Automated detection and location of indications in eddy current signals

DOEpatents

Brudnoy, David M.; Oppenlander, Jane E.; Levy, Arthur J.

2000-01-01

A computer implemented information extraction process that locates and identifies eddy current signal features in digital point-ordered signals, signals representing data from inspection of test materials, by enhancing the signal features relative to signal noise, detecting features of the signals, verifying the location of the signal features that can be known in advance, and outputting information about the identity and location of all detected signal features.

Design and Performance of the Astro-E/XRS Signal Processing System

NASA Technical Reports Server (NTRS)

Boyce, Kevin R.; Audley, M. D.; Baker, R. G.; Dumonthier, J. J.; Fujimoto, R.; Gendreau, K. C.; Ishisaki, Y.; Kelley, R. L.; Stahle, C. K.; Szymkowiak, A. E.

1999-01-01

We describe the signal processing system of the Astro-E XRS instrument. The Calorimeter Analog Processor (CAP) provides bias and power for the detectors and amplifies the detector signals by a factor of 20,000. The Calorimeter Digital Processor (CDP) performs the digital processing of the calorimeter signals, detecting X-ray pulses and analyzing them by optimal filtering. We describe the operation of pulse detection, Pulse height analysis. and risetime determination. We also discuss performance, including the three event grades (hi-res mid-res, and low-res). anticoincidence detection, counting rate dependence, and noise rejection.

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.

DETECTOR FOR MODULATED AND UNMODULATED SIGNALS

DOEpatents

Patterson, H.H.; Webber, G.H.

1959-08-25

An r-f signal-detecting device is described, which is embodied in a compact coaxial circuit principally comprising a detecting crystal diode and a modulating crystal diode connected in parallel. Incoming modulated r-f signals are demodulated by the detecting crystal diode to furnish an audio input to an audio amplifier. The detecting diode will not, however, produce an audio signal from an unmodulated r-f signal. In order that unmodulated signals may be detected, such incoming signals have a locally produced audio signal superimposed on them at the modulating crystal diode and then the"induced or artificially modulated" signal is reflected toward the detecting diode which in the process of demodulation produces an audio signal for the audio amplifier.

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.

Does the Butcher-on-the-Bus Phenomenon Require a Dual-Process Explanation? A Signal Detection Analysis

PubMed Central

Tunney, Richard J.; Mullett, Timothy L.; Moross, Claudia J.; Gardner, Anna

2012-01-01

The butcher-on-the-bus is a rhetorical device or hypothetical phenomenon that is often used to illustrate how recognition decisions can be based on different memory processes (Mandler, 1980). The phenomenon describes a scenario in which a person is recognized but the recognition is accompanied by a sense of familiarity or knowing characterized by an absence of contextual details such as the person’s identity. We report two recognition memory experiments that use signal detection analyses to determine whether this phenomenon is evidence for a recollection plus familiarity model of recognition or is better explained by a univariate signal detection model. We conclude that there is an interaction between confidence estimates and remember-know judgments which is not explained fully by either single-process signal detection or traditional dual-process models. PMID:22745631

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.

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.

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.

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

Tang, Yanmei; Li, Xinli; Bai, Yan

The measurement of multiphase flow parameters is of great importance in a wide range of industries. In the measurement of multiphase, the signals from the sensors are extremely weak and often buried in strong background noise. It is thus desirable to develop effective signal processing techniques that can detect the weak signal from the sensor outputs. In this paper, two methods, i.e., lock-in-amplifier (LIA) and improved Duffing chaotic oscillator are compared to detect and process the weak signal. For sinusoidal signal buried in noise, the correlation detection with sinusoidal reference signal is simulated by using LIA. The improved Duffing chaoticmore » oscillator method, which based on the Wigner transformation, can restore the signal waveform and detect the frequency. Two methods are combined to detect and extract the weak signal. Simulation results show the effectiveness and accuracy of the proposed improved method. The comparative analysis shows that the improved Duffing chaotic oscillator method can restrain noise strongly since it is sensitive to initial conditions.« less

Dual-Process Theory and Signal-Detection Theory of Recognition Memory

ERIC Educational Resources Information Center

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…

Automated feature detection and identification in digital point-ordered signals

DOEpatents

Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

1998-01-01

A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

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.

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.

See-through Detection and 3D Reconstruction Using Terahertz Leaky-Wave Radar Based on Sparse Signal Processing

NASA Astrophysics Data System (ADS)

Murata, Koji; Murano, Kosuke; Watanabe, Issei; Kasamatsu, Akifumi; Tanaka, Toshiyuki; Monnai, Yasuaki

2018-02-01

We experimentally demonstrate see-through detection and 3D reconstruction using terahertz leaky-wave radar based on sparse signal processing. The application of terahertz waves to radar has received increasing attention in recent years for its potential to high-resolution and see-through detection. Among others, the implementation using a leaky-wave antenna is promising for compact system integration with beam steering capability based on frequency sweep. However, the use of a leaky-wave antenna poses a challenge on signal processing. Since a leaky-wave antenna combines the entire signal captured by each part of the aperture into a single output, the conventional array signal processing assuming access to a respective antenna element is not applicable. In this paper, we apply an iterative recovery algorithm "CoSaMP" to signals acquired with terahertz leaky-wave radar for clutter mitigation and aperture synthesis. We firstly demonstrate see-through detection of target location even when the radar is covered with an opaque screen, and therefore, the radar signal is disturbed by clutter. Furthermore, leveraging the robustness of the algorithm against noise, we also demonstrate 3D reconstruction of distributed targets by synthesizing signals collected from different orientations. The proposed approach will contribute to the smart implementation of terahertz leaky-wave radar.

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.

Electronic polarization-division demultiplexing based on digital signal processing in intensity-modulation direct-detection optical communication systems.

PubMed

Kikuchi, Kazuro

2014-01-27

We propose a novel configuration of optical receivers for intensity-modulation direct-detection (IM · DD) systems, which can cope with dual-polarization (DP) optical signals electrically. Using a Stokes analyzer and a newly-developed digital signal-processing (DSP) algorithm, we can achieve polarization tracking and demultiplexing in the digital domain after direct detection. Simulation results show that the power penalty stemming from digital polarization manipulations is negligibly small.

The role of extra-foveal processing in 3D imaging

NASA Astrophysics Data System (ADS)

Eckstein, Miguel P.; Lago, Miguel A.; Abbey, Craig K.

2017-03-01

The field of medical image quality has relied on the assumption that metrics of image quality for simple visual detection tasks are a reliable proxy for the more clinically realistic visual search tasks. Rank order of signal detectability across conditions often generalizes from detection to search tasks. Here, we argue that search in 3D images represents a paradigm shift in medical imaging: radiologists typically cannot exhaustively scrutinize all regions of interest with the high acuity fovea requiring detection of signals with extra-foveal areas (visual periphery) of the human retina. We hypothesize that extra-foveal processing can alter the detectability of certain types of signals in medical images with important implications for search in 3D medical images. We compare visual search of two different types of signals in 2D vs. 3D images. We show that a small microcalcification-like signal is more highly detectable than a larger mass-like signal in 2D search, but its detectability largely decreases (relative to the larger signal) in the 3D search task. Utilizing measurements of observer detectability as a function retinal eccentricity and observer eye fixations we can predict the pattern of results in the 2D and 3D search studies. Our findings: 1) suggest that observer performance findings with 2D search might not always generalize to 3D search; 2) motivate the development of a new family of model observers that take into account the inhomogeneous visual processing across the retina (foveated model observers).

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.

SIGNAL DETECTION BEHAVIOR IN HUMANS AND RATS: A COMPARISON WITH MATCHED TASKS.

EPA Science Inventory

Animal models of human cognitive processes are essential for studying the neurobiological mechanisms of these processes and for developing therapies for intoxication and neurodegenerative diseases. A discrete-trial signal detection task was developed for assessing sustained atten...

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.

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.

[Real-time detection and processing of medical signals under windows using Lcard analog interfaces].

PubMed

Kuz'min, A A; Belozerov, A E; Pronin, T V

2008-01-01

Multipurpose modular software for an analog interface based on Lcard 761 is considered. Algorithms for pipeline processing of medical signals under Windows with dynamic control of computational resources are suggested. The software consists of user-friendly completable modifiable modules. The module hierarchy is based on object-oriented heritage principles, which make it possible to construct various real-time systems for long-term detection, processing, and imaging of multichannel medical signals.

WE-G-204-01: BEST IN PHYSICS (IMAGING): Effect of Image Processing Parameters On Nodule Detectability in Chest Radiography

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

Little, K; Lu, Z; MacMahon, H

Purpose: To investigate the effect of varying system image processing parameters on lung nodule detectability in digital radiography. Methods: An anthropomorphic chest phantom was imaged in the posterior-anterior position using a GE Discovery XR656 digital radiography system. To simulate lung nodules, a polystyrene board with 6.35mm diameter PMMA spheres was placed adjacent to the phantom (into the x-ray path). Due to magnification, the projected simulated nodules had a diameter in the radiographs of approximately 7.5 mm. The images were processed using one of GE’s default chest settings (Factory3) and reprocessed by varying the “Edge” and “Tissue Contrast” processing parameters, whichmore » were the two user-configurable parameters for a single edge and contrast enhancement algorithm. For each parameter setting, the nodule signals were calculated by subtracting the chest-only image from the image with simulated nodules. Twenty nodule signals were averaged, Gaussian filtered, and radially averaged in order to generate an approximately noiseless signal. For each processing parameter setting, this noise-free signal and 180 background samples from across the lung were used to estimate ideal observer performance in a signal-known-exactly detection task. Performance was estimated using a channelized Hotelling observer with 10 Laguerre-Gauss channel functions. Results: The “Edge” and “Tissue Contrast” parameters each had an effect on the detectability as calculated by the model observer. The CHO-estimated signal detectability ranged from 2.36 to 2.93 and was highest for “Edge” = 4 and “Tissue Contrast” = −0.15. In general, detectability tended to decrease as “Edge” was increased and as “Tissue Contrast” was increased. A human observer study should be performed to validate the relation to human detection performance. Conclusion: Image processing parameters can affect lung nodule detection performance in radiography. While validation with a human observer study is needed, model observer detectability for common tasks could provide a means for optimizing image processing parameters.« less

Evaluation of hardware costs of implementing PSK signal detection circuit based on "system on chip"

NASA Astrophysics Data System (ADS)

Sokolovskiy, A. V.; Dmitriev, D. D.; Veisov, E. A.; Gladyshev, A. B.

2018-05-01

The article deals with the choice of the architecture of digital signal processing units for implementing the PSK signal detection scheme. As an assessment of the effectiveness of architectures, the required number of shift registers and computational processes are used when implementing the "system on a chip" on the chip. A statistical estimation of the normalized code sequence offset in the signal synchronization scheme for various hardware block architectures is used.

Signal Detection with Criterion Noise: Applications to Recognition Memory

ERIC Educational Resources Information Center

Benjamin, Aaron S.; Diaz, Michael; Wee, Serena

2009-01-01

A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of…

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.

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.

Incorporating signal-dependent noise for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Morman, Christopher J.; Meola, Joseph

2015-05-01

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

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.

Signal Detection Theory-Based Information Processing for the Detection of Breast Cancer at Microwave Frequencies

DTIC Science & Technology

2002-08-01

the measurement noise, as well as the physical model of the forward scattered electric field. The Bayesian algorithms for the Uncertain Permittivity...received at multiple sensors. In this research project a tissue- model -based signal-detection theory approach for the detection of mammary tumors in the...oriented information processors. In this research project a tissue- model - based signal detection theory approach for the detection of mammary tumors in the

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.

Obstacle penetrating dynamic radar imaging system

DOEpatents

Romero, Carlos E [Livermore, CA; Zumstein, James E [Livermore, CA; Chang, John T [Danville, CA; Leach, Jr Richard R. [Castro Valley, CA

2006-12-12

An obstacle penetrating dynamic radar imaging system for the detection, tracking, and imaging of an individual, animal, or object comprising a multiplicity of low power ultra wideband radar units that produce a set of return radar signals from the individual, animal, or object, and a processing system for said set of return radar signals for detection, tracking, and imaging of the individual, animal, or object. The system provides a radar video system for detecting and tracking an individual, animal, or object by producing a set of return radar signals from the individual, animal, or object with a multiplicity of low power ultra wideband radar units, and processing said set of return radar signals for detecting and tracking of the individual, animal, or object.

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

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

PubMed

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Stream computing for biomedical signal processing: A QRS complex detection case-study.

PubMed

Murphy, B M; O'Driscoll, C; Boylan, G B; Lightbody, G; Marnane, W P

2015-01-01

Recent developments in "Big Data" have brought significant gains in the ability to process large amounts of data on commodity server hardware. Stream computing is a relatively new paradigm in this area, addressing the need to process data in real time with very low latency. While this approach has been developed for dealing with large scale data from the world of business, security and finance, there is a natural overlap with clinical needs for physiological signal processing. In this work we present a case study of streams processing applied to a typical physiological signal processing problem: QRS detection from ECG data.

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.

A cloud masking algorithm for EARLINET lidar systems

NASA Astrophysics Data System (ADS)

Binietoglou, Ioannis; Baars, Holger; D'Amico, Giuseppe; Nicolae, Doina

2015-04-01

Cloud masking is an important first step in any aerosol lidar processing chain as most data processing algorithms can only be applied on cloud free observations. Up to now, the selection of a cloud-free time interval for data processing is typically performed manually, and this is one of the outstanding problems for automatic processing of lidar data in networks such as EARLINET. In this contribution we present initial developments of a cloud masking algorithm that permits the selection of the appropriate time intervals for lidar data processing based on uncalibrated lidar signals. The algorithm is based on a signal normalization procedure using the range of observed values of lidar returns, designed to work with different lidar systems with minimal user input. This normalization procedure can be applied to measurement periods of only few hours, even if no suitable cloud-free interval exists, and thus can be used even when only a short period of lidar measurements is available. Clouds are detected based on a combination of criteria including the magnitude of the normalized lidar signal and time-space edge detection performed using the Sobel operator. In this way the algorithm avoids misclassification of strong aerosol layers as clouds. Cloud detection is performed using the highest available time and vertical resolution of the lidar signals, allowing the effective detection of low-level clouds (e.g. cumulus humilis). Special attention is given to suppress false cloud detection due to signal noise that can affect the algorithm's performance, especially during day-time. In this contribution we present the details of algorithm, the effect of lidar characteristics (space-time resolution, available wavelengths, signal-to-noise ratio) to detection performance, and highlight the current strengths and limitations of the algorithm using lidar scenes from different lidar systems in different locations across Europe.

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.

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

Initial Evaluation of Signal-Based Bayesian Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.; Russell, S.

2016-12-01

We present SIGVISA (Signal-based Vertically Integrated Seismic Analysis), a next-generation system for global seismic monitoring through Bayesian inference on seismic signals. Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a network of stations. We report results from an evaluation of SIGVISA monitoring the western United States for a two-week period following the magnitude 6.0 event in Wells, NV in February 2008. During this period, SIGVISA detects more than twice as many events as NETVISA, and three times as many as SEL3, while operating at the same precision; at lower precisions it detects up to five times as many events as SEL3. At the same time, signal-based monitoring reduces mean location errors by a factor of four relative to detection-based systems. We provide evidence that, given only IMS data, SIGVISA detects events that are missed by regional monitoring networks, indicating that our evaluations may even underestimate its performance. Finally, SIGVISA matches or exceeds the detection rates of existing systems for de novo events - events with no nearby historical seismicity - and detects through automated processing a number of such events missed even by the human analysts generating the LEB.

High efficiency processing for reduced amplitude zones detection in the HRECG signal

NASA Astrophysics Data System (ADS)

Dugarte, N.; Álvarez, A.; Balacco, J.; Mercado, G.; Gonzalez, A.; Dugarte, E.; Olivares, A.

2016-04-01

Summary - This article presents part of a more detailed research proposed in the medium to long term, with the intention of establishing a new philosophy of electrocardiogram surface analysis. This research aims to find indicators of cardiovascular disease in its early stage that may go unnoticed with conventional electrocardiography. This paper reports the development of a software processing which collect some existing techniques and incorporates novel methods for detection of reduced amplitude zones (RAZ) in high resolution electrocardiographic signal (HRECG).The algorithm consists of three stages, an efficient processing for QRS detection, averaging filter using correlation techniques and a step for RAZ detecting. Preliminary results show the efficiency of system and point to incorporation of techniques new using signal analysis with involving 12 leads.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

NASA Astrophysics Data System (ADS)

Deng, Ke; Zhang, Lu; Luo, Mao-Kang

2010-03-01

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises (even white noise). In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

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.

Acoustic firearm discharge detection and classification in an enclosed environment

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

Luzi, Lorenzo; Gonzalez, Eric; Bruillard, Paul

2016-05-01

Two different signal processing algorithms are described for detection and classification of acoustic signals generated by firearm discharges in small enclosed spaces. The first is based on the logarithm of the signal energy. The second is a joint entropy. The current study indicates that a system using both signal energy and joint entropy would be able to both detect weapon discharges and classify weapon type, in small spaces, with high statistical certainty.

Decoding Signal Processing at the Single-Cell Level

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

Wiley, H. Steven

The ability of cells to detect and decode information about their extracellular environment is critical to generating an appropriate response. In multicellular organisms, cells must decode dozens of signals from their neighbors and extracellular matrix to maintain tissue homeostasis while still responding to environmental stressors. How cells detect and process information from their surroundings through a surprisingly limited number of signal transduction pathways is one of the most important question in biology. Despite many decades of research, many of the fundamental principles that underlie cell signal processing remain obscure. However, in this issue of Cell Systems, Gillies et al presentmore » compelling evidence that the early response gene circuit can act as a linear signal integrator, thus providing significant insight into how cells handle fluctuating signals and noise in their environment.« less

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.

Phase analysis of coherent radial-breathing-mode phonons in carbon nanotubes: Implications for generation and detection processes

NASA Astrophysics Data System (ADS)

Shimura, Akihiko; Yanagi, Kazuhiro; Yoshizawa, Masayuki

2018-01-01

In time-resolved pump-probe spectroscopy of carbon nanotubes, the fundamental understanding of the optical generation and detection processes of radial-breathing-mode (RBM) phonons has been inconsistent among the previous reports. In this study, the tunable-pumping/broadband-probing scheme was used to fully reveal the amplitude and phase of the phonon-modulated signals. We observed that signals detected off resonantly to excitonic transitions are delayed by π /2 radians with respect to resonantly detected signals, which demonstrates that RBM phonons are detected through dynamically modulating the linear response, not through adiabatically modulating the light absorption. Furthermore, we found that the initial phases are independent of the pump detuning across the first (E11) and the second (E22) excitonic resonances, evidencing that the RBM phonons are generated by the displacive excitation rather than stimulated Raman process.

Nuclear Explosion and Infrasound Event Resources of the SMDC Monitoring Research Program

DTIC Science & Technology

2008-09-01

2008 Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies 928 Figure 7. Dozens of detected infrasound signals from...investigate alternative detection schemes at the two infrasound arrays based on frequency-wavenumber (fk) processing and the F-statistic. The results of... infrasound signal - detection processing schemes. REFERENCES Bahavar, M., B. Barker, J. Bennett, R. Bowman, H. Israelsson, B. Kohl, Y-L. Kung, J. Murphy

Detecting modulated signals in modulated noise: (II) neural thresholds in the songbird forebrain.

PubMed

Bee, Mark A; Buschermöhle, Michael; Klump, Georg M

2007-10-01

Sounds in the real world fluctuate in amplitude. The vertebrate auditory system exploits patterns of amplitude fluctuations to improve signal detection in noise. One experimental paradigm demonstrating these general effects has been used in psychophysical studies of 'comodulation detection difference' (CDD). The CDD effect refers to the fact that thresholds for detecting a modulated, narrowband noise signal are lower when the envelopes of flanking bands of modulated noise are comodulated with each other, but fluctuate independently of the signal compared with conditions in which the envelopes of the signal and flanking bands are all comodulated. Here, we report results from a study of the neural correlates of CDD in European starlings (Sturnus vulgaris). We manipulated: (i) the envelope correlations between a narrowband noise signal and a masker comprised of six flanking bands of noise; (ii) the signal onset delay relative to masker onset; (iii) signal duration; and (iv) masker spectrum level. Masked detection thresholds were determined from neural responses using signal detection theory. Across conditions, the magnitude of neural CDD ranged between 2 and 8 dB, which is similar to that reported in a companion psychophysical study of starlings [U. Langemann & G.M. Klump (2007) Eur. J. Neurosci., 26, 1969-1978]. We found little evidence to suggest that neural CDD resulted from the across-channel processing of auditory grouping cues related to common envelope fluctuations and synchronous onsets between the signal and flanking bands. We discuss a within-channel model of peripheral processing that explains many of our results.

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.

Jitter model and signal processing techniques for pulse width modulation optical recording

NASA Technical Reports Server (NTRS)

Liu, Max M.-K.

1991-01-01

A jitter model and signal processing techniques are discussed for data recovery in Pulse Width Modulation (PWM) optical recording. In PWM, information is stored through modulating sizes of sequential marks alternating in magnetic polarization or in material structure. Jitter, defined as the deviation from the original mark size in the time domain, will result in error detection if it is excessively large. A new approach is taken in data recovery by first using a high speed counter clock to convert time marks to amplitude marks, and signal processing techniques are used to minimize jitter according to the jitter model. The signal processing techniques include motor speed and intersymbol interference equalization, differential and additive detection, and differential and additive modulation.

A continuous dual-process model of remember/know judgments.

PubMed

Wixted, John T; Mickes, Laura

2010-10-01

The dual-process theory of recognition memory holds that recognition decisions can be based on recollection or familiarity, and the remember/know procedure is widely used to investigate those 2 processes. Dual-process theory in general and the remember/know procedure in particular have been challenged by an alternative strength-based interpretation based on signal-detection theory, which holds that remember judgments simply reflect stronger memories than do know judgments. Although supported by a considerable body of research, the signal-detection account is difficult to reconcile with G. Mandler's (1980) classic "butcher-on-the-bus" phenomenon (i.e., strong, familiarity-based recognition). In this article, a new signal-detection model is proposed that does not deny either the validity of dual-process theory or the possibility that remember/know judgments can-when used in the right way-help to distinguish between memories that are largely recollection based from those that are largely familiarity based. It does, however, agree with all prior signal-detection-based critiques of the remember/know procedure, which hold that, as it is ordinarily used, the procedure mainly distinguishes strong memories from weak memories (not recollection from familiarity).

ADAPTIVE WATER SENSOR SIGNAL PROCESSING: EXPERIMENTAL RESULTS AND IMPLICATIONS FOR ONLINE CONTAMINANT WARNING SYSTEMS

EPA Science Inventory

A contaminant detection technique and its optimization algorithms have two principal functions. One is the adaptive signal treatment that suppresses background noise and enhances contaminant signals, leading to a promising detection of water quality changes at a false rate as low...

Hearing aid malfunction detection system

NASA Technical Reports Server (NTRS)

Kessinger, R. L. (Inventor)

1977-01-01

A malfunction detection system for detecting malfunctions in electrical signal processing circuits is disclosed. Malfunctions of a hearing aid in the form of frequency distortion and/or inadequate amplification by the hearing aid amplifier, as well as weakening of the hearing aid power supply are detectable. A test signal is generated and a timed switching circuit periodically applies the test signal to the input of the hearing aid amplifier in place of the input signal from the microphone. The resulting amplifier output is compared with the input test signal used as a reference signal. The hearing aid battery voltage is also periodically compared to a reference voltage. Deviations from the references beyond preset limits cause a warning system to operate.

Methods of DNA methylation detection

NASA Technical Reports Server (NTRS)

Maki, Wusi Chen (Inventor); Filanoski, Brian John (Inventor); Mishra, Nirankar (Inventor); Rastogi, Shiva (Inventor)

2010-01-01

The present invention provides for methods of DNA methylation detection. The present invention provides for methods of generating and detecting specific electronic signals that report the methylation status of targeted DNA molecules in biological samples.Two methods are described, direct and indirect detection of methylated DNA molecules in a nano transistor based device. In the direct detection, methylated target DNA molecules are captured on the sensing surface resulting in changes in the electrical properties of a nano transistor. These changes generate detectable electronic signals. In the indirect detection, antibody-DNA conjugates are used to identify methylated DNA molecules. RNA signal molecules are generated through an in vitro transcription process. These RNA molecules are captured on the sensing surface change the electrical properties of nano transistor thereby generating detectable electronic signals.

Enhancing the Contribution of the T-Stations of the IMS Hydroacoustic Network to IDC Processing and Tsunami Warning

DTIC Science & Technology

2010-09-01

Processing at the International Data Centre (IDC) classifies these signals into three types (T for underground- generated , H for in-water generated ...other hand, present the double disadvantages of a lower detection threshold for in-water propagated hydroacoustic signals and high sensitivity to the...the IMS because of their potential (deGroot-Hedlin, 2001) to detect water-borne signals from in-water explosions (H-phases) and crustal events (T

An electronic circuit that detects left ventricular ejection events by processing the arterial pressure waveform

NASA Technical Reports Server (NTRS)

Gebben, V. D.; Webb, J. A., Jr.

1972-01-01

An electronic circuit for processing arterial blood pressure waveform signals is described. The circuit detects blood pressure as the heart pumps blood through the aortic valve and the pressure distribution caused by aortic valve closure. From these measurements, timing signals for use in measuring the left ventricular ejection time is determined, and signals are provided for computer monitoring of the cardiovascular system. Illustrations are given of the circuit and pressure waveforms.

Evidence for the contribution of a threshold retrieval process to semantic memory.

PubMed

Kempnich, Maria; Urquhart, Josephine A; O'Connor, Akira R; Moulin, Chris J A

2017-10-01

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

The detection and analysis of point processes in biological signals

NASA Technical Reports Server (NTRS)

Anderson, D. J.; Correia, M. J.

1977-01-01

A pragmatic approach to the detection and analysis of discrete events in biomedical signals is taken. Examples from both clinical and basic research are provided. Introductory sections discuss not only discrete events which are easily extracted from recordings by conventional threshold detectors but also events embedded in other information carrying signals. The primary considerations are factors governing event-time resolution and the effects limits to this resolution have on the subsequent analysis of the underlying process. The analysis portion describes tests for qualifying the records as stationary point processes and procedures for providing meaningful information about the biological signals under investigation. All of these procedures are designed to be implemented on laboratory computers of modest computational capacity.

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

This algorithm processes high-rate 3-phase signals to identify the start time of each signal and estimate its envelope as data features. The start time and magnitude of each signal during the steady state is also extracted. The features can be used to detect abnormal signals. This algorithm is developed to analyze Exxeno's 3-phase voltage and current data recorded from refrigeration systems to detect device failure or degradation.

Photonics-based real-time ultra-high-range-resolution radar with broadband signal generation and processing.

PubMed

Zhang, Fangzheng; Guo, Qingshui; Pan, Shilong

2017-10-23

Real-time and high-resolution target detection is highly desirable in modern radar applications. Electronic techniques have encountered grave difficulties in the development of such radars, which strictly rely on a large instantaneous bandwidth. In this article, a photonics-based real-time high-range-resolution radar is proposed with optical generation and processing of broadband linear frequency modulation (LFM) signals. A broadband LFM signal is generated in the transmitter by photonic frequency quadrupling, and the received echo is de-chirped to a low frequency signal by photonic frequency mixing. The system can operate at a high frequency and a large bandwidth while enabling real-time processing by low-speed analog-to-digital conversion and digital signal processing. A conceptual radar is established. Real-time processing of an 8-GHz LFM signal is achieved with a sampling rate of 500 MSa/s. Accurate distance measurement is implemented with a maximum error of 4 mm within a range of ~3.5 meters. Detection of two targets is demonstrated with a range-resolution as high as 1.875 cm. We believe the proposed radar architecture is a reliable solution to overcome the limitations of current radar on operation bandwidth and processing speed, and it is hopefully to be used in future radars for real-time and high-resolution target detection and imaging.

Parallel optimization of signal detection in active magnetospheric signal injection experiments

NASA Astrophysics Data System (ADS)

Gowanlock, Michael; Li, Justin D.; Rude, Cody M.; Pankratius, Victor

2018-05-01

Signal detection and extraction requires substantial manual parameter tuning at different stages in the processing pipeline. Time-series data depends on domain-specific signal properties, necessitating unique parameter selection for a given problem. The large potential search space makes this parameter selection process time-consuming and subject to variability. We introduce a technique to search and prune such parameter search spaces in parallel and select parameters for time series filters using breadth- and depth-first search strategies to increase the likelihood of detecting signals of interest in the field of magnetospheric physics. We focus on studying geomagnetic activity in the extremely and very low frequency ranges (ELF/VLF) using ELF/VLF transmissions from Siple Station, Antarctica, received at Québec, Canada. Our technique successfully detects amplified transmissions and achieves substantial speedup performance gains as compared to an exhaustive parameter search. We present examples where our algorithmic approach reduces the search from hundreds of seconds down to less than 1 s, with a ranked signal detection in the top 99th percentile, thus making it valuable for real-time monitoring. We also present empirical performance models quantifying the trade-off between the quality of signal recovered and the algorithm response time required for signal extraction. In the future, improved signal extraction in scenarios like the Siple experiment will enable better real-time diagnostics of conditions of the Earth's magnetosphere for monitoring space weather activity.

Detecting blast-induced infrasound in wind noise.

PubMed

Howard, Wheeler B; Dillion, Kevin L; Shields, F Douglas

2010-03-01

Current efforts seek to monitor and investigate such naturally occurring events as volcanic eruptions, hurricanes, bolides entering the atmosphere, earthquakes, and tsunamis by the infrasound they generate. Often, detection of the infrasound signal is limited by the masking effect of wind noise. This paper describes the use of a distributed array to detect infrasound signals from four atmospheric detonations at White Sands Missile Range in New Mexico, USA in 2006. Three of the blasts occurred during times of low wind noise and were easily observed with array processing techniques. One blast was obscured by high wind conditions. The results of signal processing are presented that allowed localization of the blast-induced signals in the presence of wind noise in the array response.

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.

Biosensor system-on-a-chip including CMOS-based signal processing circuits and 64 carbon nanotube-based sensors for the detection of a neurotransmitter.

PubMed

Lee, Byung Yang; Seo, Sung Min; Lee, Dong Joon; Lee, Minbaek; Lee, Joohyung; Cheon, Jun-Ho; Cho, Eunju; Lee, Hyunjoong; Chung, In-Young; Park, Young June; Kim, Suhwan; Hong, Seunghun

2010-04-07

We developed a carbon nanotube (CNT)-based biosensor system-on-a-chip (SoC) for the detection of a neurotransmitter. Here, 64 CNT-based sensors were integrated with silicon-based signal processing circuits in a single chip, which was made possible by combining several technological breakthroughs such as efficient signal processing, uniform CNT networks, and biocompatible functionalization of CNT-based sensors. The chip was utilized to detect glutamate, a neurotransmitter, where ammonia, a byproduct of the enzymatic reaction of glutamate and glutamate oxidase on CNT-based sensors, modulated the conductance signals to the CNT-based sensors. This is a major technological advancement in the integration of CNT-based sensors with microelectronics, and this chip can be readily integrated with larger scale lab-on-a-chip (LoC) systems for various applications such as LoC systems for neural networks.

Spin-Off Successes of SETI Research at Berkeley

NASA Astrophysics Data System (ADS)

Douglas, K. A.; Anderson, D. P.; Bankay, R.; Chen, H.; Cobb, J.; Korpela, E. J.; Lebofsky, M.; Parsons, A.; von Korff, J.; Werthimer, D.

2009-12-01

Our group contributes to the Search for Extra-Terrestrial Intelligence (SETI) by developing and using world-class signal processing computers to analyze data collected on the Arecibo telescope. Although no patterned signal of extra-terrestrial origin has yet been detected, and the immediate prospects for making such a detection are highly uncertain, the SETI@home project has nonetheless proven the value of pursuing such research through its impact on the fields of distributed computing, real-time signal processing, and radio astronomy. The SETI@home project has spun off the Center for Astronomy Signal Processing and Electronics Research (CASPER) and the Berkeley Open Infrastructure for Networked Computing (BOINC), both of which are responsible for catalyzing a smorgasbord of new research in scientific disciplines in countries around the world. Futhermore, the data collected and archived for the SETI@home project is proving valuable in data-mining experiments for mapping neutral galatic hydrogen and for detecting black-hole evaporation.

A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals

PubMed Central

Wang, Baojun; Barahona, Mauricio; Buck, Martin

2013-01-01

Cells perceive a wide variety of cellular and environmental signals, which are often processed combinatorially to generate particular phenotypic responses. Here, we employ both single and mixed cell type populations, pre-programmed with engineered modular cell signalling and sensing circuits, as processing units to detect and integrate multiple environmental signals. Based on an engineered modular genetic AND logic gate, we report the construction of a set of scalable synthetic microbe-based biosensors comprising exchangeable sensory, signal processing and actuation modules. These cellular biosensors were engineered using distinct signalling sensory modules to precisely identify various chemical signals, and combinations thereof, with a quantitative fluorescent output. The genetic logic gate used can function as a biological filter and an amplifier to enhance the sensing selectivity and sensitivity of cell-based biosensors. In particular, an Escherichia coli consortium-based biosensor has been constructed that can detect and integrate three environmental signals (arsenic, mercury and copper ion levels) via either its native two-component signal transduction pathways or synthetic signalling sensors derived from other bacteria in combination with a cell-cell communication module. We demonstrate how a modular cell-based biosensor can be engineered predictably using exchangeable synthetic gene circuit modules to sense and integrate multiple-input signals. This study illustrates some of the key practical design principles required for the future application of these biosensors in broad environmental and healthcare areas. PMID:22981411

Bayesian Inference for Signal-Based Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.

2015-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. SIG-VISA (Signal-based Vertically Integrated Seismic Analysis) is a system for global seismic monitoring through Bayesian inference on seismic signals. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of recent geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a global network of stations. We demonstrate recent progress in scaling up SIG-VISA to efficiently process the data stream of global signals recorded by the International Monitoring System (IMS), including comparisons against existing processing methods that show increased sensitivity from our signal-based model and in particular the ability to locate events (including aftershock sequences that can tax analyst processing) precisely from waveform correlation effects. We also provide a Bayesian analysis of an alleged low-magnitude event near the DPRK test site in May 2010 [1] [2], investigating whether such an event could plausibly be detected through automated processing in a signal-based monitoring system. [1] Zhang, Miao and Wen, Lianxing. "Seismological Evidence for a Low-Yield Nuclear Test on 12 May 2010 in North Korea". Seismological Research Letters, January/February 2015. [2] Richards, Paul. "A Seismic Event in North Korea on 12 May 2010". CTBTO SnT 2015 oral presentation, video at https://video-archive.ctbto.org/index.php/kmc/preview/partner_id/103/uiconf_id/4421629/entry_id/0_ymmtpps0/delivery/http

Fpga based L-band pulse doppler radar design and implementation

NASA Astrophysics Data System (ADS)

Savci, Kubilay

As its name implies RADAR (Radio Detection and Ranging) is an electromagnetic sensor used for detection and locating targets from their return signals. Radar systems propagate electromagnetic energy, from the antenna which is in part intercepted by an object. Objects reradiate a portion of energy which is captured by the radar receiver. The received signal is then processed for information extraction. Radar systems are widely used for surveillance, air security, navigation, weather hazard detection, as well as remote sensing applications. In this work, an FPGA based L-band Pulse Doppler radar prototype, which is used for target detection, localization and velocity calculation has been built and a general-purpose Pulse Doppler radar processor has been developed. This radar is a ground based stationary monopulse radar, which transmits a short pulse with a certain pulse repetition frequency (PRF). Return signals from the target are processed and information about their location and velocity is extracted. Discrete components are used for the transmitter and receiver chain. The hardware solution is based on Xilinx Virtex-6 ML605 FPGA board, responsible for the control of the radar system and the digital signal processing of the received signal, which involves Constant False Alarm Rate (CFAR) detection and Pulse Doppler processing. The algorithm is implemented in MATLAB/SIMULINK using the Xilinx System Generator for DSP tool. The field programmable gate arrays (FPGA) implementation of the radar system provides the flexibility of changing parameters such as the PRF and pulse length therefore it can be used with different radar configurations as well. A VHDL design has been developed for 1Gbit Ethernet connection to transfer digitized return signal and detection results to PC. An A-Scope software has been developed with C# programming language to display time domain radar signals and detection results on PC. Data are processed both in FPGA chip and on PC. FPGA uses fixed point arithmetic operations as it is fast and facilitates source requirement as it consumes less hardware than floating point arithmetic operations. The software uses floating point arithmetic operations, which ensure precision in processing at the expense of speed. The functionality of the radar system has been tested for experimental validation in the field with a moving car and the validation of submodules are tested with synthetic data simulated on MATLAB.

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).

Design of an integrated sensor system for the detection of traces of different molecules in the air

NASA Astrophysics Data System (ADS)

Strle, D.; Muševič, I.

2015-04-01

This article presents the design of a miniature detection system and its associated signal processing electronics, which can detect and selectively recognize vapor traces of different materials in the air - including explosives. It is based on the array of surface-functionalized COMB capacitive sensors and extremely low noise, analog, integrated electronic circuit, hardwired digital signal processing hardware and additional software running on a PC. The instrument is sensitive and selective, consumes a minimum amount of energy, is very small (few mm3) and cheap to produce in large quantities, and is insensitive to mechanical influences. Using an electronic detection system built of low noise analog front-end and hard-wired digital signal processing, it is possible to detect less than 0.3ppt of TNT molecules in the atmosphere (3 TNT molecules in 1013 molecules of the air) at 25°C on a 1 Hz bandwidth using very small volume and approx. 10 mA current from a 5V supply voltage. The sensors are implemented in a modified MEMS process and analog electronics in 0.18 um CMOS technology.

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

Advances in Mixed Signal Processing for Regional and Teleseismic Arrays

DTIC Science & Technology

2006-08-15

1: Mixture of signals from two earthquakes from south of Africa and the Philippines observed at USAEDS long-period seismic array in Korea. Correct...window where the detector will miss valid signals . 2 Approaches to detecting signals on arrays all focus on the basic model that expresses the observed...possible use in detecting infrasound signals . The approach is based on orthogonal- ity properties of the eigen vectors of the spectral matrix under a

Fail-safe fire detection system

NASA Technical Reports Server (NTRS)

Bloam, E. T.

1974-01-01

Fire detection control system continually monitors its own integrity, automatically signals any malfunction, and separately signals fire in any zone being monitored. Should be of interest in fields of chemical and petroleum processing, power generation, equipment testing, and building protection.

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

NASA Astrophysics Data System (ADS)

Kora, Padmavathi; Sri Rama Krishna, K.

