. Naval Observatory for three stars with low mass stellar companions. Periodic perturbations were confirmed. A comparison of the accuracy of different astrometric systems shows that the detection accuracy of a system is determined by the measurement accuracy and the number of observations, although the detection efficiency can be maximized by minimizing the number of data points for the case when observational errors are proportional to the square root of the number of data points. It is suggested that a space-based astrometric telescope is best suited to take advantage of the method.

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

Buried object detection in GPR images

DOEpatents

Paglieroni, David W; Chambers, David H; Bond, Steven W; Beer, W. Reginald

2014-04-29

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Electromyography as a recording system for eyeblink conditioning with functional magnetic resonance imaging.

PubMed

Knuttinen, M-G; Parrish, T B; Weiss, C; LaBar, K S; Gitelman, D R; Power, J M; Mesulam, M-M; Disterhoft, J F

2002-10-01

This study was designed to develop a suitable method of recording eyeblink responses while conducting functional magnetic resonance imaging (fMRI). Given the complexity of this behavioral setup outside of the magnet, this study sought to adapt and further optimize an approach to eyeblink conditioning that would be suitable for conducting event-related fMRI experiments. This method involved the acquisition of electromyographic (EMG) signals from the orbicularis oculi of the right eye, which were subsequently amplified and converted into an optical signal outside of the head coil. This optical signal was converted back into an electrical signal once outside the magnet room. Electromyography (EMG)-detected eyeblinks were used to measure responses in a delay eyeblink conditioning paradigm. Our results indicate that: (1) electromyography is a sensitive method for the detection of eyeblinks during fMRI; (2) minimal interactions or artifacts of the EMG signal were created from the magnetic resonance pulse sequence; and (3) no electromyography-related artifacts were detected in the magnetic resonance images. Furthermore, an analysis of the functional data showed areas of activation that have previously been shown in positron emission tomography studies of human eyeblink conditioning. Our results support the strength of this behavioral setup as a suitable method to be used in association with fMRI.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Continuous Grading of Early Fibrosis in NAFLD Using Label-Free Imaging: A Proof-of-Concept Study

PubMed Central

Pirhonen, Juho; Arola, Johanna; Sädevirta, Sanja; Luukkonen, Panu; Karppinen, Sanna-Maria; Pihlajaniemi, Taina; Isomäki, Antti; Hukkanen, Mika

2016-01-01

Background and Aims Early detection of fibrosis is important in identifying individuals at risk for advanced liver disease in non-alcoholic fatty liver disease (NAFLD). We tested whether second-harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy, detecting fibrillar collagen and fat in a label-free manner, might allow automated and sensitive quantification of early fibrosis in NAFLD. Methods We analyzed 32 surgical biopsies from patients covering histological fibrosis stages 0–4, using multimodal label-free microscopy. Native samples were visualized by SHG and CARS imaging for detecting fibrillar collagen and fat. Furthermore, we developed a method for quantitative assessment of early fibrosis using automated analysis of SHG signals. Results We found that the SHG mean signal intensity correlated well with fibrosis stage and the mean CARS signal intensity with liver fat. Little overlap in SHG signal intensities between fibrosis stages 0 and 1 was observed. A specific fibrillar SHG signal was detected in the liver parenchyma outside portal areas in all samples histologically classified as having no fibrosis. This signal correlated with immunohistochemical location of fibrillar collagens I and III. Conclusions This study demonstrates that label-free SHG imaging detects fibrillar collagen deposition in NAFLD more sensitively than routine histological staging and enables observer-independent quantification of early fibrosis in NAFLD with continuous grading. PMID:26808140

Detection and identification of substances using noisy THz signal

NASA Astrophysics Data System (ADS)

Trofimov, Vyacheslav A.; Zakharova, Irina G.; Zagursky, Dmitry Yu.; Varentsova, Svetlana A.

2017-05-01

We discuss an effective method for the detection and identification of substances using a high noisy THz signal. In order to model such a noisy signal, we add to the THz signal transmitted through a pure substance, a noisy THz signal obtained in real conditions at a long distance (more than 3.5 m) from the receiver in air. The insufficiency of the standard THz-TDS method is demonstrated. The method discussed in the paper is based on time-dependent integral correlation criteria calculated using spectral dynamics of medium response. A new type of the integral correlation criterion, which is less dependent on spectral characteristics of the noisy signal under investigation, is used for the substance identification. To demonstrate the possibilities of the integral correlation criteria in real experiment, they are applied for the identification of explosive HMX in the reflection mode. To explain the physical mechanism for the false absorption frequencies appearance in the signal we make a computer simulation using 1D Maxwell's equations and density matrix formalism. We propose also new method for the substance identification by using the THz pulse frequency up-conversion and discuss an application of the cascade mechanism of molecules high energy levels excitation for the substance identification.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

Direct Extraction of Tumor Response Based on Ensemble Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB.

PubMed

Li, Qinwei; Xiao, Xia; Wang, Liang; Song, Hang; Kono, Hayato; Liu, Peifang; Lu, Hong; Kikkawa, Takamaro

2015-10-01

A direct extraction method of tumor response based on ensemble empirical mode decomposition (EEMD) is proposed for early breast cancer detection by ultra-wide band (UWB) microwave imaging. With this approach, the image reconstruction for the tumor detection can be realized with only extracted signals from as-detected waveforms. The calibration process executed in the previous research for obtaining reference waveforms which stand for signals detected from the tumor-free model is not required. The correctness of the method is testified by successfully detecting a 4 mm tumor located inside the glandular region in one breast model and by the model located at the interface between the gland and the fat, respectively. The reliability of the method is checked by distinguishing a tumor buried in the glandular tissue whose dielectric constant is 35. The feasibility of the method is confirmed by showing the correct tumor information in both simulation results and experimental results for the realistic 3-D printed breast phantom.

New optical method for enhanced detection of colon cancer by capsule endoscopy

NASA Astrophysics Data System (ADS)

AnkriEqually Contributed, Rinat; Peretz, Dolev; Motiei, Menachem; Sella-Tavor, Osnat; Popovtzer, Rachela

2013-09-01

PillCam®COLON capsule endoscopy (CE), a non-invasive diagnostic tool of the digestive tract, has dramatically changed the diagnostic approach and has become an attractive alternative to the conventional colonoscopy for early detection of colorectal cancer. However, despite the significant progress and non-invasive detection capability, studies have shown that its sensitivity and specificity is lower than that of conventional colonoscopy. This work presents a new optical detection method, specifically tailored to colon cancer detection and based on the well-known optical properties of immune-conjugated gold nanorods (GNRs). We show, on a colon cancer model implanted in a chick chorioallantoic membrane (CAM), that this detection method enables conclusive differentiation between cancerous and normal tissues, where neither the distance between the light source and the intestinal wall, nor the background signal, affects the monitored signal. This optical method, which can easily be integrated in CE, is expected to reduce false positive and false negative results and improve identification of tumors and micro metastases.

The contactless detection of local normal transitions in superconducting coils by using Poynting’s vector method

NASA Astrophysics Data System (ADS)

Habu, K.; Kaminohara, S.; Kimoto, T.; Kawagoe, A.; Sumiyoshi, F.; Okamoto, H.

2010-11-01

We have developed a new monitoring system to detect an unusual event in the superconducting coils without direct contact on the coils, using Poynting's vector method. In this system, the potential leads and pickup coils are set around the superconducting coils to measure local electric and magnetic fields, respectively. By measuring the sets of magnetic and electric fields, the Poynting's vectors around the coil can be obtained. An unusual event in the coil can be detected as the result of the change of the Poynting's vector. This system has no risk of the voltage breakdown which may happen with the balance voltage method, because there is no need of direct contacts on the coil windings. In a previous paper, we have demonstrated that our system can detect the normal transitions in the Bi-2223 coil without direct contact on the coil windings by using a small test system. For our system to be applied to practical devices, it is necessary for the early detection of an unusual event in the coils to be able to detect local normal transitions in the coils. The signal voltages of the small sensors to measure local magnetic and electric fields are small. Although the increase in signals of the pickup coils is attained easily by an increase in the number of turns of the pickup coils, an increase in the signals of the potential lead is not easily attained. In this paper, a new method to amplify the signal of local electric fields around the coil is proposed. The validity of the method has been confirmed by measuring local electric fields around the Bi-2223 coil.

Systems and methods for biometric identification using the acoustic properties of the ear canal

DOEpatents

Bouchard, Ann Marie; Osbourn, Gordon Cecil

1998-01-01

The present invention teaches systems and methods for verifying or recognizing a person's identity based on measurements of the acoustic response of the individual's ear canal. The system comprises an acoustic emission device, which emits an acoustic source signal s(t), designated by a computer, into the ear canal of an individual, and an acoustic response detection device, which detects the acoustic response signal f(t). A computer digitizes the response (detected) signal f(t) and stores the data. Computer-implemented algorithms analyze the response signal f(t) to produce ear-canal feature data. The ear-canal feature data obtained during enrollment is stored on the computer, or some other recording medium, to compare the enrollment data with ear-canal feature data produced in a subsequent access attempt, to determine if the individual has previously been enrolled. The system can also be adapted for remote access applications.

Systems and methods for biometric identification using the acoustic properties of the ear canal

DOEpatents

Bouchard, A.M.; Osbourn, G.C.

1998-07-28

The present invention teaches systems and methods for verifying or recognizing a person`s identity based on measurements of the acoustic response of the individual`s ear canal. The system comprises an acoustic emission device, which emits an acoustic source signal s(t), designated by a computer, into the ear canal of an individual, and an acoustic response detection device, which detects the acoustic response signal f(t). A computer digitizes the response (detected) signal f(t) and stores the data. Computer-implemented algorithms analyze the response signal f(t) to produce ear-canal feature data. The ear-canal feature data obtained during enrollment is stored on the computer, or some other recording medium, to compare the enrollment data with ear-canal feature data produced in a subsequent access attempt, to determine if the individual has previously been enrolled. The system can also be adapted for remote access applications. 5 figs.

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

PubMed Central

Zhu, Bohui; Ding, Yongsheng; Hao, Kuangrong

2013-01-01

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. PMID:23690875

Method and apparatus for monitoring characteristics of a flow path having solid components flowing therethrough

DOEpatents

Hoskinson, Reed L [Rigby, ID; Svoboda, John M [Idaho Falls, ID; Bauer, William F [Idaho Falls, ID; Elias, Gracy [Idaho Falls, ID

2008-05-06

A method and apparatus is provided for monitoring a flow path having plurality of different solid components flowing therethrough. For example, in the harvesting of a plant material, many factors surrounding the threshing, separating or cleaning of the plant material and may lead to the inadvertent inclusion of the component being selectively harvested with residual plant materials being discharged or otherwise processed. In accordance with the present invention the detection of the selectively harvested component within residual materials may include the monitoring of a flow path of such residual materials by, for example, directing an excitation signal toward of flow path of material and then detecting a signal initiated by the presence of the selectively harvested component responsive to the excitation signal. The detected signal may be used to determine the presence or absence of a selected plant component within the flow path of residual materials.

Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

PubMed

Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

2017-05-24

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

Strand-specific transcriptome profiling with directly labeled RNA on genomic tiling microarrays

PubMed Central

2011-01-01

Background With lower manufacturing cost, high spot density, and flexible probe design, genomic tiling microarrays are ideal for comprehensive transcriptome studies. Typically, transcriptome profiling using microarrays involves reverse transcription, which converts RNA to cDNA. The cDNA is then labeled and hybridized to the probes on the arrays, thus the RNA signals are detected indirectly. Reverse transcription is known to generate artifactual cDNA, in particular the synthesis of second-strand cDNA, leading to false discovery of antisense RNA. To address this issue, we have developed an effective method using RNA that is directly labeled, thus by-passing the cDNA generation. This paper describes this method and its application to the mapping of transcriptome profiles. Results RNA extracted from laboratory cultures of Porphyromonas gingivalis was fluorescently labeled with an alkylation reagent and hybridized directly to probes on genomic tiling microarrays specifically designed for this periodontal pathogen. The generated transcriptome profile was strand-specific and produced signals close to background level in most antisense regions of the genome. In contrast, high levels of signal were detected in the antisense regions when the hybridization was done with cDNA. Five antisense areas were tested with independent strand-specific RT-PCR and none to negligible amplification was detected, indicating that the strong antisense cDNA signals were experimental artifacts. Conclusions An efficient method was developed for mapping transcriptome profiles specific to both coding strands of a bacterial genome. This method chemically labels and uses extracted RNA directly in microarray hybridization. The generated transcriptome profile was free of cDNA artifactual signals. In addition, this method requires fewer processing steps and is potentially more sensitive in detecting small amount of RNA compared to conventional end-labeling methods due to the incorporation of more fluorescent molecules per RNA fragment. PMID:21235785

Multivariate evoked response detection based on the spectral F-test.

PubMed

Rocha, Paulo Fábio F; Felix, Leonardo B; Miranda de Sá, Antonio Mauricio F L; Mendes, Eduardo M A M

2016-05-01

Objective response detection techniques, such as magnitude square coherence, component synchrony measure, and the spectral F-test, have been used to automate the detection of evoked responses. The performance of these detectors depends on both the signal-to-noise ratio (SNR) and the length of the electroencephalogram (EEG) signal. Recently, multivariate detectors were developed to increase the detection rate even in the case of a low signal-to-noise ratio or of short data records originated from EEG signals. In this context, an extension to the multivariate case of the spectral F-test detector is proposed. The performance of this technique is assessed using Monte Carlo. As an example, EEG data from 12 subjects during photic stimulation is used to demonstrate the usefulness of the proposed detector. The multivariate method showed detection rates consistently higher than those ones when only one signal was used. It is shown that the response detection in EEG signals with the multivariate technique was statistically significant if two or more EEG derivations were used. Copyright © 2016 Elsevier B.V. All rights reserved.

Leak detection in medium density polyethylene (MDPE) pipe using pressure transient method

NASA Astrophysics Data System (ADS)

Amin, M. M.; Ghazali, M. F.; PiRemli, M. A.; Hamat, A. M. A.; Adnan, N. F.

2015-12-01

Water is an essential part of commodity for a daily life usage for an average person, from personal uses such as residential or commercial consumers to industries utilization. This study emphasizes on detection of leaking in medium density polyethylene (MDPE) pipe using pressure transient method. This type of pipe is used to analyze the position of the leakage in the pipeline by using Ensemble Empirical Mode Decomposition Method (EEMD) with signal masking. Water hammer would induce an impulse throughout the pipeline that caused the system turns into a surge of water wave. Thus, solenoid valve is used to create a water hammer through the pipelines. The data from the pressure sensor is collected using DASYLab software. The data analysis of the pressure signal will be decomposed into a series of wave composition using EEMD signal masking method in matrix laboratory (MATLAB) software. The series of decomposition of signals is then carefully selected which reflected intrinsic mode function (IMF). These IMFs will be displayed by using a mathematical algorithm, known as Hilbert transform (HT) spectrum. The IMF signal was analysed to capture the differences. The analyzed data is compared with the actual measurement of the leakage in term of percentage error. The error recorded is below than 1% and it is proved that this method highly reliable and accurate for leak detection.

Optimization design of spectral discriminator for high-spectral-resolution lidar based on error analysis.

PubMed

Di, Huige; Zhang, Zhanfei; Hua, Hangbo; Zhang, Jiaqi; Hua, Dengxin; Wang, Yufeng; He, Tingyao

2017-03-06

Accurate aerosol optical properties could be obtained via the high spectral resolution lidar (HSRL) technique, which employs a narrow spectral filter to suppress the Rayleigh or Mie scattering in lidar return signals. The ability of the filter to suppress Rayleigh or Mie scattering is critical for HSRL. Meanwhile, it is impossible to increase the rejection of the filter without limitation. How to optimize the spectral discriminator and select the appropriate suppression rate of the signal is important to us. The HSRL technology was thoroughly studied based on error propagation. Error analyses and sensitivity studies were carried out on the transmittance characteristics of the spectral discriminator. Moreover, ratwo different spectroscopic methods for HSRL were described and compared: one is to suppress the Mie scattering; the other is to suppress the Rayleigh scattering. The corresponding HSRLs were simulated and analyzed. The results show that excessive suppression of Rayleigh scattering or Mie scattering in a high-spectral channel is not necessary if the transmittance of the spectral filter for molecular and aerosol scattering signals can be well characterized. When the ratio of transmittance of the spectral filter for aerosol scattering and molecular scattering is less than 0.1 or greater than 10, the detection error does not change much with its value. This conclusion implies that we have more choices for the high-spectral discriminator in HSRL. Moreover, the detection errors of HSRL regarding the two spectroscopic methods vary greatly with the atmospheric backscattering ratio. To reduce the detection error, it is necessary to choose a reasonable spectroscopic method. The detection method of suppressing the Rayleigh signal and extracting the Mie signal can achieve less error in a clear atmosphere, while the method of suppressing the Mie signal and extracting the Rayleigh signal can achieve less error in a polluted atmosphere.

A New Approach to Detect Mover Position in Linear Motors Using Magnetic Sensors

PubMed Central

Paul, Sarbajit; Chang, Junghwan

2015-01-01

A new method to detect the mover position of a linear motor is proposed in this paper. This method employs a simple cheap Hall Effect sensor-based magnetic sensor unit to detect the mover position of the linear motor. With the movement of the linear motor, Hall Effect sensor modules electrically separated 120° along with the idea of three phase balanced condition (va + vb + vc = 0) are used to produce three phase signals. The amplitude of the sensor output voltage signals are adjusted to unit amplitude to minimize the amplitude errors. With the unit amplitude signals three to two phase transformation is done to reduce the three multiples of harmonic components. The final output thus obtained is converted to position data by the use of arctangent function. The measurement accuracy of the new method is analyzed by experiments and compared with the conventional two phase method. Using the same number of sensor modules as the conventional two phase method, the proposed method gives more accurate position information compared to the conventional system where sensors are separated by 90° electrical angles. PMID:26506348

Astrometric Research of Asteroidal Satellites

NASA Astrophysics Data System (ADS)

Kikwaya, J.-B.; Thuillot, W.; Rocher, P.; Vieira Martins, R.; Arlot, J.-E.; Angeli, Cl.

2002-09-01

Several observational methods have been applied in order to detect asteroidal satellites. Some of them were rather successful, such as the stellar occultations and mutual eclipse methods. Recently other techniques such as the space imaging, the adaptive optics and the radar imaging inferred a great improvement in the search for these objects. However several limitations appear in the type of data that each of them allow us to access. We propose to apply an astrometric method in order as well to detect new asteroidal satellites as to get complementary data of some already detected objects (mainly their orbital period). This method is founded on the search of the reflex effect of the primary object due to the orbital motion of a possible satellite. Such an astrometric signature, already searched by Monet & Monet (1998), may reach several tens of MAS. Only a spectral analysis could then detect this signal under good conditions of signal/noise ratio and thanks to high quality astrometric measurements and coverage by different sites of observation. We have applied such a method for several asteroids. A preliminary result is obtained thanks to 377 CCD observations of 146 Lucina made at the Haute-Provence Observatory in South of France. A periodical signal appears in this analysis, leading to data compatible with a first detection of a probable satellite made previously (Arlot et al. 1985) by the occultation method.

Non-contact detection of cardiac rate based on visible light imaging device

NASA Astrophysics Data System (ADS)

Zhu, Huishi; Zhao, Yuejin; Dong, Liquan

2012-10-01

We have developed a non-contact method to detect human cardiac rate at a distance. This detection is based on the general lighting condition. Using the video signal of human face region captured by webcam, we acquire the cardiac rate based on the PhotoPlethysmoGraphy theory. In this paper, the cardiac rate detecting method is mainly in view of the blood's different absorptivities of the lights various wavelengths. Firstly, we discompose the video signal into RGB three color signal channels and choose the face region as region of interest to take average gray value. Then, we draw three gray-mean curves on each color channel with time as variable. When the imaging device has good fidelity of color, the green channel signal shows the PhotoPlethysmoGraphy information most clearly. But the red and blue channel signals can provide more other physiological information on the account of their light absorptive characteristics of blood. We divide red channel signal by green channel signal to acquire the pulse wave. With the passband from 0.67Hz to 3Hz as a filter of the pulse wave signal and the frequency spectrum superimposed algorithm, we design frequency extracted algorithm to achieve the cardiac rate. Finally, we experiment with 30 volunteers, containing different genders and different ages. The results of the experiments are all relatively agreeable. The difference is about 2bmp. Through the experiment, we deduce that the PhotoPlethysmoGraphy theory based on visible light can also be used to detect other physiological information.

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm for Alaska and the Himalaya.

