Multiple Source DF (Direction Finding) Signal Processing: An Experimental System,

DTIC Science & Technology

The MUltiple SIgnal Characterization ( MUSIC ) algorithm is an implementation of the Signal Subspace Approach to provide parameter estimates of...the signal subspace (obtained from the received data) and the array manifold (obtained via array calibration). The MUSIC algorithm has been

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.

Distributed Estimation using Bayesian Consensus Filtering

DTIC Science & Technology

2014-06-06

Convergence rate analysis of distributed gossip (linear parameter) estimation: Fundamental limits and tradeoffs,” IEEE J. Sel. Topics Signal Process...Dimakis, S. Kar, J. Moura, M. Rabbat, and A. Scaglione, “ Gossip algorithms for distributed signal processing,” Proc. of the IEEE, vol. 98, no. 11, pp

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

plasticity of TGF-β signaling

PubMed Central

2011-01-01

Background The family of TGF-β ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-β ligands eliciting both sustained or transient responses. Members of the TGF-β family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration. A number of different models have been proposed to reconcile these different behaviours. We were interested to define the set of minimal modifications that are required to change the type of signal processing in the TGF-β signaling network. Results To define the key aspects for signaling plasticity we focused on the core of the TGF-β signaling network. With the help of a parameter screen we identified ranges of kinetic parameters and protein concentrations that give rise to transient, sustained, or oscillatory responses to constant stimuli, as well as those parameter ranges that enable a proportional response to time-varying ligand concentrations (as expected in the read-out of morphogens). A combination of a strong negative feedback and fast shuttling to the nucleus biases signaling to a transient rather than a sustained response, while oscillations were obtained if ligand binding to the receptor is weak and the turn-over of the I-Smad is fast. A proportional read-out required inefficient receptor activation in addition to a low affinity of receptor-ligand binding. We find that targeted modification of single parameters suffices to alter the response type. The intensity of a constant signal (i.e. the ligand concentration), on the other hand, affected only the strength but not the type of the response. Conclusions The architecture of the TGF-β pathway enables the observed signaling plasticity. The observed range of signaling outputs to TGF-β ligand in different cell types and under different conditions can be explained with differences in cellular protein concentrations and with changes in effective rate constants due to cross-talk with other signaling pathways. It will be interesting to uncover the exact cellular differences as well as the details of the cross-talks in future work. PMID:22051045

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.

Modeling laser velocimeter signals as triply stochastic Poisson processes

NASA Technical Reports Server (NTRS)

Mayo, W. T., Jr.

1976-01-01

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

Processing circuit with asymmetry corrector and convolutional encoder for digital data

NASA Technical Reports Server (NTRS)

Pfiffner, Harold J. (Inventor)

1987-01-01

A processing circuit is provided for correcting for input parameter variations, such as data and clock signal symmetry, phase offset and jitter, noise and signal amplitude, in incoming data signals. An asymmetry corrector circuit performs the correcting function and furnishes the corrected data signals to a convolutional encoder circuit. The corrector circuit further forms a regenerated clock signal from clock pulses in the incoming data signals and another clock signal at a multiple of the incoming clock signal. These clock signals are furnished to the encoder circuit so that encoded data may be furnished to a modulator at a high data rate for transmission.

Processing oscillatory signals by incoherent feedforward loops

NASA Astrophysics Data System (ADS)

Zhang, Carolyn; Wu, Feilun; Tsoi, Ryan; Shats, Igor; You, Lingchong

From the timing of amoeba development to the maintenance of stem cell pluripotency,many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression.While networks underlying this signal decoding are diverse,many are built around a common motif, the incoherent feedforward loop (IFFL),where an input simultaneously activates an output and an inhibitor of the output.With appropriate parameters,this motif can generate temporal adaptation,where the system is desensitized to a sustained input.This property serves as the foundation for distinguishing signals with varying temporal profiles.Here,we use quantitative modeling to examine another property of IFFLs,the ability to process oscillatory signals.Our results indicate that the system's ability to translate pulsatile dynamics is limited by two constraints.The kinetics of IFFL components dictate the input range for which the network can decode pulsatile dynamics.In addition,a match between the network parameters and signal characteristics is required for optimal ``counting''.We elucidate one potential mechanism by which information processing occurs in natural networks with implications in the design of synthetic gene circuits for this purpose. This work was partially supported by the National Science Foundation Graduate Research Fellowship (CZ).

Processing Oscillatory Signals by Incoherent Feedforward Loops

PubMed Central

Zhang, Carolyn; You, Lingchong

2016-01-01

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose. PMID:27623175

A feasibility study on age-related factors of wrist pulse using principal component analysis.

PubMed

Jang-Han Bae; Young Ju Jeon; Sanghun Lee; Jaeuk U Kim

2016-08-01

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

Effect of crystallographical and geometrical changes of a ferrite head on magnetic signals during the sliding process with magnetic tape

NASA Technical Reports Server (NTRS)

Miyoshi, K.; Buckley, D. H.; Tanaka, K.

1986-01-01

This paper reviews changes in the crystalline structure and geometry of lapped Mn-Zn ferrite heads in sliding contact with magnetic tape and the effects of these changes on magnetic signals. A highly textured, polycrystalline structure was produced on the surface of a single-crystal Mn-Zn ferrite head when it was finished with an aluminum oxide lapping tape. Sliding this lapped surface against a magnetic tape produced a nearly amorphous structure. The sliding process led to a degradation in readback signal of 1 to 2 dB (short-wavelength recording). Furthermore, wear of the magnetic head caused geometrical changes in the head surface. The signal read back with the worn magnetic head was sensitive to operating parameters such as head displacement and tape tension. A change in operating parameters created head-to-tape spacings and, consequently, excessive gains or losses in the readback signal.

Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images

NASA Astrophysics Data System (ADS)

Uss, M.; Vozel, B.; Lukin, V.; Chehdi, K.

2017-10-01

The processing chain of Sentinel-2 MultiSpectral Instrument (MSI) data involves filtering and compression stages that modify MSI sensor noise. As a result, noise in Sentinel-2 Level-1C data distributed to users becomes processed. We demonstrate that processed noise variance model is bivariate: noise variance depends on image intensity (caused by signal-dependency of photon counting detectors) and signal-to-noise ratio (SNR; caused by filtering/compression). To provide information on processed noise parameters, which is missing in Sentinel-2 metadata, we propose to use blind noise parameter estimation approach. Existing methods are restricted to univariate noise model. Therefore, we propose extension of existing vcNI+fBm blind noise parameter estimation method to multivariate noise model, mvcNI+fBm, and apply it to each band of Sentinel-2A data. Obtained results clearly demonstrate that noise variance is affected by filtering/compression for SNR less than about 15. Processed noise variance is reduced by a factor of 2 - 5 in homogeneous areas as compared to noise variance for high SNR values. Estimate of noise variance model parameters are provided for each Sentinel-2A band. Sentinel-2A MSI Level-1C noise models obtained in this paper could be useful for end users and researchers working in a variety of remote sensing applications.

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series.

PubMed

Hoppe, Elisabeth; Körzdörfer, Gregor; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Pfeuffer, Josef; Maier, Andreas

2017-01-01

The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals. To accelerate the computation of quantitative maps we train a Convolutional Neural Network (CNN) on simulated dictionary data. As a proof of principle we show that the neural network implicitly encodes the dictionary and can replace the matching process.

Experimental research of the influence of the strength of ore samples on the parameters of an electromagnetic signal during acoustic excitation in the process of uniaxial compression

NASA Astrophysics Data System (ADS)

Yavorovich, L. V.; Bespal`ko, A. A.; Fedotov, P. I.

2018-01-01

Parameters of electromagnetic responses (EMRe) generated during uniaxial compression of rock samples under excitation by deterministic acoustic pulses are presented and discussed. Such physical modeling in the laboratory allows to reveal the main regularities of electromagnetic signals (EMS) generation in rock massive. The influence of the samples mechanical properties on the parameters of the EMRe excited by an acoustic signal in the process of uniaxial compression is considered. It has been established that sulfides and quartz in the rocks of the Tashtagol iron ore deposit (Western Siberia, Russia) contribute to the conversion of mechanical energy into the energy of the electromagnetic field, which is expressed in an increase in the EMS amplitude. The decrease in the EMS amplitude when the stress-strain state of the sample changes during the uniaxial compression is observed when the amount of conductive magnetite contained in the rock is increased. The obtained results are important for the physical substantiation of testing methods and monitoring of changes in the stress-strain state of the rock massive by the parameters of electromagnetic signals and the characteristics of electromagnetic emission.

Loss tolerant speech decoder for telecommunications

NASA Technical Reports Server (NTRS)

Prieto, Jr., Jaime L. (Inventor)

1999-01-01

A method and device for extrapolating past signal-history data for insertion into missing data segments in order to conceal digital speech frame errors. The extrapolation method uses past-signal history that is stored in a buffer. The method is implemented with a device that utilizes a finite-impulse response (FIR) multi-layer feed-forward artificial neural network that is trained by back-propagation for one-step extrapolation of speech compression algorithm (SCA) parameters. Once a speech connection has been established, the speech compression algorithm device begins sending encoded speech frames. As the speech frames are received, they are decoded and converted back into speech signal voltages. During the normal decoding process, pre-processing of the required SCA parameters will occur and the results stored in the past-history buffer. If a speech frame is detected to be lost or in error, then extrapolation modules are executed and replacement SCA parameters are generated and sent as the parameters required by the SCA. In this way, the information transfer to the SCA is transparent, and the SCA processing continues as usual. The listener will not normally notice that a speech frame has been lost because of the smooth transition between the last-received, lost, and next-received speech frames.

A simple analytical model for signal amplification by reversible exchange (SABRE) process.

PubMed

Barskiy, Danila A; Pravdivtsev, Andrey N; Ivanov, Konstantin L; Kovtunov, Kirill V; Koptyug, Igor V

2016-01-07

We demonstrate an analytical model for the description of the signal amplification by reversible exchange (SABRE) process. The model relies on a combined analysis of chemical kinetics and the evolution of the nuclear spin system during the hyperpolarization process. The presented model for the first time provides rationale for deciding which system parameters (i.e. J-couplings, relaxation rates, reaction rate constants) have to be optimized in order to achieve higher signal enhancement for a substrate of interest in SABRE experiments.

An Introduction to Data Analysis in Asteroseismology

NASA Astrophysics Data System (ADS)

Campante, Tiago L.

A practical guide is presented to some of the main data analysis concepts and techniques employed contemporarily in the asteroseismic study of stars exhibiting solar-like oscillations. The subjects of digital signal processing and spectral analysis are introduced first. These concern the acquisition of continuous physical signals to be subsequently digitally analyzed. A number of specific concepts and techniques relevant to asteroseismology are then presented as we follow the typical workflow of the data analysis process, namely, the extraction of global asteroseismic parameters and individual mode parameters (also known as peak-bagging) from the oscillation spectrum.

A microcomputer based frequency-domain processor for laser Doppler anemometry

NASA Technical Reports Server (NTRS)

Horne, W. Clifton; Adair, Desmond

1988-01-01

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

Relationships between digital signal processing and control and estimation theory

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1978-01-01

Research directions in the fields of digital signal processing and modern control and estimation theory are discussed. Stability theory, linear prediction and parameter identification, system synthesis and implementation, two-dimensional filtering, decentralized control and estimation, and image processing are considered in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the disciplines.

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.

n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation

PubMed Central

Palma Orozco, Rosaura

2018-01-01

Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal. PMID:29568310

Real time identification of the internal combustion engine combustion parameters based on the vibration velocity signal

NASA Astrophysics Data System (ADS)

Zhao, Xiuliang; Cheng, Yong; Wang, Limei; Ji, Shaobo

2017-03-01

Accurate combustion parameters are the foundations of effective closed-loop control of engine combustion process. Some combustion parameters, including the start of combustion, the location of peak pressure, the maximum pressure rise rate and its location, can be identified from the engine block vibration signals. These signals often include non-combustion related contributions, which limit the prompt acquisition of the combustion parameters computationally. The main component in these non-combustion related contributions is considered to be caused by the reciprocating inertia force excitation (RIFE) of engine crank train. A mathematical model is established to describe the response of the RIFE. The parameters of the model are recognized with a pattern recognition algorithm, and the response of the RIFE is predicted and then the related contributions are removed from the measured vibration velocity signals. The combustion parameters are extracted from the feature points of the renovated vibration velocity signals. There are angle deviations between the feature points in the vibration velocity signals and those in the cylinder pressure signals. For the start of combustion, a system bias is adopted to correct the deviation and the error bound of the predicted parameters is within 1.1°. To predict the location of the maximum pressure rise rate and the location of the peak pressure, algorithms based on the proportion of high frequency components in the vibration velocity signals are introduced. Tests results show that the two parameters are able to be predicted within 0.7° and 0.8° error bound respectively. The increase from the knee point preceding the peak value point to the peak value in the vibration velocity signals is used to predict the value of the maximum pressure rise rate. Finally, a monitoring frame work is inferred to realize the combustion parameters prediction. Satisfactory prediction for combustion parameters in successive cycles is achieved, which validates the proposed methods.

Parallel Processing of Broad-Band PPM Signals

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

Tracer SWIW tests in propped and un-propped fractures: parameter sensitivity issues, revisited

NASA Astrophysics Data System (ADS)

Ghergut, Julia; Behrens, Horst; Sauter, Martin

2017-04-01

Single-well injection-withdrawal (SWIW) or 'push-then-pull' tracer methods appear attractive for a number of reasons: less uncertainty on design and dimensioning, and lower tracer quantities required than for inter-well tests; stronger tracer signals, enabling easier and cheaper metering, and shorter metering duration required, reaching higher tracer mass recovery than in inter-well tests; last not least: no need for a second well. However, SWIW tracer signal inversion faces a major issue: the 'push-then-pull' design weakens the correlation between tracer residence times and georeservoir transport parameters, inducing insensitivity or ambiguity of tracer signal inversion w. r. to some of those georeservoir parameters that are supposed to be the target of tracer tests par excellence: pore velocity, transport-effective porosity, fracture or fissure aperture and spacing or density (where applicable), fluid/solid or fluid/fluid phase interface density. Hydraulic methods cannot measure the transport-effective values of such parameters, because pressure signals correlate neither with fluid motion, nor with material fluxes through (fluid-rock, or fluid-fluid) phase interfaces. The notorious ambiguity impeding parameter inversion from SWIW test signals has nourished several 'modeling attitudes': (i) regard dispersion as the key process encompassing whatever superposition of underlying transport phenomena, and seek a statistical description of flow-path collectives enabling to characterize dispersion independently of any other transport parameter, as proposed by Gouze et al. (2008), with Hansen et al. (2016) offering a comprehensive analysis of the various ways dispersion model assumptions interfere with parameter inversion from SWIW tests; (ii) regard diffusion as the key process, and seek for a large-time, asymptotically advection-independent regime in the measured tracer signals (Haggerty et al. 2001), enabling a dispersion-independent characterization of multiple-scale diffusion; (iii) attempt to determine both advective and non-advective transport parameters from one and the same conservative-tracer signal (relying on 'third-party' knowledge), or from twin signals of a so-called 'dual' tracer pair, e. g.: using tracers with contrasting reactivity and partitioning behavior to determine residual saturation in depleted oilfields (Tomich et al. 1973), or to determine advective parameters (Ghergut et al. 2014); using early-time signals of conservative and sorptive tracers for propped-fracture characterization (Karmakar et al. 2015); using mid-time signals of conservative tracers for a reservoir-borne inflow profiling in multi-frac systems (Ghergut et al. 2016), etc. The poster describes new uses of type-(iii) techniques for the specific purposes of shale-gas reservoir characterization, productivity monitoring, diagnostics and engineering of 're-frac' treatments, based on parameter sensitivity findings from German BMWi research project "TRENDS" (Federal Ministry for Economic Affairs and Energy, FKZ 0325515) and from the EU-H2020 project "FracRisk" (grant no. 640979).

A first approach to the distortion analysis of nonlinear analog circuits utilizing X-parameters

NASA Astrophysics Data System (ADS)

Weber, H.; Widemann, C.; Mathis, W.

2013-07-01

In this contribution a first approach to the distortion analysis of nonlinear 2-port-networks with X-parameters1 is presented. The X-parameters introduced by Verspecht and Root (2006) offer the possibility to describe nonlinear microwave 2-port-networks under large signal conditions. On the basis of X-parameter measurements with a nonlinear network analyzer (NVNA) behavioral models can be extracted for the networks. These models can be used to consider the nonlinear behavior during the design process of microwave circuits. The idea of the present work is to extract the behavioral models in order to describe the influence of interfering signals on the output behavior of the nonlinear circuits. Hereby, a simulator is used instead of a NVNA to extract the X-parameters. Assuming that the interfering signals are relatively small compared to the nominal input signal, the output signal can be described as a superposition of the effects of each input signal. In order to determine the functional correlation between the scattering variables, a polynomial dependency is assumed. The required datasets for the approximation of the describing functions are simulated by a directional coupler model in Cadence Design Framework. The polynomial coefficients are obtained by a least-square method. The resulting describing functions can be used to predict the system's behavior under certain conditions as well as the effects of the interfering signal on the output signal. 1 X-parameter is a registered trademark of Agilent Technologies, Inc.

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.

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.

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.

Architecture and settings optimization procedure of a TES frequency domain multiplexed readout firmware

NASA Astrophysics Data System (ADS)

Clenet, A.; Ravera, L.; Bertrand, B.; den Hartog, R.; Jackson, B.; van Leeuwen, B.-J.; van Loon, D.; Parot, Y.; Pointecouteau, E.; Sournac, A.

2014-11-01

IRAP is developing the readout electronics of the SPICA-SAFARI's TES bolometer arrays. Based on the frequency domain multiplexing technique the readout electronics provides the AC-signals to voltage-bias the detectors; it demodulates the data; and it computes a feedback to linearize the detection chain. The feedback is computed with a specific technique, so called baseband feedback (BBFB) which ensures that the loop is stable even with long propagation and processing delays (i.e. several μ s) and with fast signals (i.e. frequency carriers of the order of 5 MHz). To optimize the power consumption we took advantage of the reduced science signal bandwidth to decouple the signal sampling frequency and the data processing rate. This technique allowed a reduction of the power consumption of the circuit by a factor of 10. Beyond the firmware architecture the optimization of the instrument concerns the characterization routines and the definition of the optimal parameters. Indeed, to operate an array TES one has to properly define about 21000 parameters. We defined a set of procedures to automatically characterize these parameters and find out the optimal settings.

Noncoherent sampling technique for communications parameter estimations

NASA Technical Reports Server (NTRS)

Su, Y. T.; Choi, H. J.

1985-01-01

This paper presents a method of noncoherent demodulation of the PSK signal for signal distortion analysis at the RF interface. The received RF signal is downconverted and noncoherently sampled for further off-line processing. Any mismatch in phase and frequency is then compensated for by the software using the estimation techniques to extract the baseband waveform, which is needed in measuring various signal parameters. In this way, various kinds of modulated signals can be treated uniformly, independent of modulation format, and additional distortions introduced by the receiver or the hardware measurement instruments can thus be eliminated. Quantization errors incurred by digital sampling and ensuing software manipulations are analyzed and related numerical results are presented also.

Computer algorithm for analyzing and processing borehole strainmeter data

USGS Publications Warehouse

Langbein, John O.

2010-01-01

The newly installed Plate Boundary Observatory (PBO) strainmeters record signals from tectonic activity, Earth tides, and atmospheric pressure. Important information about tectonic processes may occur at amplitudes at and below tidal strains and pressure loading. If incorrect assumptions are made regarding the background noise in the strain data, then the estimates of tectonic signal amplitudes may be incorrect. Furthermore, the use of simplifying assumptions that data are uncorrelated can lead to incorrect results and pressure loading and tides may not be completely removed from the raw data. Instead, any algorithm used to process strainmeter data must incorporate the strong temporal correlations that are inherent with these data. The technique described here uses least squares but employs data covariance that describes the temporal correlation of strainmeter data. There are several advantages to this method since many parameters are estimated simultaneously. These parameters include: (1) functional terms that describe the underlying error model, (2) the tidal terms, (3) the pressure loading term(s), (4) amplitudes of offsets, either those from earthquakes or from the instrument, (5) rate and changes in rate, and (6) the amplitudes and time constants of either logarithmic or exponential curves that can characterize postseismic deformation or diffusion of fluids near the strainmeter. With the proper error model, realistic estimates of the standard errors of the various parameters are obtained; this is especially critical in determining the statistical significance of a suspected, tectonic strain signal. The program also provides a method of tracking the various adjustments required to process strainmeter data. In addition, the program provides several plots to assist with identifying either tectonic signals or other signals that may need to be removed before any geophysical signal can be identified.

Randomized Hough transform filter for echo extraction in DLR

NASA Astrophysics Data System (ADS)

Liu, Tong; Chen, Hao; Shen, Ming; Gao, Pengqi; Zhao, You

2016-11-01

The signal-to-noise ratio (SNR) of debris laser ranging (DLR) data is extremely low, and the valid returns in the DLR range residuals are distributed on a curve in a long observation time. Therefore, it is hard to extract the signals from noise in the Observed-minus-Calculated (O-C) residuals with low SNR. In order to autonomously extract the valid returns, we propose a new algorithm based on randomized Hough transform (RHT). We firstly pre-process the data using histogram method to find the zonal area that contains all the possible signals to reduce large amount of noise. Then the data is processed with RHT algorithm to find the curve that the signal points are distributed on. A new parameter update strategy is introduced in the RHT to get the best parameters. We also analyze the values of the parameters in the algorithm. We test our algorithm on the 10 Hz repetition rate DLR data from Yunnan Observatory and 100 Hz repetition rate DLR data from Graz SLR station. For 10 Hz DLR data with relative larger and similar range gate, we can process it in real time and extract all the signals autonomously with a few false readings. For 100 Hz DLR data with longer observation time, we autonomously post-process DLR data of 0.9%, 2.7%, 8% and 33% return rate with high reliability. The extracted points contain almost all signals and a low percentage of noise. Additional noise is added to 10 Hz DLR data to get lower return rate data. The valid returns can also be well extracted for DLR data with 0.18% and 0.1% return rate.

Fluorescence lifetime measurements in flow cytometry

NASA Astrophysics Data System (ADS)

Beisker, Wolfgang; Klocke, Axel

1997-05-01

Fluorescence lifetime measurements provide insights int eh dynamic and structural properties of dyes and their micro- environment. The implementation of fluorescence lifetime measurements in flow cytometric systems allows to monitor large cell and particle populations with high statistical significance. In our system, a modulated laser beam is used for excitation and the phase shift of the fluorescence signal recorded with a fast computer controlled digital oscilloscope is processed digitally to determine the phase shift with respect to a reference beam by fast fourier transform. Total fluorescence intensity as well as other parameters can be determined simultaneously from the same fluorescence signal. We use the epi-illumination design to allow the use of high numerical apertures to collect as much light as possible to ensure detection of even weak fluorescence. Data storage and processing is done comparable to slit-scan flow cytometric data using data analysis system. The results are stored, displayed, combined with other parameters and analyzed as normal listmode data. In our report we discuss carefully the signal to noise ratio for analog and digital processed lifetime signals to evaluate the theoretical minimum fluorescence intensity for lifetime measurements. Applications to be presented include DNA staining, parameters of cell functions as well as different applications in non-mammalian cells such as algae.

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.

Description, characteristics and testing of the NASA airborne radar

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

Spaceborne SAR Imaging Algorithm for Coherence Optimized.

PubMed

Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun

2016-01-01

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application.

Spaceborne SAR Imaging Algorithm for Coherence Optimized

PubMed Central

Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun

2016-01-01

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application. PMID:26871446

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.

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.

Non Destructive Analysis of Fsw Welds using Ultrasonic Signal Analysis

NASA Astrophysics Data System (ADS)

Pavan Kumar, T.; Prabhakar Reddy, P.

2017-08-01

Friction Stir Welding is an evolving metal joining technique and is mostly used in joining materials which cannot be easily joined by other available welding techniques. It is a technique which can be used for welding dissimilar materials also. The strength of the weld joint is determined by the way in which these material are mixing with each other, since we are not using any filler material for the welding process the intermixing has a significant importance. The complication with the friction stir welding process is that there are many process parameters which effect this intermixing process such as tool geometry, rotating speed of the tool, transverse speed etc., In this study an attempt is made to compare the material flow and weld quality of various weldments by changing the parameters. Ultrasonic signal Analysis is used to characterize the microstructure of the weldments. use of ultrasonic waves is a non destructive, accurate and fast way of characterization of microstructure. In this method the relationship between the ultrasonic measured parameters and microstructures are evaluated using background echo and backscattered signal process techniques. The ultrasonic velocity and attenuation measurements are dependent on the elastic modulus and any change in the microstructure is reflected in the ultrasonic velocity. An insight into material flow is essential to determine the quality of the weld. Hence an attempt is made in this study to know the relationship between tool geometry and the pattern of material flow and resulting weld quality the experiments are conducted to weld dissimilar aluminum alloys and the weldments are characterized using and ultra Sonic signal processing. Characterization is also done using Scanning Electron Microscopy. It is observed that there is a good correlation between the ultrasonic signal processing results and Scanning Electron Microscopy on the observed precipitates. Tensile tests and hardness tests are conducted on the weldments and compared for determining the weld quality.

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.

Support-vector-machines-based multidimensional signal classification for fetal activity characterization

NASA Astrophysics Data System (ADS)

Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

2011-03-01

Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

CHAM: weak signals detection through a new multivariate algorithm for process control

NASA Astrophysics Data System (ADS)

Bergeret, François; Soual, Carole; Le Gratiet, B.

2016-10-01

Derivatives technologies based on core CMOS processes are significantly aggressive in term of design rules and process control requirements. Process control plan is a derived from Process Assumption (PA) calculations which result in a design rule based on known process variability capabilities, taking into account enough margin to be safe not only for yield but especially for reliability. Even though process assumptions are calculated with a 4 sigma known process capability margin, efficient and competitive designs are challenging the process especially for derivatives technologies in 40 and 28nm nodes. For wafer fab process control, PA are declined in monovariate (layer1 CD, layer2 CD, layer2 to layer1 overlay, layer3 CD etc….) control charts with appropriated specifications and control limits which all together are securing the silicon. This is so far working fine but such system is not really sensitive to weak signals coming from interactions of multiple key parameters (high layer2 CD combined with high layer3 CD as an example). CHAM is a software using an advanced statistical algorithm specifically designed to detect small signals, especially when there are many parameters to control and when the parameters can interact to create yield issues. In this presentation we will first present the CHAM algorithm, then the case-study on critical dimensions, with the results, and we will conclude on future work. This partnership between Ippon and STM is part of E450LMDAP, European project dedicated to metrology and lithography development for future technology nodes, especially 10nm.

Chaos based encryption system for encrypting electroencephalogram signals.

PubMed

Lin, Chin-Feng; Shih, Shun-Han; Zhu, Jin-De

2014-05-01

In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

27-day variation in solar-terrestrial parameters: Global characteristics and an origin based approach of the signals

NASA Astrophysics Data System (ADS)

Poblet, Facundo L.; Azpilicueta, Francisco

2018-05-01

The Earth and the near interplanetary medium are affected by the Sun in different ways. Those processes generated in the Sun that induce perturbations into the Magnetosphere-Ionosphere system are called geoeffective processes and show a wide range of temporal variations, like the 11-year solar cycle (long term variations), the variation of ∼27 days (recurrent variations), solar storms enduring for some days, particle acceleration events lasting for some hours, etc. In this article, the periodicity of ∼27 days associated with the solar synodic rotation period is investigated. The work is mainly focused on studying the resulting 27-day periodic signal in the magnetic activity, by the analysis of the horizontal component of the magnetic field registered on a set of 103 magnetic observatories distributed around the world. For this a new method to isolate the periodicity of interest has been developed consisting of two main steps: the first one consists of removing the linear trend corresponding to every calendar year from the data series, and the second one of removing from the resulting series a smoothed version of it obtained by applying a 30-day moving average. The result at the end of this process is a data series in which all the signal with periods larger than 30 days are canceled. The most important characteristics observed in the resulting signals are two main amplitude modulations: the first and most prominent related to the 11-year solar cycle and the second one with a semiannual pattern. In addition, the amplitude of the signal shows a dependence on the geomagnetic latitude of the observatory with a significant discontinuity at approx. ±60°. The processing scheme was also applied to other parameters that are widely used to characterize the energy transfer from the Sun to the Earth: F10.7 and Mg II indices and the ionospheric vertical total electron content (vTEC) were considered for radiative interactions; and the solar wind velocity for the non-radiative interactions between the solar wind and the magnetosphere. The 27-day signal obtained in the magnetic activity was compared with the signals found in the other parameters resulting in a series of cross-correlations curves with maximum correlation between 3 and 5 days of delays for the radiative and between 0 and 1 days of delay for the non-radiative parameters. This result supports the idea that the physical process responsible for the 27-day signal in the magnetic activity is related to the solar wind and not to the solar electromagnetic radiation.

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.

Research on human physiological parameters intelligent clothing based on distributed Fiber Bragg Grating

NASA Astrophysics Data System (ADS)

Miao, Changyun; Shi, Boya; Li, Hongqiang

2008-12-01

A human physiological parameters intelligent clothing is researched with FBG sensor technology. In this paper, the principles and methods of measuring human physiological parameters including body temperature and heart rate in intelligent clothing with distributed FBG are studied, the mathematical models of human physiological parameters measurement are built; the processing method of body temperature and heart rate detection signals is presented; human physiological parameters detection module is designed, the interference signals are filtered out, and the measurement accuracy is improved; the integration of the intelligent clothing is given. The intelligent clothing can implement real-time measurement, processing, storage and output of body temperature and heart rate. It has accurate measurement, portability, low cost, real-time monitoring, and other advantages. The intelligent clothing can realize the non-contact monitoring between doctors and patients, timely find the diseases such as cancer and infectious diseases, and make patients get timely treatment. It has great significance and value for ensuring the health of the elders and the children with language dysfunction.

A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.

PubMed

Gupta, Anubha; Singh, Pushpendra; Karlekar, Mandar

2018-05-01

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

Taguchi Method Applied in Optimization of Shipley SJR 5740 Positive Resist Deposition

NASA Technical Reports Server (NTRS)

Hui, A.; Blosiu, J. O.; Wiberg, D. V.

1998-01-01

Taguchi Methods of Robust Design presents a way to optimize output process performance through an organized set of experiments by using orthogonal arrays. Analysis of variance and signal-to-noise ratio is used to evaluate the contribution of each of the process controllable parameters in the realization of the process optimization. In the photoresist deposition process, there are numerous controllable parameters that can affect the surface quality and thickness of the final photoresist layer.

Mössbauer spectra linearity improvement by sine velocity waveform followed by linearization process

NASA Astrophysics Data System (ADS)

Kohout, Pavel; Frank, Tomas; Pechousek, Jiri; Kouril, Lukas

2018-05-01

This note reports the development of a new method for linearizing the Mössbauer spectra recorded with a sine drive velocity signal. Mössbauer spectra linearity is a critical parameter to determine Mössbauer spectrometer accuracy. Measuring spectra with a sine velocity axis and consecutive linearization increases the linearity of spectra in a wider frequency range of a drive signal, as generally harmonic movement is natural for velocity transducers. The obtained data demonstrate that linearized sine spectra have lower nonlinearity and line width parameters in comparison with those measured using a traditional triangle velocity signal.

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.

Experiments on Adaptive Self-Tuning of Seismic Signal Detector Parameters

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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.

Image informative maps for component-wise estimating parameters of signal-dependent noise

NASA Astrophysics Data System (ADS)

Uss, Mykhail L.; Vozel, Benoit; Lukin, Vladimir V.; Chehdi, Kacem

2013-01-01

We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramér-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

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.

Porting Gravitational Wave Signal Extraction to Parallel Virtual Machine (PVM)

NASA Technical Reports Server (NTRS)

Thirumalainambi, Rajkumar; Thompson, David E.; Redmon, Jeffery

2009-01-01

Laser Interferometer Space Antenna (LISA) is a planned NASA-ESA mission to be launched around 2012. The Gravitational Wave detection is fundamentally the determination of frequency, source parameters, and waveform amplitude derived in a specific order from the interferometric time-series of the rotating LISA spacecrafts. The LISA Science Team has developed a Mock LISA Data Challenge intended to promote the testing of complicated nested search algorithms to detect the 100-1 millihertz frequency signals at amplitudes of 10E-21. However, it has become clear that, sequential search of the parameters is very time consuming and ultra-sensitive; hence, a new strategy has been developed. Parallelization of existing sequential search algorithms of Gravitational Wave signal identification consists of decomposing sequential search loops, beginning with outermost loops and working inward. In this process, the main challenge is to detect interdependencies among loops and partitioning the loops so as to preserve concurrency. Existing parallel programs are based upon either shared memory or distributed memory paradigms. In PVM, master and node programs are used to execute parallelization and process spawning. The PVM can handle process management and process addressing schemes using a virtual machine configuration. The task scheduling and the messaging and signaling can be implemented efficiently for the LISA Gravitational Wave search process using a master and 6 nodes. This approach is accomplished using a server that is available at NASA Ames Research Center, and has been dedicated to the LISA Data Challenge Competition. Historically, gravitational wave and source identification parameters have taken around 7 days in this dedicated single thread Linux based server. Using PVM approach, the parameter extraction problem can be reduced to within a day. The low frequency computation and a proxy signal-to-noise ratio are calculated in separate nodes that are controlled by the master using message and vector of data passing. The message passing among nodes follows a pattern of synchronous and asynchronous send-and-receive protocols. The communication model and the message buffers are allocated dynamically to address rapid search of gravitational wave source information in the Mock LISA data sets.

Secure chaotic transmission of electrocardiography signals with acousto-optic modulation under profiled beam propagation.

PubMed

Almehmadi, Fares S; Chatterjee, Monish R

2015-01-10

Electrocardiography (ECG) signals are used for both medical purposes and identifying individuals. It is often necessary to encrypt this highly sensitive information before it is transmitted over any channel. A closed-loop acousto-optic hybrid device acting as a chaotic modulator is applied to ECG signals to achieve this encryption. Recently improved modeling of this approach using profiled optical beams has shown it to be very sensitive to key parameters that characterize the encryption and decryption process, exhibiting its potential for secure transmission of analog and digital signals. Here the encryption and decryption is demonstrated for ECG signals, both analog and digital versions, illustrating strong encryption without significant distortion. Performance analysis pertinent to both analog and digital transmission of the ECG waveform is also carried out using output signal-to-noise, signal-to-distortion, and bit-error-rate measures relative to the key parameters and presence of channel noise in the system.

Evaluation of Magnetic Diagnostics for MHD Equilibrium Reconstruction of LHD Discharges

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

Sontag, Aaron C; Hanson, James D.; Lazerson, Sam

2011-01-01

Equilibrium reconstruction is the process of determining the set of parameters of an MHD equilibrium that minimize the difference between expected and experimentally observed signals. This is routinely performed in axisymmetric devices, such as tokamaks, and the reconstructed equilibrium solution is then the basis for analysis of stability and transport properties. The V3FIT code [1] has been developed to perform equilibrium reconstruction in cases where axisymmetry cannot be assumed, such as in stellarators. The present work is focused on using V3FIT to analyze plasmas in the Large Helical Device (LHD) [2], a superconducting, heliotron type device with over 25 MWmore » of heating power that is capable of achieving both high-beta ({approx}5%) and high density (>1 x 10{sup 21}/m{sup 3}). This high performance as well as the ability to drive tens of kiloamperes of toroidal plasma current leads to deviations in the equilibrium state from the vacuum flux surfaces. This initial study examines the effectiveness of using magnetic diagnostics as the observed signals in reconstructing experimental plasma parameters for LHD discharges. V3FIT uses the VMEC [3] 3D equilibrium solver to calculate an initial equilibrium solution with closed, nested flux surfaces based on user specified plasma parameters. This equilibrium solution is then used to calculate the expected signals for specified diagnostics. The differences between these expected signal values and the observed values provides a starting {chi}{sup 2} value. V3FIT then varies all of the fit parameters independently, calculating a new equilibrium and corresponding {chi}{sup 2} for each variation. A quasi-Newton algorithm [1] is used to find the path in parameter space that leads to a minimum in {chi}{sup 2}. Effective diagnostic signals must vary in a predictable manner with the variations of the plasma parameters and this signal variation must be of sufficient amplitude to be resolved from the signal noise. Signal effectiveness can be defined for a specific signal and specific reconstruction parameter as the dimensionless fractional reduction in the posterior parameter variance with respect to the signal variance. Here, {sigma}{sub i}{sup sig} is the variance of the ith signal and {sigma}{sub j}{sup param} param is the posterior variance of the jth fit parameter. The sum of all signal effectiveness values for a given reconstruction parameter is normalized to one. This quantity will be used to determine signal effectiveness for various reconstruction cases. The next section will examine the variation of the expected signals with changes in plasma pressure and the following section will show results for reconstructing model plasmas using these signals.« less

Closed loop adaptive control of spectrum-producing step using neural networks

DOEpatents

Fu, Chi Yung

1998-01-01

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

Closed loop adaptive control of spectrum-producing step using neural networks

DOEpatents

Fu, C.Y.

1998-11-24

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

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.

Application of pattern recognition techniques to acousto-ultrasonic testing of Kevlar composite panels

NASA Astrophysics Data System (ADS)

Hinton, Yolanda L.

An acousto-ultrasonic evaluation of panels fabricated from woven Kevlar and PVB/phenolic resin is being conducted. The panels were fabricated with various simulated defects. They were examined by pulsing with one acoustic emission sensor, and detecting the signal with another sensor, on the same side of the panel at a fixed distance. The acoustic emission signals were filtered through high (400-600 KHz), low (100-300 KHz) and wide (100-1200 KHz) bandpass filters. Acoustic emission signal parameters, including amplitude, counts, rise time, duration, 'energy', rms, and counts to peak, were recorded. These were statistically analyzed to determine which of the AE parameters best characterize the simulated defects. The wideband filtered acoustic emission signal was also digitized and recorded for further processing. Seventy-one features of the signals in both the time and frequency domains were calculated and compared to determine which subset of these features uniquely characterize the defects in the panels. The objective of the program is to develop a database of AE signal parameters and features to be used in pattern recognition as an inspection tool for material fabricated from these materials.