2016-12-01

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

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

A mobile ferromagnetic shape detection sensor using a Hall sensor array and magnetic imaging.

PubMed

Misron, Norhisam; Shin, Ng Wei; Shafie, Suhaidi; Marhaban, Mohd Hamiruce; Mailah, Nashiren Farzilah

2011-01-01

This paper presents a mobile Hall sensor array system for the shape detection of ferromagnetic materials that are embedded in walls or floors. The operation of the mobile Hall sensor array system is based on the principle of magnetic flux leakage to describe the shape of the ferromagnetic material. Two permanent magnets are used to generate the magnetic flux flow. The distribution of magnetic flux is perturbed as the ferromagnetic material is brought near the permanent magnets and the changes in magnetic flux distribution are detected by the 1-D array of the Hall sensor array setup. The process for magnetic imaging of the magnetic flux distribution is done by a signal processing unit before it displays the real time images using a netbook. A signal processing application software is developed for the 1-D Hall sensor array signal acquisition and processing to construct a 2-D array matrix. The processed 1-D Hall sensor array signals are later used to construct the magnetic image of ferromagnetic material based on the voltage signal and the magnetic flux distribution. The experimental results illustrate how the shape of specimens such as square, round and triangle shapes is determined through magnetic images based on the voltage signal and magnetic flux distribution of the specimen. In addition, the magnetic images of actual ferromagnetic objects are also illustrated to prove the functionality of mobile Hall sensor array system for actual shape detection. The results prove that the mobile Hall sensor array system is able to perform magnetic imaging in identifying various ferromagnetic materials.

A Mobile Ferromagnetic Shape Detection Sensor Using a Hall Sensor Array and Magnetic Imaging

PubMed Central

Misron, Norhisam; Shin, Ng Wei; Shafie, Suhaidi; Marhaban, Mohd Hamiruce; Mailah, Nashiren Farzilah

2011-01-01

This paper presents a Mobile Hall Sensor Array system for the shape detection of ferromagnetic materials that are embedded in walls or floors. The operation of the Mobile Hall Sensor Array system is based on the principle of magnetic flux leakage to describe the shape of the ferromagnetic material. Two permanent magnets are used to generate the magnetic flux flow. The distribution of magnetic flux is perturbed as the ferromagnetic material is brought near the permanent magnets and the changes in magnetic flux distribution are detected by the 1-D array of the Hall sensor array setup. The process for magnetic imaging of the magnetic flux distribution is done by a signal processing unit before it displays the real time images using a netbook. A signal processing application software is developed for the 1-D Hall sensor array signal acquisition and processing to construct a 2-D array matrix. The processed 1-D Hall sensor array signals are later used to construct the magnetic image of ferromagnetic material based on the voltage signal and the magnetic flux distribution. The experimental results illustrate how the shape of specimens such as square, round and triangle shapes is determined through magnetic images based on the voltage signal and magnetic flux distribution of the specimen. In addition, the magnetic images of actual ferromagnetic objects are also illustrated to prove the functionality of Mobile Hall Sensor Array system for actual shape detection. The results prove that the Mobile Hall Sensor Array system is able to perform magnetic imaging in identifying various ferromagnetic materials. PMID:22346653

Signal Processing and Interpretation Using Multilevel Signal Abstractions.

DTIC Science & Technology

1986-06-01

mappings expressed in the Fourier domain. Pre- viously proposed causal analysis techniques for diagnosis are based on the analysis of intermediate data ...can be processed either as individual one-dimensional waveforms or as multichannel data 26 I P- - . . . ." " ." h9. for source detection and direction...microphone data . The signal processing for both spectral analysis of microphone signals and direc- * tion determination of acoustic sources involves

Relative Saliency in Change Signals Affects Perceptual Comparison and Decision Processes in Change Detection

ERIC Educational Resources Information Center

Yang, Cheng-Ta

2011-01-01

Change detection requires perceptual comparison and decision processes on different features of multiattribute objects. How relative salience between two feature-changes influences the processes has not been addressed. This study used the systems factorial technology to investigate the processes when detecting changes in a Gabor patch with visual…

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.

Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

PubMed

Housh, Mashor; Ohar, Ziv

2017-03-01

The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parallel Processing of Broad-Band PPM Signals

NASA Technical Reports Server (NTRS)

Gray, Andrew; Kang, Edward; Lay, Norman; Vilnrotter, Victor; Srinivasan, Meera; Lee, Clement

2010-01-01

A parallel-processing algorithm and a hardware architecture to implement the algorithm have been devised for timeslot synchronization in the reception of pulse-position-modulated (PPM) optical or radio signals. As in the cases of some prior algorithms and architectures for parallel, discrete-time, digital processing of signals other than PPM, an incoming broadband signal is divided into multiple parallel narrower-band signals by means of sub-sampling and filtering. The number of parallel streams is chosen so that the frequency content of the narrower-band signals is low enough to enable processing by relatively-low speed complementary metal oxide semiconductor (CMOS) electronic circuitry. The algorithm and architecture are intended to satisfy requirements for time-varying time-slot synchronization and post-detection filtering, with correction of timing errors independent of estimation of timing errors. They are also intended to afford flexibility for dynamic reconfiguration and upgrading. The architecture is implemented in a reconfigurable CMOS processor in the form of a field-programmable gate array. The algorithm and its hardware implementation incorporate three separate time-varying filter banks for three distinct functions: correction of sub-sample timing errors, post-detection filtering, and post-detection estimation of timing errors. The design of the filter bank for correction of timing errors, the method of estimating timing errors, and the design of a feedback-loop filter are governed by a host of parameters, the most critical one, with regard to processing very broadband signals with CMOS hardware, being the number of parallel streams (equivalently, the rate-reduction parameter).

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.

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.

Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals.

PubMed

Ze Wang; Chi Man Wong; Feng Wan

2017-07-01

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.

An integrated microfluidic analysis microsystems with bacterial capture enrichment and in-situ impedance detection

NASA Astrophysics Data System (ADS)

Liu, Hai-Tao; Wen, Zhi-Yu; Xu, Yi; Shang, Zheng-Guo; Peng, Jin-Lan; Tian, Peng

2017-09-01

In this paper, an integrated microfluidic analysis microsystems with bacterial capture enrichment and in-situ impedance detection was purposed based on microfluidic chips dielectrophoresis technique and electrochemical impedance detection principle. The microsystems include microfluidic chip, main control module, and drive and control module, and signal detection and processing modulet and result display unit. The main control module produce the work sequence of impedance detection system parts and achieve data communication functions, the drive and control circuit generate AC signal which amplitude and frequency adjustable, and it was applied on the foodborne pathogens impedance analysis microsystems to realize the capture enrichment and impedance detection. The signal detection and processing circuit translate the current signal into impendence of bacteria, and transfer to computer, the last detection result is displayed on the computer. The experiment sample was prepared by adding Escherichia coli standard sample into chicken sample solution, and the samples were tested on the dielectrophoresis chip capture enrichment and in-situ impedance detection microsystems with micro-array electrode microfluidic chips. The experiments show that the Escherichia coli detection limit of microsystems is 5 × 104 CFU/mL and the detection time is within 6 min in the optimization of voltage detection 10 V and detection frequency 500 KHz operating conditions. The integrated microfluidic analysis microsystems laid the solid foundation for rapid real-time in-situ detection of bacteria.

Real-time digital signal processing for live electro-optic imaging.

PubMed

Sasagawa, Kiyotaka; Kanno, Atsushi; Tsuchiya, Masahiro

2009-08-31

We present an imaging system that enables real-time magnitude and phase detection of modulated signals and its application to a Live Electro-optic Imaging (LEI) system, which realizes instantaneous visualization of RF electric fields. The real-time acquisition of magnitude and phase images of a modulated optical signal at 5 kHz is demonstrated by imaging with a Si-based high-speed CMOS image sensor and real-time signal processing with a digital signal processor. In the LEI system, RF electric fields are probed with light via an electro-optic crystal plate and downconverted to an intermediate frequency by parallel optical heterodyning, which can be detected with the image sensor. The artifacts caused by the optics and the image sensor characteristics are corrected by image processing. As examples, we demonstrate real-time visualization of electric fields from RF circuits.

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.

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.

Solar loading thermography: Time-lapsed thermographic survey and advanced thermographic signal processing for the inspection of civil engineering and cultural heritage structures

NASA Astrophysics Data System (ADS)

Ibarra-Castanedo, Clemente; Sfarra, Stefano; Klein, Matthieu; Maldague, Xavier

2017-05-01

The experimental results from infrared thermography surveys over two buildings externally exposed walls are presented. Data acquisition was performed on a static configuration by recording direct and indirect solar loading during several days and was processed using advanced signal processing techniques in order to increase signal-to-noise ratio and signature contrast of the elements of interest. It is demonstrated that it is possible to detect the thermal signature of large internal structures as well as surface features under such thermographic scenarios. Results from a long-wave microbolometer compared favorably to those from a mid-wave cooled infrared camera for the detection of large subsurface features from unprocessed images. In both cases, however, advanced signal processing greatly improved contrast of the internal features.

Design and Experiment of Electrooculogram (EOG) System and Its Application to Control Mobile Robot

NASA Astrophysics Data System (ADS)

Sanjaya, W. S. M.; Anggraeni, D.; Multajam, R.; Subkhi, M. N.; Muttaqien, I.

2017-03-01

In this paper, we design and investigate a biological signal detection of eye movements (Electrooculogram). To detect a signal of Electrooculogram (EOG) used 4 instrument amplifier process; differential instrumentation amplifier, High Pass Filter (HPF) with 3 stage filters, Low Pass Filter (LPF) with 3 stage filters and Level Shifter circuit. The total of amplifying is 1000 times of gain, with frequency range 0.5-30 Hz. IC OP-Amp OP07 was used for all amplifying process. EOG signal will be read as analog input for Arduino microprocessor, and will interfaced with serial communication to PC Monitor using Processing® software. The result of this research show a differences value of eye movements. Differences signal of EOG have been applied to navigation control of the mobile robot. In this research, all communication process using Bluetooth HC-05.

Design of an FMCW radar baseband signal processing system for automotive application.

PubMed

Lin, Jau-Jr; Li, Yuan-Ping; Hsu, Wei-Chiang; Lee, Ta-Sung

2016-01-01

For a typical FMCW automotive radar system, a new design of baseband signal processing architecture and algorithms is proposed to overcome the ghost targets and overlapping problems in the multi-target detection scenario. To satisfy the short measurement time constraint without increasing the RF front-end loading, a three-segment waveform with different slopes is utilized. By introducing a new pairing mechanism and a spatial filter design algorithm, the proposed detection architecture not only provides high accuracy and reliability, but also requires low pairing time and computational loading. This proposed baseband signal processing architecture and algorithms balance the performance and complexity, and are suitable to be implemented in a real automotive radar system. Field measurement results demonstrate that the proposed automotive radar signal processing system can perform well in a realistic application scenario.

FPGA based hardware optimized implementation of signal processing system for LFM pulsed radar

NASA Astrophysics Data System (ADS)

Azim, Noor ul; Jun, Wang

2016-11-01

Signal processing is one of the main parts of any radar system. Different signal processing algorithms are used to extract information about different parameters like range, speed, direction etc, of a target in the field of radar communication. This paper presents LFM (Linear Frequency Modulation) pulsed radar signal processing algorithms which are used to improve target detection, range resolution and to estimate the speed of a target. Firstly, these algorithms are simulated in MATLAB to verify the concept and theory. After the conceptual verification in MATLAB, the simulation is converted into implementation on hardware using Xilinx FPGA. Chosen FPGA is Xilinx Virtex-6 (XC6LVX75T). For hardware implementation pipeline optimization is adopted and also other factors are considered for resources optimization in the process of implementation. Focusing algorithms in this work for improving target detection, range resolution and speed estimation are hardware optimized fast convolution processing based pulse compression and pulse Doppler processing.

Optimized signal detection and analysis methods for in vivo photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Wang, Qiyan; Zhou, Quanyu; Yang, Ping; Wang, Xiaoling; Niu, Zhenyu; Suo, Yuanzhen; He, Hao; Gao, Wenyuan; Tang, Shuo; Wei, Xunbin

2017-02-01

Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.

Certification of windshear performance with RTCA class D radomes

NASA Technical Reports Server (NTRS)

Mathews, Bruce D.; Miller, Fran; Rittenhouse, Kirk; Barnett, Lee; Rowe, William

1994-01-01

Superposition testing of detection range performance forms a digital signal for input into a simulation of signal and data processing equipment and algorithms to be employed in a sensor system for advanced warning of hazardous windshear. For suitable pulse-Doppler radar, recording of the digital data at the input to the digital signal processor furnishes a realistic operational scenario and environmentally responsive clutter signal including all sidelobe clutter, ground moving target indications (GMTI), and large signal spurious due to mainbeam clutter and/or RFI respective of the urban airport clutter and aircraft scenarios (approach and landing antenna pointing). For linear radar system processes, a signal at the same point in the process from a hazard phenomena may be calculated from models of the scattering phenomena, for example, as represented in fine 3 dimensional reflectivity and velocity grid structures. Superposition testing furnishes a competing signal environment for detection and warning time performance confirmation of phenomena uncontrollable in a natural environment.

Recognition Memory zROC Slopes for Items with Correct versus Incorrect Source Decisions Discriminate the Dual Process and Unequal Variance Signal Detection Models

ERIC Educational Resources Information Center

Starns, Jeffrey J.; Rotello, Caren M.; Hautus, Michael J.

2014-01-01

We tested the dual process and unequal variance signal detection models by jointly modeling recognition and source confidence ratings. The 2 approaches make unique predictions for the slope of the recognition memory zROC function for items with correct versus incorrect source decisions. The standard bivariate Gaussian version of the unequal…

Non-invasive assessment of skeletal muscle activity

NASA Astrophysics Data System (ADS)

Merletti, Roberto; Orizio, Claudio; di Prampero, Pietro E.; Tesch, Per

2005-10-01

After the first 3 years (2002-2005), the MAP project has made available: - systems fo electrodes, signal conditioning and digital processing for multichannel simultaneously-detected EMG and MMG as well as for simultaneous electrical stimulation and EMG detection with artifact cancellation. - innovative non-invasive techniques for the extraction of individual motor unit action potentials (MUAPS) and individual motor and MMG contributions from the surface EMG interference signal and the MMG signal. - processing techniques for extractions of indicators of progressive fatigue from the electrically-elicited (M-wave) EMG signal. - techniques for the analysis of dynamic multichannel EMG during cyclic or explosive exercise (in collaboration with project EXER/MAP-MED-027).

Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

NASA Astrophysics Data System (ADS)

Heck, Matthias; Hammer, Conny; van Herwijnen, Alec; Schweizer, Jürg; Fäh, Donat

2018-01-01

Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

Watchdog Sensor Network with Multi-Stage RF Signal Identification and Cooperative Intrusion Detection

DTIC Science & Technology

2012-03-01

detection and physical layer authentication in mobile Ad Hoc networks and wireless sensor networks (WSNs) have been investigated. Résume Le rapport...IEEE 802.16 d and e (WiMAX); (b) IEEE 802.11 (Wi-Fi) family of a, b, g, n, and s (c) Sensor networks based on IEEE 802.15.4: Wireless USB, Bluetooth... sensor network are investigated for standard compatible wireless signals. The proposed signal existence detection and identification process consists

Analog CMOS design for optical coherence tomography signal detection and processing.

PubMed

Xu, Wei; Mathine, David L; Barton, Jennifer K

2008-02-01

A CMOS circuit was designed and fabricated for optical coherence tomography (OCT) signal detection and processing. The circuit includes a photoreceiver, differential gain stage and lock-in amplifier based demodulator. The photoreceiver consists of a CMOS photodetector and low noise differential transimpedance amplifier which converts the optical interference signal into a voltage. The differential gain stage further amplifies the signal. The in-phase and quadrature channels of the lock-in amplifier each include an analog mixer and switched-capacitor low-pass filter with an external mixer reference signal. The interferogram envelope and phase can be extracted with this configuration, enabling Doppler OCT measurements. A sensitivity of -80 dB is achieved with faithful reproduction of the interferometric signal envelope. A sample image of finger tip is presented.

Detection of buried mines with seismic sonar

NASA Astrophysics Data System (ADS)

Muir, Thomas G.; Baker, Steven R.; Gaghan, Frederick E.; Fitzpatrick, Sean M.; Hall, Patrick W.; Sheetz, Kraig E.; Guy, Jeremie

2003-10-01

Prior research on seismo-acoustic sonar for detection of buried targets [J. Acoust. Soc. Am. 103, 2333-2343 (1998)] has continued with examination of the target strengths of buried test targets as well as targets of interest, and has also examined detection and confirmatory classification of these, all using arrays of seismic sources and receivers as well as signal processing techniques to enhance target recognition. The target strengths of two test targets (one a steel gas bottle, the other an aluminum powder keg), buried in a sand beach, were examined as a function of internal mass load, to evaluate theory developed for seismic sonar target strength [J. Acoust. Soc. Am. 103, 2344-2353 (1998)]. The detection of buried naval and military targets of interest was achieved with an array of 7 shaker sources and 5, three-axis seismometers, at a range of 5 m. Vector polarization filtering was the main signal processing technique for detection. It capitalizes on the fact that the vertical and horizontal components in Rayleigh wave echoes are 90 deg out of phase, enabling complex variable processing to obtain the imaginary component of the signal power versus time, which is unique to Rayleigh waves. Gabor matrix processing of this signal component was the main technique used to determine whether the target was man-made or just a natural target in the environment. [Work sponsored by ONR.

Thermal noise limit for ultra-high vacuum noncontact atomic force microscopy

PubMed Central

Lübbe, Jannis; Temmen, Matthias; Rode, Sebastian; Rahe, Philipp; Kühnle, Angelika

2013-01-01

Summary The noise of the frequency-shift signal Δf in noncontact atomic force microscopy (NC-AFM) consists of cantilever thermal noise, tip–surface-interaction noise and instrumental noise from the detection and signal processing systems. We investigate how the displacement-noise spectral density d z at the input of the frequency demodulator propagates to the frequency-shift-noise spectral density d Δ f at the demodulator output in dependence of cantilever properties and settings of the signal processing electronics in the limit of a negligible tip–surface interaction and a measurement under ultrahigh-vacuum conditions. For a quantification of the noise figures, we calibrate the cantilever displacement signal and determine the transfer function of the signal-processing electronics. From the transfer function and the measured d z, we predict d Δ f for specific filter settings, a given level of detection-system noise spectral density d z ds and the cantilever-thermal-noise spectral density d z th. We find an excellent agreement between the calculated and measured values for d Δ f. Furthermore, we demonstrate that thermal noise in d Δ f, defining the ultimate limit in NC-AFM signal detection, can be kept low by a proper choice of the cantilever whereby its Q-factor should be given most attention. A system with a low-noise signal detection and a suitable cantilever, operated with appropriate filter and feedback-loop settings allows room temperature NC-AFM measurements at a low thermal-noise limit with a significant bandwidth. PMID:23400758

Thermal noise limit for ultra-high vacuum noncontact atomic force microscopy.

PubMed

Lübbe, Jannis; Temmen, Matthias; Rode, Sebastian; Rahe, Philipp; Kühnle, Angelika; Reichling, Michael

2013-01-01

The noise of the frequency-shift signal Δf in noncontact atomic force microscopy (NC-AFM) consists of cantilever thermal noise, tip-surface-interaction noise and instrumental noise from the detection and signal processing systems. We investigate how the displacement-noise spectral density d(z) at the input of the frequency demodulator propagates to the frequency-shift-noise spectral density d(Δ) (f) at the demodulator output in dependence of cantilever properties and settings of the signal processing electronics in the limit of a negligible tip-surface interaction and a measurement under ultrahigh-vacuum conditions. For a quantification of the noise figures, we calibrate the cantilever displacement signal and determine the transfer function of the signal-processing electronics. From the transfer function and the measured d(z), we predict d(Δ) (f) for specific filter settings, a given level of detection-system noise spectral density d(z) (ds) and the cantilever-thermal-noise spectral density d(z) (th). We find an excellent agreement between the calculated and measured values for d(Δ) (f). Furthermore, we demonstrate that thermal noise in d(Δ) (f), defining the ultimate limit in NC-AFM signal detection, can be kept low by a proper choice of the cantilever whereby its Q-factor should be given most attention. A system with a low-noise signal detection and a suitable cantilever, operated with appropriate filter and feedback-loop settings allows room temperature NC-AFM measurements at a low thermal-noise limit with a significant bandwidth.

Lateral position detection and control for friction stir systems

DOEpatents

Fleming, Paul; Lammlein, David; Cook, George E.; Wilkes, Don Mitchell; Strauss, Alvin M.; Delapp, David; Hartman, Daniel A.

2010-12-14

A friction stir system for processing at least a first workpiece includes a spindle actuator coupled to a rotary tool comprising a rotating member for contacting and processing the first workpiece. A detection system is provided for obtaining information related to a lateral alignment of the rotating member. The detection system comprises at least one sensor for measuring a force experienced by the rotary tool or a parameter related to the force experienced by the rotary tool during processing, wherein the sensor provides sensor signals. A signal processing system is coupled to receive and analyze the sensor signals and determine a lateral alignment of the rotating member relative to a selected lateral position, a selected path, or a direction to decrease a lateral distance relative to the selected lateral position or selected path. In one embodiment, the friction stir system can be embodied as a closed loop tracking system, such as a robot-based tracked friction stir welding (FSW) or friction stir processing (FSP) 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.

Simulate different environments TDLAS On the analysis of the test signal strength

NASA Astrophysics Data System (ADS)

Li, Xin; Zhou, Tao; Jia, Xiaodong

2014-12-01

TDLAS system is the use of the wavelength tuning characteristics of the laser diode, for detecting the absorption spectrum of the gas absorption line. Detecting the gas space, temperature, pressure and flow rate and concentration. The use of laboratory techniques TDLAS gas detection, experimental simulation engine combustion water vapor and smoke. using an optical lens system receives the signal acquisition and signal interference test analysis. Analog water vapor and smoke in two different environments in the sample pool interference. In both experiments environmental interference gas absorption in the optical signal acquisition, signal amplitude variation analysis, and records related to the signal data. In order to study site conditions in the engine combustion process for signal acquisition provides an ideal experimental data .

Study of interhemispheric asymmetries in electroencephalographic signals by frequency analysis

NASA Astrophysics Data System (ADS)

Zapata, J. F.; Garzón, J.

2011-01-01

This study provides a new method for the detection of interhemispheric asymmetries in patients with continuous video-electroencephalography (EEG) monitoring at Intensive Care Unit (ICU), using wavelet energy. We obtained the registration of EEG signals in 42 patients with different pathologies, and then we proceeded to perform signal processing using the Matlab program, we compared the abnormalities recorded in the report by the neurophysiologist, the images of each patient and the result of signals analysis with the Discrete Wavelet Transform (DWT). Conclusions: there exists correspondence between the abnormalities found in the processing of the signal with the clinical reports of findings in patients; according to previous conclusion, the methodology used can be a useful tool for diagnosis and early quantitative detection of interhemispheric asymmetries.

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

A Preliminary Investigation of the Reinforcement Function of Signal Detections in Simulated Baggage Screening: Further Support for the Vigilance Reinforcement Hypothesis

ERIC Educational Resources Information Center

Hogan, Lindsey C.; Bell, Matthew; Olson, Ryan

2009-01-01

The vigilance reinforcement hypothesis (VRH) asserts that errors in signal detection tasks are partially explained by operant reinforcement and extinction processes. VRH predictions were tested with a computerized baggage screening task. Our experiment evaluated the effects of signal schedule (extinction vs. variable interval 6 min) and visual…

Picosecond time-resolved photoluminescence using picosecond excitation correlation spectroscopy

NASA Astrophysics Data System (ADS)

Johnson, M. B.; McGill, T. C.; Hunter, A. T.

1988-03-01

We present a study of the temporal decay of photoluminescence (PL) as detected by picosecond excitation correlation spectroscopy (PECS). We analyze the correlation signal that is obtained from two simple models; one where radiative recombination dominates, the other where trapping processes dominate. It is found that radiative recombination alone does not lead to a correlation signal. Parallel trapping type processes are found to be required to see a signal. To illustrate this technique, we examine the temporal decay of the PL signal for In-alloyed, semi-insulating GaAs substrates. We find that the PL signal indicates a carrier lifetime of roughly 100 ps, for excitation densities of 1×1016-5×1017 cm-3. PECS is shown to be an easy technique to measure the ultrafast temporal behavior of PL processes because it requires no ultrafast photon detection. It is particularly well suited to measuring carrier lifetimes.

Detecting the spatial chirp signals by fractional Fourier lens with transformation materials

NASA Astrophysics Data System (ADS)

Chen, J.; Hu, J.

2018-02-01

Fractional Fourier transform (FrFT) is the general form of the Fourier transform and is an important tool in signal processing. As one typical application of FrFT, detecting the chirp rate (CR, or known as the rate of frequency change) of a chirp signal is important in many optical measurements. The optical FrFT that based on graded index lens fails to detect the high CR chirp because the short wave propagation distance of the impulse in the lens will weaken the paraxial approximation condition. With the help of transformation optics, the improved FrFT lens is proposed to adjust the high CR as well as the impulse location of the given input chirp signal. The designed transformation materials can implement the effect of space compression, making the input chirp signal is equivalent to have lower CR, therefore the system can satisfy the paraxial approximation better. As a result, this lens can improve the detection precision for the high CR. The numerical simulations verified the design. The proposed device may have both theoretical and practical values, and the design demonstrates the ability and flexibility of TO in spatial signal processing.

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.

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

PubMed Central

Chen, Fuming; Li, Sheng; Zhang, Yang; Wang, Jianqi

2017-01-01

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance. PMID:28282892

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

NASA Astrophysics Data System (ADS)

Golafshan, Reza; Yuce Sanliturk, Kenan

2016-03-01

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

Human Engineer’s Guide to Auditory Displays. Volume 2. Elements of Signal Reception and Resolution Affecting Auditory Displays.

DTIC Science & Technology

1984-08-01

90de It noce..etrv wnd identify by block numberl .’-- This work reviews the areas of monaural and binaural signal detection, auditory discrimination And...AUDITORY DISPLAYS This work reviews the areas of monaural and binaural signal detection, auditory discrimination and localization, and reaction times to...pertaining to the major areas of auditory processing in humans. The areas covered in the reviews presented here are monaural and binaural siqnal detection

On Adaptive Cell-Averaging CFAR (Constant False-Alarm Rate) Radar Signal Detection

DTIC Science & Technology

1987-10-01

SIICILE COPY 4 F FInI Tedwill Rlmrt to October 197 00 C\\JT ON ADAPTIVE CELL-AVERA81NG CFAR I RADAR SIGNAL DETECTION Syracuse University Mourud krket...NY 13441-5700 ELEMENT NO. NO. NO ACCESSION NO. 11. TITLE (Include Security Classification) 61102F 2’ 05 J8 PD - ON ADAPTIVE CELL-AVERAGING CFAR RADAR... CFAR ). One approach to adaptive detection in nonstationary noise and clutter background is to compare the processed target signal to an adaptive

Seismic data fusion anomaly detection

NASA Astrophysics Data System (ADS)

Harrity, Kyle; Blasch, Erik; Alford, Mark; Ezekiel, Soundararajan; Ferris, David

2014-06-01

Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

Phase editing as a signal pre-processing step for automated bearing fault detection

NASA Astrophysics Data System (ADS)

Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

2017-07-01

Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

Possibility of deriving the Hermean surface composition through low energy neutral atom detection

NASA Astrophysics Data System (ADS)

Milillo, A.; Orsini, S.; Massetti, S.; Mura, A.; de Angelis, E.; Lammer, H.; Wurz, P.; di Lellis, A. M.

2003-04-01

The release processes induced by ion sputtering and/or micrometeoroids impacts induces erosion of the Mercury surface. The sputtered neutrals exhibit spectra peaked at low energies (few eV). Nevertheless, a high-energy neutral signal also emerges, due to these release processes. In principle, the directional neutral signal can be detected, providing information on the local surface composition. In this study, we simulate the neutral signal due to ion sputtering below the cusp regions, assuming a highly anisotropic surface composition. The NPA SERENA / ELENA instrument proposed on board the ESA mission BepiColombo is a nadir-pointing 1-D sensor, able to detect neutral atoms, form tens of eV to about 5 keV with a capability of resolving the major species. The ELENA field-of-view (FOV) is ~ 60 degrees, with the FOV plane perpendicular to the MPO orbital plane. Here, we speculate on the possibility of discriminating composition anisotropies by detecting the high-energy portion of the sputtered signal.

Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy.

PubMed

Mammone, Nadia; Morabito, Francesco Carlo

2008-09-01

Artifacts are disturbances that may occur during signal acquisition and may affect their processing. The aim of this paper is to propose a technique for automatically detecting artifacts from the electroencephalographic (EEG) recordings. In particular, a technique based on both Independent Component Analysis (ICA) to extract artifactual signals and on Renyi's entropy to automatically detect them is presented. This technique is compared to the widely known approach based on ICA and the joint use of kurtosis and Shannon's entropy. The novel processing technique is shown to detect on average 92.6% of the artifactual signals against the average 68.7% of the previous technique on the studied available database. Moreover, Renyi's entropy is shown to be able to detect muscle and very low frequency activity as well as to discriminate them from other kinds of artifacts. In order to achieve an efficient rejection of the artifacts while minimizing the information loss, future efforts will be devoted to the improvement of blind artifact separation from EEG in order to ensure a very efficient isolation of the artifactual activity from any signals deriving from other brain tasks.

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

Relative saliency in change signals affects perceptual comparison and decision processes in change detection.

PubMed

Yang, Cheng-Ta

2011-12-01

Change detection requires perceptual comparison and decision processes on different features of multiattribute objects. How relative salience between two feature-changes influences the processes has not been addressed. This study used the systems factorial technology to investigate the processes when detecting changes in a Gabor patch with visual inputs from orientation and spatial frequency channels. Two feature-changes were equally salient in Experiment 1, but a frequency-change was more salient than an orientation-change in Experiment 2. Results showed that all four observers adopted parallel self-terminating processing with limited- to unlimited-capacity processing in Experiment 1. In Experiment 2, one observer used parallel self-terminating processing with unlimited-capacity processing, and the others adopted serial self-terminating processing with limited- to unlimited-capacity processing to detect changes. Postexperimental interview revealed that subjective utility of feature information underlay the adoption of a decision strategy. These results highlight that observers alter decision strategies in change detection depending on the relative saliency in change signals, with relative saliency being determined by both physical salience and subjective weight of feature information. When relative salience exists, individual differences in the process characteristics emerge.

Smartphone Cortex Controlled Real-Time Image Processing and Reprocessing for Concentration Independent LED Induced Fluorescence Detection in Capillary Electrophoresis.

PubMed

Szarka, Mate; Guttman, Andras

2017-10-17

We present the application of a smartphone anatomy based technology in the field of liquid phase bioseparations, particularly in capillary electrophoresis. A simple capillary electrophoresis system was built with LED induced fluorescence detection and a credit card sized minicomputer to prove the concept of real time fluorescent imaging (zone adjustable time-lapse fluorescence image processor) and separation controller. The system was evaluated by analyzing under- and overloaded aminopyrenetrisulfonate (APTS)-labeled oligosaccharide samples. The open source software based image processing tool allowed undistorted signal modulation (reprocessing) if the signal was inappropriate for the actual detection system settings (too low or too high). The novel smart detection tool for fluorescently labeled biomolecules greatly expands dynamic range and enables retrospective correction for injections with unsuitable signal levels without the necessity to repeat the analysis.

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.

Central Auditory Processing of Temporal and Spectral-Variance Cues in Cochlear Implant Listeners

PubMed Central

Pham, Carol Q.; Bremen, Peter; Shen, Weidong; Yang, Shi-Ming; Middlebrooks, John C.; Zeng, Fan-Gang; Mc Laughlin, Myles

2015-01-01

Cochlear implant (CI) listeners have difficulty understanding speech in complex listening environments. This deficit is thought to be largely due to peripheral encoding problems arising from current spread, which results in wide peripheral filters. In normal hearing (NH) listeners, central processing contributes to segregation of speech from competing sounds. We tested the hypothesis that basic central processing abilities are retained in post-lingually deaf CI listeners, but processing is hampered by degraded input from the periphery. In eight CI listeners, we measured auditory nerve compound action potentials to characterize peripheral filters. Then, we measured psychophysical detection thresholds in the presence of multi-electrode maskers placed either inside (peripheral masking) or outside (central masking) the peripheral filter. This was intended to distinguish peripheral from central contributions to signal detection. Introduction of temporal asynchrony between the signal and masker improved signal detection in both peripheral and central masking conditions for all CI listeners. Randomly varying components of the masker created spectral-variance cues, which seemed to benefit only two out of eight CI listeners. Contrastingly, the spectral-variance cues improved signal detection in all five NH listeners who listened to our CI simulation. Together these results indicate that widened peripheral filters significantly hamper central processing of spectral-variance cues but not of temporal cues in post-lingually deaf CI listeners. As indicated by two CI listeners in our study, however, post-lingually deaf CI listeners may retain some central processing abilities similar to NH listeners. PMID:26176553

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.

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.

Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques.

PubMed

Hajihosseini, Payman; Anzehaee, Mohammad Mousavi; Behnam, Behzad

2018-05-22

The early fault detection and isolation in industrial systems is a critical factor in preventing equipment damage. In the proposed method, instead of using the time signals of sensors, the 2D image obtained by placing these signals next to each other in a matrix has been used; and then a novel fault detection and isolation procedure has been carried out based on image processing techniques. Different features including texture, wavelet transform, mean and standard deviation of the image accompanied with MLP and RBF neural networks based classifiers have been used for this purpose. Obtained results indicate the notable efficacy and success of the proposed method in detecting and isolating faults of the Tennessee Eastman benchmark process and its superiority over previous techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Doppler ultrasound monitoring technology.

PubMed

Docker, M F

1993-03-01

Developments in the signal processing of Doppler ultrasound used for the detection of fetal heart rate (FHR) have improved the operation of cardiotocographs. These developments are reviewed and the advantages and disadvantages of the various Doppler and signal processing methods are compared.

Knowledge-based tracking algorithm

NASA Astrophysics Data System (ADS)

Corbeil, Allan F.; Hawkins, Linda J.; Gilgallon, Paul F.

1990-10-01

This paper describes the Knowledge-Based Tracking (KBT) algorithm for which a real-time flight test demonstration was recently conducted at Rome Air Development Center (RADC). In KBT processing, the radar signal in each resolution cell is thresholded at a lower than normal setting to detect low RCS targets. This lower threshold produces a larger than normal false alarm rate. Therefore, additional signal processing including spectral filtering, CFAR and knowledge-based acceptance testing are performed to eliminate some of the false alarms. TSC's knowledge-based Track-Before-Detect (TBD) algorithm is then applied to the data from each azimuth sector to detect target tracks. In this algorithm, tentative track templates are formed for each threshold crossing and knowledge-based association rules are applied to the range, Doppler, and azimuth measurements from successive scans. Lastly, an M-association out of N-scan rule is used to declare a detection. This scan-to-scan integration enhances the probability of target detection while maintaining an acceptably low output false alarm rate. For a real-time demonstration of the KBT algorithm, the L-band radar in the Surveillance Laboratory (SL) at RADC was used to illuminate a small Cessna 310 test aircraft. The received radar signal wa digitized and processed by a ST-100 Array Processor and VAX computer network in the lab. The ST-100 performed all of the radar signal processing functions, including Moving Target Indicator (MTI) pulse cancelling, FFT Doppler filtering, and CFAR detection. The VAX computers performed the remaining range-Doppler clustering, beamsplitting and TBD processing functions. The KBT algorithm provided a 9.5 dB improvement relative to single scan performance with a nominal real time delay of less than one second between illumination and display.

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.

Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection

NASA Astrophysics Data System (ADS)

Manolakis, D.; Model, J.; Rossacci, M.; Zhang, D.; Ontiveros, E.; Pieper, M.; Seeley, J.; Weitz, D.

2008-04-01

The long-wave infrared (LWIR) hyperpectral sensing modality is one that is often used for the problem of detection and identification of chemical warfare agents (CWA) which apply to both military and civilian situations. The inherent nature and complexity of background clutter dictates a need for sophisticated and robust statistical models which are then used in the design of optimum signal processing algorithms that then provide the best exploitation of hyperspectral data to ultimately make decisions on the absence or presence of potentially harmful CWAs. This paper describes the basic elements of an automated signal processing pipeline developed at MIT Lincoln Laboratory. In addition to describing this signal processing architecture in detail, we briefly describe the key signal models that form the foundation of these algorithms as well as some spatial processing techniques used for false alarm mitigation. Finally, we apply this processing pipeline to real data measured by the Telops FIRST hyperspectral (FIRST) sensor to demonstrate its practical utility for the user community.

Sequential Adaptive Multi-Modality Target Detection and Classification Using Physics Based Models

DTIC Science & Technology

2006-09-01

estimation," R. Raghuram, R. Raich and A.O. Hero, IEEE Intl. Conf. on Acoustics, Speech , and Signal Processing, Toulouse France, June 2006, <http...can then be solved using off-the-shelf classifiers such as radial basis functions, SVM, or kNN classifier structures. When applied to mine detection we...stage waveform selection for adaptive resource constrained state estimation," 2006 IEEE Intl. Conf. on Acoustics, Speech , and Signal Processing

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.

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.

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.

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

Generation of optical OFDM signals using 21.4 GS/s real time digital signal processing.

PubMed

Benlachtar, Yannis; Watts, Philip M; Bouziane, Rachid; Milder, Peter; Rangaraj, Deepak; Cartolano, Anthony; Koutsoyannis, Robert; Hoe, James C; Püschel, Markus; Glick, Madeleine; Killey, Robert I

2009-09-28

We demonstrate a field programmable gate array (FPGA) based optical orthogonal frequency division multiplexing (OFDM) transmitter implementing real time digital signal processing at a sample rate of 21.4 GS/s. The QPSK-OFDM signal is generated using an 8 bit, 128 point inverse fast Fourier transform (IFFT) core, performing one transform per clock cycle at a clock speed of 167.2 MHz and can be deployed with either a direct-detection or a coherent receiver. The hardware design and the main digital signal processing functions are described, and we show that the main performance limitation is due to the low (4-bit) resolution of the digital-to-analog converter (DAC) and the 8-bit resolution of the IFFT core used. We analyze the back-to-back performance of the transmitter generating an 8.36 Gb/s optical single sideband (SSB) OFDM signal using digital up-conversion, suitable for direct-detection. Additionally, we use the device to transmit 8.36 Gb/s SSB OFDM signals over 200 km of uncompensated standard single mode fiber achieving an overall BER<10(-3).

The recognition of extraterrestrial artificial signals

NASA Technical Reports Server (NTRS)

Seeger, C. L.

