Data processing method for a weak, moving telemetry signal

NASA Technical Reports Server (NTRS)

Kendall, W. B.; Levy, G. S.; Nixon, D. L.; Panson, P. L.

1969-01-01

Method of processing data from a spacecraft, where the carrier has a low signal-to-noise ratio and wide unpredictable frequency shifts, consists of analogue recording of the noisy signal along with a high-frequency tone that is used as a clock to trigger a digitizer.

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

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.

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.

System for monitoring an industrial or biological process

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.; Vilim, Rick B.; White, Andrew M.

1998-01-01

A method and apparatus for monitoring and responding to conditions of an industrial process. Industrial process signals, such as repetitive manufacturing, testing and operational machine signals, are generated by a system. Sensor signals characteristic of the process are generated over a time length and compared to reference signals over the time length. The industrial signals are adjusted over the time length relative to the reference signals, the phase shift of the industrial signals is optimized to the reference signals and the resulting signals output for analysis by systems such as SPRT.

System for monitoring an industrial or biological process

DOEpatents

Gross, K.C.; Wegerich, S.W.; Vilim, R.B.; White, A.M.

1998-06-30

A method and apparatus are disclosed for monitoring and responding to conditions of an industrial process. Industrial process signals, such as repetitive manufacturing, testing and operational machine signals, are generated by a system. Sensor signals characteristic of the process are generated over a time length and compared to reference signals over the time length. The industrial signals are adjusted over the time length relative to the reference signals, the phase shift of the industrial signals is optimized to the reference signals and the resulting signals output for analysis by systems such as SPRT. 49 figs.

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.

System and methods for determining masking signals for applying empirical mode decomposition (EMD) and for demodulating intrinsic mode functions obtained from application of EMD

DOEpatents

Senroy, Nilanjan [New Delhi, IN; Suryanarayanan, Siddharth [Littleton, CO

2011-03-15

A computer-implemented method of signal processing is provided. The method includes generating one or more masking signals based upon a computed Fourier transform of a received signal. The method further includes determining one or more intrinsic mode functions (IMFs) of the received signal by performing a masking-signal-based empirical mode decomposition (EMD) using the at least one masking signal.

Analysis of acoustic emission signals and monitoring of machining processes

PubMed

Govekar; Gradisek; Grabec

2000-03-01

Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.

Method and apparatus for improving resolution in spectrometers processing output steps from non-ideal signal sources

DOEpatents

Warburton, William K.; Momayezi, Michael

2006-06-20

A method and apparatus for processing step-like output signals (primary signals) generated by non-ideal, for example, nominally single-pole ("N-1P ") devices. An exemplary method includes creating a set of secondary signals by directing the primary signal along a plurality of signal paths to a signal summation point, summing the secondary signals reaching the signal summation point after propagating along the signal paths to provide a summed signal, performing a filtering or delaying operation in at least one of said signal paths so that the secondary signals reaching said summing point have a defined time correlation with respect to one another, applying a set of weighting coefficients to the secondary signals propagating along said signal paths, and performing a capturing operation after any filtering or delaying operations so as to provide a weighted signal sum value as a measure of the integrated area Q_gT of the input signal.

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

NASA Astrophysics Data System (ADS)

Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

2017-12-01

We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

[Analysis of scatterer microstructure feature based on Chirp-Z transform cepstrum].

PubMed

Guo, Jianzhong; Lin, Shuyu

2007-12-01

The fundamental research field of medical ultrasound has been the characterization of tissue scatterers. The signal processing method is widely used in this research field. A new method of Chirp-Z Transform Cepstrum for mean spacing estimation of tissue scatterers using ultrasonic scattered signals has been developed. By using this method together with conventional AR cepstrum method, we processed the backscattered signals of mimic tissue and pig liver in vitro. The results illustrated that the Chirp-Z Transform Cepstrum method is effective for signal analysis of ultrasonic scattering and characterization of tissue scatterers, and it can improve the resolution for mean spacing estimation of tissue scatterers.

Low-pass parabolic FFT filter for airborne and satellite lidar signal processing.

PubMed

Jiao, Zhongke; Liu, Bo; Liu, Enhai; Yue, Yongjian

2015-10-14

In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing.

Processing Electromyographic Signals to Recognize Words

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Lee, D. D.

2009-01-01

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

Electronic filters, signal conversion apparatus, hearing aids and methods

NASA Technical Reports Server (NTRS)

Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)

1994-01-01

An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits associated with the filter stages for storing electrical representations of filter parameters. The filter stages include circuits for respectively adding the electrical representations of the filter parameters to the electrical signal to be filtered thereby producing a set of filter sum signals. At least one of the filter stages includes circuitry for producing a filter signal in substantially logarithmic form at its output by combining a filter sum signal for that filter stage with a signal from an output of another filter stage. The signal processing circuitry produces an intermediate output signal, and a multiplexer connected to the signal processing circuit multiplexes the intermediate output signal with the electrical signal to be filtered so that the logarithmic filter operates as both a logarithmic prefilter and a logarithmic postfilter. Other electronic filters, signal conversion apparatus, electroacoustic systems, hearing aids and methods are also disclosed.

Optical signal processing

NASA Technical Reports Server (NTRS)

Casasent, D.

1978-01-01

The article discusses several optical configurations used for signal processing. Electronic-to-optical transducers are outlined, noting fixed window transducers and moving window acousto-optic transducers. Folded spectrum techniques are considered, with reference to wideband RF signal analysis, fetal electroencephalogram analysis, engine vibration analysis, signal buried in noise, and spatial filtering. Various methods for radar signal processing are described, such as phased-array antennas, the optical processing of phased-array data, pulsed Doppler and FM radar systems, a multichannel one-dimensional optical correlator, correlations with long coded waveforms, and Doppler signal processing. Means for noncoherent optical signal processing are noted, including an optical correlator for speech recognition and a noncoherent optical correlator.

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

Device and method to enhance availability of cluster-based processing systems

NASA Technical Reports Server (NTRS)

Lupia, David J. (Inventor); Ramos, Jeremy (Inventor); Samson, Jr., John R. (Inventor)

2010-01-01

An electronic computing device including at least one processing unit that implements a specific fault signal upon experiencing an associated fault, a control unit that generates a specific recovery signal upon receiving the fault signal from the at least one processing unit, and at least one input memory unit. The recovery signal initiates specific recovery processes in the at least one processing unit. The input memory buffers input data signals input to the at least one processing unit that experienced the fault during the recovery period.

Frequency-feature based antistrong-disturbance signal processing method and system for vortex flowmeter with single sensor

NASA Astrophysics Data System (ADS)

Xu, Ke-Jun; Luo, Qing-Lin; Wang, Gang; Liu, San-Shan; Kang, Yi-Bo

2010-07-01

Digital signal processing methods have been applied to vortex flowmeter for extracting the useful information from noisy output of the vortex flow sensor. But these approaches are unavailable when the power of the mechanical vibration noise is larger than that of the vortex flow signal. In order to solve this problem, an antistrong-disturbance signal processing method is proposed based on frequency features of the vortex flow signal and mechanical vibration noise for the vortex flowmeter with single sensor. The frequency bandwidth of the vortex flow signal is different from that of the mechanical vibration noise. The autocorrelation function can represent bandwidth features of the signal and noise. The output of the vortex flow sensor is processed by the spectrum analysis, filtered by bandpass filters, and calculated by autocorrelation function at the fixed delaying time and at τ =0 to obtain ratios. The frequency corresponding to the minimal ratio is regarded as the vortex flow frequency. With an ultralow-power microcontroller, a digital signal processing system is developed to implement the antistrong-disturbance algorithm, and at the same time to ensure low-power and two-wire mode for meeting the requirement of process instrumentation. The water flow-rate calibration and vibration test experiments are conducted, and the experimental results show that both the algorithm and system are effective.

Frequency-feature based antistrong-disturbance signal processing method and system for vortex flowmeter with single sensor.

PubMed

Xu, Ke-Jun; Luo, Qing-Lin; Wang, Gang; Liu, San-Shan; Kang, Yi-Bo

2010-07-01

Digital signal processing methods have been applied to vortex flowmeter for extracting the useful information from noisy output of the vortex flow sensor. But these approaches are unavailable when the power of the mechanical vibration noise is larger than that of the vortex flow signal. In order to solve this problem, an antistrong-disturbance signal processing method is proposed based on frequency features of the vortex flow signal and mechanical vibration noise for the vortex flowmeter with single sensor. The frequency bandwidth of the vortex flow signal is different from that of the mechanical vibration noise. The autocorrelation function can represent bandwidth features of the signal and noise. The output of the vortex flow sensor is processed by the spectrum analysis, filtered by bandpass filters, and calculated by autocorrelation function at the fixed delaying time and at tau=0 to obtain ratios. The frequency corresponding to the minimal ratio is regarded as the vortex flow frequency. With an ultralow-power microcontroller, a digital signal processing system is developed to implement the antistrong-disturbance algorithm, and at the same time to ensure low-power and two-wire mode for meeting the requirement of process instrumentation. The water flow-rate calibration and vibration test experiments are conducted, and the experimental results show that both the algorithm and system are effective.

Seismoelectric data processing for surface surveys of shallow targets

USGS Publications Warehouse

Haines, S.S.; Guitton, A.; Biondi, B.

2007-01-01

The utility of the seismoelectric method relies on the development of methods to extract the signal of interest from background and source-generated coherent noise that may be several orders-of-magnitude stronger. We compare data processing approaches to develop a sequence of preprocessing and signal/noise separation and to quantify the noise level from which we can extract signal events. Our preferred sequence begins with the removal of power line harmonic noise and the use of frequency filters to minimize random and source-generated noise. Mapping to the linear Radon domain with an inverse process incorporating a sparseness constraint provides good separation of signal from noise, though it is ineffective on noise that shows the same dip as the signal. Similarly, the seismoelectric signal and noise do not separate cleanly in the Fourier domain, so f-k filtering can not remove all of the source-generated noise and it also disrupts signal amplitude patterns. We find that prediction-error filters provide the most effective method to separate signal and noise, while also preserving amplitude information, assuming that adequate pattern models can be determined for the signal and noise. These Radon-domain and prediction-error-filter methods successfully separate signal from <33 dB stronger noise in our test data. ?? 2007 Society of Exploration Geophysicists.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

Method and apparatus for reconstructing in-cylinder pressure and correcting for signal decay

DOEpatents

Huang, Jian

2013-03-12

A method comprises steps for reconstructing in-cylinder pressure data from a vibration signal collected from a vibration sensor mounted on an engine component where it can generate a signal with a high signal-to-noise ratio, and correcting the vibration signal for errors introduced by vibration signal charge decay and sensor sensitivity. The correction factors are determined as a function of estimated motoring pressure and the measured vibration signal itself with each of these being associated with the same engine cycle. Accordingly, the method corrects for charge decay and changes in sensor sensitivity responsive to different engine conditions to allow greater accuracy in the reconstructed in-cylinder pressure data. An apparatus is also disclosed for practicing the disclosed method, comprising a vibration sensor, a data acquisition unit for receiving the vibration signal, a computer processing unit for processing the acquired signal and a controller for controlling the engine operation based on the reconstructed in-cylinder pressure.

Processing of Antenna-Array Signals on the Basis of the Interference Model Including a Rank-Deficient Correlation Matrix

NASA Astrophysics Data System (ADS)

Rodionov, A. A.; Turchin, V. I.

2017-06-01

We propose a new method of signal processing in antenna arrays, which is called the Maximum-Likelihood Signal Classification. The proposed method is based on the model in which interference includes a component with a rank-deficient correlation matrix. Using numerical simulation, we show that the proposed method allows one to ensure variance of the estimated arrival angle of the plane wave, which is close to the Cramer-Rao lower boundary and more efficient than the best-known MUSIC method. It is also shown that the proposed technique can be efficiently used for estimating the time dependence of the useful signal.

New methods of multimode fiber interferometer signal processing

NASA Astrophysics Data System (ADS)

Vitrik, Oleg B.; Kulchin, Yuri N.; Maxaev, Oleg G.; Kirichenko, Oleg V.; Kamenev, Oleg T.; Petrov, Yuri S.

1995-06-01

New methods of multimode fiber interferometers signal processing are suggested. For scheme of single fiber multimode interferometers with two excited modes, the method based on using of special fiber unit is developed. This unit provides the modes interaction and further sum optical field filtering. As a result the amplitude of output signal is modulated by external influence on interferometer. The stabilization of interferometer sensitivity is achieved by using additional special modulation of output signal. For scheme of single fiber multimode interferometers with excitation of wide mode spectrum, the signal of intermode interference is registered by photodiode matrix and then special electronic unit performs correlation processing. For elimination of temperature destabilization, the registered signal is adopted to multimode interferometers optical signal temperature changes. The achieved parameters for double mode scheme: temporary stability--0.6% per hour, sensitivity to interferometer length deviations--3,2 nm; for multimode scheme: temperature stability--(0.5%)/(K), temporary nonstability--0.2% per hour, sensitivity to interferometer length deviations--20 nm, dynamic range--35 dB.

Implementation and optimization of ultrasound signal processing algorithms on mobile GPU

NASA Astrophysics Data System (ADS)

Kong, Woo Kyu; Lee, Wooyoul; Kim, Kyu Cheol; Yoo, Yangmo; Song, Tai-Kyong

2014-03-01

A general-purpose graphics processing unit (GPGPU) has been used for improving computing power in medical ultrasound imaging systems. Recently, a mobile GPU becomes powerful to deal with 3D games and videos at high frame rates on Full HD or HD resolution displays. This paper proposes the method to implement ultrasound signal processing on a mobile GPU available in the high-end smartphone (Galaxy S4, Samsung Electronics, Seoul, Korea) with programmable shaders on the OpenGL ES 2.0 platform. To maximize the performance of the mobile GPU, the optimization of shader design and load sharing between vertex and fragment shader was performed. The beamformed data were captured from a tissue mimicking phantom (Model 539 Multipurpose Phantom, ATS Laboratories, Inc., Bridgeport, CT, USA) by using a commercial ultrasound imaging system equipped with a research package (Ultrasonix Touch, Ultrasonix, Richmond, BC, Canada). The real-time performance is evaluated by frame rates while varying the range of signal processing blocks. The implementation method of ultrasound signal processing on OpenGL ES 2.0 was verified by analyzing PSNR with MATLAB gold standard that has the same signal path. CNR was also analyzed to verify the method. From the evaluations, the proposed mobile GPU-based processing method has no significant difference with the processing using MATLAB (i.e., PSNR<52.51 dB). The comparable results of CNR were obtained from both processing methods (i.e., 11.31). From the mobile GPU implementation, the frame rates of 57.6 Hz were achieved. The total execution time was 17.4 ms that was faster than the acquisition time (i.e., 34.4 ms). These results indicate that the mobile GPU-based processing method can support real-time ultrasound B-mode processing on the smartphone.

Informational approach to the analysis of acoustic signals

NASA Astrophysics Data System (ADS)

Senkevich, Yuriy; Dyuk, Vyacheslav; Mishchenko, Mikhail; Solodchuk, Alexandra

2017-10-01

The example of linguistic processing of acoustic signals of a seismic event would be an information approach to the processing of non-stationary signals. The method for converting an acoustic signal into an information message is described by identifying repetitive self-similar patterns. The definitions of the event selection indicators in the symbolic recording of the acoustic signal are given. The results of processing an acoustic signal by a computer program realizing the processing of linguistic data are shown. Advantages and disadvantages of using software algorithms are indicated.

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.

Signal processing in ultrasound. [for diagnostic medicine

NASA Technical Reports Server (NTRS)

Le Croissette, D. H.; Gammell, P. M.

1978-01-01

Signal is the term used to denote the characteristic in the time or frequency domain of the probing energy of the system. Processing of this signal in diagnostic ultrasound occurs as the signal travels through the ultrasonic and electrical sections of the apparatus. The paper discusses current signal processing methods, postreception processing, display devices, real-time imaging, and quantitative measurements in noninvasive cardiology. The possibility of using deconvolution in a single transducer system is examined, and some future developments using digital techniques are outlined.

A source number estimation method for single optical fiber sensor

NASA Astrophysics Data System (ADS)

Hu, Junpeng; Huang, Zhiping; Su, Shaojing; Zhang, Yimeng; Liu, Chunwu

2015-10-01

The single-channel blind source separation (SCBSS) technique makes great significance in many fields, such as optical fiber communication, sensor detection, image processing and so on. It is a wide range application to realize blind source separation (BSS) from a single optical fiber sensor received data. The performance of many BSS algorithms and signal process methods will be worsened with inaccurate source number estimation. Many excellent algorithms have been proposed to deal with the source number estimation in array signal process which consists of multiple sensors, but they can not be applied directly to the single sensor condition. This paper presents a source number estimation method dealing with the single optical fiber sensor received data. By delay process, this paper converts the single sensor received data to multi-dimension form. And the data covariance matrix is constructed. Then the estimation algorithms used in array signal processing can be utilized. The information theoretic criteria (ITC) based methods, presented by AIC and MDL, Gerschgorin's disk estimation (GDE) are introduced to estimate the source number of the single optical fiber sensor's received signal. To improve the performance of these estimation methods at low signal noise ratio (SNR), this paper make a smooth process to the data covariance matrix. By the smooth process, the fluctuation and uncertainty of the eigenvalues of the covariance matrix are reduced. Simulation results prove that ITC base methods can not estimate the source number effectively under colored noise. The GDE method, although gets a poor performance at low SNR, but it is able to accurately estimate the number of sources with colored noise. The experiments also show that the proposed method can be applied to estimate the source number of single sensor received data.

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

PubMed

Jan, Shau-Shiun; Sun, Chih-Cheng

2010-01-01

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

Electronic filters, repeated signal charge conversion apparatus, hearing aids and methods

NASA Technical Reports Server (NTRS)

Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)

1993-01-01

An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits associated with the filter stages for storing electrical representations of filter parameters. The filter stages include circuits for respectively adding the electrical representations of the filter parameters to the electrical signal to be filtered thereby producing a set of filter sum signals. At least one of the filter stages includes circuitry for producing a filter signal in substantially logarithmic form at its output by combining a filter sum signal for that filter stage with a signal from an output of another filter stage. The signal processing circuitry produces an intermediate output signal, and a multiplexer connected to the signal processing circuit multiplexes the intermediate output signal with the electrical signal to be filtered so that the logarithmic filter operates as both a logarithmic prefilter and a logarithmic postfilter. Other electronic filters, signal conversion apparatus, electroacoustic systems, hearing aids and methods are also disclosed.

P-Code-Enhanced Encryption-Mode Processing of GPS Signals

NASA Technical Reports Server (NTRS)

Young, Lawrence; Meehan, Thomas; Thomas, Jess B.

2003-01-01

A method of processing signals in a Global Positioning System (GPS) receiver has been invented to enable the receiver to recover some of the information that is otherwise lost when GPS signals are encrypted at the transmitters. The need for this method arises because, at the option of the military, precision GPS code (P-code) is sometimes encrypted by a secret binary code, denoted the A code. Authorized users can recover the full signal with knowledge of the A-code. However, even in the absence of knowledge of the A-code, one can track the encrypted signal by use of an estimate of the A-code. The present invention is a method of making and using such an estimate. In comparison with prior such methods, this method makes it possible to recover more of the lost information and obtain greater accuracy.

Verification of Non-Invasive Blood Glucose Measurement Method Based on Pulse Wave Signal Detected by FBG Sensor System.

PubMed

Kurasawa, Shintaro; Koyama, Shouhei; Ishizawa, Hiroaki; Fujimoto, Keisaku; Chino, Shun

2017-11-23

This paper describes and verifies a non-invasive blood glucose measurement method using a fiber Bragg grating (FBG) sensor system. The FBG sensor is installed on the radial artery, and the strain (pulse wave) that is propagated from the heartbeat is measured. The measured pulse wave signal was used as a collection of feature vectors for multivariate analysis aiming to determine the blood glucose level. The time axis of the pulse wave signal was normalized by two signal processing methods: the shortest-time-cut process and 1-s-normalization process. The measurement accuracy of the calculated blood glucose level was compared with the accuracy of these signal processing methods. It was impossible to calculate a blood glucose level exceeding 200 mg/dL in the calibration curve that was constructed by the shortest-time-cut process. In the 1-s-normalization process, the measurement accuracy of the blood glucose level was improved, and a blood glucose level exceeding 200 mg/dL could be calculated. By verifying the loading vector of each calibration curve to calculate the blood glucose level with a high measurement accuracy, we found the gradient of the peak of the pulse wave at the acceleration plethysmogram greatly affected.

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.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Genomic signal processing methods for computation of alignment-free distances from DNA sequences.

PubMed

Borrayo, Ernesto; Mendizabal-Ruiz, E Gerardo; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Mendizabal, Adriana P; Morales, J Alejandro

2014-01-01

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments.

Genomic Signal Processing Methods for Computation of Alignment-Free Distances from DNA Sequences

PubMed Central

Borrayo, Ernesto; Mendizabal-Ruiz, E. Gerardo; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Mendizabal, Adriana P.; Morales, J. Alejandro

2014-01-01

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments. PMID:25393409

Biologically-based signal processing system applied to noise removal for signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren I.

2004-07-13

The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Digital signal processing for velocity measurements in dynamical material's behaviour studies.

PubMed

Devlaminck, Julien; Luc, Jérôme; Chanal, Pierre-Yves

2014-03-01

In this work, we describe different configurations of optical fiber interferometers (types Michelson and Mach-Zehnder) used to measure velocities during dynamical material's behaviour studies. We detail the algorithms of processing developed and optimized to improve the performance of these interferometers especially in terms of time and frequency resolutions. Three methods of analysis of interferometric signals were studied. For Michelson interferometers, the time-frequency analysis of signals by Short-Time Fourier Transform (STFT) is compared to a time-frequency analysis by Continuous Wavelet Transform (CWT). The results have shown that the CWT was more suitable than the STFT for signals with low signal-to-noise, and low velocity and high acceleration areas. For Mach-Zehnder interferometers, the measurement is carried out by analyzing the phase shift between three interferometric signals (Triature processing). These three methods of digital signal processing were evaluated, their measurement uncertainties estimated, and their restrictions or operational limitations specified from experimental results performed on a pulsed power machine.

Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Chang; Wu, Xing; Mao, Jianlin; Liu, Xiaoqin

2017-07-01

In the signal processing domain, there has been growing interest in using acoustic emission (AE) signals for the fault diagnosis and condition assessment instead of vibration signals, which has been advocated as an effective technique for identifying fracture, crack or damage. The AE signal has high frequencies up to several MHz which can avoid some signals interference, such as the parts of bearing (i.e. rolling elements, ring and so on) and other rotating parts of machine. However, acoustic emission signal necessitates advanced signal sampling capabilities and requests ability to deal with large amounts of sampling data. In this paper, compressive sensing (CS) is introduced as a processing framework, and then a compressive features extraction method is proposed. We use it for extracting the compressive features from compressively-sensed data directly, and also prove the energy preservation properties. First, we study the AE signals under the CS framework. The sparsity of AE signal of the rolling bearing is checked. The observation and reconstruction of signal is also studied. Second, we present a method of extraction AE compressive feature (AECF) from compressively-sensed data directly. We demonstrate the energy preservation properties and the processing of the extracted AECF feature. We assess the running state of the bearing using the AECF trend. The AECF trend of the running state of rolling bearings is consistent with the trend of traditional features. Thus, the method is an effective way to evaluate the running trend of rolling bearings. The results of the experiments have verified that the signal processing and the condition assessment based on AECF is simpler, the amount of data required is smaller, and the amount of computation is greatly reduced.

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method for monitoring an apparatus or process asset including creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto; acquiring a set of observed signal data values from the asset; determining an operating mode of the asset for the set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a set of estimated signal data values from the selected process submodel for the determined operating mode; and determining asset status as a function of the calculated set of estimated signal data values for providing asset surveillance and/or control.

Quality status display for a vibration welding process

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

Spicer, John Patrick; Abell, Jeffrey A.; Wincek, Michael Anthony

A method includes receiving, during a vibration welding process, a set of sensory signals from a collection of sensors positioned with respect to a work piece during formation of a weld on or within the work piece. The method also includes receiving control signals from a welding controller during the process, with the control signals causing the welding horn to vibrate at a calibrated frequency, and processing the received sensory and control signals using a host machine. Additionally, the method includes displaying a predicted weld quality status on a surface of the work piece using a status projector. The methodmore » may include identifying and display a quality status of a suspect weld. The laser projector may project a laser beam directly onto or immediately adjacent to the suspect welds, e.g., as a red, green, blue laser or a gas laser having a switched color filter.« less

Digital Signal Processing Methods for Ultrasonic Echoes.

PubMed

Sinding, Kyle; Drapaca, Corina; Tittmann, Bernhard

2016-04-28

Digital signal processing has become an important component of data analysis needed in industrial applications. In particular, for ultrasonic thickness measurements the signal to noise ratio plays a major role in the accurate calculation of the arrival time. For this application a band pass filter is not sufficient since the noise level cannot be significantly decreased such that a reliable thickness measurement can be performed. This paper demonstrates the abilities of two regularization methods - total variation and Tikhonov - to filter acoustic and ultrasonic signals. Both of these methods are compared to a frequency based filtering for digitally produced signals as well as signals produced by ultrasonic transducers. This paper demonstrates the ability of the total variation and Tikhonov filters to accurately recover signals from noisy acoustic signals faster than a band pass filter. Furthermore, the total variation filter has been shown to reduce the noise of a signal significantly for signals with clear ultrasonic echoes. Signal to noise ratios have been increased over 400% by using a simple parameter optimization. While frequency based filtering is efficient for specific applications, this paper shows that the reduction of noise in ultrasonic systems can be much more efficient with regularization methods.

System for monitoring an industrial process and determining sensor status

DOEpatents

Gross, K.C.; Hoyer, K.K.; Humenik, K.E.

1995-10-17

A method and system for monitoring an industrial process and a sensor are disclosed. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test. 17 figs.

System for monitoring an industrial process and determining sensor status

DOEpatents

Gross, K.C.; Hoyer, K.K.; Humenik, K.E.

1997-05-13

A method and system are disclosed for monitoring an industrial process and a sensor. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test. 17 figs.

System for monitoring an industrial process and determining sensor status

DOEpatents

Gross, Kenneth C.; Hoyer, Kristin K.; Humenik, Keith E.

1995-01-01

A method and system for monitoring an industrial process and a sensor. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

System for monitoring an industrial process and determining sensor status

DOEpatents

Gross, Kenneth C.; Hoyer, Kristin K.; Humenik, Keith E.

1997-01-01

A method and system for monitoring an industrial process and a sensor. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

System For Surveillance Of Spectral Signals

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.; Criss-Puszkiewicz, Cynthia; Wilks, Alan D.

2004-10-12

A method and system for monitoring at least one of a system, a process and a data source. A method and system have been developed for carrying out surveillance, testing and modification of an ongoing process or other source of data, such as a spectroscopic examination. A signal from the system under surveillance is collected and compared with a reference signal, a frequency domain transformation carried out for the system signal and reference signal, a frequency domain difference function established. The process is then repeated until a full range of data is accumulated over the time domain and a Sequential Probability Ratio Test ("SPRT") methodology applied to determine a three-dimensional surface plot characteristic of the operating state of the system under surveillance.

System For Surveillance Of Spectral Signals

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan; Criss-Puszkiewicz, Cynthia; Wilks, Alan D.

2003-04-22

A method and system for monitoring at least one of a system, a process and a data source. A method and system have been developed for carrying out surveillance, testing and modification of an ongoing process or other source of data, such as a spectroscopic examination. A signal from the system under surveillance is collected and compared with a reference signal, a frequency domain transformation carried out for the system signal and reference signal, a frequency domain difference function established. The process is then repeated until a full range of data is accumulated over the time domain and a Sequential Probability Ratio Test methodology applied to determine a three-dimensional surface plot characteristic of the operating state of the system under surveillance.

System for surveillance of spectral signals

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.; Criss-Puszkiewicz, Cynthia; Wilks, Alan D.

2006-02-14

A method and system for monitoring at least one of a system, a process and a data source. A method and system have been developed for carrying out surveillance, testing and modification of an ongoing process or other source of data, such as a spectroscopic examination. A signal from the system under surveillance is collected and compared with a reference signal, a frequency domain transformation carried out for the system signal and reference signal, a frequency domain difference function established. The process is then repeated until a full range of data is accumulated over the time domain and a Sequential Probability Ratio Test ("SPRT") methodology applied to determine a three-dimensional surface plot characteristic of the operating state of the system under surveillance.

System for surveillance of spectral signals

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.; Criss-Puszkiewicz, Cynthia; Wilks, Alan D.

2001-01-01

A method and system for monitoring at least one of a system, a process and a data source. A method and system have been developed for carrying out surveillance, testing and modification of an ongoing process or other source of data, such as a spectroscopic examination. A signal from the system under surveillance is collected and compared with a reference signal, a frequency domain transformation carried out for the system signal and reference signal, a frequency domain difference function established. The process is then repeated until a full range of data is accumulated over the time domain and a SPRT sequential probability ratio test methodology applied to determine a three-dimensional surface plot characteristic of the operating state of the system under surveillance.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Interferometric architectures based All-Optical logic design methods and their implementations

NASA Astrophysics Data System (ADS)

Singh, Karamdeep; Kaur, Gurmeet

2015-06-01

All-Optical Signal Processing is an emerging technology which can avoid costly Optical-electronic-optical (O-E-O) conversions which are usually compulsory in traditional Electronic Signal Processing systems, thus greatly enhancing operating bit rate with some added advantages such as electro-magnetic interference immunity and low power consumption etc. In order to implement complex signal processing tasks All-Optical logic gates are required as backbone elements. This review describes the advances in the field of All-Optical logic design methods based on interferometric architectures such as Mach-Zehnder Interferometer (MZI), Sagnac Interferometers and Ultrafast Non-Linear Interferometer (UNI). All-Optical logic implementations for realization of arithmetic and signal processing applications based on each interferometric arrangement are also presented in a categorized manner.

Platform for Post-Processing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Don J.

2010-01-01

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals

PubMed Central

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method. PMID:27382610

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals.

PubMed

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method.

A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

PubMed

Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K; Birch, Gary E

2007-06-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

TOPICAL REVIEW: A survey of signal processing algorithms in brain computer interfaces based on electrical brain signals

NASA Astrophysics Data System (ADS)

Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K.; Birch, Gary E.

2007-06-01

Brain computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

Damage localization by statistical evaluation of signal-processed mode shapes

NASA Astrophysics Data System (ADS)

Ulriksen, M. D.; Damkilde, L.

2015-07-01

Due to their inherent, ability to provide structural information on a local level, mode shapes and t.lieir derivatives are utilized extensively for structural damage identification. Typically, more or less advanced mathematical methods are implemented to identify damage-induced discontinuities in the spatial mode shape signals, hereby potentially facilitating damage detection and/or localization. However, by being based on distinguishing damage-induced discontinuities from other signal irregularities, an intrinsic deficiency in these methods is the high sensitivity towards measurement, noise. The present, article introduces a damage localization method which, compared to the conventional mode shape-based methods, has greatly enhanced robustness towards measurement, noise. The method is based on signal processing of spatial mode shapes by means of continuous wavelet, transformation (CWT) and subsequent, application of a generalized discrete Teager-Kaiser energy operator (GDTKEO) to identify damage-induced mode shape discontinuities. In order to evaluate whether the identified discontinuities are in fact, damage-induced, outlier analysis of principal components of the signal-processed mode shapes is conducted on the basis of T2-statistics. The proposed method is demonstrated in the context, of analytical work with a free-vibrating Euler-Bernoulli beam under noisy conditions.

Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways.

PubMed

Hardy, Simon; Robillard, Pierre N

2008-01-15

Cellular signaling networks are dynamic systems that propagate and process information, and, ultimately, cause phenotypical responses. Understanding the circuitry of the information flow in cells is one of the keys to understanding complex cellular processes. The development of computational quantitative models is a promising avenue for attaining this goal. Not only does the analysis of the simulation data based on the concentration variations of biological compounds yields information about systemic state changes, but it is also very helpful for obtaining information about the dynamics of signal propagation. This article introduces a new method for analyzing the dynamics of signal propagation in signaling pathways using Petri net theory. The method is demonstrated with the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) regulation network. The results constitute temporal information about signal propagation in the network, a simplified graphical representation of the network and of the signal propagation dynamics and a characterization of some signaling routes as regulation motifs.

Method and apparatus for obtaining complete speech signals for speech recognition applications

NASA Technical Reports Server (NTRS)

Abrash, Victor (Inventor); Cesari, Federico (Inventor); Franco, Horacio (Inventor); George, Christopher (Inventor); Zheng, Jing (Inventor)

2009-01-01

The present invention relates to a method and apparatus for obtaining complete speech signals for speech recognition applications. In one embodiment, the method continuously records an audio stream comprising a sequence of frames to a circular buffer. When a user command to commence or terminate speech recognition is received, the method obtains a number of frames of the audio stream occurring before or after the user command in order to identify an augmented audio signal for speech recognition processing. In further embodiments, the method analyzes the augmented audio signal in order to locate starting and ending speech endpoints that bound at least a portion of speech to be processed for recognition. At least one of the speech endpoints is located using a Hidden Markov Model.

Investigation of signal models and methods for evaluating structures of processing telecommunication information exchange systems under acoustic noise conditions

NASA Astrophysics Data System (ADS)

Kropotov, Y. A.; Belov, A. A.; Proskuryakov, A. Y.; Kolpakov, A. A.

2018-05-01

The paper considers models and methods for estimating signals during the transmission of information messages in telecommunication systems of audio exchange. One-dimensional probability distribution functions that can be used to isolate useful signals, and acoustic noise interference are presented. An approach to the estimation of the correlation and spectral functions of the parameters of acoustic signals is proposed, based on the parametric representation of acoustic signals and the components of the noise components. The paper suggests an approach to improving the efficiency of interference cancellation and highlighting the necessary information when processing signals from telecommunications systems. In this case, the suppression of acoustic noise is based on the methods of adaptive filtering and adaptive compensation. The work also describes the models of echo signals and the structure of subscriber devices in operational command telecommunications systems.

Digital signal processing methods for biosequence comparison.

PubMed Central

Benson, D C

1990-01-01

A method is discussed for DNA or protein sequence comparison using a finite field fast Fourier transform, a digital signal processing technique; and statistical methods are discussed for analyzing the output of this algorithm. This method compares two sequences of length N in computing time proportional to N log N compared to N2 for methods currently used. This method makes it feasible to compare very long sequences. An example is given to show that the method correctly identifies sites of known homology. PMID:2349096

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.

Polarization-insensitive techniques for optical signal processing

NASA Astrophysics Data System (ADS)

Salem, Reza

2006-12-01

This thesis investigates polarization-insensitive methods for optical signal processing. Two signal processing techniques are studied: clock recovery based on two-photon absorption in silicon and demultiplexing based on cross-phase modulation in highly nonlinear fiber. The clock recovery system is tested at an 80 Gb/s data rate for both back-to-back and transmission experiments. The demultiplexer is tested at a 160 Gb/s data rate in a back-to-back experiment. We experimentally demonstrate methods for eliminating polarization dependence in both systems. Our experimental results are confirmed by theoretical and numerical analysis.

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

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.

Ridge extraction from the time-frequency representation (TFR) of signals based on an image processing approach: application to the analysis of uterine electromyogram AR TFR.

PubMed

Terrien, Jérémy; Marque, Catherine; Germain, Guy

2008-05-01

Time-frequency representations (TFRs) of signals are increasingly being used in biomedical research. Analysis of such representations is sometimes difficult, however, and is often reduced to the extraction of ridges, or local energy maxima. In this paper, we describe a new ridge extraction method based on the image processing technique of active contours or snakes. We have tested our method on several synthetic signals and for the analysis of uterine electromyogram or electrohysterogram (EHG) recorded during gestation in monkeys. We have also evaluated a postprocessing algorithm that is especially suited for EHG analysis. Parameters are evaluated on real EHG signals in different gestational periods. The presented method gives good results when applied to synthetic as well as EHG signals. We have been able to obtain smaller ridge extraction errors when compared to two other methods specially developed for EHG. The gradient vector flow (GVF) snake method, or GVF-snake method, appears to be a good ridge extraction tool, which could be used on TFR of mono or multicomponent signals with good results.