NASA Astrophysics Data System (ADS)

Hibert, Clement; Malet, Jean-Philippe; Provost, Floriane; Michéa, David; Geertsema, Marten

2017-04-01

Detection of landslide occurrences and measurement of their dynamics properties during run-out is a high research priority but a logistical and technical challenge. Seismology has started to help in several important ways. Taking advantage of the densification of global, regional and local networks of broadband seismic stations, recent advances now permit the seismic detection and location of landslides in near-real-time. This seismic detection could potentially greatly increase the spatio-temporal resolution at which we study landslides triggering, which is critical to better understand the influence of external forcings such as rainfalls and earthquakes. However, detecting automatically seismic signals generated by landslides still represents a challenge, especially for events with volumes below one millions of cubic meters. The low signal-to-noise ratio classically observed for landslide-generated seismic signals and the difficulty to discriminate these signals from those generated by regional earthquakes or anthropogenic and natural noises are some of the obstacles that have to be circumvented. We present a new method for automatically constructing instrumental landslide catalogues from continuous seismic data. We developed a robust and versatile solution, which can be implemented in any context where a seismic detection of landslides or other mass movements is relevant. The method is based on a spectral detection of the seismic signals and the identification of the sources with a Random Forest algorithm. The spectral detection allows detecting signals with low signal-to-noise ratio, while the Random Forest algorithm achieve a high rate of positive identification of the seismic signals generated by landslides and other seismic sources. We present here the preliminary results of the application of this processing chain in two contexts: i) In Himalaya with the data acquired between 2002 and 2005 by the Hi-Climb network; ii) In Alaska using data recorded by the permanent regional network and the USArray, which is currently being deployed in this region. The landslide seismic catalogues are compared to geomorphological catalogues in terms of number of events and dates when possible.

Automated Monitoring with a BSP Fault-Detection Test

NASA Technical Reports Server (NTRS)

Bickford, Randall L.; Herzog, James P.

2003-01-01

The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.

Detection and quantification system for monitoring instruments

DOEpatents

Dzenitis, John M [Danville, CA; Hertzog, Claudia K [Houston, TX; Makarewicz, Anthony J [Livermore, CA; Henderer, Bruce D [Livermore, CA; Riot, Vincent J [Oakland, CA

2008-08-12

A method of detecting real events by obtaining a set of recent signal results, calculating measures of the noise or variation based on the set of recent signal results, calculating an expected baseline value based on the set of recent signal results, determining sample deviation, calculating an allowable deviation by multiplying the sample deviation by a threshold factor, setting an alarm threshold from the baseline value plus or minus the allowable deviation, and determining whether the signal results exceed the alarm threshold.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

NASA Astrophysics Data System (ADS)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Automated detection and cataloging of global explosive volcanism using the International Monitoring System infrasound network

NASA Astrophysics Data System (ADS)

Matoza, Robin S.; Green, David N.; Le Pichon, Alexis; Shearer, Peter M.; Fee, David; Mialle, Pierrick; Ceranna, Lars

2017-04-01

We experiment with a new method to search systematically through multiyear data from the International Monitoring System (IMS) infrasound network to identify explosive volcanic eruption signals originating anywhere on Earth. Detecting, quantifying, and cataloging the global occurrence of explosive volcanism helps toward several goals in Earth sciences and has direct applications in volcanic hazard mitigation. We combine infrasound signal association across multiple stations with source location using a brute-force, grid-search, cross-bearings approach. The algorithm corrects for a background prior rate of coherent unwanted infrasound signals (clutter) in a global grid, without needing to screen array processing detection lists from individual stations prior to association. We develop the algorithm using case studies of explosive eruptions: 2008 Kasatochi, Alaska; 2009 Sarychev Peak, Kurile Islands; and 2010 Eyjafjallajökull, Iceland. We apply the method to global IMS infrasound data from 2005-2010 to construct a preliminary acoustic catalog that emphasizes sustained explosive volcanic activity (long-duration signals or sequences of impulsive transients lasting hours to days). This work represents a step toward the goal of integrating IMS infrasound data products into global volcanic eruption early warning and notification systems. Additionally, a better understanding of volcanic signal detection and location with the IMS helps improve operational event detection, discrimination, and association capabilities.

G-CNV: A GPU-Based Tool for Preparing Data to Detect CNVs with Read-Depth Methods.

PubMed

Manconi, Andrea; Manca, Emanuele; Moscatelli, Marco; Gnocchi, Matteo; Orro, Alessandro; Armano, Giuliano; Milanesi, Luciano

2015-01-01

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals.

A new method of time difference measurement: The time difference method by dual phase coincidence points detection

NASA Technical Reports Server (NTRS)

Zhou, Wei

1993-01-01

In the high accurate measurement of periodic signals, the greatest common factor frequency and its characteristics have special functions. A method of time difference measurement - the time difference method by dual 'phase coincidence points' detection is described. This method utilizes the characteristics of the greatest common factor frequency to measure time or phase difference between periodic signals. It can suit a very wide frequency range. Measurement precision and potential accuracy of several picoseconds were demonstrated with this new method. The instrument based on this method is very simple, and the demand for the common oscillator is low. This method and instrument can be used widely.

Highly Sensitive and Automated Surface Enhanced Raman Scattering-based Immunoassay for H5N1 Detection with Digital Microfluidics.

PubMed

Wang, Yang; Ruan, Qingyu; Lei, Zhi-Chao; Lin, Shui-Chao; Zhu, Zhi; Zhou, Leiji; Yang, Chaoyong

2018-04-17

Digital microfluidics (DMF) is a powerful platform for a broad range of applications, especially immunoassays having multiple steps, due to the advantages of low reagent consumption and high automatization. Surface enhanced Raman scattering (SERS) has been proven as an attractive method for highly sensitive and multiplex detection, because of its remarkable signal amplification and excellent spatial resolution. Here we propose a SERS-based immunoassay with DMF for rapid, automated, and sensitive detection of disease biomarkers. SERS tags labeled with Raman reporter 4-mercaptobenzoic acid (4-MBA) were synthesized with a core@shell nanostructure and showed strong signals, good uniformity, and high stability. A sandwich immunoassay was designed, in which magnetic beads coated with antibodies were used as solid support to capture antigens from samples to form a beads-antibody-antigen immunocomplex. By labeling the immunocomplex with a detection antibody-functionalized SERS tag, antigen can be sensitively detected through the strong SERS signal. The automation capability of DMF can greatly simplify the assay procedure while reducing the risk of exposure to hazardous samples. Quantitative detection of avian influenza virus H5N1 in buffer and human serum was implemented to demonstrate the utility of the DMF-SERS method. The DMF-SERS method shows excellent sensitivity (LOD of 74 pg/mL) and selectivity for H5N1 detection with less assay time (<1 h) and lower reagent consumption (∼30 μL) compared to the standard ELISA method. Therefore, this DMF-SERS method holds great potentials for automated and sensitive detection of a variety of infectious diseases.

Automated infrasound signal detection algorithms implemented in MatSeis - Infra Tool.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, Darren

2004-07-01

MatSeis's infrasound analysis tool, Infra Tool, uses frequency slowness processing to deconstruct the array data into three outputs per processing step: correlation, azimuth and slowness. Until now, an experienced analyst trained to recognize a pattern observed in outputs from signal processing manually accomplished infrasound signal detection. Our goal was to automate the process of infrasound signal detection. The critical aspect of infrasound signal detection is to identify consecutive processing steps where the azimuth is constant (flat) while the time-lag correlation of the windowed waveform is above background value. These two statements describe the arrival of a correlated set of wavefrontsmore » at an array. The Hough Transform and Inverse Slope methods are used to determine the representative slope for a specified number of azimuth data points. The representative slope is then used in conjunction with associated correlation value and azimuth data variance to determine if and when an infrasound signal was detected. A format for an infrasound signal detection output file is also proposed. The detection output file will list the processed array element names, followed by detection characteristics for each method. Each detection is supplied with a listing of frequency slowness processing characteristics: human time (YYYY/MM/DD HH:MM:SS.SSS), epochal time, correlation, fstat, azimuth (deg) and trace velocity (km/s). As an example, a ground truth event was processed using the four-element DLIAR infrasound array located in New Mexico. The event is known as the Watusi chemical explosion, which occurred on 2002/09/28 at 21:25:17 with an explosive yield of 38,000 lb TNT equivalent. Knowing the source and array location, the array-to-event distance was computed to be approximately 890 km. This test determined the station-to-event azimuth (281.8 and 282.1 degrees) to within 1.6 and 1.4 degrees for the Inverse Slope and Hough Transform detection algorithms, respectively, and the detection window closely correlated to the theoretical stratospheric arrival time. Further testing will be required for tuning of detection threshold parameters for different types of infrasound events.« less

Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals

NASA Astrophysics Data System (ADS)

Bai, F.; Gagar, D.; Foote, P.; Zhao, Y.

2017-02-01

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of < 1 - 7.1 % of the monitored region compared to 2.7% for the AIC method and a range of 1.8-9.4% for the conventional Fixed Threshold method at different threshold levels.

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

NASA Astrophysics Data System (ADS)

Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

2009-04-01

Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

Gold Nanoparticles Used as Protein Scavengers Enhance Surface Plasmon Resonance Signal

PubMed Central

Ferreira de Macedo, Erenildo; Ducatti Formaggio, Daniela Maria; Salles Santos, Nivia; Batista Tada, Dayane

2017-01-01

Although several researchers had reported on methodologies for surface plasmon resonance (SPR) signal amplification based on the use of nanoparticles (NPs), the majority addressed the sandwich technique and low protein concentration. In this work, a different approach for SPR signal enhancement based on the use of gold NPs was evaluated. The method was used in the detection of two lectins, peanut agglutinin (PNA) and concanavalin A (ConA). Gold NPs were functionalized with antibodies anti-PNA and anti-ConA, and these NPs were used as protein scavengers in a solution. After being incubated with solutions of PNA or ConA, the gold NPs coupled with the collected lectins were injected on the sensor containing the immobilized antibodies. The signal amplification provided by this method was compared to the signal amplification provided by the direct coupling of PNA and ConA to gold NPs. Furthermore, both methods, direct coupling and gold NPs as protein scavengers, were compared to the direct detection of PNA and ConA in solution. Compared to the analysis of free protein, the direct coupling of PNA and ConA to gold NPs resulted in a signal amplification of 10–40-fold and a 13-fold decrease of the limit of detection (LOD), whereas the use of gold NPs as protein scavengers resulted in an SPR signal 40–50-times higher and an LOD 64-times lower. PMID:29186024

Adaptive EMG noise reduction in ECG signals using noise level approximation

NASA Astrophysics Data System (ADS)

Marouf, Mohamed; Saranovac, Lazar

2017-12-01

In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.

Multiple-Bit Differential Detection of OQPSK

NASA Technical Reports Server (NTRS)

Simon, Marvin

2005-01-01

A multiple-bit differential-detection method has been proposed for the reception of radio signals modulated with offset quadrature phase-shift keying (offset QPSK or OQPSK). The method is also applicable to other spectrally efficient offset quadrature modulations. This method is based partly on the same principles as those of a multiple-symbol differential-detection method for M-ary QPSK, which includes QPSK (that is, non-offset QPSK) as a special case. That method was introduced more than a decade ago by the author of the present method as a means of improving performance relative to a traditional (two-symbol observation) differential-detection scheme. Instead of symbol-by-symbol detection, both that method and the present one are based on a concept of maximum-likelihood sequence estimation (MLSE). As applied to the modulations in question, MLSE involves consideration of (1) all possible binary data sequences that could have been received during an observation time of some number, N, of symbol periods and (2) selection of the sequence that yields the best match to the noise-corrupted signal received during that time. The performance of the prior method was shown to range from that of traditional differential detection for short observation times (small N) to that of ideal coherent detection (with differential encoding) for long observation times (large N).

The stratospheric QBO signal in the NCEP reanalysis, 1948-2001

NASA Astrophysics Data System (ADS)

Ribera, P.; Gallego, D.; Pena-Ortiz, C.; Gimeno, L.; Garcia, R.; Hernandez, E.; Calvo, N.

2003-04-01

The spatiotemporal evolution of the zonal wind in the stratosphere is analyzed based on the use of the NCEP reanalysis dataset (1948-2001). MTM-SVD, a frequency-domain analysis method, is applied to isolate significant spatially-coherent variability with narrowband, oscillatory character. A quasibiennial oscillation is detected as the most intense coherent signal in the whole mid and high stratosphere, being the signal less intense in the lower levels, closer to the troposphere. There is a clear downward propagation of the signal with time over low latitudes, from 10 to 100 hPa, that is not evident over mid and high latitudes. A different behavior of the signal is detected over the Northern and the Southern Hemisphere. In the NH an anomaly in the zonal wind field, in phase with the equatorial signal, is detected to run around the whole hemisphere at 60º, and two regions in subtropical latitudes show wind anomalies with their sing opposed to that of the equator. In the SH no signal is detected in extratropical areas.

Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

NASA Astrophysics Data System (ADS)

Li, Jimeng; Li, Ming; Zhang, Jinfeng

2017-08-01

Rolling bearings are the key components in the modern machinery, and tough operation environments often make them prone to failure. However, due to the influence of the transmission path and background noise, the useful feature information relevant to the bearing fault contained in the vibration signals is weak, which makes it difficult to identify the fault symptom of rolling bearings in time. Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the fault diagnosis of rolling bearings. The MED method is employed to preprocess the vibration signals, which can deconvolve the effect of transmission path and clarify the defect-induced impulses. And a modified power spectrum kurtosis (MPSK) index is constructed to realize the adaptive selection of filter length in the MED algorithm. By introducing the time-delayed feedback item in to an over-damped monostable system, the TFMSR method can effectively utilize the historical information of input signal to enhance the periodicity of SR output, which is beneficial to the detection of periodic signal. Furthermore, the influence of time delay and feedback intensity on the SR phenomenon is analyzed, and by selecting appropriate time delay, feedback intensity and re-scaling ratio with genetic algorithm, the SR can be produced to realize the resonance detection of weak signal. The combination of the adaptive MED (AMED) method and TFMSR method is conducive to extracting the feature information from strong background noise and realizing the fault diagnosis of rolling bearings. Finally, some experiments and engineering application are performed to evaluate the effectiveness of the proposed AMED-TFMSR method in comparison with a traditional bistable SR method.

Dual-frequency ultrasound for detecting and sizing bubbles.

PubMed

Buckey, Jay C; Knaus, Darin A; Alvarenga, Donna L; Kenton, Marc A; Magari, Patrick J

2005-01-01

ISS construction and Mars exploration require extensive extravehicular activity (EVA), exposing crewmembers to increased decompression sickness risk. Improved bubble detection technologies could help increase EVA efficiency and safety. Creare Inc. has developed a bubble detection and sizing instrument using dual-frequency ultrasound. The device emits "pump" and "image" signals at two frequencies. The low-frequency pump signal causes an appropriately-sized bubble to resonate. When the image frequency hits a resonating bubble, mixing signals are returned at the sum and difference of the two frequencies. To test the feasibility of transcutaneous intravascular detection, intravascular bubbles in anesthetized swine were produced using agitated saline and decompression stress. Ultrasonic transducers on the chest provided the two frequencies. Mixing signals were detected transthoracically in the right atrium using both methods. A histogram of estimated bubble sizes could be constructed. Bubbles can be detected and sized transthoracically in the right atrium using dual-frequency ultrasound. c2005 Elsevier Ltd. All rights reserved.

Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

NASA Astrophysics Data System (ADS)

Bagherzadeh, Seyed Amin; Asadi, Davood

2017-05-01

In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

Average Likelihood Methods for Code Division Multiple Access (CDMA)

DTIC Science & Technology

2014-05-01

lengths in the range of 22 to 213 and possibly higher. Keywords: DS / CDMA signals, classification, balanced CDMA load, synchronous CDMA , decision...likelihood ratio test (ALRT). We begin this classification problem by finding the size of the spreading matrix that generated the DS - CDMA signal. As...Theoretical Background The classification of DS / CDMA signals should not be confused with the problem of multiuser detection. The multiuser detection deals

Fading-free transmission of 124-Gb/s PDM-DMT signal over 100-km SSMF using digital carrier regeneration.

PubMed

Li, Cai; Hu, Rong; Yang, Qi; Luo, Ming; Li, Wei; Yu, Shaohua

2016-01-25

The coherent reception of intensity modulated signal has been recently widely investigated, in which the signal is recovered by the envelop detection. High linewidth tolerance is achieved with such scheme. However, strong optical carrier exists during the transmission, which degrades the optical power efficiency. In this paper, an efficient modulation scheme for discrete multi-tone (DMT) signal is proposed based on the Mach-Zehnder modulator (MZM). Different from the traditional intensity modulation, the proposed method employs both intensity and phase domain. Thus, the optical carrier power can be greatly reduced by adjusting the bias of MZM around the null point. By employing coherent detection and digital carrier regeneration (DCR), the carrier suppressed DMT signal can be recovered using envelop detection. No carrier frequency or phase estimation is required. Numerical investigations are made to demonstrate the feasibility, in which significant improvements are found for the proposed DCR method, showing great tolerance against laser linewidth and carrier power reduction. Finally, a 124-Gb/s transmission of polarization-division multiplexed DMT (PDM-DMT) signal is demonstrated over 100-km SSMF, with only -8 dB optical carrier to signal power ratio (CSPR).

Extraction and analysis of neuron firing signals from deep cortical video microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerekes, Ryan A; Blundon, Jay

We introduce a method for extracting and analyzing neuronal activity time signals from video of the cortex of a live animal. The signals correspond to the firing activity of individual cortical neurons. Activity signals are based on the changing fluorescence of calcium indicators in the cells over time. We propose a cell segmentation method that relies on a user-specified center point, from which the signal extraction method proceeds. A stabilization approach is used to reduce tissue motion in the video. The extracted signal is then processed to flatten the baseline and detect action potentials. We show results from applying themore » method to a cortical video of a live mouse.« less

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Association rule mining in the US Vaccine Adverse Event Reporting System (VAERS).

PubMed

Wei, Lai; Scott, John

2015-09-01

Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product-adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data-mining approach to identify product-multiple adverse event associations. We describe an application of stepwise association rule mining (Step-ARM) to detect potential vaccine-symptom group associations in the US Vaccine Adverse Event Reporting System. Step-ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step-ARM, we also propose a clustering method for the post-processing of association rules. In sample applications to a trivalent intradermal inactivated influenza virus vaccine and to measles, mumps, rubella, and varicella (MMRV) vaccine and in simulation studies, we find that Step-ARM can detect a variety of medically coherent potential vaccine-symptom group signals efficiently. In the MMRV example, Step-ARM appears to outperform univariate methods in detecting a known safety signal. Our approach is sensitive to potentially complex signals, which may be particularly important when monitoring novel medical countermeasure products such as pandemic influenza vaccines. The post-processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation. Copyright © 2015 John Wiley & Sons, Ltd.

Spot restoration for GPR image post-processing

DOEpatents

Paglieroni, David W; Beer, N. Reginald

2014-05-20

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Constant-Envelope Waveform Design for Optimal Target-Detection and Autocorrelation Performances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Satyabrata

2013-01-01

We propose an algorithm to directly synthesize in time-domain a constant-envelope transmit waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. This approach is in contrast to the traditional indirect methods that synthesize the transmit signal following the computation of the optimal energy spectral density. Additionally, we aim to maintain a good autocorrelation property of the designed signal. Therefore, our waveform design technique solves a bi-objective optimization problem in order to simultaneously improve the detection and autocorrelation performances, which are in general conflicting in nature. We demonstrate this compromising characteristics of themore » detection and autocorrelation performances with numerical examples. Furthermore, in the absence of the autocorrelation criterion, our designed signal is shown to achieve a near-optimum detection performance.« less

Low-Complexity Noncoherent Signal Detection for Nanoscale Molecular Communications.

PubMed

Li, Bin; Sun, Mengwei; Wang, Siyi; Guo, Weisi; Zhao, Chenglin

2016-01-01

Nanoscale molecular communication is a viable way of exchanging information between nanomachines. In this investigation, a low-complexity and noncoherent signal detection technique is proposed to mitigate the inter-symbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed noncoherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the molecular concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The difference in molecular concentration is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nanoscale molecular communication systems with a small energy budget or limited computation resource.

Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators.

PubMed

Ross, Joseph S; Bates, Jonathan; Parzynski, Craig S; Akar, Joseph G; Curtis, Jeptha P; Desai, Nihar R; Freeman, James V; Gamble, Ginger M; Kuntz, Richard; Li, Shu-Xia; Marinac-Dabic, Danica; Masoudi, Frederick A; Normand, Sharon-Lise T; Ranasinghe, Isuru; Shaw, Richard E; Krumholz, Harlan M

2017-01-01

Machine learning methods may complement traditional analytic methods for medical device surveillance. Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%-20.9%; nonfatal ICD-related adverse events, 19.3%-26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%-37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k =0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k =-0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k =-0.042). Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance.

Detection and Classification of Transformer Winding Mechanical Faults Using UWB Sensors and Bayesian Classifier

NASA Astrophysics Data System (ADS)

Alehosseini, Ali; A. Hejazi, Maryam; Mokhtari, Ghassem; B. Gharehpetian, Gevork; Mohammadi, Mohammad

2015-06-01

In this paper, the Bayesian classifier is used to detect and classify the radial deformation and axial displacement of transformer windings. The proposed method is tested on a model of transformer for different volumes of radial deformation and axial displacement. In this method, ultra-wideband (UWB) signal is sent to the simplified model of the transformer winding. The received signal from the winding model is recorded and used for training and testing of Bayesian classifier in different axial displacement and radial deformation states of the winding. It is shown that the proposed method has a good accuracy to detect and classify the axial displacement and radial deformation of the winding.

Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space complexities of GModPCA are less as compared to PCA. This study suggests that GModPCA and SVM could be used for automated epileptic seizure detection in EEG signal.

SPEPlip: the detection of signal peptide and lipoprotein cleavage sites.

PubMed

Fariselli, Piero; Finocchiaro, Giacomo; Casadio, Rita

2003-12-12

SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins. It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/

Balanced detection for self-mixing interferometry.

PubMed

Li, Kun; Cavedo, Federico; Pesatori, Alessandro; Zhao, Changming; Norgia, Michele

2017-01-15

We propose a new detection scheme for self-mixing interferometry using two photodiodes for implementing a differential acquisition. The method is based on the phase opposition of the self-mixing signal measured between the two laser diode facet outputs. The subtraction of the two outputs implements a sort of balanced detection that improves the signal quality, and allows canceling of unwanted signals due to laser modulation and disturbances on laser supply and transimpedance amplifier. Experimental results demonstrate the benefits of differential acquisition in a system for both absolute distance and displacement-vibration measurement. This Letter provides guidance for the design of self-mixing interferometers using balanced detection.

Reverse phase protein microarrays: fluorometric and colorimetric detection.

PubMed

Gallagher, Rosa I; Silvestri, Alessandra; Petricoin, Emanuel F; Liotta, Lance A; Espina, Virginia

2011-01-01

The Reverse Phase Protein Microarray (RPMA) is an array platform used to quantitate proteins and their posttranslationally modified forms. RPMAs are applicable for profiling key cellular signaling pathways and protein networks, allowing direct comparison of the activation state of proteins from multiple samples within the same array. The RPMA format consists of proteins immobilized directly on a nitrocellulose substratum. The analyte is subsequently probed with a primary antibody and a series of reagents for signal amplification and detection. Due to the diversity, low concentration, and large dynamic range of protein analytes, RPMAs require stringent signal amplification methods, high quality image acquisition, and software capable of precisely analyzing spot intensities on an array. Microarray detection strategies can be either fluorescent or colorimetric. The choice of a detection system depends on (a) the expected analyte concentration, (b) type of microarray imaging system, and (c) type of sample. The focus of this chapter is to describe RPMA detection and imaging using fluorescent and colorimetric (diaminobenzidine (DAB)) methods.

Electrochemiluminescence-PCR detection of genetically modified organisms

NASA Astrophysics Data System (ADS)

Liu, Jinfeng; Xing, Da; Shen, Xingyan; Zhu, Debin

2005-01-01

The detection methods for genetically modified (GM) components in foods have been developed recently. But many of them are complicated and time-consuming; some of them need to use the carcinogenic substance, and can"t avoid false-positive results. In this study, an electrochemiluminescence polymerase chain reaction (ECL-PCR) method for detection GM tobaccos is proposed. The Cauliflower mosaic virus 35S (CaMV35S) promoter was amplified by PCR, Then hybridized with a Ru(bpy)32+ (TBR)-labeled and a biotinylated probe. The hybridization products were captured onto streptavidin-coated paramagnetic beads, and detected by measuring the electrochemiluminescence (ECL) signal of the TBR label. Whether the tobaccos contain GM components was discriminated by detecting the ECL signal of CaMV35S promoter. The experiment results show that the detection limit for CaMV35S promoter is 100 fmol, and the GM components can be clearly identified in GM tobaccos. The ECL-PCR method provide a new means in GMOs detection due to its safety, simplicity and high efficiency.

Real-time system for imaging and object detection with a multistatic GPR array

DOEpatents

Paglieroni, David W; Beer, N Reginald; Bond, Steven W; Top, Philip L; Chambers, David H; Mast, Jeffrey E; Donetti, John G; Mason, Blake C; Jones, Steven M

2014-10-07

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Method for curing polymers using variable-frequency microwave heating

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lauf, R.J.; Bible, D.W.; Paulauskas, F.L.

1998-02-24

A method for curing polymers incorporating a variable frequency microwave furnace system designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity is disclosed. By varying the frequency of the microwave signal, non-uniformities within the cavity are minimized, thereby achieving a more uniform cure throughout the workpiece. A directional coupler is provided for detecting the direction of a signal and further directing the signal depending on the detected direction. A first power meter is provided for measuring the power delivered to the microwave furnace. A second power meter detects the magnitude of reflected power. Themore » furnace cavity may be adapted to be used to cure materials defining a continuous sheet or which require compressive forces during curing. 15 figs.« less

Detection of Interference Phase by Digital Computation of Quadrature Signals in Homodyne Laser Interferometry

PubMed Central

Rerucha, Simon; Buchta, Zdenek; Sarbort, Martin; Lazar, Josef; Cip, Ondrej

2012-01-01

We have proposed an approach to the interference phase extraction in the homodyne laser interferometry. The method employs a series of computational steps to reconstruct the signals for quadrature detection from an interference signal from a non-polarising interferometer sampled by a simple photodetector. The complexity trade-off is the use of laser beam with frequency modulation capability. It is analytically derived and its validity and performance is experimentally verified. The method has proven to be a feasible alternative for the traditional homodyne detection since it performs with comparable accuracy, especially where the optical setup complexity is principal issue and the modulation of laser beam is not a heavy burden (e.g., in multi-axis sensor or laser diode based systems). PMID:23202038

Method for curing polymers using variable-frequency microwave heating

DOEpatents

Lauf, Robert J.; Bible, Don W.; Paulauskas, Felix L.

1998-01-01

A method for curing polymers (11) incorporating a variable frequency microwave furnace system (10) designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity (34). By varying the frequency of the microwave signal, non-uniformities within the cavity (34) are minimized, thereby achieving a more uniform cure throughout the workpiece (36). A directional coupler (24) is provided for detecting the direction of a signal and further directing the signal depending on the detected direction. A first power meter (30) is provided for measuring the power delivered to the microwave furnace (32). A second power meter (26) detects the magnitude of reflected power. The furnace cavity (34) may be adapted to be used to cure materials defining a continuous sheet or which require compressive forces during curing.

A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal

NASA Astrophysics Data System (ADS)

Luján, José M.; Bermúdez, Vicente; Guardiola, Carlos; Abbad, Ali

2010-10-01

In-cylinder pressure measurement has historically been used for off-line combustion diagnosis, but online application for real-time combustion control has become of great interest. This work considers low computing-cost methods for analysing the instant variation of the chamber pressure, directly obtained from the electric signal provided by a traditional piezoelectric sensor. Presented methods are based on the detection of sudden changes in the chamber pressure, which are amplified by the pressure derivative, and which are due to thermodynamic phenomena within the cylinder. Signal analysis tools both in time and in time-frequency domains are used for detecting the start of combustion, the end of combustion and the heat release peak. Results are compared with classical thermodynamic analysis and validated in several turbocharged diesel engines.

Identification of myoelectric signals of pregnant rat uterus: new method to detect myometrial contraction

PubMed Central

Szűcs, Kálmán F.; Grosz, György; Süle, Miklós; Nagy, Anikó; Tiszai, Zita; Samavati, Reza; Gáspár, Róbert

2017-01-01

Aim To develop an electromyography method for pregnant rat uterus in vivo and to separate myometrial signals from the gastrointestinal tract signals. Methods Pregnant Sprague-Dawley rats (n = 8) were anaesthetized and their stomach, small intestine, and large intestine were removed from the abdomen. A pair of thread electrodes was inserted into the uterus, while a pair of disk electrodes was placed subcutaneously above the myometrium. Additionally, a strain gauge sensor was fixed on the surface of the myometrium and cecum for the parallel detection of mechanical contractions in rats (n = 18) with intact gastrointestinal tract. The filtered electric signals were amplified and recorded by an online computer system and analyzed by fast Fourier transformation. The frequency of the electric activity was characterized by cycle per minute (cpm), the magnitude of the activity was described as power spectrum density maximum (PsDmax). Results The frequency of the pregnant uterine activity was 1-3 cpm, which falls within the same range as that of cecum. Measuring by both electrodes, oxytocin (1 µg/kg) increased and terbutaline (50 µg/kg) decreased the PsDmax by 25%-50% (P < 0.001) and 25%-40% (P < 0.01), respectively. We found a strong positive correlation between the alterations of PsDmax values and the strain gauge sensor-detected mechanical contractions (area under curve). The GI specific compounds (neostigmine, atropine) mainly affected the cecal activity, while myometrium specific drugs (oxytocin, terbutaline) influenced the myometrial signals only. Conclusion Our method proved to be able to detect the myoelectric activity that reflects the mechanical contraction. The overlapping myometrial and cecal signals are not separable, but they can be distinguished based on the much higher activity and different pharmacological reactivity of the pregnant uterus. Thus, the early signs of contractions can be detected and labor may be predicted in a fast and sensitive way. PMID:28409497

Detection of biological molecules using chemical amplification and optical sensors

DOEpatents

Van Antwerp, William Peter; Mastrototaro, John Joseph

2000-01-01

Methods are provided for the determination of the concentration of biological levels of polyhydroxylated compounds, particularly glucose. The methods utilize an amplification system that is an analyte transducer immobilized in a polymeric matrix, where the system is implantable and biocompatible. Upon interrogation by an optical system, the amplification system produces a signal capable of detection external to the skin of the patient. Quantitation of the analyte of interest is achieved by measurement of the emitted signal.

Detection of biological molecules using chemical amplification and optical sensors

DOEpatents

Van Antwerp, William Peter; Mastrototaro, John Joseph

2004-10-12

Methods are provided for the determination of the concentration of biological levels of polyhydroxylated compounds, particularly glucose. The methods utilize an amplification system that is an analyte transducer immobilized in a polymeric matrix, where the system is implantable and biocompatible. Upon interrogation by an optical system, the amplification system produces a signal capable of detection external to the skin of the patient. Quantitation of the analyte of interest is achieved by measurement of the emitted signal.

Real-Time Rotational Activity Detection in Atrial Fibrillation

PubMed Central

Ríos-Muñoz, Gonzalo R.; Arenal, Ángel; Artés-Rodríguez, Antonio

2018-01-01

Rotational activations, or spiral waves, are one of the proposed mechanisms for atrial fibrillation (AF) maintenance. We present a system for assessing the presence of rotational activity from intracardiac electrograms (EGMs). Our system is able to operate in real-time with multi-electrode catheters of different topologies in contact with the atrial wall, and it is based on new local activation time (LAT) estimation and rotational activity detection methods. The EGM LAT estimation method is based on the identification of the highest sustained negative slope of unipolar signals. The method is implemented as a linear filter whose output is interpolated on a regular grid to match any catheter topology. Its operation is illustrated on selected signals and compared to the classical Hilbert-Transform-based phase analysis. After the estimation of the LAT on the regular grid, the detection of rotational activity in the atrium is done by a novel method based on the optical flow of the wavefront dynamics, and a rotation pattern match. The methods have been validated using in silico and real AF signals. PMID:29593566

Hypothesis tests for the detection of constant speed radiation moving sources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dumazert, Jonathan; Coulon, Romain; Kondrasovs, Vladimir

2015-07-01

Radiation Portal Monitors are deployed in linear network to detect radiological material in motion. As a complement to single and multichannel detection algorithms, inefficient under too low signal to noise ratios, temporal correlation algorithms have been introduced. Test hypothesis methods based on empirically estimated mean and variance of the signals delivered by the different channels have shown significant gain in terms of a tradeoff between detection sensitivity and false alarm probability. This paper discloses the concept of a new hypothesis test for temporal correlation detection methods, taking advantage of the Poisson nature of the registered counting signals, and establishes amore » benchmark between this test and its empirical counterpart. The simulation study validates that in the four relevant configurations of a pedestrian source carrier under respectively high and low count rate radioactive background, and a vehicle source carrier under the same respectively high and low count rate radioactive background, the newly introduced hypothesis test ensures a significantly improved compromise between sensitivity and false alarm, while guaranteeing the stability of its optimization parameter regardless of signal to noise ratio variations between 2 to 0.8. (authors)« less

Methods for the selective detection of alkyne-presenting molecules and related compositions and systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valdez, Carlos A.; Vu, Alexander K.

Provided herein are methods for selectively detecting an alkyne-presenting molecule in a sample and related detection reagents, compositions, methods and systems. The methods include contacting a detection reagent with the sample for a time and under a condition to allow binding of the detection reagent to the one or more alkyne-presenting molecules possibly present in the matrix to the detection reagent. The detection reagent includes an organic label moiety presenting an azide group. The binding of the azide group to the alkyne-presenting molecules results in emission of a signal from the organic label moiety.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

A method for reduction of Acoustic Emission (AE) data with application in machine failure detection and diagnosis

NASA Astrophysics Data System (ADS)

Vicuña, Cristián Molina; Höweler, Christoph

2017-12-01

The use of AE in machine failure diagnosis has increased over the last years. Most AE-based failure diagnosis strategies use digital signal processing and thus require the sampling of AE signals. High sampling rates are required for this purpose (e.g. 2 MHz or higher), leading to streams of large amounts of data. This situation is aggravated if fine resolution and/or multiple sensors are required. These facts combine to produce bulky data, typically in the range of GBytes, for which sufficient storage space and efficient signal processing algorithms are required. This situation probably explains why, in practice, AE-based methods consist mostly in the calculation of scalar quantities such as RMS and Kurtosis, and the analysis of their evolution in time. While the scalar-based approach offers the advantage of maximum data reduction; it has the disadvantage that most part of the information contained in the raw AE signal is lost unrecoverably. This work presents a method offering large data reduction, while keeping the most important information conveyed by the raw AE signal, useful for failure detection and diagnosis. The proposed method consist in the construction of a synthetic, unevenly sampled signal which envelopes the AE bursts present on the raw AE signal in a triangular shape. The constructed signal - which we call TriSignal - also permits the estimation of most scalar quantities typically used for failure detection. But more importantly, it contains the information of the time of occurrence of the bursts, which is key for failure diagnosis. Lomb-Scargle normalized periodogram is used to construct the TriSignal spectrum, which reveals the frequency content of the TriSignal and provides the same information as the classic AE envelope. The paper includes application examples in planetary gearbox and low-speed rolling element bearing.

A contactless approach for respiratory gating in PET using continuous-wave radar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ersepke, Thomas, E-mail: Thomas.Ersepke@rub.de; Büther, Florian; Heß, Mirco

Purpose: Respiratory gating is commonly used to reduce motion artifacts in positron emission tomography (PET). Clinically established methods for respiratory gating in PET require contact to the patient or a direct optical line between the sensor and the patient’s torso and time consuming preparation. In this work, a contactless method for capturing a respiratory signal during PET is presented based on continuous-wave radar. Methods: The proposed method relies on the principle of emitting an electromagnetic wave and detecting the phase shift of the reflected wave, modulated due to the respiratory movement of the patient’s torso. A 24 GHz carrier frequencymore » was chosen allowing wave propagation through plastic and clothing with high reflections at the skin surface. A detector module and signal processing algorithms were developed to extract a quantitative respiratory signal. The sensor was validated using a high precision linear table. During volunteer measurements and [{sup 18}F] FDG PET scans, the radar sensor was positioned inside the scanner bore of a PET/computed tomography scanner. As reference, pressure belt (one volunteer), depth camera-based (two volunteers, two patients), and PET data-driven (six patients) signals were acquired simultaneously and the signal correlation was quantified. Results: The developed system demonstrated a high measurement accuracy for movement detection within the submillimeter range. With the proposed method, small displacements of 25 μm could be detected, not considerably influenced by clothing or blankets. From the patient studies, the extracted respiratory radar signals revealed high correlation (Pearson correlation coefficient) to those derived from the external pressure belt and depth camera signals (r = 0.69–0.99) and moderate correlation to those of the internal data-driven signals (r = 0.53–0.70). In some cases, a cardiac signal could be visualized, due to the representation of the mechanical heart motion on the skin. Conclusions: Accurate respiratory signals were obtained successfully by the proposed method with high spatial and temporal resolution. By working without contact and passing through clothing and blankets, this approach minimizes preparation time and increases the convenience of the patient during the scan.« less

A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices.

PubMed

Chen, Chieh-Li; Chuang, Chun-Te

2017-08-26

In the new-generation wearable Electrocardiogram (ECG) system, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. The QRS complex is a combination of three of the graphic deflection seen on a typical ECG. This study proposes a real-time QRS detection and R point recognition method with low computational complexity while maintaining a high accuracy. The enhancement of QRS segments and restraining of P and T waves are carried out by the proposed ECG signal transformation, which also leads to the elimination of baseline wandering. In this study, the QRS fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four QRS waveform templates and preliminary heart rhythm classification can be also achieved at the same time. The performance of the proposed approach is demonstrated using the benchmark of the MIT-BIH Arrhythmia Database, where the QRS detected sensitivity (Se) and positive prediction (+P) are 99.82% and 99.81%, respectively. The result reveals the approach's advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system.

Stripline split-ring resonator with integrated optogalvanic sample cell

NASA Astrophysics Data System (ADS)

Persson, Anders; Berglund, Martin; Thornell, Greger; Possnert, Göran; Salehpour, Mehran

2014-04-01

Intracavity optogalvanic spectroscopy (ICOGS) has been proposed as a method for unambiguous detection of rare isotopes. Of particular interest is 14C, where detection of extremely low concentrations in the 1:1015 range (14C: 12C), is of interest in, e.g., radiocarbon dating and pharmaceutical sciences. However, recent reports show that ICOGS suffers from substantial problems with reproducibility. To qualify ICOGS as an analytical method, more stable and reliable plasma generation and signal detection are needed. In our proposed setup, critical parameters have been improved. We have utilized a stripline split-ring resonator microwave-induced microplasma source to excite and sustain the plasma. Such a microplasma source offers several advantages over conventional ICOGS plasma sources. For example, the stripline split-ring resonator concept employs separated plasma generation and signal detection, which enables sensitive detection at stable plasma conditions. The concept also permits in situ observation of the discharge conditions, which was found to improve reproducibility. Unique to the stripline split-ring resonator microplasma source in this study, is that the optogalvanic sample cell has been embedded in the device itself. This integration enables improved temperature control and more stable and accurate signal detection. Significant improvements are demonstrated, including reproducibility, signal-to-noise ratio, and precision.

A dynamic sandwich assay on magnetic beads for selective detection of single-nucleotide mutations at room temperature.

PubMed

Wang, Junxiu; Xiong, Guoliang; Ma, Liang; Wang, Shihui; Zhou, Xu; Wang, Lei; Xiao, Lehui; Su, Xin; Yu, Changyuan

2017-08-15

Single-nucleotide mutation (SNM) has proven to be associated with a variety of human diseases. Development of reliable methods for the detection of SNM is crucial for molecular diagnosis and personalized medicine. The sandwich assays are widely used tools for detecting nucleic acid biomarkers due to their low cost and rapid signaling. However, the poor hybridization specificity of signal probe at room temperature hampers the discrimination of mutant and wild type. Here, we demonstrate a dynamic sandwich assay on magnetic beads for SNM detection based on the transient binding between signal probe and target. By taking the advantage of mismatch sensitive thermodynamics of transient DNA binding, the dynamic sandwich assay exhibits high discrimination factor for mutant with a broad range of salt concentration at room temperature. The beads used in this assay serve as a tool for separation, and might be helpful to enhance SNM selectivity. Flexible design of signal probe and facile magnetic separation allow multiple-mode downstream analysis including colorimetric detection and isothermal amplification. With this method, BRAF mutations in the genomic DNA extracted from cancer cell lines were tested, allowing sensitive detection of SNM at very low abundances (0.1-0.5% mutant/wild type). Copyright © 2017 Elsevier B.V. All rights reserved.

System and method for investigating sub-surface features of a rock formation with acoustic sources generating coded signals

DOEpatents

Vu, Cung Khac; Nihei, Kurt; Johnson, Paul A; Guyer, Robert; Ten Cate, James A; Le Bas, Pierre-Yves; Larmat, Carene S

2014-12-30

A system and a method for investigating rock formations includes generating, by a first acoustic source, a first acoustic signal comprising a first plurality of pulses, each pulse including a first modulated signal at a central frequency; and generating, by a second acoustic source, a second acoustic signal comprising a second plurality of pulses. A receiver arranged within the borehole receives a detected signal including a signal being generated by a non-linear mixing process from the first-and-second acoustic signal in a non-linear mixing zone within the intersection volume. The method also includes-processing the received signal to extract the signal generated by the non-linear mixing process over noise or over signals generated by a linear interaction process, or both.

Developing a reliable method for signal wire attachment : [research results].

DOT National Transportation Integrated Search

2013-03-01

Railroad signaling systems detect trains on the track, identify track fractures, prevent derailments, and alert signal crossing stations when trains approach. These systems are vital to safe train operation; therefore, each component of this system h...