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.

The effects of parameter variation on MSET models of the Crystal River-3 feedwater flow system.

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

Miron, A.

1998-04-01

In this paper we develop further the results reported in Reference 1 to include a systematic study of the effects of varying MSET models and model parameters for the Crystal River-3 (CR) feedwater flow system The study used archived CR process computer files from November 1-December 15, 1993 that were provided by Florida Power Corporation engineers Fairman Bockhorst and Brook Julias. The results support the conclusion that an optimal MSET model, properly trained and deriving its inputs in real-time from no more than 25 of the sensor signals normally provided to a PWR plant process computer, should be able tomore » reliably detect anomalous variations in the feedwater flow venturis of less than 0.1% and in the absence of a venturi sensor signal should be able to generate a virtual signal that will be within 0.1% of the correct value of the missing signal.« less

Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal.

PubMed

Panigrahy, D; Sahu, P K

2017-03-01

This paper proposes a five-stage based methodology to extract the fetal electrocardiogram (FECG) from the single channel abdominal ECG using differential evolution (DE) algorithm, extended Kalman smoother (EKS) and adaptive neuro fuzzy inference system (ANFIS) framework. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is necessary. The pre-processing stage is used to remove the noise from the abdominal ECG signal. The EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG components are required to develop the state and measurement equation of the EKS framework. These optimized maternal ECG parameters are selected by the differential evolution algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship the ANFIS is used. Inputs to the ANFIS framework are the output of EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting the output of ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. The proposed methodology shows a sensitivity of 94.21%, accuracy of 90.66%, and positive predictive value of 96.05% from the non-invasive fetal ECG database. The proposed methodology also shows a sensitivity of 91.47%, accuracy of 84.89%, and positive predictive value of 92.18% from the set A of PCDB.

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.

Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator.

PubMed

Wang, Zhiqiang; Li, Xiaolong; Xie, Yunde; Long, Zhiqiang

2018-05-24

In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator's frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.

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.

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.

Digital signal processing and control and estimation theory -- Points of tangency, area of intersection, and parallel directions

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1976-01-01

A number of current research directions in the fields of digital signal processing and modern control and estimation theory were studied. Topics such as stability theory, linear prediction and parameter identification, system analysis and implementation, two-dimensional filtering, decentralized control and estimation, image processing, and nonlinear system theory were examined in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the two disciplines. An extensive bibliography is included.

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

Jang, Junhwan; Hwang, Sungui; Park, Kyihwan, E-mail: khpark@gist.ac.kr

To utilize a time-of-flight-based laser scanner as a distance measurement sensor, the measurable distance and accuracy are the most important performance parameters to consider. For these purposes, the optical system and electronic signal processing of the laser scanner should be optimally designed in order to reduce a distance error caused by the optical crosstalk and wide dynamic range input. Optical system design for removing optical crosstalk problem is proposed in this work. Intensity control is also considered to solve the problem of a phase-shift variation in the signal processing circuit caused by object reflectivity. The experimental results for optical systemmore » and signal processing design are performed using 3D measurements.« less

Performance analysis of gamma ray spectrometric parameters on digital signal and analog signal processing based MCA systems using NaI(Tl) detector.

PubMed

Kukreti, B M; Sharma, G K

2012-05-01

Accurate and speedy estimations of ppm range uranium and thorium in the geological and rock samples are most useful towards ongoing uranium investigations and identification of favorable radioactive zones in the exploration field areas. In this study with the existing 5 in. × 4 in. NaI(Tl) detector setup, prevailing background and time constraints, an enhanced geometrical setup has been worked out to improve the minimum detection limits for primordial radioelements K(40), U(238) and Th(232). This geometrical setup has been integrated with the newly introduced, digital signal processing based MCA system for the routine spectrometric analysis of low concentration rock samples. Stability performance, during the long counting hours, for digital signal processing MCA system and its predecessor NIM bin based MCA system has been monitored, using the concept of statistical process control. Monitored results, over a time span of few months, have been quantified in terms of spectrometer's parameters such as Compton striping constants and Channel sensitivities, used for evaluating primordial radio element concentrations (K(40), U(238) and Th(232)) in geological samples. Results indicate stable dMCA performance, with a tendency of higher relative variance, about mean, particularly for Compton stripping constants. Copyright © 2012 Elsevier Ltd. All rights reserved.

Getting Astrophysical Information from LISA Data

NASA Technical Reports Server (NTRS)

Stebbins, R. T.; Bender, P. L.; Folkner, W. M.

1997-01-01

Gravitational wave signals from a large number of astrophysical sources will be present in the LISA data. Information about as many sources as possible must be estimated from time series of strain measurements. Several types of signals are expected to be present: simple periodic signals from relatively stable binary systems, chirped signals from coalescing binary systems, complex waveforms from highly relativistic binary systems, stochastic backgrounds from galactic and extragalactic binary systems and possibly stochastic backgrounds from the early Universe. The orbital motion of the LISA antenna will modulate the phase and amplitude of all these signals, except the isotropic backgrounds and thereby give information on the directions of sources. Here we describe a candidate process for disentangling the gravitational wave signals and estimating the relevant astrophysical parameters from one year of LISA data. Nearly all of the sources will be identified by searching with templates based on source parameters and directions.

Smart Vest: wearable multi-parameter remote physiological monitoring system.

PubMed

Pandian, P S; Mohanavelu, K; Safeer, K P; Kotresh, T M; Shakunthala, D T; Gopal, Parvati; Padaki, V C

2008-05-01

The wearable physiological monitoring system is a washable shirt, which uses an array of sensors connected to a central processing unit with firmware for continuously monitoring physiological signals. The data collected can be correlated to produce an overall picture of the wearer's health. In this paper, we discuss the wearable physiological monitoring system called 'Smart Vest'. The Smart Vest consists of a comfortable to wear vest with sensors integrated for monitoring physiological parameters, wearable data acquisition and processing hardware and remote monitoring station. The wearable data acquisition system is designed using microcontroller and interfaced with wireless communication and global positioning system (GPS) modules. The physiological signals monitored are electrocardiogram (ECG), photoplethysmogram (PPG), body temperature, blood pressure, galvanic skin response (GSR) and heart rate. The acquired physiological signals are sampled at 250samples/s, digitized at 12-bit resolution and transmitted wireless to a remote physiological monitoring station along with the geo-location of the wearer. The paper describes a prototype Smart Vest system used for remote monitoring of physiological parameters and the clinical validation of the data are also presented.

Radar signal transmission and switching over optical networks

NASA Astrophysics Data System (ADS)

Esmail, Maged A.; Ragheb, Amr; Seleem, Hussein; Fathallah, Habib; Alshebeili, Saleh

2018-03-01

In this paper, we experimentally demonstrate a radar signal distribution over optical networks. The use of fiber enables us to distribute radar signals to distant sites with a low power loss. Moreover, fiber networks can reduce the radar system cost, by sharing precise and expensive radar signal generation and processing equipment. In order to overcome the bandwidth challenges in electrical switches, a semiconductor optical amplifier (SOA) is used as an all-optical device for wavelength conversion to the desired port (or channel) of a wavelength division multiplexing (WDM) network. Moreover, the effect of chromatic dispersion in double sideband (DSB) signals is combated by generating optical single sideband (OSSB) signals. The optimal values of the SOA device parameters required to generate an OSSB with a high sideband suppression ratio (SSR) are determined. We considered various parameters such as injection current, pump power, and probe power. In addition, the effect of signal wavelength conversion and transmission over fiber are studied in terms of signal dynamic range.

Evaluation of Ultrasonic Fiber Structure Extraction Technique Using Autopsy Specimens of Liver

NASA Astrophysics Data System (ADS)

Yamaguchi, Tadashi; Hirai, Kazuki; Yamada, Hiroyuki; Ebara, Masaaki; Hachiya, Hiroyuki

2005-06-01

It is very important to diagnose liver cirrhosis noninvasively and correctly. In our previous studies, we proposed a processing technique to detect changes in liver tissue in vivo. In this paper, we propose the evaluation of the relationship between liver disease and echo information using autopsy specimens of a human liver in vitro. It is possible to verify the function of a processing parameter clearly and to compare the processing result and the actual human liver tissue structure by in vitro experiment. In the results of our processing technique, information that did not obey a Rayleigh distribution from the echo signal of the autopsy liver specimens was extracted depending on changes in a particular processing parameter. The fiber tissue structure of the same specimen was extracted from a number of histological images of stained tissue. We constructed 3D structures using the information extracted from the echo signal and the fiber structure of the stained tissue and compared the two. By comparing the 3D structures, it is possible to evaluate the relationship between the information that does not obey a Rayleigh distribution of the echo signal and the fibrosis structure.

Crosslinking EEG time-frequency decomposition and fMRI in error monitoring.

PubMed

Hoffmann, Sven; Labrenz, Franziska; Themann, Maria; Wascher, Edmund; Beste, Christian

2014-03-01

Recent studies implicate a common response monitoring system, being active during erroneous and correct responses. Converging evidence from time-frequency decompositions of the response-related ERP revealed that evoked theta activity at fronto-central electrode positions differentiates correct from erroneous responses in simple tasks, but also in more complex tasks. However, up to now it is unclear how different electrophysiological parameters of error processing, especially at the level of neural oscillations are related, or predictive for BOLD signal changes reflecting error processing at a functional-neuroanatomical level. The present study aims to provide crosslinks between time domain information, time-frequency information, MRI BOLD signal and behavioral parameters in a task examining error monitoring due to mistakes in a mental rotation task. The results show that BOLD signal changes reflecting error processing on a functional-neuroanatomical level are best predicted by evoked oscillations in the theta frequency band. Although the fMRI results in this study account for an involvement of the anterior cingulate cortex, middle frontal gyrus, and the Insula in error processing, the correlation of evoked oscillations and BOLD signal was restricted to a coupling of evoked theta and anterior cingulate cortex BOLD activity. The current results indicate that although there is a distributed functional-neuroanatomical network mediating error processing, only distinct parts of this network seem to modulate electrophysiological properties of error monitoring.

Stability of Intercellular Exchange of Biochemical Substances Affected by Variability of Environmental Parameters

NASA Astrophysics Data System (ADS)

Mihailović, Dragutin T.; Budinčević, Mirko; Balaž, Igor; Mihailović, Anja

Communication between cells is realized by exchange of biochemical substances. Due to internal organization of living systems and variability of external parameters, the exchange is heavily influenced by perturbations of various parameters at almost all stages of the process. Since communication is one of essential processes for functioning of living systems it is of interest to investigate conditions for its stability. Using previously developed simplified model of bacterial communication in a form of coupled difference logistic equations we investigate stability of exchange of signaling molecules under variability of internal and external parameters.

Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

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

Gongzhang, R.; Xiao, B.; Lardner, T.

2014-02-18

This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signalsmore » through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data.« less

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

NASA Astrophysics Data System (ADS)

Azim, Noor ul; Jun, Wang

2016-11-01

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

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.

Monitoring Different Phonological Parameters of Sign Language Engages the Same Cortical Language Network but Distinctive Perceptual Ones.

PubMed

Cardin, Velia; Orfanidou, Eleni; Kästner, Lena; Rönnberg, Jerker; Woll, Bencie; Capek, Cheryl M; Rudner, Mary

2016-01-01

The study of signed languages allows the dissociation of sensorimotor and cognitive neural components of the language signal. Here we investigated the neurocognitive processes underlying the monitoring of two phonological parameters of sign languages: handshape and location. Our goal was to determine if brain regions processing sensorimotor characteristics of different phonological parameters of sign languages were also involved in phonological processing, with their activity being modulated by the linguistic content of manual actions. We conducted an fMRI experiment using manual actions varying in phonological structure and semantics: (1) signs of a familiar sign language (British Sign Language), (2) signs of an unfamiliar sign language (Swedish Sign Language), and (3) invented nonsigns that violate the phonological rules of British Sign Language and Swedish Sign Language or consist of nonoccurring combinations of phonological parameters. Three groups of participants were tested: deaf native signers, deaf nonsigners, and hearing nonsigners. Results show that the linguistic processing of different phonological parameters of sign language is independent of the sensorimotor characteristics of the language signal. Handshape and location were processed by different perceptual and task-related brain networks but recruited the same language areas. The semantic content of the stimuli did not influence this process, but phonological structure did, with nonsigns being associated with longer RTs and stronger activations in an action observation network in all participants and in the supramarginal gyrus exclusively in deaf signers. These results suggest higher processing demands for stimuli that contravene the phonological rules of a signed language, independently of previous knowledge of signed languages. We suggest that the phonological characteristics of a language may arise as a consequence of more efficient neural processing for its perception and production.

GNSS Space-Time Interference Mitigation and Attitude Determination in the Presence of Interference Signals

PubMed Central

Daneshmand, Saeed; Jahromi, Ali Jafarnia; Broumandan, Ali; Lachapelle, Gérard

2015-01-01

The use of Space-Time Processing (STP) in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its effectiveness for both narrowband and wideband interference suppression. However, the resulting distortion and bias on the cross correlation functions due to space-time filtering is a major limitation of this technique. Employing the steering vector of the GNSS signals in the filter structure can significantly reduce the distortion on cross correlation functions and lead to more accurate pseudorange measurements. This paper proposes a two-stage interference mitigation approach in which the first stage estimates an interference-free subspace before the acquisition and tracking phases and projects all received signals into this subspace. The next stage estimates array attitude parameters based on detecting and employing GNSS signals that are less distorted due to the projection process. Attitude parameters enable the receiver to estimate the steering vector of each satellite signal and use it in the novel distortionless STP filter to significantly reduce distortion and maximize Signal-to-Noise Ratio (SNR). GPS signals were collected using a six-element antenna array under open sky conditions to first calibrate the antenna array. Simulated interfering signals were then added to the digitized samples in software to verify the applicability of the proposed receiver structure and assess its performance for several interference scenarios. PMID:26016909

GNSS space-time interference mitigation and attitude determination in the presence of interference signals.

PubMed

Daneshmand, Saeed; Jahromi, Ali Jafarnia; Broumandan, Ali; Lachapelle, Gérard

2015-05-26

The use of Space-Time Processing (STP) in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its effectiveness for both narrowband and wideband interference suppression. However, the resulting distortion and bias on the cross correlation functions due to space-time filtering is a major limitation of this technique. Employing the steering vector of the GNSS signals in the filter structure can significantly reduce the distortion on cross correlation functions and lead to more accurate pseudorange measurements. This paper proposes a two-stage interference mitigation approach in which the first stage estimates an interference-free subspace before the acquisition and tracking phases and projects all received signals into this subspace. The next stage estimates array attitude parameters based on detecting and employing GNSS signals that are less distorted due to the projection process. Attitude parameters enable the receiver to estimate the steering vector of each satellite signal and use it in the novel distortionless STP filter to significantly reduce distortion and maximize Signal-to-Noise Ratio (SNR). GPS signals were collected using a six-element antenna array under open sky conditions to first calibrate the antenna array. Simulated interfering signals were then added to the digitized samples in software to verify the applicability of the proposed receiver structure and assess its performance for several interference scenarios.

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

PubMed

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

[Design of blood-pressure parameter auto-acquisition circuit].

PubMed

Chen, Y P; Zhang, D L; Bai, H W; Zhang, D A

2000-02-01

This paper presents the realization and design of a kind of blood-pressure parameter auto-acquisition circuit. The auto-acquisition of blood-pressure parameter controlled by 89C2051 single chip microcomputer is accomplished by collecting and processing the driving signal of LCD. The circuit that is successfully applied in the home unit of telemedicine system has the simple and reliable properties.

Deducing receptor signaling parameters from in vivo analysis: LuxN/AI-1 quorum sensing in Vibrio harveyi

PubMed Central

Swem, Lee R.; Swem, Danielle L.; Wingreen, Ned S.; Bassler, Bonnie L.

2008-01-01

Summary Quorum sensing, a process of bacterial cell-cell communication, relies on production, detection, and response to autoinducer signaling molecules. Here we focus on LuxN, a nine transmembrane domain protein from Vibrio harveyi, and the founding example of membrane-bound receptors for acyl-homoserine lactone (AHL) autoinducers. Previously, nothing was known about signal recognition by membrane-bound AHL receptors. We used mutagenesis and suppressor analyses to identify the AHL-binding domain of LuxN, and discovered LuxN mutants that confer decreased and increased AHL sensitivity. Our analysis of dose-response curves of multiple LuxN mutants pins these inverse phenotypes on quantifiable opposing shifts in the free-energy bias of LuxN for its kinase and phosphatase states. To extract signaling parameters, we exploited a strong LuxN antagonist, one of fifteen small-molecule antagonists we identified. We find that quorum-sensing-mediated communication can be manipulated positively and negatively to control bacterial behavior, and that signaling parameters can be deduced from in vivo data. PMID:18692469

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.

SVD compression for magnetic resonance fingerprinting in the time domain.

PubMed

McGivney, Debra F; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A

2014-12-01

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

PubMed Central

McGivney, Debra F.; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A.

2016-01-01

Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously. PMID:25029380

Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI.

PubMed

Koush, Yury; Zvyagintsev, Mikhail; Dyck, Miriam; Mathiak, Krystyna A; Mathiak, Klaus

2012-01-02

Real-time fMRI allows analysis and visualization of the brain activity online, i.e. within one repetition time. It can be used in neurofeedback applications where subjects attempt to control an activation level in a specified region of interest (ROI) of their brain. The signal derived from the ROI is contaminated with noise and artifacts, namely with physiological noise from breathing and heart beat, scanner drift, motion-related artifacts and measurement noise. We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs several signal processing operations: subtraction of constant and low-frequency signal components, spike removal and signal smoothing. Quantitative feedback signal quality analysis was used to estimate the quality of the neurofeedback time series and performance of the applied signal processing on different ROIs. The signal-to-noise ratio (SNR) across the entire time series and the group event-related SNR (eSNR) were significantly higher for the processed time series in comparison to the raw data. Applied signal processing improved the t-statistic increasing the significance of blood oxygen level-dependent (BOLD) signal changes. Accordingly, the contrast-to-noise ratio (CNR) of the feedback time series was improved as well. In addition, the data revealed increase of localized self-control across feedback sessions. The new signal processing approach provided reliable neurofeedback, performed precise artifacts removal, reduced noise, and required minimal manual adjustments of parameters. Advanced and fast online signal processing algorithms considerably increased the quality as well as the information content of the control signal which in turn resulted in higher contingency in the neurofeedback loop. Copyright © 2011 Elsevier Inc. All rights reserved.

Software for Acoustic Rendering

NASA Technical Reports Server (NTRS)

Miller, Joel D.

2003-01-01

SLAB is a software system that can be run on a personal computer to simulate an acoustic environment in real time. SLAB was developed to enable computational experimentation in which one can exert low-level control over a variety of signal-processing parameters, related to spatialization, for conducting psychoacoustic studies. Among the parameters that can be manipulated are the number and position of reflections, the fidelity (that is, the number of taps in finite-impulse-response filters), the system latency, and the update rate of the filters. Another goal in the development of SLAB was to provide an inexpensive means of dynamic synthesis of virtual audio over headphones, without need for special-purpose signal-processing hardware. SLAB has a modular, object-oriented design that affords the flexibility and extensibility needed to accommodate a variety of computational experiments and signal-flow structures. SLAB s spatial renderer has a fixed signal-flow architecture corresponding to a set of parallel signal paths from each source to a listener. This fixed architecture can be regarded as a compromise that optimizes efficiency at the expense of complete flexibility. Such a compromise is necessary, given the design goal of enabling computational psychoacoustic experimentation on inexpensive personal computers.

High-resolution time-frequency representation of EEG data using multi-scale wavelets

NASA Astrophysics Data System (ADS)

Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

2017-09-01

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Mechanomyographic Parameter Extraction Methods: An Appraisal for Clinical Applications

PubMed Central

Ibitoye, Morufu Olusola; Hamzaid, Nur Azah; Zuniga, Jorge M.; Hasnan, Nazirah; Wahab, Ahmad Khairi Abdul

2014-01-01

The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity. PMID:25479326

Evaluating Acoustic Emission Signals as an in situ process monitoring technique for Selective Laser Melting (SLM)

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

Fisher, Karl A.; Candy, Jim V.; Guss, Gabe

2016-10-14

In situ real-time monitoring of the Selective Laser Melting (SLM) process has significant implications for the AM community. The ability to adjust the SLM process parameters during a build (in real-time) can save time, money and eliminate expensive material waste. Having a feedback loop in the process would allow the system to potentially ‘fix’ problem regions before a next powder layer is added. In this study we have investigated acoustic emission (AE) phenomena generated during the SLM process, and evaluated the results in terms of a single process parameter, of an in situ process monitoring technique.

Pulse-firing winner-take-all networks

NASA Technical Reports Server (NTRS)

Meador, Jack L.

1991-01-01

Winner-take-all (WTA) neural networks using pulse-firing processing elements are introduced. In the pulse-firing WTA (PWTA) networks described, input and activation signal shunting is controlled by one shared lateral inhibition signal. This organization yields an O(n) area complexity that is convenient for integrated circuit implementation. Appropriately specified network parameters allow for the accurate continuous evaluation of inputs using a signal representation compatible with established pulse-firing neural network implementations.

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.

SNDR enhancement in noisy sinusoidal signals by non-linear processing elements

NASA Astrophysics Data System (ADS)

Martorell, Ferran; McDonnell, Mark D.; Abbott, Derek; Rubio, Antonio

2007-06-01

We investigate the possibility of building linear amplifiers capable of enhancing the Signal-to-Noise and Distortion Ratio (SNDR) of sinusoidal input signals using simple non-linear elements. Other works have proven that it is possible to enhance the Signal-to-Noise Ratio (SNR) by using limiters. In this work we study a soft limiter non-linear element with and without hysteresis. We show that the SNDR of sinusoidal signals can be enhanced by 0.94 dB using a wideband soft limiter and up to 9.68 dB using a wideband soft limiter with hysteresis. These results indicate that linear amplifiers could be constructed using non-linear circuits with hysteresis. This paper presents mathematical descriptions for the non-linear elements using statistical parameters. Using these models, the input-output SNDR enhancement is obtained by optimizing the non-linear transfer function parameters to maximize the output SNDR.

Optimal experimental design for parameter estimation of a cell signaling model.

PubMed

Bandara, Samuel; Schlöder, Johannes P; Eils, Roland; Bock, Hans Georg; Meyer, Tobias

2009-11-01

Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP(3) lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay.

Hybrid photonic signal processing

NASA Astrophysics Data System (ADS)

Ghauri, Farzan Naseer

This thesis proposes research of novel hybrid photonic signal processing systems in the areas of optical communications, test and measurement, RF signal processing and extreme environment optical sensors. It will be shown that use of innovative hybrid techniques allows design of photonic signal processing systems with superior performance parameters and enhanced capabilities. These applications can be divided into domains of analog-digital hybrid signal processing applications and free-space---fiber-coupled hybrid optical sensors. The analog-digital hybrid signal processing applications include a high-performance analog-digital hybrid MEMS variable optical attenuator that can simultaneously provide high dynamic range as well as high resolution attenuation controls; an analog-digital hybrid MEMS beam profiler that allows high-power watt-level laser beam profiling and also provides both submicron-level high resolution and wide area profiling coverage; and all optical transversal RF filters that operate on the principle of broadband optical spectral control using MEMS and/or Acousto-Optic tunable Filters (AOTF) devices which can provide continuous, digital or hybrid signal time delay and weight selection. The hybrid optical sensors presented in the thesis are extreme environment pressure sensors and dual temperature-pressure sensors. The sensors employ hybrid free-space and fiber-coupled techniques for remotely monitoring a system under simultaneous extremely high temperatures and pressures.

A Preliminary Assessment of Soviet Development of Optimum Signal Discrimination Techniques: Optimum Space-Time Processing

DTIC Science & Technology

1982-10-01

thermal noise and radioastronomy is probably the application Shirman had in mind for that work. Kuriksha considers a wide class of two-dimensional...this point has been discussed In terms of EM wave propagation, signal detection, and parameter estimation in such fields as radar and radioastronomy

Influence of signal processing strategy in auditory abilities.

PubMed

Melo, Tatiana Mendes de; Bevilacqua, Maria Cecília; Costa, Orozimbo Alves; Moret, Adriane Lima Mortari

2013-01-01

The signal processing strategy is a parameter that may influence the auditory performance of cochlear implant and is important to optimize this parameter to provide better speech perception, especially in difficult listening situations. To evaluate the individual's auditory performance using two different signal processing strategy. Prospective study with 11 prelingually deafened children with open-set speech recognition. A within-subjects design was used to compare performance with standard HiRes and HiRes 120 in three different moments. During test sessions, subject's performance was evaluated by warble-tone sound-field thresholds, speech perception evaluation, in quiet and in noise. In the silence, children S1, S4, S5, S7 showed better performance with the HiRes 120 strategy and children S2, S9, S11 showed better performance with the HiRes strategy. In the noise was also observed that some children performed better using the HiRes 120 strategy and other with HiRes. Not all children presented the same pattern of response to the different strategies used in this study, which reinforces the need to look at optimizing cochlear implant clinical programming.

Plasma Charge Current for Controlling and Monitoring Electron Beam Welding with Beam Oscillation

PubMed Central

Trushnikov, Dmitriy; Belenkiy, Vladimir; Shchavlev, Valeriy; Piskunov, Anatoliy; Abdullin, Aleksandr; Mladenov, Georgy

2012-01-01

Electron beam welding (EBW) shows certain problems with the control of focus regime. The electron beam focus can be controlled in electron-beam welding based on the parameters of a secondary signal. In this case, the parameters like secondary emissions and focus coil current have extreme relationships. There are two values of focus coil current which provide equal value signal parameters. Therefore, adaptive systems of electron beam focus control use low-frequency scanning of focus, which substantially limits the operation speed of these systems and has a negative effect on weld joint quality. The purpose of this study is to develop a method for operational control of the electron beam focus during welding in the deep penetration mode. The method uses the plasma charge current signal as an additional informational parameter. This parameter allows identification of the electron beam focus regime in electron-beam welding without application of additional low-frequency scanning of focus. It can be used for working out operational electron beam control methods focusing exactly on the welding. In addition, use of this parameter allows one to observe the shape of the keyhole during the welding process. PMID:23242276

Optimizing Methods of Obtaining Stellar Parameters for the H3 Survey

NASA Astrophysics Data System (ADS)

Ivory, KeShawn; Conroy, Charlie; Cargile, Phillip

2018-01-01

The Stellar Halo at High Resolution with Hectochelle Survey (H3) is in the process of observing and collecting stellar parameters for stars in the Milky Way's halo. With a goal of measuring radial velocities for fainter stars, it is crucial that we have optimal methods of obtaining this and other parameters from the data from these stars.The method currently developed is The Payne, named after Cecilia Payne-Gaposchkin, a code that uses neural networks and Markov Chain Monte Carlo methods to utilize both spectra and photometry to obtain values for stellar parameters. This project was to investigate the benefit of fitting both spectra and spectral energy distributions (SED). Mock spectra using the parameters of the Sun were created and noise was inserted at various signal to noise values. The Payne then fit each mock spectrum with and without a mock SED also generated from solar parameters. The result was that at high signal to noise, the spectrum dominated and the effect of fitting the SED was minimal. But at low signal to noise, the addition of the SED greatly decreased the standard deviation of the data and resulted in more accurate values for temperature and metallicity.

Plasma charge current for controlling and monitoring electron beam welding with beam oscillation.

PubMed

Trushnikov, Dmitriy; Belenkiy, Vladimir; Shchavlev, Valeriy; Piskunov, Anatoliy; Abdullin, Aleksandr; Mladenov, Georgy

2012-12-14

Electron beam welding (EBW) shows certain problems with the control of focus regime. The electron beam focus can be controlled in electron-beam welding based on the parameters of a secondary signal. In this case, the parameters like secondary emissions and focus coil current have extreme relationships. There are two values of focus coil current which provide equal value signal parameters. Therefore, adaptive systems of electron beam focus control use low-frequency scanning of focus, which substantially limits the operation speed of these systems and has a negative effect on weld joint quality. The purpose of this study is to develop a method for operational control of the electron beam focus during welding in the deep penetration mode. The method uses the plasma charge current signal as an additional informational parameter. This parameter allows identification of the electron beam focus regime in electron-beam welding without application of additional low-frequency scanning of focus. It can be used for working out operational electron beam control methods focusing exactly on the welding. In addition, use of this parameter allows one to observe the shape of the keyhole during the welding process.

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.

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

PubMed Central

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

2011-01-01

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

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.

Automatic detection of slight parameter changes associated to complex biomedical signals using multiresolution q-entropy1.

PubMed

Torres, M E; Añino, M M; Schlotthauer, G

2003-12-01

It is well known that, from a dynamical point of view, sudden variations in physiological parameters which govern certain diseases can cause qualitative changes in the dynamics of the corresponding physiological process. The purpose of this paper is to introduce a technique that allows the automated temporal localization of slight changes in a parameter of the law that governs the nonlinear dynamics of a given signal. This tool takes, from the multiresolution entropies, the ability to show these changes as statistical variations at each scale. These variations are held in the corresponding principal component. Appropriately combining these techniques with a statistical changes detector, a complexity change detection algorithm is obtained. The relevance of the approach, together with its robustness in the presence of moderate noise, is discussed in numerical simulations and the automatic detector is applied to real and simulated biological signals.

The Novel Nonlinear Adaptive Doppler Shift Estimation Technique and the Coherent Doppler Lidar System Validation Lidar

NASA Technical Reports Server (NTRS)

Beyon, Jeffrey Y.; Koch, Grady J.

2006-01-01

The signal processing aspect of a 2-m wavelength coherent Doppler lidar system under development at NASA Langley Research Center in Virginia is investigated in this paper. The lidar system is named VALIDAR (validation lidar) and its signal processing program estimates and displays various wind parameters in real-time as data acquisition occurs. The goal is to improve the quality of the current estimates such as power, Doppler shift, wind speed, and wind direction, especially in low signal-to-noise-ratio (SNR) regime. A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is developed on such behalf and its performance is analyzed using the wind data acquired over a long period of time by VALIDAR. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly as SNR decreases. NADSET compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in a strong SNR range and identifying sporadic range bins where good Doppler shift estimates are found. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the long-period lidar return data.

Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; Semelin, Benoit

2017-07-01

The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

The Effect of Improved Sub-Daily Earth Rotation Models on Global GPS Data Processing

NASA Astrophysics Data System (ADS)

Yoon, S.; Choi, K. K.

2017-12-01

Throughout the various International GNSS Service (IGS) products, strong periodic signals have been observed around the 14 day period. This signal is clearly visible in all IGS time-series such as those related to orbit ephemerides, Earth rotation parameters (ERP) and ground station coordinates. Recent studies show that errors in the sub-daily Earth rotation models are the main factors that induce such noise. Current IGS orbit processing standards adopted the IERS 2010 convention and its sub-daily Earth rotation model. Since the IERS convention had published, recent advances in the VLBI analysis have made contributions to update the sub-daily Earth rotation models. We have compared several proposed sub-daily Earth rotation models and show the effect of using those models on orbit ephemeris, Earth rotation parameters and ground station coordinates generated by the NGS global GPS data processing strategy.

Evaluation of higher order statistics parameters for multi channel sEMG using different force levels.

PubMed

Naik, Ganesh R; Kumar, Dinesh K

2011-01-01

The electromyograpy (EMG) signal provides information about the performance of muscles and nerves. The shape of the muscle signal and motor unit action potential (MUAP) varies due to the movement of the position of the electrode or due to changes in contraction level. This research deals with evaluating the non-Gaussianity in Surface Electromyogram signal (sEMG) using higher order statistics (HOS) parameters. To achieve this, experiments were conducted for four different finger and wrist actions at different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density functions (PDF) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) PDF measures tends to be Gaussian process. The above measures were verified by computing the Kurtosis values for different MVCs.

A computer controlled signal preprocessor for laser fringe anemometer applications

NASA Technical Reports Server (NTRS)

Oberle, Lawrence G.

1987-01-01

The operation of most commercially available laser fringe anemometer (LFA) counter-processors assumes that adjustments are made to the signal processing independent of the computer used for reducing the data acquired. Not only does the researcher desire a record of these parameters attached to the data acquired, but changes in flow conditions generally require that these settings be changed to improve data quality. Because of this limitation, on-line modification of the data acquisition parameters can be difficult and time consuming. A computer-controlled signal preprocessor has been developed which makes possible this optimization of the photomultiplier signal as a normal part of the data acquisition process. It allows computer control of the filter selection, signal gain, and photo-multiplier voltage. The raw signal from the photomultiplier tube is input to the preprocessor which, under the control of a digital computer, filters the signal and amplifies it to an acceptable level. The counter-processor used at Lewis Research Center generates the particle interarrival times, as well as the time-of-flight of the particle through the probe volume. The signal preprocessor allows computer control of the acquisition of these data.Through the preprocessor, the computer also can control the hand shaking signals for the interface between itself and the counter-processor. Finally, the signal preprocessor splits the pedestal from the signal before filtering, and monitors the photo-multiplier dc current, sends a signal proportional to this current to the computer through an analog to digital converter, and provides an alarm if the current exceeds a predefined maximum. Complete drawings and explanations are provided in the text as well as a sample interface program for use with the data acquisition software.

Relating memory to functional performance in normal aging to dementia using hierarchical Bayesian cognitive processing models.

PubMed

Shankle, William R; Pooley, James P; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D

2013-01-01

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers.

Nuclear test ban treaty verification: Improving test ban monitoring with empirical and model-based signal processing

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

Harris, David B.; Gibbons, Steven J.; Rodgers, Arthur J.

In this approach, small scale-length medium perturbations not modeled in the tomographic inversion might be described as random fields, characterized by particular distribution functions (e.g., normal with specified spatial covariance). Conceivably, random field parameters (scatterer density or scale length) might themselves be the targets of tomographic inversions of the scattered wave field. As a result, such augmented models may provide processing gain through the use of probabilistic signal sub spaces rather than deterministic waveforms.

Nuclear test ban treaty verification: Improving test ban monitoring with empirical and model-based signal processing

DOE PAGES

Harris, David B.; Gibbons, Steven J.; Rodgers, Arthur J.; ...

2012-05-01

In this approach, small scale-length medium perturbations not modeled in the tomographic inversion might be described as random fields, characterized by particular distribution functions (e.g., normal with specified spatial covariance). Conceivably, random field parameters (scatterer density or scale length) might themselves be the targets of tomographic inversions of the scattered wave field. As a result, such augmented models may provide processing gain through the use of probabilistic signal sub spaces rather than deterministic waveforms.

Relationship Among Signal Fidelity, Hearing Loss, and Working Memory for Digital Noise Suppression.

PubMed

Arehart, Kathryn; Souza, Pamela; Kates, James; Lunner, Thomas; Pedersen, Michael Syskind

2015-01-01

This study considered speech modified by additive babble combined with noise-suppression processing. The purpose was to determine the relative importance of the signal modifications, individual peripheral hearing loss, and individual cognitive capacity on speech intelligibility and speech quality. The participant group consisted of 31 individuals with moderate high-frequency hearing loss ranging in age from 51 to 89 years (mean = 69.6 years). Speech intelligibility and speech quality were measured using low-context sentences presented in babble at several signal-to-noise ratios. Speech stimuli were processed with a binary mask noise-suppression strategy with systematic manipulations of two parameters (error rate and attenuation values). The cumulative effects of signal modification produced by babble and signal processing were quantified using an envelope-distortion metric. Working memory capacity was assessed with a reading span test. Analysis of variance was used to determine the effects of signal processing parameters on perceptual scores. Hierarchical linear modeling was used to determine the role of degree of hearing loss and working memory capacity in individual listener response to the processed noisy speech. The model also considered improvements in envelope fidelity caused by the binary mask and the degradations to envelope caused by error and noise. The participants showed significant benefits in terms of intelligibility scores and quality ratings for noisy speech processed by the ideal binary mask noise-suppression strategy. This benefit was observed across a range of signal-to-noise ratios and persisted when up to a 30% error rate was introduced into the processing. Average intelligibility scores and average quality ratings were well predicted by an objective metric of envelope fidelity. Degree of hearing loss and working memory capacity were significant factors in explaining individual listener's intelligibility scores for binary mask processing applied to speech in babble. Degree of hearing loss and working memory capacity did not predict listeners' quality ratings. The results indicate that envelope fidelity is a primary factor in determining the combined effects of noise and binary mask processing for intelligibility and quality of speech presented in babble noise. Degree of hearing loss and working memory capacity are significant factors in explaining variability in listeners' speech intelligibility scores but not in quality ratings.

Formation of the image on the receiver of thermal radiation

NASA Astrophysics Data System (ADS)

Akimenko, Tatiana A.

2018-04-01

The formation of the thermal picture of the observed scene with the verification of the quality of the thermal images obtained is one of the important stages of the technological process that determine the quality of the thermal imaging observation system. In this article propose to consider a model for the formation of a thermal picture of a scene, which must take into account: the features of the object of observation as the source of the signal; signal transmission through the physical elements of the thermal imaging system that produce signal processing at the optical, photoelectronic and electronic stages, which determines the final parameters of the signal and its compliance with the requirements for thermal information and measurement systems.

Research on photodiode detector-based spatial transient light detection and processing system

NASA Astrophysics Data System (ADS)

Liu, Meiying; Wang, Hu; Liu, Yang; Zhao, Hui; Nan, Meng

2016-10-01

In order to realize real-time signal identification and processing of spatial transient light, the features and the energy of the captured target light signal are first described and quantitatively calculated. Considering that the transient light signal has random occurrence, a short duration and an evident beginning and ending, a photodiode detector based spatial transient light detection and processing system is proposed and designed in this paper. This system has a large field of view and is used to realize non-imaging energy detection of random, transient and weak point target under complex background of spatial environment. Weak signal extraction under strong background is difficult. In this paper, considering that the background signal changes slowly and the target signal changes quickly, filter is adopted for signal's background subtraction. A variable speed sampling is realized by the way of sampling data points with a gradually increased interval. The two dilemmas that real-time processing of large amount of data and power consumption required by the large amount of data needed to be stored are solved. The test results with self-made simulative signal demonstrate the effectiveness of the design scheme. The practical system could be operated reliably. The detection and processing of the target signal under the strong sunlight background was realized. The results indicate that the system can realize real-time detection of target signal's characteristic waveform and monitor the system working parameters. The prototype design could be used in a variety of engineering applications.