Methods and Systems for Advanced Spaceport Information Management

NASA Technical Reports Server (NTRS)

Fussell, Ronald M. (Inventor); Ely, Donald W. (Inventor); Meier, Gary M. (Inventor); Halpin, Paul C. (Inventor); Meade, Phillip T. (Inventor); Jacobson, Craig A. (Inventor); Blackwell-Thompson, Charlie (Inventor)

2007-01-01

Advanced spaceport information management methods and systems are disclosed. In one embodiment, a method includes coupling a test system to the payload and transmitting one or more test signals that emulate an anticipated condition from the test system to the payload. One or more responsive signals are received from the payload into the test system and are analyzed to determine whether one or more of the responsive signals comprises an anomalous signal. At least one of the steps of transmitting, receiving, analyzing and determining includes transmitting at least one of the test signals and the responsive signals via a communications link from a payload processing facility to a remotely located facility. In one particular embodiment, the communications link is an Internet link from a payload processing facility to a remotely located facility (e.g. a launch facility, university, etc.).

Methods and systems for advanced spaceport information management

NASA Technical Reports Server (NTRS)

Ely, Donald W. (Inventor); Fussell, Ronald M. (Inventor); Halpin, Paul C. (Inventor); Blackwell-Thompson, Charlie (Inventor); Meier, Gary M. (Inventor); Meade, Phillip T. (Inventor); Jacobson, Craig A. (Inventor)

2007-01-01

Advanced spaceport information management methods and systems are disclosed. In one embodiment, a method includes coupling a test system to the payload and transmitting one or more test signals that emulate an anticipated condition from the test system to the payload. One or more responsive signals are received from the payload into the test system and are analyzed to determine whether one or more of the responsive signals comprises an anomalous signal. At least one of the steps of transmitting, receiving, analyzing and determining includes transmitting at least one of the test signals and the responsive signals via a communications link from a payload processing facility to a remotely located facility. In one particular embodiment, the communications link is an Internet link from a payload processing facility to a remotely located facility (e.g. a launch facility, university, etc.).

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2003-01-01

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

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.

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.

Device and method for skull-melting depth measurement

DOEpatents

Lauf, R.J.; Heestand, R.L.

1993-02-09

A method of skull-melting comprises the steps of: (a) providing a vessel adapted for a skull-melting process, the vessel having an interior, an underside, and an orifice connecting the interior and the underside; (b) disposing a waveguide in the orifice so that the waveguide protrudes sufficiently into the interior to interact with the skull-melting process; (c) providing a signal energy transducer in signal communication with the waveguide; (d) introducing into the vessel a molten working material; (e) carrying out the skull-melting process so that a solidified skull of the working material is formed, the skull and the vessel having an interface therebetween, the skull becoming fused to the waveguide so the signal energy can be transmitted through the waveguide and the skull without interference from the interface; (f) activating the signal energy transducer so that a signal is propagated through the waveguide; and, (g) controlling at least one variable of the skull-melting process utilizing feedback information derived from the propagated signal energy.

Device and method for skull-melting depth measurement

DOEpatents

Lauf, Robert J.; Heestand, Richard L.

1993-01-01

A method of skull-melting comprises the steps of: a. providing a vessel adapted for a skull-melting process, the vessel having an interior, an underside, and an orifice in connecting the interior and the underside; b. disposing a waveguide in the orifice so that the waveguide protrudes sufficiently into the interior to interact with the skull-melting process; c. providing a signal energy transducer in signal communication with the waveguide; d. introducing into the vessel a molten working material; e. carrying out the skull-melting process so that a solidified skull of the working material is formed, the skull and the vessel having an interface therebetween, the skull becoming fused to the waveguide so the signal energy can be transmitted through the waveguide and the skull without interference from the interface; f. activating the signal energy transducer so that a signal is propagated through the waveguide; and, g. controlling at least one variable of the skull-melting process utilizing feedback information derived from the propagated signal energy.

Signal processing for order 10 pm accuracy displacement metrology in real-world scientific applications

NASA Technical Reports Server (NTRS)

Halverson, Peter G.; Loya, Frank M.

2004-01-01

This paper describes heterodyne displacement metrology gauge signal processing methods that achieve satisfactory robustness against low signal strength and spurious signals, and good long-term stability. We have a proven displacement-measuring approach that is useful not only to space-optical projects at JPL, but also to the wider field of distance measurements.

Gas turbine engine control system

NASA Technical Reports Server (NTRS)

Idelchik, Michael S. (Inventor)

1991-01-01

A control system and method of controlling a gas turbine engine. The control system receives an error signal and processes the error signal to form a primary fuel control signal. The control system also receives at least one anticipatory demand signal and processes the signal to form an anticipatory fuel control signal. The control system adjusts the value of the anticipatory fuel control signal based on the value of the error signal to form an adjusted anticipatory signal and then the adjusted anticipatory fuel control signal and the primary fuel control signal are combined to form a fuel command signal.

lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain.

PubMed

Sepehrband, Farshid; Choupan, Jeiran; Caruyer, Emmanuel; Kurniawan, Nyoman D; Gal, Yaniv; Tieng, Quang M; McMahon, Katie L; Vegh, Viktor; Reutens, David C; Yang, Zhengyi

2014-01-01

We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion-gradient directions for high angular resolution diffusion magnetic resonance imaging. Our pre-processing method incorporates prior knowledge about the acquired diffusion-weighted signal, facilitating noise reduction. Periodic spiral sampling of gradient direction encodings results in an acquired signal in each voxel that is pseudo-periodic with characteristics that allow separation of low-frequency signal from high frequency noise. Consequently, it enhances local reconstruction of the orientation distribution function used to define fiber tracks in the brain. Denoising with periodic spiral sampling was tested using synthetic data and in vivo human brain images. The level of improvement in signal-to-noise ratio and in the accuracy of local reconstruction of fiber tracks was significantly improved using our method.

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.

Digital methods of recording color television images on film tape

NASA Astrophysics Data System (ADS)

Krivitskaya, R. Y.; Semenov, V. M.

1985-04-01

Three methods are now available for recording color television images on film tape, directly or after appropriate finish of signal processing. Conventional recording of images from the screens of three kinescopes with synthetic crystal face plates is still most effective for high fidelity. This method was improved by digital preprocessing of brightness color-difference signal. Frame-by-frame storage of these signals in the memory in digital form is followed by gamma and aperture correction and electronic correction of crossover distortions in the color layers of the film with fixing in accordance with specific emulsion procedures. The newer method of recording color television images with line arrays of light-emitting diodes involves dichromic superposing mirrors and a movable scanning mirror. This method allows the use of standard movie cameras, simplifies interlacing-to-linewise conversion and the mechanical equipment, and lengthens exposure time while it shortens recording time. The latest image transform method requires an audio-video recorder, a memory disk, a digital computer, and a decoder. The 9-step procedure includes preprocessing the total color television signal with reduction of noise level and time errors, followed by frame frequency conversion and setting the number of lines. The total signal is then resolved into its brightness and color-difference components and phase errors and image blurring are also reduced. After extraction of R,G,B signals and colorimetric matching of TV camera and film tape, the simultaneous R,B, B signals are converted from interlacing to sequential triades of color-quotient frames with linewise scanning at triple frequency. Color-quotient signals are recorded with an electron beam on a smoothly moving black-and-white film tape under vacuum. While digital techniques improve the signal quality and simplify the control of processes, not requiring stabilization of circuits, image processing is still analog.

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.

The modeling of MMI structures for signal processing applications

NASA Astrophysics Data System (ADS)

Le, Thanh Trung; Cahill, Laurence W.

2008-02-01

Microring resonators are promising candidates for photonic signal processing applications. However, almost all resonators that have been reported so far use directional couplers or 2×2 multimode interference (MMI) couplers as the coupling element between the ring and the bus waveguides. In this paper, instead of using 2×2 couplers, novel structures for microring resonators based on 3×3 MMI couplers are proposed. The characteristics of the device are derived using the modal propagation method. The device parameters are optimized by using numerical methods. Optical switches and filters using Silicon on Insulator (SOI) then have been designed and analyzed. This device can become a new basic component for further applications in optical signal processing. The paper concludes with some further examples of photonic signal processing circuits based on MMI couplers.

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

PubMed

Zhang, Yanjun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

2016-02-01

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

Noise reduction methods for nucleic acid and macromolecule sequencing

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

Schuller, Ivan K.; Di Ventra, Massimiliano; Balatsky, Alexander

Methods, systems, and devices are disclosed for processing macromolecule sequencing data with substantial noise reduction. In one aspect, a method for reducing noise in a sequential measurement of a macromolecule comprising serial subunits includes cross-correlating multiple measured signals of a physical property of subunits of interest of the macromolecule, the multiple measured signals including the time data associated with the measurement of the signal, to remove or at least reduce signal noise that is not in the same frequency and in phase with the systematic signal contribution of the measured signals.

Signal Restoration of Non-stationary Acoustic Signals in the Time Domain

NASA Technical Reports Server (NTRS)

Babkin, Alexander S.

1988-01-01

Signal restoration is a method of transforming a nonstationary signal acquired by a ground based microphone to an equivalent stationary signal. The benefit of the signal restoration is a simplification of the flight test requirements because it could dispense with the need to acquire acoustic data with another aircraft flying in concert with the rotorcraft. The data quality is also generally improved because the contamination of the signal by the propeller and wind noise is not present. The restoration methodology can also be combined with other data acquisition methods, such as a multiple linear microphone array for further improvement of the test results. The methodology and software are presented for performing the signal restoration in the time domain. The method has no restrictions on flight path geometry or flight regimes. Only requirement is that the aircraft spatial position be known relative to the microphone location and synchronized with the acoustic data. The restoration process assumes that the moving source radiates a stationary signal, which is then transformed into a nonstationary signal by various modulation processes. The restoration contains only the modulation due to the source motion.

Estimation of source location and ground impedance using a hybrid multiple signal classification and Levenberg-Marquardt approach

NASA Astrophysics Data System (ADS)

Tam, Kai-Chung; Lau, Siu-Kit; Tang, Shiu-Keung

2016-07-01

A microphone array signal processing method for locating a stationary point source over a locally reactive ground and for estimating ground impedance is examined in detail in the present study. A non-linear least square approach using the Levenberg-Marquardt method is proposed to overcome the problem of unknown ground impedance. The multiple signal classification method (MUSIC) is used to give the initial estimation of the source location, while the technique of forward backward spatial smoothing is adopted as a pre-processer of the source localization to minimize the effects of source coherence. The accuracy and robustness of the proposed signal processing method are examined. Results show that source localization in the horizontal direction by MUSIC is satisfactory. However, source coherence reduces drastically the accuracy in estimating the source height. The further application of Levenberg-Marquardt method with the results from MUSIC as the initial inputs improves significantly the accuracy of source height estimation. The present proposed method provides effective and robust estimation of the ground surface impedance.

Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations

NASA Astrophysics Data System (ADS)

García Plaza, E.; Núñez López, P. J.

2018-01-01

The wavelet packet transform method decomposes a time signal into several independent time-frequency signals called packets. This enables the temporary location of transient events occurring during the monitoring of the cutting processes, which is advantageous in monitoring condition and fault diagnosis. This paper proposes the monitoring of surface roughness using a single low cost sensor that is easily implemented in numerical control machine tools in order to make on-line decisions on workpiece surface finish quality. Packet feature extraction in vibration signals was applied to correlate the sensor signals to measured surface roughness. For the successful application of the WPT method, mother wavelets, packet decomposition level, and appropriate packet selection methods should be considered, but are poorly understood aspects in the literature. In this novel contribution, forty mother wavelets, optimal decomposition level, and packet reduction methods were analysed, as well as identifying the effective frequency range providing the best packet feature extraction for monitoring surface finish. The results show that mother wavelet biorthogonal 4.4 in decomposition level L3 with the fusion of the orthogonal vibration components (ax + ay + az) were the best option in the vibration signal and surface roughness correlation. The best packets were found in the medium-high frequency DDA (6250-9375 Hz) and high frequency ADA (9375-12500 Hz) ranges, and the feed acceleration component ay was the primary source of information. The packet reduction methods forfeited packets with relevant features to the signal, leading to poor results for the prediction of surface roughness. WPT is a robust vibration signal processing method for the monitoring of surface roughness using a single sensor without other information sources, satisfactory results were obtained in comparison to other processing methods with a low computational cost.

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

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.

Signal processing method of the diameter measurement system based on CCD parallel light projection method

NASA Astrophysics Data System (ADS)

Song, Qing; Zhu, Sijia; Yan, Han; Wu, Wenqian

2008-03-01

Parallel light projection method for the diameter measurement is to project the workpiece to be measured on the photosensitive units of CCD, but the original signal output from CCD cannot be directly used for counting or measurement. The weak signal with high-frequency noise should be filtered and amplified firstly. This paper introduces RC low-pass filter and multiple feed-back second-order low-pass filter with infinite gain. Additionally there is always dispersion on the light band and the output signal has a transition between the irradiant area and the shadow, because of the instability of the light source intensity and the imperfection of the light system adjustment. To obtain exactly the shadow size related to the workpiece diameter, binary-value processing is necessary to achieve a square wave. Comparison method and differential method can be adopted for binary-value processing. There are two ways to decide the threshold value when using voltage comparator: the fixed level method and the floated level method. The latter has a high accuracy. Deferential method is to output two spike pulses with opposite pole by the rising edge and the failing edge of the video signal related to the differential circuit firstly, then the rising edge of the signal output from the differential circuit is acquired by half-wave rectifying circuit. After traveling through the zero passing comparator and the maintain- resistance edge trigger, the square wave which indicates the measured size is acquired at last. And then it is used for filling through standard pulses and for counting through the counter. Data acquisition and information processing is accomplished by the computer and the control software. This paper will introduce in detail the design and analysis of the filter circuit, binary-value processing circuit and the interface circuit towards the computer.

Simplified signal processing for impedance spectroscopy with spectrally sparse sequences

NASA Astrophysics Data System (ADS)

Annus, P.; Land, R.; Reidla, M.; Ojarand, J.; Mughal, Y.; Min, M.

2013-04-01

Classical method for measurement of the electrical bio-impedance involves excitation with sinusoidal waveform. Sinusoidal excitation at fixed frequency points enables wide variety of signal processing options, most general of them being Fourier transform. Multiplication with two quadrature waveforms at desired frequency could be easily accomplished both in analogue and in digital domains, even simplest quadrature square waves can be considered, which reduces signal processing task in analogue domain to synchronous switching followed by low pass filter, and in digital domain requires only additions. So called spectrally sparse excitation sequences (SSS), which have been recently introduced into bio-impedance measurement domain, are very reasonable choice when simultaneous multifrequency excitation is required. They have many good properties, such as ease of generation and good crest factor compared to similar multisinusoids. Typically, the usage of discrete or fast Fourier transform in signal processing step is considered so far. Usage of simplified methods nevertheless would reduce computational burden, and enable simpler, less costly and less energy hungry signal processing platforms. Accuracy of the measurement with SSS excitation when using different waveforms for quadrature demodulation will be compared in order to evaluate the feasibility of the simplified signal processing. Sigma delta modulated sinusoid (binary signal) is considered to be a good alternative for a synchronous demodulation.

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

Development of Coriolis mass flowmeter with digital drive and signal processing technology.

PubMed

Hou, Qi-Li; Xu, Ke-Jun; Fang, Min; Liu, Cui; Xiong, Wen-Jun

2013-09-01

Coriolis mass flowmeter (CMF) often suffers from two-phase flowrate which may cause flowtube stalling. To solve this problem, a digital drive method and a digital signal processing method of CMF is studied and implemented in this paper. A positive-negative step signal is used to initiate the flowtube oscillation without knowing the natural frequency of the flowtube. A digital zero-crossing detection method based on Lagrange interpolation is adopted to calculate the frequency and phase difference of the sensor output signals in order to synthesize the digital drive signal. The digital drive approach is implemented by a multiplying digital to analog converter (MDAC) and a direct digital synthesizer (DDS). A digital Coriolis mass flow transmitter is developed with a digital signal processor (DSP) to control the digital drive, and realize the signal processing. Water flow calibrations and gas-liquid two-phase flowrate experiments are conducted to examine the performance of the transmitter. The experimental results show that the transmitter shortens the start-up time and can maintain the oscillation of flowtube in two-phase flowrate condition. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Digital signal processing in the radio science stability analyzer

NASA Technical Reports Server (NTRS)

Greenhall, C. A.

1995-01-01

The Telecommunications Division has built a stability analyzer for testing Deep Space Network installations during flight radio science experiments. The low-frequency part of the analyzer operates by digitizing wave signals with bandwidths between 80 Hz and 45 kHz. Processed outputs include spectra of signal, phase, amplitude, and differential phase; time series of the same quantities; and Allan deviation of phase and differential phase. This article documents the digital signal-processing methods programmed into the analyzer.

Neural network based system for equipment surveillance

DOEpatents

Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

1998-01-01

A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

Neural network based system for equipment surveillance

DOEpatents

Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

1998-04-28

A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

Directional dual-tree rational-dilation complex wavelet transform.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2014-01-01

Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

PubMed

Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng

2013-08-01

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

DOE PAGES

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

2014-12-18

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

NASA Astrophysics Data System (ADS)

Lee, Dong-Sup; Cho, Dae-Seung; Kim, Kookhyun; Jeon, Jae-Jin; Jung, Woo-Jin; Kang, Myeng-Hwan; Kim, Jae-Ho

2015-01-01

Independent Component Analysis (ICA), one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: instability and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to validate the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

Covariance-based direction-of-arrival estimation of wideband coherent chirp signals via sparse representation.

PubMed

Sha, Zhichao; Liu, Zhengmeng; Huang, Zhitao; Zhou, Yiyu

2013-08-29

This paper addresses the problem of direction-of-arrival (DOA) estimation of multiple wideband coherent chirp signals, and a new method is proposed. The new method is based on signal component analysis of the array output covariance, instead of the complicated time-frequency analysis used in previous literatures, and thus is more compact and effectively avoids possible signal energy loss during the hyper-processes. Moreover, the a priori information of signal number is no longer a necessity for DOA estimation in the new method. Simulation results demonstrate the performance superiority of the new method over previous ones.

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.

Total focusing method with correlation processing of antenna array signals

NASA Astrophysics Data System (ADS)

Kozhemyak, O. A.; Bortalevich, S. I.; Loginov, E. L.; Shinyakov, Y. A.; Sukhorukov, M. P.

2018-03-01

The article proposes a method of preliminary correlation processing of a complete set of antenna array signals used in the image reconstruction algorithm. The results of experimental studies of 3D reconstruction of various reflectors using and without correlation processing are presented in the article. Software ‘IDealSystem3D’ by IDeal-Technologies was used for experiments. Copper wires of different diameters located in a water bath were used as a reflector. The use of correlation processing makes it possible to obtain more accurate reconstruction of the image of the reflectors and to increase the signal-to-noise ratio. The experimental results were processed using an original program. This program allows varying the parameters of the antenna array and sampling frequency.

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.

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

PubMed

Sutha, P; Jayanthi, V E

2017-12-08

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis

NASA Astrophysics Data System (ADS)

Qiao, Zijian; Lei, Yaguo; Lin, Jing; Jia, Feng

2017-02-01

In mechanical fault diagnosis, most traditional methods for signal processing attempt to suppress or cancel noise imbedded in vibration signals for extracting weak fault characteristics, whereas stochastic resonance (SR), as a potential tool for signal processing, is able to utilize the noise to enhance fault characteristics. The classical bistable SR (CBSR), as one of the most widely used SR methods, however, has the disadvantage of inherent output saturation. The output saturation not only reduces the output signal-to-noise ratio (SNR) but also limits the enhancement capability for fault characteristics. To overcome this shortcoming, a novel method is proposed to extract the fault characteristics, where a piecewise bistable potential model is established. Simulated signals are used to illustrate the effectiveness of the proposed method, and the results show that the method is able to extract weak fault characteristics and has good enhancement performance and anti-noise capability. Finally, the method is applied to fault diagnosis of bearings and planetary gearboxes, respectively. The diagnosis results demonstrate that the proposed method can obtain larger output SNR, higher spectrum peaks at fault characteristic frequencies and therefore larger recognizable degree than the CBSR method.

Method and apparatus for measuring flow velocity using matched filters

DOEpatents

Raptis, Apostolos C.

1983-01-01

An apparatus and method for measuring the flow velocities of individual phase flow components of a multiphase flow utilizes matched filters. Signals arising from flow noise disturbance are extracted from the flow, at upstream and downstream locations. The signals are processed through pairs of matched filters which are matched to the flow disturbance frequency characteristics of the phase flow component to be measured. The processed signals are then cross-correlated to determine the transit delay time of the phase flow component between sensing positions.

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.

Elucidating the Functional Roles of Spatial Organization in Cross-Membrane Signal Transduction by a Hybrid Simulation Method.

PubMed

Chen, Jiawen; Xie, Zhong-Ru; Wu, Yinghao

2016-07-01

The ligand-binding of membrane receptors on cell surfaces initiates the dynamic process of cross-membrane signal transduction. It is an indispensable part of the signaling network for cells to communicate with external environments. Recent experiments revealed that molecular components in signal transduction are not randomly mixed, but spatially organized into distinctive patterns. These patterns, such as receptor clustering and ligand oligomerization, lead to very different gene expression profiles. However, little is understood about the molecular mechanisms and functional impacts of this spatial-temporal regulation in cross-membrane signal transduction. In order to tackle this problem, we developed a hybrid computational method that decomposes a model of signaling network into two simulation modules. The physical process of binding between receptors and ligands on cell surfaces are simulated by a diffusion-reaction algorithm, while the downstream biochemical reactions are modeled by stochastic simulation of Gillespie algorithm. These two processes are coupled together by a synchronization framework. Using this method, we tested the dynamics of a simple signaling network in which the ligand binding of cell surface receptors triggers the phosphorylation of protein kinases, and in turn regulates the expression of target genes. We found that spatial aggregation of membrane receptors at cellular interfaces is able to either amplify or inhibit downstream signaling outputs, depending on the details of clustering mechanism. Moreover, by providing higher binding avidity, the co-localization of ligands into multi-valence complex modulates signaling in very different ways that are closely related to the binding affinity between ligand and receptor. We also found that the temporal oscillation of the signaling pathway that is derived from genetic feedback loops can be modified by the spatial clustering of membrane receptors. In summary, our method demonstrates the functional importance of spatial organization in cross-membrane signal transduction. The method can be applied to any specific signaling pathway in cells.

Robust Methods for Sensing and Reconstructing Sparse Signals

ERIC Educational Resources Information Center

Carrillo, Rafael E.

2012-01-01

Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…

Dynamic decomposition of spatiotemporal neural signals

PubMed Central

2017-01-01

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. PMID:28558039

Research on on-line monitoring technology for steel ball's forming process based on load signal analysis method

NASA Astrophysics Data System (ADS)

Li, Ying-jun; Ai, Chang-sheng; Men, Xiu-hua; Zhang, Cheng-liang; Zhang, Qi

2013-04-01

This paper presents a novel on-line monitoring technology to obtain forming quality in steel ball's forming process based on load signal analysis method, in order to reveal the bottom die's load characteristic in initial cold heading forging process of steel balls. A mechanical model of the cold header producing process is established and analyzed by using finite element method. The maximum cold heading force is calculated. The results prove that the monitoring on the cold heading process with upsetting force is reasonable and feasible. The forming defects are inflected on the three feature points of the bottom die signals, which are the initial point, infection point, and peak point. A novel PVDF piezoelectric force sensor which is simple on construction and convenient on installation is designed. The sensitivity of the PVDF force sensor is calculated. The characteristics of PVDF force sensor are analyzed by FEM. The PVDF piezoelectric force sensor is fabricated to acquire the actual load signals in the cold heading process, and calibrated by a special device. The measuring system of on-line monitoring is built. The characteristics of the actual signals recognized by learning and identification algorithm are in consistence with simulation results. Identification of actual signals shows that the timing difference values of all feature points for qualified products are not exceed ±6 ms, and amplitude difference values are less than ±3%. The calibration and application experiments show that PVDF force sensor has good static and dynamic performances, and is competent at dynamic measuring on upsetting force. It greatly improves automatic level and machining precision. Equipment capacity factor with damages identification method depends on grade of steel has been improved to 90%.

Frequency-time coherence for all-optical sampling without optical pulse source

PubMed Central

Preußler, Stefan; Raoof Mehrpoor, Gilda; Schneider, Thomas

2016-01-01

Sampling is the first step to convert an analogue optical signal into a digital electrical signal. The latter can be further processed and analysed by well-known electrical signal processing methods. Optical pulse sources like mode-locked lasers are commonly incorporated for all-optical sampling, but have several drawbacks. A novel approach for a simple all-optical sampling is to utilise the frequency-time coherence of each signal. The method is based on only using two coupled modulators driven with an electrical sine wave. Since no optical source is required, a simple integration in appropriate platforms, such as Silicon Photonics might be possible. The presented method grants all-optical sampling with electrically tunable bandwidth, repetition rate and time shift. PMID:27687495

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.

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

Invariance algorithms for processing NDE signals

NASA Astrophysics Data System (ADS)

Mandayam, Shreekanth; Udpa, Lalita; Udpa, Satish S.; Lord, William

1996-11-01

Signals that are obtained in a variety of nondestructive evaluation (NDE) processes capture information not only about the characteristics of the flaw, but also reflect variations in the specimen's material properties. Such signal changes may be viewed as anomalies that could obscure defect related information. An example of this situation occurs during in-line inspection of gas transmission pipelines. The magnetic flux leakage (MFL) method is used to conduct noninvasive measurements of the integrity of the pipe-wall. The MFL signals contain information both about the permeability of the pipe-wall and the dimensions of the flaw. Similar operational effects can be found in other NDE processes. This paper presents algorithms to render NDE signals invariant to selected test parameters, while retaining defect related information. Wavelet transform based neural network techniques are employed to develop the invariance algorithms. The invariance transformation is shown to be a necessary pre-processing step for subsequent defect characterization and visualization schemes. Results demonstrating the successful application of the method are presented.

Digital Signal Processing Based on a Clustering Algorithm for Ir/Au TES Microcalorimeter

NASA Astrophysics Data System (ADS)

Zen, N.; Kunieda, Y.; Takahashi, H.; Hiramoto, K.; Nakazawa, M.; Fukuda, D.; Ukibe, M.; Ohkubo, M.

2006-02-01

In recent years, cryogenic microcalorimeters using their superconducting transition edge have been under development for possible application to the research for astronomical X-ray observations. To improve the energy resolution of superconducting transition edge sensors (TES), several correction methods have been developed. Among them, a clustering method based on digital signal processing has recently been proposed. In this paper, we applied the clustering method to Ir/Au bilayer TES. This method resulted in almost a 10% improvement in the energy resolution. Conversely, from the point of view of imaging X-ray spectroscopy, we applied the clustering method to pixellated Ir/Au-TES devices. We will thus show how a clustering method which sorts signals by their shapes is also useful for position identification

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

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

Kerekes, Ryan A; Blundon, Jay

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

Joint Carrier-Phase Synchronization and LDPC Decoding

NASA Technical Reports Server (NTRS)

Simon, Marvin; Valles, Esteban

2009-01-01

A method has been proposed to increase the degree of synchronization of a radio receiver with the phase of a suppressed carrier signal modulated with a binary- phase-shift-keying (BPSK) or quaternary- phase-shift-keying (QPSK) signal representing a low-density parity-check (LDPC) code. This method is an extended version of the method described in Using LDPC Code Constraints to Aid Recovery of Symbol Timing (NPO-43112), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 54. Both methods and the receiver architectures in which they would be implemented belong to a class of timing- recovery methods and corresponding receiver architectures characterized as pilotless in that they do not require transmission and reception of pilot signals. The proposed method calls for the use of what is known in the art as soft decision feedback to remove the modulation from a replica of the incoming signal prior to feeding this replica to a phase-locked loop (PLL) or other carrier-tracking stage in the receiver. Soft decision feedback refers to suitably processed versions of intermediate results of iterative computations involved in the LDPC decoding process. Unlike a related prior method in which hard decision feedback (the final sequence of decoded symbols) is used to remove the modulation, the proposed method does not require estimation of the decoder error probability. In a basic digital implementation of the proposed method, the incoming signal (having carrier phase theta theta (sub c) plus noise would first be converted to inphase (I) and quadrature (Q) baseband signals by mixing it with I and Q signals at the carrier frequency [wc/(2 pi)] generated by a local oscillator. The resulting demodulated signals would be processed through one-symbol-period integrate and- dump filters, the outputs of which would be sampled and held, then multiplied by a soft-decision version of the baseband modulated signal. The resulting I and Q products consist of terms proportional to the cosine and sine of the carrier phase cc as well as correlated noise components. These products would be fed as inputs to a digital PLL that would include a number-controlled oscillator (NCO), which provides an estimate of the carrier phase, theta(sub c).

Estimating the delay-Doppler of target echo in a high clutter underwater environment using wideband linear chirp signals: Evaluation of performance with experimental data.

PubMed

Yu, Ge; Yang, T C; Piao, Shengchun

2017-10-01

A chirp signal is a signal with linearly varying instantaneous frequency over the signal bandwidth, also known as a linear frequency modulated (LFM) signal. It is widely used in communication, radar, active sonar, and other applications due to its Doppler tolerance property in signal detection using the matched filter (MF) processing. Modern sonar uses high-gain, wideband signals to improve the signal to reverberation ratio. High gain implies a high product of the signal bandwidth and duration. However, wideband and/or long duration LFM signals are no longer Doppler tolerant. The shortcoming of the standard MF processing is loss of performance, and bias in range estimation. This paper uses the wideband ambiguity function and the fractional Fourier transform method to estimate the target velocity and restore the performance. Target velocity or Doppler provides a clue for differentiating the target from the background reverberation and clutter. The methods are applied to simulated and experimental data.

Pulse-echo probe of rock permeability near oil wells

NASA Technical Reports Server (NTRS)

Narasimhan, K. Y.; Parthasarathy, S. P.

1978-01-01

Processing method involves sequential insonifications of borehole wall at number of different frequencies. Return signals are normalized in amplitude, and root-mean-square (rms) value of each signal is determined. Values can be processed to yield information on size and number density of microfractures at various depths in rock matrix by using averaging methods developed for pulse-echo technique.

Advanced Signal Processing for High Temperatures Health Monitoring of Condensed Water Height in Steam Pipes

NASA Technical Reports Server (NTRS)

Lih, Shyh-Shiuh; Bar-Cohen, Yoseph; Lee, Hyeong Jae; Takano, Nobuyuki; Bao, Xiaoqi

2013-01-01

An advanced signal processing methodology is being developed to monitor the height of condensed water thru the wall of a steel pipe while operating at temperatures as high as 250deg. Using existing techniques, previous study indicated that, when the water height is low or there is disturbance in the environment, the predicted water height may not be accurate. In recent years, the use of the autocorrelation and envelope techniques in the signal processing has been demonstrated to be a very useful tool for practical applications. In this paper, various signal processing techniques including the auto correlation, Hilbert transform, and the Shannon Energy Envelope methods were studied and implemented to determine the water height in the steam pipe. The results have shown that the developed method provides a good capability for monitoring the height in the regular conditions. An alternative solution for shallow water or no water conditions based on a developed hybrid method based on Hilbert transform (HT) with a high pass filter and using the optimized windowing technique is suggested. Further development of the reported methods would provide a powerful tool for the identification of the disturbances of water height inside the pipe.

Method and apparatus for large motor control

DOEpatents

Rose, Chris R [Santa Fe, NM; Nelson, Ronald O [White Rock, NM

2003-08-12

Apparatus and method for providing digital signal processing method for controlling the speed and phase of a motor involves inputting a reference signal having a frequency and relative phase indicative of a time based signal; modifying the reference signal to introduce a slew-rate limited portion of each cycle of the reference signal; inputting a feedback signal having a frequency and relative phase indicative of the operation of said motor; modifying the feedback signal to introduce a slew-rate limited portion of each cycle of the feedback signal; analyzing the modified reference signal and the modified feedback signal to determine the frequency of the modified reference signal and of the modified feedback signal and said relative phase between said modified reference signal and said modified feedback signal; and outputting control signals to the motor for adjusting said speed and phase of the motor based on the frequency determination and determination of the relative phase.

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.

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

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Sparse signal representation and its applications in ultrasonic NDE.

PubMed

Zhang, Guang-Ming; Zhang, Cheng-Zhong; Harvey, David M

2012-03-01

Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration. Copyright © 2011 Elsevier B.V. All rights reserved.

Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning

NASA Astrophysics Data System (ADS)

García Plaza, E.; Núñez López, P. J.

2018-01-01

On-line monitoring of surface finish in machining processes has proven to be a substantial advancement over traditional post-process quality control techniques by reducing inspection times and costs and by avoiding the manufacture of defective products. This study applied techniques for processing cutting force signals based on the wavelet packet transform (WPT) method for the monitoring of surface finish in computer numerical control (CNC) turning operations. The behaviour of 40 mother wavelets was analysed using three techniques: global packet analysis (G-WPT), and the application of two packet reduction criteria: maximum energy (E-WPT) and maximum entropy (SE-WPT). The optimum signal decomposition level (Lj) was determined to eliminate noise and to obtain information correlated to surface finish. The results obtained with the G-WPT method provided an in-depth analysis of cutting force signals, and frequency ranges and signal characteristics were correlated to surface finish with excellent results in the accuracy and reliability of the predictive models. The radial and tangential cutting force components at low frequency provided most of the information for the monitoring of surface finish. The E-WPT and SE-WPT packet reduction criteria substantially reduced signal processing time, but at the expense of discarding packets with relevant information, which impoverished the results. The G-WPT method was observed to be an ideal procedure for processing cutting force signals applied to the real-time monitoring of surface finish, and was estimated to be highly accurate and reliable at a low analytical-computational cost.

Chaotic Signal Denoising Based on Hierarchical Threshold Synchrosqueezed Wavelet Transform

NASA Astrophysics Data System (ADS)

Wang, Wen-Bo; Jing, Yun-yu; Zhao, Yan-chao; Zhang, Lian-Hua; Wang, Xiang-Li

2017-12-01

In order to overcoming the shortcoming of single threshold synchrosqueezed wavelet transform(SWT) denoising method, an adaptive hierarchical threshold SWT chaotic signal denoising method is proposed. Firstly, a new SWT threshold function is constructed based on Stein unbiased risk estimation, which is two order continuous derivable. Then, by using of the new threshold function, a threshold process based on the minimum mean square error was implemented, and the optimal estimation value of each layer threshold in SWT chaotic denoising is obtained. The experimental results of the simulating chaotic signal and measured sunspot signals show that, the proposed method can filter the noise of chaotic signal well, and the intrinsic chaotic characteristic of the original signal can be recovered very well. Compared with the EEMD denoising method and the single threshold SWT denoising method, the proposed method can obtain better denoising result for the chaotic signal.

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.

A sequential method for spline approximation with variable knots. [recursive piecewise polynomial signal processing

NASA Technical Reports Server (NTRS)

Mier Muth, A. M.; Willsky, A. S.

1978-01-01

In this paper we describe a method for approximating a waveform by a spline. The method is quite efficient, as the data are processed sequentially. The basis of the approach is to view the approximation problem as a question of estimation of a polynomial in noise, with the possibility of abrupt changes in the highest derivative. This allows us to bring several powerful statistical signal processing tools into play. We also present some initial results on the application of our technique to the processing of electrocardiograms, where the knot locations themselves may be some of the most important pieces of diagnostic information.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Method and apparatus for relative navigation using reflected GPS signals

NASA Technical Reports Server (NTRS)

Cohen, Ian R. (Inventor); Boegner, Jr., Gregory J. (Inventor)

2010-01-01

A method and system to passively navigate an orbiting moving body towards an orbiting target using reflected GPS signals. A pair of antennas is employed to receive both direct signals from a plurality of GPS satellites and a second antenna to receive GPS signals reflected off an orbiting target. The direct and reflected signals are processed and compared to determine the relative distance and position of the orbiting moving body relative to the orbiting target.