[Application of the mixed programming with Labview and Matlab in biomedical signal analysis].

PubMed

Yu, Lu; Zhang, Yongde; Sha, Xianzheng

2011-01-01

This paper introduces the method of mixed programming with Labview and Matlab, and applies this method in a pulse wave pre-processing and feature detecting system. The method has been proved suitable, efficient and accurate, which has provided a new kind of approach for biomedical signal analysis.

Reconfigurable environmentally adaptive computing

NASA Technical Reports Server (NTRS)

Coxe, Robin L. (Inventor); Galica, Gary E. (Inventor)

2008-01-01

Described are methods and apparatus, including computer program products, for reconfigurable environmentally adaptive computing technology. An environmental signal representative of an external environmental condition is received. A processing configuration is automatically selected, based on the environmental signal, from a plurality of processing configurations. A reconfigurable processing element is reconfigured to operate according to the selected processing configuration. In some examples, the environmental condition is detected and the environmental signal is generated based on the detected condition.

Modulation-format-free and automatic bias control for optical IQ modulators based on dither-correlation detection.

PubMed

Li, Xiaolei; Deng, Lei; Chen, Xiaoman; Cheng, Mengfan; Fu, Songnian; Tang, Ming; Liu, Deming

2017-04-17

A novel automatic bias control (ABC) method for optical in-phase and quadrature (IQ) modulator is proposed and experimentally demonstrated. In the proposed method, two different low frequency sine wave dither signals are generated and added on to the I/Q bias signal respectively. Instead of power monitoring of the harmonics of the dither signal, dither-correlation detection is proposed and used to adjust the bias voltages of the optical IQ modulator. By this way, not only frequency spectral analysis isn't required but also the directional bias adjustment could be realized, resulting in the decrease of algorithm complexity and the growth of convergence rate of ABC algorithm. The results show that the sensitivity of the proposed ABC method outperforms that of the traditional dither frequency monitoring method. Moreover, the proposed ABC method is proved to be modulation-format-free, and the transmission penalty caused by this method for both 10 Gb/s optical QPSK and 17.9 Gb/s optical 16QAM-OFDM signal transmission are negligible in our experiment.

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing

PubMed Central

2018-01-01

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets. PMID:29562642

Median Filtering Methods for Non-volcanic Tremor Detection

NASA Astrophysics Data System (ADS)

Damiao, L. G.; Nadeau, R. M.; Dreger, D. S.; Luna, B.; Zhang, H.

2016-12-01

Various properties of median filtering over time and space are used to address challenges posed by the Non-volcanic tremor detection problem. As part of a "Big-Data" effort to characterize the spatial and temporal distribution of ambient tremor throughout the Northern San Andreas Fault system, continuous seismic data from multiple seismic networks with contrasting operational characteristics and distributed over a variety of regions are being used. Automated median filtering methods that are flexible enough to work consistently with these data are required. Tremor is characterized by a low-amplitude, long-duration signal-train whose shape is coherent at multiple stations distributed over a large area. There are no consistent phase arrivals or mechanisms in a given tremor's signal and even the durations and shapes among different tremors vary considerably. A myriad of masquerading noise, anthropogenic and natural-event signals must also be discriminated in order to obtain accurate tremor detections. We present here results of the median methods applied to data from four regions of the San Andreas Fault system in northern California (Geysers Geothermal Field, Napa, Bitterwater and Parkfield) to illustrate the ability of the methods to detect tremor under diverse conditions.

Vital sign sensing method based on EMD in terahertz band

NASA Astrophysics Data System (ADS)

Xu, Zhengwu; Liu, Tong

2014-12-01

Non-contact respiration and heartbeat rates detection could be applied to find survivors trapped in the disaster or the remote monitoring of the respiration and heartbeat of a patient. This study presents an improved algorithm that extracts the respiration and heartbeat rates of humans by utilizing the terahertz radar, which further lessens the effects of noise, suppresses the cross-term, and enhances the detection accuracy. A human target echo model for the terahertz radar is first presented. Combining the over-sampling method, low-pass filter, and Empirical Mode Decomposition improves the signal-to-noise ratio. The smoothed pseudo Wigner-Ville distribution time-frequency technique and the centroid of the spectrogram are used to estimate the instantaneous velocity of the target's cardiopulmonary motion. The down-sampling method is adopted to prevent serious distortion. Finally, a second time-frequency analysis is applied to the centroid curve to extract the respiration and heartbeat rates of the individual. Simulation results show that compared with the previously presented vital sign sensing method, the improved algorithm enhances the signal-to-noise ratio to 1 dB with a detection accuracy of 80%. The improved algorithm is an effective approach for the detection of respiration and heartbeat signal in a complicated environment.

Sensitive detection of microRNA in complex biological samples by using two stages DSN-assisted target recycling signal amplification method.

PubMed

Zhang, Kai; Wang, Ke; Zhu, Xue; Xu, Fei; Xie, Minhao

2017-01-15

MicroRNA (miRNA) has become an important biomarker candidate for cancer diagnosis, prognosis, and therapy. In this study, we have developed a novel fluorescence method for sensitive and specific miRNA detection via duplex specific nuclease (DSN) signal amplification and demonstrated its practical application in biological samples. Malachite green (MG) was employed as a "label-free" signal transducer since fluorescence of MG could be enhanced by 100-fold when MG were binding to a G-quadruplex structure formed within the d(G 2 T) 13 G sequence. The proposed signal amplification strategy is an integrated "biological circuit" designed to initiate a cascade of enzymatic reactions in order to detect, amplify, and measure a specific miRNA sequence by using the isothermal cleavage property of a DSN. The circuit is composed of two molecular switches operating in series: the amplification reaction activated by a specific miRNA and the strand-displacement polymerization reaction designed to initiate molecular beacon-assisted amplification and signal transduction by using MG/G-quadruplex complex. The hsa-miR-141 (miR141) was chosen as a target miRNA because its level specifically abnormal in a wide range of common human cancers including breast, lung, colon, and prostate cancer. The proposed method allowed quantitative sequence-specific detection of miR141 (with a detection limit of 1.03pM) in a dynamic range from 1pM to 10μM, with an excellent ability to discriminate differences in miRNAs. Moreover, the detection assay was applied to quantify miR141 in cancerous cell lysates. On the basis of these findings, we believe that this proposed sensitive and specific assay has great potential as a miRNA quantification method for use in biomedical research and clinical diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of three methods for extracting respiration from the surface ECG: a review.

PubMed

Helfenbein, Eric; Firoozabadi, Reza; Chien, Simon; Carlson, Eric; Babaeizadeh, Saeed

2014-01-01

Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patient's respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal. The first step in all three techniques is to analyze the ECG to detect beat locations and classify them. 1) The EDR method is based on analyzing the heart axis shift due to respiration. In our method, one respiration waveform value is calculated for each normal QRS complex by measuring the peak to QRS trough amplitude. Compared to other similar EDR techniques, this method does not need removal of baseline wander from the ECG signal. 2) The RSA method uses instantaneous heart rate variability to derive a respiratory signal. It is based on the observed respiratory sinus arrhythmia governed by baroreflex sensitivity. 3) Our EMGDR method for computing a respiratory waveform uses measurement of electromyogram (EMG) activity created by respiratory effort of the intercostal muscles and diaphragm. The ECG signal is high-pass filtered and processed to reduce ECG components and accentuate the EMG signal before applying RMS and smoothing. Over the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements. Copyright © 2014 Elsevier Inc. All rights reserved.

Detection of driving fatigue by using noncontact EMG and ECG signals measurement system.

PubMed

Fu, Rongrong; Wang, Hong

2014-05-01

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov-Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

Detection of low-amplitude in vivo intrinsic signals from an optical imager of retinal function

NASA Astrophysics Data System (ADS)

Barriga, Eduardo S.; T'so, Dan; Pattichis, Marios; Kwon, Young; Kardon, Randy; Abramoff, Michael; Soliz, Peter

2006-02-01

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with today's clinical instruments. Many of today's instruments focus on detecting changes in anatomical structures, such as the nerve fiber layer. Our device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. The functional imager uses a patterned stimulus at wavelength of 535nm. An intrinsic functional signal is collected at a near infrared wavelength. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by imaging system noise. In this paper, we analyze the video sequences from a set of 60 experiments with different patterned stimuli from cats. Using a set of statistical techniques known as Independent Component Analysis (ICA), we estimate the signals present in the videos. Through controlled simulation experiments, we quantify the limits of signal strength in order to detect the physiological signal of interest. The results of the analysis show that, in principle, signal levels of 0.1% (-30dB) can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted. The analysis of the different responses extracted from the videos can give an insight of the functional processes present during the stimulation of the retina.

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

PubMed Central

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

PubMed

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

Damage detection methodology on beam-like structures based on combined modal Wavelet Transform strategy

NASA Astrophysics Data System (ADS)

Serra, Roger; Lopez, Lautaro

2018-05-01

Different approaches on the detection of damages based on dynamic measurement of structures have appeared in the last decades. They were based, amongst others, on changes in natural frequencies, modal curvatures, strain energy or flexibility. Wavelet analysis has also been used to detect the abnormalities on modal shapes induced by damages. However the majority of previous work was made with non-corrupted by noise signals. Moreover, the damage influence for each mode shape was studied separately. This paper proposes a new methodology based on combined modal wavelet transform strategy to cope with noisy signals, while at the same time, able to extract the relevant information from each mode shape. The proposed methodology will be then compared with the most frequently used and wide-studied methods from the bibliography. To evaluate the performance of each method, their capacity to detect and localize damage will be analyzed in different cases. The comparison will be done by simulating the oscillations of a cantilever steel beam with and without defect as a numerical case. The proposed methodology proved to outperform classical methods in terms of noisy signals.

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

PubMed

Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

2018-03-01

Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Sensitive detection of malachite green and crystal violet by nonlinear laser wave mixing and capillary electrophoresis.

PubMed

Maxwell, Eric J; Tong, William G

2016-05-01

An ultrasensitive label-free antibody-free detection method for malachite green and crystal violet is presented using nonlinear laser wave-mixing spectroscopy and capillary zone electrophoresis. Wave-mixing spectroscopy provides a sensitive absorption-based detection method for trace analytes. This is accomplished by forming dynamic gratings within a sample cell, which diffracts light to create a coherent laser-like signal beam with high optical efficiency and high signal-to-noise ratio. A cubic dependence on laser power and square dependence on analyte concentration make wave mixing sensitive enough to detect molecules in their native form without the use of fluorescent labels for signal enhancement. A 532 nm laser and a 635 nm laser were used for malachite green and crystal violet sample excitation. The use of two lasers of different wavelengths allows the method to simultaneously detect both analytes. Selectivity is obtained through the capillary zone electrophoresis separation, which results in characteristic migration times. Measurement in capillary zone electrophoresis resulted in a limit of detection of 6.9 × 10(-10)M (2.5 × 10(-19) mol) for crystal violet and 8.3 × 10(-11)M (3.0 × 10(-20) mol) for malachite green at S/N of 2. Copyright © 2016. Published by Elsevier B.V.

Method of Menu Selection by Gaze Movement Using AC EOG Signals

NASA Astrophysics Data System (ADS)

Kanoh, Shin'ichiro; Futami, Ryoko; Yoshinobu, Tatsuo; Hoshimiya, Nozomu

A method to detect the direction and the distance of voluntary eye gaze movement from EOG (electrooculogram) signals was proposed and tested. In this method, AC-amplified vertical and horizontal transient EOG signals were classified into 8-class directions and 2-class distances of voluntary eye gaze movements. A horizontal and a vertical EOGs during eye gaze movement at each sampling time were treated as a two-dimensional vector, and the center of gravity of the sample vectors whose norms were more than 80% of the maximum norm was used as a feature vector to be classified. By the classification using the k-nearest neighbor algorithm, it was shown that the averaged correct detection rates on each subject were 98.9%, 98.7%, 94.4%, respectively. This method can avoid strict EOG-based eye tracking which requires DC amplification of very small signal. It would be useful to develop robust human interfacing systems based on menu selection for severely paralyzed patients.

Spatially assisted down-track median filter for GPR image post-processing

DOEpatents

Paglieroni, David W; Beer, N Reginald

2014-10-07

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Spatially adaptive migration tomography for multistatic GPR imaging

DOEpatents

Paglieroni, David W; Beer, N. Reginald

2013-08-13

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Synthetic aperture integration (SAI) algorithm for SAR imaging

DOEpatents

Chambers, David H; Mast, Jeffrey E; Paglieroni, David W; Beer, N. Reginald

2013-07-09

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Zero source insertion technique to account for undersampling in GPR imaging

DOEpatents

Chambers, David H; Mast, Jeffrey E; Paglieroni, David W

2014-02-25

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Tacholess order-tracking approach for wind turbine gearbox fault detection

NASA Astrophysics Data System (ADS)

Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

2017-09-01

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Analysis of degree of nonlinearity and stochastic nature of HRV signal during meditation using delay vector variance method.

PubMed

Reddy, L Ram Gopal; Kuntamalla, Srinivas

2011-01-01

Heart rate variability analysis is fast gaining acceptance as a potential non-invasive means of autonomic nervous system assessment in research as well as clinical domains. In this study, a new nonlinear analysis method is used to detect the degree of nonlinearity and stochastic nature of heart rate variability signals during two forms of meditation (Chi and Kundalini). The data obtained from an online and widely used public database (i.e., MIT/BIH physionet database), is used in this study. The method used is the delay vector variance (DVV) method, which is a unified method for detecting the presence of determinism and nonlinearity in a time series and is based upon the examination of local predictability of a signal. From the results it is clear that there is a significant change in the nonlinearity and stochastic nature of the signal before and during the meditation (p value > 0.01). During Chi meditation there is a increase in stochastic nature and decrease in nonlinear nature of the signal. There is a significant decrease in the degree of nonlinearity and stochastic nature during Kundalini meditation.

Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently.

PubMed

Sabherwal, Pooja; Singh, Latika; Agrawal, Monika

2018-03-30

In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.

Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter–defibrillators

PubMed Central

Ross, Joseph S; Bates, Jonathan; Parzynski, Craig S; Akar, Joseph G; Curtis, Jeptha P; Desai, Nihar R; Freeman, James V; Gamble, Ginger M; Kuntz, Richard; Li, Shu-Xia; Marinac-Dabic, Danica; Masoudi, Frederick A; Normand, Sharon-Lise T; Ranasinghe, Isuru; Shaw, Richard E; Krumholz, Harlan M

2017-01-01

Background Machine learning methods may complement traditional analytic methods for medical device surveillance. Methods and results Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; nonfatal ICD-related adverse events, 19.3%–26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%–37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=−0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=−0.042). Conclusion Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. PMID:28860874

Bubble measuring instrument and method

NASA Technical Reports Server (NTRS)

Magari, Patrick J. (Inventor); Kline-Schoder, Robert (Inventor)

2003-01-01

Method and apparatus are provided for a non-invasive bubble measuring instrument operable for detecting, distinguishing, and counting gaseous embolisms such as bubbles over a selectable range of bubble sizes of interest. A selected measurement volume in which bubbles may be detected is insonified by two distinct frequencies from a pump transducer and an image transducer, respectively. The image transducer frequency is much higher than the pump transducer frequency. The relatively low-frequency pump signal is used to excite bubbles to resonate at a frequency related to their diameter. The image transducer is operated in a pulse-echo mode at a controllable repetition rate that transmits bursts of high-frequency ultrasonic signal to the measurement volume in which bubbles may be detected and then receives the echo. From the echo or received signal, a beat signal related to the repetition rate may be extracted and used to indicate the presence or absence of a resonant bubble. In a preferred embodiment, software control maintains the beat signal at a preselected frequency while varying the pump transducer frequency to excite bubbles of different diameters to resonate depending on the range of bubble diameters selected for investigation.

Bubble Measuring Instrument and Method

NASA Technical Reports Server (NTRS)

Kline-Schoder, Robert (Inventor); Magari, Patrick J. (Inventor)

2002-01-01

Method and apparatus are provided for a non-invasive bubble measuring instrument operable for detecting, distinguishing, and counting gaseous embolisms such as bubbles over a selectable range of bubble sizes of interest. A selected measurement volume in which bubbles may be detected is insonified by two distinct frequencies from a pump transducer and an image transducer. respectively. The image transducer frequency is much higher than the pump transducer frequency. The relatively low-frequency pump signal is used to excite bubbles to resonate at a frequency related to their diameter. The image transducer is operated in a pulse-echo mode at a controllable repetition rate that transmits bursts of high-frequency ultrasonic signal to the measurement volume in which bubbles may be detected and then receives the echo. From the echo or received signal, a beat signal related to the repetition rate may be extracted and used to indicate the presence or absence of a resonant bubble. In a preferred embodiment, software control maintains the beat signal at a preselected frequency while varying the pump transducer frequency to excite bubbles of different diameters to resonate depending on the range of bubble diameters selected for investigation.

Double-labeled donor probe can enhance the signal of fluorescence resonance energy transfer (FRET) in detection of nucleic acid hybridization

PubMed Central

Okamura, Yukio; Kondo, Satoshi; Sase, Ichiro; Suga, Takayuki; Mise, Kazuyuki; Furusawa, Iwao; Kawakami, Shigeki; Watanabe, Yuichiro

2000-01-01

A set of fluorescently-labeled DNA probes that hybridize with the target RNA and produce fluorescence resonance energy transfer (FRET) signals can be utilized for the detection of specific RNA. We have developed probe sets to detect and discriminate single-strand RNA molecules of plant viral genome, and sought a method to improve the FRET signals to handle in vivo applications. Consequently, we found that a double-labeled donor probe labeled with Bodipy dye yielded a remarkable increase in fluorescence intensity compared to a single-labeled donor probe used in an ordinary FRET. This double-labeled donor system can be easily applied to improve various FRET probes since the dependence upon sequence and label position in enhancement is not as strict. Furthermore this method could be applied to other nucleic acid substances, such as oligo RNA and phosphorothioate oligonucleotides (S-oligos) to enhance FRET signal. Although the double-labeled donor probes labeled with a variety of fluorophores had unexpected properties (strange UV-visible absorption spectra, decrease of intensity and decay of donor fluorescence) compared with single-labeled ones, they had no relation to FRET enhancement. This signal amplification mechanism cannot be explained simply based on our current results and knowledge of FRET. Yet it is possible to utilize this double-labeled donor system in various applications of FRET as a simple signal-enhancement method. PMID:11121494

Method and apparatus for determining return stroke polarity of distant lightning

NASA Technical Reports Server (NTRS)

Blakeslee, Richard J. (Inventor); Brook, Marx (Inventor)

1992-01-01

A method is described for determining the return stroke polarity of distant lightning for distances beyond 600 km by detecting the electric field associated with a return stroke of distant lightning, and processing the electric field signal to determine the polarity of the slow tail of the VLF waveform signal associated with the detected electric field. The polarity of the return stroke of distant lightning is determined based upon the polarity of the slow tail portion of the waveform.

Method and apparatus for determining return stroke polarity of distant lightning

NASA Technical Reports Server (NTRS)

Blakeslee, Richard J. (Inventor); Brook, Marx (Inventor)

1990-01-01

A method is described for determining the return stroke polarity of distant lightning for distances beyond 600 km by detecting the electric field associated with a return stroke of distant lightning, and processing the electric field signal to determine the polarity of the slow tail of the VLF waveform signal associated with the detected electric field. The polarity of the return stroke of distant lightning is determined based upon the polarity of the slow tail portion of the waveform.

Detection of biological molecules using boronate-based chemical amplification and optical sensors

DOEpatents

Van Antwerp, William Peter; Mastrototaro, John Joseph; Lane, Stephen M.; Satcher, Jr., Joe H.; Darrow, Christopher B.; Peyser, Thomas A.; Harder, Jennifer

1999-01-01

Methods are provided for the determination of the concentration of biological levels of polyhydroxylated compounds, particularly glucose. The methods utilize an amplification system that is an analyte transducer immobilized in a polymeric matrix, where the system is implantable and biocompatible. Upon interrogation by an optical system, the amplification system produces a signal capable of detection external to the skin of the patient. Quantitation of the analyte of interest is achieved by measurement of the emitted signal.

Detection of biological molecules using boronate-based chemical amplification and optical sensors

DOEpatents

Van Antwerp, William Peter; Mastrototaro, John Joseph; Lane, Stephen M.; Satcher, Jr., Joe H.; Darrow, Christopher B.; Peyser, Thomas A.; Harder, Jennifer

2004-06-15

Methods are provided for the determination of the concentration of biological levels of polyhydroxylated compounds, particularly glucose. The methods utilize an amplification system that is an analyte transducer immobilized in a polymeric matrix, where the system is implantable and biocompatible. Upon interrogation by an optical system, the amplification system produces a signal capable of detection external to the skin of the patient. Quantitation of the analyte of interest is achieved by measurement of the emitted signal.

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

PubMed

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

Method of enhancing radiation response of radiation detection materials

DOEpatents

Miller, Steven D.