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

PubMed

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

2018-04-25

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

Parmeterization of spectra

NASA Technical Reports Server (NTRS)

Cornish, C. R.

1983-01-01

Following reception and analog to digital conversion (A/D) conversion, atmospheric radar backscatter echoes need to be processed so as to obtain desired information about atmospheric processes and to eliminate or minimize contaminating contributions from other sources. Various signal processing techniques have been implemented at mesosphere-stratosphere-troposphere (MST) radar facilities to estimate parameters of interest from received spectra. Such estimation techniques need to be both accurate and sufficiently efficient to be within the capabilities of the particular data-processing system. The various techniques used to parameterize the spectra of received signals are reviewed herein. Noise estimation, electromagnetic interference, data smoothing, correlation, and the Doppler effect are among the specific points addressed.

Signal dependence of inter-pixel capacitance in hybridized HgCdTe H2RG arrays for use in James Webb space telescope's NIRcam

NASA Astrophysics Data System (ADS)

Donlon, Kevan; Ninkov, Zoran; Baum, Stefi

2016-08-01

Interpixel capacitance (IPC) is a deterministic electronic coupling by which signal generated in one pixel is measured in neighboring pixels. Examination of dark frames from test NIRcam arrays corroborates earlier results and simulations illustrating a signal dependent coupling. When the signal on an individual pixel is larger, the fractional coupling to nearest neighbors is lesser than when the signal is lower. Frames from test arrays indicate a drop in average coupling from approximately 1.0% at low signals down to approximately 0.65% at high signals depending on the particular array in question. The photometric ramifications for this non-uniformity are not fully understood. This non-uniformity intro-duces a non-linearity in the current mathematical model for IPC coupling. IPC coupling has been mathematically formalized as convolution by a blur kernel. Signal dependence requires that the blur kernel be locally defined as a function of signal intensity. Through application of a signal dependent coupling kernel, the IPC coupling can be modeled computationally. This method allows for simultaneous knowledge of the intrinsic parameters of the image scene, the result of applying a constant IPC, and the result of a signal dependent IPC. In the age of sub-pixel precision in astronomy these effects must be properly understood and accounted for in order for the data to accurately represent the object of observation. Implementation of this method is done through python scripted processing of images. The introduction of IPC into simulated frames is accomplished through convolution of the image with a blur kernel whose parameters are themselves locally defined functions of the image. These techniques can be used to enhance the data processing pipeline for NIRcam.

Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

PubMed Central

Nassiri, Isar; Masoudi-Nejad, Ali; Jalili, Mahdi; Moeini, Ali

2012-01-01

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process. PMID:22737250

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

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

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

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Multistage estimation of received carrier signal parameters under very high dynamic conditions of the receiver

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor)

1991-01-01

A multistage estimator is provided for the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc., as may arise, for example, in the case of Global Positioning Systems (GPS) where the signal parameters are directly related to the position, velocity and jerk of the GPS ground-based receiver. In a two-stage embodiment of the more general multistage scheme, the first stage, selected to be a modified least squares algorithm referred to as differential least squares (DLS), operates as a coarse estimator resulting in higher rms estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency, provides relatively coarse estimates of the frequency and its derivatives. The second stage of the estimator, an extended Kalman filter (EKF), operates on the error signal available from the first stage refining the overall estimates of the phase along with a more refined estimate of frequency as well and in the process also reduces the number of cycle slips.

Multistage estimation of received carrier signal parameters under very high dynamic conditions of the receiver

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor)

1990-01-01

A multistage estimator is provided for the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc., as may arise, for example, in the case of Global Positioning Systems (GPS) where the signal parameters are directly related to the position, velocity and jerk of the GPS ground-based receiver. In a two-stage embodiment of the more general multistage scheme, the first stage, selected to be a modified least squares algorithm referred to as differential least squares (DLS), operates as a coarse estimator resulting in higher rms estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency, provides relatively coarse estimates of the frequency and its derivatives. The second stage of the estimator, an extended Kalman filter (EKF), operates on the error signal available from the first stage refining the overall estimates of the phase along with a more refined estimate of frequency as well and in the process also reduces the number of cycle slips.

[The parallelisms in of sound signal of domestic sheep and Northern fur seals].

PubMed

Nikol'skiĭ, A A; Lisitsina, T Iu

2011-01-01

The parallelisms in communicative behavior of domestic sheep and Northern fur seals within a herd are accompanied by parallelisms in parameters of sound signal, the calling scream. This signal ensures ties between babies and their mothers at a long distance. The basis of parallelisms is formed by amplitude modulation at two levels: the one being a direct amplitude modulation of the carrier frequency and the other--modulation of the carrier frequency oscillation. Parallelisms in the signal oscillatory process result in corresponding parallelisms in the structure of its frequency spectrum.

Characterization of electrical appliances in transient state

NASA Astrophysics Data System (ADS)

Wójcik, Augustyn; Winiecki, Wiesław

2017-08-01

The article contains the study about electrical appliance characterization on the basis of power grid signals. To represent devices, parameters of current and voltage signals recorded during transient states are used. In this paper only transients occurring as a result of switching on devices are considered. The way of data acquisition performed in specialized measurement setup developed for electricity load monitoring is described. The paper presents the method of transients detection and the method of appliance parameters calculation. Using the set of acquired measurement data and appropriate software the set of parameters for several household appliances operating in different operating conditions was processed. Usefulness of appliances characterization in Non-Intrusive Appliance Load Monitoring System (NIALMS) with the use of proposed method is discussed focusing on obtained results.

TkPl_SU: An Open-source Perl Script Builder for Seismic Unix

NASA Astrophysics Data System (ADS)

Lorenzo, J. M.

2017-12-01

TkPl_SU (beta) is a graphical user interface (GUI) to select parameters for Seismic Unix (SU) modules. Seismic Unix (Stockwell, 1999) is a widely distributed free software package for processing seismic reflection and signal processing. Perl/Tk is a mature, well-documented and free object-oriented graphical user interface for Perl. In a classroom environment, shell scripting of SU modules engages students and helps focus on the theoretical limitations and strengths of signal processing. However, complex interactive processing stages, e.g., selection of optimal stacking velocities, killing bad data traces, or spectral analysis requires advanced flows beyond the scope of introductory classes. In a research setting, special functionality from other free seismic processing software such as SioSeis (UCSD-NSF) can be incorporated readily via an object-oriented style to programming. An object oriented approach is a first step toward efficient extensible programming of multi-step processes, and a simple GUI simplifies parameter selection and decision making. Currently, in TkPl_SU, Perl 5 packages wrap 19 of the most common SU modules that are used in teaching undergraduate and first-year graduate student classes (e.g., filtering, display, velocity analysis and stacking). Perl packages (classes) can advantageously add new functionality around each module and clarify parameter names for easier usage. For example, through the use of methods, packages can isolate the user from repetitive control structures, as well as replace the names of abbreviated parameters with self-describing names. Moose, an extension of the Perl 5 object system, greatly facilitates an object-oriented style. Perl wrappers are self-documenting via Perl programming document markup language.

Statistical Signal Process in R Language in the Pharmacovigilance Programme of India.

PubMed

Kumar, Aman; Ahuja, Jitin; Shrivastava, Tarani Prakash; Kumar, Vipin; Kalaiselvan, Vivekanandan

2018-05-01

The Ministry of Health & Family Welfare, Government of India, initiated the Pharmacovigilance Programme of India (PvPI) in July 2010. The purpose of the PvPI is to collect data on adverse reactions due to medications, analyze it, and use the reference to recommend informed regulatory intervention, besides communicating the risk to health care professionals and the public. The goal of the present study was to apply statistical tools to find the relationship between drugs and ADRs for signal detection by R programming. Four statistical parameters were proposed for quantitative signal detection. These 4 parameters are IC 025 , PRR and PRR lb , chi-square, and N 11 ; we calculated these 4 values using R programming. We analyzed 78,983 drug-ADR combinations, and the total count of drug-ADR combination was 4,20,060. During the calculation of the statistical parameter, we use 3 variables: (1) N 11 (number of counts), (2) N 1. (Drug margin), and (3) N .1 (ADR margin). The structure and calculation of these 4 statistical parameters in R language are easily understandable. On the basis of the IC value (IC value >0), out of the 78,983 drug-ADR combination (drug-ADR combination), we found the 8,667 combinations to be significantly associated. The calculation of statistical parameters in R language is time saving and allows to easily identify new signals in the Indian ICSR (Individual Case Safety Reports) database.

Stability evaluation of short-circuiting gas metal arc welding based on ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Huang, Yong; Wang, Kehong; Zhou, Zhilan; Zhou, Xiaoxiao; Fang, Jimi

2017-03-01

The arc of gas metal arc welding (GMAW) contains abundant information about its stability and droplet transition, which can be effectively characterized by extracting the arc electrical signals. In this study, ensemble empirical mode decomposition (EEMD) was used to evaluate the stability of electrical current signals. The welding electrical signals were first decomposed by EEMD, and then transformed to a Hilbert-Huang spectrum and a marginal spectrum. The marginal spectrum is an approximate distribution of amplitude with frequency of signals, and can be described by a marginal index. Analysis of various welding process parameters showed that the marginal index of current signals increased when the welding process was more stable, and vice versa. Thus EEMD combined with the marginal index can effectively uncover the stability and droplet transition of GMAW.

EMG-Torque correction on Human Upper extremity using Evolutionary Computation

NASA Astrophysics Data System (ADS)

JL, Veronica; Parasuraman, S.; Khan, M. K. A. Ahamed; Jeba DSingh, Kingsly

2016-09-01

There have been many studies indicating that control system of rehabilitative robot plays an important role in determining the outcome of the therapy process. Existing works have done the prediction of feedback signal in the controller based on the kinematics parameters and EMG readings of upper limb's skeletal system. Kinematics and kinetics based control signal system is developed by reading the output of the sensors such as position sensor, orientation sensor and F/T (Force/Torque) sensor and there readings are to be compared with the preceding measurement to decide on the amount of assistive force. There are also other works that incorporated the kinematics parameters to calculate the kinetics parameters via formulation and pre-defined assumptions. Nevertheless, these types of control signals analyze the movement of the upper limb only based on the movement of the upper joints. They do not anticipate the possibility of muscle plasticity. The focus of the paper is to make use of the kinematics parameters and EMG readings of skeletal system to predict the individual torque of upper extremity's joints. The surface EMG signals are fed into different mathematical models so that these data can be trained through Genetic Algorithm (GA) to find the best correlation between EMG signals and torques acting on the upper limb's joints. The estimated torque attained from the mathematical models is called simulated output. The simulated output will then be compared with the actual individual joint which is calculated based on the real time kinematics parameters of the upper movement of the skeleton when the muscle cells are activated. The findings from this contribution are extended into the development of the active control signal based controller for rehabilitation robot.

Informed spectral analysis: audio signal parameter estimation using side information

NASA Astrophysics Data System (ADS)

Fourer, Dominique; Marchand, Sylvain

2013-12-01

Parametric models are of great interest for representing and manipulating sounds. However, the quality of the resulting signals depends on the precision of the parameters. When the signals are available, these parameters can be estimated, but the presence of noise decreases the resulting precision of the estimation. Furthermore, the Cramér-Rao bound shows the minimal error reachable with the best estimator, which can be insufficient for demanding applications. These limitations can be overcome by using the coding approach which consists in directly transmitting the parameters with the best precision using the minimal bitrate. However, this approach does not take advantage of the information provided by the estimation from the signal and may require a larger bitrate and a loss of compatibility with existing file formats. The purpose of this article is to propose a compromised approach, called the 'informed approach,' which combines analysis with (coded) side information in order to increase the precision of parameter estimation using a lower bitrate than pure coding approaches, the audio signal being known. Thus, the analysis problem is presented in a coder/decoder configuration where the side information is computed and inaudibly embedded into the mixture signal at the coder. At the decoder, the extra information is extracted and is used to assist the analysis process. This study proposes applying this approach to audio spectral analysis using sinusoidal modeling which is a well-known model with practical applications and where theoretical bounds have been calculated. This work aims at uncovering new approaches for audio quality-based applications. It provides a solution for challenging problems like active listening of music, source separation, and realistic sound transformations.

Hydrograph structure informed calibration in the frequency domain with time localization

NASA Astrophysics Data System (ADS)

Kumarasamy, K.; Belmont, P.

2015-12-01

Complex models with large number of parameters are commonly used to estimate sediment yields and predict changes in sediment loads as a result of changes in management or conservation practice at large watershed (>2000 km2) scales. As sediment yield is a strongly non-linear function that responds to channel (peak or mean) velocity or flow depth, it is critical to accurately represent flows. The process of calibration in such models (e.g., SWAT) generally involves the adjustment of several parameters to obtain better estimates of goodness of fit metrics such as Nash Sutcliff Efficiency (NSE). However, such indicators only provide a global view of model performance, potentially obscuring accuracy of the timing or magnitude of specific flows of interest. We describe an approach for streamflow calibration that will greatly reduce the black-box nature of calibration, when response from a parameter adjustment is not clearly known. Fourier Transform or the Short Term Fourier Transform could be used to characterize model performance in the frequency domain as well, however, the ambiguity of a Fourier transform with regards to time localization renders its implementation in a model calibration setting rather useless. Brief and sudden changes (e.g. stream flow peaks) in signals carry the most interesting information from parameter adjustments, which are completely lost in the transform without time localization. Wavelet transform captures the frequency component in the signal without compromising time and is applied to contrast changes in signal response to parameter adjustments. Here we employ the mother wavelet called the Mexican hat wavelet and apply a Continuous Wavelet Transform to understand the signal in the frequency domain. Further, with the use of the cross-wavelet spectrum we examine the relationship between the two signals (prior or post parameter adjustment) in the time-scale plane (e.g., lower scales correspond to higher frequencies). The non-stationarity of the streamflow signal does not hinder this assessment and regions of change called boundaries of influence (seasons or time when such change occurs in the hydrograph) for each parameter are delineated. In addition, we can discover the structural component of the signal (e.g., shifts or amplitude change) that has changed.

Hierarchial mark-recapture models: a framework for inference about demographic processes

USGS Publications Warehouse

Link, W.A.; Barker, R.J.

2004-01-01

The development of sophisticated mark-recapture models over the last four decades has provided fundamental tools for the study of wildlife populations, allowing reliable inference about population sizes and demographic rates based on clearly formulated models for the sampling processes. Mark-recapture models are now routinely described by large numbers of parameters. These large models provide the next challenge to wildlife modelers: the extraction of signal from noise in large collections of parameters. Pattern among parameters can be described by strong, deterministic relations (as in ultrastructural models) but is more flexibly and credibly modeled using weaker, stochastic relations. Trend in survival rates is not likely to be manifest by a sequence of values falling precisely on a given parametric curve; rather, if we could somehow know the true values, we might anticipate a regression relation between parameters and explanatory variables, in which true value equals signal plus noise. Hierarchical models provide a useful framework for inference about collections of related parameters. Instead of regarding parameters as fixed but unknown quantities, we regard them as realizations of stochastic processes governed by hyperparameters. Inference about demographic processes is based on investigation of these hyperparameters. We advocate the Bayesian paradigm as a natural, mathematically and scientifically sound basis for inference about hierarchical models. We describe analysis of capture-recapture data from an open population based on hierarchical extensions of the Cormack-Jolly-Seber model. In addition to recaptures of marked animals, we model first captures of animals and losses on capture, and are thus able to estimate survival probabilities w (i.e., the complement of death or permanent emigration) and per capita growth rates f (i.e., the sum of recruitment and immigration rates). Covariation in these rates, a feature of demographic interest, is explicitly described in the model.

Characteristics of High Latitude Ionosphere Scintillations

NASA Astrophysics Data System (ADS)

Morton, Y.

2012-12-01

As we enter a new solar maximum period, global navigation satellite systems (GNSS) receivers, especially the ones operating in high latitude and equatorial regions, are facing an increasing threat from ionosphere scintillations. The increased solar activities, however, also offer a great opportunity to collect scintillation data to characterize scintillation signal parameters and ionosphere irregularities. While there are numerous GPS receivers deployed around the globe to monitor ionosphere scintillations, most of them are commercial receivers whose signal processing mechanisms are not designed to operate under ionosphere scintillation. As a result, they may distort scintillation signal parameters or lose lock of satellite signals under strong scintillations. Since 2008, we have established and continuously improved a unique GNSS receiver array at HAARP, Alaska. The array contains high ends commercial receivers and custom RF front ends which can be automatically triggered to collect high quality GPS and GLONASS satellite signals during controlled heating experiments and natural scintillation events. Custom designed receiver signal tracking algorithms aim to preserve true scintillation signatures are used to process the raw RF samples. Signal strength, carrier phase, and relative TEC measurements generated by the receiver array since its inception have been analyzed to characterize high latitude scintillation phenomena. Daily, seasonal, and solar events dependency of scintillation occurrence, spectral contents of scintillation activities, and plasma drifts derived from these measurements will be presented. These interesting results demonstrate the feasibility and effectiveness of our experimental data collection system in providing insightful details of ionosphere responses to active perturbations and natural disturbances.

Synchronization transmission of laser pattern signal within uncertain switched network

NASA Astrophysics Data System (ADS)

Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

Chemometrics.

ERIC Educational Resources Information Center

Delaney, Michael F.

1984-01-01

This literature review on chemometrics (covering December 1981 to December 1983) is organized under these headings: personal supermicrocomputers; education and books; statistics; modeling and parameter estimation; resolution; calibration; signal processing; image analysis; factor analysis; pattern recognition; optimization; artificial…

Analysis of droplet transfer mode and forming process of weld bead in CO 2 laser-MAG hybrid welding process

NASA Astrophysics Data System (ADS)

Liu, Shuangyu; Liu, Fengde; Zhang, Hong; Shi, Yan

2012-06-01

In this paper, CO 2 laser-metal active gas (MAG) hybrid welding technique is used to weld high strength steel and the optimized process parameters are obtained. Using LD Pumped laser with an emission wavelength of 532 nm to overcome the strong interference from the welding arc, a computer-based system is developed to collect and visualize the waveforms of the electrical welding parameters and metal transfer processes in laser-MAG. The welding electric signals of hybrid welding processes are quantitatively described and analyzed using the ANALYSATOR HANNOVER. The effect of distance between laser and arc ( DLA) on weld bead geometry, forming process of weld shape, electric signals, arc characteristic and droplet transfer behavior is investigated. It is found that arc characteristic, droplet transfer mode and final weld bead geometry are strongly affected by the distance between laser and arc. The weld bead geometry is changed from "cocktail cup" to "cone-shaped" with the increasing DLA. The droplet transfer mode is changed from globular transfer to projected transfer with the increasing DLA. Projected transfer mode is an advantage for the stability of hybrid welding processes.

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

NASA Astrophysics Data System (ADS)

Su, Zhongqing; Ye, Lin

2004-08-01

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Model-based damage evaluation of layered CFRP structures

NASA Astrophysics Data System (ADS)

Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.

2015-03-01

An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.

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.

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

Phosphate Sink Containing Two-Component Signaling Systems as Tunable Threshold Devices

PubMed Central

Amin, Munia; Kothamachu, Varun B.; Feliu, Elisenda; Scharf, Birgit E.; Porter, Steven L.; Soyer, Orkun S.

2014-01-01

Synthetic biology aims to design de novo biological systems and reengineer existing ones. These efforts have mostly focused on transcriptional circuits, with reengineering of signaling circuits hampered by limited understanding of their systems dynamics and experimental challenges. Bacterial two-component signaling systems offer a rich diversity of sensory systems that are built around a core phosphotransfer reaction between histidine kinases and their output response regulator proteins, and thus are a good target for reengineering through synthetic biology. Here, we explore the signal-response relationship arising from a specific motif found in two-component signaling. In this motif, a single histidine kinase (HK) phosphotransfers reversibly to two separate output response regulator (RR) proteins. We show that, under the experimentally observed parameters from bacteria and yeast, this motif not only allows rapid signal termination, whereby one of the RRs acts as a phosphate sink towards the other RR (i.e. the output RR), but also implements a sigmoidal signal-response relationship. We identify two mathematical conditions on system parameters that are necessary for sigmoidal signal-response relationships and define key parameters that control threshold levels and sensitivity of the signal-response curve. We confirm these findings experimentally, by in vitro reconstitution of the one HK-two RR motif found in the Sinorhizobium meliloti chemotaxis pathway and measuring the resulting signal-response curve. We find that the level of sigmoidality in this system can be experimentally controlled by the presence of the sink RR, and also through an auxiliary protein that is shown to bind to the HK (yielding Hill coefficients of above 7). These findings show that the one HK-two RR motif allows bacteria and yeast to implement tunable switch-like signal processing and provides an ideal basis for developing threshold devices for synthetic biology applications. PMID:25357192

Dual Rate Adaptive Control for an Industrial Heat Supply Process Using Signal Compensation Approach

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

Chai, Tianyou; Jia, Yao; Wang, Hong

The industrial heat supply process (HSP) is a highly nonlinear cascaded process which uses a steam valve opening as its control input, the steam flow-rate as its inner loop output and the supply water temperature as its outer loop output. The relationship between the heat exchange rate and the model parameters, such as steam density, entropy, and fouling correction factor and heat exchange efficiency are unknown and nonlinear. Moreover, these model parameters vary in line with steam pressure, ambient temperature and the residuals caused by the quality variations of the circulation water. When the steam pressure and the ambient temperaturemore » are of high values and are subjected to frequent external random disturbances, the supply water temperature and the steam flow-rate would interact with each other and fluctuate a lot. This is also true when the process exhibits unknown characteristic variations of the process dynamics caused by the unexpected changes of the heat exchange residuals. As a result, it is difficult to control the supply water temperature and the rates of changes of steam flow-rate well inside their targeted ranges. In this paper, a novel compensation signal based dual rate adaptive controller is developed by representing the unknown variations of dynamics as unmodeled dynamics. In the proposed controller design, such a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based feedback control design. Such a compensation signal aims at eliminating the unmodeled dynamics and the rate of changes of the currently sample unmodeled dynamics. A successful industrial application is carried out, where it has been shown that both the supply water temperature and the rate of the changes of the steam flow-rate can be controlled well inside their targeted ranges when the process is subjected to unknown variations of its dynamics.« less

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

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Comparison of digital signal processing modules in gamma-ray spectrometry.

PubMed

Lépy, Marie-Christine; Cissé, Ousmane Ibrahima; Pierre, Sylvie

2014-05-01

Commercial digital signal-processing modules have been tested for their applicability to gamma-ray spectrometry. The tests were based on the same n-type high purity germanium detector. The spectrum quality was studied in terms of energy resolution and peak area versus shaping parameters, using a Eu-152 point source. The stability of a reference peak count rate versus the total count rate was also examined. The reliability of the quantitative results is discussed for their use in measurement at the metrological level. © 2013 Published by Elsevier Ltd.

Method for laser spot welding monitoring

NASA Astrophysics Data System (ADS)

Manassero, Giorgio

1994-09-01

As more powerful solid state laser sources appear on the market, new applications become technically possible and important from the economical point of view. For every process a preliminary optimization phase is necessary. The main parameters, used for a welding application by a high power Nd-YAG laser, are: pulse energy, pulse width, repetition rate and process duration or speed. In this paper an experimental methodology, for the development of an electrooptical laser spot welding monitoring system, is presented. The electromagnetic emission from the molten pool was observed and measured with appropriate sensors. The statistical method `Parameter Design' was used to obtain an accurate analysis of the process parameter that influence process results. A laser station with a solid state laser coupled to an optical fiber (1 mm in diameter) was utilized for the welding tests. The main material used for the experimental plan was zinc coated steel sheet 0.8 mm thick. This material and the related spot welding technique are extensively used in the automotive industry, therefore, the introduction of laser technology in production line will improve the quality of the final product. A correlation, between sensor signals and `through or not through' welds, was assessed. The investigation has furthermore shown the necessity, for the modern laser production systems, to use multisensor heads for process monitoring or control with more advanced signal elaboration procedures.

Method and apparatus for analog signal conditioner for high speed, digital x-ray spectrometer

DOEpatents

Warburton, William K.; Hubbard, Bradley

1999-01-01

A signal processing system which accepts input from an x-ray detector-preamplifier and produces a signal of reduced dynamic range for subsequent analog-to-digital conversion. The system conditions the input signal to reduce the number of bits required in the analog-to-digital converter by removing that part of the input signal which varies only slowly in time and retaining the amplitude of the pulses which carry information about the x-rays absorbed by the detector. The parameters controlling the signal conditioner's operation can be readily supplied in digital form, allowing it to be integrated into a feedback loop as part of a larger digital x-ray spectroscopy system.

Methods for automatically analyzing humpback song units.

PubMed

Rickwood, Peter; Taylor, Andrew

2008-03-01

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

Sequential pattern formation governed by signaling gradients

NASA Astrophysics Data System (ADS)

Jörg, David J.; Oates, Andrew C.; Jülicher, Frank

2016-10-01

Rhythmic and sequential segmentation of the embryonic body plan is a vital developmental patterning process in all vertebrate species. However, a theoretical framework capturing the emergence of dynamic patterns of gene expression from the interplay of cell oscillations with tissue elongation and shortening and with signaling gradients, is still missing. Here we show that a set of coupled genetic oscillators in an elongating tissue that is regulated by diffusing and advected signaling molecules can account for segmentation as a self-organized patterning process. This system can form a finite number of segments and the dynamics of segmentation and the total number of segments formed depend strongly on kinetic parameters describing tissue elongation and signaling molecules. The model accounts for existing experimental perturbations to signaling gradients, and makes testable predictions about novel perturbations. The variety of different patterns formed in our model can account for the variability of segmentation between different animal species.

Influence of ionospheric disturbances onto long-baseline relative positioning in kinematic mode

NASA Astrophysics Data System (ADS)

Wezka, Kinga; Herrera, Ivan; Cokrlic, Marija; Galas, Roman

2013-04-01

Ionospheric disturbances are fast and random variabilities in the ionosphere and they are difficult to detect and model. Some strong disturbances can cause, among others, interruption of GNSS signal or even lead to loss of signal lock. These phenomena are especially harmful for kinematic real-time applications, where the system availability is one of the most important parameters influencing positioning reliability. Our investigations were conducted using long time series of GNSS observations gathered at high latitude, where ionospheric disturbances more frequently occur. Selected processing strategy was used to monitor ionospheric signatures in time series of the coordinates. Quality of the data of input and of the processing results were examined and described by a set of proposed parameters. Variations in the coordinates were compared with available information about the state of ionosphere derived from Neustrelitz TEC Model (NTCM) and with the time series of raw observations. Some selected parameters were also calculated with the "iono-tools" module of the TUB-NavSolutions software developed by the Precise Navigation and Positioning Group at Technische Universitaet Berlin. The paper presents very first results of evaluation of the robustness of positioning algorithms with respect to ionospheric anomalies using the NTCM model and our calculated ionospheric parameters.

AGARD Flight Test Instrumentation Series. Volume 14. The Analysis of Random Data

DTIC Science & Technology

1981-11-01

obtained at arbitrary times during a number of flights. No constraints have been placed upon the controlling parameters, so that the process is non ...34noisy" environment controlling a non -linear system (the aircraft) using a redundant net of control parameters. when aircraft were flown manually with...structure. Cuse 2. Non -Stationary Measurements. When the 114S value of a random signal varies with parameters which cannot be controlled , then the method

Automation of extrusion of porous cable products based on a digital controller

NASA Astrophysics Data System (ADS)

Chostkovskii, B. K.; Mitroshin, V. N.

2017-07-01

This paper presents a new approach to designing an automated system for monitoring and controlling the process of applying porous insulation material on a conductive cable core, which is based on using structurally and parametrically optimized digital controllers of an arbitrary order instead of calculating typical PID controllers using known methods. The digital controller is clocked by signals from the clock length sensor of a measuring wheel, instead of a timer signal, and this provides the robust properties of the system with respect to the changing insulation speed. Digital controller parameters are tuned to provide the operating parameters of the manufactured cable using a simulation model of stochastic extrusion and are minimized by moving a regular simplex in the parameter space of the tuned controller.

Ultra-low-power and robust digital-signal-processing hardware for implantable neural interface microsystems.

PubMed

Narasimhan, S; Chiel, H J; Bhunia, S

2011-04-01

Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

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

Potential accuracy of methods of laser Doppler anemometry in the single-particle scattering mode

NASA Astrophysics Data System (ADS)

Sobolev, V. S.; Kashcheeva, G. A.

2017-05-01

Potential accuracy of methods of laser Doppler anemometry is determined for the singleparticle scattering mode where the only disturbing factor is shot noise generated by the optical signal itself. The problem is solved by means of computer simulations with the maximum likelihood method. The initial parameters of simulations are chosen to be the number of real or virtual interference fringes in the measurement volume of the anemometer, the signal discretization frequency, and some typical values of the signal/shot noise ratio. The parameters to be estimated are the Doppler frequency as the basic parameter carrying information about the process velocity, the signal amplitude containing information about the size and concentration of scattering particles, and the instant when the particles arrive at the center of the measurement volume of the anemometer, which is needed for reconstruction of the examined flow velocity as a function of time. The estimates obtained in this study show that shot noise produces a minor effect (0.004-0.04%) on the frequency determination accuracy in the entire range of chosen values of the initial parameters. For the signal amplitude and the instant when the particles arrive at the center of the measurement volume of the anemometer, the errors induced by shot noise are in the interval of 0.2-3.5%; if the number of interference fringes is sufficiently large (more than 20), the errors do not exceed 0.2% regardless of the shot noise level.

An experimental adaptive array to suppress weak interfering signals

NASA Technical Reports Server (NTRS)

Walton, Eric K.; Gupta, Inder J.; Ksienski, Aharon A.; Ward, James

1988-01-01

An experimental adaptive antenna system to suppress weak interfering signals is described. It is a sidelobe canceller with two auxiliary elements. Modified feedback loops are used to control the array weights. The received signals are simulated in hardware for parameter control. Digital processing is used for algorithm implementation and performance evaluation. The experimental results are presented. They show that interfering signals as much as 10 dB below the thermal noise level in the main channel are suppressed by 20-30 dB. Such a system has potential application in suppressing the interference encountered in direct broadcast satellite communication systems.

Single-chip microcomputer for image processing in the photonic measuring system

NASA Astrophysics Data System (ADS)

Smoleva, Olga S.; Ljul, Natalia Y.

2002-04-01

The non-contact measuring system has been designed for rail- track parameters control on the Moscow Metro. It detects some significant parameters: rail-track width, rail-track height, gage, rail-slums, crosslevel, pickets, and car speed. The system consists of three subsystems: non-contact system of rail-track width, height, and gage inspection, non-contact system of rail-slums inspection and subsystem for crosslevel, speed, and pickets detection. Data from subsystems is transferred to pre-processing unit. In order to process data received from subsystems, the single-chip signal processor ADSP-2185 must be used due to providing required processing speed. After data will be processed, it is send to PC, which processes it and outputs it in the readable form.

Monitoring the quality of welding based on welding current and ste analysis

NASA Astrophysics Data System (ADS)

Mazlan, Afidatusshimah; Daniyal, Hamdan; Izzani Mohamed, Amir; Ishak, Mahadzir; Hadi, Amran Abdul

2017-10-01

Qualities of welding play an important part in industry especially in manufacturing field. Post-welding non-destructive test is one of the importance process to ensure the quality of welding but it is time consuming and costly. To reduce the chance of defects, online monitoring had been utilized by continuously sense some of welding parameters and predict welding quality. One of the parameters is welding current, which is rich of information but lack of study focus on extract them at signal analysis level. This paper presents the analysis of welding current using Short Time Energy (STE) signal processing to quantify the pattern of the current. GMAW set with carbon steel specimens are used in this experimental study with high-bandwidth and high sampling rate oscilloscope capturing the welding current. The results indicate welding current as signatures have high correlation with the welding process. Continue with STE analysis, the value below 5000 is declare as good welding, meanwhile the STE value more than 6000 is contained defect.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

Use phase signals to promote lifetime extension for Windows PCs.

PubMed

Hickey, Stewart; Fitzpatrick, Colin; O'Connell, Maurice; Johnson, Michael

2009-04-01

This paper proposes a signaling methodology for personal computers. Signaling may be viewed as an ecodesign strategy that can positively influence the consumer to consumer (C2C) market process. A number of parameters are identified that can provide the basis for signal implementation. These include operating time, operating temperature, operating voltage, power cycle counts, hard disk drive (HDD) self-monitoring, and reporting technology (SMART) attributes and operating system (OS) event information. All these parameters are currently attainable or derivable via embedded technologies in modern desktop systems. A case study detailing a technical implementation of how the development of signals can be achieved in personal computers that incorporate Microsoft Windows operating systems is presented. Collation of lifetime temperature data from a system processor is demonstrated as a possible means of characterizing a usage profile for a desktop system. In addition, event log data is utilized for devising signals indicative of OS quality. The provision of lifetime usage data in the form of intuitive signals indicative of both hardware and software quality can in conjunction with consumer education facilitate an optimal remarketing strategy for used systems. This implementation requires no additional hardware.

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

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.

Qualitative modeling of the decision-making process using electrooculography.

PubMed

Zargari Marandi, Ramtin; Sabzpoushan, S H

2015-12-01

A novel method based on electrooculography (EOG) has been introduced in this work to study the decision-making process. An experiment was designed and implemented wherein subjects were asked to choose between two items from the same category that were presented within a limited time. The EOG and voice signals of the subjects were recorded during the experiment. A calibration task was performed to map the EOG signals to their corresponding gaze positions on the screen by using an artificial neural network. To analyze the data, 16 parameters were extracted from the response time and EOG signals of the subjects. Evaluation and comparison of the parameters, together with subjects' choices, revealed functional information. On the basis of this information, subjects switched their eye gazes between items about three times on average. We also found, according to statistical hypothesis testing-that is, a t test, t(10) = 71.62, SE = 1.25, p < .0001-that the correspondence rate of a subjects' gaze at the moment of selection with the selected item was significant. Ultimately, on the basis of these results, we propose a qualitative choice model for the decision-making task.

ISLE (Image and Signal Processing LISP Environment) reference manual

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

Sherwood, R.J.; Searfus, R.M.

1990-01-01

ISLE is a rapid prototyping system for performing image and signal processing. It is designed to meet the needs of a person doing development of image and signal processing algorithms in a research environment. The image and signal processing modules in ISLE form a very capable package in themselves. They also provide a rich environment for quickly and easily integrating user-written software modules into the package. ISLE is well suited to applications in which there is a need to develop a processing algorithm in an interactive manner. It is straightforward to develop the algorithms, load it into ISLE, apply themore » algorithm to an image or signal, display the results, then modify the algorithm and repeat the develop-load-apply-display cycle. ISLE consists of a collection of image and signal processing modules integrated into a cohesive package through a standard command interpreter. ISLE developer elected to concentrate their effort on developing image and signal processing software rather than developing a command interpreter. A COMMON LISP interpreter was selected for the command interpreter because it already has the features desired in a command interpreter, it supports dynamic loading of modules for customization purposes, it supports run-time parameter and argument type checking, it is very well documented, and it is a commercially supported product. This manual is intended to be a reference manual for the ISLE functions The functions are grouped into a number of categories and briefly discussed in the Function Summary chapter. The full descriptions of the functions and all their arguments are given in the Function Descriptions chapter. 6 refs.« less

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration.

PubMed

Carrière, Olivier; Hermand, Jean-Pierre

2012-04-01

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

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.

A Frequency-Domain Multipath Parameter Estimation and Mitigation Method for BOC-Modulated GNSS Signals

PubMed Central

Sun, Chao; Feng, Wenquan; Du, Songlin

2018-01-01

As multipath is one of the dominating error sources for high accuracy Global Navigation Satellite System (GNSS) applications, multipath mitigation approaches are employed to minimize this hazardous error in receivers. Binary offset carrier modulation (BOC), as a modernized signal structure, is adopted to achieve significant enhancement. However, because of its multi-peak autocorrelation function, conventional multipath mitigation techniques for binary phase shift keying (BPSK) signal would not be optimal. Currently, non-parametric and parametric approaches have been studied specifically aiming at multipath mitigation for BOC signals. Non-parametric techniques, such as Code Correlation Reference Waveforms (CCRW), usually have good feasibility with simple structures, but suffer from low universal applicability for different BOC signals. Parametric approaches can thoroughly eliminate multipath error by estimating multipath parameters. The problems with this category are at the high computation complexity and vulnerability to the noise. To tackle the problem, we present a practical parametric multipath estimation method in the frequency domain for BOC signals. The received signal is transferred to the frequency domain to separate out the multipath channel transfer function for multipath parameter estimation. During this process, we take the operations of segmentation and averaging to reduce both noise effect and computational load. The performance of the proposed method is evaluated and compared with the previous work in three scenarios. Results indicate that the proposed averaging-Fast Fourier Transform (averaging-FFT) method achieves good robustness in severe multipath environments with lower computational load for both low-order and high-order BOC signals. PMID:29495589

A Taguchi approach on optimal process control parameters for HDPE pipe extrusion process

NASA Astrophysics Data System (ADS)

Sharma, G. V. S. S.; Rao, R. Umamaheswara; Rao, P. Srinivasa

2017-06-01

High-density polyethylene (HDPE) pipes find versatile applicability for transportation of water, sewage and slurry from one place to another. Hence, these pipes undergo tremendous pressure by the fluid carried. The present work entails the optimization of the withstanding pressure of the HDPE pipes using Taguchi technique. The traditional heuristic methodology stresses on a trial and error approach and relies heavily upon the accumulated experience of the process engineers for determining the optimal process control parameters. This results in setting up of less-than-optimal values. Hence, there arouse a necessity to determine optimal process control parameters for the pipe extrusion process, which can ensure robust pipe quality and process reliability. In the proposed optimization strategy, the design of experiments (DoE) are conducted wherein different control parameter combinations are analyzed by considering multiple setting levels of each control parameter. The concept of signal-to-noise ratio ( S/ N ratio) is applied and ultimately optimum values of process control parameters are obtained as: pushing zone temperature of 166 °C, Dimmer speed at 08 rpm, and Die head temperature to be 192 °C. Confirmation experimental run is also conducted to verify the analysis and research result and values proved to be in synchronization with the main experimental findings and the withstanding pressure showed a significant improvement from 0.60 to 1.004 Mpa.