Apparatus and Method for Assessing Vestibulo-Ocular Function

NASA Technical Reports Server (NTRS)

Shelhamer, Mark J. (Inventor)

2015-01-01

A system for assessing vestibulo-ocular function includes a motion sensor system adapted to be coupled to a user's head; a data processing system configured to communicate with the motion sensor system to receive the head-motion signals; a visual display system configured to communicate with the data processing system to receive image signals from the data processing system; and a gain control device arranged to be operated by the user and to communicate gain adjustment signals to the data processing system.

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

Method and apparatus for measuring flow velocity using matched filters

DOEpatents

Raptis, A.C.

1983-09-06

An apparatus and method for measuring the flow velocities of individual phase flow components of a multiphase flow utilizes matched filters. Signals arising from flow noise disturbance are extracted from the flow, at upstream and downstream locations. The signals are processed through pairs of matched filters which are matched to the flow disturbance frequency characteristics of the phase flow component to be measured. The processed signals are then cross-correlated to determine the transit delay time of the phase flow component between sensing positions. 8 figs.

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.

Quantitative analysis of phosphoinositide 3-kinase (PI3K) signaling using live-cell total internal reflection fluorescence (TIRF) microscopy.

PubMed

Johnson, Heath E; Haugh, Jason M

2013-12-02

This unit focuses on the use of total internal reflection fluorescence (TIRF) microscopy and image analysis methods to study the dynamics of signal transduction mediated by class I phosphoinositide 3-kinases (PI3Ks) in mammalian cells. The first four protocols cover live-cell imaging experiments, image acquisition parameters, and basic image processing and segmentation. These methods are generally applicable to live-cell TIRF experiments. The remaining protocols outline more advanced image analysis methods, which were developed in our laboratory for the purpose of characterizing the spatiotemporal dynamics of PI3K signaling. These methods may be extended to analyze other cellular processes monitored using fluorescent biosensors. Copyright © 2013 John Wiley & Sons, Inc.

Phytometric intelligence sensors

NASA Technical Reports Server (NTRS)

Seelig, Hans-Dieter (Inventor); Stoner, II, Richard J. (Inventor); Hoehn, Alexander (Inventor); Adams, III, William Walter (Inventor)

2010-01-01

Methods and apparatus for determining when plants require watering, and methods of attending to the watering of plants including signaling the grower that the plants are in need of hydration are provided. The novel methods include real-time measurement of plant metabolics and phytometric physiology changes of intrinsic physical or behavioral traits within the plant such as determining physiological flux measurement of enzyme flux due to environmental changes such as the wind and drought stress, soil and plant mineral deficiencies, or the interaction with a bio-control for organic disease control including, cell movement, signal transduction, internal chemical processes and external environmental processes including when plants require watering, and methods of attending to the watering of plants including signaling the grower that the plants are in need of hydration.

Signal analysis and radioholographic methods for airborne radio occultations

NASA Astrophysics Data System (ADS)

Wang, Kuo-Nung

Global Positioning System (GPS) radio occultation (RO) is an atmospheric sounding technique utilizing the change in propagation direction and delay of the GPS signal to measure refractivity, which provides information on temperature and humidity. The GPS-RO technique is now operational on several Low Earth Orbiting (LEO) satellite missions. Nevertheless, when observing localized transient events, such as tropical storms, current LEO satellite systems cannot provide sufficiently high temporal and spatial resolution soundings. An airborne RO (ARO) system has therefore been developed for localized GPS-RO campaigns. The open-loop (OL) tracking in post-processing is used to cross-correlates the received Global Navigation Satellite System (GNSS) signal with an internally generated local carrier signal predicted from a Doppler model and extract the atmospheric refractivity information. OL tracking also allows robust processing of rising GPS signals using backward tracking, which will double the observed occultation event numbers. RO signals in the lower troposphere are adversely affected by rapid phase accelerations and severe signal power fading, however. The negative bias caused by low signal-to-noise ratio (SNR) and multipath ray propagation limits the depth of tracking in the atmosphere. Therefore, we developed a model relating the SNR to the variance in the residual phase of the observed signal produced from OL tracking, and its applicability to airborne data is demonstrated. We then apply this model to set a threshold on refractivity retrieval, based upon the cumulative unwrapping error bias, to determine the altitude limit for reliable signal tracking. To enhance the SNR and decrease the unwrapping error rate, the CIRA-Q climatological model and signal residual phase pre-filtering are utilized to process the ARO residual phase. This more accurately modeled phase and less noisy received signal are shown to greatly reduce the bias caused by unwrapping error at lower altitude. On the other hand, to process the superimposed signal in the lower troposphere with its highly variable moisture distribution, Radio-Holographic (RH) methods such as Phase Matching (PM) have been adapted for ARO platforms to untangle the bending angle of each signal path. Under the assumption of spherically symmetric atmosphere, ARO PM can identify different subsignals using the Method of the Stationary Phase (MSP) and determine the arrival angle for each impact parameter. As a result, each subsignal can be distinguished and its corresponding bending angle can be retrieved without producing a negative bias. The refractivity retrieval results using ARO PM are compared to those using the traditional Geometrical Optics (GO) method. The improvements are shown and discussed in the dissertation. We applied these new methods to the received ARO data collected by the GNSS instrument system for multistatic and occultation sensing (GISMOS) in the 2010 PREDepression Investigation of Cloud systems (PREDICT) campaign. A data set of 5 research flights with 57 occultation events during the formation stage of the Hurricane Karl are processed and analyzed. In this research, the refractivity fractional difference with ERA-I model can be maintained at an average 2% above a height of 2km with a climatological model and ARO PM. Compared to the traditional geometrical optics (GO) method without climatological method assistance, the new ARO processing can effectively decrease the refractivity negative bias and significantly improve the retrieval depth of ARO.

Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

PubMed Central

Pappachan, Bobby K; Caesarendra, Wahyu; Tjahjowidodo, Tegoeh; Wijaya, Tomi

2017-01-01

Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process. PMID:28556809

Spectroscopic analysis and control

DOEpatents

Tate; , James D.; Reed, Christopher J.; Domke, Christopher H.; Le, Linh; Seasholtz, Mary Beth; Weber, Andy; Lipp, Charles

2017-04-18

Apparatus for spectroscopic analysis which includes a tunable diode laser spectrometer having a digital output signal and a digital computer for receiving the digital output signal from the spectrometer, the digital computer programmed to process the digital output signal using a multivariate regression algorithm. In addition, a spectroscopic method of analysis using such apparatus. Finally, a method for controlling an ethylene cracker hydrogenator.

Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

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

Anant, K.S.

1997-06-01

In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the Pmore » as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The `pick` time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the feature identification methods of Chapter 3, the compression methods of Chapter 4, as well as the wavelet design methods of Chapter 5, are general enough to be easily applied to other transient signals.« less

[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.

Investigation of optical current transformer signal processing method based on an improved Kalman algorithm

NASA Astrophysics Data System (ADS)

Shen, Yan; Ge, Jin-ming; Zhang, Guo-qing; Yu, Wen-bin; Liu, Rui-tong; Fan, Wei; Yang, Ying-xuan

2018-01-01

This paper explores the problem of signal processing in optical current transformers (OCTs). Based on the noise characteristics of OCTs, such as overlapping signals, noise frequency bands, low signal-to-noise ratios, and difficulties in acquiring statistical features of noise power, an improved standard Kalman filtering algorithm was proposed for direct current (DC) signal processing. The state-space model of the OCT DC measurement system is first established, and then mixed noise can be processed by adding mixed noise into measurement and state parameters. According to the minimum mean squared error criterion, state predictions and update equations of the improved Kalman algorithm could be deduced based on the established model. An improved central difference Kalman filter was proposed for alternating current (AC) signal processing, which improved the sampling strategy and noise processing of colored noise. Real-time estimation and correction of noise were achieved by designing AC and DC noise recursive filters. Experimental results show that the improved signal processing algorithms had a good filtering effect on the AC and DC signals with mixed noise of OCT. Furthermore, the proposed algorithm was able to achieve real-time correction of noise during the OCT filtering process.

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

NASA Astrophysics Data System (ADS)

Han, G.; Lin, B.; Xu, Z.

2017-03-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

Synchronous acquisition of multi-channel signals by single-channel ADC based on square wave modulation

NASA Astrophysics Data System (ADS)

Yi, Xiaoqing; Hao, Liling; Jiang, Fangfang; Xu, Lisheng; Song, Shaoxiu; Li, Gang; Lin, Ling

2017-08-01

Synchronous acquisition of multi-channel biopotential signals, such as electrocardiograph (ECG) and electroencephalograph, has vital significance in health care and clinical diagnosis. In this paper, we proposed a new method which is using single channel ADC to acquire multi-channel biopotential signals modulated by square waves synchronously. In this method, a specific modulate and demodulate method has been investigated without complex signal processing schemes. For each channel, the sampling rate would not decline with the increase of the number of signal channels. More specifically, the signal-to-noise ratio of each channel is n times of the time-division method or an improvement of 3.01 ×log2n dB, where n represents the number of the signal channels. A numerical simulation shows the feasibility and validity of this method. Besides, a newly developed 8-lead ECG based on the new method has been introduced. These experiments illustrate that the method is practicable and thus is potential for low-cost medical monitors.

Robust signal recovery using the prolate spherical wave functions and maximum correntropy criterion

NASA Astrophysics Data System (ADS)

Zou, Cuiming; Kou, Kit Ian

2018-05-01

Signal recovery is one of the most important problem in signal processing. This paper proposes a novel signal recovery method based on prolate spherical wave functions (PSWFs). PSWFs are a kind of special functions, which have been proved having good performance in signal recovery. However, the existing PSWFs based recovery methods used the mean square error (MSE) criterion, which depends on the Gaussianity assumption of the noise distributions. For the non-Gaussian noises, such as impulsive noise or outliers, the MSE criterion is sensitive, which may lead to large reconstruction error. Unlike the existing PSWFs based recovery methods, our proposed PSWFs based recovery method employs the maximum correntropy criterion (MCC), which is independent of the noise distribution. The proposed method can reduce the impact of the large and non-Gaussian noises. The experimental results on synthetic signals with various types of noises show that the proposed MCC based signal recovery method has better robust property against various noises compared to other existing methods.

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas

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

Washeleski, Robert L.; Meyer, Edmond J. IV; King, Lyon B.

2013-10-15

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. Themore » key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.« less

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas.

PubMed

Washeleski, Robert L; Meyer, Edmond J; King, Lyon B

2013-10-01

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. The key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.

Apparatus and method for temperature correction and expanded count rate of inorganic scintillation detectors

DOEpatents

Ianakiev, Kiril D [Los Alamos, NM; Hsue, Sin Tao [Santa Fe, NM; Browne, Michael C [Los Alamos, NM; Audia, Jeffrey M [Abiquiu, NM

2006-07-25

The present invention includes an apparatus and corresponding method for temperature correction and count rate expansion of inorganic scintillation detectors. A temperature sensor is attached to an inorganic scintillation detector. The inorganic scintillation detector, due to interaction with incident radiation, creates light pulse signals. A photoreceiver processes the light pulse signals to current signals. Temperature correction circuitry that uses a fast light component signal, a slow light component signal, and the temperature signal from the temperature sensor to corrected an inorganic scintillation detector signal output and expanded the count rate.

Estimating Transmitted-Signal Phase Variations for Uplink Array Antennas

NASA Technical Reports Server (NTRS)

Paal, Leslie; Mukai, Ryan; Vilntrotter, Victor; Cornish, Timothy; Lee, Dennis

2009-01-01

A method of estimating phase drifts of microwave signals distributed to, and transmitted by, antennas in an array involves the use of the signals themselves as phase references. The method was conceived as part of the solution of the problem of maintaining precise phase calibration required for proper operation of an array of Deep Space Network (DSN) antennas on Earth used for communicating with distant spacecraft at frequencies between 7 and 8 GHz. The method could also be applied to purely terrestrial phased-array radar and other radio antenna array systems. In the DSN application, the electrical lengths (effective signal-propagation path lengths) of the various branches of the system for distributing the transmitted signals to the antennas are not precisely known, and they vary with time. The variations are attributable mostly to thermal expansion and contraction of fiber-optic and electrical signal cables and to a variety of causes associated with aging of signal-handling components. The variations are large enough to introduce large phase drifts at the signal frequency. It is necessary to measure and correct for these phase drifts in order to maintain phase calibration of the antennas. A prior method of measuring phase drifts involves the use of reference-frequency signals separate from the transmitted signals. A major impediment to accurate measurement of phase drifts over time by the prior method is the fact that although DSN reference-frequency sources separate from the transmitting signal sources are stable and accurate enough for most DSN purposes, they are not stable enough for use in maintaining phase calibrations, as required, to within a few degrees over times as long as days or possibly even weeks. By eliminating reliance on the reference-frequency subsystem, the present method overcomes this impediment. In a DSN array to which the present method applies (see figure), the microwave signals to be transmitted are generated by exciters in a signal-processing center, then distributed to the antennas via optical fibers. At each antenna, the signals are used to drive a microwave power-amplifier train, the output of which is coupled to the antenna for transmission. A small fraction of the power-amplifier-train output is sent back to the signal-processing center along another optical fiber that is part of the same fiber-optic cable used to distribute the transmitted signal to the antenna. In the signal-processing center, the signal thus returned from each antenna is detected and its phase is compared with the phase of the signal sampled directly from the corresponding exciter. It is known, from other measurements, that the signal-propagation path length from the power-amplifier-train output port to the phase center of each antenna is sufficiently stable and, hence, that sampling the signal at the power-amplifier-train output port suffices for the purpose of characterizing the phase drift of the transmitted signal at the phase center of the antenna

Photo-Spectrometer Realized In A Standard Cmos Ic Process

DOEpatents

Simpson, Michael L.; Ericson, M. Nance; Dress, William B.; Jellison, Gerald E.; Sitter, Jr., David N.; Wintenberg, Alan L.

1999-10-12

A spectrometer, comprises: a semiconductor having a silicon substrate, the substrate having integrally formed thereon a plurality of layers forming photo diodes, each of the photo diodes having an independent spectral response to an input spectra within a spectral range of the semiconductor and each of the photo diodes formed only from at least one of the plurality of layers of the semiconductor above the substrate; and, a signal processing circuit for modifying signals from the photo diodes with respective weights, the weighted signals being representative of a specific spectral response. The photo diodes have different junction depths and different polycrystalline silicon and oxide coverings. The signal processing circuit applies the respective weights and sums the weighted signals. In a corresponding method, a spectrometer is manufactured by manipulating only the standard masks, materials and fabrication steps of standard semiconductor processing, and integrating the spectrometer with a signal processing circuit.

New signal processing technique for density profile reconstruction using reflectometry.

PubMed

Clairet, F; Ricaud, B; Briolle, F; Heuraux, S; Bottereau, C

2011-08-01

Reflectometry profile measurement requires an accurate determination of the plasma reflected signal. Along with a good resolution and a high signal to noise ratio of the phase measurement, adequate data analysis is required. A new data processing based on time-frequency tomographic representation is used. It provides a clearer separation between multiple components and improves isolation of the relevant signals. In this paper, this data processing technique is applied to two sets of signals coming from two different reflectometer devices used on the Tore Supra tokamak. For the standard density profile reflectometry, it improves the initialization process and its reliability, providing a more accurate profile determination in the far scrape-off layer with density measurements as low as 10(16) m(-1). For a second reflectometer, which provides measurements in front of a lower hybrid launcher, this method improves the separation of the relevant plasma signal from multi-reflection processes due to the proximity of the plasma.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

NASA Astrophysics Data System (ADS)

Assaad, Bassel; Eltabach, Mario; Antoni, Jérôme

2014-01-01

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.

Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex.

PubMed

Kirilina, Evgeniya; Yu, Na; Jelzow, Alexander; Wabnitz, Heidrun; Jacobs, Arthur M; Tachtsidis, Ilias

2013-01-01

Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. The goal of the present study was to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data. To achieve this goal we combined concurrent time-domain fNIRS and peripheral physiology recordings with wavelet coherence analysis (WCA). Depth selectivity was achieved by analyzing moments of photon time-of-flight distributions provided by time-domain fNIRS. Simultaneously, mean arterial blood pressure (MAP), heart rate (HR), and skin blood flow (SBF) on the forehead were recorded. WCA was employed to quantify the impact of physiological processes on fNIRS signals separately for different time scales. We identified three main processes contributing to physiological noise in fNIRS signals on the forehead. The first process with the period of about 3 s is induced by respiration. The second process is highly correlated with time lagged MAP and HR fluctuations with a period of about 10 s often referred as Mayer waves. The third process is local regulation of the facial SBF time locked to the task-evoked fNIRS signals. All processes affect oxygenated haemoglobin concentration more strongly than that of deoxygenated haemoglobin. Based on these results we developed a set of physiological regressors, which were used for physiological de-noising of fNIRS signals. Our results demonstrate that proposed de-noising method can significantly improve the sensitivity of fNIRS to cerebral signals.

Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.

PubMed

Wilson, J Adam; Williams, Justin C

2009-01-01

The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

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

Quantifying two-dimensional nonstationary signal with power-law correlations by detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Fan, Qingju; Wu, Yonghong

2015-08-01

In this paper, we develop a new method for the multifractal characterization of two-dimensional nonstationary signal, which is based on the detrended fluctuation analysis (DFA). By applying to two artificially generated signals of two-component ARFIMA process and binomial multifractal model, we show that the new method can reliably determine the multifractal scaling behavior of two-dimensional signal. We also illustrate the applications of this method in finance and physiology. The analyzing results exhibit that the two-dimensional signals under investigation are power-law correlations, and the electricity market consists of electricity price and trading volume is multifractal, while the two-dimensional EEG signal in sleep recorded for a single patient is weak multifractal. The new method based on the detrended fluctuation analysis may add diagnostic power to existing statistical methods.

Signal evaluation environment: a new method for the design of peripheral in-vehicle warning signals.

PubMed

Werneke, Julia; Vollrath, Mark

2011-06-01

An evaluation method called the Signal Evaluation Environment (SEE) was developed for use in the early stages of the design process of peripheral warning signals while driving. Accident analyses have shown that with complex driving situations such as intersections, the visual scan strategies of the driver contribute to overlooking other road users who have the right of way. Salient peripheral warning signals could disrupt these strategies and direct drivers' attention towards these road users. To select effective warning signals, the SEE was developed as a laboratory task requiring visual-cognitive processes similar to those used at intersections. For validation of the SEE, four experiments were conducted using different stimulus characteristics (size, colour contrast, shape, flashing) that influence peripheral vision. The results confirm that the SEE is able to differentiate between the selected stimulus characteristics. The SEE is a useful initial tool for designing peripheral signals, allowing quick and efficient preselection of beneficial signals.

Reducing Artifacts in TMS-Evoked EEG

NASA Astrophysics Data System (ADS)

Fuertes, Juan José; Travieso, Carlos M.; Álvarez, A.; Ferrer, M. A.; Alonso, J. B.

Transcranial magnetic stimulation induces weak currents within the cranium to activate neuronal firing and its response is recorded using electroencephalography in order to study the brain directly. However, different artifacts contaminate the results. The goal of this study is to process these artifacts and reduce them digitally. Electromagnetic, blink and auditory artifacts are considered, and Signal-Space Projection, Independent Component Analysis and Wiener Filtering methods are used to reduce them. These last two produce a successful solution for electromagnetic artifacts. Regarding the other artifacts, processed with Signal-Space Projection, the method reduces the artifact but modifies the signal as well. Nonetheless, they are modified in an exactly known way and the vector used for the projection is conserved to be taken into account when analyzing the resulting signals. A system which combines the proposed methods would improve the quality of the information presented to physicians.

Method and system for downhole clock synchronization

DOEpatents

Hall, David R.; Bartholomew, David B.; Johnson, Monte; Moon, Justin; Koehler, Roger O.

2006-11-28

A method and system for use in synchronizing at least two clocks in a downhole network are disclosed. The method comprises determining a total signal latency between a controlling processing element and at least one downhole processing element in a downhole network and sending a synchronizing time over the downhole network to the at least one downhole processing element adjusted for the signal latency. Electronic time stamps may be used to measure latency between processing elements. A system for electrically synchronizing at least two clocks connected to a downhole network comprises a controlling processing element connected to a synchronizing clock in communication over a downhole network with at least one downhole processing element comprising at least one downhole clock. Preferably, the downhole network is integrated into a downhole tool string.

Time-frequency signal analysis and synthesis - The choice of a method and its application

NASA Astrophysics Data System (ADS)

Boashash, Boualem

In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It is shown that a natural approach leads to the introduction of the concepts of the analytic signal and instantaneous frequency. The Wigner-Ville Distribution (WVD) is a method of analysis based upon these concepts and it is shown that an accurate Time-Frequency representation of a signal can be obtained by using the WVD for the analysis of a class of signals referred to as 'asymptotic'. For this class of signals, the instantaneous frequency describes an important physical parameter characteristic of the process under investigation. The WVD procedure for signal analysis and synthesis is outlined and its properties are reviewed for deterministic and random signals.

Time-Frequency Signal Analysis And Synthesis The Choice Of A Method And Its Application

NASA Astrophysics Data System (ADS)

Boashash, Boualem

1988-02-01

In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It is shown that a natural approach leads to the introduction of the concepts of the analytic signal and in-stantaneous frequency. The Wigner-Ville Distribution (WVD) is a method of analysis based upon these concepts and it is shown that an accurate Time-Frequency representation of a signal can be obtained by using the WVD for the analysis of a class of signals referred to as "asymptotic". For this class of signals, the instantaneous frequency describes an important physical parameter characteristic of the process under investigation. The WVD procedure for signal analysis and synthesis is outlined and its properties are reviewed for deterministic and random signals.

Method and Apparatus for Non-Invasive Measurement of Changes in Intracranial Pressure

NASA Technical Reports Server (NTRS)

Yost, William T. (Inventor); Cantrell, John H., Jr. (Inventor)

2004-01-01

A method and apparatus for measuring intracranial pressure. In one embodiment, the method comprises the steps of generating an information signal that comprises components (e.g., pulsatile changes and slow changes) that are related to intracranial pressure and blood pressure, generating a reference signal comprising pulsatile components that are solely related to blood pressure, processing the information and reference signals to determine the pulsatile components of the information signal that have generally the same phase as the pulsatile components of the reference signal, and removing from the information signal the pulsatile components determined to have generally the same phase as the pulsatile components of the reference signal so as to provide a data signal having components wherein substantially all of the components are related to intracranial pressure.

Waveguide-type optical circuits for recognition of optical 8QAM-coded label

NASA Astrophysics Data System (ADS)

Surenkhorol, Tumendemberel; Kishikawa, Hiroki; Goto, Nobuo; Gonchigsumlaa, Khishigjargal

2017-10-01

Optical signal processing is expected to be applied in network nodes. In photonic routers, label recognition is one of the important functions. We have studied different kinds of label recognition methods so far for on-off keying, binary phase-shift keying, quadrature phase-shift keying, and 16 quadrature amplitude modulation-coded labels. We propose a method based on waveguide circuits to recognize an optical eight quadrature amplitude modulation (8QAM)-coded label by simple passive optical signal processing. The recognition of the proposed method is theoretically analyzed and numerically simulated by the finite difference beam propagation method. The noise tolerance is discussed, and bit-error rate against optical signal-to-noise ratio is evaluated. The scalability of the proposed method is also discussed theoretically for two-symbol length 8QAM-coded labels.

Research on signal demodulation technology of Mach-Zehnder optical fiber sensor vibration system

NASA Astrophysics Data System (ADS)

Liu, Juncheng; Cheng, Pengshen; Hu, Tong

2017-08-01

Mach-Zehnder (M-Z) interferometer is frequently used in optical fiber vibration system. And signal demodulation technology plays an important role in the signal processing of M-Z optical fiber vibration system. In order to accurately get the phase information of the vibration signals, the signal demodulation technique based on M-Z interference principle is studied. In this paper, by analyzing the principles of 3 × 3 fiber coupler homodyne demodulation method and phase-generating carrier (PGC) technology, the advantages and disadvantages of the two demodulation methods for different vibration signal are presented. Then the method of judging signal strength is proposed. The correlation between the demodulation effects and strength of the perturbation signals is analyzed. Finally, the simulation experiments are carried out to compare the demodulation effects of the two demodulation methods, the results demonstrate that PGC demodulation technology has great advantages in weak signals, and the 3 × 3 fiber coupler is more suitable for strong signals.

Application of wavelet filtering and Barker-coded pulse compression hybrid method to air-coupled ultrasonic testing

NASA Astrophysics Data System (ADS)

Zhou, Zhenggan; Ma, Baoquan; Jiang, Jingtao; Yu, Guang; Liu, Kui; Zhang, Dongmei; Liu, Weiping

2014-10-01

Air-coupled ultrasonic testing (ACUT) technique has been viewed as a viable solution in defect detection of advanced composites used in aerospace and aviation industries. However, the giant mismatch of acoustic impedance in air-solid interface makes the transmission efficiency of ultrasound low, and leads to poor signal-to-noise (SNR) ratio of received signal. The utilisation of signal-processing techniques in non-destructive testing is highly appreciated. This paper presents a wavelet filtering and phase-coded pulse compression hybrid method to improve the SNR and output power of received signal. The wavelet transform is utilised to filter insignificant components from noisy ultrasonic signal, and pulse compression process is used to improve the power of correlated signal based on cross-correction algorithm. For the purpose of reasonable parameter selection, different families of wavelets (Daubechies, Symlet and Coiflet) and decomposition level in discrete wavelet transform are analysed, different Barker codes (5-13 bits) are also analysed to acquire higher main-to-side lobe ratio. The performance of the hybrid method was verified in a honeycomb composite sample. Experimental results demonstrated that the proposed method is very efficient in improving the SNR and signal strength. The applicability of the proposed method seems to be a very promising tool to evaluate the integrity of high ultrasound attenuation composite materials using the ACUT.

Time-frequency domain SNR estimation and its application in seismic data processing

NASA Astrophysics Data System (ADS)

Zhao, Yan; Liu, Yang; Li, Xuxuan; Jiang, Nansen

2014-08-01

Based on an approach estimating frequency domain signal-to-noise ratio (FSNR), we propose a method to evaluate time-frequency domain signal-to-noise ratio (TFSNR). This method adopts short-time Fourier transform (STFT) to estimate instantaneous power spectrum of signal and noise, and thus uses their ratio to compute TFSNR. Unlike FSNR describing the variation of SNR with frequency only, TFSNR depicts the variation of SNR with time and frequency, and thus better handles non-stationary seismic data. By considering TFSNR, we develop methods to improve the effects of inverse Q filtering and high frequency noise attenuation in seismic data processing. Inverse Q filtering considering TFSNR can better solve the problem of amplitude amplification of noise. The high frequency noise attenuation method considering TFSNR, different from other de-noising methods, distinguishes and suppresses noise using an explicit criterion. Examples of synthetic and real seismic data illustrate the correctness and effectiveness of the proposed methods.

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis

PubMed Central

2018-01-01

Objective To study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects. Methods MfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated syndrome (CIS)). The conventional method of processing mfVEPs consists of using a 1–35 Hz bandpass frequency filter (XDFT). The EMD algorithm was used to decompose the XDFT signals into several intrinsic mode functions (IMFs). This signal processing was assessed by computing the amplitudes and latencies of the XDFT and IMF signals (XEMD). The amplitudes from the full visual field and from ring 5 (9.8–15° eccentricity) were studied. The discrimination index was calculated between controls and patients. Interocular latency values were computed from the XDFT and XEMD signals in a control database to study variability. Results Using the amplitude of the mfVEP signals filtered with EMD (XEMD) obtains higher discrimination index values than the conventional method when control, MS-risk progression (RIS and CIS) and MS subjects are studied. The lowest variability in interocular latency computations from the control patient database was obtained by comparing the XEMD signals with the XDFT signals. Even better results (amplitude discrimination and latency variability) were obtained in ring 5 (9.8–15° eccentricity of the visual field). Conclusions Filtering mfVEP signals using the EMD algorithm will result in better identification of subjects at risk of developing MS and better accuracy in latency studies. This could be applied to assess visual cortex activity in MS diagnosis and evolution studies. PMID:29677200

A de-noising method using the improved wavelet threshold function based on noise variance estimation

NASA Astrophysics Data System (ADS)

Liu, Hui; Wang, Weida; Xiang, Changle; Han, Lijin; Nie, Haizhao

2018-01-01

The precise and efficient noise variance estimation is very important for the processing of all kinds of signals while using the wavelet transform to analyze signals and extract signal features. In view of the problem that the accuracy of traditional noise variance estimation is greatly affected by the fluctuation of noise values, this study puts forward the strategy of using the two-state Gaussian mixture model to classify the high-frequency wavelet coefficients in the minimum scale, which takes both the efficiency and accuracy into account. According to the noise variance estimation, a novel improved wavelet threshold function is proposed by combining the advantages of hard and soft threshold functions, and on the basis of the noise variance estimation algorithm and the improved wavelet threshold function, the research puts forth a novel wavelet threshold de-noising method. The method is tested and validated using random signals and bench test data of an electro-mechanical transmission system. The test results indicate that the wavelet threshold de-noising method based on the noise variance estimation shows preferable performance in processing the testing signals of the electro-mechanical transmission system: it can effectively eliminate the interference of transient signals including voltage, current, and oil pressure and maintain the dynamic characteristics of the signals favorably.

Parallel demodulation system and signal-processing method for extrinsic Fabry-Perot interferometer and fiber Bragg grating sensors.

PubMed

Jiang, Junfeng; Liu, Tiegen; Zhang, Yimo; Liu, Lina; Zha, Ying; Zhang, Fan; Wang, Yunxin; Long, Pin

2005-03-15

A parallel demodulation system for extrinsic Fabry-Perot interferometer (EFPI) and fiber Bragg grating (FBG) sensors is presented that is based on a Michelson interferometer and combines the methods of low-coherence interference and Fourier transform spectrum. Signals from EFPI and FBG sensors are obtained simultaneously by scanning one arm of a Michelson interferometer, and an algorithm model is established to process the signals and retrieve both the wavelength of the FBG and the cavity length of the EFPI at the same time, which are then used to determine the strain and temperature.

Estimation of frequency offset in mobile satellite modems

NASA Technical Reports Server (NTRS)

Cowley, W. G.; Rice, M.; Mclean, A. N.

1993-01-01

In mobilesat applications, frequency offset on the received signal must be estimated and removed prior to further modem processing. A straightforward method of estimating the carrier frequency offset is to raise the received MPSK signal to the M-th power, and then estimate the location of the peak spectral component. An analysis of the lower signal to noise threshold of this method is carried out for BPSK signals. Predicted thresholds are compared to simulation results. It is shown how the method can be extended to pi/M MPSK signals. A real-time implementation of frequency offset estimation for the Australian mobile satellite system is described.

[Application of the mixed programming with Labview and Matlab in biomedical signal analysis].

PubMed

Yu, Lu; Zhang, Yongde; Sha, Xianzheng

2011-01-01

This paper introduces the method of mixed programming with Labview and Matlab, and applies this method in a pulse wave pre-processing and feature detecting system. The method has been proved suitable, efficient and accurate, which has provided a new kind of approach for biomedical signal analysis.

Transient high frequency signal estimation: A model-based processing approach

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

Barnes, F.L.

1985-03-22

By utilizing the superposition property of linear systems a method of estimating the incident signal from reflective nondispersive data is developed. One of the basic merits of this approach is that, the reflections were removed by direct application of a Weiner type estimation algorithm, after the appropriate input was synthesized. The structure of the nondispersive signal model is well documented, and thus its' credence is established. The model is stated and more effort is devoted to practical methods of estimating the model parameters. Though a general approach was developed for obtaining the reflection weights, a simpler approach was employed here,more » since a fairly good reflection model is available. The technique essentially consists of calculating ratios of the autocorrelation function at lag zero and that lag where the incident and first reflection coincide. We initially performed our processing procedure on a measurement of a single signal. Multiple application of the processing procedure was required when we applied the reflection removal technique on a measurement containing information from the interaction of two physical phenomena. All processing was performed using SIG, an interactive signal processing package. One of the many consequences of using SIG was that repetitive operations were, for the most part, automated. A custom menu was designed to perform the deconvolution process.« less

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.

A Software Platform for Post-Processing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Donald J.; Martin, Richard E.; Seebo, Jeff P.; Trinh, Long B.; Walker, James L.; Winfree, William P.

2007-01-01

Ultrasonic, microwave, and terahertz nondestructive evaluation imaging systems generally require the acquisition of waveforms at each scan point to form an image. For such systems, signal and image processing methods are commonly needed to extract information from the waves and improve resolution of, and highlight, defects in the image. Since some similarity exists for all waveform-based NDE methods, it would seem a common software platform containing multiple signal and image processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. This presentation describes NASA Glenn Research Center's approach in developing a common software platform for processing waveform-based NDE signals and images. This platform is currently in use at NASA Glenn and at Lockheed Martin Michoud Assembly Facility for processing of pulsed terahertz and ultrasonic data. Highlights of the software operation will be given. A case study will be shown for use with terahertz data. The authors also request scientists and engineers who are interested in sharing customized signal and image processing algorithms to contribute to this effort by letting the authors code up and include these algorithms in future releases.

Application of Advanced Signal Processing Techniques to Angle of Arrival Estimation in ATC Navigation and Surveillance Systems

DTIC Science & Technology

1982-06-23

Administration Systems Research and Development Service 14, Spseq Aese Ce ’ Washington, D.C. 20591 It. SeppkW•aae metm The work reported in this document was...consider sophisticated signal processing techniques as an alternative method of improving system performanceH Some work in this area has already taken place...demands on the frequency spectrum. As noted in Table 1-1, there has been considerable work on advanced signal processing in the MLS context

New instantaneous frequency estimation method based on the use of image processing techniques

NASA Astrophysics Data System (ADS)

Borda, Monica; Nafornita, Ioan; Isar, Alexandru

2003-05-01

The aim of this paper is to present a new method for the estimation of the instantaneous frequency of a frequency modulated signal, corrupted by additive noise. This method represents an example of fusion of two theories: the time-frequency representations and the mathematical morphology. Any time-frequency representation of a useful signal is concentrated around its instantaneous frequency law and realizes the diffusion of the noise that perturbs the useful signal in the time - frequency plane. In this paper a new time-frequency representation, useful for the estimation of the instantaneous frequency, is proposed. This time-frequency representation is the product of two others time-frequency representations: the Wigner - Ville time-frequency representation and a new one obtained by filtering with a hard thresholding filter the Gabor representation of the signal to be processed. Using the image of this new time-frequency representation the instantaneous frequency of the useful signal can be extracted with the aid of some mathematical morphology operators: the conversion in binary form, the dilation and the skeleton. The simulations of the proposed method have proved its qualities. It is better than other estimation methods, like those based on the use of adaptive notch filters.

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.

Non-contact capacitance based image sensing method and system

DOEpatents

Novak, James L.; Wiczer, James J.

1995-01-01

A system and a method is provided for imaging desired surfaces of a workpiece. A sensor having first and second sensing electrodes which are electrically isolated from the workpiece is positioned above and in proximity to the desired surfaces of the workpiece. An electric field is developed between the first and second sensing electrodes of the sensor in response to input signals being applied thereto and capacitance signals are developed which are indicative of any disturbances in the electric field as a result of the workpiece. An image signal of the workpiece may be developed by processing the capacitance signals. The image signals may provide necessary control information to a machining device for machining the desired surfaces of the workpiece in processes such as deburring or chamfering. Also, the method and system may be used to image dimensions of weld pools on a workpiece and surfaces of glass vials. The sensor may include first and second preview sensors used to determine the feed rate of a workpiece with respect to the machining device.

Non-contact capacitance based image sensing method and system

DOEpatents

Novak, James L.; Wiczer, James J.