1980-01-01

Considerations in the design of receivers for the detection and recognition of artificial microwave signals of extraterrestrial origin are discussed. Following a review of the objectives of SETI and the probable reception and detection characteristics of extraterrestrial signals, means for the improvement of the sensitivity, signal-to-noise ratios and on-line data processing capabilities of SETI receivers are indicated. The characteristics of the signals likely to be present at the output of an ultra-low-noise microwave receiver are then examined, including the system background noise, terrestrial radiations, astrophysical radiations, accidental artificial radiations of terrestrial origin, and intentional radiations produced by humans and by extraterrestrial intelligence. The classes of extraterrestrial signals likely to be detected, beacons and leakage signals, are considered, and options in the specification of gating and thresholding for a high-spectral resolution, high-time-resolution signal discriminator are indicated. Possible tests for the nonhuman origin of a received signal are also pointed out.

Dual signal amplification for highly sensitive electrochemical detection of uropathogens via enzyme-based catalytic target recycling.

PubMed

Su, Jiao; Zhang, Haijie; Jiang, Bingying; Zheng, Huzhi; Chai, Yaqin; Yuan, Ruo; Xiang, Yun

2011-11-15

We report an ultrasensitive electrochemical approach for the detection of uropathogen sequence-specific DNA target. The sensing strategy involves a dual signal amplification process, which combines the signal enhancement by the enzymatic target recycling technique with the sensitivity improvement by the quantum dot (QD) layer-by-layer (LBL) assembled labels. The enzyme-based catalytic target DNA recycling process results in the use of each target DNA sequence for multiple times and leads to direct amplification of the analytical signal. Moreover, the LBL assembled QD labels can further enhance the sensitivity of the sensing system. The coupling of these two effective signal amplification strategies thus leads to low femtomolar (5fM) detection of the target DNA sequences. The proposed strategy also shows excellent discrimination between the target DNA and the single-base mismatch sequences. The advantageous intrinsic sequence-independent property of exonuclease III over other sequence-dependent enzymes makes our new dual signal amplification system a general sensing platform for monitoring ultralow level of various types of target DNA sequences. Copyright © 2011 Elsevier B.V. All rights reserved.

Multi-Array Detection, Association and Location of Infrasound and Seismo-Acoustic Events in Utah

DTIC Science & Technology

2008-09-30

techniques for detecting , associating, and locating infrasound signals at single and multiple arrays and then combining the processed results with...was detected and located by both infrasound and seismic instruments (Figure 3). Infrasound signals at all three arrays , from one of the explosions, are...COVERED (From - To) 30-Sep-2008 REPRINT 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER MULTI- ARRAY DETECTION , ASSOCIATION AND LOCATION OF INFRASOUND FA8718

Application of Data Mining and Knowledge Discovery Techniques to Enhance Binary Target Detection and Decision-Making for Compromised Visual Images

DTIC Science & Technology

2004-11-01

affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual...rendering of a scene. Since image processing is an area in which the original data are stationary in some sense ( auditory signals suffer from...11 Example 1 of SR - Identification of a Subliminal Signal below a Threshold .......................... 13 Example 2 of SR

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

NASA Astrophysics Data System (ADS)

Bayram, Eda; Vural, Elif; Alatan, Aydin

2017-10-01

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

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.

Research on moving object detection based on frog's eyes

NASA Astrophysics Data System (ADS)

Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

2008-12-01

On the basis of object's information processing mechanism with frog's eyes, this paper discussed a bionic detection technology which suitable for object's information processing based on frog's vision. First, the bionics detection theory by imitating frog vision is established, it is an parallel processing mechanism which including pick-up and pretreatment of object's information, parallel separating of digital image, parallel processing, and information synthesis. The computer vision detection system is described to detect moving objects which has special color, special shape, the experiment indicates that it can scheme out the detecting result in the certain interfered background can be detected. A moving objects detection electro-model by imitating biologic vision based on frog's eyes is established, the video simulative signal is digital firstly in this system, then the digital signal is parallel separated by FPGA. IN the parallel processing, the video information can be caught, processed and displayed in the same time, the information fusion is taken by DSP HPI ports, in order to transmit the data which processed by DSP. This system can watch the bigger visual field and get higher image resolution than ordinary monitor systems. In summary, simulative experiments for edge detection of moving object with canny algorithm based on this system indicate that this system can detect the edge of moving objects in real time, the feasibility of bionic model was fully demonstrated in the engineering system, and it laid a solid foundation for the future study of detection technology by imitating biologic vision.

Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures

NASA Astrophysics Data System (ADS)

Zhang, Guang-Ming; Harvey, David M.

2012-03-01

Various signal processing techniques have been used for the enhancement of defect detection and defect characterisation. Cross-correlation, filtering, autoregressive analysis, deconvolution, neural network, wavelet transform and sparse signal representations have all been applied in attempts to analyse ultrasonic signals. In ultrasonic nondestructive evaluation (NDE) applications, a large number of materials have multilayered structures. NDE of multilayered structures leads to some specific problems, such as penetration, echo overlap, high attenuation and low signal-to-noise ratio. The signals recorded from a multilayered structure are a class of very special signals comprised of limited echoes. Such signals can be assumed to have a sparse representation in a proper signal dictionary. Recently, a number of digital signal processing techniques have been developed by exploiting the sparse constraint. This paper presents a review of research to date, showing the up-to-date developments of signal processing techniques made in ultrasonic NDE. A few typical ultrasonic signal processing techniques used for NDE of multilayered structures are elaborated. The practical applications and limitations of different signal processing methods in ultrasonic NDE of multilayered structures are analysed.

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.

Simple Sample Processing Enhances Malaria Rapid Diagnostic Test Performance

PubMed Central

Davis, K. M.; Gibson, L. E.; Haselton, F. R.; Wright, D. W.

2016-01-01

Lateral flow immunochromatographic rapid diagnostic tests (RDTs) are the primary form of medical diagnostic used for malaria in underdeveloped nations. Unfortunately, many of these tests do not detect asymptomatic malaria carriers. In order for eradication of the disease to be achieved, this problem must be solved. In this study, we demonstrate enhancement in the performance of six RDT brands when a simple sample-processing step is added to the front of the diagnostic process. Greater than a 4-fold RDT signal enhancement was observed as a result of the sample processing step. This lowered the limit of detection for RDT brands to submicroscopic parasitemias. For the best performing RDTs the limits of detection were found to be as low as 3 parasites/μL. Finally, through individual donor samples, the correlations between donor source, WHO panel detection scores and RDT signal intensities were explored. PMID:24787948

Simple sample processing enhances malaria rapid diagnostic test performance.

PubMed

Davis, K M; Gibson, L E; Haselton, F R; Wright, D W

2014-06-21

Lateral flow immunochromatographic rapid diagnostic tests (RDTs) are the primary form of medical diagnostic used for malaria in underdeveloped nations. Unfortunately, many of these tests do not detect asymptomatic malaria carriers. In order for eradication of the disease to be achieved, this problem must be solved. In this study, we demonstrate enhancement in the performance of six RDT brands when a simple sample-processing step is added to the front of the diagnostic process. Greater than a 4-fold RDT signal enhancement was observed as a result of the sample processing step. This lowered the limit of detection for RDT brands to submicroscopic parasitemias. For the best performing RDTs the limits of detection were found to be as low as 3 parasites per μL. Finally, through individual donor samples, the correlations between donor source, WHO panel detection scores and RDT signal intensities were explored.

Perseveration effects in detection tasks with correlated decision intervals. [applied to pilot collision avoidance

NASA Technical Reports Server (NTRS)

Gai, E. G.; Curry, R. E.

1978-01-01

An investigation of the behavior of the human decisionmaker is described for a task related to the problem of a pilot using a traffic situation display to avoid collisions. This sequential signal detection task is characterized by highly correlated signals with time varying strength. Experimental results are presented and the behavior of the observers is analyzed using the theory of Markov processes and classical signal detection theory. Mathematical models are developed which describe the main result of the experiment: that correlation in sequential signals induced perseveration in the observer response and a strong tendency to repeat their previous decision, even when they were wrong.

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

PubMed Central

Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2014-01-01

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

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.

Experiments in ultrasonic flaw detection using a MEMS transducer

NASA Astrophysics Data System (ADS)

Jain, Akash; Greve, David W.; Oppenheim, Irving J.

2003-08-01

In earlier work we developed a MEMS phased array transducer, fabricated in the MUMPs process, and we reported on initial experimental studies in which the device was affixed into contact with solids. We demonstrated the successful detection of signals from a conventional ultrasonic source, and the successful localization of the source in an off-axis geometry using phased array signal processing. We now describe the predicted transmission and coupling characteristics for such devices in contact with solids, demonstrating reasonable agreement with experimental behavior. We then describe the results of flaw detection experiments, as well as results for fluid-coupled detectors.

Cradling Side Preference Is Associated with Lateralized Processing of Baby Facial Expressions in Females

ERIC Educational Resources Information Center

Huggenberger, Harriet J.; Suter, Susanne E.; Reijnen, Ester; Schachinger, Hartmut

2009-01-01

Women's cradling side preference has been related to contralateral hemispheric specialization of processing emotional signals; but not of processing baby's facial expression. Therefore, 46 nulliparous female volunteers were characterized as left or non-left holders (HG) during a doll holding task. During a signal detection task they were then…

On the Satisfaction of Modulus and Ambiguity Function Constraints in Radar Waveform Optimization for Detection

DTIC Science & Technology

2010-06-01

sense that the two waveforms are as close as possible in a Euclidean sense . Li et al. [33] later devised an algorithm that provides the optimal waveform...respectively), and the SWORD algorithm in [33]. These algorithms were designed for the problem of detecting a known signal in the presence of wide- sense ... sensing , astronomy, crystallography, signal processing, and image processing. (See references in the works cited below for examples.) In the general

Some Memories are Odder than Others: Judgments of Episodic Oddity Violate Known Decision Rules

PubMed Central

O’Connor, Akira R.; Guhl, Emily N.; Cox, Justin C.; Dobbins, Ian G.

2011-01-01

Current decision models of recognition memory are based almost entirely on one paradigm, single item old/new judgments accompanied by confidence ratings. This task results in receiver operating characteristics (ROCs) that are well fit by both signal-detection and dual-process models. Here we examine an entirely new recognition task, the judgment of episodic oddity, whereby participants select the mnemonically odd members of triplets (e.g., a new item hidden among two studied items). Using the only two known signal-detection rules of oddity judgment derived from the sensory perception literature, the unequal variance signal-detection model predicted that an old item among two new items would be easier to discover than a new item among two old items. In contrast, four separate empirical studies demonstrated the reverse pattern: triplets with two old items were the easiest to resolve. This finding was anticipated by the dual-process approach as the presence of two old items affords the greatest opportunity for recollection. Furthermore, a bootstrap-fed Monte Carlo procedure using two independent datasets demonstrated that the dual-process parameters typically observed during single item recognition correctly predict the current oddity findings, whereas unequal variance signal-detection parameters do not. Episodic oddity judgments represent a case where dual- and single-process predictions qualitatively diverge and the findings demonstrate that novelty is “odder” than familiarity. PMID:22833695

Novel approach for low-cost muzzle flash detection system

NASA Astrophysics Data System (ADS)

Voskoboinik, Asher

2008-04-01

A low-cost muzzle flash detection based on CMOS sensor technology is proposed. This low-cost technology makes it possible to detect various transient events with characteristic times between dozens of microseconds up to dozens of milliseconds while sophisticated algorithms successfully separate them from false alarms by utilizing differences in geometrical characteristics and/or temporal signatures. The proposed system consists of off-the-shelf smart CMOS cameras with built-in signal and image processing capabilities for pre-processing together with allocated memory for storing a buffer of images for further post-processing. Such a sensor does not require sending giant amounts of raw data to a real-time processing unit but provides all calculations in-situ where processing results are the output of the sensor. This patented CMOS muzzle flash detection concept exhibits high-performance detection capability with very low false-alarm rates. It was found that most false-alarms due to sun glints are from sources at distances of 500-700 meters from the sensor and can be distinguished by time examination techniques from muzzle flash signals. This will enable to eliminate up to 80% of falsealarms due to sun specular reflections in the battle field. Additional effort to distinguish sun glints from suspected muzzle flash signal is made by optimization of the spectral band in Near-IR region. The proposed system can be used for muzzle detection of small arms, missiles and rockets and other military applications.

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

PubMed

Liu, Jinjun; Leng, Yonggang; Lai, Zhihui; Fan, Shengbo

2018-04-25

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.

Isospin equilibration processes and dipolar signals: Coherent cluster production

NASA Astrophysics Data System (ADS)

Papa, M.; Berceanu, I.; Acosta, L.; Agodi, C.; Auditore, L.; Cardella, G.; Chatterjee, M. B.; Dell'Aquila, D.; De Filippo, E.; Francalanza, L.; Lanzalone, G.; Lombardo, I.; Maiolino, C.; Martorana, N.; Pagano, A.; Pagano, E. V.; Pirrone, S.; Politi, G.; Quattrocchi, L.; Rizzo, F.; Russotto, P.; Trifiró, A.; Trimarchi, M.; Verde, G.; Vigilante, M.

2017-11-01

The total dipolar signal related to multi-break-up processes induced on the system ^{48}Ca +{^{27}Al} at 40MeV/nucleon has been investigated with the CHIMERA multi-detector. Experimental data related to semi-peripheral collisions are shown and compared with CoMD-III calculations. The strong connection between the dipolar signal as obtained from the detected fragments and the dynamics of the isospin equilibration processes is also shortly discussed.

Integrated Data and Control Level Fault Tolerance Techniques for Signal Processing Computer Design

DTIC Science & Technology

1990-09-01

TOLERANCE TECHNIQUES FOR SIGNAL PROCESSING COMPUTER DESIGN G. Robert Redinbo I. INTRODUCTION High-speed signal processing is an important application of...techniques and mathematical approaches will be expanded later to the situation where hardware errors and roundoff and quantization noise affect all...detect errors equal in number to the degree of g(X), the maximum permitted by the Singleton bound [13]. Real cyclic codes, primarily applicable to

A UWB Radar Signal Processing Platform for Real-Time Human Respiratory Feature Extraction Based on Four-Segment Linear Waveform Model.

PubMed

Hsieh, Chi-Hsuan; Chiu, Yu-Fang; Shen, Yi-Hsiang; Chu, Ta-Shun; Huang, Yuan-Hao

2016-02-01

This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

SSME propellant path leak detection real-time

NASA Technical Reports Server (NTRS)

Crawford, R. A.; Smith, L. M.

1994-01-01

Included are four documents that outline the technical aspects of the research performed on NASA Grant NAG8-140: 'A System for Sequential Step Detection with Application to Video Image Processing'; 'Leak Detection from the SSME Using Sequential Image Processing'; 'Digital Image Processor Specifications for Real-Time SSME Leak Detection'; and 'A Color Change Detection System for Video Signals with Applications to Spectral Analysis of Rocket Engine Plumes'.

[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.

Frequency domain laser velocimeter signal processor: A new signal processing scheme

NASA Technical Reports Server (NTRS)

Meyers, James F.; Clemmons, James I., Jr.

1987-01-01

A new scheme for processing signals from laser velocimeter systems is described. The technique utilizes the capabilities of advanced digital electronics to yield a smart instrument that is able to configure itself, based on the characteristics of the input signals, for optimum measurement accuracy. The signal processor is composed of a high-speed 2-bit transient recorder for signal capture and a combination of adaptive digital filters with energy and/or zero crossing detection signal processing. The system is designed to accept signals with frequencies up to 100 MHz with standard deviations up to 20 percent of the average signal frequency. Results from comparative simulation studies indicate measurement accuracies 2.5 times better than with a high-speed burst counter, from signals with as few as 150 photons per burst.

Analysis and improved design considerations for airborne pulse Doppler radar signal processing in the detection of hazardous windshear

NASA Technical Reports Server (NTRS)

Lee, Jonggil

1990-01-01

High resolution windspeed profile measurements are needed to provide reliable detection of hazardous low altitude windshear with an airborne pulse Doppler radar. The system phase noise in a Doppler weather radar may degrade the spectrum moment estimation quality and the clutter cancellation capability which are important in windshear detection. Also the bias due to weather return Doppler spectrum skewness may cause large errors in pulse pair spectral parameter estimates. These effects are analyzed for the improvement of an airborne Doppler weather radar signal processing design. A method is presented for the direct measurement of windspeed gradient using low pulse repetition frequency (PRF) radar. This spatial gradient is essential in obtaining the windshear hazard index. As an alternative, the modified Prony method is suggested as a spectrum mode estimator for both the clutter and weather signal. Estimation of Doppler spectrum modes may provide the desired windshear hazard information without the need of any preliminary processing requirement such as clutter filtering. The results obtained by processing a NASA simulation model output support consideration of mode identification as one component of a windshear detection algorithm.

Microwave life detector for buried victims using neutrodyning loop based system

NASA Astrophysics Data System (ADS)

Tahar J., Bel Hadj

2009-07-01

This paper describes a new design of an electromagnetic life detector for the detection of buried victims. The principle of the microwave life sensor is based on the detection of the modulated part of a scattered wave which is generated by the breathing activity of the victim. Those movements generate a spectral component located in the low frequency range, which for most of the cases, is located in a spectrum extending from 0.18 Hz to 0.34 Hz. The detection process requires high sensitivity with respect to breathing movements and, simultaneously, a relative insensitivity for other non-modulated or modulated parasitic signals. Developed microwave system, generating a frequency adjustable between 500 MHz and 1 GHz, is based on a neutrodyning loop required to cancel any non-modulated background and reflected signals in order to get better receiver sensitivity without introducing supplementary distortions on the received signal. Life signal is considered practically periodic that facilitates the extraction of this spectral component using several processing techniques, such as adaptive filtering and correlation permitting to ameliorate the detection range to be more than 15 m in low-loss medium. Detection range is a fundamental parameter for a microwave life detector. A range around 1 m doesn't have a large interest for this application. To attain a range more than 15 m, while guaranteeing professional performances, the technology has to optimize the system parameters as well as the involved signal processing for the purpose of overcoming the presence of obstacles, attenuation, and noise perturbation. This constitutes the main contribution of the present work. Experimental measurements have confirmed the potentiality of this microwave technique for life detector with best space covering detection.

Modeling laser velocimeter signals as triply stochastic Poisson processes

NASA Technical Reports Server (NTRS)

Mayo, W. T., Jr.

1976-01-01

Previous models of laser Doppler velocimeter (LDV) systems have not adequately described dual-scatter signals in a manner useful for analysis and simulation of low-level photon-limited signals. At low photon rates, an LDV signal at the output of a photomultiplier tube is a compound nonhomogeneous filtered Poisson process, whose intensity function is another (slower) Poisson process with the nonstationary rate and frequency parameters controlled by a random flow (slowest) process. In the present paper, generalized Poisson shot noise models are developed for low-level LDV signals. Theoretical results useful in detection error analysis and simulation are presented, along with measurements of burst amplitude statistics. Computer generated simulations illustrate the difference between Gaussian and Poisson models of low-level signals.

High resolution, high rate x-ray spectrometer

DOEpatents

Goulding, F.S.; Landis, D.A.

1983-07-14

It is an object of the invention to provide a pulse processing system for use with detected signals of a wide dynamic range which is capable of very high counting rates, with high throughput, with excellent energy resolution and a high signal-to-noise ratio. It is a further object to provide a pulse processing system wherein the fast channel resolving time is quite short and substantially independent of the energy of the detected signals. Another object is to provide a pulse processing system having a pile-up rejector circuit which will allow the maximum number of non-interfering pulses to be passed to the output. It is also an object of the invention to provide new methods for generating substantially symmetrically triangular pulses for use in both the main and fast channels of a pulse processing system.

Ultrasonic sensor based defect detection and characterisation of ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh; Zhang, Tonzhua; Crouch, Ian

2014-01-01

Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique that is both time consuming and very expensive. The aim of this research is to develop a methodology to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide (RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon carbide material and conventional porosity are often difficult to detect using conventional X-radiography. An alternative inspection system was developed to detect defects in ceramic components using an Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and post-processing of the signals for classification purposes. This research contributes to developing an on-line inspection system that would be far more cost effective than present methods and, moreover, assist manufacturers in checking the location of high density areas, defects and enable real time quality control, including the implementation of accept/reject criteria. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

On the Measurement of Criterion Noise in Signal Detection Theory: The Case of Recognition Memory

ERIC Educational Resources Information Center

Kellen, David; Klauer, Karl Christoph; Singmann, Henrik

2012-01-01

Traditional approaches within the framework of signal detection theory (SDT; Green & Swets, 1966), especially in the field of recognition memory, assume that the positioning of response criteria is not a noisy process. Recent work (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008) has challenged this assumption, arguing not only…

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

Review of Peak Detection Algorithms in Liquid-Chromatography-Mass Spectrometry

PubMed Central

Zhang, Jianqiu; Gonzalez, Elias; Hestilow, Travis; Haskins, William; Huang, Yufei

2009-01-01

In this review, we will discuss peak detection in Liquid-Chromatography-Mass Spectrometry (LC/MS) from a signal processing perspective. A brief introduction to LC/MS is followed by a description of the major processing steps in LC/MS. Specifically, the problem of peak detection is formulated and various peak detection algorithms are described and compared. PMID:20190954

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.

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.

Noncoherent detection of periodic signals

NASA Technical Reports Server (NTRS)

Gagliardi, R. M.

1974-01-01

The optimal Bayes detector for a general periodic waveform having uniform delay and additive white Gaussian noise is examined. It is shown that the detector is much more complex than that for the well known cases of pure sine waves (i.e. classical noncoherent detection) and narrowband signals. An interpretation of the optimal processing is presented, and several implementations are discussed. The results have application to the noncoherent detection of optical square waves.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

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.

A design study of a signal detection system. [for search of extraterrestrial radio sources

NASA Technical Reports Server (NTRS)

Healy, T. J.

1980-01-01

A system is described which can aid in the search for radio signals from extraterrestrial sources, or in other applications characterized by low signal-to-noise ratios and very high data rates. The system follows a multichannel (16 million bin) spectrum analyzer, and has critical processing, system control, and memory fuctions. The design includes a moderately rich set of algorithms to be used in parallel to detect signals of unknown form. A multi-threshold approach is used to obtain high and low signal sensitivities. Relatively compact and transportable memory systems are specified.

SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

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

Möller, A.; Ruhlmann-Kleider, V.; Neveu, J.

2015-04-01

Detection of supernovae (SNe) and, more generally, of transient events in large surveys can provide numerous false detections. In the case of a deferred processing of survey images, this implies reconstructing complete light curves for all detections, requiring sizable processing time and resources. Optimizing the detection of transient events is thus an important issue for both present and future surveys. We present here the optimization done in the SuperNova Legacy Survey (SNLS) for the 5-year data deferred photometric analysis. In this analysis, detections are derived from stacks of subtracted images with one stack per lunation. The 3-year analysis provided 300,000more » detections dominated by signals of bright objects that were not perfectly subtracted. Allowing these artifacts to be detected leads not only to a waste of resources but also to possible signal coordinate contamination. We developed a subtracted image stack treatment to reduce the number of non SN-like events using morphological component analysis. This technique exploits the morphological diversity of objects to be detected to extract the signal of interest. At the level of our subtraction stacks, SN-like events are rather circular objects while most spurious detections exhibit different shapes. A two-step procedure was necessary to have a proper evaluation of the noise in the subtracted image stacks and thus a reliable signal extraction. We also set up a new detection strategy to obtain coordinates with good resolution for the extracted signal. SNIa Monte-Carlo (MC) generated images were used to study detection efficiency and coordinate resolution. When tested on SNLS 3-year data this procedure decreases the number of detections by a factor of two, while losing only 10% of SN-like events, almost all faint ones. MC results show that SNIa detection efficiency is equivalent to that of the original method for bright events, while the coordinate resolution is improved.« less

Sensitivity Simulation of Compressed Sensing Based Electronic Warfare Receiver Using Orthogonal Matching Pursuit Algorithm

DTIC Science & Technology

2016-02-01

algorithm is used to process CS data. The insufficient nature of the sparcity of the signal adversely affects the signal detection probability for...with equal probability. The scheme was proposed [2] for image processing using single pixel camera, where the field of view was masked by a grid...modulation. The orthogonal matching pursuit (OMP) algorithm is used to process CS data. The insufficient nature of the sparcity of the signal

A Statistical Analysis of the Output Signals of an Acousto-Optic Spectrum Analyzer for CW (Continuous-Wave) Signals

DTIC Science & Technology

1988-10-01

A statistical analysis on the output signals of an acousto - optic spectrum analyzer (AOSA) is performed for the case when the input signal is a...processing, Electronic warfare, Radar countermeasures, Acousto - optic , Spectrum analyzer, Statistical analysis, Detection, Estimation, Canada, Modelling.

Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction.

PubMed

Holan, Scott H; Viator, John A

2008-06-21

Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

Golay Complementary Waveforms in Reed–Müller Sequences for Radar Detection of Nonzero Doppler Targets

PubMed Central

Wang, Xuezhi; Huang, Xiaotao; Suvorova, Sofia; Moran, Bill

2018-01-01

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods. PMID:29324708

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

Topics in the Detection of Gravitational Waves from Compact Binary Inspirals

NASA Astrophysics Data System (ADS)

Kapadia, Shasvath Jagat

Orbiting compact binaries - such as binary black holes, binary neutron stars and neutron star-black hole binaries - are among the most promising sources of gravitational waves observable by ground-based interferometric detectors. Despite numerous sophisticated engineering techniques, the gravitational wave signals will be buried deep within noise generated by various instrumental and environmental processes, and need to be extracted via a signal processing technique referred to as matched filtering. Matched filtering requires large banks of signal templates that are faithful representations of the true gravitational waveforms produced by astrophysical binaries. The accurate and efficient production of templates is thus crucial to the success of signal processing and data analysis. To that end, the dissertation presents a numerical technique that calibrates existing analytical (Post-Newtonian) waveforms, which are relatively inexpensive, to more accurate fiducial waveforms that are computationally expensive to generate. The resulting waveform family is significantly more accurate than the analytical waveforms, without incurring additional computational costs of production. Certain kinds of transient background noise artefacts, called "glitches'', can masquerade as gravitational wave signals for short durations and throw-off the matched-filter algorithm. Identifying glitches from true gravitational wave signals is a highly non-trivial exercise in data analysis which has been attempted with varying degrees of success. We present here a machine-learning based approach that exploits the various attributes of glitches and signals within detector data to provide a classification scheme that is a significant improvement over previous methods. The dissertation concludes by investigating the possibility of detecting a non-linear DC imprint, called the Christodoulou memory, produced in the arms of ground-based interferometers by the recently detected gravitational waves. The memory, which is even smaller in amplitude than the primary (detected) gravitational waves, will almost certainly not be seen in the current detection event. Nevertheless, future space-based detectors will likely be sensitive enough to observe the memory.

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.

Search for Extraterrestrial Intelligence (SETI)

NASA Technical Reports Server (NTRS)

Billingham, John

1993-01-01

Various aspects of project SETI are discussed. Some of the topics discussed include spectrum analyzers, signal processing, sky surveys, radiotelescopes, high resolution microwave survey, Deep Space Network, and signal detection.

Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects.

PubMed

Muszynski, C; Happillon, T; Azudin, K; Tylcz, J-B; Istrate, D; Marque, C

2018-05-08

Preterm birth is a major public health problem in developed countries. In this context, we have conducted research into outpatient monitoring of uterine electrical activity in women at risk of preterm delivery. The objective of this preliminary study was to perform automated detection of uterine contractions (without human intervention or tocographic signal, TOCO) by processing the EHG recorded on the abdomen of pregnant women. The feasibility and accuracy of uterine contraction detection based on EHG processing were tested and compared to expert decision using external tocodynamometry (TOCO) . The study protocol was approved by local Ethics Committees under numbers ID-RCB 2016-A00663-48 for France and VSN 02-0006-V2 for Iceland. Two populations of women were included (threatened preterm birth and labour) in order to test our system of recognition of the various types of uterine contractions. EHG signal acquisition was performed according to a standardized protocol to ensure optimal reproducibility of EHG recordings. A system of 18 Ag/AgCl surface electrodes was used by placing 16 recording electrodes between the woman's pubis and umbilicus according to a 4 × 4 matrix. TOCO was recorded simultaneously with EHG recording. EHG signals were analysed in real-time by calculation of the nonlinear correlation coefficient H 2 . A curve representing the number of correlated pairs of signals according to the value of H 2 calculated between bipolar signals was then plotted. High values of H 2 indicated the presence of an event that may correspond to a contraction. Two tests were performed after detection of an event (fusion and elimination of certain events) in order to increase the contraction detection rate. The EHG database contained 51 recordings from pregnant women, with a total of 501 contractions previously labelled by analysis of the corresponding tocographic recording. The percentage recognitions obtained by application of the method based on coefficient H 2 was 100% with 782% of false alarms. Addition of fusion and elimination tests to the previously obtained detections allowed the false alarm rate to be divided by 8.5, while maintaining an excellent detection rate (96%). These preliminary results appear to be encouraging for monitoring of uterine contractions by algorithm-based automated detection to process the electrohysterographic signal (EHG). This compact recording system, based on the use of surface electrodes attached to the skin, appears to be particularly suitable for outpatient monitoring of uterine contractions, possibly at home, allowing telemonitoring of pregnancies. One of the advantages of EHG processing is that useful information concerning contraction efficiency can be extracted from this signal, which is not possible with the TOCO signal.

Development of Advanced Signal Processing and Source Imaging Methods for Superparamagnetic Relaxometry

PubMed Central

Huang, Ming-Xiong; Anderson, Bill; Huang, Charles W.; Kunde, Gerd J.; Vreeland, Erika C.; Huang, Jeffrey W.; Matlashov, Andrei N.; Karaulanov, Todor; Nettles, Christopher P.; Gomez, Andrew; Minser, Kayla; Weldon, Caroline; Paciotti, Giulio; Harsh, Michael; Lee, Roland R.; Flynn, Edward R.

2017-01-01

Superparamagnetic Relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using Super-conducting Quantum Interference Device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to: 1) remove trials contaminated with artifacts, 2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data, 3) automatically detect and correct flux jumps, and 4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings. PMID:28072579

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.

Breaking cover: neural responses to slow and fast camouflage-breaking motion.

PubMed

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M; McLoughlin, Niall; Wang, Wei

2015-08-22

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. © 2015 The Authors.

Breaking cover: neural responses to slow and fast camouflage-breaking motion

PubMed Central

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M.; McLoughlin, Niall; Wang, Wei

2015-01-01

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. PMID:26269500

Detection of Artificially Generated Seismic Signals Using Balloon-Borne Infrasound Sensors

NASA Astrophysics Data System (ADS)

Krishnamoorthy, Siddharth; Komjathy, Attila; Pauken, Michael T.; Cutts, James A.; Garcia, Raphael F.; Mimoun, David; Cadu, Alexandre; Sournac, Anthony; Jackson, Jennifer M.; Lai, Voon Hui; Bowman, Daniel C.

2018-04-01

We conducted an experiment in Pahrump, Nevada, in June 2017, where artificial seismic signals were created using a seismic hammer, and the possibility of detecting them from their acoustic signature was examined. In this work, we analyze the pressure signals recorded by highly sensitive barometers deployed on the ground and on tethers suspended from balloons. Our signal processing results show that wind noise experienced by a barometer on a free-flying balloon is lower compared to one on a moored balloon. This has never been experimentally demonstrated in the lower troposphere. While seismoacoustic signals were not recorded on the hot air balloon platform owing to operational challenges, we demonstrate the detection of seismoacoustic signals on our moored balloon platform. Our results have important implications for performing seismology in harsh surface environments such as Venus through atmospheric remote sensing.

Fisher information matrix for branching processes with application to electron-multiplying charge-coupled devices

PubMed Central

Chao, Jerry; Ward, E. Sally; Ober, Raimund J.

2012-01-01

The high quantum efficiency of the charge-coupled device (CCD) has rendered it the imaging technology of choice in diverse applications. However, under extremely low light conditions where few photons are detected from the imaged object, the CCD becomes unsuitable as its readout noise can easily overwhelm the weak signal. An intended solution to this problem is the electron-multiplying charge-coupled device (EMCCD), which stochastically amplifies the acquired signal to drown out the readout noise. Here, we develop the theory for calculating the Fisher information content of the amplified signal, which is modeled as the output of a branching process. Specifically, Fisher information expressions are obtained for a general and a geometric model of amplification, as well as for two approximations of the amplified signal. All expressions pertain to the important scenario of a Poisson-distributed initial signal, which is characteristic of physical processes such as photon detection. To facilitate the investigation of different data models, a “noise coefficient” is introduced which allows the analysis and comparison of Fisher information via a scalar quantity. We apply our results to the problem of estimating the location of a point source from its image, as observed through an optical microscope and detected by an EMCCD. PMID:23049166

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.

A mixed signal ECG processing platform with an adaptive sampling ADC for portable monitoring applications.

PubMed

Kim, Hyejung; Van Hoof, Chris; Yazicioglu, Refet Firat

2011-01-01

This paper describes a mixed-signal ECG processing platform with an 12-bit ADC architecture that can adapt its sampling rate according to the input signals rate of change. This enables the sampling of ECG signals with significantly reduced data rate without loss of information. The presented adaptive sampling scheme reduces the ADC power consumption, enables the processing of ECG signals with lower power consumption, and reduces the power consumption of the radio while streaming the ECG signals. The test results show that running a CWT-based R peak detection algorithm using the adaptively sampled ECG signals consumes only 45.6 μW and it leads to 36% less overall system power consumption.

A microcomputer based frequency-domain processor for laser Doppler anemometry

NASA Technical Reports Server (NTRS)

Horne, W. Clifton; Adair, Desmond

1988-01-01

A prototype multi-channel laser Doppler anemometry (LDA) processor was assembled using a wideband transient recorder and a microcomputer with an array processor for fast Fourier transform (FFT) computations. The prototype instrument was used to acquire, process, and record signals from a three-component wind tunnel LDA system subject to various conditions of noise and flow turbulence. The recorded data was used to evaluate the effectiveness of burst acceptance criteria, processing algorithms, and selection of processing parameters such as record length. The recorded signals were also used to obtain comparative estimates of signal-to-noise ratio between time-domain and frequency-domain signal detection schemes. These comparisons show that the FFT processing scheme allows accurate processing of signals for which the signal-to-noise ratio is 10 to 15 dB less than is practical using counter processors.

Signal processing methodologies for an acoustic fetal heart rate monitor

NASA Technical Reports Server (NTRS)

Pretlow, Robert A., III; Stoughton, John W.

1992-01-01

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use.

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

PubMed

Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali

2017-12-01

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

An ultra low power ECG signal processor design for cardiovascular disease detection.

PubMed

Jain, Sanjeev Kumar; Bhaumik, Basabi

2015-08-01

This paper presents an ultra low power ASIC design based on a new cardiovascular disease diagnostic algorithm. This new algorithm based on forward search is designed for real time ECG signal processing. The algorithm is evaluated for Physionet PTB database from the point of view of cardiovascular disease diagnosis. The failed detection rate of QRS complex peak detection of our algorithm ranges from 0.07% to 0.26% for multi lead ECG signal. The ASIC is designed using 130-nm CMOS low leakage process technology. The area of ASIC is 1.21 mm(2). This ASIC consumes only 96 nW at an operating frequency of 1 kHz with a supply voltage of 0.9 V. Due to ultra low power consumption, our proposed ASIC design is most suitable for energy efficient wearable ECG monitoring devices.

Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome.

PubMed

Moret-Bonillo, Vicente; Alvarez-Estévez, Diego; Fernández-Leal, Angel; Hernández-Pereira, Elena

2014-01-01

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints.

Intelligent Approach for Analysis of Respiratory Signals and Oxygen Saturation in the Sleep Apnea/Hypopnea Syndrome

PubMed Central

Moret-Bonillo, Vicente; Alvarez-Estévez, Diego; Fernández-Leal, Angel; Hernández-Pereira, Elena

2014-01-01

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints. PMID:25035712

Stepped-frequency GPR for utility line detection using polarization-dependent scattering

NASA Astrophysics Data System (ADS)

Jensen, Ole K.; Gregersen, Ole G.

2000-04-01

A GPR for detection of buried cables and pipes is developed by Ekko Dane Production in cooperation with Aalborg University. The appearance is a 'lawn mower' model including antennas, electronics and on-line data processing. A successful result is obtained by combining dedicated hardware and signal processing. The inherent signal to clutter ratio is bad, but making measurements at many polarization angles and subsequent signal processing improves the ratio. A simple model of the polarization dependence of the scattering from the target is used. The method is improved by combining the polarization filtering with averaging over small horizontal displacements. A stepped frequency measurement system is used. The method often implies long measurement times, but this problem is overcome by development of fast RF-electronics. Standard signal processors are used for real-time data processing. Several antenna array configurations are tested and optimized for low coupling between transmitter and receiver and for a short impulse response. A large number of tests have been made for different targets, e.g. metal cables and plastic pipes filled with air or water. Tests have been made under realistic ground conditions, including sand, wet clay, pavements and grass covered soil. The results show reliable detection even when the conditions are difficult.

Comparison of digital signal-signal beat interference compensation techniques in direct-detection subcarrier modulation systems.

PubMed

Li, Zhe; Erkilinc, M Sezer; Galdino, Lidia; Shi, Kai; Thomsen, Benn C; Bayvel, Polina; Killey, Robert I

2016-12-12

Single-polarization direct-detection transceivers may offer advantages compared to digital coherent technology for some metro, back-haul, access and inter-data center applications since they offer low-cost and complexity solutions. However, a direct-detection receiver introduces nonlinearity upon photo detection, since it is a square-law device, which results in signal distortion due to signal-signal beat interference (SSBI). Consequently, it is desirable to develop effective and low-cost SSBI compensation techniques to improve the performance of such transceivers. In this paper, we compare the performance of a number of recently proposed digital signal processing-based SSBI compensation schemes, including the use of single- and two-stage linearization filters, an iterative linearization filter and a SSBI estimation and cancellation technique. Their performance is assessed experimentally using a 7 × 25 Gb/s wavelength division multiplexed (WDM) single-sideband 16-QAM Nyquist-subcarrier modulation system operating at a net information spectral density of 2.3 (b/s)/Hz.

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.

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.

No Special K! A Signal Detection Framework for the Strategic Regulation of Memory Accuracy

ERIC Educational Resources Information Center

Higham, Philip A.

2007-01-01

Two experiments investigated criterion setting and metacognitive processes underlying the strategic regulation of accuracy on the Scholastic Aptitude Test (SAT) using Type-2 signal detection theory (SDT). In Experiment 1, report bias was manipulated by penalizing participants either 0.25 (low incentive) or 4 (high incentive) points for each error.…

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

NASA Astrophysics Data System (ADS)

Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo

2016-04-01

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

Full-duplex bidirectional transmission of 10-Gb/s millimeter-wave QPSK signal in E-band optical wireless link.