The DCU: the detector control unit for SPICA-SAFARI

NASA Astrophysics Data System (ADS)

Clénet, Antoine; Ravera, Laurent; Bertrand, Bernard; den Hartog, Roland H.; Jackson, Brian D.; van Leeuven, Bert-Joost; van Loon, Dennis; Parot, Yann; Pointecouteau, Etienne; Sournac, Anthony

2014-08-01

IRAP is developing the warm electronic, so called Detector Control Unit" (DCU), in charge of the readout of the SPICA-SAFARI's TES type detectors. The architecture of the electronics used to readout the 3 500 sensors of the 3 focal plane arrays is based on the frequency domain multiplexing technique (FDM). In each of the 24 detection channels the data of up to 160 pixels are multiplexed in frequency domain between 1 and 3:3 MHz. The DCU provides the AC signals to voltage-bias the detectors; it demodulates the detectors data which are readout in the cold by a SQUID; and it computes a feedback signal for the SQUID to linearize the detection chain in order to optimize its dynamic range. The feedback is computed with a specific technique, so called baseband feedback (BBFB) which ensures that the loop is stable even with long propagation and processing delays (i.e. several µs) and with fast signals (i.e. frequency carriers at 3:3 MHz). This digital signal processing is complex and has to be done at the same time for the 3 500 pixels. It thus requires an optimisation of the power consumption. We took the advantage of the relatively reduced science signal bandwidth (i.e. 20 - 40 Hz) to decouple the signal sampling frequency (10 MHz) and the data processing rate. Thanks to this method we managed to reduce the total number of operations per second and thus the power consumption of the digital processing circuit by a factor of 10. Moreover we used time multiplexing techniques to share the resources of the circuit (e.g. a single BBFB module processes 32 pixels). The current version of the firmware is under validation in a Xilinx Virtex 5 FPGA, the final version will be developed in a space qualified digital ASIC. Beyond the firmware architecture the optimization of the instrument concerns the characterization routines and the definition of the optimal parameters. Indeed the operation of the detection and readout chains requires to properly define more than 17 500 parameters (about 5 parameters per pixel). Thus it is mandatory to work out an automatic procedure to set up these optimal values. We defined a fast algorithm which characterizes the phase correction to be applied by the BBFB firmware and the pixel resonance frequencies. We also defined a technique to define the AC-carrier initial phases in such a way that the amplitude of their sum is minimized (for a better use of the DAC dynamic range).

Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP).

PubMed

Addison, Paul S; Wang, Rui; Uribe, Alberto A; Bergese, Sergio D

2015-06-01

DPOP (∆POP or Delta-POP) is a non-invasive parameter which measures the strength of respiratory modulations present in the pulse oximetry photoplethysmogram (pleth) waveform. It has been proposed as a non-invasive surrogate parameter for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. Many groups have reported on the DPOP parameter and its correlation with PPV using various semi-automated algorithmic implementations. The study reported here demonstrates the performance gains made by adding increasingly sophisticated signal processing components to a fully automated DPOP algorithm. A DPOP algorithm was coded and its performance systematically enhanced through a series of code module alterations and additions. Each algorithm iteration was tested on data from 20 mechanically ventilated OR patients. Correlation coefficients and ROC curve statistics were computed at each stage. For the purposes of the analysis we split the data into a manually selected 'stable' region subset of the data containing relatively noise free segments and a 'global' set incorporating the whole data record. Performance gains were measured in terms of correlation against PPV measurements in OR patients undergoing controlled mechanical ventilation. Through increasingly advanced pre-processing and post-processing enhancements to the algorithm, the correlation coefficient between DPOP and PPV improved from a baseline value of R = 0.347 to R = 0.852 for the stable data set, and, correspondingly, R = 0.225 to R = 0.728 for the more challenging global data set. Marked gains in algorithm performance are achievable for manually selected stable regions of the signals using relatively simple algorithm enhancements. Significant additional algorithm enhancements, including a correction for low perfusion values, were required before similar gains were realised for the more challenging global data set.

Evaluation of dispersive Bragg gratings (BG) structures for the processing of RF signals with large time delays and bandwidths

NASA Astrophysics Data System (ADS)

Kaba, M.; Zhou, F. C.; Lim, A.; Decoster, D.; Huignard, J.-P.; Tonda, S.; Dolfi, D.; Chazelas, J.

2007-11-01

The applications of microwave optoelectronics are extremely large since they extend from the Radio-over-Fibre to the Homeland security and defence systems. Then, the improved maturity of the optoelectronic components operating up to 40GHz permit to consider new optical processing functions (filtering, beamforming, ...) which can operate over very wideband microwave analogue signals. Specific performances are required which imply optical delay lines able to exhibit large Time-Bandwidth product values. It is proposed to evaluate slow light approach through highly dispersive structures based on either uniform or chirped Bragg Gratings. Therefore, we highlight the impact of the major parameters of such structures: index modulation depth, grating length, grating period, chirp coefficient and demonstrate the high potentiality of Bragg Grating for Large RF signals bandwidth processing under slow-light propagation.

Sensors, Volume 1, Fundamentals and General Aspects

NASA Astrophysics Data System (ADS)

Grandke, Thomas; Ko, Wen H.

1996-12-01

'Sensors' is the first self-contained series to deal with the whole area of sensors. It describes general aspects, technical and physical fundamentals, construction, function, applications and developments of the various types of sensors. This volume deals with the fundamentals and common principles of sensors and covers the wide areas of principles, technologies, signal processing, and applications. Contents include: Sensor Fundamentals, e.g. Sensor Parameters, Modeling, Design and Packaging; Basic Sensor Technologies, e.g. Thin and Thick Films, Integrated Magnetic Sensors, Optical Fibres and Intergrated Optics, Ceramics and Oxides; Sensor Interfaces, e.g. Signal Processing, Multisensor Signal Processing, Smart Sensors, Interface Systems; Sensor Applications, e.g. Automotive: On-board Sensors, Traffic Surveillance and Control, Home Appliances, Environmental Monitoring, etc. This volume is an indispensable reference work and text book for both specialits and newcomers, researchers and developers.

Integration of local motion is normal in amblyopia

NASA Astrophysics Data System (ADS)

Hess, Robert F.; Mansouri, Behzad; Dakin, Steven C.; Allen, Harriet A.

2006-05-01

We investigate the global integration of local motion direction signals in amblyopia, in a task where performance is equated between normal and amblyopic eyes at the single element level. We use an equivalent noise model to derive the parameters of internal noise and number of samples, both of which we show are normal in amblyopia for this task. This result is in apparent conflict with a previous study in amblyopes showing that global motion processing is defective in global coherence tasks [Vision Res. 43, 729 (2003)]. A similar discrepancy between the normalcy of signal integration [Vision Res. 44, 2955 (2004)] and anomalous global coherence form processing has also been reported [Vision Res. 45, 449 (2005)]. We suggest that these discrepancies for form and motion processing in amblyopia point to a selective problem in separating signal from noise in the typical global coherence task.

Modal parameter identification based on combining transmissibility functions and blind source separation techniques

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Sánchez, Jesús Antonio García; Andersen, Palle

2018-05-01

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility-based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios.

Novel multireceiver communication systems configurations based on optimal estimation theory

NASA Technical Reports Server (NTRS)

Kumar, Rajendra

1992-01-01

A novel multireceiver configuration for carrier arraying and/or signal arraying is presented. The proposed configuration is obtained by formulating the carrier and/or signal arraying problem as an optimal estimation problem, and it consists of two stages. The first stage optimally estimates various phase processes received at different receivers with coupled phase-locked loops wherein the individual loops acquire and track their respective receivers' phase processes but are aided by each other in an optimal manner via LF error signals. The proposed configuration results in the minimization of the the effective radio loss at the combiner output, and thus maximization of energy per bit to noise power spectral density ratio is achieved. A novel adaptive algorithm for the estimator of the signal model parameters when these are not known a priori is also presented.

The "Haptic Finger"- a new device for monitoring skin condition.

PubMed

Tanaka, Mami; Lévêque, Jean Luc; Tagami, Hachiro; Kikuchi, Katsuko; Chonan, Seifi

2003-05-01

Touching the skin is of great importance for the Clinician for assessing roughness, softness, firmness, etc. This type of clinical assessment is very subjective and therefore non-reproducible from one Clinician to another one or even from time to time for the same Clinician. In order to objectively monitor skin texture, we developed a new sensor, placed directly on the Clinician's finger, which generate some electric signal when slid over the skin surface. The base of this Haptic Finger sensor is a thin stainless steel plate on which sponge rubber, PVDF foil, acetate film and gauze are layered. The signal generated by the sensor was filtered and digitally stored before processing. In a first in vitro experiment, the sensor was moved over different skin models (sponge rubber covered by silicon rubber) of varying hardness and roughness. These experiments allowed the definition of two parameters characterizing textures. The first parameter is variance of the signal processed using wavelet analysis, representing an index of roughness. The second parameter is dispersion of the power spectrum density in the frequency domain, corresponding to hardness. To validate these parameters, the Haptic Finger was used to scan skin surfaces of 30 people, 14 of whom displayed a skin disorder: xerosis (n = 5), atopic dermatitis (n = 7), and psoriasis (n = 2). The results obtained by means of the sensor were compared with subjective, clinical evaluations by a Clinician who scored both roughness and hardness of the skin. Good agreement was observed between clinical assessment of the skin and the two parameters generated using the Haptic Finger. Use of this sensor could prove extremely valuable in cosmetic research where skin surface texture (in terms of tactile properties) is difficult to measure.

Systems for monitoring and digitally recording water-quality parameters

USGS Publications Warehouse

Smoot, George F.; Blakey, James F.

1966-01-01

Digital recording of water-quality parameters is a link in the automated data collection and processing system of the U.S. Geological Survey. The monitoring and digital recording systems adopted by the Geological Survey, while punching all measurements on a standard paper tape, provide a choice of compatible components to construct a system to meet specific physical problems and data needs. As many as 10 parameters can be recorded by an Instrument, with the only limiting criterion being that measurements are expressed as electrical signals.

Quantitative Förster resonance energy transfer analysis for kinetic determinations of SUMO-specific protease.

PubMed

Liu, Yan; Song, Yang; Madahar, Vipul; Liao, Jiayu

2012-03-01

Förster resonance energy transfer (FRET) technology has been widely used in biological and biomedical research, and it is a very powerful tool for elucidating protein interactions in either dynamic or steady state. SUMOylation (the process of SUMO [small ubiquitin-like modifier] conjugation to substrates) is an important posttranslational protein modification with critical roles in multiple biological processes. Conjugating SUMO to substrates requires an enzymatic cascade. Sentrin/SUMO-specific proteases (SENPs) act as an endopeptidase to process the pre-SUMO or as an isopeptidase to deconjugate SUMO from its substrate. To fully understand the roles of SENPs in the SUMOylation cycle, it is critical to understand their kinetics. Here, we report a novel development of a quantitative FRET-based protease assay for SENP1 kinetic parameter determination. The assay is based on the quantitative analysis of the FRET signal from the total fluorescent signal at acceptor emission wavelength, which consists of three components: donor (CyPet-SUMO1) emission, acceptor (YPet) emission, and FRET signal during the digestion process. Subsequently, we developed novel theoretical and experimental procedures to determine the kinetic parameters, k(cat), K(M), and catalytic efficiency (k(cat)/K(M)) of catalytic domain SENP1 toward pre-SUMO1. Importantly, the general principles of this quantitative FRET-based protease kinetic determination can be applied to other proteases. Copyright © 2011 Elsevier Inc. All rights reserved.

Lateral position detection and control for friction stir systems

DOEpatents

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

2012-06-05

An apparatus and computer program are disclosed for processing at least one workpiece using a rotary tool with rotating member for contacting and processing the workpiece. The methods include oscillating the rotary tool laterally with respect to a selected propagation path for the rotating member with respect to the workpiece to define an oscillation path for the rotating member. The methods further include obtaining force signals or parameters related to the force experienced by the rotary tool at least while the rotating member is disposed at the extremes of the oscillation. The force signals or parameters associated with the extremes can then be analyzed to determine a lateral position of the selected path with respect to a target path and a lateral offset value can be determined based on the lateral position. The lateral distance between the selected path and the target path can be decreased based on the lateral offset value.

Lateral position detection and control for friction stir systems

DOEpatents

Fleming, Paul [Boulder, CO; Lammlein, David H [Houston, TX; Cook, George E [Brentwood, TN; Wilkes, Don Mitchell [Nashville, TN; Strauss, Alvin M [Nashville, TN; Delapp, David R [Ashland City, TN; Hartman, Daniel A [Fairhope, AL

2011-11-08

Friction stir methods are disclosed for processing at least one workpiece using a rotary tool with rotating member for contacting and processing the workpiece. The methods include oscillating the rotary tool laterally with respect to a selected propagation path for the rotating member with respect to the workpiece to define an oscillation path for the rotating member. The methods further include obtaining force signals or parameters related to the force experienced by the rotary tool at least while the rotating member is disposed at the extremes of the oscillation. The force signals or parameters associated with the extremes can then be analyzed to determine a lateral position of the selected path with respect to a target path and a lateral offset value can be determined based on the lateral position. The lateral distance between the selected path and the target path can be decreased based on the lateral offset value.

Foraging for brain stimulation: toward a neurobiology of computation.

PubMed

Gallistel, C R

1994-01-01

The self-stimulating rat performs foraging tasks mediated by simple computations that use interreward intervals and subjective reward magnitudes to determine stay durations. This is a simplified preparation in which to study the neurobiology of the elementary computational operations that make cognition possible, because the neural signal specifying the value of a computationally relevant variable is produced by direct electrical stimulation of a neural pathway. Newly developed measurement methods yield functions relating the subjective reward magnitude to the parameters of the neural signal. These measurements also show that the decision process that governs foraging behavior divides the subjective reward magnitude by the most recent interreward interval to determine the preferability of an option (a foraging patch). The decision process sets the parameters that determine stay durations (durations of visits to foraging patches) so that the ratios of the stay durations match the ratios of the preferabilities.

Error free all optical wavelength conversion in highly nonlinear As-Se chalcogenide glass fiber.

PubMed

Ta'eed, Vahid G; Fu, Libin; Pelusi, Mark; Rochette, Martin; Littler, Ian C; Moss, David J; Eggleton, Benjamin J

2006-10-30

We present the first demonstration of all optical wavelength conversion in chalcogenide glass fiber including system penalty measurements at 10 Gb/s. Our device is based on As2Se3 chalcogenide glass fiber which has the highest Kerr nonlinearity (n(2)) of any fiber to date for which either advanced all optical signal processing functions or system penalty measurements have been demonstrated. We achieve wavelength conversion via cross phase modulation over a 10 nm wavelength range near 1550 nm with 7 ps pulses at 2.1 W peak pump power in 1 meter of fiber, achieving only 1.4 dB excess system penalty. Analysis and comparison of the fundamental fiber parameters, including nonlinear coefficient, two-photon absorption coefficient and dispersion parameter with other nonlinear glasses shows that As(2)Se(3) based devices show considerable promise for radically integrated nonlinear signal processing devices.

Validation of Ionosonde Electron Density Reconstruction Algorithms with IONOLAB-RAY in Central Europe

NASA Astrophysics Data System (ADS)

Gok, Gokhan; Mosna, Zbysek; Arikan, Feza; Arikan, Orhan; Erdem, Esra

2016-07-01

Ionospheric observation is essentially accomplished by specialized radar systems called ionosondes. The time delay between the transmitted and received signals versus frequency is measured by the ionosondes and the received signals are processed to generate ionogram plots, which show the time delay or reflection height of signals with respect to transmitted frequency. The critical frequencies of ionospheric layers and virtual heights, that provide useful information about ionospheric structurecan be extracted from ionograms . Ionograms also indicate the amount of variability or disturbances in the ionosphere. With special inversion algorithms and tomographical methods, electron density profiles can also be estimated from the ionograms. Although structural pictures of ionosphere in the vertical direction can be observed from ionosonde measurements, some errors may arise due to inaccuracies that arise from signal propagation, modeling, data processing and tomographic reconstruction algorithms. Recently IONOLAB group (www.ionolab.org) developed a new algorithm for effective and accurate extraction of ionospheric parameters and reconstruction of electron density profile from ionograms. The electron density reconstruction algorithm applies advanced optimization techniques to calculate parameters of any existing analytical function which defines electron density with respect to height using ionogram measurement data. The process of reconstructing electron density with respect to height is known as the ionogram scaling or true height analysis. IONOLAB-RAY algorithm is a tool to investigate the propagation path and parameters of HF wave in the ionosphere. The algorithm models the wave propagation using ray representation under geometrical optics approximation. In the algorithm , the structural ionospheric characteristics arerepresented as realistically as possible including anisotropicity, inhomogenity and time dependence in 3-D voxel structure. The algorithm is also used for various purposes including calculation of actual height and generation of ionograms. In this study, the performance of electron density reconstruction algorithm of IONOLAB group and standard electron density profile algorithms of ionosondes are compared with IONOLAB-RAY wave propagation simulation in near vertical incidence. The electron density reconstruction and parameter extraction algorithms of ionosondes are validated with the IONOLAB-RAY results both for quiet anddisturbed ionospheric states in Central Europe using ionosonde stations such as Pruhonice and Juliusruh . It is observed that IONOLAB ionosonde parameter extraction and electron density reconstruction algorithm performs significantly better compared to standard algorithms especially for disturbed ionospheric conditions. IONOLAB-RAY provides an efficient and reliable tool to investigate and validate ionosonde electron density reconstruction algorithms, especially in determination of reflection height (true height) of signals and critical parameters of ionosphere. This study is supported by TUBITAK 114E541, 115E915 and Joint TUBITAK 114E092 and AS CR 14/001 projects.

Lateral position detection and control for friction stir systems

DOEpatents

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

2010-12-14

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

The effect of epoch length on time and frequency domain parameters of electromyographic and mechanomyographic signals.

PubMed

Keller, Joshua L; Housh, Terry J; Camic, Clayton L; Bergstrom, Haley C; Smith, Doug B; Smith, Cory M; Hill, Ethan C; Schmidt, Richard J; Johnson, Glen O; Zuniga, Jorge M

2018-06-01

The selection of epoch lengths affects the time and frequency resolution of electromyographic (EMG) and mechanomyographic (MMG) signals, as well as decisions regarding the signal processing techniques to use for determining the power density spectrum. No previous studies, however, have examined the effects of epoch length on parameters of the MMG signal. The purpose of this study was to examine the differences between epoch lengths for EMG amplitude, EMG mean power frequency (MPF), MMG amplitude, and MMG MPF from the VL and VM muscles during MVIC muscle actions as well as at each 10% of the time to exhaustion (TTE) during a continuous isometric muscle action of the leg extensors at 50% of MVIC. During the MVIC trial, there were no significant (p > 0.05) differences between epoch lengths (0.25, 0.50, 1.00, and 2.00-s) for mean absolute values for any of the EMG or MMG parameters. During the submaximal, sustained muscle action, however, absolute MMG amplitude and MMG MPF were affected by the length of epoch. All epoch related differences were eliminated by normalizing the absolute values to MVIC. These findings supported normalizing EMG and MMG parameter values to MVIC and utilizing epoch lengths that ranged from 0.25 to 2.00-s. Copyright © 2018 Elsevier Ltd. All rights reserved.

A novel approach for the elimination of artefacts from EEG signals employing an improved Artificial Immune System algorithm

NASA Astrophysics Data System (ADS)

Suja Priyadharsini, S.; Edward Rajan, S.; Femilin Sheniha, S.

2016-03-01

Electroencephalogram (EEG) is the recording of electrical activities of the brain. It is contaminated by other biological signals, such as cardiac signal (electrocardiogram), signals generated by eye movement/eye blinks (electrooculogram) and muscular artefact signal (electromyogram), called artefacts. Optimisation is an important tool for solving many real-world problems. In the proposed work, artefact removal, based on the adaptive neuro-fuzzy inference system (ANFIS) is employed, by optimising the parameters of ANFIS. Artificial Immune System (AIS) algorithm is used to optimise the parameters of ANFIS (ANFIS-AIS). Implementation results depict that ANFIS-AIS is effective in removing artefacts from EEG signal than ANFIS. Furthermore, in the proposed work, improved AIS (IAIS) is developed by including suitable selection processes in the AIS algorithm. The performance of the proposed method IAIS is compared with AIS and with genetic algorithm (GA). Measures such as signal-to-noise ratio, mean square error (MSE) value, correlation coefficient, power spectrum density plot and convergence time are used for analysing the performance of the proposed method. From the results, it is found that the IAIS algorithm converges faster than the AIS and performs better than the AIS and GA. Hence, IAIS tuned ANFIS (ANFIS-IAIS) is effective in removing artefacts from EEG signals.

Hemodynamic and electrophysiological signals of conflict processing in the Chinese-character Stroop task: a simultaneous near-infrared spectroscopy and event-related potential study

NASA Astrophysics Data System (ADS)

Zhai, Jiahuan; Li, Ting; Zhang, Zhongxing; Gong, Hui

2009-09-01

A dual-modality method combining continuous-wave near-infrared spectroscopy (NIRS) and event-related potentials (ERPs) was developed for the Chinese-character color-word Stroop task, which included congruent, incongruent, and neutral stimuli. Sixteen native Chinese speakers participated in this study. Hemodynamic and electrophysiological signals in the prefrontal cortex (PFC) were monitored simultaneously by NIRS and ERP. The hemodynamic signals were represented by relative changes in oxy-, deoxy-, and total hemoglobin concentration, whereas the electrophysiological signals were characterized by the parameters P450, N500, and P600. Both types of signals measured at four regions of the PFC were analyzed and compared spatially and temporally among the three different stimuli. We found that P600 signals correlated significantly with the hemodynamic parameters, suggesting that the PFC executes conflict-solving function. Additionally, we observed that the change in deoxy-Hb concentration showed higher sensitivity in response to the Stroop task than other hemodynamic signals. Correlation between NIRS and ERP signals revealed that the vascular response reflects the cumulative effect of neural activities. Taken together, our findings demonstrate that this new dual-modality method is a useful approach to obtaining more information during cognitive and physiological studies.

Hemodynamic and electrophysiological signals of conflict processing in the Chinese-character Stroop task: a simultaneous near-infrared spectroscopy and event-related potential study.

PubMed

Zhai, Jiahuan; Li, Ting; Zhang, Zhongxing; Gong, Hui

2009-01-01

A dual-modality method combining continuous-wave near-infrared spectroscopy (NIRS) and event-related potentials (ERPs) was developed for the Chinese-character color-word Stroop task, which included congruent, incongruent, and neutral stimuli. Sixteen native Chinese speakers participated in this study. Hemodynamic and electrophysiological signals in the prefrontal cortex (PFC) were monitored simultaneously by NIRS and ERP. The hemodynamic signals were represented by relative changes in oxy-, deoxy-, and total hemoglobin concentration, whereas the electrophysiological signals were characterized by the parameters P450, N500, and P600. Both types of signals measured at four regions of the PFC were analyzed and compared spatially and temporally among the three different stimuli. We found that P600 signals correlated significantly with the hemodynamic parameters, suggesting that the PFC executes conflict-solving function. Additionally, we observed that the change in deoxy-Hb concentration showed higher sensitivity in response to the Stroop task than other hemodynamic signals. Correlation between NIRS and ERP signals revealed that the vascular response reflects the cumulative effect of neural activities. Taken together, our findings demonstrate that this new dual-modality method is a useful approach to obtaining more information during cognitive and physiological studies.

In-line mixing states monitoring of suspensions using ultrasonic reflection technique.

PubMed

Zhan, Xiaobin; Yang, Yili; Liang, Jian; Zou, Dajun; Zhang, Jiaqi; Feng, Luyi; Shi, Tielin; Li, Xiwen

2016-02-01

Based on the measurement of echo signal changes caused by different concentration distributions in the mixing process, a simple ultrasonic reflection technique is proposed for in-line monitoring of the mixing states of suspensions in an agitated tank in this study. The relation between the echo signals and the concentration of suspensions is studied, and the mixing process of suspensions is tracked by in-line measurement of ultrasonic echo signals using two ultrasonic sensors. Through the analysis of echo signals over time, the mixing states of suspensions are obtained, and the homogeneity of suspensions is quantified. With the proposed technique, the effects of impeller diameter and agitation speed on the mixing process are studied, and the optimal agitation speed and the minimum mixing time to achieve the maximum homogeneity are acquired under different operating conditions and design parameters. The proposed technique is stable and feasible and shows great potential for in-line monitoring of mixing states of suspensions. Copyright © 2015 Elsevier B.V. All rights reserved.

Development of Parallel Architectures for Sensor Array Processing. Volume 1

DTIC Science & Technology

1993-08-01

required for the DOA estimation [ 1-7]. The Multiple Signal Classification ( MUSIC ) [ 1] and the Estimation of Signal Parameters by Rotational...manifold and the estimated subspace. Although MUSIC is a high resolution algorithm, it has several drawbacks including the fact that complete knowledge of...thoroughly, MUSIC algorithm was selected to develop special purpose hardware for real time computation. Summary of the MUSIC algorithm is as follows

Development of a battery status monitor

NASA Technical Reports Server (NTRS)

Zimmerman, R. I.

1974-01-01

A prototype battery status monitor system has been developed. The functions of the system are: (1) to provide the energy status of the battery, (2) to measure and transmit basic battery parameters, (3) to process these measurements required to determine abnormal functioning of the battery, and (4) to transmit warning signals of the abnormal condition along with a go/no go signal. The system was developed for use with the space shuttle.

A real-time GNSS-R system based on software-defined radio and graphics processing units

NASA Astrophysics Data System (ADS)

Hobiger, Thomas; Amagai, Jun; Aida, Masanori; Narita, Hideki

2012-04-01

Reflected signals of the Global Navigation Satellite System (GNSS) from the sea or land surface can be utilized to deduce and monitor physical and geophysical parameters of the reflecting area. Unlike most other remote sensing techniques, GNSS-Reflectometry (GNSS-R) operates as a passive radar that takes advantage from the increasing number of navigation satellites that broadcast their L-band signals. Thereby, most of the GNSS-R receiver architectures are based on dedicated hardware solutions. Software-defined radio (SDR) technology has advanced in the recent years and enabled signal processing in real-time, which makes it an ideal candidate for the realization of a flexible GNSS-R system. Additionally, modern commodity graphic cards, which offer massive parallel computing performances, allow to handle the whole signal processing chain without interfering with the PC's CPU. Thus, this paper describes a GNSS-R system which has been developed on the principles of software-defined radio supported by General Purpose Graphics Processing Units (GPGPUs), and presents results from initial field tests which confirm the anticipated capability of the system.

Aerospace Applications Conference, Steamboat Springs, CO, Feb. 1-8, 1986, Digest

NASA Astrophysics Data System (ADS)

The present conference considers topics concerning the projected NASA Space Station's systems, digital signal and data processing applications, and space science and microwave applications. Attention is given to Space Station video and audio subsystems design, clock error, jitter, phase error and differential time-of-arrival in satellite communications, automation and robotics in space applications, target insertion into synthetic background scenes, and a novel scheme for the computation of the discrete Fourier transform on a systolic processor. Also discussed are a novel signal parameter measurement system employing digital signal processing, EEPROMS for spacecraft applications, a unique concurrent processor architecture for high speed simulation of dynamic systems, a dual polarization flat plate antenna, Fresnel diffraction, and ultralinear TWTs for high efficiency satellite communications.

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.

Method and apparatus for analog signal conditioner for high speed, digital x-ray spectrometer

DOEpatents

Warburton, W.K.; Hubbard, B.

1999-02-09

A signal processing system which accepts input from an x-ray detector-preamplifier and produces a signal of reduced dynamic range for subsequent analog-to-digital conversion is disclosed. The system conditions the input signal to reduce the number of bits required in the analog-to-digital converter by removing that part of the input signal which varies only slowly in time and retaining the amplitude of the pulses which carry information about the x-rays absorbed by the detector. The parameters controlling the signal conditioner`s operation can be readily supplied in digital form, allowing it to be integrated into a feedback loop as part of a larger digital x-ray spectroscopy system. 13 figs.

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).

Data Unfolding with Wiener-SVD Method

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

Tang, W.; Li, X.; Qian, X.

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Data Unfolding with Wiener-SVD Method

DOE PAGES

Tang, W.; Li, X.; Qian, X.; ...

2017-10-04

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Intraoperative monitoring of somatosensory-evoked potential in the spinal cord rectification operation by means of wavelet analysis

NASA Astrophysics Data System (ADS)

Liu, W.; Du, M. H.; Chan, Francis H. Y.; Lam, F. K.; Luk, D. K.; Hu, Y.; Fung, Kan S. M.; Qiu, W.

1998-09-01

Recently there has been a considerable interest in the use of a somatosensory evoked potential (SEP) for monitoring the functional integrity of the spinal cord during surgery such as spinal scoliosis. This paper describes a monitoring system and signal processing algorithms, which consists of 50 Hz mains filtering and a wavelet signal analyzer. Our system allows fast detection of changes in SEP peak latency, amplitude and signal waveform, which are the main parameters of interest during intra-operative procedures.

Split-spectrum processing technique for SNR enhancement of ultrasonic guided wave.

PubMed

Pedram, Seyed Kamran; Fateri, Sina; Gan, Lu; Haig, Alex; Thornicroft, Keith

2018-02-01

Ultrasonic guided wave (UGW) systems are broadly used in several branches of industry where the structural integrity is of concern. In those systems, signal interpretation can often be challenging due to the multi-modal and dispersive propagation of UGWs. This results in degradation of the signals in terms of signal-to-noise ratio (SNR) and spatial resolution. This paper employs the split-spectrum processing (SSP) technique in order to enhance the SNR and spatial resolution of UGW signals using the optimized filter bank parameters in real time scenario for pipe inspection. SSP technique has already been developed for other applications such as conventional ultrasonic testing for SNR enhancement. In this work, an investigation is provided to clarify the sensitivity of SSP performance to the filter bank parameter values for UGWs such as processing bandwidth, filter bandwidth, filter separation and a number of filters. As a result, the optimum values are estimated to significantly improve the SNR and spatial resolution of UGWs. The proposed method is synthetically and experimentally compared with conventional approaches employing different SSP recombination algorithms. The Polarity Thresholding (PT) and PT with Minimization (PTM) algorithms were found to be the best recombination algorithms. They substantially improved the SNR up to 36.9dB and 38.9dB respectively. The outcome of the work presented in this paper paves the way to enhance the reliability of UGW inspections. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling the Atmospheric Phase Effects of a Digital Antenna Array Communications System

NASA Technical Reports Server (NTRS)

Tkacenko, A.

2006-01-01

In an antenna array system such as that used in the Deep Space Network (DSN) for satellite communication, it is often necessary to account for the effects due to the atmosphere. Typically, the atmosphere induces amplitude and phase fluctuations on the transmitted downlink signal that invalidate the assumed stationarity of the signal model. The degree to which these perturbations affect the stationarity of the model depends both on parameters of the atmosphere, including wind speed and turbulence strength, and on parameters of the communication system, such as the sampling rate used. In this article, we focus on modeling the atmospheric phase fluctuations in a digital antenna array communications system. Based on a continuous-time statistical model for the atmospheric phase effects, we show how to obtain a related discrete-time model based on sampling the continuous-time process. The effects of the nonstationarity of the resulting signal model are investigated using the sample matrix inversion (SMI) algorithm for minimum mean-squared error (MMSE) equalization of the received signal

Noise source and reactor stability estimation in a boiling water reactor using a multivariate autoregressive model

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

Kanemoto, S.; Andoh, Y.; Sandoz, S.A.

1984-10-01

A method for evaluating reactor stability in boiling water reactors has been developed. The method is based on multivariate autoregressive (M-AR) modeling of steady-state neutron and process noise signals. In this method, two kinds of power spectral densities (PSDs) for the measured neutron signal and the corresponding noise source signal are separately identified by the M-AR modeling. The closed- and open-loop stability parameters are evaluated from these PSDs. The method is applied to actual plant noise data that were measured together with artificial perturbation test data. Stability parameters identified from noise data are compared to those from perturbation test data,more » and it is shown that both results are in good agreement. In addition to these stability estimations, driving noise sources for the neutron signal are evaluated by the M-AR modeling. Contributions from void, core flow, and pressure noise sources are quantitatively evaluated, and the void noise source is shown to be the most dominant.« less

Modeling and control parameters for GMAW, short-circuiting transfer

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

Cook, G.E.; DeLapp, D.R.; Barnett, R.J.

1996-12-31

Digital signal processing was used to analyze the electrical arc signals of the gas metal arc welding process with short-circuiting transfer. Among the features extracted were arc voltage and current (both average and peak values), short-circuiting frequency, arc period, shorting period, and the ratio of the arcing to shorting period. Additionally , a Joule heating model was derived which accurately predicted the melt-back distance during each short. The short-circuiting frequency, the ratio of the arc period to short periods, and the melt-back distance were found to be good indicators for monitoring and control of stable arc conditions.

Gmti Motion Compensation

DOEpatents

Doerry, Armin W.

2004-07-20

Movement of a GMTI radar during a coherent processing interval over which a set of radar pulses are processed may cause defocusing of a range-Doppler map in the video signal. This problem may be compensated by varying waveform or sampling parameters of each pulse to compensate for distortions caused by variations in viewing angles from the radar to the target.

Multivariate analysis techniques

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

Bendavid, Josh; Fisher, Wade C.; Junk, Thomas R.

2016-01-01

The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually bothmore » be improved by separating signal events from background events with higher efficiency and purity.« less

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.

The Thirty Gigahertz Instrument Receiver for the QUIJOTE Experiment: Preliminary Polarization Measurements and Systematic-Error Analysis.

PubMed

Casas, Francisco J; Ortiz, David; Villa, Enrique; Cano, Juan L; Cagigas, Jaime; Pérez, Ana R; Aja, Beatriz; Terán, J Vicente; de la Fuente, Luisa; Artal, Eduardo; Hoyland, Roger; Génova-Santos, Ricardo

2015-08-05

This paper presents preliminary polarization measurements and systematic-error characterization of the Thirty Gigahertz Instrument receiver developed for the QUIJOTE experiment. The instrument has been designed to measure the polarization of Cosmic Microwave Background radiation from the sky, obtaining the Q, U, and I Stokes parameters of the incoming signal simultaneously. Two kinds of linearly polarized input signals have been used as excitations in the polarimeter measurement tests in the laboratory; these show consistent results in terms of the Stokes parameters obtained. A measurement-based systematic-error characterization technique has been used in order to determine the possible sources of instrumental errors and to assist in the polarimeter calibration process.

Experimental Research and Mathematical Modeling of Parameters Effecting on Cutting Force and SurfaceRoughness in CNC Turning Process

NASA Astrophysics Data System (ADS)

Zeqiri, F.; Alkan, M.; Kaya, B.; Toros, S.

2018-01-01

In this paper, the effects of cutting parameters on cutting forces and surface roughness based on Taguchi experimental design method are determined. Taguchi L9 orthogonal array is used to investigate the effects of machining parameters. Optimal cutting conditions are determined using the signal/noise (S/N) ratio which is calculated by average surface roughness and cutting force. Using results of analysis, effects of parameters on both average surface roughness and cutting forces are calculated on Minitab 17 using ANOVA method. The material that was investigated is Inconel 625 steel for two cases with heat treatment and without heat treatment. The predicted and calculated values with measurement are very close to each other. Confirmation test of results showed that the Taguchi method was very successful in the optimization of machining parameters for maximum surface roughness and cutting forces in the CNC turning process.

Dyes assay for measuring physicochemical parameters.

PubMed

Moczko, Ewa; Meglinski, Igor V; Bessant, Conrad; Piletsky, Sergey A

2009-03-15

A combination of selective fluorescent dyes has been developed for simultaneous quantitative measurements of several physicochemical parameters. The operating principle of the assay is similar to electronic nose and tongue systems, which combine nonspecific or semispecific elements for the determination of diverse analytes and chemometric techniques for multivariate data analysis. The analytical capability of the proposed mixture is engendered by changes in fluorescence signal in response to changes in environment such as pH, temperature, ionic strength, and presence of oxygen. The signal is detected by a three-dimensional spectrofluorimeter, and the acquired data are processed using an artificial neural network (ANN) for multivariate calibration. The fluorescence spectrum of a solution of selected dyes allows discreet reading of emission maxima of all dyes composing the mixture. The variations in peaks intensities caused by environmental changes provide distinctive fluorescence patterns which can be handled in the same way as the signals collected from nose/tongue electrochemical or piezoelectric devices. This optical system opens possibilities for rapid, inexpensive, real-time detection of a multitude of physicochemical parameters and analytes of complex samples.

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

Microwave life detector for buried victims using neutrodyning loop based system

NASA Astrophysics Data System (ADS)

Tahar J., Bel Hadj

2009-07-01

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

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

NASA Astrophysics Data System (ADS)

Kora, Padmavathi; Sri Rama Krishna, K.

2016-12-01

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

Distributed traffic signal control using fuzzy logic

NASA Technical Reports Server (NTRS)

Chiu, Stephen

1992-01-01

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

Vector network analyzer ferromagnetic resonance spectrometer with field differential detection

NASA Astrophysics Data System (ADS)

Tamaru, S.; Tsunegi, S.; Kubota, H.; Yuasa, S.

2018-05-01

This work presents a vector network analyzer ferromagnetic resonance (VNA-FMR) spectrometer with field differential detection. This technique differentiates the S-parameter by applying a small binary modulation field in addition to the DC bias field to the sample. By setting the modulation frequency sufficiently high, slow sensitivity fluctuations of the VNA, i.e., low-frequency components of the trace noise, which limit the signal-to-noise ratio of the conventional VNA-FMR spectrometer, can be effectively removed, resulting in a very clean FMR signal. This paper presents the details of the hardware implementation and measurement sequence as well as the data processing and analysis algorithms tailored for the FMR spectrum obtained with this technique. Because the VNA measures a complex S-parameter, it is possible to estimate the Gilbert damping parameter from the slope of the phase variation of the S-parameter with respect to the bias field. We show that this algorithm is more robust against noise than the conventional algorithm based on the linewidth.

Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments

NASA Astrophysics Data System (ADS)

Zhaunerchyk, V.; Frasinski, L. J.; Eland, J. H. D.; Feifel, R.

2014-05-01

Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.

Implant for in-vivo parameter monitoring, processing and transmitting

DOEpatents

Ericson, Milton N [Knoxville, TN; McKnight, Timothy E [Greenback, TN; Smith, Stephen F [London, TN; Hylton, James O [Clinton, TN

2009-11-24

The present invention relates to a completely implantable intracranial pressure monitor, which can couple to existing fluid shunting systems as well as other internal monitoring probes. The implant sensor produces an analog data signal which is then converted electronically to a digital pulse by generation of a spreading code signal and then transmitted to a location outside the patient by a radio-frequency transmitter to an external receiver. The implanted device can receive power from an internal source as well as an inductive external source. Remote control of the implant is also provided by a control receiver which passes commands from an external source to the implant system logic. Alarm parameters can be programmed into the device which are capable of producing an audible or visual alarm signal. The utility of the monitor can be greatly expanded by using multiple pressure sensors simultaneously or by combining sensors of various physiological types.

Amplitude and Phase Characteristics of Signals at the Output of Spatially Separated Antennas for Paths with Scattering

NASA Astrophysics Data System (ADS)

Anikin, A. S.

2018-06-01

Conditional statistical characteristics of the phase difference are considered depending on the ratio of instantaneous output signal amplitudes of spatially separated weakly directional antennas for the normal field model for paths with radio-wave scattering. The dependences obtained are related to the physical processes on the radio-wave propagation path. The normal model parameters are established at which the statistical characteristics of the phase difference depend on the ratio of the instantaneous amplitudes and hence can be used to measure the phase difference. Using Shannon's formula, the amount of information on the phase difference of signals contained in the ratio of their amplitudes is calculated depending on the parameters of the normal field model. Approaches are suggested to reduce the shift of phase difference measured for paths with radio-wave scattering. A comparison with results of computer simulation by the Monte Carlo method is performed.

Cardiac Care Assistance using Self Configured Sensor Network—a Remote Patient Monitoring System

NASA Astrophysics Data System (ADS)

Sarma Dhulipala, V. R.; Kanagachidambaresan, G. R.

2014-04-01

Pervasive health care systems are used to monitor patients remotely without disturbing the normal day-to-day activities in real-time. Wearable physiological sensors required to monitor various significant ecological parameters of the patients are connected to Body Central Unit (BCU). Body Sensor Network (BSN) updates data in real-time and are designed to transmit alerts against abnormalities which enables quick response by medical units in case of an emergency. BSN helps monitoring patient without any need for attention to the subject. BSN helps in reducing the stress and strain caused by hospital environment. In this paper, mathematical models for heartbeat signal, electro cardio graph (ECG) signal and pulse rate are introduced. These signals are compared and their RMS difference-fast Fourier transforms (PRD-FFT) are processed. In the context of cardiac arrest, alert messages of these parameters and first aid for post-surgical operations has been suggested.

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Negative BOLD response and serotonin concentration within rostral subgenual portion of the anterior cingulate cortex for long-allele carriers during perceptual processing of emotional tasks

NASA Astrophysics Data System (ADS)

Hadi, Shamil M.; Siadat, Mohamad R.; Babajani-Feremi, Abbas

2012-03-01

We investigated the effect of synaptic serotonin concentration on hemodynamic responses. The stimuli paradigm involved the presentation of fearful and threatening facial expressions to a set of 24 subjects who were either5HTTLPR long- or short-allele carriers (12 of each type in each group). The BOLD signals of the rACC from subjects of each group were averaged to increase the signal-to-noise ratio. We used a Bayesian approach to estimate the parameters of the underlying hemodynamic model. Our results, during this perceptual processing of emotional task, showed a negative BOLD signal in the rACC in the subjects with long-alleles. In contrast, the subjects with short-alleles showed positive BOLD signals in the rACC. These results suggest that high synaptic serotonin concentration in the rACC inhibits neuronal activity in a fashion similar to GABA, and a consequent negative BOLD signal ensues.

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.

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

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

Impact of the Test Device on the Behavior of the Acoustic Emission Signals: Contribution of the Numerical Modeling to Signal Processing

NASA Astrophysics Data System (ADS)

Issiaka Traore, Oumar; Cristini, Paul; Favretto-Cristini, Nathalie; Pantera, Laurent; Viguier-Pla, Sylvie

2018-01-01

In a context of nuclear safety experiment monitoring with the non destructive testing method of acoustic emission, we study the impact of the test device on the interpretation of the recorded physical signals by using spectral finite element modeling. The numerical results are validated by comparison with real acoustic emission data obtained from previous experiments. The results show that several parameters can have significant impacts on acoustic wave propagation and then on the interpretation of the physical signals. The potential position of the source mechanism, the positions of the receivers and the nature of the coolant fluid have to be taken into account in the definition a pre-processing strategy of the real acoustic emission signals. In order to show the relevance of such an approach, we use the results to propose an optimization of the positions of the acoustic emission sensors in order to reduce the estimation bias of the time-delay and then improve the localization of the source mechanisms.

Collision avoidance system cost-benefit analysis : volume I - technical manual

DOT National Transportation Integrated Search

1981-09-01

Collision-avoidance systems under development in the U.S.A., Japan and Germany were evaluated. The performance evaluation showed that the signal processing and the control law of a system were the key parameters that decided the system's capability, ...

Real-time windowing in imaging radar using FPGA technique

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Escamilla-Hernandez, Enrique

2005-02-01

The imaging radar uses the high frequency electromagnetic waves reflected from different objects for estimating of its parameters. Pulse compression is a standard signal processing technique used to minimize the peak transmission power and to maximize SNR, and to get a better resolution. Usually the pulse compression can be achieved using a matched filter. The level of the side-lobes in the imaging radar can be reduced using the special weighting function processing. There are very known different weighting functions: Hamming, Hanning, Blackman, Chebyshev, Blackman-Harris, Kaiser-Bessel, etc., widely used in the signal processing applications. Field Programmable Gate Arrays (FPGAs) offers great benefits like instantaneous implementation, dynamic reconfiguration, design, and field programmability. This reconfiguration makes FPGAs a better solution over custom-made integrated circuits. This work aims at demonstrating a reasonably flexible implementation of FM-linear signal and pulse compression using Matlab, Simulink, and System Generator. Employing FPGA and mentioned software we have proposed the pulse compression design on FPGA using classical and novel windows technique to reduce the side-lobes level. This permits increasing the detection ability of the small or nearly placed targets in imaging radar. The advantage of FPGA that can do parallelism in real time processing permits to realize the proposed algorithms. The paper also presents the experimental results of proposed windowing procedure in the marine radar with such the parameters: signal is linear FM (Chirp); frequency deviation DF is 9.375MHz; the pulse width T is 3.2μs taps number in the matched filter is 800 taps; sampling frequency 253.125*106 MHz. It has been realized the reducing of side-lobes levels in real time permitting better resolution of the small targets.

Discovery of Intramolecular Signal Transduction Network Based on a New Protein Dynamics Model of Energy Dissipation

PubMed Central

Ma, Cheng-Wei; Xiu, Zhi-Long; Zeng, An-Ping

2012-01-01

A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins. PMID:22363664

Direct match data flow machine apparatus and process for data driven computing

DOEpatents

Davidson, G.S.; Grafe, V.G.

1997-08-12

A data flow computer and method of computing are disclosed which utilizes a data driven processor node architecture. The apparatus in a preferred embodiment includes a plurality of First-In-First-Out (FIFO) registers, a plurality of related data flow memories, and a processor. The processor makes the necessary calculations and includes a control unit to generate signals to enable the appropriate FIFO register receiving the result. In a particular embodiment, there are three FIFO registers per node: an input FIFO register to receive input information form an outside source and provide it to the data flow memories; an output FIFO register to provide output information from the processor to an outside recipient; and an internal FIFO register to provide information from the processor back to the data flow memories. The data flow memories are comprised of four commonly addressed memories. A parameter memory holds the A and B parameters used in the calculations; an opcode memory holds the instruction; a target memory holds the output address; and a tag memory contains status bits for each parameter. One status bit indicates whether the corresponding parameter is in the parameter memory and one status but to indicate whether the stored information in the corresponding data parameter is to be reused. The tag memory outputs a ``fire`` signal (signal R VALID) when all of the necessary information has been stored in the data flow memories, and thus when the instruction is ready to be fired to the processor. 11 figs.

Direct match data flow machine apparatus and process for data driven computing

DOEpatents

Davidson, George S.; Grafe, Victor Gerald

1997-01-01

A data flow computer and method of computing is disclosed which utilizes a data driven processor node architecture. The apparatus in a preferred embodiment includes a plurality of First-In-First-Out (FIFO) registers, a plurality of related data flow memories, and a processor. The processor makes the necessary calculations and includes a control unit to generate signals to enable the appropriate FIFO register receiving the result. In a particular embodiment, there are three FIFO registers per node: an input FIFO register to receive input information form an outside source and provide it to the data flow memories; an output FIFO register to provide output information from the processor to an outside recipient; and an internal FIFO register to provide information from the processor back to the data flow memories. The data flow memories are comprised of four commonly addressed memories. A parameter memory holds the A and B parameters used in the calculations; an opcode memory holds the instruction; a target memory holds the output address; and a tag memory contains status bits for each parameter. One status bit indicates whether the corresponding parameter is in the parameter memory and one status but to indicate whether the stored information in the corresponding data parameter is to be reused. The tag memory outputs a "fire" signal (signal R VALID) when all of the necessary information has been stored in the data flow memories, and thus when the instruction is ready to be fired to the processor.

United States Air Force Summer Research Program -- 1993 Summer Research Program Final Reports. Volume 11. Arnold Engineering Development Center, Frank J. Seiler Research Laboratory, Wilford Hall Medical Center

DTIC Science & Technology

1993-01-01

external parameters such as airflow, temperature, pressure, etc, are measured. Turbine Engine testing generates massive volumes of data at very high...a form that describes the signal flow graph topology as well as specific parameters of the processing blocks in the diagram. On multiprocessor...provides an interface to the symbolic builder and control functions such that parameters may be set during the build operation that will affect the

Hopf bifurcation and chaos in a third-order phase-locked loop

NASA Astrophysics Data System (ADS)

Piqueira, José Roberto C.

2017-01-01

Phase-locked loops (PLLs) are devices able to recover time signals in several engineering applications. The literature regarding their dynamical behavior is vast, specifically considering that the process of synchronization between the input signal, coming from a remote source, and the PLL local oscillation is robust. For high-frequency applications it is usual to increase the PLL order by increasing the order of the internal filter, for guarantying good transient responses; however local parameter variations imply structural instability, thus provoking a Hopf bifurcation and a route to chaos for the phase error. Here, one usual architecture for a third-order PLL is studied and a range of permitted parameters is derived, providing a rule of thumb for designers. Out of this range, a Hopf bifurcation appears and, by increasing parameters, the periodic solution originated by the Hopf bifurcation degenerates into a chaotic attractor, therefore, preventing synchronization.

Process observation in fiber laser-based selective laser melting

NASA Astrophysics Data System (ADS)

Thombansen, Ulrich; Gatej, Alexander; Pereira, Milton

2015-01-01

The process observation in selective laser melting (SLM) focuses on observing the interaction point where the powder is processed. To provide process relevant information, signals have to be acquired that are resolved in both time and space. Especially in high-power SLM, where more than 1 kW of laser power is used, processing speeds of several meters per second are required for a high-quality processing results. Therefore, an implementation of a suitable process observation system has to acquire a large amount of spatially resolved data at low sampling speeds or it has to restrict the acquisition to a predefined area at a high sampling speed. In any case, it is vitally important to synchronously record the laser beam position and the acquired signal. This is a prerequisite that allows the recorded data become information. Today, most SLM systems employ f-theta lenses to focus the processing laser beam onto the powder bed. This report describes the drawbacks that result for process observation and suggests a variable retro-focus system which solves these issues. The beam quality of fiber lasers delivers the processing laser beam to the powder bed at relevant focus diameters, which is a key prerequisite for this solution to be viable. The optical train we present here couples the processing laser beam and the process observation coaxially, ensuring consistent alignment of interaction zone and observed area. With respect to signal processing, we have developed a solution that synchronously acquires signals from a pyrometer and the position of the laser beam by sampling the data with a field programmable gate array. The relevance of the acquired signals has been validated by the scanning of a sample filament. Experiments with grooved samples show a correlation between different powder thicknesses and the acquired signals at relevant processing parameters. This basic work takes a first step toward self-optimization of the manufacturing process in SLM. It enables the addition of cognitive functions to the manufacturing system to the extent that the system could track its own process. The results are based on analyzing and redesigning the optical train, in combination with a real-time signal acquisition system which provides a solution to certain technological barriers.

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

PubMed

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

2010-01-01

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

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.

Reference analysis of the signal + background model in counting experiments II. Approximate reference prior

NASA Astrophysics Data System (ADS)

Casadei, D.

2014-10-01

The objective Bayesian treatment of a model representing two independent Poisson processes, labelled as ``signal'' and ``background'' and both contributing additively to the total number of counted events, is considered. It is shown that the reference prior for the parameter of interest (the signal intensity) can be well approximated by the widely (ab)used flat prior only when the expected background is very high. On the other hand, a very simple approximation (the limiting form of the reference prior for perfect prior background knowledge) can be safely used over a large portion of the background parameters space. The resulting approximate reference posterior is a Gamma density whose parameters are related to the observed counts. This limiting form is simpler than the result obtained with a flat prior, with the additional advantage of representing a much closer approximation to the reference posterior in all cases. Hence such limiting prior should be considered a better default or conventional prior than the uniform prior. On the computing side, it is shown that a 2-parameter fitting function is able to reproduce extremely well the reference prior for any background prior. Thus, it can be useful in applications requiring the evaluation of the reference prior for a very large number of times.

Improved performance of analog and digital acousto-optic modulation with feedback under profiled beam propagation for secure communication using chaos

NASA Astrophysics Data System (ADS)

Almehmadi, Fares S.; Chatterjee, Monish R.

2014-12-01

Using intensity feedback, the closed-loop behavior of an acousto-optic hybrid device under profiled beam propagation has been recently shown to exhibit wider chaotic bands potentially leading to an increase in both the dynamic range and sensitivity to key parameters that characterize the encryption. In this work, a detailed examination is carried out vis-à-vis the robustness of the encryption/decryption process relative to parameter mismatch for both analog and pulse code modulation signals, and bit error rate (BER) curves are used to examine the impact of additive white noise. The simulations with profiled input beams are shown to produce a stronger encryption key (i.e., much lower parametric tolerance thresholds) relative to simulations with uniform plane wave input beams. In each case, it is shown that the tolerance for key parameters drops by factors ranging from 10 to 20 times below those for uniform plane wave propagation. Results are shown to be at consistently lower tolerances for secure transmission of analog and digital signals using parameter tolerance measures, as well as BER performance measures for digital signals. These results hold out the promise for considerably greater information transmission security for such a system.

Hybrid graphene-copper UWB array sensor for brain tumor detection via scattering parameters in microwave detection system

NASA Astrophysics Data System (ADS)

Jamlos, Mohd Aminudin; Ismail, Abdul Hafiizh; Jamlos, Mohd Faizal; Narbudowicz, Adam

2017-01-01

Hybrid graphene-copper ultra-wideband array sensor applied to microwave imaging technique is successfully used in detecting and visualizing tumor inside human brain. The sensor made of graphene coated film for the patch while copper for both the transmission line and parasitic element. The hybrid sensor performance is better than fully copper sensor. Hybrid sensor recorded wider bandwidth of 2.0-10.1 GHz compared with fully copper sensor operated from 2.5 to 10.1 GHz. Higher gain of 3.8-8.5 dB is presented by hybrid sensor, while fully copper sensor stated lower gain ranging from 2.6 to 6.7 dB. Both sensors recorded excellent total efficiency averaged at 97 and 94%, respectively. The sensor used for both transmits equivalent signal and receives backscattering signal from stratified human head model in detecting tumor. Difference in the data of the scattering parameters recorded from the head model with presence and absence of tumor is used as the main data to be further processed in confocal microwave imaging algorithm in generating image. MATLAB software is utilized to analyze S-parameter signals obtained from measurement. Tumor presence is indicated by lower S-parameter values compared to higher values recorded by tumor absence.

Behavioural and neuroanatomical correlates of auditory speech analysis in primary progressive aphasias.

PubMed

Hardy, Chris J D; Agustus, Jennifer L; Marshall, Charles R; Clark, Camilla N; Russell, Lucy L; Bond, Rebecca L; Brotherhood, Emilie V; Thomas, David L; Crutch, Sebastian J; Rohrer, Jonathan D; Warren, Jason D

2017-07-27

Non-verbal auditory impairment is increasingly recognised in the primary progressive aphasias (PPAs) but its relationship to speech processing and brain substrates has not been defined. Here we addressed these issues in patients representing the non-fluent variant (nfvPPA) and semantic variant (svPPA) syndromes of PPA. We studied 19 patients with PPA in relation to 19 healthy older individuals. We manipulated three key auditory parameters-temporal regularity, phonemic spectral structure and prosodic predictability (an index of fundamental information content, or entropy)-in sequences of spoken syllables. The ability of participants to process these parameters was assessed using two-alternative, forced-choice tasks and neuroanatomical associations of task performance were assessed using voxel-based morphometry of patients' brain magnetic resonance images. Relative to healthy controls, both the nfvPPA and svPPA groups had impaired processing of phonemic spectral structure and signal predictability while the nfvPPA group additionally had impaired processing of temporal regularity in speech signals. Task performance correlated with standard disease severity and neurolinguistic measures. Across the patient cohort, performance on the temporal regularity task was associated with grey matter in the left supplementary motor area and right caudate, performance on the phoneme processing task was associated with grey matter in the left supramarginal gyrus, and performance on the prosodic predictability task was associated with grey matter in the right putamen. Our findings suggest that PPA syndromes may be underpinned by more generic deficits of auditory signal analysis, with a distributed cortico-subcortical neuraoanatomical substrate extending beyond the canonical language network. This has implications for syndrome classification and biomarker development.

Collision avoidance system cost-benefit analysis : volume III - appendices F-M

DOT National Transportation Integrated Search

1981-09-01

Collision-avoidance systems under development in the U.S.A., Japan and Germany were evaluated. The performance evaluation showed that the signal processing and the control law of a system were the key parameters that decided the system's capability, ...

Collision avoidance system cost-benefit analysis : volume II - appendices A-E

DOT National Transportation Integrated Search

1981-09-01

Collision-avoidance systems under development in the U.S.A., Japan and Germany were evaluated. The performance evaluation showed that the signal processing and the control law of a system were the key parameters that decided the system's capability, ...

Control of power to an inductively heated part

DOEpatents

Adkins, Douglas R.; Frost, Charles A.; Kahle, Philip M.; Kelley, J. Bruce; Stanton, Suzanne L.

1997-01-01

A process for induction hardening a part to a desired depth with an AC signal applied to the part from a closely coupled induction coil includes measuring the voltage of the AC signal at the coil and the current passing through the coil; and controlling the depth of hardening of the part from the measured voltage and current. The control system determines parameters of the part that are functions of applied voltage and current to the induction coil, and uses a neural network to control the application of the AC signal based on the detected functions for each part.

Control of power to an inductively heated part

DOEpatents

Adkins, D.R.; Frost, C.A.; Kahle, P.M.; Kelley, J.B.; Stanton, S.L.

1997-05-20

A process for induction hardening a part to a desired depth with an AC signal applied to the part from a closely coupled induction coil includes measuring the voltage of the AC signal at the coil and the current passing through the coil; and controlling the depth of hardening of the part from the measured voltage and current. The control system determines parameters of the part that are functions of applied voltage and current to the induction coil, and uses a neural network to control the application of the AC signal based on the detected functions for each part. 6 figs.

Multi-parameter Observations and Validation of Pre-earthquake Atmospheric Signals

NASA Astrophysics Data System (ADS)

Ouzounov, D.; Pulinets, S. A.; Hattori, K.; Mogi, T.; Kafatos, M.

2014-12-01

We are presenting the latest development in multi-sensors observations of short-term pre-earthquake phenomena preceding major earthquakes. We are exploring the potential of pre-seismic atmospheric and ionospheric signals to alert for large earthquakes. To achieve this, we start validating anomalous ionospheric /atmospheric signals in retrospective and prospective modes. The integrated satellite and terrestrial framework (ISTF) is our method for validation and is based on a joint analysis of several physical and environmental parameters (Satellite thermal infrared radiation (OLR), electron concentration in the ionosphere (GPS/TEC), VHF-bands radio waves, radon/ion activities, air temperature and seismicity patterns) that were found to be associated with earthquakes. The science rationale for multidisciplinary analysis is based on concept Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) [Pulinets and Ouzounov, 2011], which explains the synergy of different geospace processes and anomalous variations, usually named short-term pre-earthquake anomalies. Our validation processes consist in two steps: (1) A continuous retrospective analysis preformed over two different regions with high seismicity- Taiwan and Japan for 2003-2009 The retrospective tests (100+ major earthquakes, M>5.9, Taiwan and Japan) show OLR anomalous behavior before all of these events with no false negatives. False alarm ratio for false positives is less then 25%. (2) Prospective testing using multiple parameters with potential for M5.5+ events. The initial testing shows systematic appearance of atmospheric anomalies in advance (days) to the M5.5+ events for Taiwan and Japan (Honshu and Hokkaido areas). Our initial prospective results suggest that our approach show a systematic appearance of atmospheric anomalies, one to several days prior to the largest earthquakes That feature could be further studied and tested for advancing the multi-sensors detection of pre-earthquake atmospheric signals.

The influence of laser pulse duration and energy on ICP-MS signal intensity, elemental fractionation, and particle size distribution in NIR fs-LA-ICP-MS

PubMed Central

Diwakar, Prasoon K.; Harilal, Sivanandan S.; LaHaye, Nicole L.; Hassanein, Ahmed; Kulkarni, Pramod

2015-01-01

Laser parameters, typically wavelength, pulse width, irradiance, repetition rate, and pulse energy, are critical parameters which influence the laser ablation process and thereby influence the LA-ICP-MS signal. In recent times, femtosecond laser ablation has gained popularity owing to the reduction in fractionation related issues and improved analytical performance which can provide matrix-independent sampling. The advantage offered by fs-LA is due to shorter pulse duration of the laser as compared to the phonon relaxation time and heat diffusion time. Hence the thermal effects are minimized in fs-LA. Recently, fs-LA-ICP-MS demonstrated improved analytical performance as compared to ns-LA-ICP-MS, but detailed mechanisms and processes are still not clearly understood. Improvement of fs-LA-ICP-MS over ns-LA-ICP-MS elucidates the importance of laser pulse duration and related effects on the ablation process. In this study, we have investigated the influence of laser pulse width (40 fs to 0.3 ns) and energy on LA-ICP-MS signal intensity and repeatability using a brass sample. Experiments were performed in single spot ablation mode as well as rastering ablation mode to monitor the Cu/Zn ratio. The recorded ICP-MS signal was correlated with total particle counts generated during laser ablation as well as particle size distribution. Our results show the importance of pulse width effects in the fs regime that becomes more pronounced when moving from femtosecond to picosecond and nanosecond regimes. PMID:26664120

[INVITED] Evaluation of process observation features for laser metal welding

NASA Astrophysics Data System (ADS)

Tenner, Felix; Klämpfl, Florian; Nagulin, Konstantin Yu.; Schmidt, Michael

2016-06-01

In the present study we show how fast the fluid dynamics change when changing the laser power for different feed rates during laser metal welding. By the use of two high-speed cameras and a data acquisition system we conclude how fast we have to image the process to measure the fluid dynamics with a very high certainty. Our experiments show that not all process features which can be measured during laser welding do represent the process behavior similarly well. Despite the good visibility of the vapor plume the monitoring of its movement is less suitable as an input signal for a closed-loop control. The features measured inside the keyhole show a good correlation with changes of process parameters. Due to its low noise, the area of the keyhole opening is well suited as an input signal for a closed-loop control of the process.

Temporal characterization of plasma cw high-power CO2 laser-matter interaction: contribution to the welding process control

NASA Astrophysics Data System (ADS)

Engel, Thierry; Kane, M.; Fontaine, Joel

1997-08-01

During high-power laser welding, gas ionization occurs above the sample. The resulting plasma ignition threshold is related to ionization potential of metallic vapors from the sample, and shielding gases used in the process. In this work, we have characterized the temporal behavior of the radiation emitted by the plasma during laser welding in order to relate the observed signals to the process parameters.

Signal optimization in urban transport: A totally asymmetric simple exclusion process with traffic lights.

PubMed

Arita, Chikashi; Foulaadvand, M Ebrahim; Santen, Ludger

2017-03-01

We consider the exclusion process on a ring with time-dependent defective bonds at which the hopping rate periodically switches between zero and one. This system models main roads in city traffics, intersecting with perpendicular streets. We explore basic properties of the system, in particular dependence of the vehicular flow on the parameters of signalization as well as the system size and the car density. We investigate various types of the spatial distribution of the vehicular density, and show existence of a shock profile. We also measure waiting time behind traffic lights, and examine its relationship with the traffic flow.

Signal optimization in urban transport: A totally asymmetric simple exclusion process with traffic lights

NASA Astrophysics Data System (ADS)

Arita, Chikashi; Foulaadvand, M. Ebrahim; Santen, Ludger

2017-03-01

We consider the exclusion process on a ring with time-dependent defective bonds at which the hopping rate periodically switches between zero and one. This system models main roads in city traffics, intersecting with perpendicular streets. We explore basic properties of the system, in particular dependence of the vehicular flow on the parameters of signalization as well as the system size and the car density. We investigate various types of the spatial distribution of the vehicular density, and show existence of a shock profile. We also measure waiting time behind traffic lights, and examine its relationship with the traffic flow.

Smart wireless sensor for physiological monitoring.

PubMed

Tomasic, Ivan; Avbelj, Viktor; Trobec, Roman

2015-01-01

Presented is a wireless body sensor capable of measuring local potential differences on a body surface. By using on-sensor signal processing capabilities, and developed algorithms for off-line signal processing on a personal computing device, it is possible to record single channel ECG, heart rate, breathing rate, EMG, and when three sensors are applied, even the 12-lead ECG. The sensor is portable, unobtrusive, and suitable for both inpatient and outpatient monitoring. The paper presents the sensor's hardware and results of power consumption analysis. The sensor's capabilities of recording various physiological parameters are also presented and illustrated. The paper concludes with envisioned sensor's future developments and prospects.

Higgs boson production in the littlest Higgs model with T-parity at the ILC

NASA Astrophysics Data System (ADS)

Han, Jinzhong; Yang, Guang; Meng, Ming; Wang, Weijian; Li, Jingyun

2018-04-01

We investigate the Higgs boson production processes e+e‑→ ZH, e+e‑→ νν¯H, e+e‑→ tt¯H, e+e‑→ ZHH and e+e‑→ νν¯HH in the littlest Higgs model with T-parity (LHT) at the International Linear Collider (ILC). We calculate the LHT model predictions on the production rate of these processes at the ILC in the case of (un)polarized beams and the signal strengths of the production processes ZH and νν¯H with Higgs decaying to bb¯(gg,γγ). In the allowed parameter space, we find that the signal strengths μgg is most likely approach to the expected precision of the ILC.

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.

Biosensor method and system based on feature vector extraction

DOEpatents

Greenbaum, Elias; Rodriguez, Jr., Miguel; Qi, Hairong; Wang, Xiaoling

2013-07-02

A system for biosensor-based detection of toxins includes providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

Clinical study of a digital vs an analogue hearing aid.

PubMed

Bille, M; Jensen, A M; Kjaerbøl, E; Vesterager, V; Sibelle, P; Nielsen, H

1999-01-01

Digital signal processing in hearing instruments has brought new perspectives to the compensation of hearing impairment and may result in alleviation of the adverse effects of hearing problems. This study compares a commercially available digital signal processing hearing aid (HA) (Senso) with a modern analogue HA with programmable fitting (Logo). The HAs tested are identical in appearance and, in spite of a different mode of operation, the study design ensured blinding of the test subjects. Outcome parameters were: improvements in speech recognition score in noise (deltaSRSN) with the HAs; overall preference for HA; overall satisfaction; and various measures of HA performance evaluated by a self-assessment questionnaire. A total of 28 experienced HA users with sensorineural hearing impairment were included and 25 completed the trial. No significant differences were found in deltaSRSN between the two HAs. Eleven subjects indicated an overall preference for the digital HA, 10 preferred the analogue HA and 4 had no preference. Concerning overall satisfaction, 8 subjects rated the digital HA superior to the analogue one, whereas 7 indicated a superior rating for the analogue HA and 10 rated the HAs equal. Acceptability of noise from traffic was the only outcome parameter which gave a significant difference between the HAs in favour of the digital HA. It is concluded that there are no significant differences in outcome between the digital and analogue signal processing HAs tested by these experienced HA-users.

The Thirty Gigahertz Instrument Receiver for the QUIJOTE Experiment: Preliminary Polarization Measurements and Systematic-Error Analysis

PubMed Central

Casas, Francisco J.; Ortiz, David; Villa, Enrique; Cano, Juan L.; Cagigas, Jaime; Pérez, Ana R.; Aja, Beatriz; Terán, J. Vicente; de la Fuente, Luisa; Artal, Eduardo; Hoyland, Roger; Génova-Santos, Ricardo

2015-01-01

This paper presents preliminary polarization measurements and systematic-error characterization of the Thirty Gigahertz Instrument receiver developed for the QUIJOTE experiment. The instrument has been designed to measure the polarization of Cosmic Microwave Background radiation from the sky, obtaining the Q, U, and I Stokes parameters of the incoming signal simultaneously. Two kinds of linearly polarized input signals have been used as excitations in the polarimeter measurement tests in the laboratory; these show consistent results in terms of the Stokes parameters obtained. A measurement-based systematic-error characterization technique has been used in order to determine the possible sources of instrumental errors and to assist in the polarimeter calibration process. PMID:26251906

Errors in the estimation method for the rejection of vibrations in adaptive optics systems

NASA Astrophysics Data System (ADS)

Kania, Dariusz

2017-06-01

In recent years the problem of the mechanical vibrations impact in adaptive optics (AO) systems has been renewed. These signals are damped sinusoidal signals and have deleterious effect on the system. One of software solutions to reject the vibrations is an adaptive method called AVC (Adaptive Vibration Cancellation) where the procedure has three steps: estimation of perturbation parameters, estimation of the frequency response of the plant, update the reference signal to reject/minimalize the vibration. In the first step a very important problem is the estimation method. A very accurate and fast (below 10 ms) estimation method of these three parameters has been presented in several publications in recent years. The method is based on using the spectrum interpolation and MSD time windows and it can be used to estimate multifrequency signals. In this paper the estimation method is used in the AVC method to increase the system performance. There are several parameters that affect the accuracy of obtained results, e.g. CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, b - number of ADC bits, γ - damping ratio of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value for systematic error is approximately 10^-10 Hz/Hz for N = 2048 and CiR = 0.1. This paper presents equations that can used to estimate maximum systematic errors for given values of H, CiR and N before the start of the estimation process.

A theoretical signal processing framework for linear diffusion MRI: Implications for parameter estimation and experiment design.

PubMed

Varadarajan, Divya; Haldar, Justin P

2017-11-01

The data measured in diffusion MRI can be modeled as the Fourier transform of the Ensemble Average Propagator (EAP), a probability distribution that summarizes the molecular diffusion behavior of the spins within each voxel. This Fourier relationship is potentially advantageous because of the extensive theory that has been developed to characterize the sampling requirements, accuracy, and stability of linear Fourier reconstruction methods. However, existing diffusion MRI data sampling and signal estimation methods have largely been developed and tuned without the benefit of such theory, instead relying on approximations, intuition, and extensive empirical evaluation. This paper aims to address this discrepancy by introducing a novel theoretical signal processing framework for diffusion MRI. The new framework can be used to characterize arbitrary linear diffusion estimation methods with arbitrary q-space sampling, and can be used to theoretically evaluate and compare the accuracy, resolution, and noise-resilience of different data acquisition and parameter estimation techniques. The framework is based on the EAP, and makes very limited modeling assumptions. As a result, the approach can even provide new insight into the behavior of model-based linear diffusion estimation methods in contexts where the modeling assumptions are inaccurate. The practical usefulness of the proposed framework is illustrated using both simulated and real diffusion MRI data in applications such as choosing between different parameter estimation methods and choosing between different q-space sampling schemes. Copyright © 2017 Elsevier Inc. All rights reserved.

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

PubMed

Stec, Bronisław; Susek, Waldemar

2018-05-06

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

Real-Time Noise Reduction for Mossbauer Spectroscopy through Online Implementation of a Modified Kalman Filter

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

Abrecht, David G.; Schwantes, Jon M.; Kukkadapu, Ravi K.

2015-02-01

Spectrum-processing software that incorporates a gaussian smoothing kernel within the statistics of first-order Kalman filtration has been developed to provide cross-channel spectral noise reduction for increased real-time signal-to-noise ratios for Mossbauer spectroscopy. The filter was optimized for the breadth of the gaussian using the Mossbauer spectrum of natural iron foil, and comparisons between the peak broadening, signal-to-noise ratios, and shifts in the calculated hyperfine parameters are presented. The results of optimization give a maximum improvement in the signal-to-noise ratio of 51.1% over the unfiltered spectrum at a gaussian breadth of 27 channels, or 2.5% of the total spectrum width. Themore » full-width half-maximum of the spectrum peaks showed an increase of 19.6% at this optimum point, indicating a relatively weak increase in the peak broadening relative to the signal enhancement, leading to an overall increase in the observable signal. Calculations of the hyperfine parameters showed no statistically significant deviations were introduced from the application of the filter, confirming the utility of this filter for spectroscopy applications.« less

[Study of ocular surface electromyography signal analysis].

PubMed

Zhu, Bei; Qi, Li-Ping

2009-11-01

Test ocular surface electromyography signal waves and characteristic parameters to provide effective data for the diagnosis and treatment of ocular myopathy. Surface electromyography signals tests were performed in 140 normal volunteers and 30 patients with ophthalmoplegia. Surface electrodes were attached to medial canthi, lateral canthi and the middle of frontal bone. Then some alternate flashing red lamps were installed on perimeter to reduce the movement of eyeball. The computer hardware, software, and A/D adapter (12 Bit) were used. Sampling frequency could be selected within 40 kHz, frequency of amplifier was 2 kHz, and input short circuit noise was less than 3 microV. For normal volunteers, the ocular surface electromyography signals were regular, and the electric waves were similar between different sex groups and age groups. While for patients with ophthalmoplegia, the wave amplitude of ocular surface electromyography signals were declined or disappeared in the dyskinesia direction. The wave amplitude was related with the degree of pathological process. The characteristic parameters of patients with ophthalmoplegia were higher than normal volunteers. The figures of ocular surface electromyogram obtained from normal volunteers were obviously different with that from patients with ophthalmoplegia. This test can provide reliable quantized data for the diagnosis and treatment of ocular myopathy.

Improvement of the Laser-Induced Breakdown Spectroscopy method sensitivity by the usage of combination of Ag-nanoparticles and vacuum conditions

NASA Astrophysics Data System (ADS)

Sládková, Lucia; Prochazka, David; Pořízka, Pavel; Škarková, Pavlína; Remešová, Michaela; Hrdlička, Aleš; Novotný, Karel; Čelko, Ladislav; Kaiser, Jozef

2017-01-01

In this work we studied the effect of vacuum (low pressure) conditions on the behavior of laser-induced plasma (LIP) created on a sample surface covered with silver nanoparticles (Ag-NPs), i.e. Nanoparticles-Enhanced Laser-Induced Breakdown Spectroscopy (NELIBS) experiment in a vacuum. The focus was put on the step by step optimization of the measurement parameters, such as energy of the laser pulse, temporally resolved detection, ambient pressure, and different content of Ag-NPs applied on the sample surface. The measurement parameters were optimized in order to achieve the greatest enhancement represented as the signal-to-noise ratio (SNR) of NELIBS signal to the SNR of LIBS signal. The presence of NPs involved in the ablation process enhances LIP intensity; hence the improvement in the analytical sensitivity was yielded. A leaded brass standard was analyzed with the emphasis on the signal enhancement of Pb traces. We gained enhancement by a factor of four. Although the low pressure had no significant influence on the LIP signal enhancement compared to that under ambient conditions, the SNR values were noticeably improved with the implementation of the NPs.

A methodology to condition distorted acoustic emission signals to identify fracture timing from human cadaver spine impact tests.

PubMed

Arun, Mike W J; Yoganandan, Narayan; Stemper, Brian D; Pintar, Frank A

2014-12-01

While studies have used acoustic sensors to determine fracture initiation time in biomechanical studies, a systematic procedure is not established to process acoustic signals. The objective of the study was to develop a methodology to condition distorted acoustic emission data using signal processing techniques to identify fracture initiation time. The methodology was developed from testing a human cadaver lumbar spine column. Acoustic sensors were glued to all vertebrae, high-rate impact loading was applied, load-time histories were recorded (load cell), and fracture was documented using CT. Compression fracture occurred to L1 while other vertebrae were intact. FFT of raw voltage-time traces were used to determine an optimum frequency range associated with high decibel levels. Signals were bandpass filtered in this range. Bursting pattern was found in the fractured vertebra while signals from other vertebrae were silent. Bursting time was associated with time of fracture initiation. Force at fracture was determined using this time and force-time data. The methodology is independent of selecting parameters a priori such as fixing a voltage level(s), bandpass frequency and/or using force-time signal, and allows determination of force based on time identified during signal processing. The methodology can be used for different body regions in cadaver experiments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Calibration and compensation method of three-axis geomagnetic sensor based on pre-processing total least square iteration

NASA Astrophysics Data System (ADS)

Zhou, Y.; Zhang, X.; Xiao, W.

2018-04-01

As the geomagnetic sensor is susceptible to interference, a pre-processing total least square iteration method is proposed for calibration compensation. Firstly, the error model of the geomagnetic sensor is analyzed and the correction model is proposed, then the characteristics of the model are analyzed and converted into nine parameters. The geomagnetic data is processed by Hilbert transform (HHT) to improve the signal-to-noise ratio, and the nine parameters are calculated by using the combination of Newton iteration method and the least squares estimation method. The sifter algorithm is used to filter the initial value of the iteration to ensure that the initial error is as small as possible. The experimental results show that this method does not need additional equipment and devices, can continuously update the calibration parameters, and better than the two-step estimation method, it can compensate geomagnetic sensor error well.