1997-01-01

The present invention is a method of increasing radiation response of a radiation detection material for a given radiation signal by first pressurizing the radiation detection material. Pressurization may be accomplished by any means including mechanical and/or hydraulic. In this application, the term "pressure" includes fluid pressure and/or mechanical stress.

Applying the Multiple Signal Classification Method to Silent Object Detection Using Ambient Noise

NASA Astrophysics Data System (ADS)

Mori, Kazuyoshi; Yokoyama, Tomoki; Hasegawa, Akio; Matsuda, Minoru

2004-05-01

The revolutionary concept of using ocean ambient noise positively to detect objects, called acoustic daylight imaging, has attracted much attention. The authors attempted the detection of a silent target object using ambient noise and a wide-band beam former consisting of an array of receivers. In experimental results obtained in air, using the wide-band beam former, we successfully applied the delay-sum array (DSA) method to detect a silent target object in an acoustic noise field generated by a large number of transducers. This paper reports some experimental results obtained by applying the multiple signal classification (MUSIC) method to a wide-band beam former to detect silent targets. The ocean ambient noise was simulated by transducers decentralized to many points in air. Both MUSIC and DSA detected a spherical target object in the noise field. The relative power levels near the target obtained with MUSIC were compared with those obtained by DSA. Then the effectiveness of the MUSIC method was evaluated according to the rate of increase in the maximum and minimum relative power levels.

Comparing CNV detection methods for SNP arrays.

PubMed

Winchester, Laura; Yau, Christopher; Ragoussis, Jiannis

2009-09-01

Data from whole genome association studies can now be used for dual purposes, genotyping and copy number detection. In this review we discuss some of the methods for using SNP data to detect copy number events. We examine a number of algorithms designed to detect copy number changes through the use of signal-intensity data and consider methods to evaluate the changes found. We describe the use of several statistical models in copy number detection in germline samples. We also present a comparison of data using these methods to assess accuracy of prediction and detection of changes in copy number.

Non-enzymolytic adenosine barcode-mediated dual signal amplification strategy for ultrasensitive protein detection using LC-MS/MS.

PubMed

Yang, Wen; Li, Tengfei; Shu, Chang; Ji, Shunli; Wang, Lei; Wang, Yan; Li, Duo; Mtalimanja, Michael; Sun, Luning; Ding, Li

2018-05-10

A method is described for the determination of proteins with LC-MS/MS enabled by a small molecule (adenosine) barcode and based on a double-recognition sandwich structure. The coagulation protein thrombin was chosen as the model analyte. Magnetic nanoparticles were functionalized with aptamer29 (MNP/apt29) and used to capture thrombin from the samples. MNP/apt29 forms a sandwich with functionalized gold nanoparticles modified with (a) aptamer15 acting as thrombin-recognizing element and (b) a large number of adenosine as mass barcodes. The sandwich formed (MNP/apt29-thrombin-apt15/AuNP/adenosine) can ben magnetically separated from the sample. Mass barcodes are subsequently released from the sandwiched structure for further analysis by adding 11-mercaptoundecanoic acid. Adenosine is then detected by LC-MS/MS as it reflects the level of thrombin with impressively amplified signal. Numerous adenosines introduced into the sandwich proportional to the target concentration further amplify the signal. Under optimized conditions, the response is linearly proportional to the thrombin concentration in the range of 0.02 nM to 10 nM, with a detection limit of 9 fM. The application of this method to the determination of thrombin in spiked plasma samples gave recoveries that ranged from 92.3% to 104.7%. Graphical abstract Schematic representation of a method for the determination of thrombin with LC-MS/MS. The method is based on a double-recognition sandwiched structure. With LC-MS/MS, mass barcodes (adenosine) are detected to quantify thrombin, which amplifies the detection signal impressively.

Signal and image processing for early detection of coronary artery diseases: A review

NASA Astrophysics Data System (ADS)

Mobssite, Youness; Samir, B. Belhaouari; Mohamad Hani, Ahmed Fadzil B.

2012-09-01

Today biomedical signals and image based detection are a basic step to diagnose heart diseases, in particular, coronary artery diseases. The goal of this work is to provide non-invasive early detection of Coronary Artery Diseases relying on analyzing images and ECG signals as a combined approach to extract features, further classify and quantify the severity of DCAD by using B-splines method. In an aim of creating a prototype of screening biomedical imaging for coronary arteries to help cardiologists to decide the kind of treatment needed to reduce or control the risk of heart attack.

Method and means for detecting optically transmitted signals and establishing optical interference pattern between electrodes

DOEpatents

Kostenbauder, Adnah G.

1988-01-01

A photodetector for detecting signal pulses transmitted in an optical carrier signal relies on the generation of electron-hole pairs and the diffusion of the generated electrons and holes to the electrodes on the surface of the semiconductor detector body for generating photovoltaic pulses. The detector utilizes the interference of optical waves for generating an electron-hole grating within the semiconductor body, and, by establishing an electron-hole pair maximum at one electrode and a minimum at the other electrode, a detectable voltaic pulse is generated across the electrode.

Method and means for detecting optically transmitted signals and establishing optical interference pattern between electrodes

DOEpatents

Kostenbauder, A.G.

1988-06-28

A photodetector for detecting signal pulses transmitted in an optical carrier signal relies on the generation of electron-hole pairs and the diffusion of the generated electrons and holes to the electrodes on the surface of the semiconductor detector body for generating photovoltaic pulses. The detector utilizes the interference of optical waves for generating an electron-hole grating within the semiconductor body, and, by establishing an electron-hole pair maximum at one electrode and a minimum at the other electrode, a detectable voltaic pulse is generated across the electrode. 4 figs.

SIG-VISA: Signal-based Vertically Integrated Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.; Mayeda, K. M.; Myers, S. C.; Russell, S.

2013-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software; however, while such detections may constitute a useful summary of station activity, they discard large amounts of information present in the original recorded signal. We present SIG-VISA (Signal-based Vertically Integrated Seismic Analysis), a system for seismic monitoring through Bayesian inference on seismic signals. By directly modeling the recorded signal, our approach incorporates additional information unavailable to detection-based methods, enabling higher sensitivity and more accurate localization using techniques such as waveform matching. SIG-VISA's Bayesian forward model of seismic signal envelopes includes physically-derived models of travel times and source characteristics as well as Gaussian process (kriging) statistical models of signal properties that combine interpolation of historical data with extrapolation of learned physical trends. Applying Bayesian inference, we evaluate the model on earthquakes as well as the 2009 DPRK test event, demonstrating a waveform matching effect as part of the probabilistic inference, along with results on event localization and sensitivity. In particular, we demonstrate increased sensitivity from signal-based modeling, in which the SIGVISA signal model finds statistical evidence for arrivals even at stations for which the IMS station processing failed to register any detection.

Research on signal processing method for total organic carbon of water quality online monitor

NASA Astrophysics Data System (ADS)

Ma, R.; Xie, Z. X.; Chu, D. Z.; Zhang, S. W.; Cao, X.; Wu, N.

2017-08-01

At present, there is no rapid, stable and effective approach of total organic carbon (TOC) measurement in the Marine environmental online monitoring field. Therefore, this paper proposes an online TOC monitor of chemiluminescence signal processing method. The weak optical signal detected by photomultiplier tube can be enhanced and converted by a series of signal processing module: phase-locked amplifier module, fourth-order band pass filter module and AD conversion module. After a long time of comparison test & measurement, compared with the traditional method, on the premise of sufficient accuracy, this chemiluminescence signal processing method can offer greatly improved measuring speed and high practicability for online monitoring.

The Application of Time-Frequency Methods to HUMS

NASA Technical Reports Server (NTRS)

Pryor, Anna H.; Mosher, Marianne; Lewicki, David G.; Norvig, Peter (Technical Monitor)

2001-01-01

This paper reports the study of four time-frequency transforms applied to vibration signals and presents a new metric for comparing them for fault detection. The four methods to be described and compared are the Short Time Frequency Transform (STFT), the Choi-Williams Distribution (WV-CW), the Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform (DWT). Vibration data of bevel gear tooth fatigue cracks, under a variety of operating load levels, are analyzed using these methods. The new metric for automatic fault detection is developed and can be produced from any systematic numerical representation of the vibration signals. This new metric reveals indications of gear damage with all of the methods on this data set. Analysis with the CWT detects mechanical problems with the test rig not found with the other transforms. The WV-CW and CWT use considerably more resources than the STFT and the DWT. More testing of the new metric is needed to determine its value for automatic fault detection and to develop methods of setting the threshold for the metric.

An investigation of gear mesh failure prediction techniques. M.S. Thesis - Cleveland State Univ.

NASA Technical Reports Server (NTRS)

Zakrajsek, James J.

1989-01-01

A study was performed in which several gear failure prediction methods were investigated and applied to experimental data from a gear fatigue test apparatus. The primary objective was to provide a baseline understanding of the prediction methods and to evaluate their diagnostic capabilities. The methods investigated use the signal average in both the time and frequency domain to detect gear failure. Data from eleven gear fatigue tests were recorded at periodic time intervals as the gears were run from initiation to failure. Four major failure modes, consisting of heavy wear, tooth breakage, single pits, and distributed pitting were observed among the failed gears. Results show that the prediction methods were able to detect only those gear failures which involved heavy wear or distributed pitting. None of the methods could predict fatigue cracks, which resulted in tooth breakage, or single pits. It is suspected that the fatigue cracks were not detected because of limitations in data acquisition rather than in methodology. Additionally, the frequency response between the gear shaft and the transducer was found to significantly affect the vibration signal. The specific frequencies affected were filtered out of the signal average prior to application of the methods.

Quantitation of secreted proteins using mCherry fusion constructs and a fluorescent microplate reader.

PubMed

Duellman, Tyler; Burnett, John; Yang, Jay

2015-03-15

Traditional assays for secreted proteins include methods such as Western blot and enzyme-linked immunosorbent assay (ELISA) detection of the protein in the cell culture medium. We describe a method for the detection of a secreted protein based on fluorescent measurement of an mCherry fusion reporter. This microplate reader-based mCherry fluorescence detection method has a wide dynamic range of 4.5 orders of magnitude and a sensitivity that allows detection of 1 to 2fmol fusion protein. Comparison with the Western blot detection method indicated greater linearity, wider dynamic range, and a similar lower detection threshold for the microplate-based fluorescent detection assay of secreted fusion proteins. An mCherry fusion protein of matrix metalloproteinase-9 (MMP-9), a secreted glycoprotein, was created and expressed by transfection of human embryonic kidney (HEK) 293 cells. The cell culture medium was assayed for the presence of the fluorescent signal up to 32 h after transfection. The secreted MMP-9-mCherry fusion protein was detected 6h after transfection with a linear increase in signal intensity over time. Treatment with chloroquine, a drug known to inhibit the secretion of many proteins, abolished the MMP-9-mCherry secretion, demonstrating the utility of this method in a biological experiment. Copyright © 2014 Elsevier Inc. All rights reserved.

A method for real time detecting of non-uniform magnetic field

NASA Astrophysics Data System (ADS)

Marusenkov, Andriy

2015-04-01

The principle of measuring magnetic signatures for observing diverse objects is widely used in Near Surface work (unexploded ordnance (UXO); engineering & environmental; archaeology) and security and vehicle detection systems as well. As a rule, the magnitude of the signals to be measured is much lower than that of the quasi-uniform Earth magnetic field. Usually magnetometers for these purposes contain two or more spatially separated sensors to estimate the full tensor gradient of the magnetic field or, more frequently, only partial gradient components. The both types (scalar and vector) of magnetic sensors could be used. The identity of the scale factors and proper alignment of the sensitivity axes of the vector sensors are very important for deep suppression of the ambient field and detection of weak target signals. As a rule, the periodical calibration procedure is used to keep matching sensors' parameters as close as possible. In the present report we propose the technique for detection magnetic anomalies, which is almost insensitive to imperfect matching of the sensors. This method based on the idea that the difference signals between two sensors are considerably different when the instrument is rotated or moved in uniform and non-uniform fields. Due to the misfit of calibration parameters the difference signal observed at the rotation in the uniform field is similar to the total signal - the sum of the signals of both sensors. Zero change of the difference and total signals is expected, if the instrument moves in the uniform field along a straight line. In contrast, the same move in the non-uniform field produces some response of each of the sensors. In case one measures dB/dx and moves along x direction, the sensors signals is shifted in time with the lag proportional to the distance between sensors and the speed of move. It means that the difference signal looks like derivative of the total signal at move in the non-uniform field. So, using quite simple electronic schematic it is possible to detect the lag between the total and difference signals and to trigger alarms, when the instrument passes near a magnetized object. The proposed method was successfully applied in the two instruments: the low-power search coil magnetometer for vehicle detection system and the low-noise flux-gate magnetometer for magnetocardiograph. Author believes that this approach could be also useful for the fast inspection of the area during the engineering, archaeology, UXO surveys.

Combining Absorption and Dispersion Signals to Improve Signal-to-noise for Rapid Scan EPR Imaging

PubMed Central

Tseitlin, Mark; Quine, Richard W.; Rinard, George A.; Eaton, Sandra S.; Eaton, Gareth R.

2010-01-01

Direct detection of the rapid scan EPR signal with quadrature detection and without automatic frequency control provides both the absorption and dispersion components of the signal. The use of a cross-loop resonator results in similar signal-to-noise in the two channels. The dispersion signal can be converted to an equivalent absorption signal by means of Kramers-Kronig relations. The converted signal is added to the directly-measured absorption signal. Since the noise in the two channels is not correlated, this procedure increases the signal-to-noise ratio of the resultant absorption signal by up to a factor of √2. The utility of this method was demonstrated for 2D spectral-spatial imaging of a phantom containing 3 tubes of LiPc with different oxygen concentrations and therefore different linewidths. PMID:20181505

Noise reduction algorithm with the soft thresholding based on the Shannon entropy and bone-conduction speech cross- correlation bands.

PubMed

Na, Sung Dae; Wei, Qun; Seong, Ki Woong; Cho, Jin Ho; Kim, Myoung Nam

2018-01-01

The conventional methods of speech enhancement, noise reduction, and voice activity detection are based on the suppression of noise or non-speech components of the target air-conduction signals. However, air-conduced speech is hard to differentiate from babble or white noise signals. To overcome this problem, the proposed algorithm uses the bone-conduction speech signals and soft thresholding based on the Shannon entropy principle and cross-correlation of air- and bone-conduction signals. A new algorithm for speech detection and noise reduction is proposed, which makes use of the Shannon entropy principle and cross-correlation with the bone-conduction speech signals to threshold the wavelet packet coefficients of the noisy speech. The proposed method can be get efficient result by objective quality measure that are PESQ, RMSE, Correlation, SNR. Each threshold is generated by the entropy and cross-correlation approaches in the decomposed bands using the wavelet packet decomposition. As a result, the noise is reduced by the proposed method using the MATLAB simulation. To verify the method feasibility, we compared the air- and bone-conduction speech signals and their spectra by the proposed method. As a result, high performance of the proposed method is confirmed, which makes it quite instrumental to future applications in communication devices, noisy environment, construction, and military operations.

Heart rate measurement based on face video sequence

NASA Astrophysics Data System (ADS)

Xu, Fang; Zhou, Qin-Wu; Wu, Peng; Chen, Xing; Yang, Xiaofeng; Yan, Hong-jian

2015-03-01

This paper proposes a new non-contact heart rate measurement method based on photoplethysmography (PPG) theory. With this method we can measure heart rate remotely with a camera and ambient light. We collected video sequences of subjects, and detected remote PPG signals through video sequences. Remote PPG signals were analyzed with two methods, Blind Source Separation Technology (BSST) and Cross Spectral Power Technology (CSPT). BSST is a commonly used method, and CSPT is used for the first time in the study of remote PPG signals in this paper. Both of the methods can acquire heart rate, but compared with BSST, CSPT has clearer physical meaning, and the computational complexity of CSPT is lower than that of BSST. Our work shows that heart rates detected by CSPT method have good consistency with the heart rates measured by a finger clip oximeter. With good accuracy and low computational complexity, the CSPT method has a good prospect for the application in the field of home medical devices and mobile health devices.

The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

NASA Astrophysics Data System (ADS)

Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

2017-12-01

In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long-term series and achieve real-time monitoring. Finally, we employ the SDAR on a synthetic model and Tomakomai Ocean Bottom Cable (OBC) baseline data to prove the feasibility and advantage of our method.

Earth's field NMR detection of oil under arctic ice-water suppression

NASA Astrophysics Data System (ADS)

Conradi, Mark S.; Altobelli, Stephen A.; Sowko, Nicholas J.; Conradi, Susan H.; Fukushima, Eiichi

2018-03-01

Earth's field NMR has been developed to detect oil trapped under or in Arctic sea-ice. A large challenge, addressed here, is the suppression of the water signal that dominates the oil signal. Selective suppression of water is based on relaxation time T1 because of the negligible chemical shifts in the weak earth's magnetic field, making all proton signals overlap spectroscopically. The first approach is inversion-null recovery, modified for use with pre-polarization. The requirements for efficient inversion over a wide range of B1 and subsequent adiabatic reorientation of the magnetization to align with the static field are stressed. The second method acquires FIDs at two durations of pre-polarization and cancels the water component of the signal after the data are acquired. While less elegant, this technique imposes no stringent requirements. Similar water suppression is found in simulations for the two methods. Oil detection in the presence of water is demonstrated experimentally with both techniques.

Earth's field NMR detection of oil under arctic ice-water suppression.

PubMed

Conradi, Mark S; Altobelli, Stephen A; Sowko, Nicholas J; Conradi, Susan H; Fukushima, Eiichi

2018-03-01

Earth's field NMR has been developed to detect oil trapped under or in Arctic sea-ice. A large challenge, addressed here, is the suppression of the water signal that dominates the oil signal. Selective suppression of water is based on relaxation time T 1 because of the negligible chemical shifts in the weak earth's magnetic field, making all proton signals overlap spectroscopically. The first approach is inversion-null recovery, modified for use with pre-polarization. The requirements for efficient inversion over a wide range of B 1 and subsequent adiabatic reorientation of the magnetization to align with the static field are stressed. The second method acquires FIDs at two durations of pre-polarization and cancels the water component of the signal after the data are acquired. While less elegant, this technique imposes no stringent requirements. Similar water suppression is found in simulations for the two methods. Oil detection in the presence of water is demonstrated experimentally with both techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection

PubMed Central

Zhang, Haibin; He, Qingbo; Kong, Fanrang

2015-01-01

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system. PMID:26343662

Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection.

PubMed

Zhang, Haibin; He, Qingbo; Kong, Fanrang

2015-08-28

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.

Filtration of human EEG recordings from physiological artifacts with empirical mode method

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

2017-03-01

In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

Acoustic impact testing and waveform analysis for damage detection in glued laminated timber

Treesearch

Feng Xu; Xiping Wang; Marko Teder; Yunfei Liu

2017-01-01

Delamination and decay are common structural defects in old glued laminated timber (glulam) buildings, which, if left undetected, could cause severe structural damage. This paper presents a new damage detection method for glulam inspection based on moment analysis and wavelet transform (WT) of impact acoustic signals. Acoustic signals were collected from a glulam arch...

Quantitative Surface Chirality Detection with Sum Frequency Generation Vibrational Spectroscopy: Twin Polarization Angle Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Feng; Xu, Yanyan; Guo, Yuan

2009-12-27

Here we report a novel twin polarization angle (TPA) approach in the quantitative chirality detection with the surface sum-frequency generation vibrational spectroscopy (SFG-VS). Generally, the achiral contribution dominates the surface SFG-VS signal, and the pure chiral signal is usually two or three orders of magnitude smaller. Therefore, it has been difficult to make quantitative detection and analysis of the chiral contributions to the surface SFG- VS signal. In the TPA method, by varying together the polarization angles of the incoming visible light and the sum frequency signal at fixed s or p polarization of the incoming infrared beam, the polarizationmore » dependent SFG signal can give not only direct signature of the chiral contribution in the total SFG-VS signal, but also the accurate measurement of the chiral and achiral components in the surface SFG signal. The general description of the TPA method is presented and the experiment test of the TPA approach is also presented for the SFG-VS from the S- and R-limonene chiral liquid surfaces. The most accurate degree of chiral excess values thus obtained for the 2878 cm⁻¹ spectral peak of the S- and R-limonene liquid surfaces are (23.7±0.4)% and ({25.4±1.3)%, respectively.« less

Method and apparatus for telemetry adaptive bandwidth compression

NASA Technical Reports Server (NTRS)

Graham, Olin L.

1987-01-01

Methods and apparatus are provided for automatic and/or manual adaptive bandwidth compression of telemetry. An adaptive sampler samples a video signal from a scanning sensor and generates a sequence of sampled fields. Each field and range rate information from the sensor are hence sequentially transmitted to and stored in a multiple and adaptive field storage means. The field storage means then, in response to an automatic or manual control signal, transfers the stored sampled field signals to a video monitor in a form for sequential or simultaneous display of a desired number of stored signal fields. The sampling ratio of the adaptive sample, the relative proportion of available communication bandwidth allocated respectively to transmitted data and video information, and the number of fields simultaneously displayed are manually or automatically selectively adjustable in functional relationship to each other and detected range rate. In one embodiment, when relatively little or no scene motion is detected, the control signal maximizes sampling ratio and causes simultaneous display of all stored fields, thus maximizing resolution and bandwidth available for data transmission. When increased scene motion is detected, the control signal is adjusted accordingly to cause display of fewer fields. If greater resolution is desired, the control signal is adjusted to increase the sampling ratio.