PubMed

Fang, Yuan; Yu, Jianjun; Chi, Nan; Xiao, Jiangnan

2014-01-27

We experimentally demonstrated full-duplex bidirectional transmission of 10-Gb/s millimeter-wave (mm-wave) quadrature phase shift keying (QPSK) signal in E-band (71-76 GHz and 81-86 GHz) optical wireless link. Single-mode fibers (SMF) are connected at both sides of the antenna for uplink and downlink which realize 40-km SMF and 2-m wireless link for bidirectional transmission simultaneously. We utilized multi-level modulation format and coherent detection in such E-band optical wireless link for the first time. Mm-wave QPSK signal is generated by photonic technique to increase spectrum efficiency and received signal is coherently detected to improve receiver sensitivity. After the coherent detection, digital signal processing is utilized to compensate impairments of devices and transmission link.

Weak signal amplification and detection by higher-order sensory neurons

PubMed Central

Longtin, Andre; Maler, Leonard

2016-01-01

Sensory systems must extract behaviorally relevant information and therefore often exhibit a very high sensitivity. How the nervous system reaches such high sensitivity levels is an outstanding question in neuroscience. Weakly electric fish (Apteronotus leptorhynchus/albifrons) are an excellent model system to address this question because detailed background knowledge is available regarding their behavioral performance and its underlying neuronal substrate. Apteronotus use their electrosense to detect prey objects. Therefore, they must be able to detect electrical signals as low as 1 μV while using a sensory integration time of <200 ms. How these very weak signals are extracted and amplified by the nervous system is not yet understood. We studied the responses of cells in the early sensory processing areas, namely, the electroreceptor afferents (EAs) and pyramidal cells (PCs) of the electrosensory lobe (ELL), the first-order electrosensory processing area. In agreement with previous work we found that EAs cannot encode very weak signals with a spike count code. However, PCs can encode prey mimic signals by their firing rate, revealing a huge signal amplification between EAs and PCs and also suggesting differences in their stimulus encoding properties. Using a simple leaky integrate-and-fire (LIF) model we predict that the target neurons of PCs in the midbrain torus semicircularis (TS) are able to detect very weak signals. In particular, TS neurons could do so by assuming biologically plausible convergence rates as well as very simple decoding strategies such as temporal integration, threshold crossing, and combining the inputs of PCs. PMID:26843601

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

A Stochastic Detection and Retrieval Model for the Study of Metacognition

ERIC Educational Resources Information Center

Jang, Yoonhee; Wallsten, Thomas S.; Huber, David E.

2012-01-01

We present a signal detection-like model termed the stochastic detection and retrieval model (SDRM) for use in studying metacognition. Focusing on paradigms that relate retrieval (e.g., recall or recognition) and confidence judgments, the SDRM measures (1) variance in the retrieval process, (2) variance in the confidence process, (3) the extent to…

A signal-on electrochemical aptasensor for ultrasensitive detection of endotoxin using three-way DNA junction-aided enzymatic recycling and graphene nanohybrid for amplification

NASA Astrophysics Data System (ADS)

Bai, Lijuan; Chai, Yaqin; Pu, Xiaoyun; Yuan, Ruo

2014-02-01

Endotoxin, also known as lipopolysaccharide (LPS), is able to induce a strong immune response on its internalization into mammalian cells. To date, aptamer-based biosensors for LPS detection have been rarely reported. This work describes a new signal-on electrochemical aptasensor for the ultrasensitive detection of LPS by combining the three-way DNA hybridization process and nanotechnology-based amplification. With the help of DNA1 (associated with the concentration of target LPS), the capture probe hybridizes with DNA1 and the assistant probe to open its hairpin structure and form a ternary ``Y'' junction structure. The DNA1 can be released from the structure in the presence of nicking endonuclease to initiate the next hybridization process. Then a great deal of cleaved capture probe produced in the cyclic process can bind with DNA2-nanocomposite, which contains the electroactive toluidine blue (Tb) with the amplification materials graphene (Gra) and gold nanoparticles (AuNPs). Thus, an enhanced electrochemical signal can be easily read out. With the cascade signal amplification, this newly designed protocol provides an ultrasensitive electrochemical detection of LPS down to the femtogram level (8.7 fg mL-1) with a linear range of 6 orders of magnitude (from 10 fg mL-1 to 50 ng mL-1). Moreover, the high sensitivity and specificity make this method versatile for the detection of other biomolecules by changing the corresponding sequences of the capture probe and the assistant probe.

The weight and angle of depression detection and control system of a large portal crane

NASA Astrophysics Data System (ADS)

Shi, Lian-Wen; Xie, Hongxia; Wang, Meijing; Guan, Yankui; Leng, Gengxin

2008-12-01

In order to prevent overturning accidents, the lifted weight and the angle of depression should be detected when a large portal crane is working in a shipyard. However, the locations of the weight sensor and the angle of depression detection part are far away from the central control room. The long signal transmitting distance is so long that it results in a lot of interferences, even the breaking down of the system. In order to solve the above mentioned problems, a high precision analog signal amplifier and a voltage / current (V / I) transforming circuit is set at the place of the sensor to detect the weight. After the sensor signals have been amplified, they will be transformed into 4 to 20 mA current signals for transmission. Thus the interferences in the long transmitting process can be overcome. A WXJ-3 potentiometer is applied to detect the angle of depression. This device has the advantages of a high accuracy of repeated positions, a good stability and a strong anti-fatigue property. After processed by the current-strengthened circuit, the transmitted signals representing voltage value can have the characteristics of transmitting currents because of the large current value. Then the anti-jamming capability is stronger. Send the weight and the angle of depression detection signals to A/D converter, then the signals turn into digital representation and are sent to the control system composed of a PLC. The PLC calculates the current rated lifting weight depending on the different angles of depression, and when the weight is greater than the rated one, the PLC sends control signals to stop the lifting; hence the crane can only put down the weights. So the safety of the large portal crane is effectively guaranteed. At present ,the system has been applied to the 70-ton large portal cranes of the Tianjin Xingang Shipyard with a safe operation of 10 years.

Generation and detection of 80-Gbit/s return-to-zero differential phase-shift keying signals

NASA Astrophysics Data System (ADS)

Möller, Lothar; Su, Yikai; Xie, Chongjin; Liu, Xiang; Leuthold, Juerg; Gill, Douglas; Wei, Xing

2003-12-01

Nonlinear polarization rotation between a pump and a probe signal in a highly nonlinear fiber is used as a modulation process to generate 80-Gbit/s return-to-zero differential phase-shift keying signals. Its performance is analyzed and compared with a conventional on-off keying modulated signal.

Applying traditional signal processing techniques to social media exploitation for situational understanding

NASA Astrophysics Data System (ADS)

Abdelzaher, Tarek; Roy, Heather; Wang, Shiguang; Giridhar, Prasanna; Al Amin, Md. Tanvir; Bowman, Elizabeth K.; Kolodny, Michael A.

2016-05-01

Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

NASA Astrophysics Data System (ADS)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

WAMS measurements pre-processing for detecting low-frequency oscillations in power systems

NASA Astrophysics Data System (ADS)

Kovalenko, P. Y.

2017-07-01

Processing the data received from measurement systems implies the situation when one or more registered values stand apart from the sample collection. These values are referred to as “outliers”. The processing results may be influenced significantly by the presence of those in the data sample under consideration. In order to ensure the accuracy of low-frequency oscillations detection in power systems the corresponding algorithm has been developed for the outliers detection and elimination. The algorithm is based on the concept of the irregular component of measurement signal. This component comprises measurement errors and is assumed to be Gauss-distributed random. The median filtering is employed to detect the values lying outside the range of the normally distributed measurement error on the basis of a 3σ criterion. The algorithm has been validated involving simulated signals and WAMS data as well.

Frequency domain laser velocimeter signal processor

NASA Technical Reports Server (NTRS)

Meyers, James F.; Murphy, R. Jay

1991-01-01

A new scheme for processing signals from laser velocimeter systems is described. The technique utilizes the capabilities of advanced digital electronics to yield a signal processor capable of operating in the frequency domain maximizing the information obtainable from each signal burst. This allows a sophisticated approach to signal detection and processing, with a more accurate measurement of the chirp frequency resulting in an eight-fold increase in measurable signals over the present high-speed burst counter technology. Further, the required signal-to-noise ratio is reduced by a factor of 32, allowing measurements within boundary layers of wind tunnel models. Measurement accuracy is also increased up to a factor of five.

Time-Frequency Approach for Stochastic Signal Detection

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

Ghosh, Ripul; Akula, Aparna; Kumar, Satish

2011-10-20

The detection of events in a stochastic signal has been a subject of great interest. One of the oldest signal processing technique, Fourier Transform of a signal contains information regarding frequency content, but it cannot resolve the exact onset of changes in the frequency, all temporal information is contained in the phase of the transform. On the other hand, Spectrogram is better able to resolve temporal evolution of frequency content, but has a trade-off in time resolution versus frequency resolution in accordance with the uncertainty principle. Therefore, time-frequency representations are considered for energetic characterisation of the non-stationary signals. Wigner Villemore » Distribution (WVD) is the most prominent quadratic time-frequency signal representation and used for analysing frequency variations in signals.WVD allows for instantaneous frequency estimation at each data point, for a typical temporal resolution of fractions of a second. This paper through simulations describes the way time frequency models are applied for the detection of event in a stochastic signal.« less

Time-Frequency Approach for Stochastic Signal Detection

NASA Astrophysics Data System (ADS)

Ghosh, Ripul; Akula, Aparna; Kumar, Satish; Sardana, H. K.

2011-10-01

The detection of events in a stochastic signal has been a subject of great interest. One of the oldest signal processing technique, Fourier Transform of a signal contains information regarding frequency content, but it cannot resolve the exact onset of changes in the frequency, all temporal information is contained in the phase of the transform. On the other hand, Spectrogram is better able to resolve temporal evolution of frequency content, but has a trade-off in time resolution versus frequency resolution in accordance with the uncertainty principle. Therefore, time-frequency representations are considered for energetic characterisation of the non-stationary signals. Wigner Ville Distribution (WVD) is the most prominent quadratic time-frequency signal representation and used for analysing frequency variations in signals.WVD allows for instantaneous frequency estimation at each data point, for a typical temporal resolution of fractions of a second. This paper through simulations describes the way time frequency models are applied for the detection of event in a stochastic signal.

Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process

NASA Technical Reports Server (NTRS)

Racette, Paul

2010-01-01

Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

High speed preamplifier circuit, detection electronics, and radiation detection systems therefrom

DOEpatents

Riedel, Richard A [Knoxville, TN; Wintenberg, Alan L [Knoxville, TN; Clonts, Lloyd G [Knoxville, TN; Cooper, Ronald G [Oak Ridge, TN

2010-09-21

A preamplifier circuit for processing a signal provided by a radiation detector includes a transimpedance amplifier coupled to receive a current signal from a detector and generate a voltage signal at its output. A second amplification stage has an input coupled to an output of the transimpedance amplifier for providing an amplified voltage signal. Detector electronics include a preamplifier circuit having a first and second transimpedance amplifier coupled to receive a current signal from a first and second location on a detector, respectively, and generate a first and second voltage signal at respective outputs. A second amplification stage has an input coupled to an output of the transimpedance amplifiers for amplifying the first and said second voltage signals to provide first and second amplified voltage signals. A differential output stage is coupled to the second amplification stage for receiving the first and second amplified voltage signals and providing a pair of outputs from each of the first and second amplified voltage signals. Read out circuitry has an input coupled to receive both of the pair of outputs, the read out circuitry having structure for processing each of the pair of outputs, and providing a single digital output having a time-stamp therefrom.

Electrochemical sensing using comparison of voltage-current time differential values during waveform generation and detection

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

Woo, Leta Yar-Li; Glass, Robert Scott; Fitzpatrick, Joseph Jay

2018-01-02

A device for signal processing. The device includes a signal generator, a signal detector, and a processor. The signal generator generates an original waveform. The signal detector detects an affected waveform. The processor is coupled to the signal detector. The processor receives the affected waveform from the signal detector. The processor also compares at least one portion of the affected waveform with the original waveform. The processor also determines a difference between the affected waveform and the original waveform. The processor also determines a value corresponding to a unique portion of the determined difference between the original and affected waveforms.more » The processor also outputs the determined value.« less

Neural Dynamics Underlying Target Detection in the Human Brain

PubMed Central

Bansal, Arjun K.; Madhavan, Radhika; Agam, Yigal; Golby, Alexandra; Madsen, Joseph R.

2014-01-01

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in an image, the brain compares input signals and goals to elicit the correct behavior. We examined how target detection modulates visual recognition signals by recording intracranial field potential responses from 776 electrodes in 10 epileptic human subjects. We observed reliable differences in the physiological responses to stimuli when a cued target was present versus absent. Goal-related modulation was particularly strong in the inferior temporal and fusiform gyri, two areas important for object recognition. Target modulation started after 250 ms post stimulus, considerably after the onset of visual recognition signals. While broadband signals exhibited increased or decreased power, gamma frequency power showed predominantly increases during target presence. These observations support models where task goals interact with sensory inputs via top-down signals that influence the highest echelons of visual processing after the onset of selective responses. PMID:24553944

Detection of Artificially Generated Seismic Signals Using Balloon-Borne Infrasound Sensors

DOE PAGES

Krishnamoorthy, Siddharth; Komjathy, Attila; Pauken, Michael T.; ...

2018-04-24

Here, we conducted an experiment in Pahrump, Nevada, in June 2017, where artificial seismic signals were created using a seismic hammer, and the possibility of detecting them from their acoustic signature was examined. In this work, we analyze the pressure signals recorded by highly sensitive barometers deployed on the ground and on tethers suspended from balloons. Our signal processing results show that wind noise experienced by a barometer on a free-flying balloon is lower compared to one on a moored balloon. This has never been experimentally demonstrated in the lower troposphere. While seismoacoustic signals were not recorded on the hotmore » air balloon platform owing to operational challenges, we demonstrate the detection of seismoacoustic signals on our moored balloon platform. Our results have important implications for performing seismology in harsh surface environments such as Venus through atmospheric remote sensing.« less

Detection of Artificially Generated Seismic Signals Using Balloon-Borne Infrasound Sensors

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

Krishnamoorthy, Siddharth; Komjathy, Attila; Pauken, Michael T.

Here, we conducted an experiment in Pahrump, Nevada, in June 2017, where artificial seismic signals were created using a seismic hammer, and the possibility of detecting them from their acoustic signature was examined. In this work, we analyze the pressure signals recorded by highly sensitive barometers deployed on the ground and on tethers suspended from balloons. Our signal processing results show that wind noise experienced by a barometer on a free-flying balloon is lower compared to one on a moored balloon. This has never been experimentally demonstrated in the lower troposphere. While seismoacoustic signals were not recorded on the hotmore » air balloon platform owing to operational challenges, we demonstrate the detection of seismoacoustic signals on our moored balloon platform. Our results have important implications for performing seismology in harsh surface environments such as Venus through atmospheric remote sensing.« less

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.

Task effects on BOLD signal correlates of implicit syntactic processing

PubMed Central

Caplan, David

2010-01-01

BOLD signal was measured in sixteen participants who made timed font change detection judgments in visually presented sentences that varied in syntactic structure and the order of animate and inanimate nouns. Behavioral data indicated that sentences were processed to the level of syntactic structure. BOLD signal increased in visual association areas bilaterally and left supramarginal gyrus in the contrast of sentences with object- and subject-extracted relative clauses without font changes in which the animacy order of the nouns biased against the syntactically determined meaning of the sentence. This result differs from the findings in a non-word detection task (Caplan et al, 2008a), in which the same contrast led to increased BOLD signal in the left inferior frontal gyrus. The difference in areas of activation indicates that the sentences were processed differently in the two tasks. These differences were further explored in an eye tracking study using the materials in the two tasks. Issues pertaining to how parsing and interpretive operations are affected by a task that is being performed, and how this might affect BOLD signal correlates of syntactic contrasts, are discussed. PMID:20671983

Task effects on BOLD signal correlates of implicit syntactic processing.

PubMed

Caplan, David

2010-07-01

BOLD signal was measured in sixteen participants who made timed font change detection judgments in visually presented sentences that varied in syntactic structure and the order of animate and inanimate nouns. Behavioral data indicated that sentences were processed to the level of syntactic structure. BOLD signal increased in visual association areas bilaterally and left supramarginal gyrus in the contrast of sentences with object- and subject-extracted relative clauses without font changes in which the animacy order of the nouns biased against the syntactically determined meaning of the sentence. This result differs from the findings in a non-word detection task (Caplan et al, 2008a), in which the same contrast led to increased BOLD signal in the left inferior frontal gyrus. The difference in areas of activation indicates that the sentences were processed differently in the two tasks. These differences were further explored in an eye tracking study using the materials in the two tasks. Issues pertaining to how parsing and interpretive operations are affected by a task that is being performed, and how this might affect BOLD signal correlates of syntactic contrasts, are discussed.

Network Anomaly Detection Based on Wavelet Analysis

NASA Astrophysics Data System (ADS)

Lu, Wei; Ghorbani, Ali A.

2008-12-01

Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

An FPGA-Based Rapid Wheezing Detection System

PubMed Central

Lin, Bor-Shing; Yen, Tian-Shiue

2014-01-01

Wheezing is often treated as a crucial indicator in the diagnosis of obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to monitor patients over the long-term. In this study, a portable wheezing detection system based on a field-programmable gate array (FPGA) is proposed. This system accelerates wheezing detection, and can be used as either a single-process system, or as an integrated part of another biomedical signal detection system. The system segments sound signals into 2-second units. A short-time Fourier transform was used to determine the relationship between the time and frequency components of wheezing sound data. A spectrogram was processed using 2D bilateral filtering, edge detection, multithreshold image segmentation, morphological image processing, and image labeling, to extract wheezing features according to computerized respiratory sound analysis (CORSA) standards. These features were then used to train the support vector machine (SVM) and build the classification models. The trained model was used to analyze sound data to detect wheezing. The system runs on a Xilinx Virtex-6 FPGA ML605 platform. The experimental results revealed that the system offered excellent wheezing recognition performance (0.912). The detection process can be used with a clock frequency of 51.97 MHz, and is able to perform rapid wheezing classification. PMID:24481034

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

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

A reprogrammable receiver architecture for wireless signal interception

NASA Astrophysics Data System (ADS)

Yao, Timothy S.

2003-09-01

In this paper, a re-programmable receiver architecture, based on software-defined-radio concept, for wireless signal interception is presented. The radio-frequency (RF) signal that the receiver would like to intercept may come from a terrestrial cellular network or communication satellites, which their carrier frequency are in the range from 800 MHz (civilian mobile) to 15 GHz (Ku band). To intercept signals from such a wide range of frequency in these variant communication systems, the traditional way is to deploy multiple receivers to scan and detect the desired signal. This traditional approach is obviously unattractive due to the cost, efficiency, and accuracy. Instead, we propose a universal receiver, which is software-driven and re-configurable, to intercept signals of interest. The software-defined-radio based receiver first intercepts RF energy of wide spectrum (25MHz) through antenna, performs zero-IF down conversion (homodyne architecture) to baseband, and digital channelizes the baseband signal. The channelization module is a bank of high performance digital filters. The bandwidth of the filter bank is programmable according to the wireless communication protocol under watch. In the baseband processing, high-performance digital signal processors carry out the detection process and microprocessors handle the communication protocols. The baseband processing is also re-configurable for different wireless standards and protocol. The advantages of the software-defined-radio architecture over traditional RF receiver make it a favorable technology for the communication signal interception and surveillance.

Respiration detection chip with integrated temperature-insensitive MEMS sensors and CMOS signal processing circuits.

PubMed

Wei, Chia-Ling; Lin, Yu-Chen; Chen, Tse-An; Lin, Ren-Yi; Liu, Tin-Hao

2015-02-01

An airflow sensing chip, which integrates MEMS sensors with their CMOS signal processing circuits into a single chip, is proposed for respiration detection. Three micro-cantilever-based airflow sensors were designed and fabricated using a 0.35 μm CMOS/MEMS 2P4M mixed-signal polycide process. Two main differences were present among these three designs: they were either metal-covered or metal-free structures, and had either bridge-type or fixed-type reference resistors. The performances of these sensors were measured and compared, including temperature sensitivity and airflow sensitivity. Based on the measured results, the metal-free structure with fixed-type reference resistors is recommended for use, because it has the highest airflow sensitivity and also can effectively reduce the output voltage drift caused by temperature change.

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

Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing.

PubMed

Ölçer, İbrahim; Öncü, Ahmet

2017-06-05

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ -OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ -OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems.

Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing

PubMed Central

Ölçer, İbrahim; Öncü, Ahmet

2017-01-01

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ-OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ-OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems. PMID:28587240

Statistical and Adaptive Signal Processing for UXO Discrimination for Next-Generation Sensor Data

DTIC Science & Technology

2009-09-01

using the energies of all polarizations as features in a KNN classifier variant resulted in 100% probability of detection at a probability of false...International Conference on Acoustics, Speech , and Signal Processing, vol. V, 2005, pp. 885-888. [12] C. Kreucher, K. Kastella, and A. O. Hero

Hot topics: Signal processing in acoustics

NASA Astrophysics Data System (ADS)

Gaumond, Charles F.

2005-09-01

Signal processing in acoustics is a multidisciplinary group of people that work in many areas of acoustics. We have chosen two areas that have shown exciting new applications of signal processing to acoustics or have shown exciting and important results from the use of signal processing. In this session, two hot topics are shown: the use of noiselike acoustic fields to determine sound propagation structure and the use of localization to determine animal behaviors. The first topic shows the application of correlation on geo-acoustic fields to determine the Greens function for propagation through the Earth. These results can then be further used to solve geo-acoustic inverse problems. The first topic also shows the application of correlation using oceanic noise fields to determine the Greens function through the ocean. These results also have utility for oceanic inverse problems. The second topic shows exciting results from the detection, localization, and tracking of marine mammals by two different groups. Results from detection and localization of bullfrogs are shown, too. Each of these studies contributed to the knowledge of animal behavior. [Work supported by ONR.

Optimum Array Processing for Detecting Binary Signals Corrupted by Directional Interference.

DTIC Science & Technology

1972-12-01

specific cases. Two different series representations of a vector random process are discussed in Van Trees [3]. These two methods both require the... spaci ~ng d, etc.) its detection error represents a lower bound for the performance that might be obtained with other types of array processing (such...Middleton, Introduction to Statistical Communication Theory, New York: McGraw-Hill, 1960. 3. H.L. Van Trees , Detection, Estimation, and Modulation Theory

Detection system for high-resolution gamma radiation spectroscopy with neutron time-of-flight filtering

DOEpatents

Dioszegi, Istvan; Salwen, Cynthia; Vanier, Peter

2014-12-30

A .gamma.-radiation detection system that includes at least one semiconductor detector such as HPGe-Detector, a position-sensitive .alpha.-Detector, a TOF Controller, and a Digitizer/Integrator. The Digitizer/Integrator starts to process the energy signals of a .gamma.-radiation sent from the HPGe-Detector instantly when the HPGe-Detector detects the .gamma.-radiation. Subsequently, it is determined whether a coincidence exists between the .alpha.-particles and .gamma.-radiation signal, based on a determination of the time-of-flight of neutrons obtained from the .alpha.-Detector and the HPGe-Detector. If it is determined that the time-of-flight falls within a predetermined coincidence window, the Digitizer/Integrator is allowed to continue and complete the energy signal processing. If, however, there is no coincidence, the Digitizer/Integrator is instructed to be clear and reset its operation instantly.

Integrated CMOS photodetectors and signal processing for very low-level chemical sensing with the bioluminescent bioreporter integrated circuit

NASA Technical Reports Server (NTRS)

Bolton, Eric K.; Sayler, Gary S.; Nivens, David E.; Rochelle, James M.; Ripp, Steven; Simpson, Michael L.

2002-01-01

We report an integrated CMOS microluminometer optimized for the detection of low-level bioluminescence as part of the bioluminescent bioreporter integrated circuit (BBIC). This microluminometer improves on previous devices through careful management of the sub-femtoampere currents, both signal and leakage, that flow in the front-end processing circuitry. In particular, the photodiode is operated with a reverse bias of only a few mV, requiring special attention to the reset circuitry of the current-to-frequency converter (CFC) that forms the front-end circuit. We report a sub-femtoampere leakage current and a minimum detectable signal (MDS) of 0.15 fA (1510 s integration time) using a room temperature 1.47 mm2 CMOS photodiode. This microluminometer can detect luminescence from as few as 5000 fully induced Pseudomonas fluorescens 5RL bacterial cells. c2002 Elsevier Science B.V. All rights reserved.

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.

Acoustic emission detection of macro-cracks on engraving tool steel inserts during the injection molding cycle using PZT sensors.

PubMed

Svečko, Rajko; Kusić, Dragan; Kek, Tomaž; Sarjaš, Andrej; Hančič, Aleš; Grum, Janez

2013-05-14

This paper presents an improved monitoring system for the failure detection of engraving tool steel inserts during the injection molding cycle. This system uses acoustic emission PZT sensors mounted through acoustic waveguides on the engraving insert. We were thus able to clearly distinguish the defect through measured AE signals. Two engraving tool steel inserts were tested during the production of standard test specimens, each under the same processing conditions. By closely comparing the captured AE signals on both engraving inserts during the filling and packing stages, we were able to detect the presence of macro-cracks on one engraving insert. Gabor wavelet analysis was used for closer examination of the captured AE signals' peak amplitudes during the filling and packing stages. The obtained results revealed that such a system could be used successfully as an improved tool for monitoring the integrity of an injection molding process.

Acoustic Emission Detection of Macro-Cracks on Engraving Tool Steel Inserts during the Injection Molding Cycle Using PZT Sensors

PubMed Central

Svečko, Rajko; Kusić, Dragan; Kek, Tomaž; Sarjaš, Andrej; Hančič, Aleš; Grum, Janez

2013-01-01

This paper presents an improved monitoring system for the failure detection of engraving tool steel inserts during the injection molding cycle. This system uses acoustic emission PZT sensors mounted through acoustic waveguides on the engraving insert. We were thus able to clearly distinguish the defect through measured AE signals. Two engraving tool steel inserts were tested during the production of standard test specimens, each under the same processing conditions. By closely comparing the captured AE signals on both engraving inserts during the filling and packing stages, we were able to detect the presence of macro-cracks on one engraving insert. Gabor wavelet analysis was used for closer examination of the captured AE signals' peak amplitudes during the filling and packing stages. The obtained results revealed that such a system could be used successfully as an improved tool for monitoring the integrity of an injection molding process. PMID:23673677

Data processing device test apparatus and method therefor

DOEpatents

Wilcox, Richard Jacob; Mulig, Jason D.; Eppes, David; Bruce, Michael R.; Bruce, Victoria J.; Ring, Rosalinda M.; Cole, Jr., Edward I.; Tangyunyong, Paiboon; Hawkins, Charles F.; Louie, Arnold Y.

2003-04-08

A method and apparatus mechanism for testing data processing devices are implemented. The test mechanism isolates critical paths by correlating a scanning microscope image with a selected speed path failure. A trigger signal having a preselected value is generated at the start of each pattern vector. The sweep of the scanning microscope is controlled by a computer, which also receives and processes the image signals returned from the microscope. The value of the trigger signal is correlated with a set of pattern lines being driven on the DUT. The trigger is either asserted or negated depending the detection of a pattern line failure and the particular line that failed. In response to the detection of the particular speed path failure being characterized, and the trigger signal, the control computer overlays a mask on the image of the device under test (DUT). The overlaid image provides a visual correlation of the failure with the structural elements of the DUT at the level of resolution of the microscope itself.

Electronic circuit detects left ventricular ejection events in cardiovascular system

NASA Technical Reports Server (NTRS)

Gebben, V. D.; Webb, J. A., Jr.

1972-01-01

Electronic circuit processes arterial blood pressure waveform to produce discrete signals that coincide with beginning and end of left ventricular ejection. Output signals provide timing signals for computers that monitor cardiovascular systems. Circuit operates reliably for heart rates between 50 and 200 beats per minute.

Recollection is a continuous process: implications for dual-process theories of recognition memory.

PubMed

Mickes, Laura; Wais, Peter E; Wixted, John T

2009-04-01

Dual-process theory, which holds that recognition decisions can be based on recollection or familiarity, has long seemed incompatible with signal detection theory, which holds that recognition decisions are based on a singular, continuous memory-strength variable. Formal dual-process models typically regard familiarity as a continuous process (i.e., familiarity comes in degrees), but they construe recollection as a categorical process (i.e., recollection either occurs or does not occur). A continuous process is characterized by a graded relationship between confidence and accuracy, whereas a categorical process is characterized by a binary relationship such that high confidence is associated with high accuracy but all lower degrees of confidence are associated with chance accuracy. Using a source-memory procedure, we found that the relationship between confidence and source-recollection accuracy was graded. Because recollection, like familiarity, is a continuous process, dual-process theory is more compatible with signal detection theory than previously thought.

Infrasonic Monitoring,

DTIC Science & Technology

1995-08-14

seismic network. At large range, infrasound signals are oscillatory acoustic signals detected as small pressure variations about the ambient value... Infrasound Review and Background Infrasound signals are regular acoustic signals in that they are longitudinal pressure waves albeit at rather low frequency...energy is concentrated at higher frequency than that for higher yield sources. Infrasound can be generated by natural and manmade processes; moreover

Signal processing: opportunities for superconductive circuits

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

Ralston, R.W.

1985-03-01

Prime motivators in the evolution of increasingly sophisticated communication and detection systems are the needs for handling ever wider signal bandwidths and higher data-processing speeds. These same needs drive the development of electronic device technology. Until recently the superconductive community has been tightly focused on digital devices for high speed computers. The purpose of this paper is to describe opportunities and challenges which exist for both analog and digital devices in a less familiar area, that of wideband signal processing. The function and purpose of analog signal-processing components, including matched filters, correlators and Fourier transformers, will be described and examplesmore » of superconductive implementations given. A canonic signal-processing system is then configured using these components and digital output circuits to highlight the important issues of dynamic range, accuracy and equivalent computation rate. (Reprints)« less

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.

Falling Person Detection Using Multi-Sensor Signal Processing

NASA Astrophysics Data System (ADS)

Toreyin, B. Ugur; Soyer, A. Birey; Onaran, Ibrahim; Cetin, E. Enis

2007-12-01

Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.

Lidar Luminance Quantizer

NASA Technical Reports Server (NTRS)

Quilligan, Gerard; DeMonthier, Jeffrey; Suarez, George

2011-01-01

This innovation addresses challenges in lidar imaging, particularly with the detection scheme and the shapes of the detected signals. Ideally, the echoed pulse widths should be extremely narrow to resolve fine detail at high event rates. However, narrow pulses require wideband detection circuitry with increased power dissipation to minimize thermal noise. Filtering is also required to shape each received signal into a form suitable for processing by a constant fraction discriminator (CFD) followed by a time-to-digital converter (TDC). As the intervals between the echoes decrease, the finite bandwidth of the shaping circuits blends the pulses into an analog signal (luminance) with multiple modes, reducing the ability of the CFD to discriminate individual events

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

Bame, D.

To determine if seismic signals at frequencies up to 50 Hz are useful for detecting events and discriminating between earthquakes and explosions, approximately 180 events from the three-component high-frequency seismic element (HFSE) installed at the center of the Norwegian Regional Seismic Array (NRSA) have been analyzed. The attenuation of high-frequency signals in Scandinavia varies with distance, azimuth, magnitude, and source effects. Most of the events were detected with HFSE, although detections were better on the NRSA where signal processing techniques were used. Based on a preliminary analysis, high-frequency data do not appear to be a useful discriminant in Scandinavia. 21more » refs., 29 figs., 3 tabs.« less

A Signal Processing Module for the Analysis of Heart Sounds and Heart Murmurs

NASA Astrophysics Data System (ADS)

Javed, Faizan; Venkatachalam, P. A.; H, Ahmad Fadzil M.

2006-04-01

In this paper a Signal Processing Module (SPM) for the computer-aided analysis of heart sounds has been developed. The module reveals important information of cardiovascular disorders and can assist general physician to come up with more accurate and reliable diagnosis at early stages. It can overcome the deficiency of expert doctors in rural as well as urban clinics and hospitals. The module has five main blocks: Data Acquisition & Pre-processing, Segmentation, Feature Extraction, Murmur Detection and Murmur Classification. The heart sounds are first acquired using an electronic stethoscope which has the capability of transferring these signals to the near by workstation using wireless media. Then the signals are segmented into individual cycles as well as individual components using the spectral analysis of heart without using any reference signal like ECG. Then the features are extracted from the individual components using Spectrogram and are used as an input to a MLP (Multiple Layer Perceptron) Neural Network that is trained to detect the presence of heart murmurs. Once the murmur is detected they are classified into seven classes depending on their timing within the cardiac cycle using Smoothed Pseudo Wigner-Ville distribution. The module has been tested with real heart sounds from 40 patients and has proved to be quite efficient and robust while dealing with a large variety of pathological conditions.

Field programmable gate array based fuzzy neural signal processing system for differential diagnosis of QRS complex tachycardia and tachyarrhythmia in noisy ECG signals.

PubMed

Chowdhury, Shubhajit Roy

2012-04-01

The paper reports of a Field Programmable Gate Array (FPGA) based embedded system for detection of QRS complex in a noisy electrocardiogram (ECG) signal and thereafter differential diagnosis of tachycardia and tachyarrhythmia. The QRS complex has been detected after application of entropy measure of fuzziness to build a detection function of ECG signal, which has been previously filtered to remove power line interference and base line wander. Using the detected QRS complexes, differential diagnosis of tachycardia and tachyarrhythmia has been performed. The entire algorithm has been realized in hardware on an FPGA. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity (Se) of 99.74% and accuracy of 99.5% is achieved when tested using single channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in literature. Using the system, 200 patients have been diagnosed with an accuracy of 98.5%.

Interference-Detection Module in a Digital Radar Receiver

NASA Technical Reports Server (NTRS)

Fischman, Mark; Berkun, Andrew; Chu, Anhua; Freedman, Adam; Jourdan, Michael; McWatters, Dalia; Paller, Mimi

2009-01-01

A digital receiver in a 1.26-GHz spaceborne radar scatterometer now undergoing development includes a module for detecting radio-frequency interference (RFI) that could contaminate scientific data intended to be acquired by the scatterometer. The role of the RFI-detection module is to identify time intervals during which the received signal is likely to be contaminated by RFI and thereby to enable exclusion, from further scientific data processing, of signal data acquired during those intervals. The underlying concepts of detection of RFI and rejection of RFI-contaminated signal data are also potentially applicable in advanced terrestrial radio receivers, including software-defined radio receivers in general, receivers in cellular telephones and other wireless consumer electronic devices, and receivers in automotive collision-avoidance radar systems.

Higher order spectra and their use in digital communication signal estimation

NASA Astrophysics Data System (ADS)

Yayci, Cihat

1995-03-01

This thesis compared the detection ability of the spectrogram, the 1-1/2D instantaneous power spectrum (l-1/2D(sub ips)), the bispectrum, and outer product (dyadic) representation for digitally modulated signals corrupted by additive white Gaussian noise. Four detection schemes were tried on noise free BPSK, QPSK, FSK, and OOK signals using different transform lengths. After determining the optimum transform length, each test signal is corrupted by additive white Gaussian noise. Different SNR levels were used to determine the lowest SNR level at which the message or the modulation type could be extracted. The optimal transform length was found to be the symbol duration when processing BPSK, OOK, and FSK via the spectrogram, the 1-1/2D(sub ips) or the bispectrum method. The best transform size for QPSK was half of the symbol length. For the outer product (dyadic) spectral representation, the best transform size was four times larger than the symbol length. For all processing techniques, with the exception of the other product representation, the minimum detectable SNR is about 15 dB for BPSK, FSK, and OOK signals and about 20 dB for QPSK signals. For the outer product spectral method, these values tend to be about 10 dB lower.

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.

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.

A Review on Sensor, Signal, and Information Processing Algorithms (PREPRINT)

DTIC Science & Technology

2010-01-01

processing [214], ambi- guity surface averaging [215], optimum uncertain field tracking, and optimal minimum variance track - before - detect [216]. In [217, 218...2) (2001) 739–746. [216] S. L. Tantum, L. W. Nolte, J. L. Krolik, K. Harmanci, The performance of matched-field track - before - detect methods using

42 CFR 37.2 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... radiography (CR) is the term for digital X-ray image acquisition systems that detect X-ray signals using a... stimulating laser beam to convert the latent radiographic image to electronic signals which are then processed... image acquisition systems in which the X-ray signals received by the image detector are converted nearly...

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.

Weak signal amplification and detection by higher-order sensory neurons.

PubMed

Jung, Sarah N; Longtin, Andre; Maler, Leonard

2016-04-01

Sensory systems must extract behaviorally relevant information and therefore often exhibit a very high sensitivity. How the nervous system reaches such high sensitivity levels is an outstanding question in neuroscience. Weakly electric fish (Apteronotus leptorhynchus/albifrons) are an excellent model system to address this question because detailed background knowledge is available regarding their behavioral performance and its underlying neuronal substrate. Apteronotus use their electrosense to detect prey objects. Therefore, they must be able to detect electrical signals as low as 1 μV while using a sensory integration time of <200 ms. How these very weak signals are extracted and amplified by the nervous system is not yet understood. We studied the responses of cells in the early sensory processing areas, namely, the electroreceptor afferents (EAs) and pyramidal cells (PCs) of the electrosensory lobe (ELL), the first-order electrosensory processing area. In agreement with previous work we found that EAs cannot encode very weak signals with a spike count code. However, PCs can encode prey mimic signals by their firing rate, revealing a huge signal amplification between EAs and PCs and also suggesting differences in their stimulus encoding properties. Using a simple leaky integrate-and-fire (LIF) model we predict that the target neurons of PCs in the midbrain torus semicircularis (TS) are able to detect very weak signals. In particular, TS neurons could do so by assuming biologically plausible convergence rates as well as very simple decoding strategies such as temporal integration, threshold crossing, and combining the inputs of PCs. Copyright © 2016 the American Physiological Society.