Numerical simulation of electron beam welding with beam oscillations

NASA Astrophysics Data System (ADS)

Trushnikov, D. N.; Permyakov, G. L.

2017-02-01

This research examines the process of electron-beam welding in a keyhole mode with the use of beam oscillations. We study the impact of various beam oscillations and their parameters on the shape of the keyhole, the flow of heat and mass transfer processes and weld parameters to develop methodological recommendations. A numerical three-dimensional mathematical model of electron beam welding is presented. The model was developed on the basis of a heat conduction equation and a Navier-Stokes equation taking into account phase transitions at the interface of a solid and liquid phase and thermocapillary convection (Marangoni effect). The shape of the keyhole is determined based on experimental data on the parameters of the secondary signal by using the method of a synchronous accumulation. Calculations of thermal and hydrodynamic processes were carried out based on a computer cluster, using a simulation package COMSOL Multiphysics.

Spectrometer gun

DOEpatents

Waechter, David A.; Wolf, Michael A.; Umbarger, C. John

1985-01-01

A hand-holdable, battery-operated, microprocessor-based spectrometer gun includes a low-power matrix display and sufficient memory to permit both real-time observation and extended analysis of detected radiation pulses. Universality of the incorporated signal processing circuitry permits operation with various detectors having differing pulse detection and sensitivity parameters.

Self-mixing interferometry as a diagnostics tool for plasma characteristics in laser microdrilling

NASA Astrophysics Data System (ADS)

Colombo, Paolo; Demir, Ali Gökhan; Norgia, Michele; Previtali, Barbara

2017-05-01

In this work, self-mixing interferometry (SMI) was used to monitor the optical path difference induced by the ablation plasma and plume. The paper develops the analytical relationships to explain the fringe appearance in the SMI during laser microdrilling. The monitoring principle was tested under a large experimental campaign of laser microdrilling on TiAlN ceramic coating with a low-ns green fibre laser. Key process parameters namely pulse energy, number and repetition rate were varied. The effect of side gas on the SMI signal characteristic was analysed. Laser induced breakdown spectroscopy (LIBS) was used to identify the plasma temperature and electron number density. The SMI signals were correlated to the plume size and its evolution as a function of process parameters, as well as electron number density estimated by spectroscopy. In addition to proving the validity of the proposed new method, the results show insights to the micromachining of the ceramic material with low ns pulses.

Optical signal processing using photonic reservoir computing

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Dehyadegari, Louiza

2014-10-01

As a new approach to recognition and classification problems, photonic reservoir computing has such advantages as parallel information processing, power efficient and high speed. In this paper, a photonic structure has been proposed for reservoir computing which is investigated using a simple, yet, non-partial noisy time series prediction task. This study includes the application of a suitable topology with self-feedbacks in a network of SOA's - which lends the system a strong memory - and leads to adjusting adequate parameters resulting in perfect recognition accuracy (100%) for noise-free time series, which shows a 3% improvement over previous results. For the classification of noisy time series, the rate of accuracy showed a 4% increase and amounted to 96%. Furthermore, an analytical approach was suggested to solve rate equations which led to a substantial decrease in the simulation time, which is an important parameter in classification of large signals such as speech recognition, and better results came up compared with previous works.

Apparatus for sensor failure detection and correction in a gas turbine engine control system

NASA Technical Reports Server (NTRS)

Spang, H. A., III; Wanger, R. P. (Inventor)

1981-01-01

A gas turbine engine control system maintains a selected level of engine performance despite the failure or abnormal operation of one or more engine parameter sensors. The control system employs a continuously updated engine model which simulates engine performance and generates signals representing real time estimates of the engine parameter sensor signals. The estimate signals are transmitted to a control computational unit which utilizes them in lieu of the actual engine parameter sensor signals to control the operation of the engine. The estimate signals are also compared with the corresponding actual engine parameter sensor signals and the resulting difference signals are utilized to update the engine model. If a particular difference signal exceeds specific tolerance limits, the difference signal is inhibited from updating the model and a sensor failure indication is provided to the engine operator.

Identification of gene regulation models from single-cell data

NASA Astrophysics Data System (ADS)

Weber, Lisa; Raymond, William; Munsky, Brian

2018-09-01

In quantitative analyses of biological processes, one may use many different scales of models (e.g. spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g. model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ MATLAB and PYTHON software to consider a time-dependent input signal (e.g. a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g. deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast (Neuert et al 2013 Science 339 584–7) and human cells (Senecal et al 2014 Cell Rep. 8 75–83)5.

Invariant polarimetric contrast parameters of light with Gaussian fluctuations in three dimensions.

PubMed

Réfrégier, Philippe; Roche, Muriel; Goudail, François

2006-01-01

We propose a rigorous definition of the minimal set of parameters that characterize the difference between two partially polarized states of light whose electric fields vary in three dimensions with Gaussian fluctuations. Although two such states are a priori defined by eighteen parameters, we demonstrate that the performance of processing tasks such as detection, localization, or segmentation of spatial or temporal polarization variations is uniquely determined by three scalar functions of these parameters. These functions define a "polarimetric contrast" that simplifies the analysis and the specification of processing techniques on polarimetric signals and images. This result can also be used to analyze the definition of the degree of polarization of a three-dimensional state of light with Gaussian fluctuations in comparison, with respect to its polarimetric contrast parameters, with a totally depolarized light. We show that these contrast parameters are a simple function of the degrees of polarization previously proposed by Barakat [Opt. Acta 30, 1171 (1983)] and Setälä et al. [Phys. Rev. Lett. 88, 123902 (2002)]. Finally, we analyze the dimension of the set of contrast parameters in different particular situations.

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

PubMed

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis.

PubMed

Aboy, Mateo; Hornero, Roberto; Abásolo, Daniel; Alvarez, Daniel

2006-11-01

Lempel-Ziv complexity (LZ) and derived LZ algorithms have been extensively used to solve information theoretic problems such as coding and lossless data compression. In recent years, LZ has been widely used in biomedical applications to estimate the complexity of discrete-time signals. Despite its popularity as a complexity measure for biosignal analysis, the question of LZ interpretability and its relationship to other signal parameters and to other metrics has not been previously addressed. We have carried out an investigation aimed at gaining a better understanding of the LZ complexity itself, especially regarding its interpretability as a biomedical signal analysis technique. Our results indicate that LZ is particularly useful as a scalar metric to estimate the bandwidth of random processes and the harmonic variability in quasi-periodic signals.

Non-parametric PCM to ADM conversion. [Pulse Code to Adaptive Delta Modulation

NASA Technical Reports Server (NTRS)

Locicero, J. L.; Schilling, D. L.

1977-01-01

An all-digital technique to convert pulse code modulated (PCM) signals into adaptive delta modulation (ADM) format is presented. The converter developed is shown to be independent of the statistical parameters of the encoded signal and can be constructed with only standard digital hardware. The structure of the converter is simple enough to be fabricated on a large scale integrated circuit where the advantages of reliability and cost can be optimized. A concise evaluation of this PCM to ADM translation technique is presented and several converters are simulated on a digital computer. A family of performance curves is given which displays the signal-to-noise ratio for sinusoidal test signals subjected to the conversion process, as a function of input signal power for several ratios of ADM rate to Nyquist rate.

Determination of combustion parameters using engine crankshaft speed

NASA Astrophysics Data System (ADS)

Taglialatela, F.; Lavorgna, M.; Mancaruso, E.; Vaglieco, B. M.

2013-07-01

Electronic engine controls based on real time diagnosis of combustion process can significantly help in complying with the stricter and stricter regulations on pollutants emissions and fuel consumption. The most important parameter for the evaluation of combustion quality in internal combustion engines is the in-cylinder pressure, but its direct measurement is very expensive and involves an intrusive approach to the cylinder. Previous researches demonstrated the direct relationship existing between in-cylinder pressure and engine crankshaft speed and several authors tried to reconstruct the pressure cycle on the basis of the engine speed signal. In this paper we propose the use of a Multi-Layer Perceptron neural network to model the relationship between the engine crankshaft speed and some parameters derived from the in-cylinder pressure cycle. This allows to have a non-intrusive estimation of cylinder pressure and a real time evaluation of combustion quality. The structure of the model and the training procedure is outlined in the paper. A possible combustion controller using the information extracted from the crankshaft speed information is also proposed. The application of the neural network model is demonstrated on a single-cylinder spark ignition engine tested in a wide range of speeds and loads. Results confirm that a good estimation of some combustion pressure parameters can be obtained by means of a suitable processing of crankshaft speed signal.

Longitudinal bunch monitoring at the Fermilab Tevatron and Main Injector synchrotrons

DOE PAGES

Thurman-Keup, R.; Bhat, C.; Blokland, W.; ...

2011-10-17

The measurement of the longitudinal behavior of the accelerated particle beams at Fermilab is crucial to the optimization and control of the beam and the maximizing of the integrated luminosity for the particle physics experiments. Longitudinal measurements in the Tevatron and Main Injector synchrotrons are based on the analysis of signals from resistive wall current monitors. This study describes the signal processing performed by a 2 GHz-bandwidth oscilloscope together with a computer running a LabVIEW program which calculates the longitudinal beam parameters.

Cellular level models as tools for cytokine design.

PubMed

Radhakrishnan, Mala L; Tidor, Bruce

2010-01-01

Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency. (c) 2010 American Institute of Chemical Engineers

Validating of Atmospheric Signals Associated with some of the Major Earthquakes in Asia (2003-2009)

NASA Technical Reports Server (NTRS)

Ouzounov, D. P.; Pulinets, S.; Liu, J. Y.; Hattori, K.; Oarritm N,; Taylor, P. T.

2010-01-01

The recent catastrophic earthquake in Haiti (January 2010) has provided and renewed interest in the important question of the existence of precursory signals related to strong earthquakes. Latest studies (VESTO workshop in Japan 2009) have shown that there were precursory atmospheric signals observed on the ground and in space associated with several recent earthquakes. The major question, still widely debated in the scientific community is whether such signals systematically precede major earthquakes. To address this problem we have started to validate the anomalous atmospheric signals during the occurrence of large earthquakes. Our approach is based on integration analysis of several physical and environmental parameters (thermal infrared radiation, electron concentration in the ionosphere, Radon/ion activities, air temperature and seismicity) that were found to be associated with earthquakes. We performed hind-cast detection over three different regions with high seismicity Taiwan, Japan and Kamchatka for the period of 2003-2009. We are using existing thermal satellite data (Aqua and POES); in situ atmospheric data (NOAA/NCEP); and ionospheric variability data (GPS/TEC and DEMETER). The first part of this validation included 42 major earthquakes (M greater than 5.9): 10 events in Taiwan, 15 events in Japan, 15 events in Kamchatka and four most recent events for M8.0 Wenchuan earthquake (May 2008) in China and M7.9 Samoa earthquakes (Sep 2009). Our initial results suggest a systematic appearance of atmospheric anomalies near the epicentral area, 1 to 5 days prior to the largest earthquakes, that could be explained by a coupling process between the observed physical parameters, and the earthquake preparation processes.

Spectrometer gun

DOEpatents

Waechter, D.A.; Wolf, M.A.; Umbarger, C.J.

1981-11-03

A hand-holdable, battery-operated, microprocessor-based spectrometer gun is described that includes a low-power matrix display and sufficient memory to permit both real-time observation and extended analysis of detected radiation pulses. Universality of the incorporated signal processing circuitry permits operation with various detectors having differing pulse detection and sensitivity parameters.

Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices.

PubMed

Cataldo, E; Soize, C

2018-06-06

Jitter, in voice production applications, is a random phenomenon characterized by the deviation of the glottal cycle length with respect to a mean value. Its study can help in identifying pathologies related to the vocal folds according to the values obtained through the different ways to measure it. This paper aims to propose a stochastic model, considering three control parameters, to generate jitter based on a deterministic one-mass model for the dynamics of the vocal folds and to identify parameters from the stochastic model taking into account real voice signals experimentally obtained. To solve the corresponding stochastic inverse problem, the cost function used is based on the distance between probability density functions of the random variables associated with the fundamental frequencies obtained by the experimental voices and the simulated ones, and also on the distance between features extracted from the voice signals, simulated and experimental, to calculate jitter. The results obtained show that the model proposed is valid and some samples of voices are synthesized considering the identified parameters for normal and pathological cases. The strategy adopted is also a novelty and mainly because a solution was obtained. In addition to the use of three parameters to construct the model of jitter, it is the discussion of a parameter related to the bandwidth of the power spectral density function of the stochastic process to measure the quality of the signal generated. A study about the influence of all the main parameters is also performed. The identification of the parameters of the model considering pathological cases is maybe of all novelties introduced by the paper the most interesting. Copyright © 2018 Elsevier Ltd. All rights reserved.

Acoustic interference and recognition space within a complex assemblage of dendrobatid frogs

PubMed Central

Amézquita, Adolfo; Flechas, Sandra Victoria; Lima, Albertina Pimentel; Gasser, Herbert; Hödl, Walter

2011-01-01

In species-rich assemblages of acoustically communicating animals, heterospecific sounds may constrain not only the evolution of signal traits but also the much less-studied signal-processing mechanisms that define the recognition space of a signal. To test the hypothesis that the recognition space is optimally designed, i.e., that it is narrower toward the species that represent the higher potential for acoustic interference, we studied an acoustic assemblage of 10 diurnally active frog species. We characterized their calls, estimated pairwise correlations in calling activity, and, to model the recognition spaces of five species, conducted playback experiments with 577 synthetic signals on 531 males. Acoustic co-occurrence was not related to multivariate distance in call parameters, suggesting a minor role for spectral or temporal segregation among species uttering similar calls. In most cases, the recognition space overlapped but was greater than the signal space, indicating that signal-processing traits do not act as strictly matched filters against sounds other than homospecific calls. Indeed, the range of the recognition space was strongly predicted by the acoustic distance to neighboring species in the signal space. Thus, our data provide compelling evidence of a role of heterospecific calls in evolutionarily shaping the frogs' recognition space within a complex acoustic assemblage without obvious concomitant effects on the signal. PMID:21969562

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.

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.

Non-stationary least-squares complex decomposition for microseismic noise attenuation

NASA Astrophysics Data System (ADS)

Chen, Yangkang

2018-06-01

Microseismic data processing and imaging are crucial for subsurface real-time monitoring during hydraulic fracturing process. Unlike the active-source seismic events or large-scale earthquake events, the microseismic event is usually of very small magnitude, which makes its detection challenging. The biggest trouble of microseismic data is the low signal-to-noise ratio issue. Because of the small energy difference between effective microseismic signal and ambient noise, the effective signals are usually buried in strong random noise. I propose a useful microseismic denoising algorithm that is based on decomposing a microseismic trace into an ensemble of components using least-squares inversion. Based on the predictive property of useful microseismic event along the time direction, the random noise can be filtered out via least-squares fitting of multiple damping exponential components. The method is flexible and almost automated since the only parameter needed to be defined is a decomposition number. I use some synthetic and real data examples to demonstrate the potential of the algorithm in processing complicated microseismic data sets.

New instrument for on-line viscosity measurement of fermentation media.

PubMed

Picque, D; Corrieu, G

1988-01-01

In an attempt to resolve the difficult problem of on-line determination of the viscosity of non-Newtonian fermentation media, the authors have used a vibrating rod sensor mounted on a bioreactor. The sensor signal decreases nonlinearly with increased apparent viscosity. Electronic filtering of the signal damps the interfering effect of aeration and mechanical agitation. Sensor drift is very low (0.03% of measured value per hour). On the rheological level the sensor is primarily an empirical tool that must be specifically calibrated for each fermentation process. Once this is accomplished, it becomes possible to establish linear or second-degree correlations between the electrical signal from the sensor and the essential parameters of the fermentation process in question (pH of a fermented milk during acidification, concentration of extra cellular polysaccharide). In addition, by applying the power law to describe the rheological behavior of fermentation media, we observe a second-order polynomial correlation between the sensor signal and the behavior index (n).

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.

[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.

Model-based tomographic reconstruction

DOEpatents

Chambers, David H; Lehman, Sean K; Goodman, Dennis M

2012-06-26

A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

On Certain New Methodology for Reducing Sensor and Readout Electronics Circuitry Noise in Digital Domain

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Miko, Joseph; Bradley, Damon; Heinzen, Katherine

2008-01-01

NASA Hubble Space Telescope (HST) and upcoming cosmology science missions carry instruments with multiple focal planes populated with many large sensor detector arrays. These sensors are passively cooled to low temperatures for low-level light (L3) and near-infrared (NIR) signal detection, and the sensor readout electronics circuitry must perform at extremely low noise levels to enable new required science measurements. Because we are at the technological edge of enhanced performance for sensors and readout electronics circuitry, as determined by thermal noise level at given temperature in analog domain, we must find new ways of further compensating for the noise in the signal digital domain. To facilitate this new approach, state-of-the-art sensors are augmented at their array hardware boundaries by non-illuminated reference pixels, which can be used to reduce noise attributed to sensors. There are a few proposed methodologies of processing in the digital domain the information carried by reference pixels, as employed by the Hubble Space Telescope and the James Webb Space Telescope Projects. These methods involve using spatial and temporal statistical parameters derived from boundary reference pixel information to enhance the active (non-reference) pixel signals. To make a step beyond this heritage methodology, we apply the NASA-developed technology known as the Hilbert- Huang Transform Data Processing System (HHT-DPS) for reference pixel information processing and its utilization in reconfigurable hardware on-board a spaceflight instrument or post-processing on the ground. The methodology examines signal processing for a 2-D domain, in which high-variance components of the thermal noise are carried by both active and reference pixels, similar to that in processing of low-voltage differential signals and subtraction of a single analog reference pixel from all active pixels on the sensor. Heritage methods using the aforementioned statistical parameters in the digital domain (such as statistical averaging of the reference pixels themselves) zeroes out the high-variance components, and the counterpart components in the active pixels remain uncorrected. This paper describes how the new methodology was demonstrated through analysis of fast-varying noise components using the Hilbert-Huang Transform Data Processing System tool (HHT-DPS) developed at NASA and the high-level programming language MATLAB (Trademark of MathWorks Inc.), as well as alternative methods for correcting for the high-variance noise component, using an HgCdTe sensor data. The NASA Hubble Space Telescope data post-processing, as well as future deep-space cosmology projects on-board instrument data processing from all the sensor channels, would benefit from this effort.

Biosensor method and system based on feature vector extraction

DOEpatents

Greenbaum, Elias [Knoxville, TN; Rodriguez, Jr., Miguel; Qi, Hairong [Knoxville, TN; Wang, Xiaoling [San Jose, CA

2012-04-17

A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

A Full Dynamic Compound Inverse Method for output-only element-level system identification and input estimation from earthquake response signals

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Rizzi, Egidio

2016-08-01

This paper proposes a new output-only element-level system identification and input estimation technique, towards the simultaneous identification of modal parameters, input excitation time history and structural features at the element-level by adopting earthquake-induced structural response signals. The method, named Full Dynamic Compound Inverse Method (FDCIM), releases strong assumptions of earlier element-level techniques, by working with a two-stage iterative algorithm. Jointly, a Statistical Average technique, a modification process and a parameter projection strategy are adopted at each stage to achieve stronger convergence for the identified estimates. The proposed method works in a deterministic way and is completely developed in State-Space form. Further, it does not require continuous- to discrete-time transformations and does not depend on initialization conditions. Synthetic earthquake-induced response signals from different shear-type buildings are generated to validate the implemented procedure, also with noise-corrupted cases. The achieved results provide a necessary condition to demonstrate the effectiveness of the proposed identification method.

Plasmon-modulated bistable four-wave mixing signals from a metal nanoparticle-monolayer MoS2 nanoresonator hybrid system

NASA Astrophysics Data System (ADS)

Li, Jian-Bo; Tan, Xiao-Long; Ma, Jin-Hong; Xu, Si-Qin; Kuang, Zhi-Wei; Liang, Shan; Xiao, Si; He, Meng-Dong; Kim, Nam-Chol; Luo, Jian-Hua; Chen, Li-Qun

2018-06-01

We present a study for the impact of exciton-phonon and exciton-plasmon interactions on bistable four-wave mixing (FWM) signals in a metal nanoparticle (MNP)-monolayer MoS2 nanoresonator hybrid system. Via tracing the FWM response we predict that, depending on the excitation conditions and the system parameters, such a system exhibits ‘U-shaped’ bistable FWM signals. We also map out bistability phase diagrams within the system’s parameter space. Especially, we show that compared with the exciton-phonon interaction, a strong exciton-plasmon interaction plays a dominant role in the generation of optical bistability, and the bistable region will be greatly broadened by shortening the distance between the MNP and the monolayer MoS2 nanoresonator. In the weak exciton-plasmon coupling regime, the impact of exciton-phonon interaction on optical bistability will become obvious. The scheme proposed may be used for building optical switches and logic-gate devices for optical computing and quantum information processing.

A pH-activatable nanoparticle with signal-amplification capabilities for non-invasive imaging of tumour malignancy.

PubMed

Mi, Peng; Kokuryo, Daisuke; Cabral, Horacio; Wu, Hailiang; Terada, Yasuko; Saga, Tsuneo; Aoki, Ichio; Nishiyama, Nobuhiro; Kataoka, Kazunori

2016-08-01

Engineered nanoparticles that respond to pathophysiological parameters, such as pH or redox potential, have been developed as contrast agents for the magnetic resonance imaging (MRI) of tumours. However, beyond anatomic assessment, contrast agents that can sense these pathological parameters and rapidly amplify their magnetic resonance signals are desirable because they could potentially be used to monitor the biological processes of tumours and improve cancer diagnosis. Here, we report an MRI contrast agent that rapidly amplifies magnetic resonance signals in response to pH. We confined Mn(2+) within pH-sensitive calcium phosphate (CaP) nanoparticles comprising a poly(ethylene glycol) shell. At a low pH, such as in solid tumours, the CaP disintegrates and releases Mn(2+) ions. Binding to proteins increases the relaxivity of Mn(2+) and enhances the contrast. We show that these nanoparticles could rapidly and selectively brighten solid tumours, identify hypoxic regions within the tumour mass and detect invisible millimetre-sized metastatic tumours in the liver.

Coded acoustic wave sensors and system using time diversity

NASA Technical Reports Server (NTRS)

Solie, Leland P. (Inventor); Hines, Jacqueline H. (Inventor)

2012-01-01

An apparatus and method for distinguishing between sensors that are to be wirelessly detected is provided. An interrogator device uses different, distinct time delays in the sensing signals when interrogating the sensors. The sensors are provided with different distinct pedestal delays. Sensors that have the same pedestal delay as the delay selected by the interrogator are detected by the interrogator whereas other sensors with different pedestal delays are not sensed. Multiple sensors with a given pedestal delay are provided with different codes so as to be distinguished from one another by the interrogator. The interrogator uses a signal that is transmitted to the sensor and returned by the sensor for combination and integration with the reference signal that has been processed by a function. The sensor may be a surface acoustic wave device having a differential impulse response with a power spectral density consisting of lobes. The power spectral density of the differential response is used to determine the value of the sensed parameter or parameters.

Plasmon-modulated bistable four-wave mixing signals from a metal nanoparticle-monolayer MoS2 nanoresonator hybrid system.

PubMed

Li, Jian-Bo; Tan, Xiao-Long; Ma, Jin-Hong; Xu, Si-Qin; Kuang, Zhi-Wei; Liang, Shan; Xiao, Si; He, Meng-Dong; Kim, Nam-Chol; Luo, Jian-Hua; Chen, Li-Qun

2018-06-22

We present a study for the impact of exciton-phonon and exciton-plasmon interactions on bistable four-wave mixing (FWM) signals in a metal nanoparticle (MNP)-monolayer MoS 2 nanoresonator hybrid system. Via tracing the FWM response we predict that, depending on the excitation conditions and the system parameters, such a system exhibits 'U-shaped' bistable FWM signals. We also map out bistability phase diagrams within the system's parameter space. Especially, we show that compared with the exciton-phonon interaction, a strong exciton-plasmon interaction plays a dominant role in the generation of optical bistability, and the bistable region will be greatly broadened by shortening the distance between the MNP and the monolayer MoS 2 nanoresonator. In the weak exciton-plasmon coupling regime, the impact of exciton-phonon interaction on optical bistability will become obvious. The scheme proposed may be used for building optical switches and logic-gate devices for optical computing and quantum information processing.

A pH-activatable nanoparticle with signal-amplification capabilities for non-invasive imaging of tumour malignancy

NASA Astrophysics Data System (ADS)

Mi, Peng; Kokuryo, Daisuke; Cabral, Horacio; Wu, Hailiang; Terada, Yasuko; Saga, Tsuneo; Aoki, Ichio; Nishiyama, Nobuhiro; Kataoka, Kazunori

2016-08-01

Engineered nanoparticles that respond to pathophysiological parameters, such as pH or redox potential, have been developed as contrast agents for the magnetic resonance imaging (MRI) of tumours. However, beyond anatomic assessment, contrast agents that can sense these pathological parameters and rapidly amplify their magnetic resonance signals are desirable because they could potentially be used to monitor the biological processes of tumours and improve cancer diagnosis. Here, we report an MRI contrast agent that rapidly amplifies magnetic resonance signals in response to pH. We confined Mn2+ within pH-sensitive calcium phosphate (CaP) nanoparticles comprising a poly(ethylene glycol) shell. At a low pH, such as in solid tumours, the CaP disintegrates and releases Mn2+ ions. Binding to proteins increases the relaxivity of Mn2+ and enhances the contrast. We show that these nanoparticles could rapidly and selectively brighten solid tumours, identify hypoxic regions within the tumour mass and detect invisible millimetre-sized metastatic tumours in the liver.

Estimation of multiple accelerated motions using chirp-Fourier transform and clustering.

PubMed

Alexiadis, Dimitrios S; Sergiadis, George D

2007-01-01

Motion estimation in the spatiotemporal domain has been extensively studied and many methodologies have been proposed, which, however, cannot handle both time-varying and multiple motions. Extending previously published ideas, we present an efficient method for estimating multiple, linearly time-varying motions. It is shown that the estimation of accelerated motions is equivalent to the parameter estimation of superpositioned chirp signals. From this viewpoint, one can exploit established signal processing tools such as the chirp-Fourier transform. It is shown that accelerated motion results in energy concentration along planes in the 4-D space: spatial frequencies-temporal frequency-chirp rate. Using fuzzy c-planes clustering, we estimate the plane/motion parameters. The effectiveness of our method is verified on both synthetic as well as real sequences and its advantages are highlighted.

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

Monitoring of temperature fatigue failure mechanism for polyvinyl alcohol fiber concrete using acoustic emission sensors.

PubMed

Li, Dongsheng; Cao, Hai

2012-01-01

The applicability of acoustic emission (AE) techniques to monitor the mechanism of evolution of polyvinyl alcohol (PVA) fiber concrete damage under temperature fatigue loading is investigated. Using the temperature fatigue test, real-time AE monitoring data of PVA fiber concrete is achieved. Based on the AE signal characteristics of the whole test process and comparison of AE signals of PVA fiber concretes with different fiber contents, the damage evolution process of PVA fiber concrete is analyzed. Finally, a qualitative evaluation of the damage degree is obtained using the kurtosis index and b-value of AE characteristic parameters. The results obtained using both methods are discussed.

Monitoring of Temperature Fatigue Failure Mechanism for Polyvinyl Alcohol Fiber Concrete Using Acoustic Emission Sensors

PubMed Central

Li, Dongsheng; Cao, Hai

2012-01-01

The applicability of acoustic emission (AE) techniques to monitor the mechanism of evolution of polyvinyl alcohol (PVA) fiber concrete damage under temperature fatigue loading is investigated. Using the temperature fatigue test, real-time AE monitoring data of PVA fiber concrete is achieved. Based on the AE signal characteristics of the whole test process and comparison of AE signals of PVA fiber concretes with different fiber contents, the damage evolution process of PVA fiber concrete is analyzed. Finally, a qualitative evaluation of the damage degree is obtained using the kurtosis index and b-value of AE characteristic parameters. The results obtained using both methods are discussed. PMID:23012555

Moment-Based Physical Models of Broadband Clutter due to Aggregations of Fish

DTIC Science & Technology

2013-09-30

statistical models for signal-processing algorithm development. These in turn will help to develop a capability to statistically forecast the impact of...aggregations of fish based on higher-order statistical measures describable in terms of physical and system parameters. Environmentally , these models...processing. In this experiment, we had good ground truth on (1) and (2), and had control over (3) and (4) except for environmentally -imposed restrictions

SPECIFYING PHYSIOLOGICAL PARAMETERS FOR THE KINETICS OF INHALED TOLUENE IN RATS PERFORMING THE VISUAL SIGNAL DETECTION TASK (SDT).

EPA Science Inventory

A physiologically-based pharmacokinetic (PBPK) model is being developed to estimate the dosimetry of toluene in rats inhaling the VOC under various experimental conditions. The effects of physical activity are currently being estimated utilizing a three-step process. First, we d...

Combinatorial influence of environmental parameters on transcription factor activity.

PubMed

Knijnenburg, T A; Wessels, L F A; Reinders, M J T

2008-07-01

Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. The Matlab code is available from the authors upon request. Supplementary data are available at Bioinformatics online.

Dark forces in the sky: signals from Z{sup ′} and the dark Higgs

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

Bell, Nicole F.; Cai, Yi; Leane, Rebecca K.

2016-08-01

We consider the indirect detection signals for a self-consistent hidden U(1) model containing a Majorana dark matter candidate, χ, a dark gauge boson, Z{sup ′}, and a dark Higgs, s. Compared with a model containing only a dark matter candidate and Z{sup ′} mediator, the addition of the scalar provides a mass generation mechanism for the dark sector particles and is required in order to avoid unitarity violation at high energies. We find that the inclusion of the two mediators opens up a new two-body s-wave annihilation channel, χχ→sZ{sup ′}. This new process, which is missed in the usual single-mediatormore » simplified model approach, can be the dominant annihilation channel. This provides rich phenomenology for indirect detection searches, allows indirect searches to explore regions of parameter space not accessible with other commonly considered s-wave annihilation processes, and enables both the Z{sup ′} and scalar couplings to be probed. We examine the phenomenology of the sector with a focus on this new process, and determine the limits on the model parameter space from Fermi data on dwarf spheriodal galaxies and other relevant experiments.« less

FPGA-based fused smart sensor for dynamic and vibration parameter extraction in industrial robot links.

PubMed

Rodriguez-Donate, Carlos; Morales-Velazquez, Luis; Osornio-Rios, Roque Alfredo; Herrera-Ruiz, Gilberto; de Jesus Romero-Troncoso, Rene

2010-01-01

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).

FPGA-Based Fused Smart Sensor for Dynamic and Vibration Parameter Extraction in Industrial Robot Links

PubMed Central

Rodriguez-Donate, Carlos; Morales-Velazquez, Luis; Osornio-Rios, Roque Alfredo; Herrera-Ruiz, Gilberto; de Jesus Romero-Troncoso, Rene

2010-01-01

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA). PMID:22319345

An Automated Recording Method in Clinical Consultation to Rate the Limp in Lower Limb Osteoarthritis

PubMed Central

Barrois, R.; Gregory, Th.; Oudre, L.; Moreau, Th.; Truong, Ch.; Aram Pulini, A.; Vienne, A.; Labourdette, Ch.; Vayatis, N.; Buffat, S.; Yelnik, A.; de Waele, C.; Laporte, S.; Vidal, P. P.; Ricard, D.

2016-01-01

For diagnosis and follow up, it is important to be able to quantify limp in an objective, and precise way adapted to daily clinical consultation. The purpose of this exploratory study was to determine if an inertial sensor-based method could provide simple features that correlate with the severity of lower limb osteoarthritis evaluated by the WOMAC index without the use of step detection in the signal processing. Forty-eight patients with lower limb osteoarthritis formed two severity groups separated by the median of the WOMAC index (G1, G2). Twelve asymptomatic age-matched control subjects formed the control group (G0). Subjects were asked to walk straight 10 meters forward and 10 meters back at self-selected walking speeds with inertial measurement units (IMU) (3-D accelerometers, 3-D gyroscopes and 3-D magnetometers) attached on the head, the lower back (L3-L4) and both feet. Sixty parameters corresponding to the mean and the root mean square (RMS) of the recorded signals on the various sensors (head, lower back and feet), in the various axes, in the various frames were computed. Parameters were defined as discriminating when they showed statistical differences between the three groups. In total, four parameters were found discriminating: mean and RMS of the norm of the acceleration in the horizontal plane for contralateral and ipsilateral foot in the doctor’s office frame. No discriminating parameter was found on the head or the lower back. No discriminating parameter was found in the sensor linked frames. This study showed that two IMUs placed on both feet and a step detection free signal processing method could be an objective and quantitative complement to the clinical examination of the physician in everyday practice. Our method provides new automatically computed parameters that could be used for the comprehension of lower limb osteoarthritis. It may not only be used in medical consultation to score patients but also to monitor the evolution of their clinical syndrome during and after rehabilitation. Finally, it paves the way for the quantification of gait in other fields such as neurology and for monitoring the gait at a patient’s home. PMID:27776168

AGARD Flight Test Techniques Series. Volume 14. Introduction to Flight Test Engineering (Introduction a la Technique d’essais en vol)

DTIC Science & Technology

1995-09-01

path and aircraft attitude and other flight or aircraft parameters • Calculations in the frequency domain ( Fast Fourier Transform) • Data analysis...Signal filtering Image processing of video and radar data Parameter identification Statistical analysis Power spectral density Fast Fourier Transform...airspeeds both fast and slow, altitude, load factor both above and below 1g, centers of gravity (fore and aft), and with system/subsystem failures. Whether

Piezoelectric extraction of ECG signal

NASA Astrophysics Data System (ADS)

Ahmad, Mahmoud Al

2016-11-01

The monitoring and early detection of abnormalities or variations in the cardiac cycle functionality are very critical practices and have significant impact on the prevention of heart diseases and their associated complications. Currently, in the field of biomedical engineering, there is a growing need for devices capable of measuring and monitoring a wide range of cardiac cycle parameters continuously, effectively and on a real-time basis using easily accessible and reusable probes. In this paper, the revolutionary generation and extraction of the corresponding ECG signal using a piezoelectric transducer as alternative for the ECG will be discussed. The piezoelectric transducer pick up the vibrations from the heart beats and convert them into electrical output signals. To this end, piezoelectric and signal processing techniques were employed to extract the ECG corresponding signal from the piezoelectric output voltage signal. The measured electrode based and the extracted piezoelectric based ECG traces are well corroborated. Their peaks amplitudes and locations are well aligned with each other.

The Applicability of Incoherent Array Processing to IMS Seismic Array Stations

NASA Astrophysics Data System (ADS)

Gibbons, S. J.

2012-04-01

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

Efficient sensor network vehicle classification using peak harmonics of acoustic emissions

NASA Astrophysics Data System (ADS)

William, Peter E.; Hoffman, Michael W.

2008-04-01

An application is proposed for detection and classification of battlefield ground vehicles using the emitted acoustic signal captured at individual sensor nodes of an ad hoc Wireless Sensor Network (WSN). We make use of the harmonic characteristics of the acoustic emissions of battlefield vehicles, in reducing both the computations carried on the sensor node and the transmitted data to the fusion center for reliable and effcient classification of targets. Previous approaches focus on the lower frequency band of the acoustic emissions up to 500Hz; however, we show in the proposed application how effcient discrimination between battlefield vehicles is performed using features extracted from higher frequency bands (50 - 1500Hz). The application shows that selective time domain acoustic features surpass equivalent spectral features. Collaborative signal processing is utilized, such that estimation of certain signal model parameters is carried by the sensor node, in order to reduce the communication between the sensor node and the fusion center, while the remaining model parameters are estimated at the fusion center. The transmitted data from the sensor node to the fusion center ranges from 1 ~ 5% of the sampled acoustic signal at the node. A variety of classification schemes were examined, such as maximum likelihood, vector quantization and artificial neural networks. Evaluation of the proposed application, through processing of an acoustic data set with comparison to previous results, shows that the improvement is not only in the number of computations but also in the detection and false alarm rate as well.

Magnetic Resonance Fingerprinting - a promising new approach to obtain standardized imaging biomarkers from MRI.

PubMed

2015-04-01

Current routine MRI examinations rely on the acquisition of qualitative images that have a contrast "weighted" for a mixture of (magnetic) tissue properties. Recently, a novel approach was introduced, namely MR Fingerprinting (MRF) with a completely different approach to data acquisition, post-processing and visualization. Instead of using a repeated, serial acquisition of data for the characterization of individual parameters of interest, MRF uses a pseudo randomized acquisition that causes the signals from different tissues to have a unique signal evolution or 'fingerprint' that is simultaneously a function of the multiple material properties under investigation. The processing after acquisition involves a pattern recognition algorithm to match the fingerprints to a predefined dictionary of predicted signal evolutions. These can then be translated into quantitative maps of the magnetic parameters of interest. MR Fingerprinting (MRF) is a technique that could theoretically be applied to most traditional qualitative MRI methods and replaces them with acquisition of truly quantitative tissue measures. MRF is, thereby, expected to be much more accurate and reproducible than traditional MRI and should improve multi-center studies and significantly reduce reader bias when diagnostic imaging is performed. Key Points • MR fingerprinting (MRF) is a new approach to data acquisition, post-processing and visualization.• MRF provides highly accurate quantitative maps of T1, T2, proton density, diffusion.• MRF may offer multiparametric imaging with high reproducibility, and high potential for multicenter/ multivendor studies.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Testing a key assumption in animal communication: between-individual variation in female visual systems alters perception of male signals

PubMed Central

Ensminger, Amanda L.; Shawkey, Matthew D.; Lucas, Jeffrey R.; Fernández-Juricic, Esteban

2017-01-01

ABSTRACT Variation in male signal production has been extensively studied because of its relevance to animal communication and sexual selection. Although we now know much about the mechanisms that can lead to variation between males in the properties of their signals, there is still a general assumption that there is little variation in terms of how females process these male signals. Variation between females in signal processing may lead to variation between females in how they rank individual males, meaning that one single signal may not be universally attractive to all females. We tested this assumption in a group of female wild-caught brown-headed cowbirds (Molothrus ater), a species that uses a male visual signal (e.g. a wingspread display) to make its mate-choice decisions. We found that females varied in two key parameters of their visual sensory systems related to chromatic and achromatic vision: cone densities (both total and proportions) and cone oil droplet absorbance. Using visual chromatic and achromatic contrast modeling, we then found that this between-individual variation in visual physiology leads to significant between-individual differences in how females perceive chromatic and achromatic male signals. These differences may lead to variation in female preferences for male visual signals, which would provide a potential mechanism for explaining individual differences in mate-choice behavior. PMID:29247048

Testing a key assumption in animal communication: between-individual variation in female visual systems alters perception of male signals.