Smoke detection

DOEpatents

Warmack, Robert J. Bruce; Wolf, Dennis A.; Frank, Steven Shane

2016-09-06

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Smoke detection

DOEpatents

Warmack, Robert J. Bruce; Wolf, Dennis A.; Frank, Steven Shane

2015-10-27

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

Compact conscious animal positron emission tomography scanner

DOEpatents

Schyler, David J.; O'Connor, Paul; Woody, Craig; Junnarkar, Sachin Shrirang; Radeka, Veljko; Vaska, Paul; Pratte, Jean-Francois; Volkow, Nora

2006-10-24

A method of serially transferring annihilation information in a compact positron emission tomography (PET) scanner includes generating a time signal for an event, generating an address signal representing a detecting channel, generating a detector channel signal including the time and address signals, and generating a composite signal including the channel signal and similarly generated signals. The composite signal includes events from detectors in a block and is serially output. An apparatus that serially transfers annihilation information from a block includes time signal generators for detectors in a block and an address and channel signal generator. The PET scanner includes a ring tomograph that mounts onto a portion of an animal, which includes opposing block pairs. Each of the blocks in a block pair includes a scintillator layer, detection array, front-end array, and a serial encoder. The serial encoder includes time signal generators and an address signal and channel signal generator.

A study on locating the sonic source of sinusoidal magneto-acoustic signals using a vector method.

PubMed

Zhang, Shunqi; Zhou, Xiaoqing; Ma, Ren; Yin, Tao; Liu, Zhipeng

2015-01-01

Methods based on the magnetic-acoustic effect are of great significance in studying the electrical imaging properties of biological tissues and currents. The continuous wave method, which is commonly used, can only detect the current amplitude without the sound source position. Although the pulse mode adopted in magneto-acoustic imaging can locate the sonic source, the low measuring accuracy and low SNR has limited its application. In this study, a vector method was used to solve and analyze the magnetic-acoustic signal based on the continuous sine wave mode. This study includes theory modeling of the vector method, simulations to the line model, and experiments with wire samples to analyze magneto-acoustic (MA) signal characteristics. The results showed that the amplitude and phase of the MA signal contained the location information of the sonic source. The amplitude and phase obeyed the vector theory in the complex plane. This study sets a foundation for a new technique to locate sonic sources for biomedical imaging of tissue conductivity. It also aids in studying biological current detecting and reconstruction based on the magneto-acoustic effect.

Multipath interference test method for distributed amplifiers

NASA Astrophysics Data System (ADS)

Okada, Takahiro; Aida, Kazuo

2005-12-01

A method for testing distributed amplifiers is presented; the multipath interference (MPI) is detected as a beat spectrum between the multipath signal and the direct signal using a binary frequency shifted keying (FSK) test signal. The lightwave source is composed of a DFB-LD that is directly modulated by a pulse stream passing through an equalizer, and emits the FSK signal of the frequency deviation of about 430MHz at repetition rate of 80-100 kHz. The receiver consists of a photo-diode and an electrical spectrum analyzer (ESA). The base-band power spectrum peak appeared at the frequency of the FSK frequency deviation can be converted to amount of MPI using a calibration chart. The test method has improved the minimum detectable MPI as low as -70 dB, compared to that of -50 dB of the conventional test method. The detailed design and performance of the proposed method are discussed, including the MPI simulator for calibration procedure, computer simulations for evaluating the error caused by the FSK repetition rate and the fiber length under test and experiments on singlemode fibers and distributed Raman amplifier.

Background noise cancellation for improved acoustic detection of manatee vocalizations

NASA Astrophysics Data System (ADS)

Yan, Zheng; Niezrecki, Christopher; Beusse, Diedrich O.

2005-06-01

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees, so that a collision can be avoided, is desired. A practical implementation of the technology is dependent on the hydrophone spacing and range of detection. These parameters are primarily dependent on the manatee vocalization strength, the decay of the signal's strength with distance, and the background noise levels. An efficient method to extend the detection range by using background noise cancellation is proposed in this paper. An adaptive line enhancer (ALE) that can detect and track narrow band signals buried in broadband noise is implemented to cancel the background noise. The results indicate that the ALE algorithm can efficiently extract the manatee calls from the background noise. The improved signal-to-noise ratio of the signal can be used to extend the range of detection of manatee vocalizations and reduce the false alarm and missing detection rate in their natural habitat. .

Background noise cancellation for improved acoustic detection of manatee vocalizations

NASA Astrophysics Data System (ADS)

Yan, Zheng; Niezrecki, Christopher; Beusse, Diedrich O.

2005-04-01

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees, so that a collision can be avoided, is desired. Practical implementation of the technology is dependent on the hydrophone spacing and range of detection. These parameters are primarily dependent on the manatee vocalization strength, the decay of the signal strength with distance, and the background noise levels. An efficient method to extend the detection range by using background noise cancellation is proposed in this paper. An adaptive line enhancer (ALE) that can detect and track narrowband signals buried in broadband noise is implemented to cancel the background noise. The results indicate that the ALE algorithm can efficiently extract the manatee calls from the background noise. The improved signal-to-noise ratio of the signal can be used to extend the range of detection of manatee vocalizations and reduce the false alarm and missing detection rate in their natural habitat.

Zero-power receiver

DOEpatents

Brocato, Robert W.

2016-10-04

An unpowered signal receiver and a method for signal reception detects and responds to very weak signals using pyroelectric devices as impedance transformers and/or demodulators. In some embodiments, surface acoustic wave devices (SAW) are also used. Illustrative embodiments include satellite and long distance terrestrial communications applications.

Research on detecting spot selection and signal pretreatment of four-quadrant detector

NASA Astrophysics Data System (ADS)

Liu, Wenli; Han, Shaokun

2018-01-01

The four-quadrant detector is a photoelectric position sensor based on the photovoltaic effect. It is widely used in many fields such as target azimuth measurement, end-guided weapon and so on. The selection of the spot and the calculation of the center position are one of the main factors that affect the accuracy of the position measurement of the fourquadrant detector. In order to improve the positioning accuracy of the four-quadrant detector, the method of determining the best spot size is obtained from the theoretical research. The output signal of the four-quadrant detector is a weak narrow pulse signal, which needs to be magnified and widened at high magnitudes. The signal preprocessing method is simulated and experimentally studied. Detecting the spot and the signal processing is realized by the four-quadrant detector, which is important for the use of quadrant detectors for high-precision position measurements.

Automated accident detection at intersections.

DOT National Transportation Integrated Search

2004-03-01

This research aims to provide a timely and accurate accident detection method at intersections, which is : very important for the Traffic Management System(TMS). This research uses acoustic signals to detect : accident at intersections. A system is c...

Implementation of a custom time-domain firmware trigger for RADAR-based cosmic ray detection

NASA Astrophysics Data System (ADS)

Prohira, S.; Besson, D.; Kunwar, S.; Ratzlaff, K.; Young, R.

2018-05-01

Interest in Radio-based detection schemes for ultra-high energy cosmic rays (UHECR) has surged in recent years, owing to the potentially very low cost/detection ratio. The method of radio-frequency (RF) scatter has been proposed as potentially the most economical detection technology. Though the first dedicated experiment to employ this method, the Telescope Array RADAR experiment (TARA) reported no signal, efforts to develop more robust and sensitive trigger techniques continue. This paper details the development of a time-domain firmware trigger that exploits characteristics of the expected scattered signal from an UHECR extensive-air shower (EAS). The improved sensitivity of this trigger is discussed, as well as implementation in two separate field deployments from 2016 to 2017.

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

PubMed Central

Bang, Jae Won; Choi, Jong-Suk; Park, Kang Ryoung

2013-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods. PMID:23669713

Application of partial inversion pulse to ultrasonic time-domain correlation method to measure the flow rate in a pipe

NASA Astrophysics Data System (ADS)

Wada, Sanehiro; Furuichi, Noriyuki; Shimada, Takashi

2017-11-01

This paper proposes the application of a novel ultrasonic pulse, called a partial inversion pulse (PIP), to the measurement of the velocity profile and flow rate in a pipe using the ultrasound time-domain correlation (UTDC) method. In general, the measured flow rate depends on the velocity profile in the pipe; thus, on-site calibration is the only method of checking the accuracy of on-site flow rate measurements. Flow rate calculation using UTDC is based on the integration of the measured velocity profile. The advantages of this method compared with the ultrasonic pulse Doppler method include the possibility of the velocity range having no limitation and its applicability to flow fields without a sufficient amount of reflectors. However, it has been previously reported that the measurable velocity range for UTDC is limited by false detections. Considering the application of this method to on-site flow fields, the issue of velocity range is important. To reduce the effect of false detections, a PIP signal, which is an ultrasound signal that contains a partially inverted region, was developed in this study. The advantages of the PIP signal are that it requires little additional hardware cost and no additional software cost in comparison with conventional methods. The effects of inversion on the characteristics of the ultrasound transmission were estimated through numerical calculation. Then, experimental measurements were performed at a national standard calibration facility for water flow rate in Japan. The experimental results demonstrate that measurements made using a PIP signal are more accurate and yield a higher detection ratio than measurements using a normal pulse signal.

TH-CD-201-06: Experimental Characterization of Acoustic Signals Generated in Water Following Clinical Photon and Electron Beam Irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hickling, S; El Naqa, I

Purpose: Previous work has demonstrated the detectability of acoustic waves induced following the irradiation of high density metals with radiotherapy linac photon beams. This work demonstrates the ability to experimentally detect such acoustic signals following both photon and electron irradiation in a more radiotherapy relevant material. The relationship between induced acoustic signal properties in water and the deposited dose distribution is explored, and the feasibility of exploiting such signals for radiotherapy dosimetry is demonstrated. Methods: Acoustic waves were experimentally induced in a water tank via the thermoacoustic effect following a single pulse of photon or electron irradiation produced by amore » clinical linac. An immersion ultrasound transducer was used to detect these acoustic waves in water and signals were read out on an oscilloscope. Results: Peaks and troughs in the detected acoustic signals were found to correspond to the location of gradients in the deposited dose distribution following both photon and electron irradiation. Signal amplitude was linearly related to the dose per pulse deposited by photon or electron beams at the depth of detection. Flattening filter free beams induced large acoustic signals, and signal amplitude decreased with depth after the depth of maximum dose. Varying the field size resulted in a temporal shift of the acoustic signal peaks and a change in the detected signal frequency. Conclusion: Acoustic waves can be detected in a water tank following irradiation by linac photon and electron beams with basic electronics, and have characteristics related to the deposited dose distribution. The physical location of dose gradients and the amount of dose deposited can be inferred from the location and magnitude of acoustic signal peaks. Thus, the detection of induced acoustic waves could be applied to photon and electron water tank and in vivo dosimetry. This work was supported in part by CIHR grants MOP-114910 and MOP-136774. S.H. acknowledges support by the NSERC CREATE Medical Physics Research Training Network grant 432290.« less

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

PubMed Central

2009-01-01

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system. PMID:19495912

Spatial filters and automated spike detection based on brain topographies improve sensitivity of EEG-fMRI studies in focal epilepsy.

PubMed

Siniatchkin, Michael; Moeller, Friederike; Jacobs, Julia; Stephani, Ulrich; Boor, Rainer; Wolff, Stephan; Jansen, Olav; Siebner, Hartwig; Scherg, Michael

2007-09-01

The ballistocardiogram (BCG) represents one of the most prominent sources of artifacts that contaminate the electroencephalogram (EEG) during functional MRI. The BCG artifacts may affect the detection of interictal epileptiform discharges (IED) in patients with epilepsy, reducing the sensitivity of the combined EEG-fMRI method. In this study we improved the BCG artifact correction using a multiple source correction (MSC) approach. On the one hand, a source analysis of the IEDs was applied to the EEG data obtained outside the MRI scanner to prevent the distortion of EEG signals of interest during the correction of BCG artifacts. On the other hand, the topographies of the BCG artifacts were defined based on the EEG recorded inside the scanner. The topographies of the BCG artifacts were then added to the surrogate model of IED sources and a combined source model was applied to the data obtained inside the scanner. The artifact signal was then subtracted without considerable distortion of the IED topography. The MSC approach was compared with the traditional averaged artifact subtraction (AAS) method. Both methods reduced the spectral power of BCG-related harmonics and enabled better detection of IEDs. Compared with the conventional AAS method, the MSC approach increased the sensitivity of IED detection because the IED signal was less attenuated when subtracting the BCG artifacts. The proposed MSC method is particularly useful in situations in which the BCG artifact is spatially correlated and time-locked with the EEG signal produced by the focal brain activity of interest.

Eliminating ambiguity in digital signals

NASA Technical Reports Server (NTRS)

Weber, W. J., III

1979-01-01

Multiamplitude minimum shift keying (mamsk) transmission system, method of differential encoding overcomes problem of ambiguity associated with advanced digital-transmission techniques with little or no penalty in transmission rate, error rate, or system complexity. Principle of method states, if signal points are properly encoded and decoded, bits are detected correctly, regardless of phase ambiguities.

Detection of Delamination in Concrete Bridge Decks Using Mfcc of Acoustic Impact Signals

NASA Astrophysics Data System (ADS)

Zhang, G.; Harichandran, R. S.; Ramuhalli, P.

2010-02-01

Delamination of the concrete cover is a commonly observed damage in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck by impacting it with a hammer or bar, or by dragging a chain, and assesses delamination by the "hollowness" of the sound. The detection of the delamination is subjective and requires extensive training. To improve performance, this paper proposes an objective method for delamination detection. In this method, mel-frequency cepstral coefficients (MFCC) of the signal are extracted. Some MFCC are then selected as features for detection purposes using a mutual information criterion. Finally, the selected features are used to train a classifier which is subsequently used for detection. In this work, a simple quadratic Bayesian classifier is used. Different numbers of features are used to compare the performance of the detection method. The results show that the performance first increases with the number of features, but then decreases after an optimal value. The optimal number of features based on the recorded signals is four, and the mean error rate is only 3.3% when four features are used. Therefore, the proposed algorithm has sufficient accuracy to be used in field detection.

Location detection and tracking of moving targets by a 2D IR-UWB radar system.

PubMed

Nguyen, Van-Han; Pyun, Jae-Young

2015-03-19

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

Chemical Methods for the Direct Detection and Labeling of S-Nitrosothiols

PubMed Central

Bechtold, Erika

2012-01-01

Abstract Significance: Posttranslational modification of proteins through phosphorylation, glycosylation, and oxidation adds complexity to the proteome by reversibly altering the structure and function of target proteins in a highly controlled fashion. Recent Advances: The study of reversible cysteine oxidation highlights a role for this oxidative modification in complex signal transduction pathways. Nitric oxide (NO), and its respective metabolites (including reactive nitrogen species), participates in a variety of these cellular redox processes, including the reversible oxidation of cysteine to S-nitrosothiols (RSNOs). RSNOs act as endogenous transporters of NO, but also possess beneficial effects independent of NO-related signaling, which suggests a complex and versatile biological role. In this review, we highlight the importance of RSNOs as a required posttranslational modification and summarize the current methods available for detecting S-nitrosation. Critical Issues: Given the limitations of these indirect detection methods, the review covers recent developments toward the direct detection of RSNOs by phosphine-based chemical probes. The intrinsic properties that dictate this phosphine/RSNO reactivity are summarized. In general, RSNOs (both small molecule and protein) react with phosphines to yield reactive S-substituted aza-ylides that undergo further reactions leading to stable RSNO-based adducts. Future Directions: This newly explored chemical reactivity forms the basis of a number of exciting potential chemical methods for protein RSNO detection in biological systems. Antioxid. Redox Signal. 17, 981–991. PMID:22356122

Detecting gear tooth fracture in a high contact ratio face gear mesh

NASA Technical Reports Server (NTRS)

Zakrajsek, James J.; Handschuh, Robert F.; Lewicki, David G.; Decker, Harry J.

1995-01-01

This paper summarized the results of a study in which three different vibration diagnostic methods were used to detect gear tooth fracture in a high contact ratio face gear mesh. The NASA spiral bevel gear fatigue test rig was used to produce unseeded fault, natural failures of four face gear specimens. During the fatigue tests, which were run to determine load capacity and primary failure mechanisms for face gears, vibration signals were monitored and recorded for gear diagnostic purposes. Gear tooth bending fatigue and surface pitting were the primary failure modes found in the tests. The damage ranged from partial tooth fracture on a single tooth in one test to heavy wear, severe pitting, and complete tooth fracture of several teeth on another test. Three gear fault detection techniques, FM4, NA4*, and NB4, were applied to the experimental data. These methods use the signal average in both the time and frequency domain. Method NA4* was able to conclusively detect the gear tooth fractures in three out of the four fatigue tests, along with gear tooth surface pitting and heavy wear. For multiple tooth fractures, all of the methods gave a clear indication of the damage. It was also found that due to the high contact ratio of the face gear mesh, single tooth fractures did not significantly affect the vibration signal, making this type of failure difficult to detect.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors.

PubMed

Lee, Kwan Woo; Yoon, Hyo Sik; Song, Jong Min; Park, Kang Ryoung

2018-03-23

Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Improving label-free detection of circulating melanoma cells by photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Zhou, Huan; Wang, Qiyan; Pang, Kai; Zhou, Quanyu; Yang, Ping; He, Hao; Wei, Xunbin

2018-02-01

Melanoma is a kind of a malignant tumor of melanocytes with the properties of high mortality and high metastasis rate. The circulating melanoma cells with the high content of melanin can be detected by light absorption to diagnose and treat cancer at an early stage. Compared with conventional detection methods such as in vivo flow cytometry (IVFC) based on fluorescence, the in vivo photoacoustic flow cytometry (PAFC) utilizes melanin cells as biomarkers to collect the photoacoustic (PA) signals without toxic fluorescent dyes labeling in a non-invasive way. The information of target tumor cells is helpful for data analysis and cell counting. However, the raw signals in PAFC system contain numerous noises such as environmental noise, device noise and in vivo motion noise. Conventional denoising algorithms such as wavelet denoising (WD) method and means filter (MF) method are based on the local information to extract the data of clinical interest, which remove the subtle feature and leave many noises. To address the above questions, the nonlocal means (NLM) method based on nonlocal data has been proposed to suppress the noise in PA signals. Extensive experiments on in vivo PA signals from the mice with the injection of B16F10 cells in caudal vein have been conducted. All the results indicate that the NLM method has superior noise reduction performance and subtle information reservation.

Theoretical analysis of a method for extracting the phase of a phase-amplitude modulated signal generated by a direct-modulated optical injection-locked semiconductor laser

NASA Astrophysics Data System (ADS)

Lee, Hwan; Cho, Jun-Hyung; Sung, Hyuk-Kee

2017-05-01

The phase modulation (PM) and amplitude modulation (AM) of optical signals can be achieved using a direct-modulated (DM) optical injection-locked (OIL) semiconductor laser. We propose and theoretically analyze a simple method to extract the phase component of a PM signal produced by a DM-OIL semiconductor laser. The pure AM component of the combined PM-AM signal can be isolated by square-law detection in a photodetector and can then be used to compensate for the PM-AM signal based on an optical homodyne method. Using the AM compensation technique, we successfully developed a simple and cost-effective phase extraction method applicable to the PM-AM optical signal of a DM-OIL semiconductor laser.

Methods for automatic detection of artifacts in microelectrode recordings.

PubMed

Bakštein, Eduard; Sieger, Tomáš; Wild, Jiří; Novák, Daniel; Schneider, Jakub; Vostatek, Pavel; Urgošík, Dušan; Jech, Robert

2017-10-01

Extracellular microelectrode recording (MER) is a prominent technique for studies of extracellular single-unit neuronal activity. In order to achieve robust results in more complex analysis pipelines, it is necessary to have high quality input data with a low amount of artifacts. We show that noise (mainly electromagnetic interference and motion artifacts) may affect more than 25% of the recording length in a clinical MER database. We present several methods for automatic detection of noise in MER signals, based on (i) unsupervised detection of stationary segments, (ii) large peaks in the power spectral density, and (iii) a classifier based on multiple time- and frequency-domain features. We evaluate the proposed methods on a manually annotated database of 5735 ten-second MER signals from 58 Parkinson's disease patients. The existing methods for artifact detection in single-channel MER that have been rigorously tested, are based on unsupervised change-point detection. We show on an extensive real MER database that the presented techniques are better suited for the task of artifact identification and achieve much better results. The best-performing classifiers (bagging and decision tree) achieved artifact classification accuracy of up to 89% on an unseen test set and outperformed the unsupervised techniques by 5-10%. This was close to the level of agreement among raters using manual annotation (93.5%). We conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts. Copyright © 2017 Elsevier B.V. All rights reserved.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography.