The DISC Quotient

NASA Astrophysics Data System (ADS)

Elliott, John R.; Baxter, Stephen

2012-09-01

D.I.S.C: Decipherment Impact of a Signal's Content. The authors present a numerical method to characterise the significance of the receipt of a complex and potentially decipherable signal from extraterrestrial intelligence (ETI). The purpose of the scale is to facilitate the public communication of work on any such claimed signal, as such work proceeds, and to assist in its discussion and interpretation. Building on a "position" paper rationale, this paper looks at the DISC quotient proposed and develops the algorithmic steps and comprising measures that form this post detection strategy for information dissemination, based on prior work on message detection, decipherment. As argued, we require a robust and incremental strategy, to disseminate timely, accurate and meaningful information, to the scientific community and the general public, in the event we receive an "alien" signal that displays decipherable information. This post-detection strategy is to serve as a stepwise algorithm for a logical approach to information extraction and a vehicle for sequential information dissemination, to manage societal impact. The "DISC Quotient", which is based on signal analysis processing stages, includes factors based on the signal's data quantity, structure, affinity to known human languages, and likely decipherment times. Comparisons with human and other phenomena are included as a guide to assessing likely societal impact. It is submitted that the development, refinement and implementation of DISC as an integral strategy, during the complex processes involved in post detection and decipherment, is essential if we wish to minimize disruption and optimize dissemination.

Detection of underground pipeline based on Golay waveform design

NASA Astrophysics Data System (ADS)

Dai, Jingjing; Xu, Dazhuan

2017-08-01

The detection of underground pipeline is an important problem in the development of the city, but the research about it is not mature at present. In this paper, based on the principle of waveform design in wireless communication, we design an acoustic signal detection system to detect the location of underground pipelines. According to the principle of acoustic localization, we chose DSP-F28335 as the development board, and use DA and AD module as the master control chip. The DA module uses complementary Golay sequence as emission signal. The AD module acquisiting data synchronously, so that the echo signals which containing position information of the target is recovered through the signal processing. The test result shows that the method in this paper can not only calculate the sound velocity of the soil, but also can locate the location of underground pipelines accurately.

Experiments on Adaptive Self-Tuning of Seismic Signal Detector Parameters

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Chael, E. P.; Peterson, M. G.; Lawry, B.; Phillips-Alonge, K. E.; Balch, R. S.; Ziegler, A.

2016-12-01

Scientific applications, including underground nuclear test monitoring and microseismic monitoring can benefit enormously from data-driven dynamic algorithms for tuning seismic and infrasound signal detection parameters since continuous streams are producing waveform archives on the order of 1TB per month. Tuning is a challenge because there are a large number of data processing parameters that interact in complex ways, and because the underlying populating of true signal detections is generally unknown. The largely manual process of identifying effective parameters, often performed only over a subset of stations over a short time period, is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. We present improvements to an Adaptive Self-Tuning algorithm for continuously adjusting detection parameters based on consistency with neighboring sensors. Results are shown for 1) data from a very dense network ( 120 stations, 10 km radius) deployed during 2008 on Erebus Volcano, Antarctica, and 2) data from a continuous downhole seismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project. Performance is assessed in terms of missed detections and false detections relative to human analyst detections, simulated waveforms where ground-truth detections exist and visual inspection.

Subconscious detection of threat as reflected by an enhanced response bias.

PubMed

Windmann, S; Krüger, T

1998-12-01

Neurobiological and cognitive models of unconscious information processing suggest that subconscious threat detection can lead to cognitive misinterpretations and false alarms, while conscious processing is assumed to be perceptually and conceptually accurate and unambiguous. Furthermore, clinical theories suggest that pathological anxiety results from a crude preattentive warning system predominating over more sophisticated and controlled modes of processing. We investigated the hypothesis that subconscious detection of threat in a cognitive task is reflected by enhanced "false signal" detection rather than by selectively enhanced discrimination of threat items in 30 patients with panic disorder and 30 healthy controls. We presented a tachistoscopic word-nonword discrimination task and a subsequent recognition task and analyzed the data by means of process-dissociation procedures. In line with our expectations, subjects of both groups showed more false signal detection to threat than to neutral stimuli as indicated by an enhanced response bias, whereas indices of discriminative sensitivity did not show this effect. In addition, patients with panic disorder showed a generally enhanced response bias in comparison to healthy controls. They also seemed to have processed the stimuli less elaborately and less differentially. Results are consistent with the assumption that subconscious threat detection can lead to misrepresentations of stimulus significance and that pathological anxiety is characterized by a hyperactive preattentive alarm system that is insufficiently controlled by higher cognitive processes. Copyright 1998 Academic Press.

Ultrasonic imaging system for in-process fabric defect detection

DOEpatents

Sheen, Shuh-Haw; Chien, Hual-Te; Lawrence, William P.; Raptis, Apostolos C.

1997-01-01

An ultrasonic method and system are provided for monitoring a fabric to identify a defect. A plurality of ultrasonic transmitters generate ultrasonic waves relative to the fabric. An ultrasonic receiver means responsive to the generated ultrasonic waves from the transmitters receives ultrasonic waves coupled through the fabric and generates a signal. An integrated peak value of the generated signal is applied to a digital signal processor and is digitized. The digitized signal is processed to identify a defect in the fabric. The digitized signal processing includes a median value filtering step to filter out high frequency noise. Then a mean value and standard deviation of the median value filtered signal is calculated. The calculated mean value and standard deviation are compared with predetermined threshold values to identify a defect in the fabric.

A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior

PubMed Central

Viswanathan, Vijay; Sheppard, John P.; Kim, Byoung W.; Plantz, Christopher L.; Ying, Hao; Lee, Myung J.; Raman, Kalyan; Mulhern, Frank J.; Block, Martin P.; Calder, Bobby; Lee, Sang; Mortensen, Dale T.; Blood, Anne J.; Breiter, Hans C.

2017-01-01

This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher “loss aversion.” Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans. PMID:28270776

A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior.

PubMed

Viswanathan, Vijay; Sheppard, John P; Kim, Byoung W; Plantz, Christopher L; Ying, Hao; Lee, Myung J; Raman, Kalyan; Mulhern, Frank J; Block, Martin P; Calder, Bobby; Lee, Sang; Mortensen, Dale T; Blood, Anne J; Breiter, Hans C

2017-01-01

This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher "loss aversion." Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans.

Neural Correlates and Mechanisms of Spatial Release From Masking: Single-Unit and Population Responses in the Inferior Colliculus

PubMed Central

Lane, Courtney C.; Delgutte, Bertrand

2007-01-01

Spatial release from masking (SRM), a factor in listening in noisy environments, is the improvement in auditory signal detection obtained when a signal is separated in space from a masker. To study the neural mechanisms of SRM, we recorded from single units in the inferior colliculus (IC) of barbiturate-anesthetized cats, focusing on low-frequency neurons sensitive to interaural time differences. The stimulus was a broadband chirp train with a 40-Hz repetition rate in continuous broadband noise, and the unit responses were measured for several signal and masker (virtual) locations. Masked thresholds (the lowest signal-to-noise ratio, SNR, for which the signal could be detected for 75% of the stimulus presentations) changed systematically with signal and masker location. Single-unit thresholds did not necessarily improve with signal and masker separation; instead, they tended to reflect the units’ azimuth preference. Both how the signal was detected (through a rate increase or decrease) and how the noise masked the signal response (suppressive or excitatory masking) changed with signal and masker azimuth, consistent with a cross-correlator model of binaural processing. However, additional processing, perhaps related to the signal’s amplitude modulation rate, appeared to influence the units’ responses. The population masked thresholds (the most sensitive unit’s threshold at each signal and masker location) did improve with signal and masker separation as a result of the variety of azimuth preferences in our unit sample. The population thresholds were similar to human behavioral thresholds in both SNR value and shape, indicating that these units may provide a neural substrate for low-frequency SRM. PMID:15857966

Acetylcholine Release in Prefrontal Cortex Promotes Gamma Oscillations and Theta–Gamma Coupling during Cue Detection

PubMed Central

Hetrick, Vaughn L.; Berke, Joshua D.

2017-01-01

The capacity for using external cues to guide behavior (“cue detection”) constitutes an essential aspect of attention and goal-directed behavior. The cortical cholinergic input system, via phasic increases in prefrontal acetylcholine release, plays an essential role in attention by mediating such cue detection. However, the relationship between cholinergic signaling during cue detection and neural activity dynamics in prefrontal networks remains unclear. Here we combined subsecond measures of cholinergic signaling, neurophysiological recordings, and cholinergic receptor blockade to delineate the cholinergic contributions to prefrontal oscillations during cue detection in rats. We first confirmed that detected cues evoke phasic acetylcholine release. These cholinergic signals were coincident with increased neuronal synchrony across several frequency bands and the emergence of theta–gamma coupling. Muscarinic and nicotinic cholinergic receptors both contributed specifically to gamma synchrony evoked by detected cues, but the effects of blocking the two receptor subtypes were dissociable. Blocking nicotinic receptors primarily attenuated high-gamma oscillations occurring during the earliest phases of the cue detection process, while muscarinic (M1) receptor activity was preferentially involved in the transition from high to low gamma power that followed and corresponded to the mobilization of networks involved in cue-guided decision making. Detected cues also promoted coupling between gamma and theta oscillations, and both nicotinic and muscarinic receptor activity contributed to this process. These results indicate that acetylcholine release coordinates neural oscillations during the process of cue detection. SIGNIFICANCE STATEMENT The capacity of learned cues to direct attention and guide responding (“cue detection”) is a key component of goal-directed behavior. Rhythmic neural activity and increases in acetylcholine release in the prefrontal cortex contribute to this process; however, the relationship between these neuronal mechanisms is not well understood. Using a combination of in vivo neurochemistry, neurophysiology, and pharmacological methods, we demonstrate that cue-evoked acetylcholine release, through distinct actions at both nicotinic and muscarinic receptors, triggers a procession of neural oscillations that map onto the multiple stages of cue detection. Our data offer new insights into cholinergic function by revealing the temporally orchestrated changes in prefrontal network synchrony modulated by acetylcholine release during cue detection. PMID:28213446

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.

Ultrasensitive electrochemical sensing platform based on graphene wrapping SnO2 nanocorals and autonomous cascade DNA duplication strategy.

PubMed

Chen, Ying-Xu; Huang, Ke-Jing; Lin, Feng; Fang, Lin-Xia

2017-12-01

In this work, a sensitive, universal and reusable electrochemical biosensor based on stannic oxide nanocorals-graphene hybrids (SnO 2 NCs-Gr) is developed for target DNA detection by using two kinds of DNA enzymes for signal amplification through an autonomous cascade DNA duplication strategy. A hairpin probe is designed composing of a projecting part at the 3'-end as identification sequence for target, a recognition site for nicking endonuclease, and an 18-carbon shim to stop polymerization process. The designed DNA duplication-incision-replacement process is handled by KF polymerase and endonuclease, then combining with gold nanoparticles as signal carrier for further signal amplification. In the detection system, the electrochemical-chemical-chemical procedure, which uses ferrocene methanol, tris(2-carboxyethyl)phosphine and l-ascorbic acid 2-phosphate as oxidoreduction neurogen, deoxidizer and zymolyte, separately, is applied to amplify detection signal. Benefiting from the multiple signal amplification mechanism, the proposed sensor reveals a good linear connection between the peak current and logarithm of analyte concentration in range of 0.0001-1 × 10 -11 molL -1 with a detection limit of 1.25 × 10 -17 molL -1 (S/N=3). This assay also opens one promising strategy for ultrasensitive determination of other biological molecules for bioanalysis and biomedicine diagnostics. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors

PubMed Central

Broquetas, Antoni; Comerón, Adolf; Gelonch, Antoni; Fuertes, Josep M.; Castro, J. Antonio; Felip, Damià; López, Miguel A.; Pulido, José A.

2012-01-01

The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns. PMID:23443376

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.

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.

Surface Electromyography Signal Processing and Classification Techniques

PubMed Central

Chowdhury, Rubana H.; Reaz, Mamun B. I.; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A. A.; Chellappan, Kalaivani; Chang, Tae. G.

2013-01-01

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:24048337

Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy.

PubMed

Gómez, María Jesús; Corral, Eduardo; Castejón, Cristina; García-Prada, Juan Carlos

2018-05-17

Crack detection for railway axles is key to avoiding catastrophic accidents. Currently, non-destructive testing is used for that purpose. The present work applies vibration signal analysis to diagnose cracks in real railway axles installed on a real Y21 bogie working on a rig. Vibration signals were obtained from two wheelsets with cracks at the middle section of the axle with depths from 5.7 to 15 mm, at several conditions of load and speed. Vibration signals were processed by means of wavelet packet transform energy. Energies obtained were used to train an artificial neural network, with reliable diagnosis results. The success rate of 5.7 mm defects was 96.27%, and the reliability in detecting larger defects reached almost 100%, with a false alarm ratio lower than 5.5%.

A transfer of technology from engineering: use of ROC curves from signal detection theory to investigate information processing in the brain during sensory difference testing.

PubMed

Wichchukit, Sukanya; O'Mahony, Michael

2010-01-01

This article reviews a beneficial effect of technology transfer from Electrical Engineering to Food Sensory Science. Specifically, it reviews the recent adoption in Food Sensory Science of the receiver operating characteristic (ROC) curve, a tool that is incorporated in the theory of signal detection. Its use allows the information processing that takes place in the brain during sensory difference testing to be studied and understood. The review deals with how Signal Detection Theory, also called Thurstonian modeling, led to the adoption of a more sophisticated way of analyzing the data from sensory difference tests, by introducing the signal-to-noise ratio, d', as a fundamental measure of perceived small sensory differences. Generally, the method of computation of d' is a simple matter for some of the better known difference tests like the triangle, duo-trio and 2-AFC. However, there are occasions when these tests are not appropriate and other tests like the same-different and the A Not-A test are more suitable. Yet, for these, it is necessary to understand how the brain processes information during the test before d' can be computed. It is for this task that the ROC curve has a particular use. © 2010 Institute of Food Technologists®

A New Method for Suppressing Periodic Narrowband Interference Based on the Chaotic van der Pol Oscillator

NASA Astrophysics Data System (ADS)

Lu, Jia; Zhang, Xiaoxing; Xiong, Hao

The chaotic van der Pol oscillator is a powerful tool for detecting defects in electric systems by using online partial discharge (PD) monitoring. This paper focuses on realizing weak PD signal detection in the strong periodic narrowband interference by using high sensitivity to the periodic narrowband interference signals and immunity to white noise and PD signals of chaotic systems. A new approach to removing the periodic narrowband interference by using a van der Pol chaotic oscillator is described by analyzing the motion characteristic of the chaotic oscillator on the basis of the van der Pol equation. Furthermore, the Floquet index for measuring the amplitude of periodic narrowband signals is redefined. The denoising signal processed by the chaotic van der Pol oscillators is further processed by wavelet analysis. Finally, the denoising results verify that the periodic narrowband and white noise interference can be removed efficiently by combining the theory of the chaotic van der Pol oscillator and wavelet analysis.

Uniform, optimal signal processing of mapped deep-sequencing data.

PubMed

Kumar, Vibhor; Muratani, Masafumi; Rayan, Nirmala Arul; Kraus, Petra; Lufkin, Thomas; Ng, Huck Hui; Prabhakar, Shyam

2013-07-01

Despite their apparent diversity, many problems in the analysis of high-throughput sequencing data are merely special cases of two general problems, signal detection and signal estimation. Here we adapt formally optimal solutions from signal processing theory to analyze signals of DNA sequence reads mapped to a genome. We describe DFilter, a detection algorithm that identifies regulatory features in ChIP-seq, DNase-seq and FAIRE-seq data more accurately than assay-specific algorithms. We also describe EFilter, an estimation algorithm that accurately predicts mRNA levels from as few as 1-2 histone profiles (R ∼0.9). Notably, the presence of regulatory motifs in promoters correlates more with histone modifications than with mRNA levels, suggesting that histone profiles are more predictive of cis-regulatory mechanisms. We show by applying DFilter and EFilter to embryonic forebrain ChIP-seq data that regulatory protein identification and functional annotation are feasible despite tissue heterogeneity. The mathematical formalism underlying our tools facilitates integrative analysis of data from virtually any sequencing-based functional profile.

Method and apparatus for detecting combustion instability in continuous combustion systems

DOEpatents

Benson, Kelly J.; Thornton, Jimmy D.; Richards, George A.; Straub, Douglas L.

2006-08-29

An apparatus and method to sense the onset of combustion stability is presented. An electrode is positioned in a turbine combustion chamber such that the electrode is exposed to gases in the combustion chamber. A control module applies a voltage potential to the electrode and detects a combustion ionization signal and determines if there is an oscillation in the combustion ionization signal indicative of the occurrence of combustion stability or the onset of combustion instability. A second electrode held in a coplanar but spaced apart manner by an insulating member from the electrode provides a combustion ionization signal to the control module when the first electrode fails. The control module broadcasts a notice if the parameters indicate the combustion process is at the onset of combustion instability or broadcasts an alarm signal if the parameters indicate the combustion process is unstable.

Improved methods for signal processing in measurements of mercury by Tekran® 2537A and 2537B instruments

NASA Astrophysics Data System (ADS)

Ambrose, Jesse L.

2017-12-01

Atmospheric Hg measurements are commonly carried out using Tekran® Instruments Corporation's model 2537 Hg vapor analyzers, which employ gold amalgamation preconcentration sampling and detection by thermal desorption (TD) and atomic fluorescence spectrometry (AFS). A generally overlooked and poorly characterized source of analytical uncertainty in those measurements is the method by which the raw Hg atomic fluorescence (AF) signal is processed. Here I describe new software-based methods for processing the raw signal from the Tekran® 2537 instruments, and I evaluate the performances of those methods together with the standard Tekran® internal signal processing method. For test datasets from two Tekran® instruments (one 2537A and one 2537B), I estimate that signal processing uncertainties in Hg loadings determined with the Tekran® method are within ±[1 % + 1.2 pg] and ±[6 % + 0.21 pg], respectively. I demonstrate that the Tekran® method can produce significant low biases (≥ 5 %) not only at low Hg sample loadings (< 5 pg) but also at tropospheric background concentrations of gaseous elemental mercury (GEM) and total mercury (THg) (˜ 1 to 2 ng m-3) under typical operating conditions (sample loadings of 5-10 pg). Signal processing uncertainties associated with the Tekran® method can therefore represent a significant unaccounted for addition to the overall ˜ 10 to 15 % uncertainty previously estimated for Tekran®-based GEM and THg measurements. Signal processing bias can also add significantly to uncertainties in Tekran®-based gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurements, which often derive from Hg sample loadings < 5 pg. In comparison, estimated signal processing uncertainties associated with the new methods described herein are low, ranging from within ±0.053 pg, when the Hg thermal desorption peaks are defined manually, to within ±[2 % + 0.080 pg] when peak definition is automated. Mercury limits of detection (LODs) decrease by 31 to 88 % when the new methods are used in place of the Tekran® method. I recommend that signal processing uncertainties be quantified in future applications of the Tekran® 2537 instruments.

Detection and inhibition of bacterial cell-cell communication.

PubMed

Rice, Scott A; McDougald, Diane; Givskov, Michael; Kjelleberg, Staffan

2008-01-01

Bacteria communicate with other members of their community through the secretion and perception of small chemical cues or signals. The recognition of a signal normally leads to the expression of a large suite of genes, which in some bacteria are involved in the regulation of virulence factors, and as a result, these signaling compounds are key regulatory factors in many disease processes. Thus, it is of interest when studying pathogens to understand the mechanisms used to control the expression of virulence genes so that strategies might be devised for the control of those pathogens. Clearly, the ability to interfere with this process of signaling represents a novel approach for the treatment of bacterial infections. There is a broad range of compounds that bacteria can use for signaling purposes, including fatty acids, peptides, N-acylated homoserine lactones, and the signals collectively called autoinducer 2 (AI-2). This chapter will focus on the latter two signaling systems as they are present in a range of medically relevant bacteria, and here we describe assays for determining whether an organism produces a particular signal and assays that can be used to identify inhibitors of the signaling cascade. Lastly, the signal detection and inhibition assays will be directly linked to the expression of virulence factors of specific pathogens.

Combining millimeter-wave radar and communication paradigms for automotive applications : a signal processing approach.

DOT National Transportation Integrated Search

2016-05-01

As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars (RAdio Detection and Ranging) are used for object detection, visual cameras as virtual mirrors, and LIDARs (LIght Detection and...

Detection of Subsurface Defects in Levees in Correlation to Weather Conditions Utilizing Ground Penetrating Radar

NASA Astrophysics Data System (ADS)

Martinez, I. A.; Eisenmann, D.

2012-12-01

Ground Penetrating Radar (GPR) has been used for many years in successful subsurface detection of conductive and non-conductive objects in all types of material including different soils and concrete. Typical defect detection is based on subjective examination of processed scans using data collection and analysis software to acquire and analyze the data, often requiring a developed expertise or an awareness of how a GPR works while collecting data. Processing programs, such as GSSI's RADAN analysis software are then used to validate the collected information. Iowa State University's Center for Nondestructive Evaluation (CNDE) has built a test site, resembling a typical levee used near rivers, which contains known sub-surface targets of varying size, depth, and conductivity. Scientist at CNDE have developed software with the enhanced capabilities, to decipher a hyperbola's magnitude and amplitude for GPR signal processing. With this enhanced capability, the signal processing and defect detection capabilities for GPR have the potential to be greatly enhanced. This study will examine the effects of test parameters, antenna frequency (400MHz), data manipulation methods (which include data filters and restricting the range of depth in which the chosen antenna's signal can reach), and real-world conditions using this test site (such as varying weather conditions) , with the goal of improving GPR tests sensitivity for differing soil conditions.

Description, characteristics and testing of the NASA airborne radar

NASA Technical Reports Server (NTRS)

Jones, W. R.; Altiz, O.; Schaffner, P.; Schrader, J. H.; Blume, H. J. C.

1991-01-01

Presented here is a description of a coherent radar scattermeter and its associated signal processing hardware, which have been specifically designed to detect microbursts and record their radar characteristics. Radar parameters, signal processing techniques and detection algorithms, all under computer control, combine to sense and process reflectivity, clutter, and microburst data. Also presented is the system's high density, high data rate recording system. This digital system is capable of recording many minutes of the in-phase and quadrature components and corresponding receiver gains of the scattered returns for selected spatial regions, as well as other aircraft and hardware related parameters of interest for post-flight analysis. Information is given in viewgraph form.

Ultrasensitive and selective signal-on electrochemical DNA detection via exonuclease III catalysis and hybridization chain reaction amplification.

PubMed

Ren, Wang; Gao, Zhong Feng; Li, Nian Bing; Luo, Hong Qun

2015-01-15

This work reported a novel, ultrasensitive, and selective platform for electrochemical detection of DNA, employing an integration of exonuclease III (Exo-III) assisted target recycling and hybridization chain reaction (HCR) for the dual signal amplification strategy. The hairpin capture probe DNA (C-DNA) with an Exo-III 3' overhang end was self-assembled on a gold electrode. In the presence of target DNA (T-DNA), C-DNA hybridized with the T-DNA to form a duplex region, exposing its 5' complementary sequence (initiator). Exo-III was applied to selectively digest duplex region from its 3-hydroxyl termini until the duplex was fully consumed, leaving the remnant initiator. The intact T-DNA spontaneously dissociated from the structure and then initiated the next hybridization process as a result of catalysis of the Exo-III. HCR event was triggered by the initiator and two hairpin helper signal probes labeled with methylene blue, facilitating the polymerization of oligonucleotides into a long nicked dsDNA molecule. The numerous exposed remnant initiators can trigger more HCR events. Because of integration of dual signal amplification and the specific HCR process reaction, the resultant sensor showed a high sensitivity for the detection of the target DNA in a linear range from 1.0 fM to 1.0 nM, and a detection limit as low as 0.2 fM. The proposed dual signal amplification strategy provides a powerful tool for detecting different sequences of target DNA by changing the sequence of capture probe and signal probes, holding a great potential for early diagnosis in gene-related diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

Variable frequency microwave furnace system

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

Bible, D.W.; Lauf, R.J.

1994-06-14

A variable frequency microwave furnace system designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity for testing or other selected applications. The variable frequency microwave furnace system includes a microwave signal generator or microwave voltage-controlled oscillator for generating a low-power microwave signal for input to the microwave furnace. A first amplifier may be provided to amplify the magnitude of the signal output from the microwave signal generator or the microwave voltage-controlled oscillator. A second amplifier is provided for processing the signal output by the first amplifier. The second amplifier outputs the microwave signal inputmore » to the furnace cavity. In the preferred embodiment, the second amplifier is a traveling-wave tube (TWT). A power supply is provided for operation of the second amplifier. 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. Reflected power is dissipated in the reflected power load. 5 figs.« less

Variable frequency microwave furnace system

DOEpatents

Bible, D.W.; Lauf, R.J.

1994-06-14

A variable frequency microwave furnace system designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity for testing or other selected applications. The variable frequency microwave furnace system includes a microwave signal generator or microwave voltage-controlled oscillator for generating a low-power microwave signal for input to the microwave furnace. A first amplifier may be provided to amplify the magnitude of the signal output from the microwave signal generator or the microwave voltage-controlled oscillator. A second amplifier is provided for processing the signal output by the first amplifier. The second amplifier outputs the microwave signal input to the furnace cavity. In the preferred embodiment, the second amplifier is a traveling-wave tube (TWT). A power supply is provided for operation of the second amplifier. 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. Reflected power is dissipated in the reflected power load. 5 figs.

Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

NASA Astrophysics Data System (ADS)

Si, Qian

Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

Ultrahigh Detective Heterogeneous Photosensor Arrays with In-Pixel Signal Boosting Capability for Large-Area and Skin-Compatible Electronics.

PubMed

Kim, Jaehyun; Kim, Jaekyun; Jo, Sangho; Kang, Jingu; Jo, Jeong-Wan; Lee, Myungwon; Moon, Juhyuk; Yang, Lin; Kim, Myung-Gil; Kim, Yong-Hoon; Park, Sung Kyu

2016-04-01

An ultra-thin and large-area skin-compatible heterogeneous organic/metal-oxide photosensor array is demonstrated which is capable of sensing and boosting signals with high detectivity and signal-to-noise ratio. For the realization of ultra-flexible and high-sensitive heterogeneous photosensor arrays on a polyimide substrate having organic sensor arrays and metal-oxide boosting circuitry, solution-processing and room-temperature alternating photochemical conversion routes are applied. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Comparative Analysis for Selection of Appropriate Mother Wavelet for Detection of Stationary Disturbances

NASA Astrophysics Data System (ADS)

Kamble, Saurabh Prakash; Thawkar, Shashank; Gaikwad, Vinayak G.; Kothari, D. P.

2017-12-01

Detection of disturbances is the first step of mitigation. Power electronics plays a crucial role in modern power system which makes system operation efficient but it also bring stationary disturbances in the power system and added impurities to the supply. It happens because of the non-linear loads used in modern day power system which inject disturbances like harmonic disturbances, flickers, sag etc. in power grid. These impurities can damage equipments so it is necessary to mitigate these impurities present in the supply very quickly. So, digital signal processing techniques are incorporated for detection purpose. Signal processing techniques like fast Fourier transform, short-time Fourier transform, Wavelet transform etc. are widely used for the detection of disturbances. Among all, wavelet transform is widely used because of its better detection capabilities. But, which mother wavelet has to use for detection is still a mystery. Depending upon the periodicity, the disturbances are classified as stationary and non-stationary disturbances. This paper presents the importance of selection of mother wavelet for analyzing stationary disturbances using discrete wavelet transform. Signals with stationary disturbances of various frequencies are generated using MATLAB. The analysis of these signals is done using various mother wavelets like Daubechies and bi-orthogonal wavelets and the measured root mean square value of stationary disturbance is obtained. The measured value obtained by discrete wavelet transform is compared with the exact RMS value of the frequency component and the percentage differences are presented which helps to select optimum mother wavelet.

Classification of cardiac arrhythmias using competitive networks.

PubMed

Leite, Cicilia R M; Martin, Daniel L; Sizilio, Glaucia R A; Dos Santos, Keylly E A; de Araujo, Bruno G; Valentim, Ricardo A M; Neto, Adriao D D; de Melo, Jorge D; Guerreiro, Ana M G

2010-01-01

Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Method and apparatus for off-gas composition sensing

DOEpatents

Ottesen, David Keith; Allendorf, Sarah Williams; Hubbard, Gary Lee; Rosenberg, David Ezechiel

1999-01-01

An apparatus and method for non-intrusive collection of off-gas data in a steelmaking furnace includes structure and steps for transmitting a laser beam through the off-gas produced by a steelmaking furnace, for controlling the transmitting to repeatedly scan the laser beam through a plurality of wavelengths in its tuning range, and for detecting the laser beam transmitted through the off-gas and converting the detected laser beam to an electrical signal. The electrical signal is processed to determine characteristics of the off-gas that are used to analyze and/or control the steelmaking process.

Diagnosis diagrams for passing signals on an automatic block signaling railway section

NASA Astrophysics Data System (ADS)

Spunei, E.; Piroi, I.; Chioncel, C. P.; Piroi, F.

2018-01-01

This work presents a diagnosis method for railway traffic security installations. More specifically, the authors present a series of diagnosis charts for passing signals on a railway block equipped with an automatic block signaling installation. These charts are based on the exploitation electric schemes, and are subsequently used to develop a diagnosis software package. The thus developed software package contributes substantially to a reduction of failure detection and remedy for these types of installation faults. The use of the software package eliminates making wrong decisions in the fault detection process, decisions that may result in longer remedy times and, sometimes, to railway traffic events.

A Portable and Autonomous Magnetic Detection Platform for Biosensing

PubMed Central

Germano, José; Martins, Verónica C.; Cardoso, Filipe A.; Almeida, Teresa M.; Sousa, Leonel; Freitas, Paulo P.; Piedade, Moisés S.

2009-01-01

This paper presents a prototype of a platform for biomolecular recognition detection. The system is based on a magnetoresistive biochip that performs biorecognition assays by detecting magnetically tagged targets. All the electronic circuitry for addressing, driving and reading out signals from spin-valve or magnetic tunnel junctions sensors is implemented using off-the-shelf components. Taking advantage of digital signal processing techniques, the acquired signals are processed in real time and transmitted to a digital analyzer that enables the user to control and follow the experiment through a graphical user interface. The developed platform is portable and capable of operating autonomously for nearly eight hours. Experimental results show that the noise level of the described platform is one order of magnitude lower than the one presented by the previously used measurement set-up. Experimental results also show that this device is able to detect magnetic nanoparticles with a diameter of 250 nm at a concentration of about 40 fM. Finally, the biomolecular recognition detection capabilities of the platform are demonstrated by performing a hybridization assay using complementary and non-complementary probes and a magnetically tagged 20mer single stranded DNA target. PMID:22408516

Dual Electrophoresis Detection System for Rapid and Sensitive Immunoassays with Nanoparticle Signal Amplification

NASA Astrophysics Data System (ADS)

Zhang, Fangfang; Ma, Junjie; Watanabe, Junji; Tang, Jinlong; Liu, Huiyu; Shen, Heyun

2017-02-01

An electrophoretic technique was combined with an enzyme-linked immunosorbent assay (ELISA) system to achieve a rapid and sensitive immunoassay. A cellulose acetate filter modified with polyelectrolyte multilayer (PEM) was used as a solid substrate for three-dimensional antigen-antibody reactions. A dual electrophoresis process was used to induce directional migration and local condensation of antigens and antibodies at the solid substrate, avoiding the long diffusion times associated with antigen-antibody reactions in conventional ELISAs. The electrophoretic forces drove two steps in the ELISA process, namely the adsorption of antigen, and secondary antibody-labelled polystyrene nanoparticles (NP-Ab). The total time needed for dual electrophoresis-driven detection was just 4 min, nearly 2 h faster than a conventional ELISA system. Moreover, the rapid NP-Ab electrophoresis system simultaneously achieved amplification of the specific signal and a reduction in noise, leading to a more sensitive NP-Ab immunoassay with a limit of detection (LOD) of 130 fM, and wide range of detectable concentrations from 0.13 to 130 pM. These results suggest that the combination of dual electrophoresis detection and NP-Ab signal amplification has great potential for future immunoassay systems.

[Oligonucleotide derivatives in the nucleic acid hybridization analysis. II. Isothermal signal amplification in process of DNA analysis by minisequencing].

PubMed

Dmitrienko, E V; Khomiakova, E A; Pyshnaia; Bragin, A G; Vedernikov, V E; Pyshnyĭ, D V

2010-01-01

The isothermal amplification of reporter signal via limited probe extension (minisequencing) upon hybridization of nucleic acids has been studied. The intensity of reporter signal has been shown to increase due to enzymatic labeling of multiple probes upon consecutive hybridization with one DNA template both in homophase and heterophase assays using various kinds of detection signal: radioisotope label, fluorescent label, and enzyme-linked assay. The kinetic scheme of the process has been proposed and kinetic parameters for each step have been determined. The signal intensity has been shown to correlate with physicochemical characteristics of both complexes: probe/DNA and product/DNA. The maximum intensity has been observed at minimal difference between the thermodynamic stability of these complexes, provided the reaction temperature has been adjusted near their melting temperature values; rising or lowering the reaction temperature reduces the amount of reporting product. The signal intensity has been shown to decrease significantly upon hybridization with the DNA template containing single-nucleotide mismatches. Limited probe extension assay is useful not only for detection of DNA template but also for its quantitative characterization.

Low-complexity image processing for real-time detection of neonatal clonic seizures.

PubMed

Ntonfo, Guy Mathurin Kouamou; Ferrari, Gianluigi; Raheli, Riccardo; Pisani, Francesco

2012-05-01

In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

A miniaturized NQR spectrometer for a multi-channel NQR-based detection device

NASA Astrophysics Data System (ADS)

Beguš, Samo; Jazbinšek, Vojko; Pirnat, Janez; Trontelj, Zvonko

2014-10-01

A low frequency (0.5-5 MHz) battery operated sensitive pulsed NQR spectrometer with a transmitter power up to 5 W and a total mass of about 3 kg aimed at detecting 14 N NQR signals, predominantly of illicit materials, was designed and assembled. This spectrometer uses a standard software defined radio (SDR) platform for the data acquisition unit. Signal processing is done with the LabView Virtual instrument on a personal computer. We successfully tested the spectrometer by measuring 14 N NQR signals from aminotetrazole monohydrate (ATMH), potassium nitrate (PN), paracetamol (PCM) and trinitrotoluene (TNT). Such a spectrometer is a feasible component of a portable single or multichannel 14 N NQR based detection device.

Laser doppler blood flow imaging using a CMOS imaging sensor with on-chip signal processing.

PubMed

He, Diwei; Nguyen, Hoang C; Hayes-Gill, Barrie R; Zhu, Yiqun; Crowe, John A; Gill, Cally; Clough, Geraldine F; Morgan, Stephen P

2013-09-18

The first fully integrated 2D CMOS imaging sensor with on-chip signal processing for applications in laser Doppler blood flow (LDBF) imaging has been designed and tested. To obtain a space efficient design over 64 × 64 pixels means that standard processing electronics used off-chip cannot be implemented. Therefore the analog signal processing at each pixel is a tailored design for LDBF signals with balanced optimization for signal-to-noise ratio and silicon area. This custom made sensor offers key advantages over conventional sensors, viz. the analog signal processing at the pixel level carries out signal normalization; the AC amplification in combination with an anti-aliasing filter allows analog-to-digital conversion with a low number of bits; low resource implementation of the digital processor enables on-chip processing and the data bottleneck that exists between the detector and processing electronics has been overcome. The sensor demonstrates good agreement with simulation at each design stage. The measured optical performance of the sensor is demonstrated using modulated light signals and in vivo blood flow experiments. Images showing blood flow changes with arterial occlusion and an inflammatory response to a histamine skin-prick demonstrate that the sensor array is capable of detecting blood flow signals from tissue.

Theory and Measurement of Signal-to-Noise Ratio in Continuous-Wave Noise Radar.

PubMed

Stec, Bronisław; Susek, Waldemar

2018-05-06

Determination of the signal power-to-noise power ratio on the input and output of reception systems is essential to the estimation of their quality and signal reception capability. This issue is especially important in the case when both signal and noise have the same characteristic as Gaussian white noise. This article considers the problem of how a signal-to-noise ratio is changed as a result of signal processing in the correlation receiver of a noise radar in order to determine the ability to detect weak features in the presence of strong clutter-type interference. These studies concern both theoretical analysis and practical measurements of a noise radar with a digital correlation receiver for 9.2 GHz bandwidth. Firstly, signals participating individually in the correlation process are defined and the terms signal and interference are ascribed to them. Further studies show that it is possible to distinguish a signal and a noise on the input and output of a correlation receiver, respectively, when all the considered noises are in the form of white noise. Considering the above, a measurement system is designed in which it is possible to represent the actual conditions of noise radar operation and power measurement of a useful noise signal and interference noise signals—in particular the power of an internal leakage signal between a transmitter and a receiver of the noise radar. The proposed measurement stands and the obtained results show that it is possible to optimize with the use of the equipment and not with the complex processing of a noise signal. The radar parameters depend on its prospective application, such as short- and medium-range radar, ground-penetrating radar, and through-the-wall detection radar.

Photonic Materials and Devices for RF (mmW) Sensing and Imaging

DTIC Science & Technology

2012-12-31

wave encoding thereby eliminating the need for bulky LO distribution cables. Also, optical processing techniques can be utilized to provide simple... optical powers, can be close to unity and low -noise photodetectors make the detection of exceedingly low power millimeter-waves practical. In... optically -filtering the modulated signal to pass only a single sideband and detecting the resultant optical signal with a low -noise photodetector we have

Robust detection of heartbeats using association models from blood pressure and EEG signals.

PubMed

Jeon, Taegyun; Yu, Jongmin; Pedrycz, Witold; Jeon, Moongu; Lee, Boreom; Lee, Byeongcheol

2016-01-15

The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats. The proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets. To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.

Study on De-noising Technology of Radar Life Signal

NASA Astrophysics Data System (ADS)

Yang, Xiu-Fang; Wang, Lian-Huan; Ma, Jiang-Fei; Wang, Pei-Pei

2016-05-01

Radar detection is a kind of novel life detection technology, which can be applied to medical monitoring, anti-terrorism and disaster relief street fighting, etc. As the radar life signal is very weak, it is often submerged in the noise. Because of non-stationary and randomness of these clutter signals, it is necessary to denoise efficiently before extracting and separating the useful signal. This paper improves the radar life signal's theoretical model of the continuous wave, does de-noising processing by introducing lifting wavelet transform and determine the best threshold function through comparing the de-noising effects of different threshold functions. The result indicates that both SNR and MSE of the signal are better than the traditional ones by introducing lifting wave transform and using a new improved soft threshold function de-noising method..