PubMed

Ronald, Kelly L; Ensminger, Amanda L; Shawkey, Matthew D; Lucas, Jeffrey R; Fernández-Juricic, Esteban

2017-12-15

Variation in male signal production has been extensively studied because of its relevance to animal communication and sexual selection. Although we now know much about the mechanisms that can lead to variation between males in the properties of their signals, there is still a general assumption that there is little variation in terms of how females process these male signals. Variation between females in signal processing may lead to variation between females in how they rank individual males, meaning that one single signal may not be universally attractive to all females. We tested this assumption in a group of female wild-caught brown-headed cowbirds ( Molothrus ater ), a species that uses a male visual signal (e.g. a wingspread display) to make its mate-choice decisions. We found that females varied in two key parameters of their visual sensory systems related to chromatic and achromatic vision: cone densities (both total and proportions) and cone oil droplet absorbance. Using visual chromatic and achromatic contrast modeling, we then found that this between-individual variation in visual physiology leads to significant between-individual differences in how females perceive chromatic and achromatic male signals. These differences may lead to variation in female preferences for male visual signals, which would provide a potential mechanism for explaining individual differences in mate-choice behavior. © 2017. Published by The Company of Biologists Ltd.

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

Toward a model for lexical access based on acoustic landmarks and distinctive features

NASA Astrophysics Data System (ADS)

Stevens, Kenneth N.

2002-04-01

This article describes a model in which the acoustic speech signal is processed to yield a discrete representation of the speech stream in terms of a sequence of segments, each of which is described by a set (or bundle) of binary distinctive features. These distinctive features specify the phonemic contrasts that are used in the language, such that a change in the value of a feature can potentially generate a new word. This model is a part of a more general model that derives a word sequence from this feature representation, the words being represented in a lexicon by sequences of feature bundles. The processing of the signal proceeds in three steps: (1) Detection of peaks, valleys, and discontinuities in particular frequency ranges of the signal leads to identification of acoustic landmarks. The type of landmark provides evidence for a subset of distinctive features called articulator-free features (e.g., [vowel], [consonant], [continuant]). (2) Acoustic parameters are derived from the signal near the landmarks to provide evidence for the actions of particular articulators, and acoustic cues are extracted by sampling selected attributes of these parameters in these regions. The selection of cues that are extracted depends on the type of landmark and on the environment in which it occurs. (3) The cues obtained in step (2) are combined, taking context into account, to provide estimates of ``articulator-bound'' features associated with each landmark (e.g., [lips], [high], [nasal]). These articulator-bound features, combined with the articulator-free features in (1), constitute the sequence of feature bundles that forms the output of the model. Examples of cues that are used, and justification for this selection, are given, as well as examples of the process of inferring the underlying features for a segment when there is variability in the signal due to enhancement gestures (recruited by a speaker to make a contrast more salient) or due to overlap of gestures from neighboring segments.

Adsorptive removal of aniline by granular activated carbon from aqueous solutions with catechol and resorcinol.

PubMed

Suresh, S; Srivastava, V C; Mishrab, I M

2012-01-01

In the present paper, the removal of aniline by adsorption process onto granular activated carbon (GAC) is reported from aqueous solutions containing catechol and resorcinol separately. The Taguchi experimental design was applied to study the effect of such parameters as the initial component concentrations (C(0,i)) of two solutes (aniline and catechol or aniline and resorcinol) in the solution, temperature (T), adsorbent dosage (m) and contact time (t). The L27 orthogonal array consisting of five parameters each with three levels was used to determine the total amount of solutes adsorbed on GAC (q(tot), mmol/g) and the signal-to-noise ratio. The analysis of variance (ANOVA) was used to determine the optimum conditions. Under these conditions, the ANOVA shows that m is the most important parameter in the adsorption process. The most favourable levels of process parameters were T = 303 K, m = 10 g/l and t = 660 min for both the systems, qtot values in the confirmation experiments carried out at optimum conditions were 0.73 and 0.95 mmol/g for aniline-catechol and aniline-resorcinol systems, respectively.

Model of the best-of-N nest-site selection process in honeybees.

PubMed

Reina, Andreagiovanni; Marshall, James A R; Trianni, Vito; Bose, Thomas

2017-05-01

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N-1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Model of the best-of-N nest-site selection process in honeybees

NASA Astrophysics Data System (ADS)

Reina, Andreagiovanni; Marshall, James A. R.; Trianni, Vito; Bose, Thomas

2017-05-01

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N -1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Self-Contained Avionics Sensing and Flight Control System for Small Unmanned Aerial Vehicle

NASA Technical Reports Server (NTRS)

Ingham, John C. (Inventor); Shams, Qamar A. (Inventor); Logan, Michael J. (Inventor); Fox, Robert L. (Inventor); Fox, legal representative, Melanie L. (Inventor); Kuhn, III, Theodore R. (Inventor); Babel, III, Walter C. (Inventor); Fox, legal representative, Christopher L. (Inventor); Adams, James K. (Inventor); Laughter, Sean A. (Inventor)

2011-01-01

A self-contained avionics sensing and flight control system is provided for an unmanned aerial vehicle (UAV). The system includes sensors for sensing flight control parameters and surveillance parameters, and a Global Positioning System (GPS) receiver. Flight control parameters and location signals are processed to generate flight control signals. A Field Programmable Gate Array (FPGA) is configured to provide a look-up table storing sets of values with each set being associated with a servo mechanism mounted on the UAV and with each value in each set indicating a unique duty cycle for the servo mechanism associated therewith. Each value in each set is further indexed to a bit position indicative of a unique percentage of a maximum duty cycle for the servo mechanism associated therewith. The FPGA is further configured to provide a plurality of pulse width modulation (PWM) generators coupled to the look-up table. Each PWM generator is associated with and adapted to be coupled to one of the servo mechanisms.

Bioinspired Polarization Imaging Sensors: From Circuits and Optics to Signal Processing Algorithms and Biomedical Applications

PubMed Central

York, Timothy; Powell, Samuel B.; Gao, Shengkui; Kahan, Lindsey; Charanya, Tauseef; Saha, Debajit; Roberts, Nicholas W.; Cronin, Thomas W.; Marshall, Justin; Achilefu, Samuel; Lake, Spencer P.; Raman, Baranidharan; Gruev, Viktor

2015-01-01

In this paper, we present recent work on bioinspired polarization imaging sensors and their applications in biomedicine. In particular, we focus on three different aspects of these sensors. First, we describe the electro–optical challenges in realizing a bioinspired polarization imager, and in particular, we provide a detailed description of a recent low-power complementary metal–oxide–semiconductor (CMOS) polarization imager. Second, we focus on signal processing algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation. Third, the emergence of these sensors has enabled rapid progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical areas, such as label-free optical neural recording, dynamic tissue strength analysis, and early diagnosis of flat cancerous lesions in a murine colorectal tumor model. We highlight results obtained from these three areas and discuss future applications for these sensors. PMID:26538682

The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings.

PubMed

Koen, Joshua D; Barrett, Frederick S; Harlow, Iain M; Yonelinas, Andrew P

2017-08-01

Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.

Dimensional analysis of acoustically propagated signals

NASA Technical Reports Server (NTRS)

Hansen, Scott D.; Thomson, Dennis W.

1993-01-01

Traditionally, long term measurements of atmospherically propagated sound signals have consisted of time series of multiminute averages. Only recently have continuous measurements with temporal resolution corresponding to turbulent time scales been available. With modern digital data acquisition systems we now have the capability to simultaneously record both acoustical and meteorological parameters with sufficient temporal resolution to allow us to examine in detail relationships between fluctuating sound and the meteorological variables, particularly wind and temperature, which locally determine the acoustic refractive index. The atmospheric acoustic propagation medium can be treated as a nonlinear dynamical system, a kind of signal processor whose innards depend on thermodynamic and turbulent processes in the atmosphere. The atmosphere is an inherently nonlinear dynamical system. In fact one simple model of atmospheric convection, the Lorenz system, may well be the most widely studied of all dynamical systems. In this paper we report some results of our having applied methods used to characterize nonlinear dynamical systems to study the characteristics of acoustical signals propagated through the atmosphere. For example, we investigate whether or not it is possible to parameterize signal fluctuations in terms of fractal dimensions. For time series one such parameter is the limit capacity dimension. Nicolis and Nicolis were among the first to use the kind of methods we have to study the properties of low dimension global attractors.

Functional relationship-based alarm processing

DOEpatents

Corsberg, D.R.

1987-04-13

A functional relationship-based alarm processing system and method analyzes each alarm as it is activated and determines its relative importance with other currently activated alarms and signals in accordance with the relationships that the newly activated alarm has with other currently activated alarms. Once the initial level of importance of the alarm has been determined, that alarm is again evaluated if another related alarm is activated. Thus, each alarm's importance is continuously updated as the state of the process changes during a scenario. Four hierarchical relationships are defined by this alarm filtering methodology: (1) level precursor (usually occurs when there are two alarm settings on the same parameter); (2) direct precursor (based on causal factors between two alarms); (3) required action (system response or action expected within a specified time following activation of an alarm or combination of alarms and process signals); and (4) blocking condition (alarms that are normally expected and are not considered important). 11 figs.

Research and development of an electrochemical biocide reactor

NASA Technical Reports Server (NTRS)

See, G. G.; Bodo, C. A.; Glennon, J. P.

1975-01-01

An alternate disinfecting process to chemical agents, heat, or radiation in an aqueous media has been studied. The process is called an electrochemical biocide and employs cyclic, low-level voltages at chemically inert electrodes to pass alternating current through water and, in the process, to destroy microorganisms. The paper describes experimental hardware, methodology, and results with a tracer microorganism (Escherichia coli). The results presented show the effects on microorganism kill of operating parameters, including current density (15 to 55 mA/sq cm (14 to 51 ASF)), waveform of applied electrical signal (square, triangular, sine), frequency of applied electrical signal (0.5 to 1.5 Hz), process water flow rate (100 to 600 cc/min (1.6 to 9.5 gph)), and reactor resident time (0 to 4 min). Comparisons are made between the disinfecting property of the electrochemical biocide and chlorine, bromine, and iodine.

Transient Modulations of Neural Responses to Heartbeats Covary with Bodily Self-Consciousness.

PubMed

Park, Hyeong-Dong; Bernasconi, Fosco; Bello-Ruiz, Javier; Pfeiffer, Christian; Salomon, Roy; Blanke, Olaf

2016-08-10

Recent research has investigated self-consciousness associated with the multisensory processing of bodily signals (e.g., somatosensory, visual, vestibular signals), a notion referred to as bodily self-consciousness, and these studies have shown that the manipulation of bodily inputs induces changes in bodily self-consciousness such as self-identification. Another line of research has highlighted the importance of signals from the inside of the body (e.g., visceral signals) and proposed that neural representations of internal bodily signals underlie self-consciousness, which to date has been based on philosophical inquiry, clinical case studies, and behavioral studies. Here, we investigated the relationship of bodily self-consciousness with the neural processing of internal bodily signals. By combining electrical neuroimaging, analysis of peripheral physiological signals, and virtual reality technology in humans, we show that transient modulations of neural responses to heartbeats in the posterior cingulate cortex covary with changes in bodily self-consciousness induced by the full-body illusion. Additional analyses excluded that measured basic cardiorespiratory parameters or interoceptive sensitivity traits could account for this finding. These neurophysiological data link experimentally the cortical mapping of the internal body to self-consciousness. What are the brain mechanisms of self-consciousness? Prominent views propose that the neural processing associated with signals from the internal organs (such as the heart and the lung) plays a critical role in self-consciousness. Although this hypothesis dates back to influential views in philosophy and psychology (e.g., William James), definitive experimental evidence supporting this idea is lacking despite its recent impact in neuroscience. In the present study, we show that posterior cingulate activities responding to heartbeat signals covary with changes in participants' conscious self-identification with a body that were manipulated experimentally using virtual reality technology. Our finding provides important neural evidence about the long-standing proposal that self-consciousness is linked to the cortical processing of internal bodily signals. Copyright © 2016 the authors 0270-6474/16/368453-08$15.00/0.

Channel modeling, signal processing and coding for perpendicular magnetic recording

NASA Astrophysics Data System (ADS)

Wu, Zheng

With the increasing areal density in magnetic recording systems, perpendicular recording has replaced longitudinal recording to overcome the superparamagnetic limit. Studies on perpendicular recording channels including aspects of channel modeling, signal processing and coding techniques are presented in this dissertation. To optimize a high density perpendicular magnetic recording system, one needs to know the tradeoffs between various components of the system including the read/write transducers, the magnetic medium, and the read channel. We extend the work by Chaichanavong on the parameter optimization for systems via design curves. Different signal processing and coding techniques are studied. Information-theoretic tools are utilized to determine the acceptable region for the channel parameters when optimal detection and linear coding techniques are used. Our results show that a considerable gain can be achieved by the optimal detection and coding techniques. The read-write process in perpendicular magnetic recording channels includes a number of nonlinear effects. Nonlinear transition shift (NLTS) is one of them. The signal distortion induced by NLTS can be reduced by write precompensation during data recording. We numerically evaluate the effect of NLTS on the read-back signal and examine the effectiveness of several write precompensation schemes in combating NLTS in a channel characterized by both transition jitter noise and additive white Gaussian electronics noise. We also present an analytical method to estimate the bit-error-rate and use it to help determine the optimal write precompensation values in multi-level precompensation schemes. We propose a mean-adjusted pattern-dependent noise predictive (PDNP) detection algorithm for use on the channel with NLTS. We show that this detector can offer significant improvements in bit-error-rate (BER) compared to conventional Viterbi and PDNP detectors. Moreover, the system performance can be further improved by combining the new detector with a simple write precompensation scheme. Soft-decision decoding for algebraic codes can improve performance for magnetic recording systems. In this dissertation, we propose two soft-decision decoding methods for tensor-product parity codes. We also present a list decoding algorithm for generalized error locating codes.

Operational stability prediction in milling based on impact tests

NASA Astrophysics Data System (ADS)

Kiss, Adam K.; Hajdu, David; Bachrathy, Daniel; Stepan, Gabor

2018-03-01

Chatter detection is usually based on the analysis of measured signals captured during cutting processes. These techniques, however, often give ambiguous results close to the stability boundaries, which is a major limitation in industrial applications. In this paper, an experimental chatter detection method is proposed based on the system's response for perturbations during the machining process, and no system parameter identification is required. The proposed method identifies the dominant characteristic multiplier of the periodic dynamical system that models the milling process. The variation of the modulus of the largest characteristic multiplier can also be monitored, the stability boundary can precisely be extrapolated, while the manufacturing parameters are still kept in the chatter-free region. The method is derived in details, and also verified experimentally in laboratory environment.

Real-time controller for foot-drop correction by using surface electromyography sensor.

PubMed

Al Mashhadany, Yousif I; Abd Rahim, Nasrudin

2013-04-01

Foot drop is a disease caused mainly by muscle paralysis, which incapacitates the nerves generating the impulses that control feet in a heel strike. The incapacity may stem from lesions that affect the brain, the spinal cord, or peripheral nerves. The foot becomes dorsiflexed, affecting normal walking. A design and analysis of a controller for such legs is the subject of this article. Surface electromyography electrodes are connected to the skin surface of the human muscle and work on the mechanics of human muscle contraction. The design uses real surface electromyography signals for estimation of the joint angles. Various-speed flexions and extensions of the leg were analyzed. The two phases of the design began with surface electromyography of real human leg electromyography signal, which was subsequently filtered, amplified, and normalized to the maximum amplitude. Parameters extracted from the surface electromyography signal were then used to train an artificial neural network for prediction of the joint angle. The artificial neural network design included various-speed identification of the electromyography signal and estimation of the angles of the knee and ankle joints by a recognition process that depended on the parameters of the real surface electromyography signal measured through real movements. The second phase used artificial neural network estimation of the control signal, for calculation of the electromyography signal to be stimulated for the leg muscle to move the ankle joint. Satisfactory simulation (MATLAB/Simulink version 2012a) and implementation results verified the design feasibility.

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

Gilchrist, Kristin H., E-mail: kgilchrist@rti.org; Lewis, Gregory F.; Gay, Elaine A.

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak formore » field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.« less

Instantaneous and Frequency-Warped Signal Processing Techniques for Auditory Source Separation.

NASA Astrophysics Data System (ADS)

Wang, Avery Li-Chun

This thesis summarizes several contributions to the areas of signal processing and auditory source separation. The philosophy of Frequency-Warped Signal Processing is introduced as a means for separating the AM and FM contributions to the bandwidth of a complex-valued, frequency-varying sinusoid p (n), transforming it into a signal with slowly-varying parameters. This transformation facilitates the removal of p (n) from an additive mixture while minimizing the amount of damage done to other signal components. The average winding rate of a complex-valued phasor is explored as an estimate of the instantaneous frequency. Theorems are provided showing the robustness of this measure. To implement frequency tracking, a Frequency-Locked Loop algorithm is introduced which uses the complex winding error to update its frequency estimate. The input signal is dynamically demodulated and filtered to extract the envelope. This envelope may then be remodulated to reconstruct the target partial, which may be subtracted from the original signal mixture to yield a new, quickly-adapting form of notch filtering. Enhancements to the basic tracker are made which, under certain conditions, attain the Cramer -Rao bound for the instantaneous frequency estimate. To improve tracking, the novel idea of Harmonic -Locked Loop tracking, using N harmonically constrained trackers, is introduced for tracking signals, such as voices and certain musical instruments. The estimated fundamental frequency is computed from a maximum-likelihood weighting of the N tracking estimates, making it highly robust. The result is that harmonic signals, such as voices, can be isolated from complex mixtures in the presence of other spectrally overlapping signals. Additionally, since phase information is preserved, the resynthesized harmonic signals may be removed from the original mixtures with relatively little damage to the residual signal. Finally, a new methodology is given for designing linear-phase FIR filters which require a small fraction of the computational power of conventional FIR implementations. This design strategy is based on truncated and stabilized IIR filters. These signal-processing methods have been applied to the problem of auditory source separation, resulting in voice separation from complex music that is significantly better than previous results at far lower computational cost.

A hybrid silicon membrane spatial light modulator for optical information processing

NASA Technical Reports Server (NTRS)

Pape, D. R.; Hornbeck, L. J.

1984-01-01

A new two dimensional, fast, analog, electrically addressable, silicon based membrane spatial light modulator (SLM) was developed for optical information processing applications. Coherent light reflected from the mirror elements is phase modulated producing an optical Fourier transform of an analog signal input to the device. The DMD architecture and operating parameters related to this application are presented. A model is developed that describes the optical Fourier transform properties of the DMD.

Biomedical implications of information processing in chemical systems: non-classical approach to photochemistry of coordination compounds.

PubMed

Szaciłowski, Konrad

2007-01-01

Analogies between photoactive nitric oxide generators and various electronic devices: logic gates and operational amplifiers are presented. These analogies have important biological consequences: application of control parameters allows for better targeting and control of nitric oxide drugs. The same methodology may be applied in the future for other therapeutic strategies and at the same time helps to understand natural regulatory and signaling processes in biological systems.

On-line Monitoring for Cutting Tool Wear Condition Based on the Parameters

NASA Astrophysics Data System (ADS)

Han, Fenghua; Xie, Feng

2017-07-01

In the process of cutting tools, it is very important to monitor the working state of the tools. On the basis of acceleration signal acquisition under the constant speed, time domain and frequency domain analysis of relevant indicators monitor the online of tool wear condition. The analysis results show that the method can effectively judge the tool wear condition in the process of machining. It has certain application value.

Experimental facility for the study of acoustic emission registered in the primary circuit components of WWER power units

NASA Astrophysics Data System (ADS)

Petrosyan, V. G.; Hovakimyan, T. H.; Yeghoyan, E. A.; Hovhannisyan, H. T.; Mayilyan, D. G.; Petrosyan, A. P.

2017-01-01

This paper is dedicated to the creation of a facility for the experimental study of a phenomenon of background acoustic emission (AE), which is detected in the main circulation loop (MCL) of WWER power units. The analysis of the operating principle and the design of a primary feed-and-blow down system (FB) deaerator of NPP as the most likely source of continuous acoustic emission is carried out. The experimental facility for the systematic study of a phenomenon of continuous AE is developed. A physical model of a thermal deaerator is designed and constructed. A thermal monitoring system is introduced. An automatic system providing acoustic signal registration in a low frequency (0.03-30 kHz) and high frequency (30-300 kHz) bands and study of its spectral characteristics is designed. Special software for recording and processing of digitized electrical sensor signals is developed. A separate and independent principle of study of the most probable processes responsible for the generation of acoustic emission signals in the deaerator is applied. Trial series of experiments and prechecks of acoustic signals in different modes of the deaerator model are conducted. Compliance of basic technological parameters with operating range of the real deaerator was provided. It is shown that the acoustic signal time-intensity curve has several typical regions. The pilot research showed an impact of various processes that come about during the operation of the deaerator physical model on the intensity of the AE signal. The experimental results suggest that the main sources of generation of the AE signals are the processes of steam condensation, turbulent flow of gas-vapor medium, and water boiling.

Optimization of Surface Roughness Parameters of Al-6351 Alloy in EDC Process: A Taguchi Coupled Fuzzy Logic Approach

NASA Astrophysics Data System (ADS)

Kar, Siddhartha; Chakraborty, Sujoy; Dey, Vidyut; Ghosh, Subrata Kumar

2017-10-01

This paper investigates the application of Taguchi method with fuzzy logic for multi objective optimization of roughness parameters in electro discharge coating process of Al-6351 alloy with powder metallurgical compacted SiC/Cu tool. A Taguchi L16 orthogonal array was employed to investigate the roughness parameters by varying tool parameters like composition and compaction load and electro discharge machining parameters like pulse-on time and peak current. Crucial roughness parameters like Centre line average roughness, Average maximum height of the profile and Mean spacing of local peaks of the profile were measured on the coated specimen. The signal to noise ratios were fuzzified to optimize the roughness parameters through a single comprehensive output measure (COM). Best COM obtained with lower values of compaction load, pulse-on time and current and 30:70 (SiC:Cu) composition of tool. Analysis of variance is carried out and a significant COM model is observed with peak current yielding highest contribution followed by pulse-on time, compaction load and composition. The deposited layer is characterised by X-Ray Diffraction analysis which confirmed the presence of tool materials on the work piece surface.

The Monitoring System of the Operating State of the Gear Wheels of the Torque Multiplier of the Desalination Plant Steam Generator

NASA Astrophysics Data System (ADS)

Danilin, A. I.; Neverov, V. V.; Danilin, S. A.; Shimanov, A. A.; Tsapkova, A. B.

2018-01-01

The article describes a noncontact operational control method based on the processing of a microwave signal reflected from the controlled teeth of the wheel. In this paper describes the influence of wear patterns on the characteristic information parameters of the analyzed signals. The block diagram in section 3 shows the experimental system for monitoring the operating state of the gear wheels of the steam compressor torque multiplier. The design of the primary converter is briefly described.

GMTI Direction of Arrival Measurements from Multiple Phase Centers.

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

Doerry, Armin W.; Bickel, Douglas L.

2015-03-01

Ground Moving Target Indicator (GMTI) radar attempts to detect and locate targets with unknown motion. Very slow-moving targets are difficult to locate in the presence of surrounding clutter. This necessitates multiple antenna phase centers (or equivalent) to offer independent Direction of Arrival (DOA) measurements. DOA accuracy and precision generally remains dependent on target Signal-to-Noise Ratio (SNR), Clutter-toNoise Ratio (CNR), scene topography, interfering signals, and a number of antenna parameters. This is true even for adaptive techniques like Space-Time-AdaptiveProcessing (STAP) algorithms.

Data Acquisition and Processing System for Airborne Wind Profiling with a Pulsed, 2-Micron, Coherent-Detection, Doppler Lidar System

NASA Technical Reports Server (NTRS)

Beyon, J. Y.; Koch, G. J.; Kavaya, M. J.

2010-01-01

A data acquisition and signal processing system is being developed for a 2-micron airborne wind profiling coherent Doppler lidar system. This lidar, called the Doppler Aerosol Wind Lidar (DAWN), is based on a Ho:Tm:LuLiF laser transmitter and 15-cm diameter telescope. It is being packaged for flights onboard the NASA DC-8, with the first flights in the summer of 2010 in support of the NASA Genesis and Rapid Intensification Processes (GRIP) campaign for the study of hurricanes. The data acquisition and processing system is housed in a compact PCI chassis and consists of four components such as a digitizer, a digital signal processing (DSP) module, a video controller, and a serial port controller. The data acquisition and processing software (DAPS) is also being developed to control the system including real-time data analysis and display. The system detects an external 10 Hz trigger pulse and initiates the data acquisition and processing process, and displays selected wind profile parameters such as Doppler shift, power distribution, wind directions and velocities. Doppler shift created by aircraft motion is measured by an inertial navigation/GPS sensor and fed to the signal processing system for real-time removal of aircraft effects from wind measurements. A general overview of the system and the DAPS as well as the coherent Doppler lidar system is presented in this paper.

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.

GLORI (GLObal navigation satellite system Reflectometry Instrument): A New Airborne GNSS-R receiver for land surface applications

NASA Astrophysics Data System (ADS)

Motte, Erwan; Zribi, Mehrez; Fanise, Pascal

2015-04-01

GLORI (GLObal navigation satellite system Reflectometry Instrument) is a new receiver dedicated to the airborne measurement of surface parameters such as soil moisture and biomass above ground and sea state (wave height and direction) above oceans. The instrument is based on the PARIS concept [Martin-Neira, 1993] using both the direct and surface-reflected L-band signals from the GPS constellation as a multistatic radar source. The receiver is based on one up-looking and one down-looking dual polarization hemispherical active antennas feeding a low-cost 4-channel SDR direct down-conversion receiver tuned to the GPS L1 frequency. The raw measurements are sampled at 16.368MHz and stored as 2-bit, IQ binary files. In post-processing, GPS acquisition and tracking are performed on the direct up-looking signal while the down-looking signal is processed blindly using tracking parameters from the direct signal. The obtained direct and reflected code-correlation waveforms are the basic observables for geophysical parameters inversion. The instrument was designed to be installed aboard the ATR42 experimental aircraft from the French SAFIRE fleet as a permanent payload. The long term goal of the project is to provide real-time continuous surface information for every flight performed. The aircraft records attitude information through its Inertial Measurement Unit and a commercial GPS receiver records additional information such as estimated doppler and code phase, receiver location, satellites azimuth and elevation. A series of test flights were performed over both the Toulouse and Gulf of Lion (Mediterranean Sea) regions during the period 17-21 Nov 2014 together with the KuROS radar [Hauser et al., 2014]. Using processing methods from the literature [Egido et al., 2014], preliminary results demonstrate the instrument sensitivity to both ground and ocean surface parameters estimation. A dedicated scientific flight campaign is planned at the end of second quarter 2015 with collocated measurement of biomass and soil moisture ground truth in order to better characterize the instrument sensitivity to geophysical parameters. The instrument will be improved in the meanwhile including the optimization of data processing and the better integration of external data (GPS commercial receiver, Attitude) into the receiver. M.Martin-Neira. A Passive reflectometry and interferometry system (PARIS): Application to ocean altimetry. ESA J., 17:331-355, 1993 Hauser, D.; Caudal, G.; Le Gac, C.; Valentin, R.; Delaye, L.; Tison, C., "KuROS: A new airborne Ku-band Doppler radar for observation of the ocean surface," Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International , vol., no., pp.282,285, 13-18 July 2014 Egido, A.; Paloscia, S.; Motte, E.; Guerriero, L.; Pierdicca, N.; Caparrini, M.; Santi, E.; Fontanelli, G.; Floury, N., "Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol.7, no.5, pp.1522,1532, May 2014

Learning multimodal dictionaries.

PubMed

Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

2007-09-01

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

Parameter Estimation for Real Filtered Sinusoids

DTIC Science & Technology

1997-09-01

Statistical Signal Processing: Detection, Estimation and Time Series Analysis. New York: Addison-Wesley, 1991. 74. Serway , Raymond A . Physics for...Dr. Yung Kee Yeo, Dean’s Representative Dr. Robert A . Calico, Jr., Dean Table of Contents Page List of Abbreviations...Contributions ....... ...................... 5-4 5.4 Summary ........ ............................. 5-6 Appendix A . Vector-Matrix Differentiation

Fluorescence lifetime as a new parameter in analytical cytology measurements

NASA Astrophysics Data System (ADS)

Steinkamp, John A.; Deka, Chiranjit; Lehnert, Bruce E.; Crissman, Harry A.

1996-05-01

A phase-sensitive flow cytometer has been developed to quantify fluorescence decay lifetimes on fluorochrome-labeled cells/particles. This instrument combines flow cytometry (FCM) and frequency-domain fluorescence spectroscopy measurement principles to provide unique capabilities for making phase-resolved lifetime measurements, while preserving conventional FCM capabilities. Cells are analyzed as they intersect a high-frequency, intensity-modulated (sine wave) laser excitation beam. Fluorescence signals are processed by conventional and phase-sensitive signal detection electronics and displayed as frequency distribution histograms. In this study we describe results of fluorescence intensity and lifetime measurements on fluorescently labeled particles, cells, and chromosomes. Examples of measurements on intrinsic cellular autofluorescence, cells labeled with immunofluorescence markers for cell- surface antigens, mitochondria stains, and on cellular DNA and protein binding fluorochromes will be presented to illustrate unique differences in measured lifetimes and changes caused by fluorescence quenching. This innovative technology will be used to probe fluorochrome/molecular interactions in the microenvironment of cells/chromosomes as a new parameter and thus expand the researchers' understanding of biochemical processes and structural features at the cellular and molecular level.

The R package 'RLumModel': Simulating charge transfer in quartz

NASA Astrophysics Data System (ADS)

Friedrich, Johannes; Kreutzer, Sebastian; Schmidt, Christoph

2017-04-01

Kinetic models of quartz luminescence have gained an important role for predicting experimental results and for understanding charge transfers in (natural) quartz as well as for other dosimetric materials, e.g., Al2O3:C. We present the R package 'RLumModel', offering an easy-to-use tool for simulating quartz luminescence signals (TL, OSL, LM-OSL and RF) based on five integrated and published parameter sets as well as the possibility to use own parameters. Simulation commands can be created (a) using the Risø Sequence Editor, (b) a built-in SAR sequence generator or (c) self-explanatory keywords for customised sequences. Results can be analysed seamlessly using the R package 'Luminescence' along with a visualisation of concentrations of electrons and holes in every trap/centre as well as in the valence and conduction band during all stages of the simulation. Modelling luminescence signals can help understanding charge transfer processes occurring in nature or during measurements in the laboratory. This will lead to a better understanding of several processes concerning geoscientific questions, because quartz is the second most abundant mineral in the Earth's continental crust.

Stochastic subspace identification for operational modal analysis of an arch bridge

NASA Astrophysics Data System (ADS)

Loh, Chin-Hsiung; Chen, Ming-Che; Chao, Shu-Hsien

2012-04-01

In this paer the application of output-only system identification technique, known as Stochastic Subspace Identification (SSI) algorithms, for civil infrastructures is carried out. The ability of covariance driven stochastic subspace identification (SSI-COV) was proved through the analysis of the ambient data of an arch bridge under operational condition. A newly developed signal processing technique, Singular Spectrum analysis (SSA), capable to smooth noisy signals, is adopted for pre-processing the recorded data before the SSI. The conjunction of SSA and SSICOV provides a useful criterion for the system order determination. With the aim of estimating accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data Hankel matrix. Identification task of a real structure, Guandu Bridge, is carried out to identify the system natural frequencies and mode shapes. The uncertainty of the identified model parameters from output-only measurement of the bridge under operation condition, such as temperature and traffic loading conditions, is discussed.

On the role of magnetic field intensity for better micro-structural characterization during Barkhausen Noise analysis

NASA Astrophysics Data System (ADS)

Yusufzai, Mohd Zaheer Khan; Vashista, M.

2018-04-01

Barkhausen Noise analysis is a popular and preferred technique for micro-structural characterization. The root mean square value and peak value of Barkhausen Noise burst are important parameters to assess the micro-hardness and residual stress. Barkhausen Noise burst can be enveloped using a curve known as Barkhausen Noise profile. Peak position of profile changes with change in micro-structure. In the present work, raw signal of Barkhausen Noise burst was obtained from Ni based sample at various magnetic field intensity to observe the effect of variation in field intensity on Barkhausen Noise burst. Raw signal was opened using MATLAB to further process for microstructure analysis. Barkhausen Noise analysis parameters such as magnetizing frequency, number of burst, high pass and low pass filter frequency were kept constant and magnetizing field was varied in wide range between 200 Oe to 1200 Oe. The processed profiles of Barkhausen Noise burst obtained at various magnetizing field intensity clearly reveals requirement of optimum magnetic field strength for better characterization of micro-structure.

Multi-model approach to characterize human handwriting motion.

PubMed

Chihi, I; Abdelkrim, A; Benrejeb, M

2016-02-01

This paper deals with characterization and modelling of human handwriting motion from two forearm muscle activity signals, called electromyography signals (EMG). In this work, an experimental approach was used to record the coordinates of a pen tip moving on the (x, y) plane and EMG signals during the handwriting act. The main purpose is to design a new mathematical model which characterizes this biological process. Based on a multi-model approach, this system was originally developed to generate letters and geometric forms written by different writers. A Recursive Least Squares algorithm is used to estimate the parameters of each sub-model of the multi-model basis. Simulations show good agreement between predicted results and the recorded data.

Raman Amplification and Tunable Pulse Delays in Silicon Waveguides

NASA Astrophysics Data System (ADS)

Rukhlenko, Ivan D.; Garanovich, Ivan L.; Premaratne, Malin; Sukhorukov, Andrey A.; Agrawal, Govind P.

2010-10-01

The nonlinear process of stimulated Raman scattering is important for silicon photonics as it enables optical amplification and lasing. However, generally employed numerical approaches provide very little insight into the contribution of different silicon Raman amplifier (SRA) parameters. In this paper, we solve the coupled pump-signal equations analytically and derive an exact formula for the envelope of a signal pulse when picosecond optical pulses are amplified inside a SRA pumped by a continuous-wave laser beam. Our solution is valid for an arbitrary pulse shape and fully accounts for the Raman gain-dispersion effects, including temporal broadening and group-velocity reduction. Our results are useful for optimizing the performance of SRAs and for engineering controllable signal delays.

Recognition of digital characteristics based new improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Meng; Xu, Guoqiang; Lin, Zihao

2017-08-01

In the field of digital signal processing, Estimating the characteristics of signal modulation parameters is an significant research direction. The paper determines the set of eigenvalue which can show the difference of the digital signal modulation based on the deep research of the new improved genetic algorithm. Firstly take them as the best gene pool; secondly, The best gene pool will be changed in the genetic evolvement by selecting, overlapping and eliminating each other; Finally, Adapting the strategy of futher enhance competition and punishment to more optimizer the gene pool and ensure each generation are of high quality gene. The simulation results show that this method not only has the global convergence, stability and faster convergence speed.

On estimating the phase of periodic waveform in additive Gaussian noise, part 2

NASA Astrophysics Data System (ADS)

Rauch, L. L.

1984-11-01

Motivated by advances in signal processing technology that support more complex algorithms, a new look is taken at the problem of estimating the phase and other parameters of a periodic waveform in additive Gaussian noise. The general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown constant phase with an a priori distribution. Optimal algorithms are obtained for some cases where the frequency is a parameterized function of time with the unknown parameters and phase having a joint a priori distribution. In the last section, the intrinsic and extrinsic geometry of hypersurfaces is introduced to provide insight to the estimation problem for the small noise and large noise cases.

On Estimating the Phase of Periodic Waveform in Additive Gaussian Noise, Part 2

NASA Technical Reports Server (NTRS)

Rauch, L. L.

1984-01-01

Motivated by advances in signal processing technology that support more complex algorithms, a new look is taken at the problem of estimating the phase and other parameters of a periodic waveform in additive Gaussian noise. The general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown constant phase with an a priori distribution. Optimal algorithms are obtained for some cases where the frequency is a parameterized function of time with the unknown parameters and phase having a joint a priori distribution. In the last section, the intrinsic and extrinsic geometry of hypersurfaces is introduced to provide insight to the estimation problem for the small noise and large noise cases.

Optically powered oil tank multichannel detection system with optical fiber link

NASA Astrophysics Data System (ADS)

Yu, Zhijing

1998-08-01

A novel oil tanks integrative parameters measuring system with optically powered are presented. To realize optical powered and micro-power consumption multiple channels and parameters detection, the system has taken the PWM/PPM modulation, ratio measurement, time division multiplexing and pulse width division multiplexing techniques. Moreover, the system also used special pulse width discriminator and single-chip microcomputer to accomplish signal pulse separation, PPM/PWM signal demodulation, the error correction of overlapping pulse and data processing. This new transducer has provided with high characteristics: experimental transmitting distance is 500m; total consumption of the probes is less than 150 (mu) W; measurement error: +/- 0.5 degrees C and +/- 0.2 percent FS. The measurement accuracy of the liquid level and reserves is mainly determined by the pressure accuracy. Finally, some points of the experiment are given.

Molecular parameters of head and neck cancer metastasis

PubMed Central

Bhave, Sanjay L.; Teknos, Theodoros N.; Pan, Quintin; James, Arthur G.; Solove, Richard J.