1994-01-01

A system and a method for imaging desired surfaces of a workpiece. A sensor having first and second sensing electrodes which are electrically isolated from the workpiece is positioned above and in proximity to the desired surfaces of the workpiece. An electric field is developed between the first and second sensing electrodes of the sensor in response to input signals being applied thereto and capacitance signals are developed which are indicative of any disturbances in the electric field as a result of the workpiece. An image signal of the workpiece may be developed by processing the capacitance signals. The image signals may provide necessary control information to a machining device for machining the desired surfaces of the workpiece in processes such as deburring or chamfering. Also, the method and system may be used to image dimensions of weld pools on a workpiece and surfaces of glass vials. The sensor may include first and second preview sensors used to determine the feed rate of a workpiece with respect to the machining device.

Frequency division multiplexed multi-color fluorescence microscope system

NASA Astrophysics Data System (ADS)

Le, Vu Nam; Yang, Huai Dong; Zhang, Si Chun; Zhang, Xin Rong; Jin, Guo Fan

2017-10-01

Grayscale camera can only obtain gray scale image of object, while the multicolor imaging technology can obtain the color information to distinguish the sample structures which have the same shapes but in different colors. In fluorescence microscopy, the current method of multicolor imaging are flawed. Problem of these method is affecting the efficiency of fluorescence imaging, reducing the sampling rate of CCD etc. In this paper, we propose a novel multiple color fluorescence microscopy imaging method which based on the Frequency division multiplexing (FDM) technology, by modulating the excitation lights and demodulating the fluorescence signal in frequency domain. This method uses periodic functions with different frequency to modulate amplitude of each excitation lights, and then combine these beams for illumination in a fluorescence microscopy imaging system. The imaging system will detect a multicolor fluorescence image by a grayscale camera. During the data processing, the signal obtained by each pixel of the camera will be processed with discrete Fourier transform, decomposed by color in the frequency domain and then used inverse discrete Fourier transform. After using this process for signals from all of the pixels, monochrome images of each color on the image plane can be obtained and multicolor image is also acquired. Based on this method, this paper has constructed and set up a two-color fluorescence microscope system with two excitation wavelengths of 488 nm and 639 nm. By using this system to observe the linearly movement of two kinds of fluorescent microspheres, after the data processing, we obtain a two-color fluorescence dynamic video which is consistent with the original image. This experiment shows that the dynamic phenomenon of multicolor fluorescent biological samples can be generally observed by this method. Compared with the current methods, this method can obtain the image signals of each color at the same time, and the color video's frame rate is consistent with the frame rate of the camera. The optical system is simpler and does not need extra color separation element. In addition, this method has a good filtering effect on the ambient light or other light signals which are not affected by the modulation process.

Design and Processing of a Novel Chaos-Based Stepped Frequency Synthesized Wideband Radar Signal.

PubMed

Zeng, Tao; Chang, Shaoqiang; Fan, Huayu; Liu, Quanhua

2018-03-26

The linear stepped frequency and linear frequency shift keying (FSK) signal has been widely used in radar systems. However, such linear modulation signals suffer from the range-Doppler coupling that degrades radar multi-target resolution. Moreover, the fixed frequency-hopping or frequency-coded sequence can be easily predicted by the interception receiver in the electronic countermeasures (ECM) environments, which limits radar anti-jamming performance. In addition, the single FSK modulation reduces the radar low probability of intercept (LPI) performance, for it cannot achieve a large time-bandwidth product. To solve such problems, we propose a novel chaos-based stepped frequency (CSF) synthesized wideband signal in this paper. The signal introduces chaotic frequency hopping between the coherent stepped frequency pulses, and adopts a chaotic frequency shift keying (CFSK) and phase shift keying (PSK) composited coded modulation in a subpulse, called CSF-CFSK/PSK. Correspondingly, the processing method for the signal has been proposed. According to our theoretical analyses and the simulations, the proposed signal and processing method achieve better multi-target resolution and LPI performance. Furthermore, flexible modulation is able to increase the robustness against identification of the interception receiver and improve the anti-jamming performance of the radar.

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.

Digital Signal Processing and Control for the Study of Gene Networks

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun

2016-04-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Digital Signal Processing and Control for the Study of Gene Networks.

PubMed

Shin, Yong-Jun

2016-04-22

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Digital Signal Processing and Control for the Study of Gene Networks

PubMed Central

Shin, Yong-Jun

2016-01-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

PubMed Central

Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

2016-01-01

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

Nonstationary signal analysis in episodic memory retrieval

NASA Astrophysics Data System (ADS)

Ku, Y. G.; Kawasumi, Masashi; Saito, Masao

2004-04-01

The problem of blind source separation from a mixture that has nonstationarity can be seen in signal processing, speech processing, spectral analysis and so on. This study analyzed EEG signal during episodic memory retrieval using ICA and TVAR. This paper proposes a method which combines ICA and TVAR. The signal from the brain not only exhibits the nonstationary behavior, but also contain artifacts. EEG data at the frontal lobe (F3) from the scalp is collected during the episodic memory retrieval task. The method is applied to EEG data for analysis. The artifact (eye movement) is removed by ICA, and a single burst (around 6Hz) is obtained by TVAR, suggesting that the single burst is related to the brain activity during the episodic memory retrieval.

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

PubMed Central

Lv, Yong; Zhu, Qinglin; Yuan, Rui

2015-01-01

The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

Grey signal processing and data reconstruction in the non-diffracting beam triangulation measurement system

NASA Astrophysics Data System (ADS)

Meng, Hao; Wang, Zhongyu; Fu, Jihua

2008-12-01

The non-diffracting beam triangulation measurement system possesses the advantages of longer measurement range, higher theoretical measurement accuracy and higher resolution over the traditional laser triangulation measurement system. Unfortunately the measurement accuracy of the system is greatly degraded due to the speckle noise, the CCD photoelectric noise and the background light noise in practical applications. Hence, some effective signal processing methods must be applied to improve the measurement accuracy. In this paper a novel effective method for removing the noises in the non-diffracting beam triangulation measurement system is proposed. In the method the grey system theory is used to process and reconstruct the measurement signal. Through implementing the grey dynamic filtering based on the dynamic GM(1,1), the noises can be effectively removed from the primary measurement data and the measurement accuracy of the system can be improved as a result.

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

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-01

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

Acoustic emission evolution during sliding friction of Hadfield steel single crystal

NASA Astrophysics Data System (ADS)

Lychagin, D. V.; Novitskaya, O. S.; Kolubaev, A. V.; Sizova, O. V.

2017-12-01

Friction is a complex dynamic process. Direct observation of processes occurring in the friction zone is impossible due to a small size of a real contact area and, as a consequence, requires various additional methods applicable to monitor a tribological contact state. One of such methods consists in the analysis of acoustic emission data of a tribological contact. The use of acoustic emission entails the problem of interpreting physical sources of signals. In this paper, we analyze the evolution of acoustic emission signal frames in friction of Hadfield steel single crystals. The chosen crystallographic orientation of single crystals enables to identify four stages related to friction development as well as acoustic emission signals inherent in these stages. Acoustic emission signal parameters are studied in more detail by the short-time Fourier transform used to determine the time variation of the median frequency and its power spectrum. The results obtained will facilitate the development of a more precise method to monitor the tribological contact based on the acoustic emission method.

Expert system for testing industrial processes and determining sensor status

DOEpatents

Gross, K.C.; Singer, R.M.

1998-06-02

A method and system are disclosed for monitoring both an industrial process and a sensor. The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor for evaluating the state of operation of both. The technique further includes generating a first and second signal characteristic of an industrial process variable. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test. 24 figs.

Expert system for testing industrial processes and determining sensor status

DOEpatents

Gross, Kenneth C.; Singer, Ralph M.

1998-01-01

A method and system for monitoring both an industrial process and a sensor. The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor for evaluating the state of operation of both. The technique further includes generating a first and second signal characteristic of an industrial process variable. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis.

PubMed

Soedirdjo, S D H; Ullah, K; Merletti, R

2015-08-01

Electromyogram (EMG) recordings are often corrupted by power line interference (PLI) even though the skin is prepared and well-designed instruments are used. This study focuses on the analysis of some of the recent and classical existing digital signal processing approaches have been used to attenuate, if not eliminate, the power line interference from EMG signals. A comparison of the signal to interference ratio (SIR) of the output signals is presented, for four methods: classical notch filter, spectral interpolation, adaptive noise canceller with phase locked loop (ANC-PLL) and adaptive filter, applied to simulated multichannel monopolar EMG signals with different SIR. The effect of each method on the shape of the EMG signals is also analyzed. The results show that ANC-PLL method gives the best output SIR and lowest shape distortion compared to the other methods. Classical notch filtering is the simplest method but some information might be lost as it removes both the interference and the EMG signals. Thus, it is obvious that notch filter has the lowest performance and it introduces distortion into the resulting signals.

High-resolution correlation

NASA Astrophysics Data System (ADS)

Nelson, D. J.

2007-09-01

In the basic correlation process a sequence of time-lag-indexed correlation coefficients are computed as the inner or dot product of segments of two signals. The time-lag(s) for which the magnitude of the correlation coefficient sequence is maximized is the estimated relative time delay of the two signals. For discrete sampled signals, the delay estimated in this manner is quantized with the same relative accuracy as the clock used in sampling the signals. In addition, the correlation coefficients are real if the input signals are real. There have been many methods proposed to estimate signal delay to more accuracy than the sample interval of the digitizer clock, with some success. These methods include interpolation of the correlation coefficients, estimation of the signal delay from the group delay function, and beam forming techniques, such as the MUSIC algorithm. For spectral estimation, techniques based on phase differentiation have been popular, but these techniques have apparently not been applied to the correlation problem . We propose a phase based delay estimation method (PBDEM) based on the phase of the correlation function that provides a significant improvement of the accuracy of time delay estimation. In the process, the standard correlation function is first calculated. A time lag error function is then calculated from the correlation phase and is used to interpolate the correlation function. The signal delay is shown to be accurately estimated as the zero crossing of the correlation phase near the index of the peak correlation magnitude. This process is nearly as fast as the conventional correlation function on which it is based. For real valued signals, a simple modification is provided, which results in the same correlation accuracy as is obtained for complex valued signals.

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).

Electrocardiogram signal denoising based on a new improved wavelet thresholding

NASA Astrophysics Data System (ADS)

Han, Guoqiang; Xu, Zhijun

2016-08-01

Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

High-efficiency (6 + 1) × 1 pump-signal combiner based on low-deformation and high-precision alignment fabrication

NASA Astrophysics Data System (ADS)

Zou, Shuzhen; Chen, Han; Yu, Haijuan; Sun, Jing; Zhao, Pengfei; Lin, Xuechun

2017-12-01

We demonstrate a new method for fabricating a (6 + 1) × 1 pump-signal combiner based on the reduction of signal fiber diameter by corrosion. This method avoids the mismatch loss of the splice between the signal fiber and the output fiber caused by the signal fiber taper processing. The optimum radius of the corroded signal fiber was calculated according to the analysis of the influence of the cladding thickness on the laser propagating in the fiber core. Besides, we also developed a two-step splicing method to complete the high-precision alignment between the signal fiber core and the output fiber core. A high-efficiency (6 + 1) × 1 pump-signal combiner was produced with an average pump power transmission efficiency of 98.0% and a signal power transmission efficiency of 97.7%, which is well suitable for application to high-power fiber laser system.

Online tracking of instantaneous frequency and amplitude of dynamical system response

NASA Astrophysics Data System (ADS)

Frank Pai, P.

2010-05-01

This paper presents a sliding-window tracking (SWT) method for accurate tracking of the instantaneous frequency and amplitude of arbitrary dynamic response by processing only three (or more) most recent data points. Teager-Kaiser algorithm (TKA) is a well-known four-point method for online tracking of frequency and amplitude. Because finite difference is used in TKA, its accuracy is easily destroyed by measurement and/or signal-processing noise. Moreover, because TKA assumes the processed signal to be a pure harmonic, any moving average in the signal can destroy the accuracy of TKA. On the other hand, because SWT uses a constant and a pair of windowed regular harmonics to fit the data and estimate the instantaneous frequency and amplitude, the influence of any moving average is eliminated. Moreover, noise filtering is an implicit capability of SWT when more than three data points are used, and this capability increases with the number of processed data points. To compare the accuracy of SWT and TKA, Hilbert-Huang transform is used to extract accurate time-varying frequencies and amplitudes by processing the whole data set without assuming the signal to be harmonic. Frequency and amplitude trackings of different amplitude- and frequency-modulated signals, vibrato in music, and nonlinear stationary and non-stationary dynamic signals are studied. Results show that SWT is more accurate, robust, and versatile than TKA for online tracking of frequency and amplitude.

Nonlinear Processing of Auditory Brainstem Response

DTIC Science & Technology

2001-10-25

Kraków, Poland Abstract: - Auditory brainstem response potentials (ABR) are signals calculated from the EEG signals registered as responses to an...acoustic activation of the auditory system. The ABR signals provide an objective, diagnostic method, widely applied in examinations of hearing organs

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.

Multi-ball and one-ball geolocation

NASA Astrophysics Data System (ADS)

Nelson, D. J.; Townsend, J. L.

2017-05-01

We present analysis methods that may be used to geolocate emitters using one or more moving receivers. While some of the methods we present may apply to a broader class of signals, our primary interest is locating and tracking ships from short pulsed transmissions, such as the maritime Automatic Identification System (AIS.) The AIS signal is difficult to process and track since the pulse duration is only 25 milliseconds, and the pulses may only be transmitted every six to ten seconds. In this article, we address several problems including accurate TDOA and FDOA estimation methods that do not require searching a two dimensional surface such as the cross-ambiguity surface. As an example, we apply these methods to identify and process AIS pulses from a single emitter, making it possible to geolocate the AIS signal using a single moving receiver.

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.

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.

Rapid Structured Volume Grid Smoothing and Adaption Technique

NASA Technical Reports Server (NTRS)

Alter, Stephen J.

2006-01-01

A rapid, structured volume grid smoothing and adaption technique, based on signal processing methods, was developed and applied to the Shuttle Orbiter at hypervelocity flight conditions in support of the Columbia Accident Investigation. Because of the fast pace of the investigation, computational aerothermodynamicists, applying hypersonic viscous flow solving computational fluid dynamic (CFD) codes, refined and enhanced a grid for an undamaged baseline vehicle to assess a variety of damage scenarios. Of the many methods available to modify a structured grid, most are time-consuming and require significant user interaction. By casting the grid data into different coordinate systems, specifically two computational coordinates with arclength as the third coordinate, signal processing methods are used for filtering the data [Taubin, CG v/29 1995]. Using a reverse transformation, the processed data are used to smooth the Cartesian coordinates of the structured grids. By coupling the signal processing method with existing grid operations within the Volume Grid Manipulator tool, problems related to grid smoothing are solved efficiently and with minimal user interaction. Examples of these smoothing operations are illustrated for reductions in grid stretching and volume grid adaptation. In each of these examples, other techniques existed at the time of the Columbia accident, but the incorporation of signal processing techniques reduced the time to perform the corrections by nearly 60%. This reduction in time to perform the corrections therefore enabled the assessment of approximately twice the number of damage scenarios than previously possible during the allocated investigation time.

Rapid Structured Volume Grid Smoothing and Adaption Technique

NASA Technical Reports Server (NTRS)

Alter, Stephen J.

2004-01-01

A rapid, structured volume grid smoothing and adaption technique, based on signal processing methods, was developed and applied to the Shuttle Orbiter at hypervelocity flight conditions in support of the Columbia Accident Investigation. Because of the fast pace of the investigation, computational aerothermodynamicists, applying hypersonic viscous flow solving computational fluid dynamic (CFD) codes, refined and enhanced a grid for an undamaged baseline vehicle to assess a variety of damage scenarios. Of the many methods available to modify a structured grid, most are time-consuming and require significant user interaction. By casting the grid data into different coordinate systems, specifically two computational coordinates with arclength as the third coordinate, signal processing methods are used for filtering the data [Taubin, CG v/29 1995]. Using a reverse transformation, the processed data are used to smooth the Cartesian coordinates of the structured grids. By coupling the signal processing method with existing grid operations within the Volume Grid Manipulator tool, problems related to grid smoothing are solved efficiently and with minimal user interaction. Examples of these smoothing operations are illustrated for reduction in grid stretching and volume grid adaptation. In each of these examples, other techniques existed at the time of the Columbia accident, but the incorporation of signal processing techniques reduced the time to perform the corrections by nearly 60%. This reduction in time to perform the corrections therefore enabled the assessment of approximately twice the number of damage scenarios than previously possible during the allocated investigation time.

A new method of hybrid frequency hopping signals selection and blind parameter estimation

NASA Astrophysics Data System (ADS)

Zeng, Xiaoyu; Jiao, Wencheng; Sun, Huixian

2018-04-01

Frequency hopping communication is widely used in military communications at home and abroad. In the case of single-channel reception, it is scarce to process multiple frequency hopping signals both effectively and simultaneously. A method of hybrid FH signals selection and blind parameter estimation is proposed. The method makes use of spectral transformation, spectral entropy calculation and PRI transformation basic theory to realize the sorting and parameter estimation of the components in the hybrid frequency hopping signal. The simulation results show that this method can correctly classify the frequency hopping component signal, and the estimated error of the frequency hopping period is about 5% and the estimated error of the frequency hopping frequency is less than 1% when the SNR is 10dB. However, the performance of this method deteriorates seriously at low SNR.

Motion detector and analyzer

DOEpatents

Unruh, W.P.

1987-03-23

Method and apparatus are provided for deriving positive and negative Doppler spectrum to enable analysis of objects in motion, and particularly, objects having rotary motion. First and second returned radar signals are mixed with internal signals to obtain an in-phase process signal and a quadrature process signal. A broad-band phase shifter shifts the quadrature signal through 90/degree/ relative to the in-phase signal over a predetermined frequency range. A pair of signals is output from the broad-band phase shifter which are then combined to provide a first side band signal which is functionally related to a negative Doppler shift spectrum. The distinct positive and negative Doppler spectra may then be analyzed for the motion characteristics of the object being examined.

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.

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

PubMed

Soares, Fabiano Araujo; Carvalho, João Luiz Azevedo; Miosso, Cristiano Jacques; de Andrade, Marcelino Monteiro; da Rocha, Adson Ferreira

2015-09-17

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

Spatial Mnemonic Encoding: Theta Power Decreases and Medial Temporal Lobe BOLD Increases Co-Occur during the Usage of the Method of Loci

PubMed Central

Volberg, Gregor; Goldhacker, Markus; Hanslmayr, Simon

2016-01-01

Abstract The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3–8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL. PMID:28101523

FIBRE AND INTEGRATED OPTICS. OPTICAL PROCESSING OF INFORMATION: Method for optical data processing based on a two-pulse photon echo

NASA Astrophysics Data System (ADS)

Zakharov, S. M.; Manykin, Eduard A.

1995-02-01

The principles of optical processing based on dynamic spatial—temporal properties of two-pulse photon echo signals are considered. The properties of a resonant medium as an on-line filter of temporal and spatial frequencies are discussed. These properties are due to the sensitivity of such a medium to the Fourier spectrum of the second exiting pulse. Degeneracy of quantum resonant systems, demonstrated by the coherent response dependence on the square of the amplitude of the second pulse, can be used for 'simultaneous' correlation processing of optical 'signals'. Various methods for the processing of the Fourier optical image are discussed.

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.

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.

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.

Methods and Apparatus for Reducing Multipath Signal Error Using Deconvolution

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor); Lau, Kenneth H. (Inventor)

1999-01-01

A deconvolution approach to adaptive signal processing has been applied to the elimination of signal multipath errors as embodied in one preferred embodiment in a global positioning system receiver. The method and receiver of the present invention estimates then compensates for multipath effects in a comprehensive manner. Application of deconvolution, along with other adaptive identification and estimation techniques, results in completely novel GPS (Global Positioning System) receiver architecture.

BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

PubMed Central

Vidaurre, Carmen; Sander, Tilmann H.; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals. PMID:21437227

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

PubMed

Wixted, John T

2007-01-01

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

BioSig: the free and open source software library for biomedical signal processing.

PubMed

Vidaurre, Carmen; Sander, Tilmann H; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Coherent broadband sonar signal processing with the environmentally corrected matched filter

NASA Astrophysics Data System (ADS)

Camin, Henry John, III

The matched filter is the standard approach for coherently processing active sonar signals, where knowledge of the transmitted waveform is used in the detection and parameter estimation of received echoes. Matched filtering broadband signals provides higher levels of range resolution and reverberation noise suppression than can be realized through narrowband processing. Since theoretical processing gains are proportional to the signal bandwidth, it is typically desirable to utilize the widest band signals possible. However, as signal bandwidth increases, so do environmental effects that tend to decrease correlation between the received echo and the transmitted waveform. This is especially true for ultra wideband signals, where the bandwidth exceeds an octave or approximately 70% fractional bandwidth. This loss of coherence often results in processing gains and range resolution much lower than theoretically predicted. Wiener filtering, commonly used in image processing to improve distorted and noisy photos, is investigated in this dissertation as an approach to correct for these environmental effects. This improved signal processing, Environmentally Corrected Matched Filter (ECMF), first uses a Wiener filter to estimate the environmental transfer function and then again to correct the received signal using this estimate. This process can be viewed as a smarter inverse or whitening filter that adjusts behavior according to the signal to noise ratio across the spectrum. Though the ECMF is independent of bandwidth, it is expected that ultra wideband signals will see the largest improvement, since they tend to be more impacted by environmental effects. The development of the ECMF and demonstration of improved parameter estimation with its use are the primary emphases in this dissertation. Additionally, several new contributions to the field of sonar signal processing made in conjunction with the development of the ECMF are described. A new, nondimensional wideband ambiguity function is presented as a way to view the behavior of the matched filter with and without the decorrelating environmental effects; a new, integrated phase broadband angle estimation method is developed and compared to existing methods; and a new, asymptotic offset phase angle variance model is presented. Several data sets are used to demonstrate these new contributions. High fidelity Sonar Simulation Toolset (SST) synthetic data is used to characterize the theoretical performance. Two in-water data sets were used to verify assumptions that were made during the development of the ECMF. Finally, a newly collected in-air data set containing ultra wideband signals was used in lieu of a cost prohibitive underwater experiment to demonstrate the effectiveness of the ECMF at improving parameter estimates.

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.

Comparison of two hardware-based hit filtering methods for trackers in high-pileup environments

NASA Astrophysics Data System (ADS)

Gradin, J.; Mårtensson, M.; Brenner, R.

2018-04-01

As experiments in high energy physics aim to measure increasingly rare processes, the experiments continually strive to increase the expected signal yields. In the case of the High Luminosity upgrade of the LHC, the luminosity is raised by increasing the number of simultaneous proton-proton interactions, so-called pile-up. This increases the expected yields of signal and background processes alike. The signal is embedded in a large background of processes that mimic that of signal events. It is therefore imperative for the experiments to develop new triggering methods to effectively distinguish the interesting events from the background. We present a comparison of two methods for filtering detector hits to be used for triggering on particle tracks: one based on a pattern matching technique using Associative Memory (AM) chips and the other based on the Hough transform. Their efficiency and hit rejection are evaluated for proton-proton collisions with varying amounts of pile-up using a simulation of a generic silicon tracking detector. It is found that, while both methods are feasible options for a track trigger with single muon efficiencies around 98–99%, the AM based pattern matching produces a lower number of hit combinations with respect to the Hough transform whilst keeping more of the true signal hits. We also present the effect on the two methods of increasing the amount of support material in the detector and of introducing inefficiencies by deactivating detector modules. The increased support material has negligable effects on the efficiency for both methods, while dropping 5% (10%) of the available modules decreases the efficiency to about 95% (87%) for both methods, irrespective of the amount of pile-up.

Subspace-based interference removal methods for a multichannel biomagnetic sensor array.

PubMed

Sekihara, Kensuke; Nagarajan, Srikantan S

2017-10-01

In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

Subspace-based interference removal methods for a multichannel biomagnetic sensor array

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Nagarajan, Srikantan S.

2017-10-01

Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

Integrated unaligned resonant modulator tuning

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

Zortman, William A.; Lentine, Anthony L.

Methods and systems for tuning a resonant modulator are disclosed. One method includes receiving a carrier signal modulated by the resonant modulator with a stream of data having an approximately equal number of high and low bits, determining an average power of the modulated carrier signal, comparing the average power to a predetermined threshold, and operating a tuning device coupled to the resonant modulator based on the comparison of the average power and the predetermined threshold. One system includes an input structure, a plurality of processing elements, and a digital control element. The input structure is configured to receive, frommore » the resonant modulator, a modulated carrier signal. The plurality of processing elements are configured to determine an average power of the modulated carrier signal. The digital control element is configured to operate a tuning device coupled to the resonant modulator based on the average power of the modulated carrier signal.« less

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 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.

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.

Error-Rate Estimation Based on Multi-Signal Flow Graph Model and Accelerated Radiation Tests

PubMed Central

Wang, Yueke; Xing, Kefei; Deng, Wei; Zhang, Zelong

2016-01-01

A method of evaluating the single-event effect soft-error vulnerability of space instruments before launched has been an active research topic in recent years. In this paper, a multi-signal flow graph model is introduced to analyze the fault diagnosis and meantime to failure (MTTF) for space instruments. A model for the system functional error rate (SFER) is proposed. In addition, an experimental method and accelerated radiation testing system for a signal processing platform based on the field programmable gate array (FPGA) is presented. Based on experimental results of different ions (O, Si, Cl, Ti) under the HI-13 Tandem Accelerator, the SFER of the signal processing platform is approximately 10−3(error/particle/cm2), while the MTTF is approximately 110.7 h. PMID:27583533

Error-Rate Estimation Based on Multi-Signal Flow Graph Model and Accelerated Radiation Tests.

PubMed

He, Wei; Wang, Yueke; Xing, Kefei; Deng, Wei; Zhang, Zelong

2016-01-01

A method of evaluating the single-event effect soft-error vulnerability of space instruments before launched has been an active research topic in recent years. In this paper, a multi-signal flow graph model is introduced to analyze the fault diagnosis and meantime to failure (MTTF) for space instruments. A model for the system functional error rate (SFER) is proposed. In addition, an experimental method and accelerated radiation testing system for a signal processing platform based on the field programmable gate array (FPGA) is presented. Based on experimental results of different ions (O, Si, Cl, Ti) under the HI-13 Tandem Accelerator, the SFER of the signal processing platform is approximately 10-3(error/particle/cm2), while the MTTF is approximately 110.7 h.

Iteration of ultrasound aberration correction methods

NASA Astrophysics Data System (ADS)

Maasoey, Svein-Erik; Angelsen, Bjoern; Varslot, Trond

2004-05-01

Aberration in ultrasound medical imaging is usually modeled by time-delay and amplitude variations concentrated on the transmitting/receiving array. This filter process is here denoted a TDA filter. The TDA filter is an approximation to the physical aberration process, which occurs over an extended part of the human body wall. Estimation of the TDA filter, and performing correction on transmit and receive, has proven difficult. It has yet to be shown that this method works adequately for severe aberration. Estimation of the TDA filter can be iterated by retransmitting a corrected signal and re-estimate until a convergence criterion is fulfilled (adaptive imaging). Two methods for estimating time-delay and amplitude variations in receive signals from random scatterers have been developed. One method correlates each element signal with a reference signal. The other method use eigenvalue decomposition of the receive cross-spectrum matrix, based upon a receive energy-maximizing criterion. Simulations of iterating aberration correction with a TDA filter have been investigated to study its convergence properties. A weak and strong human-body wall model generated aberration. Both emulated the human abdominal wall. Results after iteration improve aberration correction substantially, and both estimation methods converge, even for the case of strong aberration.

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.

Design of signal reception and processing system of embedded ultrasonic endoscope

NASA Astrophysics Data System (ADS)

Li, Ming; Yu, Feng; Zhang, Ruiqiang; Li, Yan; Chen, Xiaodong; Yu, Daoyin

2009-11-01

Embedded Ultrasonic Endoscope, based on embedded microprocessor and embedded real-time operating system, sends a micro ultrasonic probe into coelom through the biopsy channel of the Electronic Endoscope to get the fault histology features of digestive organs by rotary scanning, and acquires the pictures of the alimentary canal mucosal surface. At the same time, ultrasonic signals are processed by signal reception and processing system, forming images of the full histology of the digestive organs. Signal Reception and Processing System is an important component of Embedded Ultrasonic Endoscope. However, the traditional design, using multi-level amplifiers and special digital processing circuits to implement signal reception and processing, is no longer satisfying the standards of high-performance, miniaturization and low power requirements that embedded system requires, and as a result of the high noise that multi-level amplifier brought, the extraction of small signal becomes hard. Therefore, this paper presents a method of signal reception and processing based on double variable gain amplifier and FPGA, increasing the flexibility and dynamic range of the Signal Reception and Processing System, improving system noise level, and reducing power consumption. Finally, we set up the embedded experiment system, using a transducer with the center frequency of 8MHz to scan membrane samples, and display the image of ultrasonic echo reflected by each layer of membrane, with a frame rate of 5Hz, verifying the correctness of the system.

Hardware design and implementation of fast DOA estimation method based on multicore DSP

NASA Astrophysics Data System (ADS)

Guo, Rui; Zhao, Yingxiao; Zhang, Yue; Lin, Qianqiang; Chen, Zengping

2016-10-01

In this paper, we present a high-speed real-time signal processing hardware platform based on multicore digital signal processor (DSP). The real-time signal processing platform shows several excellent characteristics including high performance computing, low power consumption, large-capacity data storage and high speed data transmission, which make it able to meet the constraint of real-time direction of arrival (DOA) estimation. To reduce the high computational complexity of DOA estimation algorithm, a novel real-valued MUSIC estimator is used. The algorithm is decomposed into several independent steps and the time consumption of each step is counted. Based on the statistics of the time consumption, we present a new parallel processing strategy to distribute the task of DOA estimation to different cores of the real-time signal processing hardware platform. Experimental results demonstrate that the high processing capability of the signal processing platform meets the constraint of real-time direction of arrival (DOA) estimation.

Algorithm for Aligning an Array of Receiving Radio Antennas

NASA Technical Reports Server (NTRS)

Rogstad, David

2006-01-01

A digital-signal-processing algorithm (somewhat arbitrarily) called SUMPLE has been devised as a means of aligning the outputs of multiple receiving radio antennas in a large array for the purpose of receiving a weak signal transmitted by a single distant source. As used here, aligning signifies adjusting the delays and phases of the outputs from the various antennas so that their relatively weak replicas of the desired signal can be added coherently to increase the signal-to-noise ratio (SNR) for improved reception, as though one had a single larger antenna. The method was devised to enhance spacecraft-tracking and telemetry operations in NASA's Deep Space Network (DSN); the method could also be useful in such other applications as both satellite and terrestrial radio communications and radio astronomy. Heretofore, most commonly, alignment has been effected by a process that involves correlation of signals in pairs. This approach necessitates the use of a large amount of hardware most notably, the N(N - 1)/2 correlators needed to process signals from all possible pairs of N antennas. Moreover, because the incoming signals typically have low SNRs, the delay and phase adjustments are poorly determined from the pairwise correlations. SUMPLE also involves correlations, but the correlations are not performed in pairs. Instead, in a partly iterative process, each signal is appropriately weighted and then correlated with a composite signal equal to the sum of the other signals (see Figure 1). One benefit of this approach is that only N correlators are needed; in an array of N much greater than 1 antennas, this results in a significant reduction of the amount of hardware. Another benefit is that once the array achieves coherence, the correlation SNR is N - 1 times that of a pair of antennas.

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.

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.

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.

Freeze-drying process monitoring using a cold plasma ionization device.

PubMed

Mayeresse, Y; Veillon, R; Sibille, P H; Nomine, C

2007-01-01

A cold plasma ionization device has been designed to monitor freeze-drying processes in situ by monitoring lyophilization chamber moisture content. This plasma device, which consists of a probe that can be mounted directly on the lyophilization chamber, depends upon the ionization of nitrogen and water molecules using a radiofrequency generator and spectrometric signal collection. The study performed on this probe shows that it is steam sterilizable, simple to integrate, reproducible, and sensitive. The limitations include suitable positioning in the lyophilization chamber, calibration, and signal integration. Sensitivity was evaluated in relation to the quantity of vials and the probe positioning, and correlation with existing methods, such as microbalance, was established. These tests verified signal reproducibility through three freeze-drying cycles. Scaling-up studies demonstrated a similar product signature for the same product using pilot-scale and larger-scale equipment. On an industrial scale, the method efficiently monitored the freeze-drying cycle, but in a larger industrial freeze-dryer the signal was slightly modified. This was mainly due to the positioning of the plasma device, in relation to the vapor flow pathway, which is not necessarily homogeneous within the freeze-drying chamber. The plasma tool is a relevant method for monitoring freeze-drying processes and may in the future allow the verification of current thermodynamic freeze-drying models. This plasma technique may ultimately represent a process analytical technology (PAT) approach for the freeze-drying process.

Multi-GNSS high-rate RTK, PPP and novel direct phase observation processing method: application to precise dynamic displacement detection

NASA Astrophysics Data System (ADS)

Paziewski, Jacek; Sieradzki, Rafal; Baryla, Radoslaw

2018-03-01

This paper provides the methodology and performance assessment of multi-GNSS signal processing for the detection of small-scale high-rate dynamic displacements. For this purpose, we used methods of relative (RTK) and absolute positioning (PPP), and a novel direct signal processing approach. The first two methods are recognized as providing accurate information on position in many navigation and surveying applications. The latter is an innovative method for dynamic displacement determination with the use of GNSS phase signal processing. This method is based on the developed functional model with parametrized epoch-wise topocentric relative coordinates derived from filtered GNSS observations. Current regular kinematic PPP positioning, as well as medium/long range RTK, may not offer coordinate estimates with subcentimeter precision. Thus, extended processing strategies of absolute and relative GNSS positioning have been developed and applied for displacement detection. The study also aimed to comparatively analyze the developed methods as well as to analyze the impact of combined GPS and BDS processing and the dependence of the results of the relative methods on the baseline length. All the methods were implemented with in-house developed software allowing for high-rate precise GNSS positioning and signal processing. The phase and pseudorange observations collected with a rate of 50 Hz during the field test served as the experiment’s data set. The displacements at the rover station were triggered in the horizontal plane using a device which was designed and constructed to ensure a periodic motion of GNSS antenna with an amplitude of ~3 cm and a frequency of ~4.5 Hz. Finally, a medium range RTK, PPP, and direct phase observation processing method demonstrated the capability of providing reliable and consistent results with the precision of the determined dynamic displacements at the millimeter level. Specifically, the research shows that the standard deviation of the displacement residuals obtained as the difference between a benchmark-ultra-short baseline RTK solution and selected scenarios ranged between 1.1 and 3.4 mm. At the same time, the differences in the mean amplitude of the oscillations derived from the established scenarios did not exceed 1.3 mm, whereas the frequency of the motion detected with the use of Fourier transformation was the same.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures.

PubMed

Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-05

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

An adaptive signal-processing approach to online adaptive tutoring.

PubMed

Bergeron, Bryan; Cline, Andrew

2011-01-01

Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

SNMR pulse sequence phase cycling

DOEpatents

Walsh, David O; Grunewald, Elliot D

2013-11-12

Technologies applicable to SNMR pulse sequence phase cycling are disclosed, including SNMR acquisition apparatus and methods, SNMR processing apparatus and methods, and combinations thereof. SNMR acquisition may include transmitting two or more SNMR pulse sequences and applying a phase shift to a pulse in at least one of the pulse sequences, according to any of a variety cycling techniques. SNMR processing may include combining SNMR from a plurality of pulse sequences comprising pulses of different phases, so that desired signals are preserved and indesired signals are canceled.

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.

Non-contact capacitance based image sensing method and system

DOEpatents

Novak, J.L.; Wiczer, J.J.