Experimental validation of a structural damage detection method based on marginal Hilbert spectrum

NASA Astrophysics Data System (ADS)

Banerji, Srishti; Roy, Timir B.; Sabamehr, Ardalan; Bagchi, Ashutosh

2017-04-01

Structural Health Monitoring (SHM) using dynamic characteristics of structures is crucial for early damage detection. Damage detection can be performed by capturing and assessing structural responses. Instrumented structures are monitored by analyzing the responses recorded by deployed sensors in the form of signals. Signal processing is an important tool for the processing of the collected data to diagnose anomalies in structural behavior. The vibration signature of the structure varies with damage. In order to attain effective damage detection, preservation of non-linear and non-stationary features of real structural responses is important. Decomposition of the signals into Intrinsic Mode Functions (IMF) by Empirical Mode Decomposition (EMD) and application of Hilbert-Huang Transform (HHT) addresses the time-varying instantaneous properties of the structural response. The energy distribution among different vibration modes of the intact and damaged structure depicted by Marginal Hilbert Spectrum (MHS) detects location and severity of the damage. The present work investigates damage detection analytically and experimentally by employing MHS. The testing of this methodology for different damage scenarios of a frame structure resulted in its accurate damage identification. The sensitivity of Hilbert Spectral Analysis (HSA) is assessed with varying frequencies and damage locations by means of calculating Damage Indices (DI) from the Hilbert spectrum curves of the undamaged and damaged structures.

The heart sound preprocessor

NASA Technical Reports Server (NTRS)

Chen, W. T.

1972-01-01

Technology developed for signal and data processing was applied to diagnostic techniques in the area of phonocardiography (pcg), the graphic recording of the sounds of the heart generated by the functioning of the aortic and ventricular valves. The relatively broad bandwidth of the PCG signal (20 to 2000 Hz) was reduced to less than 100 Hz by the use of a heart sound envelope. The process involves full-wave rectification of the PCG signal, envelope detection of the rectified wave, and low pass filtering of the resultant envelope.

Small Arrays for Seismic Intruder Detections: A Simulation Based Experiment

NASA Astrophysics Data System (ADS)

Pitarka, A.

2014-12-01

Seismic sensors such as geophones and fiber optic have been increasingly recognized as promising technologies for intelligence surveillance, including intruder detection and perimeter defense systems. Geophone arrays have the capability to provide cost effective intruder detection in protecting assets with large perimeters. A seismic intruder detection system uses one or multiple arrays of geophones design to record seismic signals from footsteps and ground vehicles. Using a series of real-time signal processing algorithms the system detects, classify and monitors the intruder's movement. We have carried out numerical experiments to demonstrate the capability of a seismic array to detect moving targets that generate seismic signals. The seismic source is modeled as a vertical force acting on the ground that generates continuous impulsive seismic signals with different predominant frequencies. Frequency-wave number analysis of the synthetic array data was used to demonstrate the array's capability at accurately determining intruder's movement direction. The performance of the array was also analyzed in detecting two or more objects moving at the same time. One of the drawbacks of using a single array system is its inefficiency at detecting seismic signals deflected by large underground objects. We will show simulation results of the effect of an underground concrete block at shielding the seismic signal coming from an intruder. Based on simulations we found that multiple small arrays can greatly improve the system's detection capability in the presence of underground structures. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344

Software for a GPS-Reflection Remote-Sensing System

NASA Technical Reports Server (NTRS)

Lowe, Stephen

2003-01-01

A special-purpose software Global Positioning System (GPS) receiver designed for remote sensing with reflected GPS signals is described in Delay/Doppler-Mapping GPS-Reflection Remote-Sensing System (NPO-30385), which appears elsewhere in this issue of NASA Tech Briefs. The input accepted by this program comprises raw (open-loop) digitized GPS signals sampled at a rate of about 20 MHz. The program processes the data samples to perform the following functions: detection of signals; tracking of phases and delays; mapping of delay, Doppler, and delay/Doppler waveforms; dual-frequency processing; coherent integrations as short as 125 s; decoding of navigation messages; and precise time tagging of observable quantities. The software can perform these functions on all detectable satellite signals without dead time. Open-loop data collected over water, land, or ice and processed by this software can be further processed to extract geophysical information. Possible examples include mean sea height, wind speed and direction, and significant wave height (for observations over the ocean); bistatic-radar terrain images and measures of soil moisture and biomass (for observations over land); and estimates of ice age, thickness, and surface density (for observations over ice).

Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals

PubMed Central

Yin, Jinghai; Mu, Zhendong

2016-01-01

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors’ main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue. PMID:28529761

Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals.

PubMed

Yin, Jinghai; Hu, Jianfeng; Mu, Zhendong

2017-02-01

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.

High resolution signal-processing method for extrinsic Fabry-Perot interferometric sensors

NASA Astrophysics Data System (ADS)

Xie, Jiehui; Wang, Fuyin; Pan, Yao; Wang, Junjie; Hu, Zhengliang; Hu, Yongming

2015-03-01

In this paper, a signal-processing method for optical fiber extrinsic Fabry-Perot interferometric sensors is presented. It achieves both high resolution and absolute measurement of the dynamic change of cavity length with low sampling points in wavelength domain. In order to improve the demodulation accuracy, the reflected interference spectrum is cleared by Discrete Wavelet Transform and adjusted by the Hilbert transform. Then the cavity length is interrogated by the cross correlation algorithm. The continuous tests show the resolution of cavity length is only 36.7 pm. Moreover, the corresponding resolution of cavity length is only 1 pm on the low frequency range below 420 Hz, and the corresponding power spectrum shows the possibility of detecting the ultra-low frequency signals based on spectra detection.

Interrogation, and detection system

DOEpatents

Baldwin, Howard A.; Depp, Steven W.; Koelle, Alfred R.; Freyman, Robert W.

1978-02-21

The specification relates to a telemetering apparatus comprising a generator which generates at least a single frequency rf signal, a transponder for receiving that signal and for amplitude modulating it in accordance with information selected for transmission, an antenna on the transponder for reflecting the amplitude modulated signal, and a receiver which is preferably located at the generator. The receiver processes the signal to determine the information carried thereby.

Analytical estimation of laser phase noise induced BER floor in coherent receiver with digital signal processing.

PubMed

Vanin, Evgeny; Jacobsen, Gunnar

2010-03-01

The Bit-Error-Ratio (BER) floor caused by the laser phase noise in the optical fiber communication system with differential quadrature phase shift keying (DQPSK) and coherent detection followed by digital signal processing (DSP) is analytically evaluated. An in-phase and quadrature (I&Q) receiver with a carrier phase recovery using DSP is considered. The carrier phase recovery is based on a phase estimation of a finite sum (block) of the signal samples raised to the power of four and the phase unwrapping at transitions between blocks. It is demonstrated that errors generated at block transitions cause the dominating contribution to the system BER floor when the impact of the additive noise is negligibly small in comparison with the effect of the laser phase noise. Even the BER floor in the case when the phase unwrapping is omitted is analytically derived and applied to emphasize the crucial importance of this signal processing operation. The analytical results are verified by full Monte Carlo simulations. The BER for another type of DQPSK receiver operation, which is based on differential phase detection, is also obtained in the analytical form using the principle of conditional probability. The principle of conditional probability is justified in the case of differential phase detection due to statistical independency of the laser phase noise induced signal phase error and the additive noise contributions. Based on the achieved analytical results the laser linewidth tolerance is calculated for different system cases.

Event-related cerebral hemodynamics reveal target-specific resource allocation for both "go" and "no-go" response-based vigilance tasks.

PubMed

Shaw, Tyler H; Funke, Matthew E; Dillard, Michael; Funke, Gregory J; Warm, Joel S; Parasuraman, Raja

2013-08-01

Transcranial Doppler sonography was used to measure cerebral blood flow velocity (CBFV) in the right and left cerebral hemispheres during the performance of a 50-min visual vigilance session. Observers monitored a simulated flight of unmanned aerial vehicles for cases in which one of the vehicles was flying in an inappropriate direction relative to its cohorts. Two types of vigilance tasks were employed: a traditional task in which observers made button press ("go") responses to critical signals, and a modification of the traditional task called the Sustained Attention to Response Task (SART) in which "go" responses acknowledged nonsignal events and response withholding ("no-go") signified signal detection. Signal detections and global CBFV scores declined over time. In addition, fine-grained event-related analyses revealed that the detection of signals was accompanied by an elevation of CBFV that was not present with missed signals. As was the case with the global scores, the magnitude of the transient CBFV increments associated with signal detection also declined over time, and these findings were independent of task type. The results support the view of CBFV as an index of the cognitive evaluation of stimulus significance, and a resource model of vigilance in which the need for continuous attention produces a depletion of information-processing assets that are not replenished as the task progresses. Further, temporal declines in the magnitude of event-related CBFV in response to critical signals only is evidence that the decrement function in vigilance is due to attentional processing and not specific task elements such as the required response format. Copyright © 2013. Published by Elsevier Inc.

Signal processing: opportunities for superconductive circuits

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

Ralston, R.W.

1985-03-01

Prime motivators in the evolution of increasingly sophisticated communication and detection systems are the needs for handling ever wider signal bandwidths and higher data processing speeds. These same needs drive the development of electronic device technology. Until recently the superconductive community has been tightly focused on digital devices for high speed computers. The purpose of this paper is to describe opportunities and challenges which exist for both analog and digital devices in a less familiar area, that of wideband signal processing. The function and purpose of analog signal-processing components, including matched filters, correlators and Fourier transformers, will be described andmore » examples of superconductive implementations given. A canonic signal-processing system is then configured using these components in combination with analog/digital converters and digital output circuits to highlight the important issues of dynamic range, accuracy and equivalent computation rate. Superconductive circuits hold promise for processing signals of 10-GHz bandwidth. Signal processing systems, however, can be properly designed and implemented only through a synergistic combination of the talents of device physicists, circuit designers, algorithm architects and system engineers. An immediate challenge to the applied superconductivity community is to begin sharing ideas with these other researchers.« less

The Applicability of Incoherent Array Processing to IMS Seismic Array Stations

NASA Astrophysics Data System (ADS)

Gibbons, S. J.

2012-04-01

The seismic arrays of the International Monitoring System for the CTBT differ greatly in size and geometry, with apertures ranging from below 1 km to over 60 km. Large and medium aperture arrays with large inter-site spacings complicate the detection and estimation of high frequency phases since signals are often incoherent between sensors. Many such phases, typically from events at regional distances, remain undetected since pipeline algorithms often consider only frequencies low enough to allow coherent array processing. High frequency phases that are detected are frequently attributed qualitatively incorrect backazimuth and slowness estimates and are consequently not associated with the correct event hypotheses. This can lead to missed events both due to a lack of contributing phase detections and by corruption of event hypotheses by spurious detections. Continuous spectral estimation can be used for phase detection and parameter estimation on the largest aperture arrays, with phase arrivals identified as local maxima on beams of transformed spectrograms. The estimation procedure in effect measures group velocity rather than phase velocity and the ability to estimate backazimuth and slowness requires that the spatial extent of the array is large enough to resolve time-delays between envelopes with a period of approximately 4 or 5 seconds. The NOA, AKASG, YKA, WRA, and KURK arrays have apertures in excess of 20 km and spectrogram beamforming on these stations provides high quality slowness estimates for regional phases without additional post-processing. Seven arrays with aperture between 10 and 20 km (MJAR, ESDC, ILAR, KSRS, CMAR, ASAR, and EKA) can provide robust parameter estimates subject to a smoothing of the resulting slowness grids, most effectively achieved by convolving the measured slowness grids with the array response function for a 4 or 5 second period signal. The MJAR array in Japan recorded high SNR Pn signals for both the 2006 and 2009 North Korea nuclear tests but, due to signal incoherence, failed to contribute to the automatic event detections. It is demonstrated that the smoothed incoherent slowness estimates for the MJAR Pn phases for both tests indicate unambiguously the correct type of phase and a backazimuth estimate within 5 degrees of the great-circle backazimuth. The detection part of the algorithm is applicable to all IMS arrays, and spectrogram-based processing may offer a reduction in the false alarm rate for high frequency signals. Significantly, the local maxima of the scalar functions derived from the transformed spectrogram beams provide good estimates of the signal onset time. High frequency energy is of greater significance for lower event magnitudes and in, for example, the cavity decoupling detection evasion scenario. There is a need to characterize propagation paths with low attenuation of high frequency energy and situations in which parameter estimation on array stations fails.

The endpoint detection technique for deep submicrometer plasma etching

NASA Astrophysics Data System (ADS)

Wang, Wei; Du, Zhi-yun; Zeng, Yong; Lan, Zhong-went

2009-07-01

The availability of reliable optical sensor technology provides opportunities to better characterize and control plasma etching processes in real time, they could play a important role in endpoint detection, fault diagnostics and processes feedback control and so on. The optical emission spectroscopy (OES) method becomes deficient in the case of deep submicrometer gate etching. In the newly developed high density inductively coupled plasma (HD-ICP) etching system, Interferometry endpoint (IEP) is introduced to get the EPD. The IEP fringe count algorithm is investigated to predict the end point, and then its signal is used to control etching rate and to call end point with OES signal in over etching (OE) processes step. The experiment results show that IEP together with OES provide extra process control margin for advanced device with thinner gate oxide.

[Research and realization of signal processing algorithms based on FPGA in digital ophthalmic ultrasonography imaging].

PubMed

Fang, Simin; Zhou, Sheng; Wang, Xiaochun; Ye, Qingsheng; Tian, Ling; Ji, Jianjun; Wang, Yanqun

2015-01-01

To design and improve signal processing algorithms of ophthalmic ultrasonography based on FPGA. Achieved three signal processing modules: full parallel distributed dynamic filter, digital quadrature demodulation, logarithmic compression, using Verilog HDL hardware language in Quartus II. Compared to the original system, the hardware cost is reduced, the whole image shows clearer and more information of the deep eyeball contained in the image, the depth of detection increases from 5 cm to 6 cm. The new algorithms meet the design requirements and achieve the system's optimization that they can effectively improve the image quality of existing equipment.

The Neural Substrate for Binaural Masking Level Differences in the Auditory Cortex

PubMed Central

Gilbert, Heather J.; Krumbholz, Katrin; Palmer, Alan R.

2015-01-01

The binaural masking level difference (BMLD) is a phenomenon whereby a signal that is identical at each ear (S0), masked by a noise that is identical at each ear (N0), can be made 12–15 dB more detectable by inverting the waveform of either the tone or noise at one ear (Sπ, Nπ). Single-cell responses to BMLD stimuli were measured in the primary auditory cortex of urethane-anesthetized guinea pigs. Firing rate was measured as a function of signal level of a 500 Hz pure tone masked by low-passed white noise. Responses were similar to those reported in the inferior colliculus. At low signal levels, the response was dominated by the masker. At higher signal levels, firing rate either increased or decreased. Detection thresholds for each neuron were determined using signal detection theory. Few neurons yielded measurable detection thresholds for all stimulus conditions, with a wide range in thresholds. However, across the entire population, the lowest thresholds were consistent with human psychophysical BMLDs. As in the inferior colliculus, the shape of the firing-rate versus signal-level functions depended on the neurons' selectivity for interaural time difference. Our results suggest that, in cortex, BMLD signals are detected from increases or decreases in the firing rate, consistent with predictions of cross-correlation models of binaural processing and that the psychophysical detection threshold is based on the lowest neural thresholds across the population. PMID:25568115

Acetylcholine Release in Prefrontal Cortex Promotes Gamma Oscillations and Theta-Gamma Coupling during Cue Detection.

PubMed

Howe, William M; Gritton, Howard J; Lusk, Nicholas A; Roberts, Erik A; Hetrick, Vaughn L; Berke, Joshua D; Sarter, Martin

2017-03-22

The capacity for using external cues to guide behavior ("cue detection") constitutes an essential aspect of attention and goal-directed behavior. The cortical cholinergic input system, via phasic increases in prefrontal acetylcholine release, plays an essential role in attention by mediating such cue detection. However, the relationship between cholinergic signaling during cue detection and neural activity dynamics in prefrontal networks remains unclear. Here we combined subsecond measures of cholinergic signaling, neurophysiological recordings, and cholinergic receptor blockade to delineate the cholinergic contributions to prefrontal oscillations during cue detection in rats. We first confirmed that detected cues evoke phasic acetylcholine release. These cholinergic signals were coincident with increased neuronal synchrony across several frequency bands and the emergence of theta-gamma coupling. Muscarinic and nicotinic cholinergic receptors both contributed specifically to gamma synchrony evoked by detected cues, but the effects of blocking the two receptor subtypes were dissociable. Blocking nicotinic receptors primarily attenuated high-gamma oscillations occurring during the earliest phases of the cue detection process, while muscarinic (M1) receptor activity was preferentially involved in the transition from high to low gamma power that followed and corresponded to the mobilization of networks involved in cue-guided decision making. Detected cues also promoted coupling between gamma and theta oscillations, and both nicotinic and muscarinic receptor activity contributed to this process. These results indicate that acetylcholine release coordinates neural oscillations during the process of cue detection. SIGNIFICANCE STATEMENT The capacity of learned cues to direct attention and guide responding ("cue detection") is a key component of goal-directed behavior. Rhythmic neural activity and increases in acetylcholine release in the prefrontal cortex contribute to this process; however, the relationship between these neuronal mechanisms is not well understood. Using a combination of in vivo neurochemistry, neurophysiology, and pharmacological methods, we demonstrate that cue-evoked acetylcholine release, through distinct actions at both nicotinic and muscarinic receptors, triggers a procession of neural oscillations that map onto the multiple stages of cue detection. Our data offer new insights into cholinergic function by revealing the temporally orchestrated changes in prefrontal network synchrony modulated by acetylcholine release during cue detection. Copyright © 2017 the authors 0270-6474/17/373215-16$15.00/0.

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

Multistage audiovisual integration of speech: dissociating identification and detection.

PubMed

Eskelund, Kasper; Tuomainen, Jyrki; Andersen, Tobias S

2011-02-01

Speech perception integrates auditory and visual information. This is evidenced by the McGurk illusion where seeing the talking face influences the auditory phonetic percept and by the audiovisual detection advantage where seeing the talking face influences the detectability of the acoustic speech signal. Here, we show that identification of phonetic content and detection can be dissociated as speech-specific and non-specific audiovisual integration effects. To this end, we employed synthetically modified stimuli, sine wave speech (SWS), which is an impoverished speech signal that only observers informed of its speech-like nature recognize as speech. While the McGurk illusion only occurred for informed observers, the audiovisual detection advantage occurred for naïve observers as well. This finding supports a multistage account of audiovisual integration of speech in which the many attributes of the audiovisual speech signal are integrated by separate integration processes.

Falls event detection using triaxial accelerometry and barometric pressure measurement.

PubMed

Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Celler, Branko G; Lovell, Nigel H

2009-01-01

A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

Exploitation of Ubiquitous Wi-Fi Devices as Building Blocks for Improvised Motion Detection Systems.

PubMed

Soldovieri, Francesco; Gennarelli, Gianluca

2016-02-27

This article deals with a feasibility study on the detection of human movements in indoor scenarios based on radio signal strength variations. The sensing principle exploits the fact that the human body interacts with wireless signals, introducing variations of the radiowave fields due to shadowing and multipath phenomena. As a result, human motion can be inferred from fluctuations of radiowave power collected by a receiving terminal. In this paper, we investigate the potentialities of widely available wireless communication devices in order to develop an improvised motion detection system (IMDS). Experimental tests are performed in an indoor environment by using a smartphone as a Wi-Fi access point and a laptop with dedicated software as a receiver. Simple detection strategies tailored for real-time operation are implemented to process the received signal strength measurements. The achieved results confirm the potentialities of the simple system here proposed to reliably detect human motion in operational conditions.

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues.

PubMed

Gepperth, Alexander R T; Rebhan, Sven; Hasler, Stephan; Fritsch, Jannik

2011-03-01

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.

Proactive and Reactive Stopping When Distracted: An Attentional Account

PubMed Central

2014-01-01

Performance in response inhibition paradigms is typically attributed to inhibitory control. Here we examined the idea that stopping may largely depend on the outcome of a sensory detection process. Subjects performed a speeded go task, but they were instructed to withhold their response when a visual stop signal was presented. The stop signal could occur in the center of the screen or in the periphery. On half of the trials, perceptual distractors were presented throughout the trial. We found that these perceptual distractors impaired stopping, especially when stop signals could occur in the periphery. Furthermore, the effect of the distractors on going was smallest in the central stop-signal condition, medium in a condition in which no signals could occur, and largest in the condition in which stop signals could occur in the periphery. The results show that an important component of stopping is finding a balance between ignoring irrelevant information in the environment and monitoring for the occurrence of occasional stop signals. These findings highlight the importance of sensory detection processes when stopping and could shed new light on a range of phenomena and findings in the response inhibition literature. PMID:24842070

Reflectometric measurement of plasma imaging and applications

NASA Astrophysics Data System (ADS)

Mase, A.; Ito, N.; Oda, M.; Komada, Y.; Nagae, D.; Zhang, D.; Kogi, Y.; Tobimatsu, S.; Maruyama, T.; Shimazu, H.; Sakata, E.; Sakai, F.; Kuwahara, D.; Yoshinaga, T.; Tokuzawa, T.; Nagayama, Y.; Kawahata, K.; Yamaguchi, S.; Tsuji-Iio, S.; Domier, C. W.; Luhmann, N. C., Jr.; Park, H. K.; Yun, G.; Lee, W.; Padhi, S.; Kim, K. W.

2012-01-01

Progress in microwave and millimeter-wave technologies has made possible advanced diagnostics for application to various fields, such as, plasma diagnostics, radio astronomy, alien substance detection, airborne and spaceborne imaging radars called as synthetic aperture radars, living body measurements. Transmission, reflection, scattering, and radiation processes of electromagnetic waves are utilized as diagnostic tools. In this report we focus on the reflectometric measurements and applications to biological signals (vital signal detection and breast cancer detection) as well as plasma diagnostics, specifically by use of imaging technique and ultra-wideband radar technique.

System for detecting substructure microfractures and method therefore

NASA Technical Reports Server (NTRS)

Parthasarathy, S. P.; Narasimhan, K. Y. (Inventor)

1979-01-01

Bursts of signals at different frequencies are induced into substructure, adjacent to a borehole. The return signals from each burst of signals are normalized to compensate for the attenuation, experienced by more distant return signals. The peak amplitudes of return signals, above a selected level, are cut off, and an average signal is produced from the normalized amplitude-limited return signals of each burst. The averaged signals of the return signals of all the signal bursts at the different frequencies are processed to provide a combined signal, whose amplitude is related to the microfracture density of the substructure adjacent to the borehole.

Frequency-wavenumber processing for infrasound distributed arrays.

PubMed

Costley, R Daniel; Frazier, W Garth; Dillion, Kevin; Picucci, Jennifer R; Williams, Jay E; McKenna, Mihan H

2013-10-01

The work described herein discusses the application of a frequency-wavenumber signal processing technique to signals from rectangular infrasound arrays for detection and estimation of the direction of travel of infrasound. Arrays of 100 sensors were arranged in square configurations with sensor spacing of 2 m. Wind noise data were collected at one site. Synthetic infrasound signals were superposed on top of the wind noise to determine the accuracy and sensitivity of the technique with respect to signal-to-noise ratio. The technique was then applied to an impulsive event recorded at a different site. Preliminary results demonstrated the feasibility of this approach.

Detection of and Response to Signals Involved in Host-Microbe Interactions by Plant-Associated Bacteria

PubMed Central

Brencic, Anja; Winans, Stephen C.

2005-01-01

Diverse interactions between hosts and microbes are initiated by the detection of host-released chemical signals. Detection of these signals leads to altered patterns of gene expression that culminate in specific and adaptive changes in bacterial physiology that are required for these associations. This concept was first demonstrated for the members of the family Rhizobiaceae and was later found to apply to many other plant-associated bacteria as well as to microbes that colonize human and animal hosts. The family Rhizobiaceae includes various genera of rhizobia as well as species of Agrobacterium. Rhizobia are symbionts of legumes, which fix nitrogen within root nodules, while Agrobacterium tumefaciens is a pathogen that causes crown gall tumors on a wide variety of plants. The plant-released signals that are recognized by these bacteria are low-molecular-weight, diffusible molecules and are detected by the bacteria through specific receptor proteins. Similar phenomena are observed with other plant pathogens, including Pseudomonas syringae, Ralstonia solanacearum, and Erwinia spp., although here the signals and signal receptors are not as well defined. In some cases, nutritional conditions such as iron limitation or the lack of nitrogen sources seem to provide a significant cue. While much has been learned about the process of host detection over the past 20 years, our knowledge is far from being complete. The complex nature of the plant-microbe interactions makes it extremely challenging to gain a comprehensive picture of host detection in natural environments, and thus many signals and signal recognition systems remain to be described. PMID:15755957

Fast contactless vibrating structure characterization using real time field programmable gate array-based digital signal processing: demonstrations with a passive wireless acoustic delay line probe and vision.

PubMed

Goavec-Mérou, G; Chrétien, N; Friedt, J-M; Sandoz, P; Martin, G; Lenczner, M; Ballandras, S

2014-01-01

Vibrating mechanical structure characterization is demonstrated using contactless techniques best suited for mobile and rotating equipments. Fast measurement rates are achieved using Field Programmable Gate Array (FPGA) devices as real-time digital signal processors. Two kinds of algorithms are implemented on FPGA and experimentally validated in the case of the vibrating tuning fork. A first application concerns in-plane displacement detection by vision with sampling rates above 10 kHz, thus reaching frequency ranges above the audio range. A second demonstration concerns pulsed-RADAR cooperative target phase detection and is applied to radiofrequency acoustic transducers used as passive wireless strain gauges. In this case, the 250 ksamples/s refresh rate achieved is only limited by the acoustic sensor design but not by the detection bandwidth. These realizations illustrate the efficiency, interest, and potentialities of FPGA-based real-time digital signal processing for the contactless interrogation of passive embedded probes with high refresh rates.

Enhanced detection of a low-frequency signal by using broad squeezed light and a bichromatic local oscillator

NASA Astrophysics Data System (ADS)

Li, Wei; Jin, Yuanbin; Yu, Xudong; Zhang, Jing

2017-08-01

We experimentally study a protocol of using the broadband high-frequency squeezed vacuum to detect the low-frequency signal. In this scheme, the lower sideband field of the squeezed light carries the low-frequency modulation signal, and the two strong coherent light fields are applied as the bichromatic local oscillator in the homodyne detection to measure the quantum entanglement of the upper and lower sideband for the broadband squeezed light. The power of one of the local oscillators for detecting the upper sideband can be adjusted to optimize the conditional variance in the low-frequency regime by subtracting the photocurrent of the upper sideband field of the squeezed light from that of the lower sideband field. By means of the quantum correlation of the upper and lower sideband for the broadband squeezed light, the low-frequency signal beyond the standard quantum limit is measured. This scheme is appropriate for enhancing the sensitivity of the low-frequency signal by the aid of the broad squeezed light, such as gravitational waves detection, and does not need to directly produce the low-frequency squeezing in an optical parametric process.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

PubMed Central

Elgendi, Mohamed

2016-01-01

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 (W1 and W2) have to follow the inequality (8×W1)≥W2≥(2×W1). 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. PMID:27827852

Shining a light on the Arabidopsis circadian clock.

PubMed

Oakenfull, Rachael J; Davis, Seth J

2017-11-01

The circadian clock provides essential timing information to ensure optimal growth to prevailing external environmental conditions. A major time-setting mechanism (zeitgeber) in clock synchronization is light. Differing light wavelengths, intensities, and photoperiodic duration are processed for the clock-setting mechanism. Many studies on light-input pathways to the clock have focused on Arabidopsis thaliana. Photoreceptors are specific chromic proteins that detect light signals and transmit this information to the central circadian oscillator through a number of different signalling mechanisms. The most well-characterized clock-mediating photoreceptors are cryptochromes and phytochromes, detecting blue, red, and far-red wavelengths of light. Ultraviolet and shaded light are also processed signals to the oscillator. Notably, the clock reciprocally generates rhythms of photoreceptor action leading to so-called gating of light responses. Intermediate proteins, such as Phytochrome interacting factors (PIFs), constitutive photomorphogenic 1 (COP1) and EARLY FLOWERING 3 (ELF3), have been established in signalling pathways downstream of photoreceptor activation. However, the precise details for these signalling mechanisms are not fully established. This review highlights both historical and recent efforts made to understand overall light input to the oscillator, first looking at how each wavelength of light is detected, this is then related to known input mechanisms and their interactions. © 2017 John Wiley & Sons Ltd.

A software framework for real-time multi-modal detection of microsleeps.

PubMed

Knopp, Simon J; Bones, Philip J; Weddell, Stephen J; Jones, Richard D

2017-09-01

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

The research of PSD location method in micro laser welding fields

NASA Astrophysics Data System (ADS)

Zhang, Qiue; Zhang, Rong; Dong, Hua

2010-11-01

In the field of micro laser welding, besides the special requirement in the parameter of lasers, the locating in welding points accurately is very important. The article adopt position sensitive detector (PSD) as hard core, combine optic system, electric circuits and PC and software processing, confirm the location of welding points. The signal detection circuits adopt the special integrate circuit H-2476 to process weak signal. It is an integrated circuit for high-speed, high-sensitivity optical range finding, which has stronger noiseproof feature, combine digital filter arithmetic, carry out repair the any non-ideal factors, increasing the measure precision. The amplifier adopt programmable amplifier LTC6915. The system adapt two dimension stepping motor drive the workbench, computer and corresponding software processing, make sure the location of spot weld. According to different workpieces to design the clamps. The system on-line detect PSD 's output signal in the moving processing. At the workbench moves in the X direction, the filaments offset is detected dynamic. Analyze the X axes moving sampling signal direction could be estimate the Y axes moving direction, and regulate the Y axes moving values. The workbench driver adopt A3979, it is a stepping motor driver with insert transducer and operate easily. It adapts the requirement of location in micro laser welding fields, real-time control to adjust by computer. It can be content up 20 μm's laser micro welding requirement on the whole. Using laser powder cladding technology achieve inter-penetration welding of high quality and reliability.

Implementation of a portable device for real-time ECG signal analysis.

PubMed

Jeon, Taegyun; Kim, Byoungho; Jeon, Moongu; Lee, Byung-Geun

2014-12-10

Cardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient's survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases. A noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia. A portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%. Current home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

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.

Passive signal processing for a miniature Fabry-Perot interferometric sensor with a multimode laser-diode source

NASA Astrophysics Data System (ADS)

Ezbiri, A.; Tatam, R. P.

1995-09-01

A passive signal-processing technique for addressing a miniature low-finesse fiber Fabry-Perot interferometric sensor with a multimode laser diode is reported. Two modes of a multimode laser diode separated by 3 nm are used to obtain quadrature outputs from an \\similar 20 - mu m cavity. Wavelength-division demultiplexing combined with digital signal processing is used to recover the measurand-induced phase change. The technique is demonstrated for the measurement of vibration. The signal-to-noise ratio is \\similar 70 dB at 500 Hz for \\similar pi /2 rad displacement of the mirror, which results in a minimum detectable signal of \\similar 200 mu rad H z-1/2 . A quantitative discussion of miscalibration and systematic errors is presented.

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.

Detecting determinism from point processes.

PubMed

Andrzejak, Ralph G; Mormann, Florian; Kreuz, Thomas

2014-12-01

The detection of a nonrandom structure from experimental data can be crucial for the classification, understanding, and interpretation of the generating process. We here introduce a rank-based nonlinear predictability score to detect determinism from point process data. Thanks to its modular nature, this approach can be adapted to whatever signature in the data one considers indicative of deterministic structure. After validating our approach using point process signals from deterministic and stochastic model dynamics, we show an application to neuronal spike trains recorded in the brain of an epilepsy patient. While we illustrate our approach in the context of temporal point processes, it can be readily applied to spatial point processes as well.

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.

Arduino-based noise robust online heart-rate detection.

PubMed

Das, Sangita; Pal, Saurabh; Mitra, Madhuchhanda

2017-04-01

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

A Probabilistic Approach to Network Event Formation from Pre-Processed Waveform Data

NASA Astrophysics Data System (ADS)

Kohl, B. C.; Given, J.

2017-12-01

The current state of the art for seismic event detection still largely depends on signal detection at individual sensor stations, including picking accurate arrivals times and correctly identifying phases, and relying on fusion algorithms to associate individual signal detections to form event hypotheses. But increasing computational capability has enabled progress toward the objective of fully utilizing body-wave recordings in an integrated manner to detect events without the necessity of previously recorded ground truth events. In 2011-2012 Leidos (then SAIC) operated a seismic network to monitor activity associated with geothermal field operations in western Nevada. We developed a new association approach for detecting and quantifying events by probabilistically combining pre-processed waveform data to deal with noisy data and clutter at local distance ranges. The ProbDet algorithm maps continuous waveform data into continuous conditional probability traces using a source model (e.g. Brune earthquake or Mueller-Murphy explosion) to map frequency content and an attenuation model to map amplitudes. Event detection and classification is accomplished by combining the conditional probabilities from the entire network using a Bayesian formulation. This approach was successful in producing a high-Pd, low-Pfa automated bulletin for a local network and preliminary tests with regional and teleseismic data show that it has promise for global seismic and nuclear monitoring applications. The approach highlights several features that we believe are essential to achieving low-threshold automated event detection: Minimizes the utilization of individual seismic phase detections - in traditional techniques, errors in signal detection, timing, feature measurement and initial phase ID compound and propagate into errors in event formation, Has a formalized framework that utilizes information from non-detecting stations, Has a formalized framework that utilizes source information, in particular the spectral characteristics of events of interest, Is entirely model-based, i.e. does not rely on a priori's - particularly important for nuclear monitoring, Does not rely on individualized signal detection thresholds - it's the network solution that matters.

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology

PubMed Central

Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

2017-01-01

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space (H, L, and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved. PMID:28106806

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology.

PubMed

Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

2017-01-18

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space ( H , L , and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved.

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar. Thesis Technical Report No. 20

NASA Technical Reports Server (NTRS)

Lai, Jonathan Y.

1994-01-01

This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.

Sensor/amplifier for weak light sources

NASA Technical Reports Server (NTRS)

Desmet, D. J.; Jason, A. J.; Parr, A. C.

1980-01-01

Light sensor/amplifier circuit detects weak light converts it into strong electrical signal in electrically noisy environment. Circuit is relatively simple and uses inexpensive, readily available components. Device is useful in such applications as fire detection and photographic processing.

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.

Separation and reconstruction of high pressure water-jet reflective sound signal based on ICA

NASA Astrophysics Data System (ADS)

Yang, Hongtao; Sun, Yuling; Li, Meng; Zhang, Dongsu; Wu, Tianfeng

2011-12-01

The impact of high pressure water-jet on the different materials target will produce different reflective mixed sound. In order to reconstruct the reflective sound signals distribution on the linear detecting line accurately and to separate the environment noise effectively, the mixed sound signals acquired by linear mike array were processed by ICA. The basic principle of ICA and algorithm of FASTICA were described in detail. The emulation experiment was designed. The environment noise signal was simulated by using band-limited white noise and the reflective sound signal was simulated by using pulse signal. The reflective sound signal attenuation produced by the different distance transmission was simulated by weighting the sound signal with different contingencies. The mixed sound signals acquired by linear mike array were synthesized by using the above simulated signals and were whitened and separated by ICA. The final results verified that the environment noise separation and the reconstruction of the detecting-line sound distribution can be realized effectively.

Detection of the ODMR signal of a nitrogen vacancy centre in nanodiamond in propagating surface plasmons

NASA Astrophysics Data System (ADS)

Al-Baiaty, Zahraa; Cumming, Benjamin P.; Gan, Xiaosong; Gu, Min

2018-02-01

We demonstrate that the optically detected magnetic resonance (ODMR) signal of a nitrogen vacancy (NV) centre can be coupled to propagating surface plasmons for the detection of the NV centre spin states, and of external magnetic fields. By coupling the spin dependent luminescence signal of a NV centre in a nanodiamond (ND) to a chemically synthesized silver nanowire, we demonstrate the readout of the ODMR signal as a reduction in the surface plasmon polariton intensity, with improved contrast in comparison to the emission from the NV centre. Furthermore, on the application of a permanent magnetic field from zero to 13 G, we demonstrate that the Zeeman splitting of the magnetic spin states of the nitrogen vacancy centre ground states can also be detected in the coupled surface plasmons. This is an important step in the development of a compact on-chip information processing system utilizing the nitrogen vacancy in nanodiamond as an on-chip source with efficient magnetometry sensing properties.

Design of Weft Detection System in The Stenter Machine

NASA Astrophysics Data System (ADS)

Gu, Minming; Xu, Xianju; Dai, Wenzhan

2017-12-01

In order to build an effective automatic weft-straightening system, it is important for the sensing device to detect most the possible fabric styles, designs, colours and structures, an optical sensing system that detects the angular orientation of weft threads in a moving web of a textile has been built. It contains an adjustable light source, two lens systems and photodiode sensor array. The sensor array includes 13 radial pattern of photosensitive areas that each generate an electrical signal proportional to the total intensity of the light incident on the area. The moving shadow of a weft thread passing over the area will modulate the output signal. A signal processed circuit was built to do the I/V conversion, amplifying, hardware filtering. An embed micro control system then deals with the information of these signals, calculates the angle of the weft drew. Finally, the experiments were done, the results showed that the weft detection system can deal with the fabric weft skew up to 30° and has achieved good results in the application.

VLC-beacon detection with an under-sampled ambient light sensor

NASA Astrophysics Data System (ADS)

Green, Jacob; Pérez-Olivas, Huetzin; Martínez-Díaz, Saúl; García-Márquez, Jorge; Domínguez-González, Carlos; Santiago-Montero, Raúl; Guan, Hongyu; Rozenblat, Marc; Topsu, Suat

2017-08-01

LEDs will replace in a near future the current worldwide lighting mainly due to their low production-cost and energy-saving assets. Visible light communications (VLC) will turn gradually the existing lighting network into a communication network. Nowadays VLC transceivers can be found in some commercial centres in Europe; some of them broadcast continuously an identification tag that contains its coordinate position. In such a case, the transceiver acts as a geolocation beacon. Nevertheless, mobile transceivers represent a challenge in the VLC communication chain, as smartphones have not integrated yet a VLC customized detection stage. In order to make current smartphones capable to detect VLC broadcasted signals, their Ambient Light Sensor (ALS) is adapted as a VLC detector. For this to be achieved, lighting transceivers need to adapt their modulation scheme. For instance, frequencies representing start bit, 1, and 0 logic values can be set to avoid flicker from illumination and to permit detecting the under-sampled signal. Decoding the signal requires a multiple steps real-time signal processing as shown here.

On the design of a postprocessor for a search for extraterrestrial intelligence /SETI/ system

NASA Technical Reports Server (NTRS)

Healy, T. J.; Seeger, C. L.; Stull, M. A.