PubMed

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

Dual sensitivity mode system for monitoring processes and sensors

DOEpatents

Wilks, Alan D.; Wegerich, Stephan W.; Gross, Kenneth C.

2000-01-01

A method and system for analyzing a source of data. The system and method involves initially training a system using a selected data signal, calculating at least two levels of sensitivity using a pattern recognition methodology, activating a first mode of alarm sensitivity to monitor the data source, activating a second mode of alarm sensitivity to monitor the data source and generating a first alarm signal upon the first mode of sensitivity detecting an alarm condition and a second alarm signal upon the second mode of sensitivity detecting an associated alarm condition. The first alarm condition and second alarm condition can be acted upon by an operator and/or analyzed by a specialist or computer program.

Pure detection of the acoustic spin pumping in Pt/YIG/PZT structures

NASA Astrophysics Data System (ADS)

Uchida, Ken-ichi; Qiu, Zhiyong; Kikkawa, Takashi; Saitoh, Eiji

2014-11-01

The acoustic spin pumping (ASP) stands for the generation of a spin voltage from sound waves in a ferromagnet/paramagnet junction. In this letter, we propose and demonstrate a method for pure detection of the ASP, which enables the separation of sound-wave-driven spin currents from the spin Seebeck effect due to the heating of a sample caused by a sound-wave injection. Our demonstration using a Pt/YIG/PZT sample shows that the ASP signal in this structure measured by a conventional method is considerably offset by the heating signal and that the pure ASP signal is one order of magnitude greater than that reported in the previous study.

A Monte Carlo technique for signal level detection in implanted intracranial pressure monitoring.

PubMed

Avent, R K; Charlton, J D; Nagle, H T; Johnson, R N

1987-01-01

Statistical monitoring techniques like CUSUM, Trigg's tracking signal and EMP filtering have a major advantage over more recent techniques, such as Kalman filtering, because of their inherent simplicity. In many biomedical applications, such as electronic implantable devices, these simpler techniques have greater utility because of the reduced requirements on power, logic complexity and sampling speed. The determination of signal means using some of the earlier techniques are reviewed in this paper, and a new Monte Carlo based method with greater capability to sparsely sample a waveform and obtain an accurate mean value is presented. This technique may find widespread use as a trend detection method when reduced power consumption is a requirement.

Stochastic resonance investigation of object detection in images

NASA Astrophysics Data System (ADS)

Repperger, Daniel W.; Pinkus, Alan R.; Skipper, Julie A.; Schrider, Christina D.

2007-02-01

Object detection in images was conducted using a nonlinear means of improving signal to noise ratio termed "stochastic resonance" (SR). In a recent United States patent application, it was shown that arbitrarily large signal to noise ratio gains could be realized when a signal detection problem is cast within the context of a SR filter. Signal-to-noise ratio measures were investigated. For a binary object recognition task (friendly versus hostile), the method was implemented by perturbing the recognition algorithm and subsequently thresholding via a computer simulation. To fairly test the efficacy of the proposed algorithm, a unique database of images has been constructed by modifying two sample library objects by adjusting their brightness, contrast and relative size via commercial software to gradually compromise their saliency to identification. The key to the use of the SR method is to produce a small perturbation in the identification algorithm and then to threshold the results, thus improving the overall system's ability to discern objects. A background discussion of the SR method is presented. A standard test is proposed in which object identification algorithms could be fairly compared against each other with respect to their relative performance.

A new feature extraction method for signal classification applied to cord dorsum potentials detection

PubMed Central

Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

2012-01-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

A new feature extraction method for signal classification applied to cord dorsum potential detection.

PubMed

Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

2012-10-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Detection of combustion start in the controlled auto ignition engine by wavelet transform of the engine block vibration signal

NASA Astrophysics Data System (ADS)

Kim, Seonguk; Min, Kyoungdoug

2008-08-01

The CAI (controlled auto ignition) engine ignites fuel and air mixture by trapping high temperature burnt gas using a negative valve overlap. Due to auto ignition in CAI combustion, efficiency improvements and low level NOx emission can be obtained. Meanwhile, the CAI combustion regime is restricted and control parameters are limited. The start of combustion data in the compressed ignition engine are most critical for controlling the overall combustion. In this research, the engine block vibration signal is transformed by the Meyer wavelet to analyze CAI combustion more easily and accurately. Signal acquisition of the engine block vibration is a more suitable method for practical use than measurement of in-cylinder pressure. A new method for detecting combustion start in CAI engines through wavelet transformation of the engine block vibration signal was developed and results indicate that it is accurate enough to analyze the start of combustion. Experimental results show that wavelet transformation of engine block vibration can track the start of combustion in each cycle. From this newly developed method, the start of combustion data in CAI engines can be detected more easily and used as input data for controlling CAI combustion.

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

PubMed

Levman, Jacob E D; Gallego-Ortiz, Cristina; Warner, Ellen; Causer, Petrina; Martel, Anne L

2016-02-01

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.

Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials.

PubMed

Houston, Brady; Blumenfeld, Zack; Quinn, Emma; Bronte-Stewart, Helen; Chizeck, Howard

2015-01-01

Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology. Attempts to detect Parkinsonian tremor using brain signals recorded during the implantation procedure have been successful. However, the ability of these methods to accurately detect tremor over extended periods of time is unknown. Here we use local field potentials recorded during a deep brain stimulation clinical follow-up visit 1 month after initial programming to build a tremor detection algorithm and use this algorithm to detect tremor in subsequent visits up to 8 months later. Using this method, we detected the occurrence of tremor with accuracies between 68-93%. These results demonstrate the potential of tremor detection methods for efficacious closed-loop deep brain stimulation over extended periods of time.

Online Conditional Outlier Detection in Nonstationary Time Series

PubMed Central

Liu, Siqi; Wright, Adam; Hauskrecht, Milos

2017-01-01

The objective of this work is to develop methods for detecting outliers in time series data. Such methods can become the key component of various monitoring and alerting systems, where an outlier may be equal to some adverse condition that needs human attention. However, real-world time series are often affected by various sources of variability present in the environment that may influence the quality of detection; they may (1) explain some of the changes in the signal that would otherwise lead to false positive detections, as well as, (2) reduce the sensitivity of the detection algorithm leading to increase in false negatives. To alleviate these problems, we propose a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation. Our experiments on several data sets in different domains show that our method provides more accurate modeling of the time series, and that it is able to significantly improve outlier detection performance. PMID:29644345

Online Conditional Outlier Detection in Nonstationary Time Series.

PubMed

Liu, Siqi; Wright, Adam; Hauskrecht, Milos

2017-05-01

The objective of this work is to develop methods for detecting outliers in time series data. Such methods can become the key component of various monitoring and alerting systems, where an outlier may be equal to some adverse condition that needs human attention. However, real-world time series are often affected by various sources of variability present in the environment that may influence the quality of detection; they may (1) explain some of the changes in the signal that would otherwise lead to false positive detections, as well as, (2) reduce the sensitivity of the detection algorithm leading to increase in false negatives. To alleviate these problems, we propose a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation. Our experiments on several data sets in different domains show that our method provides more accurate modeling of the time series, and that it is able to significantly improve outlier detection performance.

A robust hypothesis test for the sensitive detection of constant speed radiation moving sources

NASA Astrophysics Data System (ADS)

Dumazert, Jonathan; Coulon, Romain; Kondrasovs, Vladimir; Boudergui, Karim; Moline, Yoann; Sannié, Guillaume; Gameiro, Jordan; Normand, Stéphane; Méchin, Laurence

2015-09-01

Radiation Portal Monitors are deployed in linear networks to detect radiological material in motion. As a complement to single and multichannel detection algorithms, inefficient under too low signal-to-noise ratios, temporal correlation algorithms have been introduced. Test hypothesis methods based on empirically estimated mean and variance of the signals delivered by the different channels have shown significant gain in terms of a tradeoff between detection sensitivity and false alarm probability. This paper discloses the concept of a new hypothesis test for temporal correlation detection methods, taking advantage of the Poisson nature of the registered counting signals, and establishes a benchmark between this test and its empirical counterpart. The simulation study validates that in the four relevant configurations of a pedestrian source carrier under respectively high and low count rate radioactive backgrounds, and a vehicle source carrier under the same respectively high and low count rate radioactive backgrounds, the newly introduced hypothesis test ensures a significantly improved compromise between sensitivity and false alarm. It also guarantees that the optimal coverage factor for this compromise remains stable regardless of signal-to-noise ratio variations between 2 and 0.8, therefore allowing the final user to parametrize the test with the sole prior knowledge of background amplitude.

Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis

PubMed Central

Vilar, Santiago; Harpaz, Rave; Chase, Herbert S; Costanzi, Stefano; Rabadan, Raul

2011-01-01

Background Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. Objective To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. Results The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. Conclusion The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection. PMID:21946238

Amplified electrochemical detection of nucleic acid hybridization via selective preconcentration of unmodified gold nanoparticles.

PubMed

Li, Yuan; Tian, Rui; Zheng, Xingwang; Huang, Rongfu

2016-08-31

The common drawback of optical methods for rapid detection of nucleic acid by exploiting the differential affinity of single-/double-stranded nucleic acids for unmodified gold nanoparticles (AuNPs) is its relatively low sensitivity. In this article, on the basis of selective preconcentration of AuNPs unprotected by single-stranded DNA (ssDNA) binding, a novel electrochemical strategy for nucleic acid sequence identification assay has been developed. Through detecting the redox signal mediated by AuNPs on 1, 6-hexanedithiol blocked gold electrode, the proposed method is able to ensure substantial signal amplification and a low background current. This strategy is demonstrated for quantitative analysis of the target microRNA (let-7a) in human breast adenocarcinoma cells, and a detection limit of 16 fM is readily achieved with desirable specificity and sensitivity. These results indicate that the selective preconcentration of AuNPs for electrochemical signal readout can offer a promising platform for the detection of specific nucleic acid sequence. Copyright © 2016 Elsevier B.V. All rights reserved.

Rapid and Robust Cross-Correlation-Based Seismic Phase Identification Using an Approximate Nearest Neighbor Method

NASA Astrophysics Data System (ADS)

Tibi, R.; Young, C. J.; Gonzales, A.; Ballard, S.; Encarnacao, A. V.

2016-12-01

The matched filtering technique involving the cross-correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive, and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this study, we introduce an Approximate Nearest Neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation without requiring a complex distributed computing system. Our method begins with a projection into a reduced dimensionality space based on correlation with a randomized subset of the full template archive. Searching for a specified number of nearest neighbors is accomplished by using randomized K-dimensional trees. We used the approach to search for matches to each of 2700 analyst-reviewed signal detections reported for May 2010 for the IMS station MKAR. The template library in this case consists of a dataset of more than 200,000 analyst-reviewed signal detections for the same station from 2002-2014 (excluding May 2010). Of these signal detections, 60% are teleseismic first P, and 15% regional phases (Pn, Pg, Sn, and Lg). The analyses performed on a standard desktop computer shows that the proposed approach performs the search of the large template libraries about 20 times faster than the standard full linear search, while achieving recall rates greater than 80%, with the recall rate increasing for higher correlation values. To decide whether to confirm a match, we use a hybrid method involving a cluster approach for queries with two or more matches, and correlation score for single matches. Of the signal detections that passed our confirmation process, 52% were teleseismic first P, and 30% were regional phases.

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

PubMed Central

Sivaraks, Haemwaan

2015-01-01

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

Coded Excitation Plane Wave Imaging for Shear Wave Motion Detection

PubMed Central

Song, Pengfei; Urban, Matthew W.; Manduca, Armando; Greenleaf, James F.; Chen, Shigao

2015-01-01

Plane wave imaging has greatly advanced the field of shear wave elastography thanks to its ultrafast imaging frame rate and the large field-of-view (FOV). However, plane wave imaging also has decreased penetration due to lack of transmit focusing, which makes it challenging to use plane waves for shear wave detection in deep tissues and in obese patients. This study investigated the feasibility of implementing coded excitation in plane wave imaging for shear wave detection, with the hypothesis that coded ultrasound signals can provide superior detection penetration and shear wave signal-to-noise-ratio (SNR) compared to conventional ultrasound signals. Both phase encoding (Barker code) and frequency encoding (chirp code) methods were studied. A first phantom experiment showed an approximate penetration gain of 2-4 cm for the coded pulses. Two subsequent phantom studies showed that all coded pulses outperformed the conventional short imaging pulse by providing superior sensitivity to small motion and robustness to weak ultrasound signals. Finally, an in vivo liver case study on an obese subject (Body Mass Index = 40) demonstrated the feasibility of using the proposed method for in vivo applications, and showed that all coded pulses could provide higher SNR shear wave signals than the conventional short pulse. These findings indicate that by using coded excitation shear wave detection, one can benefit from the ultrafast imaging frame rate and large FOV provided by plane wave imaging while preserving good penetration and shear wave signal quality, which is essential for obtaining robust shear elasticity measurements of tissue. PMID:26168181

A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles.

PubMed

Homaeinezhad, M R; Erfanianmoshiri-Nejad, M; Naseri, H

2014-01-01

The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection-delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory. Also, the method is applied to the MIT-BIH Arrhythmia and the QT databases for comparing some parts of its performance characteristics with a number of P and T detection-delineation algorithms. The conducted evaluations indicate that in a signal with low quality value of about 0.6, the proposed method detects the P and T events with sensitivity Se=85% and positive predictive value of P+=89%, respectively. In addition, at the same quality, the average delineation errors associated with those ECG events are 45 and 63ms, respectively. Stable delineation error, high detection accuracy and high noise tolerance were the most important aspects considered during development of the proposed method. © 2013 Elsevier Ltd. All rights reserved.

Enzyme-free detection of sequence-specific microRNAs based on nanoparticle-assisted signal amplification strategy.

PubMed

Li, Ru-Dong; Wang, Qian; Yin, Bin-Cheng; Ye, Bang-Ce

2016-03-15

Developing direct and convenient methods for microRNAs (miRNAs) analysis is of great significance in understanding biological functions of miRNAs, and early diagnosis of cancers. We have developed a rapid, enzyme-free method for miRNA detection based on nanoparticle-assisted signal amplification coupling fluorescent metal nanoclusters as signal output. The proposed method involves two processes: target miRNA-mediated nanoparticle capture, which consists of magnetic microparticle (MMP) probe and CuO nanoparticle (NP) probe, and nanoparticle-mediated amplification for signal generation, which consists of fluorescent DNA-Cu/Ag nanocluster (NC) and 3-mercaptopropionic acid (MPA). In the presence of target miRNA, MMP probe and NP probe sandwich-capture the target miRNA via their respective complementary sequence. The resultant sandwich complex (MMP probe-miRNA-CuO NP probe) is separated using a magnetic field and further dissolved by acidolysis to turn CuO NP into a great amount of copper (II) ions (Cu(2+)). Cu(2+) could disrupt the interactions between thiol moiety of MPA and the fluorescent Cu/Ag NCs by preferentially reacting with MPA to form a disulfide compound as intermediate. By this way, the fluorescence emission of the DNA-Cu/Ag NCs in the presence of MPA increases upon the increasing concentration of Cu(2+), which is directly proportional to the amount of target miRNA. The proposed method allows quantitative detection of a liver-specific miR-221-5p in the range of 5 pM to 1000 pM with a detection limit of ~0.73 pM, and shows a good ability to discriminate single-base difference. Moreover, the detection assay can be applied to detect miRNA in cancerous cell lysates in excellent agreement with that from a commercial miRNA detection kit. Copyright © 2015 Elsevier B.V. All rights reserved.

Damage localization of marine risers using time series of vibration signals

NASA Astrophysics Data System (ADS)

Liu, Hao; Yang, Hezhen; Liu, Fushun

2014-10-01

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.

Multisignal detecting system of pile integrity testing

NASA Astrophysics Data System (ADS)

Liu, Zuting; Luo, Ying; Yu, Shihai

2002-05-01

The low strain reflection wave method plays a principal rule in the integrating detection of base piles. However, there are some deficiencies with this method. For example, there is a blind area of detection on top of the tested pile; it is difficult to recognize the defects at deep-seated parts of the pile; there is still the planar of 3D domino effect, etc. It is very difficult to solve these problems only with the single-transducer pile integrity testing system. A new multi-signal piles integrity testing system is proposed in this paper, which is able to impulse and collect signals on multiple points on top of the pile. By using the multiple superposition data processing method, the detecting system can effectively restrain the interference and elevate the precision and SNR of pile integrity testing. The system can also be applied to the evaluation of engineering structure health.

Adaptive signal processing and higher order time- frequency analysis for acoustic and vibration signatures in condition monitoring

NASA Astrophysics Data System (ADS)

Lee, Sang-Kwon

This thesis is concerned with the development of a useful engineering technique to detect and analyse faults in rotating machinery. The methods developed are based on the advanced signal processing such as the adaptive signal processing and higher-order time frequency methods. The two-stage Adaptive Line Enhancer (ALE), using adaptive signal processing, has been developed for increasing the Signal to Noise Ratio of impulsive signals. The enhanced signal can then be analysed using time frequency methods to identify fault characteristics. However, if after pre-processing by the two stage ALE, the SNR of the signals is low, the residual noise often hinders clear identification of the fault characteristics in the time-frequency domain. In such cases, higher order time-frequency methods have been proposed and studied. As examples of rotating machinery, the internal combustion engine and an industrial gear box are considered in this thesis. The noise signal from an internal combustion engine and vibration signal measured on a gear box are studied in detail. Typically an impulsive signal manifests itself when the fault occurs in the machinery and is embedded in background noise, such as the fundamental frequency and its harmonic orders of the rotation speed and broadband noise. The two-stage ALE is developed for reducing this background noise. Conditions for the choice of adaptive filter parameters are studied and suitable adaptive algorithms given. The enhanced impulsive signal is analysed in the time- frequency domain using the Wigner higher order moment spectra (WHOMS) and the multi-time WHOMS (which is a dual form of the WHOMS). The WHOMS suffers from unwanted cross-terms, which increase dramatically as the order increases. Novel expressions for the cross-terms in WHOMS have been presented. The number of cross-terms can be reduced by taking the principal slice of the WHOMS. The residual cross-terms are smoothed by using a general class of kernel functions and the γ-method kernel function which is a novel development in this thesis. The WVD and the sliced WHOMS for synthesised signals and measured data from rotating machinery are analysed. The estimated ROC (Receive Operating Characteristic) curves for these methods are computed. These results lead to the conclusion that the detection performance when using the sliced WHOMS, for impulsive signals in embedded in broadband noise, is better than that of the Wigner-Ville distribution. Real data from a faulty car engine and faulty industrial gears are analysed. The car engine radiates an impulsive noise signal due to the loosening of a spark plug. The faulty industrial gear produces an impulsive vibration signal due to a spall on the tooth face in gear. The two- stage ALE and WHOMS are successfully applied to detection and analysis of these impulsive signals.

Method and apparatus for determining viscosity

DOEpatents

Chu, Benjamin; Dhadwal, Harbans S.

1990-01-01

A capillary viscometer is provided which includes a fiber-optic probe and a phototransistor which produces an output signal as a liquid meniscus falls through the field of view of a detecting fiber bundle. An analog circuit is employed for receiving the signal and starting or stopping a digital counter in response thereto. The circuit includes first and second differentiators and a zero detection portion for detecting zero value outputs from the second differentiator. The counter is started or stopped upon the generation of a triggering pulse at the time such zero value is detected.

Signal processing for passive detection and classification of underwater acoustic signals

NASA Astrophysics Data System (ADS)

Chung, Kil Woo

2011-12-01

This dissertation examines signal processing for passive detection, classification and tracking of underwater acoustic signals for improving port security and the security of coastal and offshore operations. First, we consider the problem of passive acoustic detection of a diver in a shallow water environment. A frequency-domain multi-band matched-filter approach to swimmer detection is presented. The idea is to break the frequency contents of the hydrophone signals into multiple narrow frequency bands, followed by time averaged (about half of a second) energy calculation over each band. Then, spectra composed of such energy samples over the chosen frequency bands are correlated to form a decision variable. The frequency bands with highest Signal/Noise ratio are used for detection. The performance of the proposed approach is demonstrated for experimental data collected for a diver in the Hudson River. We also propose a new referenceless frequency-domain multi-band detector which, unlike other reference-based detectors, does not require a diver specific signature. Instead, our detector matches to a general feature of the diver spectrum in the high frequency range: the spectrum is roughly periodic in time and approximately flat when the diver exhales. The performance of the proposed approach is demonstrated by using experimental data collected from the Hudson River. Moreover, we present detection, classification and tracking of small vessel signals. Hydroacoustic sensors can be applied for the detection of noise generated by vessels, and this noise can be used for vessel detection, classification and tracking. This dissertation presents recent improvements aimed at the measurement and separation of ship DEMON (Detection of Envelope Modulation on Noise) acoustic signatures in busy harbor conditions. Ship signature measurements were conducted in the Hudson River and NY Harbor. The DEMON spectra demonstrated much better temporal stability compared with the full ship spectra and were measured at distances up to 7 km. The combination of cross-correlation and DEMON methods allows separation of the acoustic signatures of ships in busy urban environments. Finally, we consider the extension of this algorithm for vessel tracking using phase measurement of the DEMON signal recorded by two or more hydrophones. Tests conducted in the Hudson River and NY Bay confirmed opportunity of Direction of Arrival (DOA) funding using the phase DEMON method.