1994-01-25

A system and a method for imaging desired surfaces of a workpiece is described. A sensor having first and second sensing electrodes which are electrically isolated from the workpiece is positioned above and in proximity to the desired surfaces of the workpiece. An electric field is developed between the first and second sensing electrodes of the sensor in response to input signals being applied thereto and capacitance signals are developed which are indicative of any disturbances in the electric field as a result of the workpiece. An image signal of the workpiece may be developed by processing the capacitance signals. The image signals may provide necessary control information to a machining device for machining the desired surfaces of the workpiece in processes such as deburring or chamfering. Also, the method and system may be used to image dimensions of weld pools on a workpiece and surfaces of glass vials. The sensor may include first and second preview sensors used to determine the feed rate of a workpiece with respect to the machining device. 18 figures.

Non-contact capacitance based image sensing method and system

DOEpatents

Novak, J.L.; Wiczer, J.J.

1995-01-03

A system and a method is provided for imaging desired surfaces of a workpiece. A sensor having first and second sensing electrodes which are electrically isolated from the workpiece is positioned above and in proximity to the desired surfaces of the workpiece. An electric field is developed between the first and second sensing electrodes of the sensor in response to input signals being applied thereto and capacitance signals are developed which are indicative of any disturbances in the electric field as a result of the workpiece. An image signal of the workpiece may be developed by processing the capacitance signals. The image signals may provide necessary control information to a machining device for machining the desired surfaces of the workpiece in processes such as deburring or chamfering. Also, the method and system may be used to image dimensions of weld pools on a workpiece and surfaces of glass vials. The sensor may include first and second preview sensors used to determine the feed rate of a workpiece with respect to the machining device. 18 figures.

Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods.

PubMed

Ferguson, B G; Lo, K W

2000-10-01

Flight parameter estimation methods for an airborne acoustic source can be divided into two categories, depending on whether the narrow-band lines or the broadband component of the received signal spectrum is processed to estimate the flight parameters. This paper provides a common framework for the formulation and test of two flight parameter estimation methods: one narrow band, the other broadband. The performances of the two methods are evaluated by applying them to the same acoustic data set, which is recorded by a planar array of passive acoustic sensors during multiple transits of a turboprop fixed-wing aircraft and two types of rotary-wing aircraft. The narrow-band method, which is based on a kinematic model that assumes the source travels in a straight line at constant speed and altitude, requires time-frequency analysis of the acoustic signal received by a single sensor during each aircraft transit. The broadband method is based on the same kinematic model, but requires observing the temporal variation of the differential time of arrival of the acoustic signal at each pair of sensors that comprises the planar array. Generalized cross correlation of each pair of sensor outputs using a cross-spectral phase transform prefilter provides instantaneous estimates of the differential times of arrival of the signal as the acoustic wavefront traverses the array.

[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.

Method for enhancing signals transmitted over optical fibers

DOEpatents

Ogle, James W.; Lyons, Peter B.

1983-01-01

A method for spectral equalization of high frequency spectrally broadband signals transmitted through an optical fiber. The broadband signal input is first dispersed by a grating. Narrow spectral components are collected into an array of equalizing fibers. The fibers serve as optical delay lines compensating for material dispersion of each spectral component during transmission. The relative lengths of the individual equalizing fibers are selected to compensate for such prior dispersion. The output of the equalizing fibers couple the spectrally equalized light onto a suitable detector for subsequent electronic processing of the enhanced broadband signal.

Radar signal pre-processing to suppress surface bounce and multipath

DOEpatents

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

2013-12-31

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

Dynamic balancing of dual-rotor system with very little rotating speed difference.

PubMed

Yang, Jian; He, Shi-zheng; Wang, Le-qin

2003-01-01

Unbalanced vibration in dual-rotor rotating machinery was studied with numerical simulations and experiments. A new method is proposed to separate vibration signals of inner and outer rotors for a system with very little difference in rotating speeds. Magnitudes and phase values of unbalance defects can be obtained directly by sampling the vibration signal synchronized with reference signal. The balancing process is completed by the reciprocity influence coefficients of inner and outer rotors method. Results showed the advantage of such method for a dual-rotor system as compared with conventional balancing.

A Method for Implementing Force-Limited Vibration Control

NASA Technical Reports Server (NTRS)

Worth, Daniel B.

1997-01-01

NASA/GSFC has implemented force-limited vibration control on a controller which can only accept one profile. The method uses a personal computer based digital signal processing board to convert force and/or moment signals into what appears to he an acceleration signal to the controller. This technique allows test centers with older controllers to use the latest force-limited control techniques for random vibration testing. The paper describes the method, hardware, and test procedures used. An example from a test performed at NASA/GSFC is used as a guide.

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.

Quantitative multi-color FRET measurements by Fourier lifetime excitation-emission matrix spectroscopy.

PubMed

Zhao, Ming; Huang, Run; Peng, Leilei

2012-11-19

Förster resonant energy transfer (FRET) is extensively used to probe macromolecular interactions and conformation changes. The established FRET lifetime analysis method measures the FRET process through its effect on the donor lifetime. In this paper we present a method that directly probes the time-resolved FRET signal with frequency domain Fourier lifetime excitation-emission matrix (FLEEM) measurements. FLEEM separates fluorescent signals by their different phonon energy pathways from excitation to emission. The FRET process generates a unique signal channel that is initiated by donor excitation but ends with acceptor emission. Time-resolved analysis of the FRET EEM channel allows direct measurements on the FRET process, unaffected by free fluorophores that might be present in the sample. Together with time-resolved analysis on non-FRET channels, i.e. donor and acceptor EEM channels, time resolved EEM analysis allows precise quantification of FRET in the presence of free fluorophores. The method is extended to three-color FRET processes, where quantification with traditional methods remains challenging because of the significantly increased complexity in the three-way FRET interactions. We demonstrate the time-resolved EEM analysis method with quantification of three-color FRET in incompletely hybridized triple-labeled DNA oligonucleotides. Quantitative measurements of the three-color FRET process in triple-labeled dsDNA are obtained in the presence of free single-labeled ssDNA and double-labeled dsDNA. The results establish a quantification method for studying multi-color FRET between multiple macromolecules in biochemical equilibrium.

Quantitative multi-color FRET measurements by Fourier lifetime excitation-emission matrix spectroscopy

PubMed Central

Zhao, Ming; Huang, Run; Peng, Leilei

2012-01-01

Förster resonant energy transfer (FRET) is extensively used to probe macromolecular interactions and conformation changes. The established FRET lifetime analysis method measures the FRET process through its effect on the donor lifetime. In this paper we present a method that directly probes the time-resolved FRET signal with frequency domain Fourier lifetime excitation-emission matrix (FLEEM) measurements. FLEEM separates fluorescent signals by their different phonon energy pathways from excitation to emission. The FRET process generates a unique signal channel that is initiated by donor excitation but ends with acceptor emission. Time-resolved analysis of the FRET EEM channel allows direct measurements on the FRET process, unaffected by free fluorophores that might be present in the sample. Together with time-resolved analysis on non-FRET channels, i.e. donor and acceptor EEM channels, time resolved EEM analysis allows precise quantification of FRET in the presence of free fluorophores. The method is extended to three-color FRET processes, where quantification with traditional methods remains challenging because of the significantly increased complexity in the three-way FRET interactions. We demonstrate the time-resolved EEM analysis method with quantification of three-color FRET in incompletely hybridized triple-labeled DNA oligonucleotides. Quantitative measurements of the three-color FRET process in triple-labeled dsDNA are obtained in the presence of free single-labeled ssDNA and double-labeled dsDNA. The results establish a quantification method for studying multi-color FRET between multiple macromolecules in biochemical equilibrium. PMID:23187535

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.

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.

A data processing method based on tracking light spot for the laser differential confocal component parameters measurement system

NASA Astrophysics Data System (ADS)

Shao, Rongjun; Qiu, Lirong; Yang, Jiamiao; Zhao, Weiqian; Zhang, Xin

2013-12-01

We have proposed the component parameters measuring method based on the differential confocal focusing theory. In order to improve the positioning precision of the laser differential confocal component parameters measurement system (LDDCPMS), the paper provides a data processing method based on tracking light spot. To reduce the error caused by the light point moving in collecting the axial intensity signal, the image centroiding algorithm is used to find and track the center of Airy disk of the images collected by the laser differential confocal system. For weakening the influence of higher harmonic noises during the measurement, Gaussian filter is used to process the axial intensity signal. Ultimately the zero point corresponding to the focus of the objective in a differential confocal system is achieved by linear fitting for the differential confocal axial intensity data. Preliminary experiments indicate that the method based on tracking light spot can accurately collect the axial intensity response signal of the virtual pinhole, and improve the anti-interference ability of system. Thus it improves the system positioning accuracy.

Signal Processing for Determining Water Height in Steam Pipes with Dynamic Surface Conditions

NASA Technical Reports Server (NTRS)

Lih, Shyh-Shiuh; Lee, Hyeong Jae; Bar-Cohen, Yoseph

2015-01-01

An enhanced signal processing method based on the filtered Hilbert envelope of the auto-correlation function of the wave signal has been developed to monitor the height of condensed water through the steel wall of steam pipes with dynamic surface conditions. The developed signal processing algorithm can also be used to estimate the thickness of the pipe to determine the cut-off frequency for the low pass filter frequency of the Hilbert Envelope. Testing and analysis results by using the developed technique for dynamic surface conditions are presented. A multiple array of transducers setup and methodology are proposed for both the pulse-echo and pitch-catch signals to monitor the fluctuation of the water height due to disturbance, water flow, and other anomaly conditions.

The recovery of weak impulsive signals based on stochastic resonance and moving least squares fitting.

PubMed

Jiang, Kuosheng; Xu, Guanghua; Liang, Lin; Tao, Tangfei; Gu, Fengshou

2014-07-29

In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test.

A Channelization-Based DOA Estimation Method for Wideband Signals

PubMed Central

Guo, Rui; Zhang, Yue; Lin, Qianqiang; Chen, Zengping

2016-01-01

In this paper, we propose a novel direction of arrival (DOA) estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM) and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS) are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR) using direct wideband radio frequency (RF) digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method. PMID:27384566

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.

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

NASA Astrophysics Data System (ADS)

Lu, W. L.; Xie, J. W.; Wang, H. M.; Sheng, C.

2016-09-01

Modern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time-frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time-frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

Realization of guitar audio effects using methods of digital signal processing

NASA Astrophysics Data System (ADS)

Buś, Szymon; Jedrzejewski, Konrad

2015-09-01

The paper is devoted to studies on possibilities of realization of guitar audio effects by means of methods of digital signal processing. As a result of research, some selected audio effects corresponding to the specifics of guitar sound were realized as the real-time system called Digital Guitar Multi-effect. Before implementation in the system, the selected effects were investigated using the dedicated application with a graphical user interface created in Matlab environment. In the second stage, the real-time system based on a microcontroller and an audio codec was designed and realized. The system is designed to perform audio effects on the output signal of an electric guitar.

Research on the method of precise alignment technology of atmospheric laser communication

NASA Astrophysics Data System (ADS)

Chen, Wen-jian; Gao, Wei; Duan, Yuan-yuan; Ma, Shi-wei; Chen, Jian

2016-10-01

Atmosphere laser communication takes advantage of laser as the carrier transmitting the voice, data, and image information in the atmosphere. Because of its high reliability, strong anti-interference ability, the advantages of easy installation, it has great potential and development space in the communications field. In the process of establish communication, the capture, targeting and tracking of the communication signal is the key technology. This paper introduce a method of targeting the signal spot in the process of atmosphere laser communication, which through the way of making analog signal addition and subtraction directly and normalized to obtain the target azimuth information to drive the servo system to achieve precise alignment of tracking.

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

NASA Technical Reports Server (NTRS)

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

2003-01-01

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

Signal detection by means of orthogonal decomposition

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

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.

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.

Signal processing methods for in-situ creep specimen monitoring

NASA Astrophysics Data System (ADS)

Guers, Manton J.; Tittmann, Bernhard R.

2018-04-01

Previous work investigated using guided waves for monitoring creep deformation during accelerated life testing. The basic objective was to relate observed changes in the time-of-flight to changes in the environmental temperature and specimen gage length. The work presented in this paper investigated several signal processing strategies for possible application in the in-situ monitoring system. Signal processing methods for both group velocity (wave-packet envelope) and phase velocity (peak tracking) time-of-flight were considered. Although the Analytic Envelope found via the Hilbert transform is commonly applied for group velocity measurements, erratic behavior in the indicated time-of-flight was observed when this technique was applied to the in-situ data. The peak tracking strategies tested had generally linear trends, and tracking local minima in the raw waveform ultimately showed the most consistent results.

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.

Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

NASA Astrophysics Data System (ADS)

Mishra, C.; Samantaray, A. K.; Chakraborty, G.

2016-05-01

Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources. In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data1

HCPCF-based in-line fiber Fabry-Perot refractometer and high sensitivity signal processing method

NASA Astrophysics Data System (ADS)

Liu, Xiaohui; Jiang, Mingshun; Sui, Qingmei; Geng, Xiangyi; Song, Furong

2017-12-01

An in-line fiber Fabry-Perot interferometer (FPI) based on the hollow-core photonic crystal fiber (HCPCF) for refractive index (RI) measurement is proposed in this paper. The FPI is formed by splicing both ends of a short section of the HCPCF to single mode fibers (SMFs) and cleaving the SMF pigtail to a proper length. The RI response of the sensor is analyzed theoretically and demonstrated experimentally. The results show that the FPI sensor has linear response to external RI and good repeatability. The sensitivity calculated from the maximum fringe contrast is -136 dB/RIU. A new spectrum differential integration (SDI) method for signal processing is also presented in this study. In this method, the RI is obtained from the integrated intensity of the absolute difference between the interference spectrum and its smoothed spectrum. The results show that the sensitivity obtained from the integrated intensity is about -1.34×105 dB/RIU. Compared with the maximum fringe contrast method, the new SDI method can provide the higher sensitivity, better linearity, improved reliability, and accuracy, and it's also convenient for automatic and fast signal processing in real-time monitoring of RI.

A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

PubMed Central

Hernandez, Wilmar

2007-01-01

In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.

A new similarity index for nonlinear signal analysis based on local extrema patterns

NASA Astrophysics Data System (ADS)

Niknazar, Hamid; Motie Nasrabadi, Ali; Shamsollahi, Mohammad Bagher

2018-02-01

Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By adding time information of local extrema as well as fuzzifying quantized values, this work proposes a new similarity index for nonlinear and long-term signal analysis, which extends the SBLE method. These new features provide more information about signals and reduce noise sensitivity by fuzzifying them. A number of practical tests were performed to demonstrate the ability of the method in nonlinear signal clustering and classification on synthetic data. In addition, epileptic seizure detection based on electroencephalography (EEG) signal processing was done by the proposed similarity to feature the potentials of the method as a real-world application tool.

Weak Defect Identification for Centrifugal Compressor Blade Crack Based on Pressure Sensors and Genetic Algorithm.

PubMed

Li, Hongkun; He, Changbo; Malekian, Reza; Li, Zhixiong

2018-04-19

The Centrifugal compressor is a piece of key equipment for petrochemical factories. As the core component of a compressor, the blades suffer periodic vibration and flow induced excitation mechanism, which will lead to the occurrence of crack defect. Moreover, the induced blade defect usually has a serious impact on the normal operation of compressors and the safety of operators. Therefore, an effective blade crack identification method is particularly important for the reliable operation of compressors. Conventional non-destructive testing and evaluation (NDT&E) methods can detect the blade defect effectively, however, the compressors should shut down during the testing process which is time-consuming and costly. In addition, it can be known these methods are not suitable for the long-term on-line condition monitoring and cannot identify the blade defect in time. Therefore, the effective on-line condition monitoring and weak defect identification method should be further studied and proposed. Considering the blade vibration information is difficult to measure directly, pressure sensors mounted on the casing are used to sample airflow pressure pulsation signal on-line near the rotating impeller for the purpose of monitoring the blade condition indirectly in this paper. A big problem is that the blade abnormal vibration amplitude induced by the crack is always small and this feature information will be much weaker in the pressure signal. Therefore, it is usually difficult to identify blade defect characteristic frequency embedded in pressure pulsation signal by general signal processing methods due to the weakness of the feature information and the interference of strong noise. In this paper, continuous wavelet transform (CWT) is used to pre-process the sampled signal first. Then, the method of bistable stochastic resonance (SR) based on Woods-Saxon and Gaussian (WSG) potential is applied to enhance the weak characteristic frequency contained in the pressure pulsation signal. Genetic algorithm (GA) is used to obtain optimal parameters for this SR system to improve its feature enhancement performance. The analysis result of experimental signal shows the validity of the proposed method for the enhancement and identification of weak defect characteristic. In the end, strain test is carried out to further verify the accuracy and reliability of the analysis result obtained by pressure pulsation signal.

Weak Defect Identification for Centrifugal Compressor Blade Crack Based on Pressure Sensors and Genetic Algorithm

PubMed Central

Li, Hongkun; He, Changbo

2018-01-01

The Centrifugal compressor is a piece of key equipment for petrochemical factories. As the core component of a compressor, the blades suffer periodic vibration and flow induced excitation mechanism, which will lead to the occurrence of crack defect. Moreover, the induced blade defect usually has a serious impact on the normal operation of compressors and the safety of operators. Therefore, an effective blade crack identification method is particularly important for the reliable operation of compressors. Conventional non-destructive testing and evaluation (NDT&E) methods can detect the blade defect effectively, however, the compressors should shut down during the testing process which is time-consuming and costly. In addition, it can be known these methods are not suitable for the long-term on-line condition monitoring and cannot identify the blade defect in time. Therefore, the effective on-line condition monitoring and weak defect identification method should be further studied and proposed. Considering the blade vibration information is difficult to measure directly, pressure sensors mounted on the casing are used to sample airflow pressure pulsation signal on-line near the rotating impeller for the purpose of monitoring the blade condition indirectly in this paper. A big problem is that the blade abnormal vibration amplitude induced by the crack is always small and this feature information will be much weaker in the pressure signal. Therefore, it is usually difficult to identify blade defect characteristic frequency embedded in pressure pulsation signal by general signal processing methods due to the weakness of the feature information and the interference of strong noise. In this paper, continuous wavelet transform (CWT) is used to pre-process the sampled signal first. Then, the method of bistable stochastic resonance (SR) based on Woods-Saxon and Gaussian (WSG) potential is applied to enhance the weak characteristic frequency contained in the pressure pulsation signal. Genetic algorithm (GA) is used to obtain optimal parameters for this SR system to improve its feature enhancement performance. The analysis result of experimental signal shows the validity of the proposed method for the enhancement and identification of weak defect characteristic. In the end, strain test is carried out to further verify the accuracy and reliability of the analysis result obtained by pressure pulsation signal. PMID:29671821

Optical signal processing of spatially distributed sensor data in smart structures

NASA Technical Reports Server (NTRS)

Bennett, K. D.; Claus, R. O.; Murphy, K. A.; Goette, A. M.

1989-01-01

Smart structures which contain dense two- or three-dimensional arrays of attached or embedded sensor elements inherently require signal multiplexing and processing capabilities to permit good spatial data resolution as well as the adequately short calculation times demanded by real time active feedback actuator drive circuitry. This paper reports the implementation of an in-line optical signal processor and its application in a structural sensing system which incorporates multiple discrete optical fiber sensor elements. The signal processor consists of an array of optical fiber couplers having tailored s-parameters and arranged to allow gray code amplitude scaling of sensor inputs. The use of this signal processor in systems designed to indicate the location of distributed strain and damage in composite materials, as well as to quantitatively characterize that damage, is described. Extension of similar signal processing methods to more complicated smart materials and structures applications are discussed.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

DOA Finding with Support Vector Regression Based Forward-Backward Linear Prediction.

PubMed

Pan, Jingjing; Wang, Yide; Le Bastard, Cédric; Wang, Tianzhen

2017-05-27

Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward-backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.

Comparison of lifetime-based methods for 2D phosphor thermometry in high-temperature environment

NASA Astrophysics Data System (ADS)

Peng, Di; Liu, Yingzheng; Zhao, Xiaofeng; Kim, Kyung Chun

2016-09-01

This paper discusses the currently available techniques for 2D phosphor thermometry, and compares the performance of two lifetime-based methods: high-speed imaging and the dual-gate. High-speed imaging resolves luminescent decay with a fast frame rate, and has become a popular method for phosphor thermometry in recent years. But it has disadvantages such as high equipment cost and long data processing time, and it would fail at sufficiently high temperature due to a low signal-to-noise ratio and short lifetime. The dual-gate method only requires two images on the decay curve and therefore greatly reduces cost in hardware and processing time. A dual-gate method for phosphor thermometry has been developed and compared with the high-speed imaging method through both calibration and a jet impingement experiment. Measurement uncertainty has been evaluated for a temperature range of 473-833 K. The effects of several key factors on uncertainty have been discussed, including the luminescent signal level, the decay lifetime and temperature sensitivity. The results show that both methods are valid for 2D temperature sensing within the given range. The high-speed imaging method shows less uncertainty at low temperatures where the signal level and the lifetime are both sufficient, but its performance is degraded at higher temperatures due to a rapidly reduced signal and lifetime. For T  >  750 K, the dual-gate method outperforms the high-speed imaging method thanks to its superiority in signal-to-noise ratio and temperature sensitivity. The dual-gate method has great potential for applications in high-temperature environments where the high-speed imaging method is not applicable.

Signal Processing Methods Monitor Cranial Pressure

NASA Technical Reports Server (NTRS)

2010-01-01

Dr. Norden Huang, of Goddard Space Flight Center, invented a set of algorithms (called the Hilbert-Huang Transform, or HHT) for analyzing nonlinear and nonstationary signals that developed into a user-friendly signal processing technology for analyzing time-varying processes. At an auction managed by Ocean Tomo Federal Services LLC, licenses of 10 U.S. patents and 1 domestic patent application related to HHT were sold to DynaDx Corporation, of Mountain View, California. DynaDx is now using the licensed NASA technology for medical diagnosis and prediction of brain blood flow-related problems, such as stroke, dementia, and traumatic brain injury.

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

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.

Signal enhancement for gradient reverse-phase high-performance liquid chromatography-electrospray ionization mass spectrometry analysis with trifluoroacetic and other strong acid modifiers by postcolumn addition of propionic acid and isopropanol.

PubMed

Kuhlmann, F E; Apffel, A; Fischer, S M; Goldberg, G; Goodley, P C

1995-12-01

Trifluoroacetic acid (TFA) and other volatile strong acids, used as modifiers in reverse-phase high-performance liquid chromatography, cause signal suppression for basic compounds when analyzed by electrospray ionization mass spectrometry (ESI-MS). Evidence is presented that signal suppression is caused by strong ion pairing between the TFA anion and the protonated sample cation of basic sample molecules. The ion-pairing process "masks" the protonated sample cations from the ESI-MS electric fields by rendering them "neutral. " Weakly basic molecules are not suppressed by this process. The TFA signal suppression effect is independent from the well-known spray problem that electrospray has with highly aqueous solutions that contain TFA. This previously reported spray problem is caused by the high conductivity and surface tension of aqueous TFA solutions. A practical method to enhance the signal for most basic analytes in the presence of signal-suppressing volatile strong acids has been developed. The method employs postcolumn addition of a solution of 75% propionic acid and 25% isopropanol in a ratio 1:2 to the column flow. Signal enhancement is typically 10-50 times for peptides and other small basic molecules. Thus, peptide maps that use ESI-MS for detection can be performed at lower levels, with conventional columns, without the need to use capillary chromatography or reduced mass spectral resolution to achieve satisfactory sensitivity. The method may be used with similar results for heptafluorobutyric acid and hydrochloric acid. A mechanism for TFA signal suppression and signal enhancement by the foregoing method, is proposed.

A method of noise reduction in heterodyne interferometric vibration metrology by combining auto-correlation analysis and spectral filtering

NASA Astrophysics Data System (ADS)

Hao, Hongliang; Xiao, Wen; Chen, Zonghui; Ma, Lan; Pan, Feng

2018-01-01

Heterodyne interferometric vibration metrology is a useful technique for dynamic displacement and velocity measurement as it can provide a synchronous full-field output signal. With the advent of cost effective, high-speed real-time signal processing systems and software, processing of the complex signals encountered in interferometry has become more feasible. However, due to the coherent nature of the laser sources, the sequence of heterodyne interferogram are corrupted by a mixture of coherent speckle and incoherent additive noise, which can severely degrade the accuracy of the demodulated signal and the optical display. In this paper, a new heterodyne interferometric demodulation method by combining auto-correlation analysis and spectral filtering is described leading to an expression for the dynamic displacement and velocity of the object under test that is significantly more accurate in both the amplitude and frequency of the vibrating waveform. We present a mathematical model of the signals obtained from interferograms that contain both vibration information of the measured objects and the noise. A simulation of the signal demodulation process is presented and used to investigate the noise from the system and external factors. The experimental results show excellent agreement with measurements from a commercial Laser Doppler Velocimetry (LDV).

Mastication noise reduction method for fully implantable hearing aid using piezo-electric sensor.

PubMed

Na, Sung Dae; Lee, Gihyoun; Wei, Qun; Seong, Ki Woong; Cho, Jin Ho; Kim, Myoung Nam

2017-07-20

Fully implantable hearing devices (FIHDs) can be affected by generated biomechanical noise such as mastication noise. To reduce the mastication noise using a piezo-electric sensor, the mastication noise is measured with the piezo-electric sensor, and noise reduction is practiced by the energy difference. For the experiment on mastication noise, a skull model was designed using artificial skull model and a piezo-electric sensor that can measure the vibration signals better than other sensors. A 1 kHz pure-tone sound through a standard speaker was applied to the model while the lower jawbone of the model was moved in a masticatory fashion. The correlation coefficients and signal-to-noise ratio (SNR) before and after application of the proposed method were compared. It was found that the signal-to-noise ratio and correlation coefficients increased by 4.48 dB and 0.45, respectively. The mastication noise is measured by piezo-electric sensor as the mastication noise that occurred during vibration. In addition, the noise was reduced by using the proposed method in conjunction with MATLAB. In order to confirm the performance of the proposed method, the correlation coefficients and signal-to-noise ratio before and after signal processing were calculated. In the future, an implantable microphone for real-time processing will be developed.

Simultaneous spectrophotometric determination of indacaterol and glycopyrronium in a newly approved pharmaceutical formulation using different signal processing techniques of ratio spectra

NASA Astrophysics Data System (ADS)

Abdel Ghany, Maha F.; Hussein, Lobna A.; Magdy, Nancy; Yamani, Hend Z.

2016-03-01

Three spectrophotometric methods have been developed and validated for determination of indacaterol (IND) and glycopyrronium (GLY) in their binary mixtures and novel pharmaceutical dosage form. The proposed methods are considered to be the first methods to determine the investigated drugs simultaneously. The developed methods are based on different signal processing techniques of ratio spectra namely; Numerical Differentiation (ND), Savitsky-Golay (SG) and Fourier Transform (FT). The developed methods showed linearity over concentration range 1-30 and 10-35 (μg/mL) for IND and GLY, respectively. The accuracy calculated as percentage recoveries were in the range of 99.00%-100.49% with low value of RSD% (< 1.5%) demonstrating an excellent accuracy of the proposed methods. The developed methods were proved to be specific, sensitive and precise for quality control of the investigated drugs in their pharmaceutical dosage form without the need for any separation process.

Method and Apparatus to Access Optimum Strength During Processing of Precipitation Strengthened Alloys

NASA Technical Reports Server (NTRS)

Cantrell, John H. (Inventor); Yost, William T. (Inventor)

2001-01-01

A method and apparatus are provided which enable the nondestructive testing of strength of a heat treated alloy. An alloy is insonified with an ultrasonic signal. The resulting convoluted signal is detected and the acoustic nonlinearity parameter is determined. The acoustic nonlinearity parameter shows a peak corresponding to a peak in material strength.

Velocity interferometer signal de-noising using modified Wiener filter

NASA Astrophysics Data System (ADS)

Rav, Amit; Joshi, K. D.; Roy, Kallol; Kaushik, T. C.

2017-05-01

The accuracy and precision of the non-contact velocity interferometer system for any reflector (VISAR) depends not only on the good optical design and linear optical-to- electrical conversion system, but also on accurate and robust post-processing techniques. The performance of these techniques, such as the phase unwrapping algorithm, depends on the signal-to-noise ratio (SNR) of the recorded signal. In the present work, a novel method of improving the SNR of the recorded VISAR signal, based on the knowledge of the noise characteristic of the signal conversion and recording system, is presented. The proposed method uses a modified Wiener filter, for which the signal power spectrum estimation is obtained using a spectral subtraction method (SSM), and the noise power spectrum estimation is obtained by taking the average of the recorded signal during the period when no target movement is expected. Since the noise power spectrum estimate is dynamic in nature, and obtained for each experimental record individually, the improved signal quality is high. The proposed method is applied to the simulated standard signals, and is not only found to be better than the SSM, but is also less sensitive to the selection of the noise floor during signal power spectrum estimation. Finally, the proposed method is applied to the recorded experimental signal and an improvement in the SNR is reported.

Method and system for controlling a synchronous machine over full operating range

DOEpatents

Walters, James E.; Gunawan, Fani S.; Xue, Yanhong

2002-01-01

System and method for controlling a synchronous machine are provided. The method allows for calculating a stator voltage index. The method further allows for relating the magnitude of the stator voltage index against a threshold voltage value. An offset signal is generated based on the results of the relating step. A respective state of operation of the machine is determined. The offset signal is processed based on the respective state of the machine.

Method to improve the blade tip-timing accuracy of fiber bundle sensor under varying tip clearance

NASA Astrophysics Data System (ADS)

Duan, Fajie; Zhang, Jilong; Jiang, Jiajia; Guo, Haotian; Ye, Dechao

2016-01-01

Blade vibration measurement based on the blade tip-timing method has become an industry-standard procedure. Fiber bundle sensors are widely used for tip-timing measurement. However, the variation of clearance between the sensor and the blade will bring a tip-timing error to fiber bundle sensors due to the change in signal amplitude. This article presents methods based on software and hardware to reduce the error caused by the tip clearance change. The software method utilizes both the rising and falling edges of the tip-timing signal to determine the blade arrival time, and a calibration process suitable for asymmetric tip-timing signals is presented. The hardware method uses an automatic gain control circuit to stabilize the signal amplitude. Experiments are conducted and the results prove that both methods can effectively reduce the impact of tip clearance variation on the blade tip-timing and improve the accuracy of measurements.

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation.

PubMed

Atluri, Sravya; Frehlich, Matthew; Mei, Ye; Garcia Dominguez, Luis; Rogasch, Nigel C; Wong, Willy; Daskalakis, Zafiris J; Farzan, Faranak

2016-01-01

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation

PubMed Central

Atluri, Sravya; Frehlich, Matthew; Mei, Ye; Garcia Dominguez, Luis; Rogasch, Nigel C.; Wong, Willy; Daskalakis, Zafiris J.; Farzan, Faranak

2016-01-01

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research. PMID:27774054

Global interrupt and barrier networks

DOEpatents

Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E; Heidelberger, Philip; Kopcsay, Gerard V.; Steinmacher-Burow, Burkhard D.; Takken, Todd E.

2008-10-28

A system and method for generating global asynchronous signals in a computing structure. Particularly, a global interrupt and barrier network is implemented that implements logic for generating global interrupt and barrier signals for controlling global asynchronous operations performed by processing elements at selected processing nodes of a computing structure in accordance with a processing algorithm; and includes the physical interconnecting of the processing nodes for communicating the global interrupt and barrier signals to the elements via low-latency paths. The global asynchronous signals respectively initiate interrupt and barrier operations at the processing nodes at times selected for optimizing performance of the processing algorithms. In one embodiment, the global interrupt and barrier network is implemented in a scalable, massively parallel supercomputing device structure comprising a plurality of processing nodes interconnected by multiple independent networks, with each node including one or more processing elements for performing computation or communication activity as required when performing parallel algorithm operations. One multiple independent network includes a global tree network for enabling high-speed global tree communications among global tree network nodes or sub-trees thereof. The global interrupt and barrier network may operate in parallel with the global tree network for providing global asynchronous sideband signals.

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.

Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

NASA Astrophysics Data System (ADS)

Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

2017-12-01

The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

Dimension from covariance matrices.

PubMed

Carroll, T L; Byers, J M

2017-02-01

We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems. The algorithm described here compares the eigenvalues of covariance matrices created from an embedded signal to the eigenvalues for a covariance matrix of a Gaussian random process with the same dimension and number of points. A statistical test gives the probability that the eigenvalues for the embedded signal did not come from the Gaussian random process.

EFFECTS OF LASER RADIATION ON MATTER. LASER PLASMA: Doppler backscattered-signal diagnostics of laser-induced surface hydrodynamic processes

NASA Astrophysics Data System (ADS)

Gordienko, Vyacheslav M.; Kurochkin, Nikolay N.; Markov, V. N.; Panchenko, Vladislav Ya; Pogosov, G. A.; Chastukhin, E. M.

1995-02-01

A method is proposed for on-line monitoring of laser industrial processing. The method is based on optical heterodyne measurements of the Doppler backscattering signal generated in the interaction zone. Qualitative and quantitative information on hydrodynamic flows in the interaction zone can be obtained. A report is given of measurements, carried out at cw CO2 laser radiation intensities up to 1 kW cm-2, on the surfaces of a number of condensed materials irradiated in the monostatic interaction configuration.

Feature Visibility Limits in the Non-Linear Enhancement of Turbid Images

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.; Rahman, Zia-ur; Woodell, Glenn A.

2003-01-01

The advancement of non-linear processing methods for generic automatic clarification of turbid imagery has led us from extensions of entirely passive multiscale Retinex processing to a new framework of active measurement and control of the enhancement process called the Visual Servo. In the process of testing this new non-linear computational scheme, we have identified that feature visibility limits in the post-enhancement image now simplify to a single signal-to-noise figure of merit: a feature is visible if the feature-background signal difference is greater than the RMS noise level. In other words, a signal-to-noise limit of approximately unity constitutes a lower limit on feature visibility.

Method of recording bioelectrical signals using a capacitive coupling

NASA Astrophysics Data System (ADS)

Simon, V. A.; Gerasimov, V. A.; Kostrin, D. K.; Selivanov, L. M.; Uhov, A. A.

2017-11-01

In this article a technique for the bioelectrical signals acquisition by means of the capacitive sensors is described. A feedback loop for the ultra-high impedance biasing of the input instrumentation amplifier, which provides receiving of the electrical cardiac signal (ECS) through a capacitive coupling, is proposed. The mains 50/60 Hz noise is suppressed by a narrow-band stop filter with an independent notch frequency and quality factor tuning. Filter output is attached to a ΣΔ analog-to-digital converter (ADC), which acquires the filtered signal with a 24-bit resolution. Signal processing board is connected through universal serial bus interface to a personal computer, where ECS in a digital form is recorded and processed.

Emg Signal Analysis of Healthy and Neuropathic Individuals

NASA Astrophysics Data System (ADS)

Gupta, Ashutosh; Sayed, Tabassum; Garg, Ridhi; Shreyam, Richa

2017-08-01

Electromyography is a method to evaluate levels of muscle activity. When a muscle contracts, an action potential is generated and this circulates along the muscular fibers. In electromyography, electrodes are connected to the skin and the electrical activity of muscles is measured and graph is plotted. The surface EMG signals picked up during the muscular activity are interfaced with a system. The EMG signals from individual suffering from Neuropathy and healthy individual, so obtained, are processed and analyzed using signal processing techniques. This project includes the investigation and interpretation of EMG signals of healthy and Neuropathic individuals using MATLAB. The prospective use of this study is in developing the prosthetic device for the people with Neuropathic disability.

Method for enhancing signals transmitted over optical fibers

DOEpatents

Ogle, J.W.; Lyons, P.B.

1981-02-11

A method for spectral equalization of high frequency spectrally broadband signals transmitted through an optical fiber is disclosed. The broadband signal input is first dispersed by a grating. Narrow spectral components are collected into an array of equalizing fibers. The fibers serve as optical delay lines compensating for material dispersion of each spectral component during transmission. The relative lengths of the individual equalizing fibers are selected to compensate for such prior dispersion. The output of the equalizing fibers couple the spectrally equalized light onto a suitable detector for subsequent electronic processing of the enhanced broadband signal.

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.