1979-01-01

The design of an on-line postprocessor for a search for extraterrestrial intelligence (SETI) system is described. Signal processing tasks of the postprocessor include: (1) analysis of power level, phase coherence, and state of polarization of single-channel signals in a search for significant signals; (2) grouping or aggregation of adjacent channel data, time averaging of data; and (3) the detection of drifting and modulated signals. Control functions include multichannel spectrum analyzer frequency and clock control, system calibration and selfdiagnostic, control of data flow to and from short-term and long-term (archival) memories, and operation of detection subsystems, such as a visual display and a tunable receiver.

An innovative nonintrusive driver assistance system for vital signal monitoring.

PubMed

Sun, Ye; Yu, Xiong Bill

2014-11-01

This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.

Transiting Planet Search in the Kepler Pipeline

NASA Technical Reports Server (NTRS)

Jenkins, Jon M.; Chandrasekaran, Hema; McCauliff, Sean D.; Caldwell, Douglas A.; Tenebaum, Peter; Li, Jie; Klaus, Todd C.; Cote, Mile T.; Middour, Christopher

2010-01-01

The Kepler Mission simultaneously measures the brightness of more than 160,000 stars every 29.4 minutes over a 3.5-year mission to search for transiting planets. Detecting transits is a signal-detection problem where the signal of interest is a periodic pulse train and the predominant noise source is non-white, non-stationary (1/f) type process of stellar variability. Many stars also exhibit coherent or quasi-coherent oscillations. The detection algorithm first identifies and removes strong oscillations followed by an adaptive, wavelet-based matched filter. We discuss how we obtain super-resolution detection statistics and the effectiveness of the algorithm for Kepler flight data.

[Study on spectral detection of green plant target].

PubMed

Deng, Wei; Zhao, Chun-jiang; He, Xiong-kui; Chen, Li-ping; Zhang, Lu-da; Wu, Guang-wei; Mueller, J; Zhai, Chang-yuan

2010-08-01

Weeds grow scatteredly in fields, where many insentient objects exist, for example, withered grasses, dry twig and barriers. In order to improve the precision level of spraying, it is important to study green plant detecting technology. The present paper discussed detecting method of green plant by using spectral recognizing technology, because of the real-time feature of spectral recognition. By analyzing the reflectivity difference between each of the two sides of the "red edge" of the spectrum from plants and surrounding environment, green plant discriminat index (GPDI) is defined as the value which equals the reflectivity ratio at the wavelength of 850 nm divided by the reflectivity ratio at the wavelength of 650 nm. The original spectral data of green plants and the background were measured by using the handhold FieldSpec 3 Spectroradiometer manufactured by ASD Inc. in USA. The spectral data were processed to get the reflectivity of each measured objects and to work out the GPDI thereof as well. The classification model of green plant and its background was built up using decision tree method in order to obtain the threshold of GPDI to distinguish green plants and the background. The threshold of GPDI was chosen as 5.54. The detected object was recognized as green plant when it is GPDI>GPDITH, and vice versa. Through another test, the accuracy rate was verified which was 100% by using the threshold. The authors designed and developed the green plant detector based on single chip microcomputer (SCM) "AT89S51" and photodiode "OPT101" to realize detecting green plants from the background. After passing through two optical filters, the center wavelengths of which are 650 and 850 nm respectively, the reflected light from measured targets was detected by two photodiodes and converted into electrical signals. These analog signals were then converted to digital signals via an analog-to-digital converter (ADS7813) after being amplified by a signal amplifier (OP400). The converted digital signal of reflected light was eventually sent to the SCM (AT89S51) and was calculated and processed there. The processing results and the control signals were given out to actuate executive device to open or close the solenoid valve. The test results show that the level of detectivity of the designed detector was affected by the species, size, and density of weeds. The detectivity of broad-leaf species is higher than that of narrow-leaf species. Broad-leaf species are more easily detected than those narrow-leaf ones; the bigger the plants and the denser the leaves are, the higher the level of detectivity is.

Portable Intelligent Tritium in Air Monitor

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

Purghel, L.; Calin, M.R.; Bartos, D.

2005-07-15

The tritium detection method used for this monitor is original, patented in Romania. The detection unit consists of a single ionization chamber, a special fast preamplifier and a dedicated software associated to the detection unit, for signals processing. Some results concerning the tritium in relative strong gamma-ray fields are presented.

A Bayesian Method for Managing Uncertainties Relating to Distributed Multistatic Sensor Search

DTIC Science & Technology

2006-07-01

before - detect process. There will also be an increased probability of high signal-to-noise ratio (SNR) detections associated with specular and near...and high target strength and high Doppler opportunities give rise to the expectation of an increased number of detections that could feed a track

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

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

42 CFR 37.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., Morgantown, WV 26505. (j) Preemployment physical examination means any medical examination which includes a... image acquisition systems that detect X-ray signals using a cassette-based photostimulable storage... radiographic image to electronic signals which are then processed and stored so they can be displayed. (2...

Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

PubMed

Goense, J B M; Ratnam, R

2003-10-01

An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

Adaptive filtering in biological signal processing.

PubMed

Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A

1990-01-01

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.

A miniaturized NQR spectrometer for a multi-channel NQR-based detection device.

PubMed

Beguš, Samo; Jazbinšek, Vojko; Pirnat, Janez; Trontelj, Zvonko

2014-10-01

A low frequency (0.5-5 MHz) battery operated sensitive pulsed NQR spectrometer with a transmitter power up to 5 W and a total mass of about 3 kg aimed at detecting (14)N NQR signals, predominantly of illicit materials, was designed and assembled. This spectrometer uses a standard software defined radio (SDR) platform for the data acquisition unit. Signal processing is done with the LabView Virtual instrument on a personal computer. We successfully tested the spectrometer by measuring (14)N NQR signals from aminotetrazole monohydrate (ATMH), potassium nitrate (PN), paracetamol (PCM) and trinitrotoluene (TNT). Such a spectrometer is a feasible component of a portable single or multichannel (14)N NQR based detection device. Copyright © 2014 Elsevier Inc. All rights reserved.

Promises and Limitations of RFI Canceling Solutions

NASA Astrophysics Data System (ADS)

Fisher, J. R.

2004-05-01

Recent years have seen a kindling of interest in signal processing solutions to radio frequency interference (RFI) to astronomical observations. Over a dozen refereed papers and several dozen conference presentations on the subject are now in the astronomical and engineering literature. This work builds on several decades of signal processing development in the fields of acoustics and communications for which there are a number of standard texts. Radio astronomy has the unique requirement that interference must be suppressed below very low detection thresholds before the scientific results of observations can be considered reliable. These detection thresholds are several orders of magnitude lower than is typical of communications signal levels. Initial trials of coherent cancellation of TV and satellite signals and blanking of pulsed interference, such as radar and aircraft signals, are encouraging, but the signal processing power required for useful bandwidths is sobering. Simultaneous cancellation of many signals and compensation for multi-path propagation effects of distant transmitters add to the processing load and are challenges that remain to be tackled. Spectrum management is becoming increasingly complex with greater emphasis on spectrum sharing in the time and spacial domains. This requires a better understanding of long-distance propagation effects and the techniques and economics of signal separation to guide the protection of the scientific use of the radio spectrum. The traditional concept of frequency allocations will be only one aspect of spectrum management in the coming years. Active users of the spectrum will expect us to devote some of our engineering and managerial resources to spectrum sharing agreements, Hence, we need to continually build a firm technical footing upon which to base our negotiating positions. The NRAO is operated for the National Science Foundation (NSF) by Associated Universities, Inc. (AUI), under a cooperative agreement.

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1997-01-01

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

The research of knitting needle status monitoring setup

NASA Astrophysics Data System (ADS)

Liu, Lu; Liao, Xiao-qing; Zhu, Yong-kang; Yang, Wei; Zhang, Pei; Zhao, Yong-kai; Huang, Hui-jie

2013-09-01

In textile production, quality control and testing is the key to ensure the process and improve the efficiency. Defect of the knitting needles is the main factor affecting the quality of the appearance of textiles. Defect detection method based on machine vision and image processing technology is universal. This approach does not effectively identify the defect generated by damaged knitting needles and raise the alarm. We developed a knitting needle status monitoring setup using optical imaging, photoelectric detection and weak signal processing technology to achieve real-time monitoring of weaving needles' position. Depending on the shape of the knitting needle, we designed a kind of Glass Optical Fiber (GOF) light guides with a rectangular port used for transmission of the signal light. To be able to capture the signal of knitting needles accurately, we adopt a optical 4F system which has better imaging quality and simple structure and there is a rectangle image on the focal plane after the system. When a knitting needle passes through position of the rectangle image, the reflected light from needle surface will back to the GOF light guides along the same optical system. According to the intensity of signals, the computer control unit distinguish that the knitting needle is broken or curving. The experimental results show that this system can accurately detect the broken needles and the curving needles on the knitting machine in operating condition.

Ultrasonic Leak Detection System

NASA Technical Reports Server (NTRS)

Youngquist, Robert C. (Inventor); Moerk, J. Steven (Inventor)

1998-01-01

A system for detecting ultrasonic vibrations. such as those generated by a small leak in a pressurized container. vessel. pipe. or the like. comprises an ultrasonic transducer assembly and a processing circuit for converting transducer signals into an audio frequency range signal. The audio frequency range signal can be used to drive a pair of headphones worn by an operator. A diode rectifier based mixing circuit provides a simple, inexpensive way to mix the transducer signal with a square wave signal generated by an oscillator, and thereby generate the audio frequency signal. The sensitivity of the system is greatly increased through proper selection and matching of the system components. and the use of noise rejection filters and elements. In addition, a parabolic collecting horn is preferably employed which is mounted on the transducer assembly housing. The collecting horn increases sensitivity of the system by amplifying the received signals. and provides directionality which facilitates easier location of an ultrasonic vibration source.

A novel multireceiver communications system configuration based on optimal estimation theory

NASA Technical Reports Server (NTRS)

Kumar, R.

1990-01-01

A multireceiver configuration for the purpose of carrier arraying and/or signal arraying is presented. Such a problem arises for example, in the NASA Deep Space Network where the same data-modulated signal from a spacecraft is received by a number of geographically separated antennas and the data detection must be efficiently performed on the basis of the various received signals. The proposed configuration is arrived at by formulating the carrier and/or signal arraying problem as an optimal estimation problem. Two specific solutions are proposed. The first solution is to simultaneously and optimally estimate the various phase processes received at different receivers with coupled phase locked loops (PLLs) wherein the individual PLLs acquire and track their respective receivers' phase processes, but are aided by each other in an optimal manner. However, when the phase processes are relatively weakly correlated, and for the case of relatively high values of symbol energy-to-noise spectral density ratio, a novel configuration for combining the data modulated, loop-output signals is proposed. The scheme can be extended to the case of low symbol energy-to-noise case by performing the combining/detection process over a multisymbol period. Such a configuration results in the minimization of the effective radio loss at the combiner output, and thus a maximization of energy per bit to noise-power spectral density ration is achieved.

System and method for generating micro-seismic events and characterizing properties of a medium with non-linear acoustic interactions

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

Vu, Cung Khac; Nihei, Kurt; Johnson, Paul A.

2015-12-29

A method and system includes generating a first coded acoustic signal including pulses each having a modulated signal at a central frequency; and a second coded acoustic signal each pulse of which includes a modulated signal a central frequency of which is a fraction d of the central frequency of the modulated signal for the corresponding pulse in the first plurality of pulses. A receiver detects a third signal generated by a non-linear mixing process in the mixing zone and the signal is processed to extract the third signal to obtain an emulated micro-seismic event signal occurring at the mixingmore » zone; and to characterize properties of the medium or creating a 3D image of the properties of the medium, or both, based on the emulated micro-seismic event signal.« less

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.

Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

NASA Astrophysics Data System (ADS)

Liu, Zenghua; Sun, Kunming; Song, Guorong; He, Cunfu; Wu, Bin

2016-03-01

In this work, a detection method for the damage in plate-like structure with a compact rectangular phased piezoelectric transducer array of 16 piezoelectric elements was presented. This compact array can not only detect and locate a single defect (through hole) in plate, but also identify multi-defects (through holes and surface defect simulated by an iron pillar glued to the plate). The experiments proved that the compact rectangular phased transducer array could detect the full range of plate structures and implement multiple-defect detection simultaneously. The processing algorithm proposed in this paper contains two parts: signal filtering and damage imaging. The former part was used to remove noise from signals. Continuous wavelet transform was applicable to signal filtering. Continuous wavelet transform can provide a plot of wavelet coefficients and the signal with narrow frequency band can be easily extracted from the plot. The latter part of processing algorithm was to implement damage detection and localization. In order to accurately locate defects and improve the imaging quality, two images were obtained from amplitude and phase information. One image was obtained with the Total Focusing Method (TFM) and another phase image was obtained with the Sign Coherence Factor (SCF). Furthermore, an image compounding technique for compact rectangular phased piezoelectric transducer array was proposed in this paper. With the proposed technique, the compounded image can be obtained by combining TFM image with SCF image, thus greatly improving the resolution and contrast of image.

Variable frequency microwave furnace system

DOEpatents

Bible, Don W.; Lauf, Robert J.

1994-01-01

A variable frequency microwave furnace system (10) designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity (34) for testing or other selected applications. The variable frequency microwave furnace system (10) includes a microwave signal generator (12) or microwave voltage-controlled oscillator (14) for generating a low-power microwave signal for input to the microwave furnace. A first amplifier (18) may be provided to amplify the magnitude of the signal output from the microwave signal generator (12) or the microwave voltage-controlled oscillator (14). A second amplifier (20) is provided for processing the signal output by the first amplifier (18). The second amplifier (20) outputs the microwave signal input to the furnace cavity (34). In the preferred embodiment, the second amplifier (20) is a traveling-wave tube (TWT). A power supply (22) is provided for operation of the second amplifier (20). 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. Reflected power is dissipated in the reflected power load (28).

Real-time monitoring of drowsiness through wireless nanosensor systems

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay K.

2016-04-01

Detection of sleepiness and drowsiness in human beings has been a daunting task for both engineering and medical technologies. Accuracy, precision and promptness of detection have always been an issue that has to be dealt by technologists. Generally, the bio potential signals - ECG, EOG, EEG and EMG are used to classify and discriminate sleep from being awake. However, the potential drawbacks may be high false detections, low precision, obtrusiveness, aftermath analysis, etc. To overcome the disadvantages, this paper reviews the design aspects of a wireless and a real time monitoring system to track sleep and detect fatigue. This concept involves the use of EOG and EEG to measure the blink rate and asses the person's condition. In this user friendly and intuitive approach, EOG and EEG signals are obtained by the textile based nanosensors mounted on the inner side of a flexible headband. The acquired signals are then electrically transmitted to the data processing and transmission unit, which transmits the processed data to the receiver/monitoring module through ZigBee communication. This system is equipped with a software program to process, feature extract, analyze, display and store the information. Thereby, immediate detection of a person falling asleep is made feasible and, tracking the sleep cycle continuously provides an insight about the fatigue level. This approach of using a wireless, real time, dry sensor on a flexible substrate mitigates obtrusiveness that is expected from a wearable system. We have previously presented the results of the aforementioned wearable systems. This paper aims to extend our work conceptually through a review of engineering and medical techniques involved in wearable systems to detect drowsiness.

Close-in detection system for the Mine Hunter/Killer program

NASA Astrophysics Data System (ADS)

Bishop, Steven S.; Campana, Stephen B.; Lang, David A.; Wiggins, Carl M.

2000-08-01

The Close-in Detection (CID) System is the vehicle-mounted multisensor landmine detection system for the Army CECOM Night Vision Electronic Sensors Directorate (NVESD) Mine Hunter/Killer (MH/K) Program. The CID System is being developed by BAE Systems in San Diego, CA. TRW Systems and Information Technology Group in Arlington, VA and a team of specialists for ERIM, E-OIR, SNL, and APL/JHU support NVESD in the development, analysis and testing of the CID and associated signal and data processing. The CID System includes tow down-looking sensor arrays: a ground- penetrating radar (GPR) array, and a set of Electro-Magnetic Induction (EMI) coils for metal detection. These arrays span a 3-meter wide swath in front of a high mobility, multipurpose wheeled vehicle. The system also includes a forward looking IR imaging system mounted on the roof of the vehicle and covering a swath of the road ahead of the vehicle. Signals from each sensor are processed separately to detect and localize objects of interest. Features of candidate objects are integrated in a processor that uses them to discriminates between anti-tank miens and clutter. Mine locations are passed to the neutralization subsystem of MH/K. This paper reviews the design of the sensors and signal processing of the CID system and gives examples and analysis of recent test results at the NVESD mine lanes. The strengths and weaknesses of each sensor are discussed, and the application of multisensor fusion is illustrated.

Wireless sleep monitoring headband to identify sleep and track fatigue

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Oh, Sechang; Varadan, Vijay K.

2014-04-01

Detection of sleepiness and drowsiness in human beings has been a daunting task for both engineering and medical technologies. Accuracy, precision and promptness of detection have always been an issue that has to be dealt by technologists. Commonly, the rudimentary bio potential signals - ECG, EOG, EEG and EMG are used to classify and discriminate sleep from being awake. However, the potential drawbacks may be high false detections, low precision, obtrusiveness, aftermath analysis, etc. To overcome the disadvantages, this paper proposes the design of a wireless and a real time monitoring system to track sleep and detect fatigue. This concept involves the use of EOG and EEG to measure the blink rate and asses the person's condition. In this user friendly and intuitive approach, EOG and EEG signals are obtained by the dry gold wire nano-sensors fabricated on the inner side of a flexible headband. The acquired signals are then electrically transmitted to the data processing and transmission unit, which transmits the processed data to the receiver/monitoring module through WCDMA/GSM communication. This module is equipped with a software program to process, feature extract, analyze, display and store the information. Thereby, immediate detection of a person falling asleep is made feasible and, tracking the sleep cycle continuously provides an insight about the experienced fatigue level. The novel approach of using a wireless, real time, dry sensor on a flexible substrate reduces the obtrusiveness, and techniques adopted in the electronics and software facilitates and substantial increase in efficiency, accuracy and precision.

Intelligent Power Swing Detection Scheme to Prevent False Relay Tripping Using S-Transform

NASA Astrophysics Data System (ADS)

Mohamad, Nor Z.; Abidin, Ahmad F.; Musirin, Ismail

2014-06-01

Distance relay design is equipped with out-of-step tripping scheme to ensure correct distance relay operation during power swing. The out-of-step condition is a consequence result from unstable power swing. It requires proper detection of power swing to initiate a tripping signal followed by separation of unstable part from the entire power system. The distinguishing process of unstable swing from stable swing poses a challenging task. This paper presents an intelligent approach to detect power swing based on S-Transform signal processing tool. The proposed scheme is based on the use of S-Transform feature of active power at the distance relay measurement point. It is demonstrated that the proposed scheme is able to detect and discriminate the unstable swing from stable swing occurring in the system. To ascertain validity of the proposed scheme, simulations were carried out with the IEEE 39 bus system and its performance has been compared with the wavelet transform-based power swing detection scheme.

Recent advances in signal amplification strategy based on oligonucleotide and nanomaterials for microRNA detection-a review.

PubMed

Chen, Ying-Xu; Huang, Ke-Jing; Niu, Ke-Xin

2018-01-15

MicroRNAs (MiRNAs) play multiple crucial regulating roles in cell which can regulate one third of protein-coding genes. MiRNAs participate in the developmental and physiological processes of human body, while their aberrant adjustment will be more likely to trigger diseases such as cancers, kidney disease, central nervous system diseases, cardiovascular diseases, diabetes, viral infections and so on. What's worse, for the detection of miRNAs, their small size, high sequence similarity, low abundance and difficult extraction from cells impose great challenges in the analysis. Hence, it's necessary to fabricate accurate and sensitive biosensing platform for miRNAs detection. Up to now, researchers have developed many signal-amplification strategies for miRNAs detection, including hybridization chain reaction, nuclease amplification, rolling circle amplification, catalyzed hairpin assembly amplification and nanomaterials based amplification. These methods are typical, feasible and frequently used. In this review, we retrospect recent advances in signal amplification strategies for detecting miRNAs and point out the pros and cons of them. Furthermore, further prospects and promising developments of the signal-amplification strategies for detecting miRNAs are proposed. Copyright © 2017 Elsevier B.V. All rights reserved.

A real time ECG signal processing application for arrhythmia detection on portable devices

NASA Astrophysics Data System (ADS)

Georganis, A.; Doulgeraki, N.; Asvestas, P.

2017-11-01

Arrhythmia describes the disorders of normal heart rate, which, depending on the case, can even be fatal for a patient with severe history of heart disease. The purpose of this work is to develop an application for heart signal visualization, processing and analysis in Android portable devices e.g. Mobile phones, tablets, etc. The application is able to retrieve the signal initially from a file and at a later stage this signal is processed and analysed within the device so that it can be classified according to the features of the arrhythmia. In the processing and analysing stage, different algorithms are included among them the Moving Average and Pan Tompkins algorithm as well as the use of wavelets, in order to extract features and characteristics. At the final stage, testing is performed by simulating our application in real-time records, using the TCP network protocol for communicating the mobile with a simulated signal source. The classification of ECG beat to be processed is performed by neural networks.

Direct bit detection receiver noise performance analysis for 32-PSK and 64-PSK modulated signals

NASA Astrophysics Data System (ADS)

Ahmed, Iftikhar

1987-12-01

Simple two channel receivers for 32-PSK and 64-PSK modulated signals have been proposed which allow digital data (namely bits), to be recovered directly instead of the traditional approach of symbol detection followed by symbol to bit mappings. This allows for binary rather than M-ary receiver decisions, reduces the amount of signal processing operations and permits parallel recovery of the bits. The noise performance of these receivers quantified by the Bit Error Rate (BER) assuming an Additive White Gaussian Noise interference model is evaluated as a function of Eb/No, the signal to noise ratio, and transmitted phase angles of the signals. The performance results of the direct bit detection receivers (DBDR) when compared to that of convectional phase measurement receivers demonstrate that DBDR's are optimum in BER sense. The simplicity of the receiver implementations and the BER of the delivered data make DBDR's attractive for high speed, spectrally efficient digital communication systems.

Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

DOE PAGES

Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; ...

2016-01-01

The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events thatmore » generated the phase picks.« less

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.

The BEFWM system for detection and phase conjugation of a weak laser beam

NASA Astrophysics Data System (ADS)

Khizhnyak, Anatoliy; Markov, Vladimir

2007-09-01

Real environmental conditions, such as atmospheric turbulence and aero-optics effects, make practical implementation of the object-in-the-loop (TIL) algorithm a very difficult task, especially when the system is set to operate with a signal from the diffuse surface image-resolved object. The problem becomes even more complex since for the remote object the intensity of the returned signal is extremely low. This presentation discusses the results of an analysis and experimental verification of a thresholdless coherent signal receiving system, capable not only in high-sensitivity detection of an ultra weak object-scattered light, but also in its high-gain amplification and phase conjugation. The process of coherent detection by using the Brillouin Enhanced Four Wave Mixing (BEFWM) enables retrieval of complete information on the received signal, including accurate measurement of its wavefront. This information can be used for direct real-time control of the adaptive mirror.

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.

The feature extraction of "cat-eye" targets based on bi-spectrum

NASA Astrophysics Data System (ADS)

Zhang, Tinghua; Fan, Guihua; Sun, Huayan

2016-10-01

In order to resolve the difficult problem of detection and identification of optical targets in complex background or in long-distance transmission, this paper mainly study the range profiles of "cat-eye" targets using bi-spectrum. For the problems of laser echo signal attenuation serious and low Signal-Noise Ratio (SNR), the multi-pulse laser signal echo signal detection algorithm which is based on high-order cumulant, filter processing and the accumulation of multi-pulse is proposed. This could improve the detection range effectively. In order to extract the stable characteristics of the one-dimensional range profile coming from the cat-eye targets, a method is proposed which extracts the bi-spectrum feature, and uses the singular value decomposition to simplify the calculation. Then, by extracting data samples of different distance, type and incidence angle, verify the stability of the eigenvector and effectiveness extracted by bi-spectrum.

Optimal causal filtering for 1 /fα-type noise in single-electrode EEG signals.

PubMed

Paris, Alan; Atia, George; Vosoughi, Azadeh; Berman, Stephen A

2016-08-01

Understanding the mode of generation and the statistical structure of neurological noise is one of the central problems of biomedical signal processing. We have developed a broad class of abstract biological noise sources we call hidden simplicial tissues. In the simplest cases, such tissue emits what we have named generalized van der Ziel-McWhorter (GVZM) noise which has a roughly 1/fα spectral roll-off. Our previous work focused on the statistical structure of GVZM frequency spectra. However, causality of processing operations (i.e., dependence only on the past) is an essential requirement for real-time applications to seizure detection and brain-computer interfacing. In this paper we outline the theoretical background for optimal causal time-domain filtering of deterministic signals embedded in GVZM noise. We present some of our early findings concerning the optimal filtering of EEG signals for the detection of steady-state visual evoked potential (SSVEP) responses and indicate the next steps in our ongoing research.

TreSpEx—Detection of Misleading Signal in Phylogenetic Reconstructions Based on Tree Information

PubMed Central

Struck, Torsten H

2014-01-01

Phylogenies of species or genes are commonplace nowadays in many areas of comparative biological studies. However, for phylogenetic reconstructions one must refer to artificial signals such as paralogy, long-branch attraction, saturation, or conflict between different datasets. These signals might eventually mislead the reconstruction even in phylogenomic studies employing hundreds of genes. Unfortunately, there has been no program allowing the detection of such effects in combination with an implementation into automatic process pipelines. TreSpEx (Tree Space Explorer) now combines different approaches (including statistical tests), which utilize tree-based information like nodal support or patristic distances (PDs) to identify misleading signals. The program enables the parallel analysis of hundreds of trees and/or predefined gene partitions, and being command-line driven, it can be integrated into automatic process pipelines. TreSpEx is implemented in Perl and supported on Linux, Mac OS X, and MS Windows. Source code, binaries, and additional material are freely available at http://www.annelida.de/research/bioinformatics/software.html. PMID:24701118

Adaptive Wiener filtering for improved acquisition of distortion product otoacoustic emissions.

PubMed

Ozdamar, O; Delgado, R E; Rahman, S; Lopez, C

1998-01-01

An innovative acoustic noise canceling method using adaptive Wiener filtering (AWF) was developed for improved acquisition of distortion product otoacoustic emissions (DPOAEs). The system used one microphone placed in the test ear for the primary signal. Noise reference signals were obtained from three different sources: (a) pre-stimulus response from the test ear microphone, (b) post-stimulus response from a microphone placed near the head of the subject and (c) post-stimulus response obtained from a microphone placed in the subject's nontest ear. In order to improve spectral estimation, block averaging of a different number of single sweep responses was used. DPOAE data were obtained from 11 ears of healthy newborns in a well-baby nursery of a hospital under typical noise conditions. Simultaneously obtained recordings from all three microphones were digitized, stored and processed off-line to evaluate the effects of AWF with respect to DPOAE detection and signal-to-noise ratio (SNR) improvement. Results show that compared to standard DPOAE processing, AWF improved signal detection and improved SNR.

An ultra low energy biomedical signal processing system operating at near-threshold.

PubMed

Hulzink, J; Konijnenburg, M; Ashouei, M; Breeschoten, A; Berset, T; Huisken, J; Stuyt, J; de Groot, H; Barat, F; David, J; Van Ginderdeuren, J

2011-12-01

This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today.

Automatic identification of alpine mass movements based on seismic and infrasound signals

NASA Astrophysics Data System (ADS)

Schimmel, Andreas; Hübl, Johannes

2017-04-01

The automatic detection and identification of alpine mass movements like debris flows, debris floods or landslides gets increasing importance for mitigation measures in the densely populated and intensively used alpine regions. Since this mass movement processes emits characteristically seismic and acoustic waves in the low frequency range this events can be detected and identified based on this signals. So already several approaches for detection and warning systems based on seismic or infrasound signals has been developed. But a combination of both methods, which can increase detection probability and reduce false alarms is currently used very rarely and can serve as a promising method for developing an automatic detection and identification system. So this work presents an approach for a detection and identification system based on a combination of seismic and infrasound sensors, which can detect sediment related mass movements from a remote location unaffected by the process. The system is based on one infrasound sensor and one geophone which are placed co-located and a microcontroller where a specially designed detection algorithm is executed which can detect mass movements in real time directly at the sensor site. Further this work tries to get out more information from the seismic and infrasound spectrum produced by different sediment related mass movements to identify the process type and estimate the magnitude of the event. The system is currently installed and tested on five test sites in Austria, two in Italy and one in Switzerland as well as one in Germany. This high number of test sites is used to get a large database of very different events which will be the basis for a new identification method for alpine mass movements. These tests shows promising results and so this system provides an easy to install and inexpensive approach for a detection and warning system.

Optical signal processing

NASA Astrophysics Data System (ADS)

Vanderlugt, A.

1993-07-01

A quasi-realtime adaptive processing system was used to correct the multipath distortion found in wideband digital radios. The measured power spectral density of the input signal was used to adaptively select one of eight equalization filters which reduce the residual distortion to less than 3.6 dB even for the most severe channel distortion. A related adaptive system was used for signal excision in which we removed narrowband interference from wideband signals with minimum signal distortion. An 8x8 acousto-optic switch in a multimode fiber-optic system was built. Insertion loss is approximately 2-4 dB, signal-to-crosstalk ratio is better than 25 dB, and the reconfiguration time is 880 nsec. Short pulses were detected by using the Fresnel transform. Pulses as short as the theoretical limit of 20 nanoseconds were detected for this system, and separated by as little as 60 nanoseconds or by as much as 17 nanoseconds. All possible acousto-optic scanning configurations were considered and classified into four basic types. A consistent set of design relationships for each of the scanning configurations was developed and presented in both tabular and graphic forms from which a preliminary design is obtained.

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.

Neuromimetic Sound Representation for Percept Detection and Manipulation

NASA Astrophysics Data System (ADS)

Zotkin, Dmitry N.; Chi, Taishih; Shamma, Shihab A.; Duraiswami, Ramani

2005-12-01

The acoustic wave received at the ears is processed by the human auditory system to separate different sounds along the intensity, pitch, and timbre dimensions. Conventional Fourier-based signal processing, while endowed with fast algorithms, is unable to easily represent a signal along these attributes. In this paper, we discuss the creation of maximally separable sounds in auditory user interfaces and use a recently proposed cortical sound representation, which performs a biomimetic decomposition of an acoustic signal, to represent and manipulate sound for this purpose. We briefly overview algorithms for obtaining, manipulating, and inverting a cortical representation of a sound and describe algorithms for manipulating signal pitch and timbre separately. The algorithms are also used to create sound of an instrument between a "guitar" and a "trumpet." Excellent sound quality can be achieved if processing time is not a concern, and intelligible signals can be reconstructed in reasonable processing time (about ten seconds of computational time for a one-second signal sampled at [InlineEquation not available: see fulltext.]). Work on bringing the algorithms into the real-time processing domain is ongoing.

Digital signal processing techniques for coherent optical communication

NASA Astrophysics Data System (ADS)

Goldfarb, Gilad

Coherent detection with subsequent digital signal processing (DSP) is developed, analyzed theoretically and numerically and experimentally demonstrated in various fiber-optic transmission scenarios. The use of DSP in conjunction with coherent detection unleashes the benefits of coherent detection which rely on the preservaton of full information of the incoming field. These benefits include high receiver sensitivity, the ability to achieve high spectral-efficiency and the use of advanced modulation formats. With the immense advancements in DSP speeds, many of the problems hindering the use of coherent detection in optical transmission systems have been eliminated. Most notably, DSP alleviates the need for hardware phase-locking and polarization tracking, which can now be achieved in the digital domain. The complexity previously associated with coherent detection is hence significantly diminished and coherent detection is once gain considered a feasible detection alternative. In this thesis, several aspects of coherent detection (with or without subsequent DSP) are addressed. Coherent detection is presented as a means to extend the dispersion limit of a duobinary signal using an analog decision-directed phase-lock loop. Analytical bit-error ratio estimation for quadrature phase-shift keying signals is derived. To validate the promise for high spectral efficiency, the orthogonal-wavelength-division multiplexing scheme is suggested. In this scheme the WDM channels are spaced at the symbol rate, thus achieving the spectral efficiency limit. Theory, simulation and experimental results demonstrate the feasibility of this approach. Infinite impulse response filtering is shown to be an efficient alternative to finite impulse response filtering for chromatic dispersion compensation. Theory, design considerations, simulation and experimental results relating to this topic are presented. Interaction between fiber dispersion and nonlinearity remains the last major challenge deterministic effects pose for long-haul optical data transmission. Experimental results which demonstrate the possibility to digitally mitigate both dispersion and nonlinearity are presented. Impairment compensation is achieved using backward propagation by implementing the split-step method. Efficient realizations of the dispersion compensation operator used in this implementation are considered. Infinite-impulse response and wavelet-based filtering are both investigated as a means to reduce the required computational load associated with signal backward-propagation. Possible future research directions conclude this dissertation.

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

PubMed Central

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin MD.; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md. Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis. PMID:25093211

Role of EEG as biomarker in the early detection and classification of dementia.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin Md; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials.

PubMed

Potas, Jason Robert; de Castro, Newton Gonçalves; Maddess, Ted; de Souza, Marcio Nogueira

2015-01-01

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.

Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials

PubMed Central

Potas, Jason Robert; de Castro, Newton Gonçalves; Maddess, Ted; de Souza, Marcio Nogueira

2015-01-01

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies. PMID:26325291

Cosmic non-TEM radiation and synthetic feed array sensor system in ASIC mixed signal technology

NASA Astrophysics Data System (ADS)

Centureli, F.; Scotti, G.; Tommasino, P.; Trifiletti, A.; Romano, F.; Cimmino, R.; Saitto, A.

2014-08-01

The paper deals with the opportunity to introduce "Not strictly TEM waves" Synthetic detection Method (NTSM), consisting in a Three Axis Digital Beam Processing (3ADBP), to enhance the performances of radio telescope and sensor systems. Current Radio Telescopes generally use the classic 3D "TEM waves" approximation Detection Method, which consists in a linear tomography process (Single or Dual axis beam forming processing) neglecting the small z component. The Synthetic FEED ARRAY three axis Sensor SYSTEM is an innovative technique using a synthetic detection of the generic "NOT strictly TEM Waves radiation coming from the Cosmo, which processes longitudinal component of Angular Momentum too. Than the simultaneous extraction from radiation of both the linear and quadratic information component, may reduce the complexity to reconstruct the Early Universe in the different requested scales. This next order approximation detection of the observed cosmologic processes, may improve the efficacy of the statistical numerical model used to elaborate the same information acquired. The present work focuses on detection of such waves at carrier frequencies in the bands ranging from LF to MMW. The work shows in further detail the new generation of on line programmable and reconfigurable Mixed Signal ASIC technology that made possible the innovative Synthetic Sensor. Furthermore the paper shows the ability of such technique to increase the Radio Telescope Array Antenna performances.

Detection and classification of concealed weapons using a magnetometer-based portal

NASA Astrophysics Data System (ADS)

Kotter, Dale K.; Roybal, Lyle G.; Polk, Robert E.

2002-08-01

A concealed weapons detection technology was developed through the support of the National Institute of Justice (NIJ) to provide a non intrusive means for rapid detection, location, and archiving of data (including visual) of potential suspects and weapon threats. This technology, developed by the Idaho National Engineering and Environmental Laboratory (INEEL), has been applied in a portal style weapons detection system using passive magnetic sensors as its basis. This paper will report on enhancements to the weapon detection system to enable weapon classification and to discriminate threats from non-threats. Advanced signal processing algorithms were used to analyze the magnetic spectrum generated when a person passes through a portal. These algorithms analyzed multiple variables including variance in the magnetic signature from random weapon placement and/or orientation. They perform pattern recognition and calculate the probability that the collected magnetic signature correlates to a known database of weapon versus non-weapon responses. Neural networks were used to further discriminate weapon type and identify controlled electronic items such as cell phones and pagers. False alarms were further reduced by analyzing the magnetic detector response by using a Joint Time Frequency Analysis digital signal processing technique. The frequency components and power spectrum for a given sensor response were derived. This unique fingerprint provided additional information to aid in signal analysis. This technology has the potential to produce major improvements in weapon detection and classification.

Nonlinear ultrasonic wave modulation for online fatigue crack detection

NASA Astrophysics Data System (ADS)

Sohn, Hoon; Lim, Hyung Jin; DeSimio, Martin P.; Brown, Kevin; Derriso, Mark

2014-02-01

This study presents a fatigue crack detection technique using nonlinear ultrasonic wave modulation. Ultrasonic waves at two distinctive driving frequencies are generated and corresponding ultrasonic responses are measured using permanently installed lead zirconate titanate (PZT) transducers with a potential for continuous monitoring. Here, the input signal at the lower driving frequency is often referred to as a 'pumping' signal, and the higher frequency input is referred to as a 'probing' signal. The presence of a system nonlinearity, such as a crack formation, can provide a mechanism for nonlinear wave modulation, and create spectral sidebands around the frequency of the probing signal. A signal processing technique combining linear response subtraction (LRS) and synchronous demodulation (SD) is developed specifically to extract the crack-induced spectral sidebands. The proposed crack detection method is successfully applied to identify actual fatigue cracks grown in metallic plate and complex fitting-lug specimens. Finally, the effect of pumping and probing frequencies on the amplitude of the first spectral sideband is investigated using the first sideband spectrogram (FSS) obtained by sweeping both pumping and probing signals over specified frequency ranges.

Lightning Detection Efficiency Analysis Process: Modeling Based on Empirical Data

NASA Technical Reports Server (NTRS)

Rompala, John T.

2005-01-01

A ground based lightning detection system employs a grid of sensors, which record and evaluate the electromagnetic signal produced by a lightning strike. Several detectors gather information on that signal s strength, time of arrival, and behavior over time. By coordinating the information from several detectors, an event solution can be generated. That solution includes the signal s point of origin, strength and polarity. Determination of the location of the lightning strike uses algorithms based on long used techniques of triangulation. Determination of the event s original signal strength relies on the behavior of the generated magnetic field over distance and time. In general the signal from the event undergoes geometric dispersion and environmental attenuation as it progresses. Our knowledge of that radial behavior together with the strength of the signal received by detecting sites permits an extrapolation and evaluation of the original strength of the lightning strike. It also limits the detection efficiency (DE) of the network. For expansive grids and with a sparse density of detectors, the DE varies widely over the area served. This limits the utility of the network in gathering information on regional lightning strike density and applying it to meteorological studies. A network of this type is a grid of four detectors in the Rondonian region of Brazil. The service area extends over a million square kilometers. Much of that area is covered by rain forests. Thus knowledge of lightning strike characteristics over the expanse is of particular value. I have been developing a process that determines the DE over the region [3]. In turn, this provides a way to produce lightning strike density maps, corrected for DE, over the entire region of interest. This report offers a survey of that development to date and a record of present activity.