Method of and device for detecting oil pollutions on water surfaces

DOEpatents

Belov, Michael Leonidovich [Moscow, RU; Gorodnichev, Victor Aleksandrovich [Moscow, RU; Kozintsev, Valentin Ivanovich [Moscow, RU; Smimova, Olga Alekseevna [Moscow, RU; Fedotov, Yurii Victorovich [Moscow, RU; Khroustaleva, Anastasiva Michailovnan [Moscow, RU

2008-08-26

Detection of oil pollution on water surfaces includes providing echo signals obtained from optical radiation of a clean water area at two wavelengths, optically radiating an investigated water area at two wavelengths and obtaining echo signals from the optical radiation of the investigated water area at the two wavelengths, comparing the echo signals obtained from the radiation of the investigated area at two wavelengths with the echo signals obtained from the radiation of the clean water area, and based on the comparison, determining presence or absence of oil pollution in the investigated water area.

Acoustic emission detection for mass fractions of materials based on wavelet packet technology.

PubMed

Wang, Xianghong; Xiang, Jianjun; Hu, Hongwei; Xie, Wei; Li, Xiongbing

2015-07-01

Materials are often damaged during the process of detecting mass fractions by traditional methods. Acoustic emission (AE) technology combined with wavelet packet analysis is used to evaluate the mass fractions of microcrystalline graphite/polyvinyl alcohol (PVA) composites in this study. Attenuation characteristics of AE signals across the composites with different mass fractions are investigated. The AE signals are decomposed by wavelet packet technology to obtain the relationships between the energy and amplitude attenuation coefficients of feature wavelet packets and mass fractions as well. Furthermore, the relationship is validated by a sample. The larger proportion of microcrystalline graphite will correspond to the higher attenuation of energy and amplitude. The attenuation characteristics of feature wavelet packets with the frequency range from 125 kHz to 171.85 kHz are more suitable for the detection of mass fractions than those of the original AE signals. The error of the mass fraction of microcrystalline graphite calculated by the feature wavelet packet (1.8%) is lower than that of the original signal (3.9%). Therefore, AE detection base on wavelet packet analysis is an ideal NDT method for evaluate mass fractions of composite materials. Copyright © 2015 Elsevier B.V. All rights reserved.

Layer-by-layer multienzyme assembly for highly sensitive electrochemical immunoassay based on tyramine signal amplification strategy.

PubMed

Zhou, Jun; Tang, Juan; Chen, Guonan; Tang, Dianping

2014-04-15

A new sandwich-type electrochemical immunosensor based on nanosilver-doped bovine serum albumin microspheres (Ag@BSA) with a high ratio of horseradish peroxidase (HRP) and detection antibody was developed for quantitative monitoring of biomarkers (carcinoembryonic antigen, CEA, used in this case) by coupling enzymatic biocatalytic precipitation with tyramine signal amplification strategy on capture antibody-modified glassy carbon electrode. Two immunosensing protocols (with and without tyramine signal amplification) were also investigated for the detection of CEA and improved analytical features were acquired with tyramine signal amplification strategy. With the labeling method, the performance and factors influencing the electrochemical immunoassay were studied and evaluated in detail. Under the optimal conditions, the electrochemical immunosensor exhibited a wide dynamic range of 0.005-80 ng mL(-1) toward CEA standards with a low detection limit of 5.0 pg mL(-1). Intra- and inter-assay coefficients of variation were below 11%. No significant differences at the 0.05 significance level were encountered in the analysis of 6 clinical serum specimens and 6 spiked new-born cattle serum samples between the electrochemical immunoassay and the commercialized electrochemiluminescent immunoassay method for the detection of CEA. © 2013 Published by Elsevier B.V.

Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.

PubMed

Elbert, Yevgeniy; Burkom, Howard S

2009-11-20

This paper discusses further advances in making robust predictions with the Holt-Winters forecasts for a variety of syndromic time series behaviors and introduces a control-chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time-to-detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt-Winters-generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control-chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt-Winters-based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance.

Quantum sensing of weak radio-frequency signals by pulsed Mollow absorption spectroscopy.

PubMed

Joas, T; Waeber, A M; Braunbeck, G; Reinhard, F

2017-10-17

Quantum sensors-qubits sensitive to external fields-have become powerful detectors for various small acoustic and electromagnetic fields. A major key to their success have been dynamical decoupling protocols which enhance sensitivity to weak oscillating (AC) signals. Currently, those methods are limited to signal frequencies below a few MHz. Here we harness a quantum-optical effect, the Mollow triplet splitting of a strongly driven two-level system, to overcome this limitation. We microscopically understand this effect as a pulsed dynamical decoupling protocol and find that it enables sensitive detection of fields close to the driven transition. Employing a nitrogen-vacancy center, we detect GHz microwave fields with a signal strength (Rabi frequency) below the current detection limit, which is set by the center's spectral linewidth [Formula: see text]. Pushing detection sensitivity to the much lower 1/T 2 limit, this scheme could enable various applications, most prominently coherent coupling to single phonons and microwave photons.Dynamical decoupling protocols can enhance the sensitivity of quantum sensors but this is limited to signal frequencies below a few MHz. Here, Joas et al. use the Mollow triplet splitting in a nitrogen-vacancy centre to overcome this limitation, enabling sensitive detection of signals in the GHz range.

Recordings of mucociliary activity in vivo: benefit of fast Fourier transformation of the photoelectric signal.

PubMed

Lindberg, S; Cervin, A; Runer, T; Thomasson, L

1996-09-01

Investigations of mucociliary activity in vivo are based on photoelectric recordings of light reflections from the mucosa. The alterations in light intensity produced by the beating cilia are picked up by a photodetector and converted to photoelectric signals. The optimal processing of these signals is not known, but in vitro recordings have been reported to benefit from fast Fourier transformation (FFT) of the signal. The aim of the investigation was to study the effect of FFT for frequency analysis of photoelectric signals originating from an artificial light source simulating mucociliary activity or from sinus or nasal mucosa in vivo, as compared to a conventional method of calculating mucociliary wave frequency, in which each peak in the signal is interpreted as a beat (old method). In the experiments with the artificial light source, the FFT system was superior to the conventional method by a factor of 50 in detecting weak signals. By using FFT signal processing, frequency could be correctly calculated in experiments with a compound signal. In experiments in the rabbit maxillary sinus, the spontaneous variations were greater when signals were processed by FFT. The correlation between the two methods was excellent: r = .92. The increase in mucociliary activity in response to the ciliary stimulant methacholine at a dosage of 0.5 microgram/kg was greater measured with the FFT than with the old method (55.3% +/- 8.3% versus 43.0% +/- 8.2%, p < .05, N = 8), and only with the FFT system could a significant effect of a threshold dose (0.05 microgram/kg) of methacholine be detected. In the human nose, recordings from aluminum foil placed on the nasal dorsum and from the nasal septa mucosa displayed some similarities in the lower frequency spectrum (< 5 Hz) attributable to artifacts. The predominant cause of these artifacts was the pulse beat, whereas in the frequency spectrum above 5 Hz, results differed for the two sources of reflected light, the mean frequency in seven healthy volunteers being 7.8 +/- 1.6 Hz for the human nasal mucosa. It is concluded that the FFT system has greater sensitivity in detecting photoelectric signals derived from the mucociliary system, and that it is also a useful tool for analyzing the contributions of artifacts to the signal.

An estimation method for echo signal energy of pipe inner surface longitudinal crack detection by 2-D energy coefficients integration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Shiyuan, E-mail: redaple@bit.edu.cn; Sun, Haoyu, E-mail: redaple@bit.edu.cn; Xu, Chunguang, E-mail: redaple@bit.edu.cn

The echo signal energy is directly affected by the incident sound beam eccentricity or angle for thick-walled pipes inner longitudinal cracks detection. A method for analyzing the relationship between echo signal energy between the values of incident eccentricity is brought forward, which can be used to estimate echo signal energy when testing inside wall longitudinal crack of pipe, using mode-transformed compression wave adaptation of shear wave with water-immersion method, by making a two-dimension integration of “energy coefficient” in both circumferential and axial directions. The calculation model is founded for cylinder sound beam case, in which the refraction and reflection energymore » coefficients of different rays in the whole sound beam are considered different. The echo signal energy is calculated for a particular cylinder sound beam testing different pipes: a beam with a diameter of 0.5 inch (12.7mm) testing a φ279.4mm pipe and a φ79.4mm one. As a comparison, both the results of two-dimension integration and one-dimension (circumferential direction) integration are listed, and only the former agrees well with experimental results. The estimation method proves to be valid and shows that the usual method of simplifying the sound beam as a single ray for estimating echo signal energy and choosing optimal incident eccentricity is not so appropriate.« less

An estimation method for echo signal energy of pipe inner surface longitudinal crack detection by 2-D energy coefficients integration

NASA Astrophysics Data System (ADS)

Zhou, Shiyuan; Sun, Haoyu; Xu, Chunguang; Cao, Xiandong; Cui, Liming; Xiao, Dingguo

2015-03-01

The echo signal energy is directly affected by the incident sound beam eccentricity or angle for thick-walled pipes inner longitudinal cracks detection. A method for analyzing the relationship between echo signal energy between the values of incident eccentricity is brought forward, which can be used to estimate echo signal energy when testing inside wall longitudinal crack of pipe, using mode-transformed compression wave adaptation of shear wave with water-immersion method, by making a two-dimension integration of "energy coefficient" in both circumferential and axial directions. The calculation model is founded for cylinder sound beam case, in which the refraction and reflection energy coefficients of different rays in the whole sound beam are considered different. The echo signal energy is calculated for a particular cylinder sound beam testing different pipes: a beam with a diameter of 0.5 inch (12.7mm) testing a φ279.4mm pipe and a φ79.4mm one. As a comparison, both the results of two-dimension integration and one-dimension (circumferential direction) integration are listed, and only the former agrees well with experimental results. The estimation method proves to be valid and shows that the usual method of simplifying the sound beam as a single ray for estimating echo signal energy and choosing optimal incident eccentricity is not so appropriate.

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

Applications of Fault Detection in Vibrating Structures

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

The effects of lossy compression on diagnostically relevant seizure information in EEG signals.

PubMed

Higgins, G; McGinley, B; Faul, S; McEvoy, R P; Glavin, M; Marnane, W P; Jones, E

2013-01-01

This paper examines the effects of compression on EEG signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach.

PubMed

Elgendi, Mohamed

2016-11-02

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages ("TERMA") involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 ) have to follow the inequality ( 8 × W 1 ) ≥ W 2 ≥ ( 2 × W 1 ) . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

Detection of the multiphoton signals in stained tissue using nonlinear optical microscopy

NASA Astrophysics Data System (ADS)

Zeng, Yaping; Xu, Jian; Kang, Deyong; Lin, Jiangbo; Chen, Jianxin

2016-10-01

Multiphoton microscopy (MPM) based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging, has become a powerful, important tool for tissue imaging at the molecular level. Recently, MPM is also used to image hematoxylin and eosin (H and E)-stained sections in cancer diagnostics. However, several studies have showed that the MPM images of tissue stained with H and E are significantly different from unstained tissue sections. Our aim was to detect of the multiphoton signals in stained tissue by using MPM. In this paper, MPM was used to image histological sections of esophageal invasive carcinoma tissues stained with H, E, H and E and fresh tissue. To detect of the multiphoton signals in stained tissue, the emission spectroscopic of tissue stained with H, E, H and E were obtained. For comparison, the fresh tissues were also investigated. Our results showed that the tissue stained with H, E, H and E could be detected by their TPEF signals. While the tissue stained with H and fresh tissue could be detected by their TPEF and SHG signals. In this work, we detect of the multiphoton signals in stained tissue. These findings will be useful for choosing suitable staining method so to improve the quality of MPM imaging in the future.

Detecting breast microcalcifications using super-resolution ultrasound imaging: a clinical study

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Labyed, Yassin; Hanson, Kenneth; Sandoval, Daniel; Pohl, Jennifer; Williamson, Michael

2013-03-01

Imaging breast microcalcifications is crucial for early detection and diagnosis of breast cancer. It is challenging for current clinical ultrasound to image breast microcalcifications. However, new imaging techniques using data acquired with a synthetic-aperture ultrasound system have the potential to significantly improve ultrasound imaging. We recently developed a super-resolution ultrasound imaging method termed the phase-coherent multiple-signal classification (PC-MUSIC). This signal subspace method accounts for the phase response of transducer elements to improve image resolution. In this paper, we investigate the clinical feasibility of our super-resolution ultrasound imaging method for detecting breast microcalcifications. We use our custom-built, real-time synthetic-aperture ultrasound system to acquire breast ultrasound data for 40 patients whose mammograms show the presence of breast microcalcifications. We apply our super-resolution ultrasound imaging method to the patient data, and produce clear images of breast calcifications. Our super-resolution ultrasound PC-MUSIC imaging with synthetic-aperture ultrasound data can provide a new imaging modality for detecting breast microcalcifications in clinic without using ionizing radiation.

Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-03-01

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

Development of microcontroller-based acquisition and processing unit for fiber optic vibration sensor

NASA Astrophysics Data System (ADS)

Suryadi; Puranto, P.; Adinanta, H.; Waluyo, T. B.; Priambodo, P. S.

2017-04-01

Microcontroller based acquisition and processing unit (MAPU) has been developed to measure vibration signal from fiber optic vibration sensor. The MAPU utilizes a 32-bit ARM microcontroller to perform acquisition and processing of the input signal. The input signal is acquired with 12 bit ADC and processed using FFT method to extract frequency information. Stability of MAPU is characterized by supplying a constant input signal at 500 Hz for 29 hours and shows a stable operation. To characterize the frequency response, input signal is swapped from 20 to 1000 Hz with 20 Hz interval. The characterization result shows that MAPU can detect input signal from 20 to 1000 Hz with minimum signal of 4 mV RMS. The experiment has been set that utilizes the MAPU with singlemode-multimode-singlemode (SMS) fiber optic sensor to detect vibration which is induced by a transducer in a wooden platform. The experimental result indicates that vibration signal from 20 to 600 Hz has been successfully detected. Due to the limitation of the vibration source used in the experiment, vibration signal above 600 Hz is undetected.

Gold nanoparticle labeling with tyramide signal amplification for highly sensitive detection of alpha fetoprotein in human serum by ICP-MS.

PubMed

Li, Xiaoting; Chen, Beibei; He, Man; Xiao, Guangyang; Hu, Bin

2018-01-01

In this work, we developed an immunoassay based on tyramide signal amplification (TSA) and gold nanoparticles (Au NPs) labeling for highly sensitive detection of alpha fetoprotein (AFP) by inductively coupled plasma mass spectrometry (ICP-MS). AFP was captured by anti-AFP1 coating on the 96-well plate and labeled by anti-AFP2-horseradish peroxidase (HRP), in which the HRP can catalyze the deposition of biotinylated tyramine on the nearby protein. Then the streptavidin (SA)-Au NPs was labeled on the deposited biotinylated tyramine as the intensive signal probe for ICP-MS measurement. Under the optimal experimental conditions, the limit of detection of the developed method for AFP was 1.85pg/mL and the linear range was 0.005-2ng/mL. The relative standard deviation for seven replicate detections of 0.01ng/mL AFP was 5.2%. The proposed method was successfully applied to the detection of AFP in human serum with good recoveries. This strategy is highly sensitive and easy to operate, and can be extended to the sensitive detection of other biomolecules in human serum. Copyright © 2017 Elsevier B.V. All rights reserved.

High temperature ion channels and pores

NASA Technical Reports Server (NTRS)

Cheley, Stephen (Inventor); Gu, Li Qun (Inventor); Bayley, Hagan (Inventor); Kang, Xiaofeng (Inventor)

2011-01-01

The present invention includes an apparatus, system and method for stochastic sensing of an analyte to a protein pore. The protein pore may be an engineer protein pore, such as an ion channel at temperatures above 55.degree. C. and even as high as near 100.degree. C. The analyte may be any reactive analyte, including chemical weapons, environmental toxins and pharmaceuticals. The analyte covalently bonds to the sensor element to produce a detectable electrical current signal. Possible signals include change in electrical current. Detection of the signal allows identification of the analyte and determination of its concentration in a sample solution. Multiple analytes present in the same solution may also be detected.

Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems.

PubMed

Wang, Chao; Guo, Xiao-Jing; Xu, Jin-Fang; Wu, Cheng; Sun, Ya-Lin; Ye, Xiao-Fei; Qian, Wei; Ma, Xiu-Qiang; Du, Wen-Min; He, Jia

2012-01-01

The detection of signals of adverse drug events (ADEs) has increased because of the use of data mining algorithms in spontaneous reporting systems (SRSs). However, different data mining algorithms have different traits and conditions for application. The objective of our study was to explore the application of association rule (AR) mining in ADE signal detection and to compare its performance with that of other algorithms. Monte Carlo simulation was applied to generate drug-ADE reports randomly according to the characteristics of SRS datasets. Thousand simulated datasets were mined by AR and other algorithms. On average, 108,337 reports were generated by the Monte Carlo simulation. Based on the predefined criterion that 10% of the drug-ADE combinations were true signals, with RR equaling to 10, 4.9, 1.5, and 1.2, AR detected, on average, 284 suspected associations with a minimum support of 3 and a minimum lift of 1.2. The area under the receiver operating characteristic (ROC) curve of the AR was 0.788, which was equivalent to that shown for other algorithms. Additionally, AR was applied to reports submitted to the Shanghai SRS in 2009. Five hundred seventy combinations were detected using AR from 24,297 SRS reports, and they were compared with recognized ADEs identified by clinical experts and various other sources. AR appears to be an effective method for ADE signal detection, both in simulated and real SRS datasets. The limitations of this method exposed in our study, i.e., a non-uniform thresholds setting and redundant rules, require further research.

Adaptive method of recognition of signals for one and two-frequency signal system in the telephony on the background of speech

NASA Astrophysics Data System (ADS)

Kuznetsov, Michael V.

2006-05-01

For reliable teamwork of various systems of automatic telecommunication including transferring systems of optical communication networks it is necessary authentic recognition of signals for one- or two-frequency service signal system. The analysis of time parameters of an accepted signal allows increasing reliability of detection and recognition of the service signal system on a background of speech.

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

PubMed Central

Cai, Suxian; Yang, Shanshan; Zheng, Fang; Lu, Meng; Wu, Yunfeng; Krishnan, Sridhar

2013-01-01

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis. PMID:23573175

An Effective Method for Substance Detection Using the Broad Spectrum THz Signal: A “Terahertz Nose”

PubMed Central

Trofimov, Vyacheslav A.; Varentsova, Svetlana A.

2015-01-01

We propose an effective method for the detection and identification of dangerous substances by using the broadband THz pulse. This pulse excites, for example, many vibrational or rotational energy levels of molecules simultaneously. By analyzing the time-dependent spectrum of the THz pulse transmitted through or reflected from a substance, we follow the average response spectrum dynamics. Comparing the absorption and emission spectrum dynamics of a substance under analysis with the corresponding data for a standard substance, one can detect and identify the substance under real conditions taking into account the influence of packing material, water vapor and substance surface. For quality assessment of the standard substance detection in the signal under analysis, we propose time-dependent integral correlation criteria. Restrictions of usually used detection and identification methods, based on a comparison between the absorption frequencies of a substance under analysis and a standard substance, are demonstrated using a physical experiment with paper napkins. PMID:26020281

Tensor Fukunaga-Koontz transform for small target detection in infrared images

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Wang, Jingzhuo; Yang, Huizhen; Gong, Chenglong; Zhou, Yuanshen; Liu, Lipeng; Zhang, Zhen; Shen, Shuli

2016-09-01

Infrared small targets detection plays a crucial role in warning and tracking systems. Some novel methods based on pattern recognition technology catch much attention from researchers. However, those classic methods must reshape images into vectors with the high dimensionality. Moreover, vectorizing breaks the natural structure and correlations in the image data. Image representation based on tensor treats images as matrices and can hold the natural structure and correlation information. So tensor algorithms have better classification performance than vector algorithms. Fukunaga-Koontz transform is one of classification algorithms and it is a vector version method with the disadvantage of all vector algorithms. In this paper, we first extended the Fukunaga-Koontz transform into its tensor version, tensor Fukunaga-Koontz transform. Then we designed a method based on tensor Fukunaga-Koontz transform for detecting targets and used it to detect small targets in infrared images. The experimental results, comparison through signal-to-clutter, signal-to-clutter gain and background suppression factor, have validated the advantage of the target detection based on the tensor Fukunaga-Koontz transform over that based on the Fukunaga-Koontz transform.