Determining Polarities Of Distant Lightning Strokes

NASA Technical Reports Server (NTRS)

Blakeslee, Richard J.; Brook, Marx

1990-01-01

Method for determining polarities of lightning strokes more than 400 km away. Two features of signal from each stroke correlated. New method based on fact each stroke observed thus far for which polarity determined unambiguously, initial polarity of tail same as polarity of initial deflection before initial-deflection signal altered by propagation effects. Receiving station equipped with electric-field-change antenna coupled to charge amplifier having time constant of order of 1 to 10 seconds. Output of amplifier fed to signal-processing circuitry, which determines initial polarity of tail.

Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking

NASA Technical Reports Server (NTRS)

Park, Han G. (Inventor); Zak, Michail (Inventor); James, Mark L. (Inventor); Mackey, Ryan M. E. (Inventor)

2003-01-01

A general method of anomaly detection from time-correlated sensor data is disclosed. Multiple time-correlated signals are received. Their cross-signal behavior is compared against a fixed library of invariants. The library is constructed during a training process, which is itself data-driven using the same time-correlated signals. The method is applicable to a broad class of problems and is designed to respond to any departure from normal operation, including faults or events that lie outside the training envelope.

Adaptive Fourier decomposition based ECG denoising.

PubMed

Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

2016-10-01

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

NASA Astrophysics Data System (ADS)

Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

2016-05-01

Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

Interpretation of interference signals in label free integrated interferometric biosensors

NASA Astrophysics Data System (ADS)

Heikkinen, Hanna; Wang, Meng; Okkonen, Matti; Hast, Jukka; Myllylä, Risto

2006-02-01

In the future fast, simple and reliable biosensors will be needed to detect various analytes from different biosamples. This is due to fact that the needs of traditional health care are changing. In the future homecare of patients and peoples' responsibility for their own health will increase. Also, different wellness applications need new parameters to be analysed, reducing costs of traditional health care, which are increasing rapidly. One fascinating and promising sensor type for these applications is an integrated optical interferometric immunosensor, which is manufactured using organic materials. The use of organic materials opens up enormous possibilities to develop different biochemical functions. In label free biosensors the measurement is based on detecting changes in refractive index, which typically are in the range of 10 -6-10 -8 [1]. In this research, theoretically generated interferograms are used to compare various signal processing methods. The goal is to develop an efficient method to analyse the interferogram. Different time domain signal processing methods are studied to determine the measuring resolution and efficiency of these methods. A low cost CCD -element is used in detecting the interferogram dynamics. It was found that in most of the signal processing methods the measuring resolution was mainly limited by pixel size. With calculation of Pearson's correlation coefficient, subpixel resolution was achieved which means that nanometer range optical path differences can be measured. This results in the refractive index resolution of the order of 10 -7.

A review of signals used in sleep analysis

PubMed Central

Roebuck, A; Monasterio, V; Gederi, E; Osipov, M; Behar, J; Malhotra, A; Penzel, T; Clifford, GD

2014-01-01

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep. PMID:24346125

An adaptive spatio-temporal Gaussian filter for processing cardiac optical mapping data.

PubMed

Pollnow, S; Pilia, N; Schwaderlapp, G; Loewe, A; Dössel, O; Lenis, G

2018-06-04

Optical mapping is widely used as a tool to investigate cardiac electrophysiology in ex vivo preparations. Digital filtering of fluorescence-optical data is an important requirement for robust subsequent data analysis and still a challenge when processing data acquired from thin mammalian myocardium. Therefore, we propose and investigate the use of an adaptive spatio-temporal Gaussian filter for processing optical mapping signals from these kinds of tissue usually having low signal-to-noise ratio (SNR). We demonstrate how filtering parameters can be chosen automatically without additional user input. For systematic comparison of this filter with standard filtering methods from the literature, we generated synthetic signals representing optical recordings from atrial myocardium of a rat heart with varying SNR. Furthermore, all filter methods were applied to experimental data from an ex vivo setup. Our developed filter outperformed the other filter methods regarding local activation time detection at SNRs smaller than 3 dB which are typical noise ratios expected in these signals. At higher SNRs, the proposed filter performed slightly worse than the methods from literature. In conclusion, the proposed adaptive spatio-temporal Gaussian filter is an appropriate tool for investigating fluorescence-optical data with low SNR. The spatio-temporal filter parameters were automatically adapted in contrast to the other investigated filters. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform

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

Tang, Hui, E-mail: corinna@seu.edu.cn; Key Laboratory of Computer Network and Information Integration; Centre de Recherche en Information Biomédicale sino-français, Laboratoire International Associé, Inserm, Université de Rennes 1, Rennes 35000

2015-04-15

Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimagesmore » using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.« less

Background Noise Reduction Using Adaptive Noise Cancellation Determined by the Cross-Correlation

NASA Technical Reports Server (NTRS)

Spalt, Taylor B.; Brooks, Thomas F.; Fuller, Christopher R.

2012-01-01

Background noise due to flow in wind tunnels contaminates desired data by decreasing the Signal-to-Noise Ratio. The use of Adaptive Noise Cancellation to remove background noise at measurement microphones is compromised when the reference sensor measures both background and desired noise. The technique proposed modifies the classical processing configuration based on the cross-correlation between the reference and primary microphone. Background noise attenuation is achieved using a cross-correlation sample width that encompasses only the background noise and a matched delay for the adaptive processing. A present limitation of the method is that a minimum time delay between the background noise and desired signal must exist in order for the correlated parts of the desired signal to be separated from the background noise in the crosscorrelation. A simulation yields primary signal recovery which can be predicted from the coherence of the background noise between the channels. Results are compared with two existing methods.

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.

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

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.

Method and apparatus for assessing weld quality

DOEpatents

Smartt, Herschel B.; Kenney, Kevin L.; Johnson, John A.; Carlson, Nancy M.; Clark, Denis E.; Taylor, Paul L.; Reutzel, Edward W.

2001-01-01

Apparatus for determining a quality of a weld produced by a welding device according to the present invention includes a sensor operatively associated with the welding device. The sensor is responsive to at least one welding process parameter during a welding process and produces a welding process parameter signal that relates to the at least one welding process parameter. A computer connected to the sensor is responsive to the welding process parameter signal produced by the sensor. A user interface operatively associated with the computer allows a user to select a desired welding process. The computer processes the welding process parameter signal produced by the sensor in accordance with one of a constant voltage algorithm, a short duration weld algorithm or a pulsed current analysis module depending on the desired welding process selected by the user. The computer produces output data indicative of the quality of the weld.

Signal processing for smart cards

NASA Astrophysics Data System (ADS)

Quisquater, Jean-Jacques; Samyde, David

2003-06-01

In 1998, Paul Kocher showed that when a smart card computes cryptographic algorithms, for signatures or encryption, its consumption or its radiations leak information. The keys or the secrets hidden in the card can then be recovered using a differential measurement based on the intercorrelation function. A lot of silicon manufacturers use desynchronization countermeasures to defeat power analysis. In this article we detail a new resynchronization technic. This method can be used to facilitate the use of a neural network to do the code recognition. It becomes possible to reverse engineer a software code automatically. Using data and clock separation methods, we show how to optimize the synchronization using signal processing. Then we compare these methods with watermarking methods for 1D and 2D signal. The very last watermarking detection improvements can be applied to signal processing for smart cards with very few modifications. Bayesian processing is one of the best ways to do Differential Power Analysis, and it is possible to extract a PIN code from a smart card in very few samples. So this article shows the need to continue to set up effective countermeasures for cryptographic processors. Although the idea to use advanced signal processing operators has been commonly known for a long time, no publication explains that results can be obtained. The main idea of differential measurement is to use the cross-correlation of two random variables and to repeat consumption measurements on the processor to be analyzed. We use two processors clocked at the same external frequency and computing the same data. The applications of our design are numerous. Two measurements provide the inputs of a central operator. With the most accurate operator we can improve the signal noise ratio, re-synchronize the acquisition clock with the internal one, or remove jitter. The analysis based on consumption or electromagnetic measurements can be improved using our structure. At first sight the same results can be obtained with only one smart card, but this idea is not completely true because the statistical properties of the signal are not the same. As the two smart cards are submitted to the same external noise during the measurement, it is more easy to reduce the influence of perturbations. This paper shows the importance of accurate countermeasures against differential analysis.

Selective suppression of CARS signal with three-beam competing stimulated Raman scattering processes.

PubMed

Choi, Dae Sik; Rao, B Jayachander; Kim, Doyeon; Shim, Sang-Hee; Rhee, Hanju; Cho, Minhaeng

2018-06-14

Coherent Raman scattering spectroscopy and microscopy are useful methods for studying the chemical and biological structures of molecules with Raman-active modes. In particular, coherent anti-Stokes Raman scattering (CARS) microscopy, which is a label-free method capable of imaging structures by displaying the vibrational contrast of the molecules, has been widely used. However, the lack of a technique for switching-off the CARS signal has prevented the development of the super-resolution Raman imaging method. Here, we demonstrate that a selective suppression of the CARS signal is possible by using a three-beam double stimulated Raman scattering (SRS) scheme; the three beams are the pump, Stokes, and depletion lights in order of frequency. Both pump-Stokes and pump-depletion beam pairs can generate SRS processes by tuning their beat frequencies to match two different vibrational modes, then two CARS signals induced by pump-Stokes-pump and pump-depletion-pump interactions can be generated, where the two CARS signals are coupled with each other because they both involve interactions with the common pump beam. Herein, we show that as the intensity of the depletion beam is increased, one can selectively suppress the pump-Stokes-pump CARS signal because the pump-depletion SRS depletes the pump photons. A detailed theoretical description of the coupled differential equations for the three incident fields and the generated CARS signal fields is presented. Taking benzene as a molecular system, we obtained a maximum CARS suppression efficiency of about 97% with our experimental scheme, where the ring breathing mode of the benzene is associated with pump-Stokes-pump CARS, while the C-H stretching mode is associated with the competing pump-depletion SRS process. We anticipate that this selective switching-off scheme will be of use in developing super-resolution label-free CARS microscopy.

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

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.

Ultrasonic inspection of carbon fiber reinforced plastic by means of sample-recognition methods

NASA Technical Reports Server (NTRS)

Bilgram, R.

1985-01-01

In the case of carbon fiber reinforced plastic (CFRP), it has not yet been possible to detect nonlocal defects and material degradation related to aging with the aid of nondestructive inspection method. An approach for overcoming difficulties regarding such an inspection involves an extension of the ultrasonic inspection procedure on the basis of a use of signal processing and sample recognition methods. The basic concept involved in this approach is related to the realization that the ultrasonic signal contains information regarding the medium which is not utilized in conventional ultrasonic inspection. However, the analytical study of the phyiscal processes involved is very complex. For this reason, an empirical approach is employed to make use of the information which has not been utilized before. This approach uses reference signals which can be obtained with material specimens of different quality. The implementation of these concepts for the supersonic inspection of CFRP laminates is discussed.

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

PubMed

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

2018-05-21

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

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

PubMed Central

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

2018-01-01

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

A miniature electronic nose system based on an MWNT-polymer microsensor array and a low-power signal-processing chip.

PubMed

Chiu, Shih-Wen; Wu, Hsiang-Chiu; Chou, Ting-I; Chen, Hsin; Tang, Kea-Tiong

2014-06-01

This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760 μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.

Tactile objects based on an amplitude disturbed diffraction pattern method

NASA Astrophysics Data System (ADS)

Liu, Yuan; Nikolovski, Jean-Pierre; Mechbal, Nazih; Hafez, Moustapha; Vergé, Michel

2009-12-01

Tactile sensing is becoming widely used in human-computer interfaces. Recent advances in acoustic approaches demonstrated the possibilities to transform ordinary solid objects into interactive interfaces. This letter proposes a static finger contact localization process using an amplitude disturbed diffraction pattern method. The localization method is based on the following physical phenomenon: a finger contact modifies the energy distribution of acoustic wave in a solid; these variations depend on the wave frequency and the contact position. The presented method first consists of exciting the object with an acoustic signal with plural frequency components. In a second step, a measured acoustic signal is compared with prerecorded values to deduce the contact position. This position is then used for human-machine interaction (e.g., finger tracking on computer screen). The selection of excitation signals is discussed and a frequency choice criterion based on contrast value is proposed. Tests on a sandwich plate (liquid crystal display screen) prove the simplicity and easiness to apply the process in various solids.

Wavelet-Based Processing for Fiber Optic Sensing Systems

NASA Technical Reports Server (NTRS)

Hamory, Philip J. (Inventor); Parker, Allen R., Jr. (Inventor)

2016-01-01

The present invention is an improved method of processing conglomerate data. The method employs a Triband Wavelet Transform that decomposes and decimates the conglomerate signal to obtain a final result. The invention may be employed to improve performance of Optical Frequency Domain Reflectometry systems.

Active control of nanolitre droplet contents with convective concentration gradients across permeable walls.

PubMed

Zeitoun, Ramsey I; Goudie, Marcus J; Zwier, Jacob; Mahawilli, David; Burns, Mark A

2011-12-07

Nanolitre droplets in microfluidic devices can be used to perform thousands of independent chemical and biological experiments while minimizing reagents, cost and time. However, the absence of simple and versatile methods capable of controlling the contents of these nanolitre chemical systems limits their scientific potential. To address this, we have developed a method that is simple to fabricate and can continuously control nanolitre chemical systems by integrating a time-resolved convective flow signal across a permeable membrane wall. With this method, we can independently control the volume and concentration of nanolitre-sized drops without ever directly contacting the fluid. Transport occurring in these systems was also analyzed and thoroughly characterized. We achieved volumetric fluid introduction and removal rates ranging from 0.23 to 4.0 pL s(-1). Furthermore, we expanded this method to perform chemical processes. We precipitated silver chloride using a flow signal of sodium chloride and silver nitrate droplets. From there, we were able to separate sodium chloride reactants with a water flow signal, and dissolve silver chloride solids with an ammonia hydroxide flow signal. Finally, we demonstrate the potential to deliver large molecules and perform physical processes like crystallization and particle packing.

Signal processing and analysis for copper layer thickness measurement within a large variation range in the CMP process.

PubMed

Li, Hongkai; Zhao, Qian; Lu, Xinchun; Luo, Jianbin

2017-11-01

In the copper (Cu) chemical mechanical planarization (CMP) process, accurate determination of a process reaching the end point is of great importance. Based on the eddy current technology, the in situ thickness measurement of the Cu layer is feasible. Previous research studies focus on the application of the eddy current method to the metal layer thickness measurement or endpoint detection. In this paper, an in situ measurement system, which is independently developed by using the eddy current method, is applied to the actual Cu CMP process. A series of experiments are done for further analyzing the dynamic response characteristic of the output signal within different thickness variation ranges. In this study, the voltage difference of the output signal is used to represent the thickness of the Cu layer, and we can extract the voltage difference variations from the output signal fast by using the proposed data processing algorithm. The results show that the voltage difference decreases as thickness decreases in the conventional measurement range and the sensitivity increases at the same time. However, it is also found that there exists a thickness threshold, and the correlation is negative, when the thickness is more than the threshold. Furthermore, it is possible that the in situ measurement system can be used within a larger Cu layer thickness variation range by creating two calibration tables.

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

Beamforming array techniques for acoustic emission monitoring of large concrete structures

NASA Astrophysics Data System (ADS)

McLaskey, Gregory C.; Glaser, Steven D.; Grosse, Christian U.

2010-06-01

This paper introduces a novel method of acoustic emission (AE) analysis which is particularly suited for field applications on large plate-like reinforced concrete structures, such as walls and bridge decks. Similar to phased-array signal processing techniques developed for other non-destructive evaluation methods, this technique adapts beamforming tools developed for passive sonar and seismological applications for use in AE source localization and signal discrimination analyses. Instead of relying on the relatively weak P-wave, this method uses the energy-rich Rayleigh wave and requires only a small array of 4-8 sensors. Tests on an in-service reinforced concrete structure demonstrate that the azimuth of an artificial AE source can be determined via this method for sources located up to 3.8 m from the sensor array, even when the P-wave is undetectable. The beamforming array geometry also allows additional signal processing tools to be implemented, such as the VESPA process (VElocity SPectral Analysis), whereby the arrivals of different wave phases are identified by their apparent velocity of propagation. Beamforming AE can reduce sampling rate and time synchronization requirements between spatially distant sensors which in turn facilitates the use of wireless sensor networks for this application.

A Surrogate Technique for Investigating Deterministic Dynamics in Discrete Human Movement.

PubMed

Taylor, Paul G; Small, Michael; Lee, Kwee-Yum; Landeo, Raul; O'Meara, Damien M; Millett, Emma L

2016-10-01

Entropy is an effective tool for investigation of human movement variability. However, before applying entropy, it can be beneficial to employ analyses to confirm that observed data are not solely the result of stochastic processes. This can be achieved by contrasting observed data with that produced using surrogate methods. Unlike continuous movement, no appropriate method has been applied to discrete human movement. This article proposes a novel surrogate method for discrete movement data, outlining the processes for determining its critical values. The proposed technique reliably generated surrogates for discrete joint angle time series, destroying fine-scale dynamics of the observed signal, while maintaining macro structural characteristics. Comparison of entropy estimates indicated observed signals had greater regularity than surrogates and were not only the result of stochastic but also deterministic processes. The proposed surrogate method is both a valid and reliable technique to investigate determinism in other discrete human movement time series.

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.

Novel auto-correction method in a fiber-optic distributed-temperature sensor using reflected anti-Stokes Raman scattering.

PubMed

Hwang, Dusun; Yoon, Dong-Jin; Kwon, Il-Bum; Seo, Dae-Cheol; Chung, Youngjoo

2010-05-10

A novel method for auto-correction of fiber optic distributed temperature sensor using anti-Stokes Raman back-scattering and its reflected signal is presented. This method processes two parts of measured signal. One part is the normal back scattered anti-Stokes signal and the other part is the reflected signal which eliminate not only the effect of local losses due to the micro-bending or damages on fiber but also the differential attenuation. Because the beams of the same wavelength are used to cancel out the local variance in transmission medium there is no differential attenuation inherently. The auto correction concept was verified by the bending experiment on different bending points. (c) 2010 Optical Society of America.

Perceptions as Hypotheses

NASA Astrophysics Data System (ADS)

Gregory, R. L.

1980-07-01

Perceptions may be compared with hypotheses in science. The methods of acquiring scientific knowledge provide a working paradigm for investigating processes of perception. Much as the information channels of instruments, such as radio telescopes, transmit signals which are processed according to various assumptions to give useful data, so neural signals are processed to give data for perception. To understand perception, the signal codes and the stored knowledge or assumptions used for deriving perceptual hypotheses must be discovered. Systematic perceptual errors are important clues for appreciating signal channel limitations, and for discovering hypothesis-generating procedures. Although this distinction between `physiological' and `cognitive' aspects of perception may be logically clear, it is in practice surprisingly difficult to establish which are responsible even for clearly established phenomena such as the classical distortion illusions. Experimental results are presented, aimed at distinguishing between and discovering what happens when there is mismatch with the neural signal channel, and when neural signals are processed inappropriately for the current situation. This leads us to make some distinctions between perceptual and scientific hypotheses, which raise in a new form the problem: What are `objects'?

Navigation system and method

NASA Technical Reports Server (NTRS)

Taylor, R. E.; Sennott, J. W. (Inventor)

1984-01-01

In a global positioning system (GPS), such as the NAVSTAR/GPS system, wherein the position coordinates of user terminals are obtained by processing multiple signals transmitted by a constellation of orbiting satellites, an acquisition-aiding signal generated by an earth-based control station is relayed to user terminals via a geostationary satellite to simplify user equipment. The aiding signal is FSK modulated on a reference channel slightly offset from the standard GPS channel. The aiding signal identifies satellites in view having best geometry and includes Doppler prediction data as well as GPS satellite coordinates and identification data associated with user terminals within an area being served by the control station and relay satellite. The aiding signal significantly reduces user equipment by simplifying spread spectrum signal demodulation and reducing data processing functions previously carried out at the user terminals.

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.

Position sensitive solid-state photomultipliers, systems and methods

DOEpatents

Shah, Kanai S; Christian, James; Stapels, Christopher; Dokhale, Purushottam; McClish, Mickel

2014-11-11

An integrated silicon solid state photomultiplier (SSPM) device includes a pixel unit including an array of more than 2.times.2 p-n photodiodes on a common substrate, a signal division network electrically connected to each photodiode, where the signal division network includes four output connections, a signal output measurement unit, a processing unit configured to identify the photodiode generating a signal or a center of mass of photodiodes generating a signal, and a global receiving unit.

Adaptive variational mode decomposition method for signal processing based on mode characteristic

NASA Astrophysics Data System (ADS)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

Utilization of a balanced steady state free precession signal model for improved fat/water decomposition.

PubMed

Henze Bancroft, Leah C; Strigel, Roberta M; Hernando, Diego; Johnson, Kevin M; Kelcz, Frederick; Kijowski, Richard; Block, Walter F

2016-03-01

Chemical shift based fat/water decomposition methods such as IDEAL are frequently used in challenging imaging environments with large B0 inhomogeneity. However, they do not account for the signal modulations introduced by a balanced steady state free precession (bSSFP) acquisition. Here we demonstrate improved performance when the bSSFP frequency response is properly incorporated into the multipeak spectral fat model used in the decomposition process. Balanced SSFP allows for rapid imaging but also introduces a characteristic frequency response featuring periodic nulls and pass bands. Fat spectral components in adjacent pass bands will experience bulk phase offsets and magnitude modulations that change the expected constructive and destructive interference between the fat spectral components. A bSSFP signal model was incorporated into the fat/water decomposition process and used to generate images of a fat phantom, and bilateral breast and knee images in four normal volunteers at 1.5 Tesla. Incorporation of the bSSFP signal model into the decomposition process improved the performance of the fat/water decomposition. Incorporation of this model allows rapid bSSFP imaging sequences to use robust fat/water decomposition methods such as IDEAL. While only one set of imaging parameters were presented, the method is compatible with any field strength or repetition time. © 2015 Wiley Periodicals, Inc.

Principal and independent component analysis of concomitant functional near infrared spectroscopy and magnetic resonance imaging data

NASA Astrophysics Data System (ADS)

Schelkanova, Irina; Toronov, Vladislav

2011-07-01

Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by using various signal processing methods, including blind signal separation techniques. In particular, principal component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired at the same wavelength and at multiple sites on the human or animal heads during functional activation. These signal processing procedures resulted in a number of principal or independent components that could be attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the original signals were quite diverse, we found very few different components which corresponded to fMRI signals at different locations in the brain and to different physiological chromophores.

Super-resolution imaging using multi- electrode CMUTs: theoretical design and simulation using point targets.

PubMed

You, Wei; Cretu, Edmond; Rohling, Robert

2013-11-01

This paper investigates a low computational cost, super-resolution ultrasound imaging method that leverages the asymmetric vibration mode of CMUTs. Instead of focusing on the broadband received signal on the entire CMUT membrane, we utilize the differential signal received on the left and right part of the membrane obtained by a multi-electrode CMUT structure. The differential signal reflects the asymmetric vibration mode of the CMUT cell excited by the nonuniform acoustic pressure field impinging on the membrane, and has a resonant component in immersion. To improve the resolution, we propose an imaging method as follows: a set of manifold matrices of CMUT responses for multiple focal directions are constructed off-line with a grid of hypothetical point targets. During the subsequent imaging process, the array sequentially steers to multiple angles, and the amplitudes (weights) of all hypothetical targets at each angle are estimated in a maximum a posteriori (MAP) process with the manifold matrix corresponding to that angle. Then, the weight vector undergoes a directional pruning process to remove the false estimation at other angles caused by the side lobe energy. Ultrasound imaging simulation is performed on ring and linear arrays with a simulation program adapted with a multi-electrode CMUT structure capable of obtaining both average and differential received signals. Because the differential signals from all receiving channels form a more distinctive temporal pattern than the average signals, better MAP estimation results are expected than using the average signals. The imaging simulation shows that using differential signals alone or in combination with the average signals produces better lateral resolution than the traditional phased array or using the average signals alone. This study is an exploration into the potential benefits of asymmetric CMUT responses for super-resolution imaging.

Removing damped sinusoidal vibrations in adaptive optics systems using a DFT-based estimation method

NASA Astrophysics Data System (ADS)

Kania, Dariusz

2017-06-01

The problem of a vibrations rejection in adaptive optics systems is still present in publications. These undesirable signals emerge because of shaking the system structure, the tracking process, etc., and they usually are damped sinusoidal signals. There are some mechanical solutions to reduce the signals but they are not very effective. One of software solutions are very popular adaptive methods. An AVC (Adaptive Vibration Cancellation) method has been presented and developed in recent years. The method is based on the estimation of three vibrations parameters and values of frequency, amplitude and phase are essential to produce and adjust a proper signal to reduce or eliminate vibrations signals. This paper presents a fast (below 10 ms) and accurate estimation method of frequency, amplitude and phase of a multifrequency signal that can be used in the AVC method to increase the AO system performance. The method accuracy depends on several parameters: CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, THD, b - number of A/D converter bits in a real time system, γ - the damping ratio of the tested signal, φ - the phase of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value of systematic error for γ = 0.1%, CiR = 1.1 and N = 32 is approximately 10^-4 Hz/Hz. This paper focuses on systematic errors of and effect of the signal phase and values of γ on the results.

Where and How Does Grammatically Geared Processing Take Place--And Why Is Broca's Area Often Involved. A Coordinated fMRI/ERBP Study of Language Processing

ERIC Educational Resources Information Center

Dogil, Grzegorz; Frese, Inga; Haider, Hubert; Rohm, Dietmar; Wokurek, Wolfgang

2004-01-01

We address the possibility of combining the results from hemodynamic and electrophysiological methods for the study of cognitive processing of language. The hemodynamic method we use is Event-Related fMRI, and the electrophysiological method measures Event-Related Band Power (ERBP) of the EEG signal. The experimental technique allows us to…

Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation

PubMed Central

White, David J.; Congedo, Marco; Ciorciari, Joseph

2014-01-01

A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback. PMID:25374520

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.

Research on motor rotational speed measurement in regenerative braking system of electric vehicle

NASA Astrophysics Data System (ADS)

Pan, Chaofeng; Chen, Liao; Chen, Long; Jiang, Haobin; Li, Zhongxing; Wang, Shaohua

2016-01-01

Rotational speed signals acquisition and processing techniques are widely used in rotational machinery. In order to realized precise and real-time control of motor drive and regenerative braking process, rotational speed measurement techniques are needed in electric vehicles. Obtaining accurate motor rotational speed signal will contribute to the regenerative braking force control steadily and realized higher energy recovery rate. This paper aims to develop a method that provides instantaneous speed information in the form of motor rotation. It addresses principles of motor rotational speed measurement in the regenerative braking systems of electric vehicle firstly. The paper then presents ideal and actual Hall position sensor signals characteristics, the relation between the motor rotational speed and the Hall position sensor signals is revealed. Finally, Hall position sensor signals conditioning and processing circuit and program for motor rotational speed measurement have been carried out based on measurement error analysis.

A real-time spike sorting method based on the embedded GPU.

PubMed

Zelan Yang; Kedi Xu; Xiang Tian; Shaomin Zhang; Xiaoxiang Zheng

2017-07-01

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

Elementary signaling modes predict the essentiality of signal transduction network components

PubMed Central

2011-01-01

Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM. PMID:21426566

Spot restoration for GPR image post-processing

DOEpatents

Paglieroni, David W; Beer, N. Reginald

2014-05-20

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

Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

NASA Astrophysics Data System (ADS)

Ha, Jong M.; Youn, Byeng D.; Oh, Hyunseok; Han, Bongtae; Jung, Yoongho; Park, Jungho

2016-03-01

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2 kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited.

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.

A novel method to detect ignition angle of diesel

NASA Astrophysics Data System (ADS)

Li, Baofu; Peng, Yong; Huang, Hongzhong

2018-04-01

This paper is based on the combustion signal collected by the combustion sensor of piezomagnetic type, taking how to get the diesel fuel to start the combustion as the starting point. It analyzes the operating principle and pressure change of the combustion sensor, the compression peak signal of the diesel engine in the process of compression, and several common methods. The author puts forward a new idea that ignition angle timing can be determined more accurately by the compression peak decomposition method. Then, the method is compared with several common methods.

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

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

Hart, Darren

2004-07-01

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

Signal-Processing Algorithm Development for the ACLAIM Sensor

NASA Technical Reports Server (NTRS)

vonLaven, Scott

1995-01-01

Methods for further minimizing the risk by making use of previous lidar observations were investigated. EOFs are likely to play an important role in these methods, and a procedure for extracting EOFs from data has been implemented, The new processing methods involving EOFs could range from extrapolation, as discussed, to more complicated statistical procedures for maintaining low unstart risk.

Gear fault diagnosis based on the structured sparsity time-frequency analysis

NASA Astrophysics Data System (ADS)

Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong

2018-03-01

Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.

Two Dimensional Processing Of Speech And Ecg Signals Using The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Abeysekera, Saman S.

1986-12-01

The Wigner-Ville Distribution (WVD) has been shown to be a valuable tool for the analysis of non-stationary signals such as speech and Electrocardiogram (ECG) data. The one-dimensional real data are first transformed into a complex analytic signal using the Hilbert Transform and then a 2-dimensional image is formed using the Wigner-Ville Transform. For speech signals, a contour plot is determined and used as a basic feature. for a pattern recognition algorithm. This method is compared with the classical Short Time Fourier Transform (STFT) and is shown, to be able to recognize isolated words better in a noisy environment. The same method together with the concept of instantaneous frequency of the signal is applied to the analysis of ECG signals. This technique allows one to classify diseased heart-beat signals. Examples are shown.

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.

Reduction hybrid artifacts of EMG-EOG in electroencephalography evoked by prefrontal transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Bai, Yang; Wan, Xiaohong; Zeng, Ke; Ni, Yinmei; Qiu, Lirong; Li, Xiaoli

2016-12-01

Objective. When prefrontal-transcranial magnetic stimulation (p-TMS) performed, it may evoke hybrid artifact mixed with muscle activity and blink activity in EEG recordings. Reducing this kind of hybrid artifact challenges the traditional preprocessing methods. We aim to explore method for the p-TMS evoked hybrid artifact removal. Approach. We propose a novel method used as independent component analysis (ICA) post processing to reduce the p-TMS evoked hybrid artifact. Ensemble empirical mode decomposition (EEMD) was used to decompose signal into multi-components, then the components were separated with artifact reduced by blind source separation (BSS) method. Three standard BSS methods, ICA, independent vector analysis, and canonical correlation analysis (CCA) were tested. Main results. Synthetic results showed that EEMD-CCA outperformed others as ICA post processing step in hybrid artifacts reduction. Its superiority was clearer when signal to noise ratio (SNR) was lower. In application to real experiment, SNR can be significantly increased and the p-TMS evoked potential could be recovered from hybrid artifact contaminated signal. Our proposed method can effectively reduce the p-TMS evoked hybrid artifacts. Significance. Our proposed method may facilitate future prefrontal TMS-EEG researches.

Method of measuring cross-flow vortices by use of an array of hot-film sensors

NASA Technical Reports Server (NTRS)

Agarwal, Aval K. (Inventor); Maddalon, Dal V. (Inventor); Mangalam, Siva M. (Inventor)

1993-01-01

The invention is a method for measuring the wavelength of cross-flow vortices of air flow having streamlines of flow traveling across a swept airfoil. The method comprises providing a plurality of hot-film sensors. Each hot-film sensor provides a signal which can be processed, and each hot-film sensor is spaced in a straight-line array such that the distance between successive hot-film sensors is less than the wavelength of the cross-flow vortices being measured. The method further comprises determining the direction of travel of the streamlines across the airfoil and positioning the straight-line array of hot film sensors perpendicular to the direction of travel of the streamlines, such that each sensor has a spanwise location. The method further comprises processing the signals provided by the sensors to provide root-mean-square values for each signal, plotting each root-mean-square value as a function of its spanwise location, and determining the wavelength of the cross-flow vortices by noting the distance between two maxima or two minima of root-mean-square values.

A self-regulating biomolecular comparator for processing oscillatory signals

PubMed Central

Agrawal, Deepak K.; Franco, Elisa; Schulman, Rebecca

2015-01-01

While many cellular processes are driven by biomolecular oscillators, precise control of a downstream on/off process by a biochemical oscillator signal can be difficult: over an oscillator's period, its output signal varies continuously between its amplitude limits and spends a significant fraction of the time at intermediate values between these limits. Further, the oscillator's output is often noisy, with particularly large variations in the amplitude. In electronic systems, an oscillating signal is generally processed by a downstream device such as a comparator that converts a potentially noisy oscillatory input into a square wave output that is predominantly in one of two well-defined on and off states. The comparator's output then controls downstream processes. We describe a method for constructing a synthetic biochemical device that likewise produces a square-wave-type biomolecular output for a variety of oscillatory inputs. The method relies on a separation of time scales between the slow rate of production of an oscillatory signal molecule and the fast rates of intermolecular binding and conformational changes. We show how to control the characteristics of the output by varying the concentrations of the species and the reaction rates. We then use this control to show how our approach could be applied to process different in vitro and in vivo biomolecular oscillators, including the p53-Mdm2 transcriptional oscillator and two types of in vitro transcriptional oscillators. These results demonstrate how modular biomolecular circuits could, in principle, be combined to build complex dynamical systems. The simplicity of our approach also suggests that natural molecular circuits may process some biomolecular oscillator outputs before they are applied downstream. PMID:26378119

Signal digitizing system and method based on amplitude-to-time optical mapping

DOEpatents

Chou, Jason; Bennett, Corey V; Hernandez, Vince

2015-01-13

A signal digitizing system and method based on analog-to-time optical mapping, optically maps amplitude information of an analog signal of interest first into wavelength information using an amplitude tunable filter (ATF) to impress spectral changes induced by the amplitude of the analog signal onto a carrier signal, i.e. a train of optical pulses, and next from wavelength information to temporal information using a dispersive element so that temporal information representing the amplitude information is encoded in the time domain in the carrier signal. Optical-to-electrical conversion of the optical pulses into voltage waveforms and subsequently digitizing the voltage waveforms into a digital image enables the temporal information to be resolved and quantized in the time domain. The digital image may them be digital signal processed to digitally reconstruct the analog signal based on the temporal information with high fidelity.

Dual Key Speech Encryption Algorithm Based Underdetermined BSS

PubMed Central

Zhao, Huan; Chen, Zuo; Zhang, Xixiang

2014-01-01

When the number of the mixed signals is less than that of the source signals, the underdetermined blind source separation (BSS) is a significant difficult problem. Due to the fact that the great amount data of speech communications and real-time communication has been required, we utilize the intractability of the underdetermined BSS problem to present a dual key speech encryption method. The original speech is mixed with dual key signals which consist of random key signals (one-time pad) generated by secret seed and chaotic signals generated from chaotic system. In the decryption process, approximate calculation is used to recover the original speech signals. The proposed algorithm for speech signals encryption can resist traditional attacks against the encryption system, and owing to approximate calculation, decryption becomes faster and more accurate. It is demonstrated that the proposed method has high level of security and can recover the original signals quickly and efficiently yet maintaining excellent audio quality. PMID:24955430

Minimally invasive surgical method to detect sound processing in the cochlear apex by optical coherence tomography

NASA Astrophysics Data System (ADS)

Ramamoorthy, Sripriya; Zhang, Yuan; Petrie, Tracy; Fridberger, Anders; Ren, Tianying; Wang, Ruikang; Jacques, Steven L.; Nuttall, Alfred L.

2016-02-01

Sound processing in the inner ear involves separation of the constituent frequencies along the length of the cochlea. Frequencies relevant to human speech (100 to 500 Hz) are processed in the apex region. Among mammals, the guinea pig cochlear apex processes similar frequencies and is thus relevant for the study of speech processing in the cochlea. However, the requirement for extensive surgery has challenged the optical accessibility of this area to investigate cochlear processing of signals without significant intrusion. A simple method is developed to provide optical access to the guinea pig cochlear apex in two directions with minimal surgery. Furthermore, all prior vibration measurements in the guinea pig apex involved opening an observation hole in the otic capsule, which has been questioned on the basis of the resulting changes to cochlear hydrodynamics. Here, this limitation is overcome by measuring the vibrations through the unopened otic capsule using phase-sensitive Fourier domain optical coherence tomography. The optically and surgically advanced method described here lays the foundation to perform minimally invasive investigation of speech-related signal processing in the cochlea.

Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep

PubMed Central

Crainiceanu, Ciprian M.; Caffo, Brian S.; Di, Chong-Zhi; Punjabi, Naresh M.

2009-01-01

We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS. PMID:20057925

Application of wavelet and Fuorier transforms as powerful alternatives for derivative spectrophotometry in analysis of binary mixtures: A comparative study

NASA Astrophysics Data System (ADS)

Hassan, Said A.; Abdel-Gawad, Sherif A.

2018-02-01

Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS. The three signal processing methods were adopted for the quantification of NTX and BUP in pure and tablet forms. The adopted methods were validated according to the ICH guideline where accuracy, precision and specificity were found to be within appropriate limits.

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.

Micromachined microwave signal control device and method for making same

DOEpatents

Forman, Michael A [San Francisco, CA

2008-09-02

A method for fabricating a signal controller, e.g., a filter or a switch, for a coplanar waveguide during the LIGA fabrication process of the waveguide. Both patterns for the waveguide and patterns for the signal controllers are created on a mask. Radiation travels through the mask and reaches a photoresist layer on a substrate. The irradiated portions are removed and channels are formed on the substrate. A metal is filled into the channels to form the conductors of the waveguide and the signal controllers. Micromachined quasi-lumped elements are used alone or together as filters. The switch includes a comb drive, a spring, a metal plunger, and anchors.

On the Use of a Signal Quality Index Applying at Tracking Stage Level to Assist the RAIM System of a GNSS Receiver.

PubMed

Berardo, Mattia; Lo Presti, Letizia

2016-07-02

In this work, a novel signal processing method is proposed to assist the Receiver Autonomous Integrity Monitoring (RAIM) module used in a receiver of Global Navigation Satellite Systems (GNSS) to improve the integrity of the estimated position. The proposed technique represents an evolution of the Multipath Distance Detector (MPDD), thanks to the introduction of a Signal Quality Index (SQI), which is both a metric able to evaluate the goodness of the signal, and a parameter used to improve the performance of the RAIM modules. Simulation results show the effectiveness of the proposed method.

Method for making a micromachined microwave signal control device

DOEpatents

Forman, Michael A [Mountain House, CA

2011-02-15

A method for fabricating a signal controller, e.g., a filter or a switch, for a coplanar waveguide during the LIGA fabrication process of the waveguide. Both patterns for the waveguide and patterns for the signal controllers are created on a mask. Radiation travels through the mask and reaches a photoresist layer on a substrate. The irradiated portions are removed and channels are formed on the substrate. A metal is filled into the channels to form the conductors of the waveguide and the signal controllers. Micromachined quasi-lumped elements are used alone or together as filters. The switch includes a comb drive, a spring, a metal plunger, and anchors.

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

NASA Technical Reports Server (NTRS)

Hearn, Tristan A.

2015-01-01

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Footwear scanning systems and methods

DOEpatents

Fernandes, Justin L.; McMakin, Douglas L.; Sheen, David M.; Tedeschi, Jonathan R.

2017-07-25

Methods and apparatus for scanning articles, such as footwear, to provide information regarding the contents of the articles are described. According to one aspect, a footwear scanning system includes a platform configured to contact footwear to be scanned, an antenna array configured to transmit electromagnetic waves through the platform into the footwear and to receive electromagnetic waves from the footwear and the platform, a transceiver coupled with antennas of the antenna array and configured to apply electrical signals to at least one of the antennas to generate the transmitted electromagnetic waves and to receive electrical signals from at least another of the antennas corresponding to the electromagnetic waves received by the others of the antennas, and processing circuitry configured to process the received electrical signals from the transceiver to provide information regarding contents within the footwear.

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.

Inverse analysis of water profile in starch by non-contact photopyroelectric method

NASA Astrophysics Data System (ADS)

Frandas, A.; Duvaut, T.; Paris, D.

2000-07-01

The photopyroelectric (PPE) method in a non-contact configuration was proposed to study water migration in starch sheets used for biodegradable packaging. A 1-D theoretical model was developed, allowing the study of samples having a water profile characterized by an arbitrary continuous function. An experimental setup was designed or this purpose which included the choice of excitation source, detection of signals, signal and data processing, and cells for conditioning the samples. We report here the development of an inversion procedure allowing for the determination of the parameters that influence the PPE signal. This procedure led to the optimization of experimental conditions in order to identify the parameters related to the water profile in the sample, and to monitor the dynamics of the process.

Mathematical approach to recover EEG brain signals with artifacts by means of Gram-Schmidt transform

NASA Astrophysics Data System (ADS)

Runnova, A. E.; Zhuravlev, M. O.; Koronovskiy, A. A.; Hramov, A. E.

2017-04-01

A novel method for removing oculomotor artifacts on electroencephalographical signals is proposed and based on the orthogonal Gram-Schmidt transform using electrooculography data. The method has shown high efficiency removal of artifacts caused by spontaneous movements of the eyeballs (about 95-97% correct remote oculomotor artifacts). This method may be recommended for multi-channel electroencephalography data processing in an automatic on-line in a variety of psycho-physiological experiments.

Sparse representation of electrodermal activity with knowledge-driven dictionaries.

PubMed

Chaspari, Theodora; Tsiartas, Andreas; Stein, Leah I; Cermak, Sharon A; Narayanan, Shrikanth S

2015-03-01

Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven method to represent electrodermal activity (EDA), a psychophysiological signal linked to stress, affect, and cognitive processing. We build EDA-specific dictionaries that accurately model both the slow varying tonic part and the signal fluctuations, called skin conductance responses (SCR), and use greedy sparse representation techniques to decompose the signal into a small number of atoms from the dictionary. Quantitative evaluation of our method considers signal reconstruction, compression rate, and information retrieval measures, that capture the ability of the model to incorporate the main signal characteristics, such as SCR occurrences. Compared to previous studies fitting a predetermined structure to the signal, results indicate that our approach provides benefits across all aforementioned criteria. This paper demonstrates the ability of appropriate dictionaries along with sparse decomposition methods to reliably represent EDA signals and provides a foundation for automatic measurement of SCR characteristics and the extraction of meaningful EDA features.

Signal processing system for electrotherapy applications

NASA Astrophysics Data System (ADS)

Płaza, Mirosław; Szcześniak, Zbigniew

2017-08-01

The system of signal processing for electrotherapeutic applications is proposed in the paper. The system makes it possible to model the curve of threshold human sensitivity to current (Dalziel's curve) in full medium frequency range (1kHz-100kHz). The tests based on the proposed solution were conducted and their results were compared with those obtained according to the assumptions of High Tone Power Therapy method and referred to optimum values. Proposed system has high dynamics and precision of mapping the curve of threshold human sensitivity to current and can be used in all methods where threshold curves are modelled.

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.

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.

Radar Methods in Urban Environments

DTIC Science & Technology

2016-10-26

to appear in IEEE Journal of Selected Topics in Signal Processing. J8. M. Wang and A. Nehorai, “Coarrays, MUSIC , and the Cramér Rao bound,” to...Journal Papers: 1. P. Chavali and A. Nehorai, "Scheduling and resource allocation in a cognitive radar network for multiple- target tracking,’’ IEEE...Processing. 33. M. Wang and A. Nehorai, "Coarrays, MUSIC , and the Cramér Rao bound," to appear in IEEE Trans. on Signal Processing. 34. J. Li and A. Nehorai

System for processing an encrypted instruction stream in hardware

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

Griswold, Richard L.; Nickless, William K.; Conrad, Ryan C.

A system and method of processing an encrypted instruction stream in hardware is disclosed. Main memory stores the encrypted instruction stream and unencrypted data. A central processing unit (CPU) is operatively coupled to the main memory. A decryptor is operatively coupled to the main memory and located within the CPU. The decryptor decrypts the encrypted instruction stream upon receipt of an instruction fetch signal from a CPU core. Unencrypted data is passed through to the CPU core without decryption upon receipt of a data fetch signal.

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Capillary Electrophoresis Sensitivity Enhancement Based on Adaptive Moving Average Method.

PubMed

Drevinskas, Tomas; Telksnys, Laimutis; Maruška, Audrius; Gorbatsova, Jelena; Kaljurand, Mihkel

2018-06-05

In the present work, we demonstrate a novel approach to improve the sensitivity of the "out of lab" portable capillary electrophoretic measurements. Nowadays, many signal enhancement methods are (i) underused (nonoptimal), (ii) overused (distorts the data), or (iii) inapplicable in field-portable instrumentation because of a lack of computational power. The described innovative migration velocity-adaptive moving average method uses an optimal averaging window size and can be easily implemented with a microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher-frequency peaks, whereas lower electrophoretic mobility analytes exhibit lower-frequency peaks. On the basis of the obtained data, a migration velocity-adaptive moving average algorithm was created, adapted, and programmed into capillary electrophoresis data-processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method, the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis and other separation methods.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Wigner-Ville distribution and Gabor transform in Doppler ultrasound signal processing.

PubMed

Ghofrani, S; Ayatollahi, A; Shamsollahi, M B

2003-01-01

Time-frequency distributions have been used extensively for nonstationary signal analysis, they describe how the frequency content of a signal is changing in time. The Wigner-Ville distribution (WVD) is the best known. The draw back of WVD is cross-term artifacts. An alternative to the WVD is Gabor transform (GT), a signal decomposition method, which displays the time-frequency energy of a signal on a joint t-f plane without generating considerable cross-terms. In this paper the WVD and GT of ultrasound echo signals are computed analytically.

Digital signal processor and processing method for GPS receivers

NASA Technical Reports Server (NTRS)

Thomas, Jr., Jess B. (Inventor)

1989-01-01

A digital signal processor and processing method therefor for use in receivers of the NAVSTAR/GLOBAL POSITIONING SYSTEM (GPS) employs a digital carrier down-converter, digital code correlator and digital tracking processor. The digital carrier down-converter and code correlator consists of an all-digital, minimum bit implementation that utilizes digital chip and phase advancers, providing exceptional control and accuracy in feedback phase and in feedback delay. Roundoff and commensurability errors can be reduced to extremely small values (e.g., less than 100 nanochips and 100 nanocycles roundoff errors and 0.1 millichip and 1 millicycle commensurability errors). The digital tracking processor bases the fast feedback for phase and for group delay in the C/A, P.sub.1, and P.sub.2 channels on the L.sub.1 C/A carrier phase thereby maintaining lock at lower signal-to-noise ratios, reducing errors in feedback delays, reducing the frequency of cycle slips and in some cases obviating the need for quadrature processing in the P channels. Simple and reliable methods are employed for data bit synchronization, data bit removal and cycle counting. Improved precision in averaged output delay values is provided by carrier-aided data-compression techniques. The signal processor employs purely digital operations in the sense that exactly the same carrier phase and group delay measurements are obtained, to the last decimal place, every time the same sampled data (i.e., exactly the same bits) are processed.

Correction of complex nonlinear signal response from a pixel array detector

PubMed Central

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella; Nielsen, Martin Meedom; Lemke, Henrik Till

2015-01-01

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering from liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics. PMID:25931072

Correction of complex nonlinear signal response from a pixel array detector.

PubMed

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella; Nielsen, Martin Meedom; Lemke, Henrik Till

2015-05-01

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering from liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics.

Signal Construction-Based Dispersion Compensation of Lamb Waves Considering Signal Waveform and Amplitude Spectrum Preservation

PubMed Central

Cai, Jian; Yuan, Shenfang; Wang, Tongguang

2016-01-01

The results of Lamb wave identification for the aerospace structures could be easily affected by the nonlinear-dispersion characteristics. In this paper, dispersion compensation of Lamb waves is of particular concern. Compared with the similar research works on the traditional signal domain transform methods, this study is based on signal construction from the viewpoint of nonlinear wavenumber linearization. Two compensation methods of linearly-dispersive signal construction (LDSC) and non-dispersive signal construction (NDSC) are proposed. Furthermore, to improve the compensation effect, the influence of the signal construction process on the other crucial signal properties, including the signal waveform and amplitude spectrum, is considered during the investigation. The linear-dispersion and non-dispersion effects are firstly analyzed. Then, after the basic signal construction principle is explored, the numerical realization of LDSC and NDSC is discussed, in which the signal waveform and amplitude spectrum preservation is especially regarded. Subsequently, associated with the delay-and-sum algorithm, LDSC or NDSC is employed for high spatial resolution damage imaging, so that the adjacent multi-damage or quantitative imaging capacity of Lamb waves can be strengthened. To verify the proposed signal construction and damage imaging methods, the experimental and numerical validation is finally arranged on the aluminum plates. PMID:28772366

Signal Construction-Based Dispersion Compensation of Lamb Waves Considering Signal Waveform and Amplitude Spectrum Preservation.

PubMed

Cai, Jian; Yuan, Shenfang; Wang, Tongguang

2016-12-23

The results of Lamb wave identification for the aerospace structures could be easily affected by the nonlinear-dispersion characteristics. In this paper, dispersion compensation of Lamb waves is of particular concern. Compared with the similar research works on the traditional signal domain transform methods, this study is based on signal construction from the viewpoint of nonlinear wavenumber linearization. Two compensation methods of linearly-dispersive signal construction (LDSC) and non-dispersive signal construction (NDSC) are proposed. Furthermore, to improve the compensation effect, the influence of the signal construction process on the other crucial signal properties, including the signal waveform and amplitude spectrum, is considered during the investigation. The linear-dispersion and non-dispersion effects are firstly analyzed. Then, after the basic signal construction principle is explored, the numerical realization of LDSC and NDSC is discussed, in which the signal waveform and amplitude spectrum preservation is especially regarded. Subsequently, associated with the delay-and-sum algorithm, LDSC or NDSC is employed for high spatial resolution damage imaging, so that the adjacent multi-damage or quantitative imaging capacity of Lamb waves can be strengthened. To verify the proposed signal construction and damage imaging methods, the experimental and numerical validation is finally arranged on the aluminum plates.

Study on negative incident photon-to-electron conversion efficiency of quantum dot-sensitized solar cells

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

Li, Chunhui; Wu, Huijue; Zhu, Lifeng

2014-02-15

Recently, negative signals are frequently observed during the measuring process of monochromatic incident photon-to-electron conversion efficiency (IPCE) for sensitized solar cells by DC method. This phenomenon is confusing and hindering the reasonable evaluation of solar cells. Here, cause of negative IPCE values is studied by taking quantum dot-sensitized solar cell (QDSC) as an example, and the accurate measurement method to avoid the negative value is suggested. The negative background signals of QDSC without illumination are found the direct cause of the negative IPCE values by DC method. Ambient noise, significant capacitance characteristics, and uncontrolled electrochemical reaction all can lead tomore » the negative background signals. When the photocurrent response of device under monochromatic light illumination is relatively weak, the actual photocurrent signals will be covered by the negative background signals and the resulting IPCE values will appear negative. To improve the signal-to-noise ratio, quasi-AC method is proposed for IPCE measurement of solar cells with weak photocurrent response based on the idea of replacing the absolute values by the relative values.« less

Method and Apparatus for Reducing Noise from Near Ocean Surface Sources

DTIC Science & Technology

2001-10-01

reducing the acoustic noise from near-surface 4 sources using an array processing technique that utilizes 5 Multiple Signal Classification ( MUSIC ...sources without 13 degrading the signal level and quality of the TOI. The present 14 invention utilizes a unique application of the MUSIC beamforming...specific algorithm that utilizes a 5 MUSIC technique and estimates the direction of arrival (DOA) of 6 the acoustic signal signals and generates output

Statistical 21-cm Signal Separation via Gaussian Process Regression Analysis

NASA Astrophysics Data System (ADS)

Mertens, F. G.; Ghosh, A.; Koopmans, L. V. E.

2018-05-01

Detecting and characterizing the Epoch of Reionization and Cosmic Dawn via the redshifted 21-cm hyperfine line of neutral hydrogen will revolutionize the study of the formation of the first stars, galaxies, black holes and intergalactic gas in the infant Universe. The wealth of information encoded in this signal is, however, buried under foregrounds that are many orders of magnitude brighter. These must be removed accurately and precisely in order to reveal the feeble 21-cm signal. This requires not only the modeling of the Galactic and extra-galactic emission, but also of the often stochastic residuals due to imperfect calibration of the data caused by ionospheric and instrumental distortions. To stochastically model these effects, we introduce a new method based on `Gaussian Process Regression' (GPR) which is able to statistically separate the 21-cm signal from most of the foregrounds and other contaminants. Using simulated LOFAR-EoR data that include strong instrumental mode-mixing, we show that this method is capable of recovering the 21-cm signal power spectrum across the entire range k = 0.07 - 0.3 {h cMpc^{-1}}. The GPR method is most optimal, having minimal and controllable impact on the 21-cm signal, when the foregrounds are correlated on frequency scales ≳ 3 MHz and the rms of the signal has σ21cm ≳ 0.1 σnoise. This signal separation improves the 21-cm power-spectrum sensitivity by a factor ≳ 3 compared to foreground avoidance strategies and enables the sensitivity of current and future 21-cm instruments such as the Square Kilometre Array to be fully exploited.

Research on vibration signal of engine based on subband energy method

NASA Astrophysics Data System (ADS)

Wu, Chunmei; Cui, Feng; Zhao, Yong; Fu, Baohong; Ma, Junchi; Yang, Guihua

2017-04-01

Based on the research of DA462 type engine cylinder and cylinder head vibration signal of the surface, the signal measured in the time domain and frequency domain are analyzed in detail, draw the following conclusions: the analysis of vibration signal of the subband energy method is applied to the engine, the concentration response of each of the motivation band can clearly be seen. Through the analysis we can see that the combustion excitation frequency response from 0k to 1K, the vibration influence on the body piston lateral impact force is mainly concentrated in 2K˜5K frequency range of Hz, valve opening and closing the excitation response frequency is mainly concentrated in the 3K˜4K range of Hz, and thus locating the valve clearance fault. This method is simple, accurate and practical for the post processing and analysis of vibration signals.

Modular continuous wavelet processing of biosignals: extracting heart rate and oxygen saturation from a video signal

PubMed Central

2016-01-01

A novel method of extracting heart rate and oxygen saturation from a video-based biosignal is described. The method comprises a novel modular continuous wavelet transform approach which includes: performing the transform, undertaking running wavelet archetyping to enhance the pulse information, extraction of the pulse ridge time–frequency information [and thus a heart rate (HRvid) signal], creation of a wavelet ratio surface, projection of the pulse ridge onto the ratio surface to determine the ratio of ratios from which a saturation trending signal is derived, and calibrating this signal to provide an absolute saturation signal (SvidO2). The method is illustrated through its application to a video photoplethysmogram acquired during a porcine model of acute desaturation. The modular continuous wavelet transform-based approach is advocated by the author as a powerful methodology to deal with noisy, non-stationary biosignals in general. PMID:27382479

BCI2000: a general-purpose brain-computer interface (BCI) system.

PubMed

Schalk, Gerwin; McFarland, Dennis J; Hinterberger, Thilo; Birbaumer, Niels; Wolpaw, Jonathan R

2004-06-01

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

Real-time nondestructive monitoring of the gas tungsten arc welding (GTAW) process by combined airborne acoustic emission and non-contact ultrasonics

NASA Astrophysics Data System (ADS)

Zhang, Lu; Basantes-Defaz, Alexandra-Del-Carmen; Abbasi, Zeynab; Yuhas, Donald; Ozevin, Didem; Indacochea, Ernesto

2018-03-01

Welding is a key manufacturing process for many industries and may introduce defects into the welded parts causing significant negative impacts, potentially ruining high-cost pieces. Therefore, a real-time process monitoring method is important to implement for avoiding producing a low-quality weld. Due to high surface temperature and possible contamination of surface by contact transducers, the welding process should be monitored via non-contact transducers. In this paper, airborne acoustic emission (AE) transducers tuned at 60 kHz and non-contact ultrasonic testing (UT) transducers tuned at 500 kHz are implemented for real time weld monitoring. AE is a passive nondestructive evaluation method that listens for the process noise, and provides information about the uniformity of manufacturing process. UT provides more quantitative information about weld defects. One of the most common weld defects as burn-through is investigated. The influences of weld defects on AE signatures (time-driven data) and UT signals (received signal energy, change in peak frequency) are presented. The level of burn-through damage is defined by using single method or combine AE/UT methods.

Method for ambiguity resolution in range-Doppler measurements

NASA Technical Reports Server (NTRS)

Heymsfield, Gerald M. (Inventor); Miller, Lee S. (Inventor)

1994-01-01

A method for resolving range and Doppler target ambiguities when the target has substantial range or has a high relative velocity in which a first signal is generated and a second signal is also generated which is coherent with the first signal but at a slightly different frequency such that there exists a difference in frequency between these two signals of Delta f(sub t). The first and second signals are converted into a dual-frequency pulsed signal, amplified, and the dual-frequency pulsed signal is transmitted towards a target. A reflected dual-frequency signal is received from the target, amplified, and changed to an intermediate dual-frequency signal. The intermediate dual-frequency signal is amplified, with extracting of a shifted difference frequency Delta f(sub r) from the amplified intermediate dual-frequency signal done by a nonlinear detector. The final step is generating two quadrature signals from the difference frequency Delta f(sub t) and the shifted difference frequency Delta f(sub r) and processing the two quadrature signals to determine range and Doppler information of the target.

Selection signature in domesticated animals.

PubMed

Pan, Zhang-yuan; He, Xiao-yun; Wang, Xiang-yu; Guo, Xiao-fei; Cao, Xiao-han; Hu, Wen-ping; Di, Ran; Liu, Qiu-yue; Chu, Ming-xing

2016-12-20

Domesticated animals play an important role in the life of humanity. All these domesticated animals undergo same process, first domesticated from wild animals, then after long time natural and artificial selection, formed various breeds that adapted to the local environment and human needs. In this process, domestication, natural and artificial selection will leave the selection signal in the genome. The research on these selection signals can find functional genes directly, is one of the most important strategies in screening functional genes. The current studies of selection signal have been performed in pigs, chickens, cattle, sheep, goats, dogs and other domestic animals, and found a great deal of functional genes. This paper provided an overview of the types and the detected methods of selection signal, and outlined researches of selection signal in domestic animals, and discussed the key issues in selection signal analysis and its prospects.

Minimal Polynomial Method for Estimating Parameters of Signals Received by an Antenna Array

NASA Astrophysics Data System (ADS)

Ermolaev, V. T.; Flaksman, A. G.; Elokhin, A. V.; Kuptsov, V. V.

2018-01-01

The effectiveness of the projection minimal polynomial method for solving the problem of determining the number of sources of signals acting on an antenna array (AA) with an arbitrary configuration and their angular directions has been studied. The method proposes estimating the degree of the minimal polynomial of the correlation matrix (CM) of the input process in the AA on the basis of a statistically validated root-mean-square criterion. Special attention is paid to the case of the ultrashort sample of the input process when the number of samples is considerably smaller than the number of AA elements, which is important for multielement AAs. It is shown that the proposed method is more effective in this case than methods based on the AIC (Akaike's Information Criterion) or minimum description length (MDL) criterion.

A Range Ambiguity Suppression Processing Method for Spaceborne SAR with Up and Down Chirp Modulation.

PubMed

Wen, Xuejiao; Qiu, Xiaolan; Han, Bing; Ding, Chibiao; Lei, Bin; Chen, Qi

2018-05-07

Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger backscattering intensity than the mainlobe imaging area in some case, which has a severe impact on visual effects and subsequent applications. In this paper, a novel hybrid range ambiguity suppression method for up and down chirp modulation is proposed. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately, by applying pulse compression using a contrary modulation rate and CFAR detecting method. The effectiveness and correctness of the approach is demonstrated by processing the archive images acquired by Chinese Gaofen-3 SAR sensor in full-polarization mode.

Combination of cylindrical confinement and spark discharge for signal improvement using laser induced breakdown spectroscopy.

PubMed

Hou, Zongyu; Wang, Zhe; Liu, Jianmin; Ni, Weidou; Li, Zheng

2014-06-02

Spark discharge has been proved to be an effective way to enhance the LIBS signal while moderate cylindrical confinement is able to increase the signal repeatability with limited signal enhancement effects. In the present work, these two methods were combined together not only to improve the pulse-to-pulse signal repeatability but also to simultaneously and significantly enhance the signal as well as SNR. Plasma images showed that the confinement stabilized the morphology of the plasma, especially for the discharge assisted process, which explained the improvement of the signal repeatability.

Theoretical and experimental study of low-finesse extrinsic Fabry-Perot interferometric fiber optic sensors

NASA Astrophysics Data System (ADS)

Han, Ming

In this dissertation, detailed and systematic theoretical and experimental study of low-finesse extrinsic Fabry-Perot interferometric (EFPI) fiber optic sensors together with their signal processing methods for white-light systems are presented. The work aims to provide a better understanding of the operational principle of EFPI fiber optic sensors, and is useful and important in the design, optimization, fabrication and application of single mode fiber(SMF) EFPI (SMF-EFPI) and multimode fiber (MMF) EFPI (MMF-EFPI) sensor systems. The cases for SMF-EFPI and MMF-EFPI sensors are separately considered. In the analysis of SMF-EFPI sensors, the light transmitted in the fiber is approximated by a Gaussian beam and the obtained spectral transfer function of the sensors includes an extra phase shift due to the light coupling in the fiber end-face. This extra phase shift has not been addressed by previous researchers and is of great importance for high accuracy and high resolution signal processing of white-light SMF-EFPI systems. Fringe visibility degradation due to gap-length increase and sensor imperfections is studied. The results indicate that the fringe visibility of a SMF-EFPI sensor is relatively insensitive to the gap-length change and sensor imperfections. Based on the spectral fringe pattern predicated by the theory of SMF-EFPI sensors, a novel curve fitting signal processing method (Type 1 curve-fitting method) is presented for white-light SMF-EFPI sensor systems. Other spectral domain signal processing methods including the wavelength-tracking, the Type 2-3 curve fitting, Fourier transform, and two-point interrogation methods are reviewed and systematically analyzed. Experiments were carried out to compare the performances of these signal processing methods. The results have shown that the Type 1 curve fitting method achieves high accuracy, high resolution, large dynamic range, and the capability of absolute measurement at the same time, while others either have less resolution, or are not capable of absolute measurement. Previous mathematical models for MMF-EFPI sensors are all based on geometric optics; therefore their applications have many limitations. In this dissertation, a modal theory is developed that can be used in any situations and is more accurate. The mathematical description of the spectral fringes of MMF-EFPI sensors is obtained by the modal theory. Effect on the fringe visibility of system parameters, including the sensor head structure, the fiber parameters, and the mode power distribution in the MMF of the MMF-EFPI sensors, is analyzed. Experiments were carried out to validate the theory. Fundamental mechanism that causes the degradation of the fringe visibility in MMF-EFPI sensors are revealed. It is shown that, in some situations at which the fringe visibility is important and difficult to achieve, a simple method of launching the light into the MMF-EFPI sensor system from the output of a SMF could be used to improve the fringe visibility and to ease the fabrication difficulties of MMF-EFPI sensors. Signal processing methods that are well-understood in white-light SMF-EFPI sensor systems may exhibit new aspects when they are applied to white-light MMF-EFPI sensor systems. This dissertation reveals that the variations of mode power distribution (MPD) in the MMF could cause phase variations of the spectral fringes from a MMF-EFPI sensor and introduce measurement errors for a signal processing method in which the phase information is used. This MPD effect on the wavelength-tracking method in white-light MMF-EFPI sensors is theoretically analyzed. The fringe phases changes caused by MPD variations were experimentally observed and thus the MFD effect is validated.

Using Imaging Methods to Interrogate Radiation-Induced Cell Signaling

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

Shankaran, Harish; Weber, Thomas J.; Freiin von Neubeck, Claere H.

2012-04-01

There is increasing emphasis on the use of systems biology approaches to define radiation induced responses in cells and tissues. Such approaches frequently rely on global screening using various high throughput 'omics' platforms. Although these methods are ideal for obtaining an unbiased overview of cellular responses, they often cannot reflect the inherent heterogeneity of the system or provide detailed spatial information. Additionally, performing such studies with multiple sampling time points can be prohibitively expensive. Imaging provides a complementary method with high spatial and temporal resolution capable of following the dynamics of signaling processes. In this review, we utilize specific examplesmore » to illustrate how imaging approaches have furthered our understanding of radiation induced cellular signaling. Particular emphasis is placed on protein co-localization, and oscillatory and transient signaling dynamics.« less

Detector signal correction method and system

DOEpatents

Carangelo, Robert M.; Duran, Andrew J.; Kudman, Irwin

1995-07-11

Corrective factors are applied so as to remove anomalous features from the signal generated by a photoconductive detector, and to thereby render the output signal highly linear with respect to the energy of incident, time-varying radiation. The corrective factors may be applied through the use of either digital electronic data processing means or analog circuitry, or through a combination of those effects.

Detector signal correction method and system

DOEpatents

Carangelo, R.M.; Duran, A.J.; Kudman, I.

1995-07-11

Corrective factors are applied so as to remove anomalous features from the signal generated by a photoconductive detector, and to thereby render the output signal highly linear with respect to the energy of incident, time-varying radiation. The corrective factors may be applied through the use of either digital electronic data processing means or analog circuitry, or through a combination of those effects. 5 figs.

Dynamic tracking down-conversion signal processing method based on reference signal for grating heterodyne interferometer

NASA Astrophysics Data System (ADS)

Wang, Guochao; Yan, Shuhua; Zhou, Weihong; Gu, Chenhui

2012-08-01

Traditional displacement measurement systems by grating, which purely make use of fringe intensity to implement fringe count and subdivision, have rigid demands for signal quality and measurement condition, so they are not easy to realize measurement with nanometer precision. Displacement measurement with the dual-wavelength and single-grating design takes advantage of the single grating diffraction theory and the heterodyne interference theory, solving quite well the contradiction between large range and high precision in grating displacement measurement. To obtain nanometer resolution and nanometer precision, high-power subdivision of interference fringes must be realized accurately. A dynamic tracking down-conversion signal processing method based on the reference signal is proposed. Accordingly, a digital phase measurement module to realize high-power subdivision on field programmable gate array (FPGA) was designed, as well as a dynamic tracking down-conversion module using phase-locked loop (PLL). Experiments validated that a carrier signal after down-conversion can constantly maintain close to 100 kHz, and the phase-measurement resolution and phase precision are more than 0.05 and 0.2 deg, respectively. The displacement resolution and the displacement precision, corresponding to the phase results, are 0.139 and 0.556 nm, respectively.

Hadamard multimode optical imaging transceiver

DOEpatents

Cooke, Bradly J; Guenther, David C; Tiee, Joe J; Kellum, Mervyn J; Olivas, Nicholas L; Weisse-Bernstein, Nina R; Judd, Stephen L; Braun, Thomas R

2012-10-30

Disclosed is a method and system for simultaneously acquiring and producing results for multiple image modes using a common sensor without optical filtering, scanning, or other moving parts. The system and method utilize the Walsh-Hadamard correlation detection process (e.g., functions/matrix) to provide an all-binary structure that permits seamless bridging between analog and digital domains. An embodiment may capture an incoming optical signal at an optical aperture, convert the optical signal to an electrical signal, pass the electrical signal through a Low-Noise Amplifier (LNA) to create an LNA signal, pass the LNA signal through one or more correlators where each correlator has a corresponding Walsh-Hadamard (WH) binary basis function, calculate a correlation output coefficient for each correlator as a function of the corresponding WH binary basis function in accordance with Walsh-Hadamard mathematical principles, digitize each of the correlation output coefficient by passing each correlation output coefficient through an Analog-to-Digital Converter (ADC), and performing image mode processing on the digitized correlation output coefficients as desired to produce one or more image modes. Some, but not all, potential image modes include: multi-channel access, temporal, range, three-dimensional, and synthetic aperture.

Nonlinear microscopy of collagen fibers

NASA Astrophysics Data System (ADS)

Strupler, M.; Pena, A.-M.; Hernest, M.; Tharaux, P.-L.; Fabre, A.; Marchal-Somme, J.; Crestani, B.; Débarre, D.; Martin, J.-L.; Beaurepaire, E.; Schanne-Klein, M.-C.

2007-02-01

We used intrinsic Second Harmonic Generation (SHG) by fibrillar collagen to visualize the three-dimensional architecture of collagen fibrosis at the micrometer scale using laser scanning nonlinear microscopy. We showed that SHG signals are highly specific to fibrillar collagen and provide a sensitive probe of the micrometer-scale structural organization of collagen in tissues. Moreover, recording simultaneously other nonlinear optical signals in a multimodal setup, we visualized the tissue morphology using Two-Photon Excited Fluorescence (2PEF) signals from endogenous chromophores such as NADH or elastin. We then compared different methods to determine accurate indexes of collagen fibrosis using nonlinear microscopy, given that most collagen fibrils are smaller than the microscope resolution and that second harmonic generation is a coherent process. In order to define a robust method to process our three-dimensional images, we either calculated the fraction of the images occupied by a significant SHG signal, or averaged SHG signal intensities. We showed that these scores provide an estimation of the extension of renal and pulmonary fibrosis in murine models, and that they clearly sort out the fibrotic mice.

A review of channel selection algorithms for EEG signal processing

NASA Astrophysics Data System (ADS)

Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq

2015-12-01

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

Signal Processing for Time-Series Functions on a Graph

DTIC Science & Technology

2018-02-01

as filtering to functions supported on graphs. These methods can be applied to scalar functions with a domain that can be described by a fixed...classical signal processing such as filtering to account for the graph domain. This work essentially divides into 2 basic approaches: graph Laplcian...based filtering and weighted adjacency matrix-based filtering . In Shuman et al.,11 and elaborated in Bronstein et al.,13 filtering operators are

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.

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

PubMed

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-06-03

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis: A Review

PubMed Central

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-01-01

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis. PMID:28587198

Robust Nonlinear Causality Analysis of Nonstationary Multivariate Physiological Time Series.

PubMed

Schack, Tim; Muma, Michael; Feng, Mengling; Guan, Cuntai; Zoubir, Abdelhak M

2018-06-01

An important research area in biomedical signal processing is that of quantifying the relationship between simultaneously observed time series and to reveal interactions between the signals. Since biomedical signals are potentially nonstationary and the measurements may contain outliers and artifacts, we introduce a robust time-varying generalized partial directed coherence (rTV-gPDC) function. The proposed method, which is based on a robust estimator of the time-varying autoregressive (TVAR) parameters, is capable of revealing directed interactions between signals. By definition, the rTV-gPDC only displays the linear relationships between the signals. We therefore suggest to approximate the residuals of the TVAR process, which potentially carry information about the nonlinear causality by a piece-wise linear time-varying moving-average model. The performance of the proposed method is assessed via extensive simulations. To illustrate the method's applicability to real-world problems, it is applied to a neurophysiological study that involves intracranial pressure, arterial blood pressure, and brain tissue oxygenation level (PtiO2) measurements. The rTV-gPDC reveals causal patterns that are in accordance with expected cardiosudoral meachanisms and potentially provides new insights regarding traumatic brain injuries. The rTV-gPDC is not restricted to the above problem but can be useful in revealing interactions in a broad range of applications.

Signal restoration through deconvolution applied to deep mantle seismic probes

NASA Astrophysics Data System (ADS)

Stefan, W.; Garnero, E.; Renaut, R. A.