Demodulation signal processing in multiphoton imaging

NASA Astrophysics Data System (ADS)

Fisher, Walter G.; Wachter, Eric A.; Piston, David W.

2002-06-01

Multiphoton laser scanning microscopy offers numerous advantages, but sensitivity can be seriously affected by contamination from ambient room light. Typically, this forces experiments to be performed in an absolutely dark room. Since mode-locked lasers are used to generate detectable signals, signal-processing can be used to avoid such problems by taking advantage of the pulsed characteristics of such lasers. Demodulation of the fluorescence signal generated at the mode-locked frequency can result in significant reduction of interference from ambient noise sources. Such demodulation can be readily adapted to existing microscopes by inserting appropriate processor circuitry between the detector and data collection system, yielding a more robust microscope.

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

[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.

Unfolding and unfoldability of digital pulses in the z-domain

NASA Astrophysics Data System (ADS)

Regadío, Alberto; Sánchez-Prieto, Sebastián

2018-04-01

The unfolding (or deconvolution) technique is used in the development of digital pulse processing systems applied to particle detection. This technique is applied to digital signals obtained by digitization of analog signals that represent the combined response of the particle detectors and the associated signal conditioning electronics. This work describes a technique to determine if the signal is unfoldable. For unfoldable signals the characteristics of the unfolding system (unfolder) are presented. Finally, examples of the method applied to real experimental setup are discussed.

Experimental photonic generation of chirped pulses using nonlinear dispersion-based incoherent processing.

PubMed

Rius, Manuel; Bolea, Mario; Mora, José; Ortega, Beatriz; Capmany, José

2015-05-18

We experimentally demonstrate, for the first time, a chirped microwave pulses generator based on the processing of an incoherent optical signal by means of a nonlinear dispersive element. Different capabilities have been demonstrated such as the control of the time-bandwidth product and the frequency tuning increasing the flexibility of the generated waveform compared to coherent techniques. Moreover, the use of differential detection improves considerably the limitation over the signal-to-noise ratio related to incoherent processing.

Processing device with self-scrubbing logic

DOEpatents

Wojahn, Christopher K.

2016-03-01

An apparatus includes a processing unit including a configuration memory and self-scrubber logic coupled to read the configuration memory to detect compromised data stored in the configuration memory. The apparatus also includes a watchdog unit external to the processing unit and coupled to the self-scrubber logic to detect a failure in the self-scrubber logic. The watchdog unit is coupled to the processing unit to selectively reset the processing unit in response to detecting the failure in the self-scrubber logic. The apparatus also includes an external memory external to the processing unit and coupled to send configuration data to the configuration memory in response to a data feed signal outputted by the self-scrubber logic.

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.

Effects of atmospheric variations on acoustic system performance

NASA Technical Reports Server (NTRS)

Nation, Robert; Lang, Stephen; Olsen, Robert; Chintawongvanich, Prasan

1993-01-01

Acoustic propagation over medium to long ranges in the atmosphere is subject to many complex, interacting effects. Of particular interest at this point is modeling low frequency (less than 500 Hz) propagation for the purpose of predicting ranges and bearing accuracies at which acoustic sources can be detected. A simple means of estimating how much of the received signal power propagated directly from the source to the receiver and how much was received by turbulent scattering was developed. The correlations between the propagation mechanism and detection thresholds, beamformer bearing estimation accuracies, and beamformer processing gain of passive acoustic signal detection systems were explored.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Low Noise Infrasonic Sensor System with High Reduction of Natural Background Noise

DTIC Science & Technology

2006-05-01

local processing allows a variety of options both in the array geometry and signal processing. A generic geometry is indicated in Figure 2. Geometric...higher frequency sound detected . Table 1 provides a comparison of piezocable and microbarograph based arrays . Piezocable Sensor Local Signal ...aliasing associated with the current infrasound sensors used at large spacing in the present designs of infrasound monitoring arrays , particularly in the

Comparative of signal processing techniques for micro-Doppler signature extraction with automotive radar systems

NASA Astrophysics Data System (ADS)

Rodriguez-Hervas, Berta; Maile, Michael; Flores, Benjamin C.

2014-05-01

In recent years, the automotive industry has experienced an evolution toward more powerful driver assistance systems that provide enhanced vehicle safety. These systems typically operate in the optical and microwave regions of the electromagnetic spectrum and have demonstrated high efficiency in collision and risk avoidance. Microwave radar systems are particularly relevant due to their operational robustness under adverse weather or illumination conditions. Our objective is to study different signal processing techniques suitable for extraction of accurate micro-Doppler signatures of slow moving objects in dense urban environments. Selection of the appropriate signal processing technique is crucial for the extraction of accurate micro-Doppler signatures that will lead to better results in a radar classifier system. For this purpose, we perform simulations of typical radar detection responses in common driving situations and conduct the analysis with several signal processing algorithms, including short time Fourier Transform, continuous wavelet or Kernel based analysis methods. We take into account factors such as the relative movement between the host vehicle and the target, and the non-stationary nature of the target's movement. A comparison of results reveals that short time Fourier Transform would be the best approach for detection and tracking purposes, while the continuous wavelet would be the best suited for classification purposes.

Fiber Bragg grating sensor for fault detection in high voltage overhead transmission lines

NASA Astrophysics Data System (ADS)

Moghadas, Amin

2011-12-01

A fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by fiber Bragg grating (FBG) sensors. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signals. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG sensors and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

PubMed Central

Moghadas, Amin A.; Shadaram, Mehdi

2010-01-01

In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system. PMID:22163416

Industrial Control System Process-Oriented Intrusion Detection (iPoid) Algorithm

DTIC Science & Technology

2016-08-01

inspection rules using an intrusion-detection system (IDS) sensor, a simulated Programmable Logic Controller (PLC), and a Modbus client operating...operating system PLC Programmable Logic Controller SCADA supervisory control and data acquisition SIGHUP signal hangup SPAN Switched Port Analyzer

Cross-modal detection using various temporal and spatial configurations.

PubMed

Schirillo, James A

2011-01-01

To better understand temporal and spatial cross-modal interactions, two signal detection experiments were conducted in which an auditory target was sometimes accompanied by an irrelevant flash of light. In the first, a psychometric function for detecting a unisensory auditory target in varying signal-to-noise ratios (SNRs) was derived. Then auditory target detection was measured while an irrelevant light was presented with light/sound stimulus onset asynchronies (SOAs) between 0 and ±700 ms. When the light preceded the sound by 100 ms or was coincident, target detection (d') improved for low SNR conditions. In contrast, for larger SOAs (350 and 700 ms), the behavioral gain resulted from a change in both d' and response criterion (β). However, when the light followed the sound, performance changed little. In the second experiment, observers detected multimodal target sounds at eccentricities of ±8°, and ±24°. Sensitivity benefits occurred at both locations, with a larger change at the more peripheral location. Thus, both temporal and spatial factors affect signal detection measures, effectively parsing sensory and decision-making processes.

Exploring methods of cGPS transient detections for the Chilean cGPS network in conjunction with displacement predictions from seismic catalogues: To what extent can we detect seismic and aseismic motion in the cGPS network?

NASA Astrophysics Data System (ADS)

Bedford, J. R.; Moreno, M.; Oncken, O.; Li, S.; Schurr, B.; Metzger, S.; Baez, J. C.; Deng, Z.; Melnick, D.

2016-12-01

Various algorithms for the detection of transient deformation in cGPS networks are under currently being developed to relieve us of by-eye detection, which is an error prone and time-expensive activity. Such algorithms aim to separate the time series into secular, seasonal, and transient components. Additional white and coloured noise, as well as common-mode (network correlated) noise, may remain in the separated transient component of the signal, depending on the processing flow before the separation step. The a-priori knowledge of regional seismicity can assist in the recognition of steps in the data, which are generally corrected for if they are above the noise-floor. Sometimes, the cumulative displacement caused by small earthquakes can create a seemingly continuous transient signal in the cGPS leading to confusion as to whether to attribute this transient motion as seismic or aseismic. Here we demonstrate the efficacy of various transient detection algorithms for subsets of the Chilean cGPS network and present the optimal processing flow for teasing out the transients. We present a step-detection and removal algorithm and estimate the seismic efficiency of any detected transient signals by forward modelling the surface displacements of the earthquakes and comparing to the recovered transient signals. A major challenge in separating signals in the Chilean cGPS network is the overlapping of postseismic effects at adjacent segments: For example, a Mw 9 earthquake will produce a postseismic viscoelastic relaxation that is sustained over decades and several hundreds of kilometres. Additionally, it has been observed in Chile and Japan that following moderately large earthquakes (e.g. Mw > 8) the secular velocities of adjacent segments in the subduction margin suddenly change and remain changed: this effect may be related to a change in speed of slab subduction rather than viscoelastic relaxation, and therefore the signal separation algorithms that assume a time-independent secular velocity at each station may need to be revised to account for this effect. Accordingly, we categorize the recovered separated secular and transient signals of a particular station in terms of the seismic cycle in both its own and adjacent segments and discuss the appropriate modelling strategy for this station given its category.

Separating Decision and Encoding Noise in Signal Detection Tasks

PubMed Central

Cabrera, Carlos Alexander; Lu, Zhong-Lin; Dosher, Barbara Anne

2015-01-01

In this paper we develop an extension to the Signal Detection Theory (SDT) framework to separately estimate internal noise arising from representational and decision processes. Our approach constrains SDT models with decision noise by combining a multi-pass external noise paradigm with confidence rating responses. In a simulation study we present evidence that representation and decision noise can be separately estimated over a range of representative underlying representational and decision noise level configurations. These results also hold across a number of decision rules and show resilience to rule miss-specification. The new theoretical framework is applied to a visual detection confidence-rating task with three and five response categories. This study compliments and extends the recent efforts of researchers (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008; Rosner & Kochanski, 2009, Kellen, Klauer, & Singmann, 2012) to separate and quantify underlying sources of response variability in signal detection tasks. PMID:26120907

Costas loop lock detection in the advanced receiver

NASA Technical Reports Server (NTRS)

Mileant, A.; Hinedi, S.

1989-01-01

The advanced receiver currently being developed uses a Costas digital loop to demodulate the subcarrier. Previous analyses of lock detector algorithms for Costas loops have ignored the effects of the inherent correlation between the samples of the phase-error process. Accounting for this correlation is necessary to achieve the desired lock-detection probability for a given false-alarm rate. Both analysis and simulations are used to quantify the effects of phase correlation on lock detection for the square-law and the absolute-value type detectors. Results are obtained which depict the lock-detection probability as a function of loop signal-to-noise ratio for a given false-alarm rate. The mathematical model and computer simulation show that the square-law detector experiences less degradation due to phase jitter than the absolute-value detector and that the degradation in detector signal-to-noise ratio is more pronounced for square-wave than for sine-wave signals.

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.

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.

Signal processing in SETI.

PubMed

Cullers, D K; Linscott, I R; Oliver, B M

1985-11-01

The development of a multi-channel spectrum analyzer (MCSA) for the SETI program is described. The spectrum analyzer is designed for both all-sky surveys and targeted searches. The mechanisms of the MCSA are explained and a diagram is provided. Detection of continuous wave signals, pulses, and patterns is examined.

Multibeam Gpu Transient Pipeline for the Medicina BEST-2 Array

NASA Astrophysics Data System (ADS)

Magro, A.; Hickish, J.; Adami, K. Z.

2013-09-01

Radio transient discovery using next generation radio telescopes will pose several digital signal processing and data transfer challenges, requiring specialized high-performance backends. Several accelerator technologies are being considered as prototyping platforms, including Graphics Processing Units (GPUs). In this paper we present a real-time pipeline prototype capable of processing multiple beams concurrently, performing Radio Frequency Interference (RFI) rejection through thresholding, correcting for the delay in signal arrival times across the frequency band using brute-force dedispersion, event detection and clustering, and finally candidate filtering, with the capability of persisting data buffers containing interesting signals to disk. This setup was deployed at the BEST-2 SKA pathfinder in Medicina, Italy, where several benchmarks and test observations of astrophysical transients were conducted. These tests show that on the deployed hardware eight 20 MHz beams can be processed simultaneously for 640 Dispersion Measure (DM) values. Furthermore, the clustering and candidate filtering algorithms employed prove to be good candidates for online event detection techniques. The number of beams which can be processed increases proportionally to the number of servers deployed and number of GPUs, making it a viable architecture for current and future radio telescopes.

Visible spectrum-based non-contact HRV and dPTT for stress detection

NASA Astrophysics Data System (ADS)

Kaur, Balvinder; Hutchinson, J. Andrew; Ikonomidou, Vasiliki N.

2017-05-01

Stress is a major health concern that not only compromises our quality of life, but also affects our physical health and well-being. Despite its importance, our ability to objectively detect and quantify it in a real-time, non-invasive manner is very limited. This capability would have a wide variety of medical, military, and security applications. We have developed a pipeline of image and signal processing algorithms to make such a system practical, which includes remote cardiac pulse detection based on visible spectrum videos and physiological stress detection based on the variability in the remotely detected cardiac signals. First, to determine a reliable cardiac pulse, principal component analysis (PCA) was applied for noise reduction and independent component analysis (ICA) was applied for source selection. To determine accurate cardiac timing for heart rate variability (HRV) analysis, a blind source separation method based least squares (LS) estimate was used to determine signal peaks that were closely related to R-peaks of the electrocardiogram (ECG) signal. A new metric, differential pulse transit time (dPTT), defined as the difference in arrival time of the remotely acquired cardiac signal at two separate distal locations, was derived. It was demonstrated that the remotely acquired metrics, HRV and dPTT, have potential for remote stress detection. The developed algorithms were tested against human subject data collected under two physiological conditions using the modified Trier Social Stress Test (TSST) and the Affective Stress Response Test (ASRT). This research provides evidence that the variability in remotely-acquired blood wave (BW) signals can be used for stress (high and mild) detection, and as a guide for further development of a real-time remote stress detection system based on remote HRV and dPTT.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

PubMed

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-09-16

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

PubMed Central

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-01-01

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. PMID:28926953

Production of high-fidelity electropherograms results in improved and consistent DNA interpretation: Standardizing the forensic validation process.

PubMed

Peters, Kelsey C; Swaminathan, Harish; Sheehan, Jennifer; Duffy, Ken R; Lun, Desmond S; Grgicak, Catherine M

2017-11-01

Samples containing low-copy numbers of DNA are routinely encountered in casework. The signal acquired from these sample types can be difficult to interpret as they do not always contain all of the genotypic information from each contributor, where the loss of genetic information is associated with sampling and detection effects. The present work focuses on developing a validation scheme to aid in mitigating the effects of the latter. We establish a scheme designed to simultaneously improve signal resolution and detection rates without costly large-scale experimental validation studies by applying a combined simulation and experimental based approach. Specifically, we parameterize an in silico DNA pipeline with experimental data acquired from the laboratory and use this to evaluate multifarious scenarios in a cost-effective manner. Metrics such as signal 1copy -to-noise resolution, false positive and false negative signal detection rates are used to select tenable laboratory parameters that result in high-fidelity signal in the single-copy regime. We demonstrate that the metrics acquired from simulation are consistent with experimental data obtained from two capillary electrophoresis platforms and various injection parameters. Once good resolution is obtained, analytical thresholds can be determined using detection error tradeoff analysis, if necessary. Decreasing the limit of detection of the forensic process to one copy of DNA is a powerful mechanism by which to increase the information content on minor components of a mixture, which is particularly important for probabilistic system inference. If the forensic pipeline is engineered such that high-fidelity electropherogram signal is obtained, then the likelihood ratio (LR) of a true contributor increases and the probability that the LR of a randomly chosen person is greater than one decreases. This is, potentially, the first step towards standardization of the analytical pipeline across operational laboratories. Copyright © 2017 Elsevier B.V. All rights reserved.

Signal processing in local neuronal circuits based on activity-dependent noise and competition

NASA Astrophysics Data System (ADS)

Volman, Vladislav; Levine, Herbert

2009-09-01

We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For nonperiodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity) and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease in input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit.

siGnum: graphical user interface for EMG signal analysis.

PubMed

Kaur, Manvinder; Mathur, Shilpi; Bhatia, Dinesh; Verma, Suresh

2015-01-01

Electromyography (EMG) signals that represent the electrical activity of muscles can be used for various clinical and biomedical applications. These are complicated and highly varying signals that are dependent on anatomical location and physiological properties of the muscles. EMG signals acquired from the muscles require advanced methods for detection, decomposition and processing. This paper proposes a novel Graphical User Interface (GUI) siGnum developed in MATLAB that will apply efficient and effective techniques on processing of the raw EMG signals and decompose it in a simpler manner. It could be used independent of MATLAB software by employing a deploy tool. This would enable researcher's to gain good understanding of EMG signal and its analysis procedures that can be utilized for more powerful, flexible and efficient applications in near future.

Generation and transmission of DPSK signals using a directly modulated passive feedback laser.

PubMed

Karar, Abdullah S; Gao, Ying; Zhong, Kang Ping; Ke, Jian Hong; Cartledge, John C

2012-12-10

The generation of differential-phase-shift keying (DPSK) signals is demonstrated using a directly modulated passive feedback laser at 10.709-Gb/s, 14-Gb/s and 16-Gb/s. The quality of the DPSK signals is assessed using both noncoherent detection for a bit rate of 10.709-Gb/s and coherent detection with digital signal processing involving a look-up table pattern-dependent distortion compensator. Transmission over a passive link consisting of 100 km of single mode fiber at a bit rate of 10.709-Gb/s is achieved with a received optical power of -45 dBm at a bit-error-ratio of 3.8 × 10(-3) and a 49 dB loss margin.

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.

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration.

PubMed

Zhao, Zi-Fang; Li, Xue-Zhu; Wan, You

2017-12-01

The local field potential (LFP) is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood (SL) is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit (GPU)-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes, delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals (like EEG and fMRI) using similar recording techniques.

Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task

PubMed Central

Motes, Michael A; Rao, Neena K; Shokri-Kojori, Ehsan; Chiang, Hsueh-Sheng; Kraut, Michael A; Hart, John

2017-01-01

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go/no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time–based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time–based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations. PMID:29276390

Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task.

PubMed

Motes, Michael A; Rao, Neena K; Shokri-Kojori, Ehsan; Chiang, Hsueh-Sheng; Kraut, Michael A; Hart, John

2017-01-01

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go / no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time-based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time-based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations.

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.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

PubMed

Georgoulas, George; Georgopoulos, Voula C; Stylios, Chrysostomos D

2006-01-01

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

ON THE RADIO DETECTION OF MULTIPLE-EXOMOON SYSTEMS DUE TO PLASMA TORUS SHARING

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

Noyola, J. P.; Satyal, S.; Musielak, Z. E., E-mail: jpnoyola@uta.edu, E-mail: ssatyal@uta.edu, E-mail: zmusielak@uta.edu

2016-04-20

The idea of single exomoon detection due to the radio emissions caused by its interaction with the host exoplanet is extended to multiple-exomoon systems. The characteristic radio emissions are made possible in part by plasma from the exomoon’s own ionosphere. In this work, it is demonstrated that neighboring exomoons and the exoplanetary magnetosphere could also provide enough plasma to generate a detectable signal. In particular, the plasma-torus-sharing phenomenon is found to be particularly well suited to facilitate the radio detection of plasma-deficient exomoons. The efficiency of this process is evaluated, and the predicted power and frequency of the resulting radiomore » signals are presented.« less

A digital boxcar integrator for IMS spectra

NASA Technical Reports Server (NTRS)

Cohen, Martin J.; Stimac, Robert M.; Wernlund, Roger F.; Parker, Donald C.

1995-01-01

When trying to detect or quantify a signal at or near the limit of detectability, it is invariably embeded in the noise. This statement is true for nearly all detectors of any physical phenomena and the limit of detectability, hopefully, occurs at very low signal-to-noise levels. This is particularly true of IMS (Ion Mobility Spectrometers) spectra due to the low vapor pressure of several chemical compounds of great interest and the small currents associated with the ionic detection process. Gated Integrators and Boxcar Integrators or Averagers are designed to recover fast, repetitive analog signals. In a typical application, a time 'Gate' or 'Window' is generated, characterized by a set delay from a trigger or gate pulse and a certain width. A Gated Integrator amplifies and integrates the signal that is present during the time the gate is open, ignoring noise and interference that may be present at other times. Boxcar Integration refers to the practice of averaging the output of the Gated Integrator over many sweeps of the detector. Since any signal present during the gate will add linearly, while noise will add in a 'random walk' fashion as the square root of the number of sweeps, averaging N sweeps will improve the 'Signal-to-Noise Ratio' by a factor of the square root of N.

Species-specific beaked whale echolocation signals.

PubMed

Baumann-Pickering, Simone; McDonald, Mark A; Simonis, Anne E; Solsona Berga, Alba; Merkens, Karlina P B; Oleson, Erin M; Roch, Marie A; Wiggins, Sean M; Rankin, Shannon; Yack, Tina M; Hildebrand, John A

2013-09-01

Beaked whale echolocation signals are mostly frequency-modulated (FM) upsweep pulses and appear to be species specific. Evolutionary processes of niche separation may have driven differentiation of beaked whale signals used for spatial orientation and foraging. FM pulses of eight species of beaked whales were identified, as well as five distinct pulse types of unknown species, but presumed to be from beaked whales. Current evidence suggests these five distinct but unidentified FM pulse types are also species-specific and are each produced by a separate species. There may be a relationship between adult body length and center frequency with smaller whales producing higher frequency signals. This could be due to anatomical and physiological restraints or it could be an evolutionary adaption for detection of smaller prey for smaller whales with higher resolution using higher frequencies. The disadvantage of higher frequencies is a shorter detection range. Whales echolocating with the highest frequencies, or broadband, likely lower source level signals also use a higher repetition rate, which might compensate for the shorter detection range. Habitat modeling with acoustic detections should give further insights into how niches and prey may have shaped species-specific FM pulse types.

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.

Comparison of multichannel wide dynamic range compression and ChannelFree processing in open canal hearing instruments.

PubMed

Plyler, Patrick N; Reber, Monika Bertges; Kovach, Amanda; Galloway, Elisabeth; Humphrey, Elizabeth

2013-02-01

Multichannel wide dynamic range compression (WDRC) and ChannelFree processing have similar goals yet differ significantly in terms of signal processing. Multichannel WDRC devices divide the input signal into separate frequency bands; a separate level is determined within each frequency band; and compression in each band is based on the level within each band. ChannelFree processing detects the wideband level, and gain adjustments are based on the wideband signal level and adjusted up to 20,000 times per second. Although both signal processing strategies are currently available in hearing aids, it is unclear if differences in these signal processing strategies affect the performance and/or preference of the end user. The purpose of the research was to determine the effects of multichannel wide dynamic range compression and ChannelFree processing on performance and/or preference of listeners using open-canal hearing instruments. An experimental study in which subjects were exposed to a repeated measures design was utilized. Fourteen adult listeners with mild sloping to moderately severe sensorineural hearing loss participated (mean age 67 yr). Participants completed two 5 wk trial periods for each signal processing strategy. Probe microphone, behavioral and subjective measures were conducted unaided and aided at the end of each trial period. Behavioral and subjective results for both signal processing strategies were significantly better than unaided results; however, behavioral and subjective results were not significantly different between the signal processing strategies. Multichannel WDRC and ChannelFree processing are both effective signal processing strategies that provide significant benefit for hearing instrument users. Overall preference between the strategies may be related to the degree of hearing loss of the user, high-frequency in-situ levels, and/or acceptance of background noise. American Academy of Audiology.

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.

System and method for investigating sub-surface features and 3D imaging of non-linear property, compressional velocity VP, shear velocity VS and velocity ratio VP/VS of a rock formation

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

Vu, Cung Khac; Skelt, Christopher; Nihei, Kurt

A system and a method for generating a three-dimensional image of a rock formation, compressional velocity VP, shear velocity VS and velocity ratio VP/VS of a rock formation are provided. A first acoustic signal includes a first plurality of pulses. A second acoustic signal from a second source includes a second plurality of pulses. A detected signal returning to the borehole includes a signal generated by a non-linear mixing process from the first and second acoustic signals in a non-linear mixing zone within an intersection volume. The received signal is processed to extract the signal over noise and/or signals resultingmore » from linear interaction and the three dimensional image of is generated.« less

Advances in optical information processing IV; Proceedings of the Meeting, Orlando, FL, Apr. 18-20, 1990

NASA Astrophysics Data System (ADS)

Pape, Dennis R.

1990-09-01

The present conference discusses topics in optical image processing, optical signal processing, acoustooptic spectrum analyzer systems and components, and optical computing. Attention is given to tradeoffs in nonlinearly recorded matched filters, miniature spatial light modulators, detection and classification using higher-order statistics of optical matched filters, rapid traversal of an image data base using binary synthetic discriminant filters, wideband signal processing for emitter location, an acoustooptic processor for autonomous SAR guidance, and sampling of Fresnel transforms. Also discussed are an acoustooptic RF signal-acquisition system, scanning acoustooptic spectrum analyzers, the effects of aberrations on acoustooptic systems, fast optical digital arithmetic processors, information utilization in analog and digital processing, optical processors for smart structures, and a self-organizing neural network for unsupervised learning.

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.

Application of higher order SVD to vibration-based system identification and damage detection

NASA Astrophysics Data System (ADS)

Chao, Shu-Hsien; Loh, Chin-Hsiung; Weng, Jian-Huang

2012-04-01

Singular value decomposition (SVD) is a powerful linear algebra tool. It is widely used in many different signal processing methods, such principal component analysis (PCA), singular spectrum analysis (SSA), frequency domain decomposition (FDD), subspace identification and stochastic subspace identification method ( SI and SSI ). In each case, the data is arranged appropriately in matrix form and SVD is used to extract the feature of the data set. In this study three different algorithms on signal processing and system identification are proposed: SSA, SSI-COV and SSI-DATA. Based on the extracted subspace and null-space from SVD of data matrix, damage detection algorithms can be developed. The proposed algorithm is used to process the shaking table test data of the 6-story steel frame. Features contained in the vibration data are extracted by the proposed method. Damage detection can then be investigated from the test data of the frame structure through subspace-based and nullspace-based damage indices.

Realtime processing of LOFAR data for the detection of nano-second pulses from the Moon

NASA Astrophysics Data System (ADS)

Winchen, T.; Bonardi, A.; Buitink, S.; Corstanje, A.; Enriquez, J. E.; Falcke, H.; Hörandel, J. R.; Mitra, P.; Mulrey, K.; Nelles, A.; Rachen, J. P.; Rossetto, L.; Schellart, P.; Scholten, O.; Thoudam, S.; Trinh, T. N. G.; ter Veen, S.; KSP, The LOFAR Cosmic Ray

2017-10-01

The low flux of the ultra-high energy cosmic rays (UHECR) at the highest energies provides a challenge to answer the long standing question about their origin and nature. Even lower fluxes of neutrinos with energies above 1022 eV are predicted in certain Grand-Unifying-Theories (GUTs) and e.g. models for super-heavy dark matter (SHDM). The significant increase in detector volume required to detect these particles can be achieved by searching for the nanosecond radio pulses that are emitted when a particle interacts in Earth’s moon with current and future radio telescopes. In this contribution we present the design of an online analysis and trigger pipeline for the detection of nano-second pulses with the LOFAR radio telescope. The most important steps of the processing pipeline are digital focusing of the antennas towards the Moon, correction of the signal for ionospheric dispersion, and synthesis of the time-domain signal from the polyphased-filtered signal in frequency domain. The implementation of the pipeline on a GPU/CPU cluster will be discussed together with the computing performance of the prototype.

Eyewitness decisions in simultaneous and sequential lineups: a dual-process signal detection theory analysis.

PubMed

Meissner, Christian A; Tredoux, Colin G; Parker, Janat F; MacLin, Otto H

2005-07-01

Many eyewitness researchers have argued for the application of a sequential alternative to the traditional simultaneous lineup, given its role in decreasing false identifications of innocent suspects (sequential superiority effect). However, Ebbesen and Flowe (2002) have recently noted that sequential lineups may merely bring about a shift in response criterion, having no effect on discrimination accuracy. We explored this claim, using a method that allows signal detection theory measures to be collected from eyewitnesses. In three experiments, lineup type was factorially combined with conditions expected to influence response criterion and/or discrimination accuracy. Results were consistent with signal detection theory predictions, including that of a conservative criterion shift with the sequential presentation of lineups. In a fourth experiment, we explored the phenomenological basis for the criterion shift, using the remember-know-guess procedure. In accord with previous research, the criterion shift in sequential lineups was associated with a reduction in familiarity-based responding. It is proposed that the relative similarity between lineup members may create a context in which fluency-based processing is facilitated to a greater extent when lineup members are presented simultaneously.

FAK and BMP-9 synergistically trigger osteogenic differentiation and bone formation of adipose derived stem cells through enhancing Wnt-β-catenin signaling.

PubMed

Yuan, Cheng; Gou, Xiaoli; Deng, Jiang; Dong, Zhijun; Ye, Peng; Hu, Zhenming

2018-06-14

Adipose derived stem cells (ADSCs) could undergo osteogenesis via focal adhesion kinase (FAK) and bone morphogenetic protein (BMP) 9 signals, both of which could affect Wnt-β-catenin signal, a signal pathway closely related to ADSCs osteogenesis. It's still enigma whether FAK and BMP-9 contribute to osteogenesis. Here, we examined the effect of FAK on BMP9-inducedosteogenic differentiation, unveiled the possible molecular mechanism underling this process. In the present study, ADSCs were isolated and purified, and cells of passage 3 underwent virus mediated transfection to prepare ADSCs with stable FAK shRNA expression. Cell viability and migration were detected by MTT and transwell assay, respectively. Expression of osteogenic gene, phosphorylation of FAK and GSK were detected by western blot. Osteogenic potential was evaluated by activity of alkaline phosphatase (ALP) and calcium deposition by ALP staining and Alizarin Red S staining. BMP-9 administration promoted ADSCs osteogenesis. Knocking down FAK attenuated this process, inhibited osteogenic proteins expression through Wnt-β-catenin signal. BMP-9 also triggered ADSCs proliferation and migration, and shFAK antagonized such effects too. Although Wnt signal is affected by FAK shRNA, Smad signal remains intact in ADSCs with shFAK. FAK and BMP-9 could cross talk on Wnt signal pathway and promote ADSCs osteogenesis. FAK could participate in BMP-9 induced ADSCs osteogenesis via Wnt signal pathway other than Smads signals (see in graph). Copyright © 2018. Published by Elsevier Masson SAS.

Coherent-subspace array processing based on wavelet covariance: an application to broad-band, seismo-volcanic signals

NASA Astrophysics Data System (ADS)

Saccorotti, G.; Nisii, V.; Del Pezzo, E.

2008-07-01

Long-Period (LP) and Very-Long-Period (VLP) signals are the most characteristic seismic signature of volcano dynamics, and provide important information about the physical processes occurring in magmatic and hydrothermal systems. These events are usually characterized by sharp spectral peaks, which may span several frequency decades, by emergent onsets, and by a lack of clear S-wave arrivals. These two latter features make both signal detection and location a challenging task. In this paper, we propose a processing procedure based on Continuous Wavelet Transform of multichannel, broad-band data to simultaneously solve the signal detection and location problems. Our method consists of two steps. First, we apply a frequency-dependent threshold to the estimates of the array-averaged WCO in order to locate the time-frequency regions spanned by coherent arrivals. For these data, we then use the time-series of the complex wavelet coefficients for deriving the elements of the spatial Cross-Spectral Matrix. From the eigenstructure of this matrix, we eventually estimate the kinematic signals' parameters using the MUltiple SIgnal Characterization (MUSIC) algorithm. The whole procedure greatly facilitates the detection and location of weak, broad-band signals, in turn avoiding the time-frequency resolution trade-off and frequency leakage effects which affect conventional covariance estimates based upon Windowed Fourier Transform. The method is applied to explosion signals recorded at Stromboli volcano by either a short-period, small aperture antenna, or a large-aperture, broad-band network. The LP (0.2 < T < 2s) components of the explosive signals are analysed using data from the small-aperture array and under the plane-wave assumption. In this manner, we obtain a precise time- and frequency-localization of the directional properties for waves impinging at the array. We then extend the wavefield decomposition method using a spherical wave front model, and analyse the VLP components (T > 2s) of the explosion recordings from the broad-band network. Source locations obtained this way are fully compatible with those retrieved from application of more traditional (and computationally expensive) time-domain techniques, such as the Radial Semblance method.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2016-03-01

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

Data Acquisition and Processing System for Airborne Wind Profiling with a Pulsed, 2-Micron, Coherent-Detection, Doppler Lidar System

NASA Technical Reports Server (NTRS)

Beyon, J. Y.; Koch, G. J.; Kavaya, M. J.

2010-01-01

A data acquisition and signal processing system is being developed for a 2-micron airborne wind profiling coherent Doppler lidar system. This lidar, called the Doppler Aerosol Wind Lidar (DAWN), is based on a Ho:Tm:LuLiF laser transmitter and 15-cm diameter telescope. It is being packaged for flights onboard the NASA DC-8, with the first flights in the summer of 2010 in support of the NASA Genesis and Rapid Intensification Processes (GRIP) campaign for the study of hurricanes. The data acquisition and processing system is housed in a compact PCI chassis and consists of four components such as a digitizer, a digital signal processing (DSP) module, a video controller, and a serial port controller. The data acquisition and processing software (DAPS) is also being developed to control the system including real-time data analysis and display. The system detects an external 10 Hz trigger pulse and initiates the data acquisition and processing process, and displays selected wind profile parameters such as Doppler shift, power distribution, wind directions and velocities. Doppler shift created by aircraft motion is measured by an inertial navigation/GPS sensor and fed to the signal processing system for real-time removal of aircraft effects from wind measurements. A general overview of the system and the DAPS as well as the coherent Doppler lidar system is presented in this paper.

A Comparison of Inductive Sensors in the Characterization of Partial Discharges and Electrical Noise Using the Chromatic Technique.

PubMed

Ardila-Rey, Jorge Alfredo; Montaña, Johny; de Castro, Bruno Albuquerque; Schurch, Roger; Covolan Ulson, José Alfredo; Muhammad-Sukki, Firdaus; Bani, Nurul Aini

2018-03-29

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals.

Research on energy transmission calculation problem on laser detecting submarine

NASA Astrophysics Data System (ADS)

Fu, Qiang; Li, Yingchao; Zhang, Lizhong; Wang, Chao; An, Yan

2014-12-01

The laser detection and identification is based on the method of using laser as the source of signal to scan the surface of ocean. If the laser detection equipment finds out the target, it will immediately reflect the returning signal, and then through receiving and disposing the returning signal by the receiving system, to realize the function of detection and identification. Two mediums channels should be though in the process of laser detection transmission, which are the atmosphere and the seawater. The energy loss in the process of water transport, mainly considering the surface reflection and scattering attenuation and internal attenuation factors such as seawater. The energy consumption though atmospheric transmission, mainly considering the absorption of atmospheric and the attenuation causing by scattering, the energy consumption though seawater transmission, mainly considering the element such as surface reflection, the attenuation of scattering and internal attenuation of seawater. On the basis of the analysis and research, through the mode of establishment of atmospheric scattering, the model of sea surface reflection and the model of internal attenuation of seawater, determine the power dissipation of emitting lasers system, calculates the signal strength that reaches the receiver. Under certain conditions, the total attenuation of -98.92 dB by calculation, and put forward the related experiment scheme by the use of Atmospheric analog channel, seawater analog channel. In the experiment of the theory, we use the simulation pool of the atmosphere and the sea to replace the real environment where the laser detection system works in this kind of situation. To start with, we need to put the target in the simulating seawater pool of 10 meters large and then control the depth of the target in the sea level. We, putting the laser detection system in position where it is 2 kilometers far from one side, secondly use the equipment to aim at the target in some distance. Lastly, by launching and detecting the signal of returning wave, identify the effect of the image produced by the system.

ALGORITHM OF CARDIO COMPLEX DETECTION AND SORTING FOR PROCESSING THE DATA OF CONTINUOUS CARDIO SIGNAL MONITORING.

PubMed

Krasichkov, A S; Grigoriev, E B; Nifontov, E M; Shapovalov, V V

The paper presents an algorithm of cardio complex classification as part of processing the data of continuous cardiac monitoring. R-wave detection concurrently with cardio complex sorting is discussed. The core of this approach is the use of prior information about. cardio complex forms, segmental structure, and degree of kindness. Results of the sorting algorithm testing are provided.

Detection and enforcement of failure-to-yield in an emergency vehicle preemption system

NASA Technical Reports Server (NTRS)

Bachelder, Aaron (Inventor); Wickline, Richard (Inventor)

2007-01-01

An intersection controlled by an intersection controller receives trigger signals from on-coming emergency vehicles responding to an emergency call. The intersection controller initiates surveillance of the intersection via cameras installed at the intersection in response to a received trigger signal. The surveillance may begin immediately upon receipt of the trigger signal from an emergency vehicle, or may wait until the intersection controller determines that the signaling emergency vehicle is in the field of view of the cameras at the intersection. Portions of the captured images are tagged by the intersection controller based on tag signals transmitted by the vehicle or based on detected traffic patterns that indicate a potential traffic violation. The captured images are downloaded to a processing facility that analyzes the images and automatically issues citations for captured traffic violations.

Read My Lips: Brain Dynamics Associated with Audiovisual Integration and Deviance Detection.

PubMed

Tse, Chun-Yu; Gratton, Gabriele; Garnsey, Susan M; Novak, Michael A; Fabiani, Monica

2015-09-01

Information from different modalities is initially processed in different brain areas, yet real-world perception often requires the integration of multisensory signals into a single percept. An example is the McGurk effect, in which people viewing a speaker whose lip movements do not match the utterance perceive the spoken sounds incorrectly, hearing them as more similar to those signaled by the visual rather than the auditory input. This indicates that audiovisual integration is important for generating the phoneme percept. Here we asked when and where the audiovisual integration process occurs, providing spatial and temporal boundaries for the processes generating phoneme perception. Specifically, we wanted to separate audiovisual integration from other processes, such as simple deviance detection. Building on previous work employing ERPs, we used an oddball paradigm in which task-irrelevant audiovisually deviant stimuli were embedded in strings of non-deviant stimuli. We also recorded the event-related optical signal, an imaging method combining spatial and temporal resolution, to investigate the time course and neuroanatomical substrate of audiovisual integration. We found that audiovisual deviants elicit a short duration response in the middle/superior temporal gyrus, whereas audiovisual integration elicits a more extended response involving also inferior frontal and occipital regions. Interactions between audiovisual integration and deviance detection processes were observed in the posterior/superior temporal gyrus. These data suggest that dynamic interactions between inferior frontal cortex and sensory regions play a significant role in multimodal integration.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.