2006-12-01

We present a method of signal restoration to improve the signal-to-noise ratio, sharpen seismic arrival onset, and act as an empirical source deconvolution of specific seismic arrivals. Observed time-series gi are modelled as a convolution of a simpler time-series fi, and an invariant point spread function (PSF) h that attempts to account for the earthquake source process. The method is used on the shear wave time window containing SKS and S, whereby using a Gaussian PSF produces more impulsive, narrower, signals in the wave train. The resulting restored time-series facilitates more accurate and objective relative traveltime estimation of the individual seismic arrivals. We demonstrate the accuracy of the reconstruction method on synthetic seismograms generated by the reflectivity method. Clean and sharp reconstructions are obtained with real data, even for signals with relatively high noise content. Reconstructed signals are simpler, more impulsive, and narrower, which allows highlighting of some details of arrivals that are not readily apparent in raw waveforms. In particular, phases nearly coincident in time can be separately identified after processing. This is demonstrated for two seismic wave pairs used to probe deep mantle and core-mantle boundary structure: (1) the Sab and Scd arrivals, which travel above and within, respectively, a 200-300-km-thick, higher than average shear wave velocity layer at the base of the mantle, observable in the 88-92 deg epicentral distance range and (2) SKS and SPdiff KS, which are core waves with the latter having short arcs of P-wave diffraction, and are nearly identical in timing near 108-110 deg in distance. A Java/Matlab algorithm was developed for the signal restoration, which can be downloaded from the authors web page, along with example data and synthetic seismograms.

A novel analysis method for near infrared spectroscopy based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zhou, Zhenyu; Yang, Hongyu; Liu, Yun; Ruan, Zongcai; Luo, Qingming; Gong, Hui; Lu, Zuhong

2007-05-01

Near Infrared Imager (NIRI) has been widely used to access the brain functional activity non-invasively. We use a portable, multi-channel and continuous-wave NIR topography instrument to measure the concentration changes of each hemoglobin species and map cerebral cortex functional activation. By extracting some essential features from the BOLD signals, optical tomography is able to be a new way of neuropsychological studies. Fourier spectral analysis provides a common framework for examining the distribution of global energy in the frequency domain. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. The hemoglobin species concentration changes are of such kind. In this work we develop a new signal processing method using Hilbert-Huang transform to perform spectral analysis of the functional NIRI signals. Compared with wavelet based multi-resolution analysis (MRA), we demonstrated the extraction of task related signal for observation of activation in the prefrontal cortex (PFC) in vision stimulation experiment. This method provides a new analysis tool for functional NIRI signals. Our experimental results show that the proposed approach provides the unique method for reconstructing target signal without losing original information and enables us to understand the episode of functional NIRI more precisely.

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.

Signal Processing Studies of a Simulated Laser Doppler Velocimetry-Based Acoustic Sensor

DTIC Science & Technology

1990-10-17

investigated using spectral correlation methods. Results indicate that it may be possible to extend demonstrated LDV-based acoustic sensor sensitivities using higher order processing techniques. (Author)

Adaptive EMG noise reduction in ECG signals using noise level approximation

NASA Astrophysics Data System (ADS)

Marouf, Mohamed; Saranovac, Lazar

2017-12-01

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

Department of Cybernetic Acoustics

NASA Astrophysics Data System (ADS)

The development of the theory, instrumentation and applications of methods and systems for the measurement, analysis, processing and synthesis of acoustic signals within the audio frequency range, particularly of the speech signal and the vibro-acoustic signal emitted by technical and industrial equipments treated as noise and vibration sources was discussed. The research work, both theoretical and experimental, aims at applications in various branches of science, and medicine, such as: acoustical diagnostics and phoniatric rehabilitation of pathological and postoperative states of the speech organ; bilateral ""man-machine'' speech communication based on the analysis, recognition and synthesis of the speech signal; vibro-acoustical diagnostics and continuous monitoring of the state of machines, technical equipments and technological processes.

Spatially assisted down-track median filter for GPR image post-processing

DOEpatents

Paglieroni, David W; Beer, N Reginald

2014-10-07

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

Development of gait segmentation methods for wearable foot pressure sensors.

PubMed

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

2012-01-01

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

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

Master-slave interferometry for parallel spectral domain interferometry sensing and versatile 3D optical coherence tomography.

PubMed

Podoleanu, Adrian Gh; Bradu, Adrian

2013-08-12

Conventional spectral domain interferometry (SDI) methods suffer from the need of data linearization. When applied to optical coherence tomography (OCT), conventional SDI methods are limited in their 3D capability, as they cannot deliver direct en-face cuts. Here we introduce a novel SDI method, which eliminates these disadvantages. We denote this method as Master - Slave Interferometry (MSI), because a signal is acquired by a slave interferometer for an optical path difference (OPD) value determined by a master interferometer. The MSI method radically changes the main building block of an SDI sensor and of a spectral domain OCT set-up. The serially provided signal in conventional technology is replaced by multiple signals, a signal for each OPD point in the object investigated. This opens novel avenues in parallel sensing and in parallelization of signal processing in 3D-OCT, with applications in high- resolution medical imaging and microscopy investigation of biosamples. Eliminating the need of linearization leads to lower cost OCT systems and opens potential avenues in increasing the speed of production of en-face OCT images in comparison with conventional SDI.

Analysis of the sleep quality of elderly people using biomedical signals.

PubMed

Moreno-Alsasua, L; Garcia-Zapirain, B; Mendez-Zorrilla, A

2015-01-01

This paper presents a technical solution that analyses sleep signals captured by biomedical sensors to find possible disorders during rest. Specifically, the method evaluates electrooculogram (EOG) signals, skin conductance (GSR), air flow (AS), and body temperature. Next, a quantitative sleep quality analysis determines significant changes in the biological signals, and any similarities between them in a given time period. Filtering techniques such as the Fourier transform method and IIR filters process the signal and identify significant variations. Once these changes have been identified, all significant data is compared and a quantitative and statistical analysis is carried out to determine the level of a person's rest. To evaluate the correlation and significant differences, a statistical analysis has been calculated showing correlation between EOG and AS signals (p=0,005), EOG, and GSR signals (p=0,037) and, finally, the EOG and Body temperature (p=0,04). Doctors could use this information to monitor changes within a patient.

Non-contact physiological signal detection using continuous wave Doppler radar.

PubMed

Qiao, Dengyu; He, Tan; Hu, Boping; Li, Ye

2014-01-01

The aim of this work is to show non-contact physiological signal monitoring system based on continuous-wave (CW) Doppler radar, which is becoming highly attractive in the field of health care monitoring of elderly people. Two radar signal processing methods were introduced in this paper: one to extract respiration and heart rates of a single person and the other to separate mixed respiration signals. To verify the validity of the methods, physiological signal is obtained from stationary human subjects using a CW Doppler radar unit. The sensor operating at 24 GHz is located 0.5 meter away from the subject. The simulation results show that the respiration and heart rates are clearly extracted, and the mixed respiration signals are successfully separated. Finally, reference respiration and heart rate signals are measured by an ECG monitor and compared with the results tracked by the CW Doppler radar monitoring system.

Fast-Acquisition/Weak-Signal-Tracking GPS Receiver for HEO

NASA Technical Reports Server (NTRS)

Wintemitz, Luke; Boegner, Greg; Sirotzky, Steve

2004-01-01

A report discusses the technical background and design of the Navigator Global Positioning System (GPS) receiver -- . a radiation-hardened receiver intended for use aboard spacecraft. Navigator is capable of weak signal acquisition and tracking as well as much faster acquisition of strong or weak signals with no a priori knowledge or external aiding. Weak-signal acquisition and tracking enables GPS use in high Earth orbits (HEO), and fast acquisition allows for the receiver to remain without power until needed in any orbit. Signal acquisition and signal tracking are, respectively, the processes of finding and demodulating a signal. Acquisition is the more computationally difficult process. Previous GPS receivers employ the method of sequentially searching the two-dimensional signal parameter space (code phase and Doppler). Navigator exploits properties of the Fourier transform in a massively parallel search for the GPS signal. This method results in far faster acquisition times [in the lab, 12 GPS satellites have been acquired with no a priori knowledge in a Low-Earth-Orbit (LEO) scenario in less than one second]. Modeling has shown that Navigator will be capable of acquiring signals down to 25 dB-Hz, appropriate for HEO missions. Navigator is built using the radiation-hardened ColdFire microprocessor and housing the most computationally intense functions in dedicated field-programmable gate arrays. The high performance of the algorithm and of the receiver as a whole are made possible by optimizing computational efficiency and carefully weighing tradeoffs among the sampling rate, data format, and data-path bit width.

Quantitative measures for redox signaling.

PubMed

Pillay, Ché S; Eagling, Beatrice D; Driscoll, Scott R E; Rohwer, Johann M

2016-07-01

Redox signaling is now recognized as an important regulatory mechanism for a number of cellular processes including the antioxidant response, phosphokinase signal transduction and redox metabolism. While there has been considerable progress in identifying the cellular machinery involved in redox signaling, quantitative measures of redox signals have been lacking, limiting efforts aimed at understanding and comparing redox signaling under normoxic and pathogenic conditions. Here we have outlined some of the accepted principles for redox signaling, including the description of hydrogen peroxide as a signaling molecule and the role of kinetics in conferring specificity to these signaling events. Based on these principles, we then develop a working definition for redox signaling and review a number of quantitative methods that have been employed to describe signaling in other systems. Using computational modeling and published data, we show how time- and concentration- dependent analyses, in particular, could be used to quantitatively describe redox signaling and therefore provide important insights into the functional organization of redox networks. Finally, we consider some of the key challenges with implementing these methods. Copyright © 2016 Elsevier Inc. All rights reserved.

Computational problems and signal processing in SETI

NASA Technical Reports Server (NTRS)

Deans, Stanley R.; Cullers, D. K.; Stauduhar, Richard

1991-01-01

The Search for Extraterrestrial Intelligence (SETI), currently being planned at NASA, will require that an enormous amount of data (on the order of 10 exp 11 distinct signal paths for a typical observation) be analyzed in real time by special-purpose hardware. Even though the SETI system design is not based on maximum entropy and Bayesian methods (partly due to the real-time processing constraint), it is expected that enough data will be saved to be able to apply these and other methods off line where computational complexity is not an overriding issue. Interesting computational problems that relate directly to the system design for processing such an enormous amount of data have emerged. Some of these problems are discussed, along with the current status on their solution.

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

Computational intelligence for target assessment in Parkinson's disease

NASA Astrophysics Data System (ADS)

Micheli-Tzanakou, Evangelia; Hamilton, J. L.; Zheng, J.; Lehman, Richard M.

2001-11-01

Recent advances in image and signal processing have created a new challenging environment for biomedical engineers. Methods that were developed for different fields are now finding a fertile ground in biomedicine, especially in the analysis of bio-signals and in the understanding of images. More and more, these methods are used in the operating room, helping surgeons, and in the physician's office as aids for diagnostic purposes. Neural Network (NN) research on the other hand, has gone a long way in the past decade. NNs now consist of many thousands of highly interconnected processing elements that can encode, store and recall relationships between different patterns by altering the weighting coefficients of inputs in a systematic way. Although they can generate reasonable outputs from unknown input patterns, and can tolerate a great deal of noise, they are very slow when run on a serial machine. We have used advanced signal processing and innovative image processing methods that are used along with computational intelligence for diagnostic purposes and as visualization aids inside and outside the operating room. Applications to be discussed include EEGs and field potentials in Parkinson's disease along with 3D reconstruction of MR or fMR brain images in Parkinson's patients, are currently used in the operating room for Pallidotomies and Deep Brain Stimulation (DBS).

Buried object detection in GPR images

DOEpatents

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

2014-04-29

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

A low-rank matrix recovery approach for energy efficient EEG acquisition for a wireless body area network.

PubMed

Majumdar, Angshul; Gogna, Anupriya; Ward, Rabab

2014-08-25

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

Photoacoustic imaging optimization with raw signal deconvolution and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Wang, Jing; Qin, Yu; Zhan, Hongchen; Yuan, Jie; Cheng, Qian; Wang, Xueding

2018-02-01

Photoacoustic (PA) signal of an ideal optical absorb particle is a single N-shape wave. PA signals of a complicated biological tissue can be considered as the combination of individual N-shape waves. However, the N-shape wave basis not only complicates the subsequent work, but also results in aliasing between adjacent micro-structures, which deteriorates the quality of the final PA images. In this paper, we propose a method to improve PA image quality through signal processing method directly working on raw signals, which including deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent point spread function (PSF) which is measured in advance. Then, EMD is adopted to adaptively re-shape the PA signals with two constraints, positive polarity and spectrum consistence. With our proposed method, the built PA images can yield more detail structural information. Micro-structures are clearly separated and revealed. To validate the effectiveness of this method, we present numerical simulations and phantom studies consist of a densely distributed point sources model and a blood vessel model. In the future, our study might hold the potential for clinical PA imaging as it can help to distinguish micro-structures from the optimized images and even measure the size of objects from deconvolved signals.

Method and apparatus for signal processing in a sensor system for use in spectroscopy

DOEpatents

O'Connor, Paul [Bellport, NY; DeGeronimo, Gianluigi [Nesconset, NY; Grosholz, Joseph [Natrona Heights, PA

2008-05-27

A method for processing pulses arriving randomly in time on at least one channel using multiple peak detectors includes asynchronously selecting a non-busy peak detector (PD) in response to a pulse-generated trigger signal, connecting the channel to the selected PD in response to the trigger signal, and detecting a pulse peak amplitude. Amplitude and time of arrival data are output in first-in first-out (FIFO) sequence. An apparatus includes trigger comparators to generate the trigger signal for the pulse-receiving channel, PDs, a switch for connecting the channel to the selected PD, and logic circuitry which maintains the write pointer. Also included, time-to-amplitude converters (TACs) convert time of arrival to analog voltage and an analog multiplexer provides FIFO output. A multi-element sensor system for spectroscopy includes detector elements, channels, trigger comparators, PDs, a switch, and a logic circuit with asynchronous write pointer. The system includes TACs, a multiplexer and analog-to-digital converter.

Full-field wrist pulse signal acquisition and analysis by 3D Digital Image Correlation

NASA Astrophysics Data System (ADS)

Xue, Yuan; Su, Yong; Zhang, Chi; Xu, Xiaohai; Gao, Zeren; Wu, Shangquan; Zhang, Qingchuan; Wu, Xiaoping

2017-11-01

Pulse diagnosis is an essential part in four basic diagnostic methods (inspection, listening, inquiring and palpation) in traditional Chinese medicine, which depends on longtime training and rich experience, so computerized pulse acquisition has been proposed and studied to ensure the objectivity. To imitate the process that doctors using three fingertips with different pressures to feel fluctuations in certain areas containing three acupoints, we established a five dimensional pulse signal acquisition system adopting a non-contacting optical metrology method, 3D digital image correlation, to record the full-field displacements of skin fluctuations under different pressures. The system realizes real-time full-field vibration mode observation with 10 FPS. The maximum sample frequency is 472 Hz for detailed post-processing. After acquisition, the signals are analyzed according to the amplitude, pressure, and pulse wave velocity. The proposed system provides a novel optical approach for digitalizing pulse diagnosis and massive pulse signal data acquisition for various types of patients.

Enzyme-Based Logic Gates and Networks with Output Signals Analyzed by Various Methods.

PubMed

Katz, Evgeny

2017-07-05

The paper overviews various methods that are used for the analysis of output signals generated by enzyme-based logic systems. The considered methods include optical techniques (optical absorbance, fluorescence spectroscopy, surface plasmon resonance), electrochemical techniques (cyclic voltammetry, potentiometry, impedance spectroscopy, conductivity measurements, use of field effect transistor devices, pH measurements), and various mechanoelectronic methods (using atomic force microscope, quartz crystal microbalance). Although each of the methods is well known for various bioanalytical applications, their use in combination with the biomolecular logic systems is rather new and sometimes not trivial. Many of the discussed methods have been combined with the use of signal-responsive materials to transduce and amplify biomolecular signals generated by the logic operations. Interfacing of biocomputing logic systems with electronics and "smart" signal-responsive materials allows logic operations be extended to actuation functions; for example, stimulating molecular release and switchable features of bioelectronic devices, such as biofuel cells. The purpose of this review article is to emphasize the broad variability of the bioanalytical systems applied for signal transduction in biocomputing processes. All bioanalytical systems discussed in the article are exemplified with specific logic gates and multi-gate networks realized with enzyme-based biocatalytic cascades. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

PubMed

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

2018-01-25

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

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.

Seismic random noise attenuation method based on empirical mode decomposition of Hausdorff dimension

NASA Astrophysics Data System (ADS)

Yan, Z.; Luan, X.

2017-12-01

Introduction Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, since the complexity of the real seismic wave field results in serious aliasing modes, it is not ideal and effective to denoise with this method alone. Based on the multi-scale decomposition characteristics of the signal EMD algorithm, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. First of all, We apply EMD algorithm adaptive decomposition of seismic data and obtain a series of intrinsic mode function (IMF)with different scales. Based on the difference of Hausdorff dimension between effectively signals and random noise, we identify IMF component mixed with random noise. Then we use threshold correlation filtering process to separate the valid signal and random noise effectively. Compared with traditional EMD method, the results show that the new method of seismic random noise attenuation has a better suppression effect. The implementation process The EMD algorithm is used to decompose seismic signals into IMF sets and analyze its spectrum. Since most of the random noise is high frequency noise, the IMF sets can be divided into three categories: the first category is the effective wave composition of the larger scale; the second category is the noise part of the smaller scale; the third category is the IMF component containing random noise. Then, the third kind of IMF component is processed by the Hausdorff dimension algorithm, and the appropriate time window size, initial step and increment amount are selected to calculate the Hausdorff instantaneous dimension of each component. The dimension of the random noise is between 1.0 and 1.05, while the dimension of the effective wave is between 1.05 and 2.0. On the basis of the previous steps, according to the dimension difference between the random noise and effective signal, we extracted the sample points, whose fractal dimension value is less than or equal to 1.05 for the each IMF components, to separate the residual noise. Using the IMF components after dimension filtering processing and the effective wave IMF components after the first selection for reconstruction, we can obtained the results of de-noising.

Using a binaural biomimetic array to identify bottom objects ensonified by echolocating dolphins

USGS Publications Warehouse

Heiweg, D.A.; Moore, P.W.; Martin, S.W.; Dankiewicz, L.A.

2006-01-01

The development of a unique dolphin biomimetic sonar produced data that were used to study signal processing methods for object identification. Echoes from four metallic objects proud on the bottom, and a substrate-only condition, were generated by bottlenose dolphins trained to ensonify the targets in very shallow water. Using the two-element ('binaural') receive array, object echo spectra were collected and submitted for identification to four neural network architectures. Identification accuracy was evaluated over two receive array configurations, and five signal processing schemes. The four neural networks included backpropagation, learning vector quantization, genetic learning and probabilistic network architectures. The processing schemes included four methods that capitalized on the binaural data, plus a monaural benchmark process. All the schemes resulted in above-chance identification accuracy when applied to learning vector quantization and backpropagation. Beam-forming or concatenation of spectra from both receive elements outperformed the monaural benchmark, with higher sensitivity and lower bias. Ultimately, best object identification performance was achieved by the learning vector quantization network supplied with beam-formed data. The advantages of multi-element signal processing for object identification are clearly demonstrated in this development of a first-ever dolphin biomimetic sonar. ?? 2006 IOP Publishing Ltd.

Multimodal neuroelectric interface development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Rosipal, Roman; Clanton, Sam T.; Matthews, Bryan; Hibbs, Andrew D.; Matthews, Robert; Krupka, Michael

2003-01-01

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

P-code enhanced method for processing encrypted GPS signals without knowledge of the encryption code

NASA Technical Reports Server (NTRS)

Young, Lawrence E. (Inventor); Meehan, Thomas K. (Inventor); Thomas, Jr., Jess Brooks (Inventor)

2000-01-01

In the preferred embodiment, an encrypted GPS signal is down-converted from RF to baseband to generate two quadrature components for each RF signal (L1 and L2). Separately and independently for each RF signal and each quadrature component, the four down-converted signals are counter-rotated with a respective model phase, correlated with a respective model P code, and then successively summed and dumped over presum intervals substantially coincident with chips of the respective encryption code. Without knowledge of the encryption-code signs, the effect of encryption-code sign flips is then substantially reduced by selected combinations of the resulting presums between associated quadrature components for each RF signal, separately and independently for the L1 and L2 signals. The resulting combined presums are then summed and dumped over longer intervals and further processed to extract amplitude, phase and delay for each RF signal. Precision of the resulting phase and delay values is approximately four times better than that obtained from straight cross-correlation of L1 and L2. This improved method provides the following options: separate and independent tracking of the L1-Y and L2-Y channels; separate and independent measurement of amplitude, phase and delay L1-Y channel; and removal of the half-cycle ambiguity in L1-Y and L2-Y carrier phase.

On recursive least-squares filtering algorithms and implementations. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hsieh, Shih-Fu

1990-01-01

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

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.

Spectrum interrogation of fiber acoustic sensor based on self-fitting and differential method.

PubMed

Fu, Xin; Lu, Ping; Ni, Wenjun; Liao, Hao; Wang, Shun; Liu, Deming; Zhang, Jiangshan

2017-02-20

In this article, we propose an interrogation method of fiber acoustic sensor to recover the time-domain signal from the sensor spectrum. The optical spectrum of the sensor will show a ripple waveform when responding to acoustic signal due to the scanning process in a certain wavelength range. The reason behind this phenomenon is the dynamic variation of the sensor spectrum while the intensity of different wavelength is acquired at different time in a scanning period. The frequency components can be extracted from the ripple spectrum assisted by the wavelength scanning speed. The signal is able to be recovered by differential between the ripple spectrum and its self-fitted curve. The differential process can eliminate the interference caused by environmental perturbations such as temperature or refractive index (RI), etc. The proposed method is appropriate for fiber acoustic sensors based on gratings or interferometers. A long period grating (LPG) is adopted as an acoustic sensor head to prove the feasibility of the interrogation method in experiment. The ability to compensate the environmental fluctuations is also demonstrated.

Systems modelling methodology for the analysis of apoptosis signal transduction and cell death decisions.

PubMed

Rehm, Markus; Prehn, Jochen H M

2013-06-01

Systems biology and systems medicine, i.e. the application of systems biology in a clinical context, is becoming of increasing importance in biology, drug discovery and health care. Systems biology incorporates knowledge and methods that are applied in mathematics, physics and engineering, but may not be part of classical training in biology. We here provide an introduction to basic concepts and methods relevant to the construction and application of systems models for apoptosis research. We present the key methods relevant to the representation of biochemical processes in signal transduction models, with a particular reference to apoptotic processes. We demonstrate how such models enable a quantitative and temporal analysis of changes in molecular entities in response to an apoptosis-inducing stimulus, and provide information on cell survival and cell death decisions. We introduce methods for analyzing the spatial propagation of cell death signals, and discuss the concepts of sensitivity analyses that enable a prediction of network responses to disturbances of single or multiple parameters. Copyright © 2013 Elsevier Inc. All rights reserved.

Acoustic inspection of concrete bridge decks

NASA Astrophysics Data System (ADS)

Henderson, Mark E.; Dion, Gary N.; Costley, R. Daniel

1999-02-01

The determination of concrete integrity, especially in concrete bridge decks, is of extreme importance. Current systems for testing concrete structures are expensive, slow, or tedious. State of the art systems use ground penetrating radar, but they have inherent problems especially with ghosting and signal signature overlap. The older method of locating delaminations in bridge decks involves either tapping on the surface with a hammer or metal rod, or dragging a chain-bar across the bridge deck. Both methods require a `calibrated' ear to determine the difference between good sections and bad sections of concrete. As a consequence, the method is highly subjective, different from person to person and even day to day for a given person. In addition, archival of such data is impractical, or at least improbable, in most situations. The Diagnostic Instrumentation and Analysis Laboratory has constructed an instrument that implements the chain-drag method of concrete inspection. The system is capable of real-time analysis of recorded signals, archival of processed data, and high-speed data acquisition so that post-processing of the data is possible for either research purposes or for listening to the recorded signals.

Modified ADALINE algorithm for harmonic estimation and selective harmonic elimination in inverters

NASA Astrophysics Data System (ADS)

Vasumathi, B.; Moorthi, S.

2011-11-01

In digital signal processing, algorithms are very well developed for the estimation of harmonic components. In power electronic applications, an objective like fast response of a system is of primary importance. An effective method for the estimation of instantaneous harmonic components, along with conventional harmonic elimination technique, is presented in this article. The primary function is to eliminate undesirable higher harmonic components from the selected signal (current or voltage) and it requires only the knowledge of the frequency of the component to be eliminated. A signal processing technique using modified ADALINE algorithm has been proposed for harmonic estimation. The proposed method stays effective as it converges to a minimum error and brings out a finer estimation. A conventional control based on pulse width modulation for selective harmonic elimination is used to eliminate harmonic components after its estimation. This method can be applied to a wide range of equipment. The validity of the proposed method to estimate and eliminate voltage harmonics is proved with a dc/ac inverter as a simulation example. Then, the results are compared with existing ADALINE algorithm for illustrating its effectiveness.

Spatial sound field synthesis and upmixing based on the equivalent source method.

PubMed

Bai, Mingsian R; Hsu, Hoshen; Wen, Jheng-Ciang

2014-01-01

Given scarce number of recorded signals, spatial sound field synthesis with an extended sweet spot is a challenging problem in acoustic array signal processing. To address the problem, a synthesis and upmixing approach inspired by the equivalent source method (ESM) is proposed. The synthesis procedure is based on the pressure signals recorded by a microphone array and requires no source model. The array geometry can also be arbitrary. Four upmixing strategies are adopted to enhance the resolution of the reproduced sound field when there are more channels of loudspeakers than the microphones. Multi-channel inverse filtering with regularization is exploited to deal with the ill-posedness in the reconstruction process. The distance between the microphone and loudspeaker arrays is optimized to achieve the best synthesis quality. To validate the proposed system, numerical simulations and subjective listening experiments are performed. The results demonstrated that all upmixing methods improved the quality of reproduced target sound field over the original reproduction. In particular, the underdetermined ESM interpolation method yielded the best spatial sound field synthesis in terms of the reproduction error, timbral quality, and spatial quality.

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.

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

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering frommore » liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics.« less

UWB communication receiver feedback loop

DOEpatents

Spiridon, Alex; Benzel, Dave; Dowla, Farid U.; Nekoogar, Faranak; Rosenbury, Erwin T.

2007-12-04

A novel technique and structure that maximizes the extraction of information from reference pulses for UWB-TR receivers is introduced. The scheme efficiently processes an incoming signal to suppress different types of UWB as well as non-UWB interference prior to signal detection. Such a method and system adds a feedback loop mechanism to enhance the signal-to-noise ratio of reference pulses in a conventional TR receiver. Moreover, sampling the second order statistical function such as, for example, the autocorrelation function (ACF) of the received signal and matching it to the ACF samples of the original pulses for each transmitted bit provides a more robust UWB communications method and system in the presence of channel distortions.

Method and apparatus for frequency spectrum analysis

NASA Technical Reports Server (NTRS)

Cole, Steven W. (Inventor)

1992-01-01

A method for frequency spectrum analysis of an unknown signal in real-time is discussed. The method is based upon integration of 1-bit samples of signal voltage amplitude corresponding to sine or cosine phases of a controlled center frequency clock which is changed after each integration interval to sweep the frequency range of interest in steps. Integration of samples during each interval is carried out over a number of cycles of the center frequency clock spanning a number of cycles of an input signal to be analyzed. The invention may be used to detect the frequency of at least two signals simultaneously. By using a reference signal of known frequency and voltage amplitude (added to the two signals for parallel processing in the same way, but in a different channel with a sampling at the known frequency and phases of the reference signal), the absolute voltage amplitude of the other two signals may be determined by squaring the sine and cosine integrals of each channel and summing the squares to obtain relative power measurements in all three channels and, from the known voltage amplitude of the reference signal, obtaining an absolute voltage measurement for the other two signals by multiplying the known voltage of the reference signal with the ratio of the relative power of each of the other two signals to the relative power of the reference signal.

Three-dimensional image signals: processing methods

NASA Astrophysics Data System (ADS)

Schiopu, Paul; Manea, Adrian; Craciun, Anca-Ileana; Craciun, Alexandru

2010-11-01

Over the years extensive studies have been carried out to apply coherent optics methods in real-time processing, communications and transmission image. This is especially true when a large amount of information needs to be processed, e.g., in high-resolution imaging. The recent progress in data-processing networks and communication systems has considerably increased the capacity of information exchange. We describe the results of literature investigation research of processing methods for the signals of the three-dimensional images. All commercially available 3D technologies today are based on stereoscopic viewing. 3D technology was once the exclusive domain of skilled computer-graphics developers with high-end machines and software. The images capture from the advanced 3D digital camera can be displayed onto screen of the 3D digital viewer with/ without special glasses. For this is needed considerable processing power and memory to create and render the complex mix of colors, textures, and virtual lighting and perspective necessary to make figures appear three-dimensional. Also, using a standard digital camera and a technique called phase-shift interferometry we can capture "digital holograms." These are holograms that can be stored on computer and transmitted over conventional networks. We present some research methods to process "digital holograms" for the Internet transmission and results.

Digital processing of array seismic recordings

USGS Publications Warehouse

Ryall, Alan; Birtill, John

1962-01-01

This technical letter contains a brief review of the operations which are involved in digital processing of array seismic recordings by the methods of velocity filtering, summation, cross-multiplication and integration, and by combinations of these operations (the "UK Method" and multiple correlation). Examples are presented of analyses by the several techniques on array recordings which were obtained by the U.S. Geological Survey during chemical and nuclear explosions in the western United States. Seismograms are synthesized using actual noise and Pn-signal recordings, such that the signal-to-noise ratio, onset time and velocity of the signal are predetermined for the synthetic record. These records are then analyzed by summation, cross-multiplication, multiple correlation and the UK technique, and the results are compared. For all of the examples presented, analysis by the non-linear techniques of multiple correlation and cross-multiplication of the traces on an array recording are preferred to analyses by the linear operations involved in summation and the UK Method.

Time reversal focusing of high amplitude sound in a reverberation chamber.

PubMed

Willardson, Matthew L; Anderson, Brian E; Young, Sarah M; Denison, Michael H; Patchett, Brian D

2018-02-01

Time reversal (TR) is a signal processing technique that can be used for intentional sound focusing. While it has been studied in room acoustics, the application of TR to produce a high amplitude focus of sound in a room has not yet been explored. The purpose of this study is to create a virtual source of spherical waves with TR that are of sufficient intensity to study nonlinear acoustic propagation. A parameterization study of deconvolution, one-bit, clipping, and decay compensation TR methods is performed to optimize high amplitude focusing and temporal signal focus quality. Of all TR methods studied, clipping is shown to produce the highest amplitude focal signal. An experiment utilizing eight horn loudspeakers in a reverberation chamber is done with the clipping TR method. A peak focal amplitude of 9.05 kPa (173.1 dB peak re 20 μPa) is achieved. Results from this experiment indicate that this high amplitude focusing is a nonlinear process.

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.

Classical-processing and quantum-processing signal separation methods for qubit uncoupling

NASA Astrophysics Data System (ADS)

Deville, Yannick; Deville, Alain

2012-12-01

The Blind Source Separation problem consists in estimating a set of unknown source signals from their measured combinations. It was only investigated in a non-quantum framework up to now. We propose its first quantum extensions. We thus introduce the Quantum Source Separation field, investigating both its blind and non-blind configurations. More precisely, we show how to retrieve individual quantum bits (qubits) only from the global state resulting from their undesired coupling. We consider cylindrical-symmetry Heisenberg coupling, which e.g. occurs when two electron spins interact through exchange. We first propose several qubit uncoupling methods which typically measure repeatedly the coupled quantum states resulting from individual qubits preparations, and which then statistically process the classical data provided by these measurements. Numerical tests prove the effectiveness of these methods. We then derive a combination of quantum gates for performing qubit uncoupling, thus avoiding repeated qubit preparations and irreversible measurements.

Method for refreshing a non-volatile memory

DOEpatents

Riekels, James E.; Schlesinger, Samuel

2008-11-04

A non-volatile memory and a method of refreshing a memory are described. The method includes allowing an external system to control refreshing operations within the memory. The memory may generate a refresh request signal and transmit the refresh request signal to the external system. When the external system finds an available time to process the refresh request, the external system acknowledges the refresh request and transmits a refresh acknowledge signal to the memory. The memory may also comprise a page register for reading and rewriting a data state back to the memory. The page register may comprise latches in lieu of supplemental non-volatile storage elements, thereby conserving real estate within the memory.

a Universal De-Noising Algorithm for Ground-Based LIDAR Signal

NASA Astrophysics Data System (ADS)

Ma, Xin; Xiang, Chengzhi; Gong, Wei

2016-06-01

Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD).

PubMed

Bermúdez Ordoñez, Juan Carlos; Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-05-16

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)

PubMed Central

Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-01-01

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain. PMID:29772731

Analysis and compensation of synchronous measurement error for multi-channel laser interferometer

NASA Astrophysics Data System (ADS)

Du, Shengwu; Hu, Jinchun; Zhu, Yu; Hu, Chuxiong

2017-05-01

Dual-frequency laser interferometer has been widely used in precision motion system as a displacement sensor, to achieve nanoscale positioning or synchronization accuracy. In a multi-channel laser interferometer synchronous measurement system, signal delays are different in the different channels, which will cause asynchronous measurement, and then lead to measurement error, synchronous measurement error (SME). Based on signal delay analysis of the measurement system, this paper presents a multi-channel SME framework for synchronous measurement, and establishes the model between SME and motion velocity. Further, a real-time compensation method for SME is proposed. This method has been verified in a self-developed laser interferometer signal processing board (SPB). The experiment result showed that, using this compensation method, at a motion velocity 0.89 m s-1, the max SME between two measuring channels in the SPB is 1.1 nm. This method is more easily implemented and applied to engineering than the method of directly testing smaller signal delay.

Method and system for photoconductive detector signal correction

DOEpatents

Carangelo, Robert M.; Hamblen, David G.; Brouillette, Carl R.

1992-08-04

A corrective factor is applied so as to remove anomalous features from the signal generated by a photoconductive detector, and to thereby render the output signal highly linear with respect to the energy of incident, time-varying radiation. The corrective factor may be applied through the use of either digital electronic data processing means or analog circuitry, or through a combination of those effects.

Method and system for photoconductive detector signal correction

DOEpatents

Carangelo, R.M.; Hamblen, D.G.; Brouillette, C.R.

1992-08-04

A corrective factor is applied so as to remove anomalous features from the signal generated by a photoconductive detector, and to thereby render the output signal highly linear with respect to the energy of incident, time-varying radiation. The corrective factor may be applied through the use of either digital electronic data processing means or analog circuitry, or through a combination of those effects. 5 figs.

Method of Laser Vibration Defect Analysis

DTIC Science & Technology

2010-06-04

415. In one embodiment, the frequencies from the reflected ultrasonic wave 430 are sensed and transformed to an electrical signal by transducer...actuator and sensor patches, respectively. Then, a process module loads sensor signal data to identify wave modes, determine the time of arrival of...conditions. An interrogation system includes at least one wave generator for generating a wave signal and optical fiber sensors applied to a structure

Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings

NASA Astrophysics Data System (ADS)

Wang, Dong; Zhao, Yang; Yi, Cai; Tsui, Kwok-Leung; Lin, Jianhui

2018-02-01

Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. Fault diagnosis of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing fault detection methods have been developed. Recently, a new adaptive signal processing method called empirical wavelet transform attracts much attention from readers and engineers and its applications to bearing fault diagnosis have been reported. The main problem of empirical wavelet transform is that Fourier segments required in empirical wavelet transform are strongly dependent on the local maxima of the amplitudes of the Fourier spectrum of a signal, which connotes that Fourier segments are not always reliable and effective if the Fourier spectrum of the signal is complicated and overwhelmed by heavy noises and other strong vibration components. In this paper, sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in empirical wavelet transform for fault diagnosis of rolling element bearings. Industrial bearing fault signals caused by single and multiple railway axle bearing defects are used to verify the effectiveness of the proposed sparsity guided empirical wavelet transform. Results show that the proposed method can automatically discover Fourier segments required in empirical wavelet transform and reveal single and multiple railway axle bearing defects. Besides, some comparisons with three popular signal processing methods including ensemble empirical mode decomposition, the fast kurtogram and the fast spectral correlation are conducted to highlight the superiority of the proposed method.