2011-01-01

Metastasis remains a major cause of mortality in patients with head and neck squamous cell carcinoma (HNSCC). HNSCC patients with metastatic disease have extremely poor prognosis with survival rate of less than a year. Metastasis is an intricate sequential process which requires a discrete population of tumor cells to possess the capacity to intravasate from the primary tumor into systemic circulation, survive in circulation, extravasate at a distant site, and proliferate in a foreign hostile environment. Literature has accumulated to provide mechanistic insight into several signal transduction pathways, receptor tyrosine kinases (RTKs), signal transducer and activator of transcription 3 (Stat3), Rho GTPases, protein kinase Cε (PKCε), and nuclear factor-κB (NF-κB), that are involved in mediating a metastatic tumor cell phenotype in HNSCC. Here we highlight accrued information regarding the key molecular parameters of HNSCC metastasis. PMID:22077153

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

Statistical properties of a filtered Poisson process with additive random noise: distributions, correlations and moment estimation

NASA Astrophysics Data System (ADS)

Theodorsen, A.; E Garcia, O.; Rypdal, M.

2017-05-01

Filtered Poisson processes are often used as reference models for intermittent fluctuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical term in a stochastic differential equation. The lowest order moments, probability density function, auto-correlation function and power spectral density are derived and used to identify and compare the effects of the two different noise terms. Monte-Carlo studies of synthetic time series are used to investigate the accuracy of model parameter estimation and to identify methods for distinguishing the noise types. It is shown that the probability density function and the three lowest order moments provide accurate estimations of the model parameters, but are unable to separate the noise types. The auto-correlation function and the power spectral density also provide methods for estimating the model parameters, as well as being capable of identifying the noise type. The number of times the signal crosses a prescribed threshold level in the positive direction also promises to be able to differentiate the noise type.

Adaptive angular-velocity Vold-Kalman filter order tracking - Theoretical basis, numerical implementation and parameter investigation

NASA Astrophysics Data System (ADS)

Pan, M.-Ch.; Chu, W.-Ch.; Le, Duc-Do

2016-12-01

The paper presents an alternative Vold-Kalman filter order tracking (VKF_OT) method, i.e. adaptive angular-velocity VKF_OT technique, to extract and characterize order components in an adaptive manner for the condition monitoring and fault diagnosis of rotary machinery. The order/spectral waveforms to be tracked can be recursively solved by using Kalman filter based on the one-step state prediction. The paper comprises theoretical derivation of computation scheme, numerical implementation, and parameter investigation. Comparisons of the adaptive VKF_OT scheme with two other ones are performed through processing synthetic signals of designated order components. Processing parameters such as the weighting factor and the correlation matrix of process noise, and data conditions like the sampling frequency, which influence tracking behavior, are explored. The merits such as adaptive processing nature and computation efficiency brought by the proposed scheme are addressed although the computation was performed in off-line conditions. The proposed scheme can simultaneously extract multiple spectral components, and effectively decouple close and crossing orders associated with multi-axial reference rotating speeds.

Mate Choice in Adult Female Bengalese Finches: Females Express Consistent Preferences for Individual Males and Prefer Female-Directed Song Performances

PubMed Central

Dunning, Jeffery L.; Pant, Santosh; Bass, Aaron; Coburn, Zachary; Prather, Jonathan F.

2014-01-01

In the process of mate selection by female songbirds, male suitors advertise their quality through reproductive displays in which song plays an important role. Females evaluate the quality of each signal and the associated male, and the results of that evaluation guide expression of selective courtship displays. Some studies reveal broad agreement among females in their preferences for specific signal characteristics, indicating that those features are especially salient in female mate choice. Other studies reveal that females differ in their preference for specific characteristics, indicating that in those cases female evaluation of signal quality is influenced by factors other than simply the physical properties of the signal. Thus, both the physical properties of male signals and specific traits of female signal evaluation can impact female mate choice. Here, we characterized the mate preferences of female Bengalese finches. We found that calls and copulation solicitation displays are equally reliable indicators of female preference. In response to songs from an array of males, each female expressed an individual-specific song preference, and those preferences were consistent across tests spanning many months. Across a population of females, songs of some males were more commonly preferred than others, and females preferred female-directed songs more than undirected songs, suggesting that some song features are broadly attractive. Preferences were indistinguishable for females that did or did not have social experience with the singers, indicating that female preference is strongly directed by song features rather than experiences associated with the singer. Analysis of song properties revealed several candidate parameters that may influence female evaluation. In an initial investigation of those parameters, females could be very selective for one song feature yet not selective for another. Therefore, multiple song parameters are evaluated independently. Together these findings reveal the nature of signal evaluation and mate choice in this species. PMID:24558501

Integrating a Linear Signal Model with Groundwater and Rainfall time-series on the Characteristic Identification of Groundwater Systems

NASA Astrophysics Data System (ADS)

Chen, Yu-Wen; Wang, Yetmen; Chang, Liang-Cheng

2017-04-01

Groundwater resources play a vital role on regional supply. To avoid irreversible environmental impact such as land subsidence, the characteristic identification of groundwater system is crucial before sustainable management of groundwater resource. This study proposes a signal process approach to identify the character of groundwater systems based on long-time hydrologic observations include groundwater level and rainfall. The study process contains two steps. First, a linear signal model (LSM) is constructed and calibrated to simulate the variation of underground hydrology based on the time series of groundwater levels and rainfall. The mass balance equation of the proposed LSM contains three major terms contain net rate of horizontal exchange, rate of rainfall recharge and rate of pumpage and four parameters are required to calibrate. Because reliable records of pumpage is rare, the time-variant groundwater amplitudes of daily frequency (P ) calculated by STFT are assumed as linear indicators of puamage instead of pumpage records. Time series obtained from 39 observation wells and 50 rainfall stations in and around the study area, Pintung Plain, are paired for model construction. Second, the well-calibrated parameters of the linear signal model can be used to interpret the characteristic of groundwater system. For example, the rainfall recharge coefficient (γ) means the transform ratio between rainfall intention and groundwater level raise. The area around the observation well with higher γ means that the saturated zone here is easily affected by rainfall events and the material of unsaturated zone might be gravel or coarse sand with high infiltration ratio. Considering the spatial distribution of γ, the values of γ decrease from the upstream to the downstream of major rivers and also are correlated to the spatial distribution of grain size of surface soil. Via the time-series of groundwater levels and rainfall, the well-calibrated parameters of LSM have ability to identify the characteristic of aquifer.

Measurand transient signal suppressor

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1994-01-01

A transient signal suppressor for use in a controls system which is adapted to respond to a change in a physical parameter whenever it crosses a predetermined threshold value in a selected direction of increasing or decreasing values with respect to the threshold value and is sustained for a selected discrete time interval is presented. The suppressor includes a sensor transducer for sensing the physical parameter and generating an electrical input signal whenever the sensed physical parameter crosses the threshold level in the selected direction. A manually operated switch is provided for adapting the suppressor to produce an output drive signal whenever the physical parameter crosses the threshold value in the selected direction of increasing or decreasing values. A time delay circuit is selectively adjustable for suppressing the transducer input signal for a preselected one of a plurality of available discrete suppression time and producing an output signal only if the input signal is sustained for a time greater than the selected suppression time. An electronic gate is coupled to receive the transducer input signal and the timer output signal and produce an output drive signal for energizing a control relay whenever the transducer input is a non-transient signal which is sustained beyond the selected time interval.

Information content in reflected signals during GPS Radio Occultation observations

NASA Astrophysics Data System (ADS)

Aparicio, Josep M.; Cardellach, Estel; Rodríguez, Hilda

2018-04-01

The possibility of extracting useful information about the state of the lower troposphere from the surface reflections that are often detected during GPS radio occultations (GPSRO) is explored. The clarity of the reflection is quantified, and can be related to properties of the surface and the low troposphere. The reflected signal is often clear enough to show good phase coherence, and can be tracked and processed as an extension of direct non-reflected GPSRO atmospheric profiles. A profile of bending angle vs. impact parameter can be obtained for these reflected signals, characterized by impact parameters that are below the apparent horizon, and that is a continuation at low altitude of the standard non-reflected bending angle profile. If there were no reflection, these would correspond to tangent altitudes below the local surface, and in particular below the local mean sea level. A forward operator is presented, for the evaluation of the bending angle of reflected GPSRO signals, given atmospheric properties as described by a numerical weather prediction system. The operator is an extension, at lower impact parameters, of standard bending angle operators, and reproduces both the direct and reflected sections of the measured profile. It can be applied to the assimilation of the reflected section of the profile as supplementary data to the direct section. Although the principle is also applicable over land, this paper is focused on ocean cases, where the topographic height of the reflecting surface, the sea level, is better known a priori.

Adaptive photoacoustic imaging quality optimization with EMD and reconstruction

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.

2016-10-01

Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.

Using the Flipchem Photochemistry Model When Fitting Incoherent Scatter Radar Data

NASA Astrophysics Data System (ADS)

Reimer, A. S.; Varney, R. H.

2017-12-01

The North face Resolute Bay Incoherent Scatter Radar (RISR-N) routinely images the dynamics of the polar ionosphere, providing measurements of the plasma density, electron temperature, ion temperature, and line of sight velocity with seconds to minutes time resolution. RISR-N does not directly measure ionospheric parameters, but backscattered signals, recording them as voltage samples. Using signal processing techniques, radar autocorrelation functions (ACF) are estimated from the voltage samples. A model of the signal ACF is then fitted to the ACF using non-linear least-squares techniques to obtain the best-fit ionospheric parameters. The signal model, and therefore the fitted parameters, depend on the ionospheric ion composition that is used [e.g. Zettergren et. al. (2010), Zou et. al. (2017)].The software used to process RISR-N ACF data includes the "flipchem" model, which is an ion photochemistry model developed by Richards [2011] that was adapted from the Field LineInterhemispheric Plasma (FLIP) model. Flipchem requires neutral densities, neutral temperatures, electron density, ion temperature, electron temperature, solar zenith angle, and F10.7 as inputs to compute ion densities, which are input to the signal model. A description of how the flipchem model is used in RISR-N fitting software will be presented. Additionally, a statistical comparison of the fitted electron density, ion temperature, electron temperature, and velocity obtained using a flipchem ionosphere, a pure O+ ionosphere, and a Chapman O+ ionosphere will be presented. The comparison covers nearly two years of RISR-N data (April 2015 - December 2016). Richards, P. G. (2011), Reexamination of ionospheric photochemistry, J. Geophys. Res., 116, A08307, doi:10.1029/2011JA016613.Zettergren, M., Semeter, J., Burnett, B., Oliver, W., Heinselman, C., Blelly, P.-L., and Diaz, M.: Dynamic variability in F-region ionospheric composition at auroral arc boundaries, Ann. Geophys., 28, 651-664, https://doi.org/10.5194/angeo-28-651-2010, 2010.Zou, S., D. Ozturk, R. Varney, and A. Reimer (2017), Effects of sudden commencement on the ionosphere: PFISR observations and global MHD simulation, Geophys. Res. Lett., 44, 3047-3058, doi:10.1002/2017GL072678.

Parametric Time-Frequency Analysis and Its Applications in Music Classification

NASA Astrophysics Data System (ADS)

Shen, Ying; Li, Xiaoli; Ma, Ngok-Wah; Krishnan, Sridhar

2010-12-01

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Analysis of thin fractures with GPR: from theory to practice

NASA Astrophysics Data System (ADS)

Arosio, Diego; Zanzi, Luigi; Longoni, Laura; Papini, Monica

2017-04-01

Whenever we perform a GPR survey to investigate a rocky medium, being the ultimate purpose of the survey either to study the stability of a rock slope or to determine the soundness of a quarried rock block, we would like mainly to detect any fracture within the investigated medium and, possibly, to estimate the parameters of the fractures, namely thickness and filling material. In most of the practical cases, rock fracture thicknesses are very small when compared to the wavelength of the electromagnetic radiation generated by the GPR systems. In such cases, fractures are to be considered as thin beds, i.e. two interfaces whose distance is smaller than GPR resolving capability, and the reflected signal is the sum of the electromagnetic reverberation within the bed. According to this, fracture parameters are encoded in the thin bed complex response and in this work we propose a methodology based on deterministic deconvolution to process amplitude and phase information in the frequency domain to estimate fracture parameters. We first present some theoretical aspects related to thin bed response and a sensitivity analysis concerning fracture thickness and filling. Secondly, we deal with GPR datasets collected both during laboratory experiments and in the facilities of quarrying activities. In the lab tests fractures were simulated by placing materials with known electromagnetic parameters and controlled thickness in between two small marble blocks, whereas field GPR surveys were performed on bigger quarried ornamental stone blocks before they were submitted to the cutting process. We show that, with basic pre-processing and the choice of a proper deconvolving signal, results are encouraging although an ambiguity between thickness and filling estimates exists when no a-priori information is available. Results can be improved by performing CMP radar surveys that are able to provide additional information (i.e., variation of thin bed response versus offset) at the expense of acquisition effort and of more complex and tricky pre-processing sequences.

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

CBL-CIPK network for calcium signaling in higher plants

NASA Astrophysics Data System (ADS)

Luan, Sheng

Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

Study on automatic ECT data evaluation by using neural network

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

Komatsu, H.; Matsumoto, Y.; Badics, Z.

1994-12-31

At the in--service inspection of the steam generator (SG) tubings in Pressurized Water Reactor (PWR) plant, eddy current testing (ECT) has been widely used at each outage. At present, ECT data evaluation is mainly performed by ECT data analyst, therefore it has the following problems. Only ECT signal configuration on the impedance trajectory is used in the evaluation. It is an enormous time consuming process. The evaluation result is influenced by the ability and experience of the analyst. Especially, it is difficult to identify the true defect signal hidden in background signals such as lift--off noise and deposit signals. Inmore » this work, the authors performed the study on the possibility of the application of neural network to ECT data evaluation. It was demonstrated that the neural network proved to be effective to identify the nature of defect, by selecting several optimum input parameters to categorize the raw ECT signals.« less

Evaluation of interaction dynamics of concurrent processes

NASA Astrophysics Data System (ADS)

Sobecki, Piotr; Białasiewicz, Jan T.; Gross, Nicholas

2017-03-01

The purpose of this paper is to present the wavelet tools that enable the detection of temporal interactions of concurrent processes. In particular, the determination of interaction coherence of time-varying signals is achieved using a complex continuous wavelet transform. This paper has used electrocardiogram (ECG) and seismocardiogram (SCG) data set to show multiple continuous wavelet analysis techniques based on Morlet wavelet transform. MATLAB Graphical User Interface (GUI), developed in the reported research to assist in quick and simple data analysis, is presented. These software tools can discover the interaction dynamics of time-varying signals, hence they can reveal their correlation in phase and amplitude, as well as their non-linear interconnections. The user-friendly MATLAB GUI enables effective use of the developed software what enables to load two processes under investigation, make choice of the required processing parameters, and then perform the analysis. The software developed is a useful tool for researchers who have a need for investigation of interaction dynamics of concurrent processes.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Luminescence and conductivity studies on CVD diamond exposed to UV light

NASA Astrophysics Data System (ADS)

Bizzarri, A.; Bogani, F.; Bruzzi, M.; Sciortino, S.

1999-04-01

The photoluminescence (PL), thermoluminescence (TL) and thermally stimulated currents (TSC) of four high-quality CVD diamond films have been investigated in the range of temperatures between 300 and 700 K. The sample excitation has been carried out by means of an UV xenon lamp and UV laser lines. The features of the signals have been found equal to those obtained from particle excitation. The TL analysis shows the existence of several deep traps with activation energies between 0.6 and 1.0 eV. The contribution to the TL signal from different traps has been singled out by means of successive annealing processes. The TL results are in good agreement with those obtained from TSC measurements. The combined use of the two techniques allows a precise determination of the trap parameters. The spectral content of the TL response has also been compared with the PL signal in order to investigate the recombination process. This analysis shows that, in this temperature range, the TL signal is likely due to recombination from bound states rather than due to radiative free to bound transitions, as generally assumed in TL theory. The TSC signal is likely to arise from impurity band rather than from free carriers conduction.

High-throughput cardiac safety evaluation and multi-parameter arrhythmia profiling of cardiomyocytes using microelectrode arrays.

PubMed

Gilchrist, Kristin H; Lewis, Gregory F; Gay, Elaine A; Sellgren, Katelyn L; Grego, Sonia

2015-10-15

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak for field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5minute recordings at multiple time points (0.5, 1, 2 and 4h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. Copyright © 2015 Elsevier Inc. All rights reserved.

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

PubMed

Singh, Omkar; Sunkaria, Ramesh Kumar

2017-12-01

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

Combinatorial influence of environmental parameters on transcription factor activity

PubMed Central

Knijnenburg, T.A.; Wessels, L.F.A.; Reinders, M.J.T.

2008-01-01

Motivation: Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. Results: We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. Availability: The Matlab code is available from the authors upon request. Contact: t.a.knijnenburg@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18586711

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

PubMed Central

Vrancken, Bram; Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Longdon, Ben; Günthard, Huldrych F.; Suchard, Marc A.

2014-01-01

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel’s λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution. PMID:25780554

Passive coherent location system simulation and evaluation

NASA Astrophysics Data System (ADS)

Slezák, Libor; Kvasnička, Michael; Pelant, Martin; Vávra, Jiř; Plšek, Radek

2006-02-01

Passive Coherent Location (PCL) is going to be important and perspective system of passive location of non cooperative and stealth targets. It works with the sources of irradiation of opportunity. PCL is intended to be a part of mobile Air Command and Control System (ACCS) as a Deployable ACCS Component (DAC). The company ERA works on PCL system parameters verification program by complete PCL simulator development since the year 2003. The Czech DoD takes financial participation on this program. The moving targets scenario, the RCS calculation by method of moment, ground clutter scattering and signal processing method (the bottle neck of the PCL) are available up to now in simulator tool. The digital signal (DSP) processing algorithms are performed both on simulated data and on real data measured at NATO C3 Agency in their Haag experiment. The Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic takes part on the implementation of the DSP algorithms in FPGA. The paper describes the simulator and signal processing structure and results both on simulated and measured data.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Delineation of karst terranes in complex environments: Application of modern developments in the wavelet theory and data mining

NASA Astrophysics Data System (ADS)

Alperovich, Leonid; Averbuch, Amir; Eppelbaum, Lev; Zheludev, Valery

2013-04-01

Karst areas occupy about 14% of the world land. Karst terranes of different origin have caused difficult conditions for building, industrial activity and tourism, and are the source of heightened danger for environment. Mapping of karst (sinkhole) hazards, obviously, will be one of the most significant problems of engineering geophysics in the XXI century. Taking into account the complexity of geological media, some unfavourable environments and known ambiguity of geophysical data analysis, a single geophysical method examination might be insufficient. Wavelet methodology as whole has a significant impact on cardinal problems of geophysical signal processing such as: denoising of signals, enhancement of signals and distinguishing of signals with closely related characteristics and integrated analysis of different geophysical fields (satellite, airborne, earth surface or underground observed data). We developed a three-phase approach to the integrated geophysical localization of subsurface karsts (the same approach could be used for following monitoring of karst dynamics). The first phase consists of modeling devoted to compute various geophysical effects characterizing karst phenomena. The second phase determines development of the signal processing approaches to analyzing of profile or areal geophysical observations. Finally, at the third phase provides integration of these methods in order to create a new method of the combined interpretation of different geophysical data. In the base of our combine geophysical analysis we put modern developments in the wavelet technique of the signal and image processing. The development of the integrated methodology of geophysical field examination will enable to recognizing the karst terranes even by a small ratio of "useful signal - noise" in complex geological environments. For analyzing the geophysical data, we used a technique based on the algorithm to characterize a geophysical image by a limited number of parameters. This set of parameters serves as a signature of the image and is to be utilized for discrimination of images containing karst cavity (K) from the images non-containing karst (N). The constructed algorithm consists of the following main phases: (a) collection of the database, (b) characterization of geophysical images, (c) and dimensionality reduction. Then, each image is characterized by the histogram of the coherency directions. As a result of the previous steps we obtain two sets K and N of the signatures vectors for images from sections containing karst cavity and non-karst subsurface, respectively.

The transfer function of neuron spike.

PubMed

Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors

PubMed Central

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

2012-01-01

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

Parametric adaptive filtering and data validation in the bar GW detector AURIGA

NASA Astrophysics Data System (ADS)

Ortolan, A.; Baggio, L.; Cerdonio, M.; Prodi, G. A.; Vedovato, G.; Vitale, S.

2002-04-01

We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio χ2 and the time of arrival are those that are expected.

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

NASA Technical Reports Server (NTRS)

Lee, Jonggil

1990-01-01

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

The time-lapse AVO difference inversion for changes in reservoir parameters

NASA Astrophysics Data System (ADS)

Longxiao, Zhi; Hanming, Gu; Yan, Li

2016-12-01

The result of conventional time-lapse seismic processing is the difference between the amplitude and the post-stack seismic data. Although stack processing can improve the signal-to-noise ratio (SNR) of seismic data, it also causes a considerable loss of important information about the amplitude changes and only gives the qualitative interpretation. To predict the changes in reservoir fluid more precisely and accurately, we also need the quantitative information of the reservoir. To achieve this aim, we develop the method of time-lapse AVO (amplitude versus offset) difference inversion. For the inversion of reservoir changes in elastic parameters, we apply the Gardner equation as the constraint and convert the three-parameter inversion of elastic parameter changes into a two-parameter inversion to make the inversion more stable. For the inversion of variations in the reservoir parameters, we infer the relation between the difference of the reflection coefficient and variations in the reservoir parameters, and then invert reservoir parameter changes directly. The results of the theoretical modeling computation and practical application show that our method can estimate the relative variations in reservoir density, P-wave and S-wave velocity, calculate reservoir changes in water saturation and effective pressure accurately, and then provide reference for the rational exploitation of the reservoir.

Discrete wavelet-aided delineation of PCG signal events via analysis of an area curve length-based decision statistic.

PubMed

Homaeinezhad, M R; Atyabi, S A; Daneshvar, E; Ghaffari, A; Tahmasebi, M

2010-12-01

The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS). Afterwards, histogram parameters of the nonlinearly enhanced DS metric are used for regulation of the α-level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. The proposed method was applied to all 85 records of Nursing Student Heart Sounds database (NSHSDB) including stenosis, insufficiency, regurgitation, gallop, septal defect, split sound, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. Also, the method was applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as the results, no false positive (FP) or false negative (FN) errors were detected. High noise robustness, acceptable detection-segmentation accuracy of PCG events in various cardiac system conditions, and having no parameters dependency to the acquisition sampling frequency can be mentioned as the principal virtues and abilities of the proposed ACL-based PCG events detection-segmentation algorithm.

Development of process parameters for 22 nm PMOS using 2-D analytical modeling

NASA Astrophysics Data System (ADS)

Maheran, A. H. Afifah; Menon, P. S.; Ahmad, I.; Shaari, S.; Faizah, Z. A. Noor

2015-04-01

The complementary metal-oxide-semiconductor field effect transistor (CMOSFET) has become major challenge to scaling and integration. Innovation in transistor structures and integration of novel materials are necessary to sustain this performance trend. CMOS variability in the scaling technology becoming very important concern due to limitation of process control; over statistically variability related to the fundamental discreteness and materials. Minimizing the transistor variation through technology optimization and ensuring robust product functionality and performance is the major issue.In this article, the continuation study on process parameters variations is extended and delivered thoroughly in order to achieve a minimum leakage current (ILEAK) on PMOS planar transistor at 22 nm gate length. Several device parameters are varies significantly using Taguchi method to predict the optimum combination of process parameters fabrication. A combination of high permittivity material (high-k) and metal gate are utilized accordingly as gate structure where the materials include titanium dioxide (TiO2) and tungsten silicide (WSix). Then the L9 of the Taguchi Orthogonal array is used to analyze the device simulation where the results of signal-to-noise ratio (SNR) of Smaller-the-Better (STB) scheme are studied through the percentage influences of the process parameters. This is to achieve a minimum ILEAK where the maximum predicted ILEAK value by International Technology Roadmap for Semiconductors (ITRS) 2011 is said to should not above 100 nA/µm. Final results shows that the compensation implantation dose acts as the dominant factor with 68.49% contribution in lowering the device's leakage current. The absolute process parameters combination results in ILEAK mean value of 3.96821 nA/µm where is far lower than the predicted value.

Development of process parameters for 22 nm PMOS using 2-D analytical modeling

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

Maheran, A. H. Afifah; Menon, P. S.; Shaari, S.

2015-04-24

The complementary metal-oxide-semiconductor field effect transistor (CMOSFET) has become major challenge to scaling and integration. Innovation in transistor structures and integration of novel materials are necessary to sustain this performance trend. CMOS variability in the scaling technology becoming very important concern due to limitation of process control; over statistically variability related to the fundamental discreteness and materials. Minimizing the transistor variation through technology optimization and ensuring robust product functionality and performance is the major issue.In this article, the continuation study on process parameters variations is extended and delivered thoroughly in order to achieve a minimum leakage current (I{sub LEAK}) onmore » PMOS planar transistor at 22 nm gate length. Several device parameters are varies significantly using Taguchi method to predict the optimum combination of process parameters fabrication. A combination of high permittivity material (high-k) and metal gate are utilized accordingly as gate structure where the materials include titanium dioxide (TiO{sub 2}) and tungsten silicide (WSi{sub x}). Then the L9 of the Taguchi Orthogonal array is used to analyze the device simulation where the results of signal-to-noise ratio (SNR) of Smaller-the-Better (STB) scheme are studied through the percentage influences of the process parameters. This is to achieve a minimum I{sub LEAK} where the maximum predicted I{sub LEAK} value by International Technology Roadmap for Semiconductors (ITRS) 2011 is said to should not above 100 nA/µm. Final results shows that the compensation implantation dose acts as the dominant factor with 68.49% contribution in lowering the device’s leakage current. The absolute process parameters combination results in I{sub LEAK} mean value of 3.96821 nA/µm where is far lower than the predicted value.« less

SU-E-T-113: Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment

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

Imae, T; Haga, A; Saotome, N

Purpose: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. Treatment plans for lung tumors using stereotactic body radiotherapy (SBRT) are calculated with planning computed tomography (CT) images only exhale phase. Purpose of this study is to evaluate dose distribution by reconstructing from only the data such as respiratory signals and machine parameters acquired during treatment. Methods: Phantom and three patients with lung tumor underwent CT scans for treatment planning. They were treated by VMAT while acquiring projection images to derive their respiratory signals and machine parameters including positions ofmore » multi leaf collimators, dose rates and integrated monitor units. The respiratory signals were divided into 4 and 10 phases and machine parameters were correlated with the divided respiratory signals based on the gantry angle. Dose distributions of each respiratory phase were calculated from plans which were reconstructed from the respiratory signals and the machine parameters during treatment. The doses at isocenter, maximum point and the centroid of target were evaluated. Results and Discussion: Dose distributions during treatment were calculated using the machine parameters and the respiratory signals detected from projection images. Maximum dose difference between plan and in treatment distribution was −1.8±0.4% at centroid of target and dose differences of evaluated points between 4 and 10 phases were no significant. Conclusion: The present method successfully evaluated dose distribution using respiratory signals and machine parameters during treatment. This method is feasible to verify the actual dose for moving target.« less

Parameter tuning method for dither compensation of a pneumatic proportional valve with friction

NASA Astrophysics Data System (ADS)

Wang, Tao; Song, Yang; Huang, Leisheng; Fan, Wei

2016-05-01

In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dynamic friction inside the valve may cause hysteresis and a dead zone. In this paper, a dither compensation mechanism is proposed to reduce negative effects on the basis of analyzing the mechanism of friction force. The specific dither signal (using a sinusoidal signal) was superimposed on the control signal of the valve. Based on the relationship between the parameters of the dither signal and the inherent characteristics of the proportional servo valve, a parameter tuning method was proposed, which uses a displacement sensor to measure the maximum static friction inside the valve. According to the experimental results, the proper amplitude ranges are determined for different pressures. In order to get the optimal parameters of the dither signal, some dither compensation experiments have been carried out on different signal amplitude and gas pressure conditions. Optimal parameters are determined under two kinds of pressure conditions. Using tuning parameters the valve spool displacement experiment has been taken. From the experiment results, hysteresis of the proportional servo valve is significantly reduced. And through simulation and experiments, the cut-off frequency of the proportional valve has also been widened. Therefore after adding the dither signal, the static and dynamic characteristics of the proportional valve are both improved to a certain degree. This research proposes a parameter tuning method of dither signal, and the validity of the method is verified experimentally.

Using diurnal temperature signals to infer vertical groundwater-surface water exchange

USGS Publications Warehouse

Irvine, Dylan J.; Briggs, Martin A.; Lautz, Laura K.; Gordon, Ryan P.; McKenzie, Jeffrey M.; Cartwright, Ian

2017-01-01

Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer.

Detecting the Lμ-Lτ gauge boson at Belle II

NASA Astrophysics Data System (ADS)

Araki, Takeshi; Hoshino, Shihori; Ota, Toshihiko; Sato, Joe; Shimomura, Takashi

2017-03-01

We discuss the feasibility of detecting the gauge boson of the U (1 )Lμ-Lτ symmetry, which possesses a mass in the range between MeV and GeV, at the Belle-II experiment. The kinetic mixing between the new gauge boson Z' and photon is forbidden at the tree level and is radiatively induced. The leptonic force mediated by such a light boson is motivated by the discrepancy in muon anomalous magnetic moment and also the gap in the energy spectrum of cosmic neutrino. Defining the process e+e-→γ Z'→γ ν ν ¯ (missing energy) to be the signal, we estimate the numbers of the signal and the background events and show the parameter region to which the Belle-II experiment will be sensitive. The signal process in the Lμ-Lτ model is enhanced with a light Z', which is a characteristic feature differing from the dark photon models with a constant kinetic mixing. We find that the Belle-II experiment with the design luminosity will be sensitive to the Z' with the mass MZ'≲1 GeV and the new gauge coupling gZ'≳8 ×10-4 , which covers a half of the unconstrained parameter region that explains the discrepancy in muon anomalous magnetic moment. The possibilities to improve the significance of the detection are also discussed.

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

PubMed

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

2017-11-01

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

Morphological characteristics of waste polyethylene/polypropylene plastics during pyrolysis and representative morphological signal characterizing pyrolysis stages.

PubMed

Wang, H; Chen, D; Yuan, G; Ma, X; Dai, X

2013-02-01

In this work, the morphological characteristics of waste polyethylene (PE)/polypropylene (PP) plastics during their pyrolysis process were investigated, and based on their basic image changing patterns representative morphological signals describing the pyrolysis stages were obtained. PE and PP granules and films were used as typical plastics for testing, and influence of impurities was also investigated. During pyrolysis experiments, photographs of the testing samples were taken sequentially with a high-speed infrared camera, and the quantitative parameters that describe the morphological characteristics of these photographs were explored using the "Image Pro Plus (v6.3)" digital image processing software. The experimental results showed that plastics pyrolysis involved four stages: melting, two stages of decomposition which are characterized with bubble formation caused by volatile evaporating, and ash deposition; and each stage was characterized with its own phase changing behaviors and morphological features. Two stages of decomposition are the key step of pyrolysis since they took up half or more of the reaction time; melting step consumed another half of reaction time in experiments when raw materials were heated up from ambient temperatures; and coke-like deposition appeared as a result of decomposition completion. Two morphological signals defined from digital image processing, namely, pixel area of the interested reaction region and bubble ratio (BR) caused by volatile evaporating were found to change regularly with pyrolysis stages. In particular, for all experimental scenarios with plastics films and granules, the BR curves always exhibited a slowly drop as melting started and then a sharp increase followed by a deep decrease corresponding to the first stage of intense decomposition, afterwards a second increase - drop section corresponding to the second stage of decomposition appeared. As ash deposition happened, the BR dropped to zero or very low values. When impurities were involved, the shape of BR curves showed that intense decomposition started earlier but morphological characteristics remained the same. In addition, compared to parameters such as pressure, the BR reflects reaction stages better and its change with pyrolysis process of PE/PP plastics with or without impurities was more intrinsically process correlated; therefore it can be adopted as a signal for pyrolysis process characterization, as well as offering guide to process improvement and reactor design. Copyright © 2012 Elsevier Ltd. All rights reserved.

Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.

PubMed

Alonso-Valerdi, Luz María

2016-01-01

A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application.

Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor

PubMed Central

Alonso-Valerdi, Luz María

2016-01-01

A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application. PMID:27445783

The zipper mechanism in phagocytosis: energetic requirements and variability in phagocytic cup shape

PubMed Central

2010-01-01

Background Phagocytosis is the fundamental cellular process by which eukaryotic cells bind and engulf particles by their cell membrane. Particle engulfment involves particle recognition by cell-surface receptors, signaling and remodeling of the actin cytoskeleton to guide the membrane around the particle in a zipper-like fashion. Despite the signaling complexity, phagocytosis also depends strongly on biophysical parameters, such as particle shape, and the need for actin-driven force generation remains poorly understood. Results Here, we propose a novel, three-dimensional and stochastic biophysical model of phagocytosis, and study the engulfment of particles of various sizes and shapes, including spiral and rod-shaped particles reminiscent of bacteria. Highly curved shapes are not taken up, in line with recent experimental results. Furthermore, we surprisingly find that even without actin-driven force generation, engulfment proceeds in a large regime of parameter values, albeit more slowly and with highly variable phagocytic cups. We experimentally confirm these predictions using fibroblasts, transfected with immunoreceptor FcγRIIa for engulfment of immunoglobulin G-opsonized particles. Specifically, we compare the wild-type receptor with a mutant receptor, unable to signal to the actin cytoskeleton. Based on the reconstruction of phagocytic cups from imaging data, we indeed show that cells are able to engulf small particles even without support from biological actin-driven processes. Conclusions This suggests that biochemical pathways render the evolutionary ancient process of phagocytic highly robust, allowing cells to engulf even very large particles. The particle-shape dependence of phagocytosis makes a systematic investigation of host-pathogen interactions and an efficient design of a vehicle for drug delivery possible. PMID:21059234

Computational Modeling and Analysis of Insulin Induced Eukaryotic Translation Initiation

PubMed Central

Lequieu, Joshua; Chakrabarti, Anirikh; Nayak, Satyaprakash; Varner, Jeffrey D.

2011-01-01

Insulin, the primary hormone regulating the level of glucose in the bloodstream, modulates a variety of cellular and enzymatic processes in normal and diseased cells. Insulin signals are processed by a complex network of biochemical interactions which ultimately induce gene expression programs or other processes such as translation initiation. Surprisingly, despite the wealth of literature on insulin signaling, the relative importance of the components linking insulin with translation initiation remains unclear. We addressed this question by developing and interrogating a family of mathematical models of insulin induced translation initiation. The insulin network was modeled using mass-action kinetics within an ordinary differential equation (ODE) framework. A family of model parameters was estimated, starting from an initial best fit parameter set, using 24 experimental data sets taken from literature. The residual between model simulations and each of the experimental constraints were simultaneously minimized using multiobjective optimization. Interrogation of the model population, using sensitivity and robustness analysis, identified an insulin-dependent switch that controlled translation initiation. Our analysis suggested that without insulin, a balance between the pro-initiation activity of the GTP-binding protein Rheb and anti-initiation activity of PTEN controlled basal initiation. On the other hand, in the presence of insulin a combination of PI3K and Rheb activity controlled inducible initiation, where PI3K was only critical in the presence of insulin. Other well known regulatory mechanisms governing insulin action, for example IRS-1 negative feedback, modulated the relative importance of PI3K and Rheb but did not fundamentally change the signal flow. PMID:22102801

DHS Internship Paper

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

Dreyer, J

2007-09-18

During my internship at Lawrence Livermore National Laboratory I worked with microcalorimeter gamma-ray and fast-neutron detectors based on superconducting Transition Edge Sensors (TESs). These instruments are being developed for fundamental science and nuclear non-proliferation applications because of their extremely high energy resolution; however, this comes at the expense of a small pixel size and slow decay times. The small pixel sizes are being addressed by developing detector arrays while the low count rate is being addressed by developing Digital Signal Processors (DSPs) that allow higher throughput than traditional pulse processing algorithms. Traditionally, low-temperature microcalorimeter pulses have been processed off-line withmore » optimum filtering routines based on the measured spectral characteristics of the signal and the noise. These optimum filters rely on the spectral content of the signal being identical for all events, and therefore require capturing the entire pulse signal without pile-up. In contrast, the DSP algorithm being developed is based on differences in signal levels before and after a trigger event, and therefore does not require the waveform to fully decay, or even the signal level to be close to the base line. The readout system allows for real time data acquisition and analysis at count rates exceeding 100 Hz for pulses with several {approx}ms decay times with minimal loss of energy resolution. Originally developed for gamma-ray analysis with HPGe detectors we have modified the hardware and firmware of the system to accommodate the slower TES signals and optimized the parameters of the filtering algorithm to maximize either resolution or throughput. The following presents an overview of the digital signal processing hardware and discusses the results of characterization measurements made to determine the systems performance.« less

Taguchi's off line method and Multivariate loss function approach for quality management and optimization of process parameters -A review

NASA Astrophysics Data System (ADS)

Bharti, P. K.; Khan, M. I.; Singh, Harbinder

2010-10-01

Off-line quality control is considered to be an effective approach to improve product quality at a relatively low cost. The Taguchi method is one of the conventional approaches for this purpose. Through this approach, engineers can determine a feasible combination of design parameters such that the variability of a product's response can be reduced and the mean is close to the desired target. The traditional Taguchi method was focused on ensuring good performance at the parameter design stage with one quality characteristic, but most products and processes have multiple quality characteristics. The optimal parameter design minimizes the total quality loss for multiple quality characteristics. Several studies have presented approaches addressing multiple quality characteristics. Most of these papers were concerned with maximizing the parameter combination of signal to noise (SN) ratios. The results reveal the advantages of this approach are that the optimal parameter design is the same as the traditional Taguchi method for the single quality characteristic; the optimal design maximizes the amount of reduction of total quality loss for multiple quality characteristics. This paper presents a literature review on solving multi-response problems in the Taguchi method and its successful implementation in various industries.

Post-processing of seismic parameter data based on valid seismic event determination

DOEpatents

McEvilly, Thomas V.

1985-01-01

An automated seismic processing system and method are disclosed, including an array of CMOS microprocessors for unattended battery-powered processing of a multi-station network. According to a characterizing feature of the invention, each channel of the network is independently operable to automatically detect, measure times and amplitudes, and compute and fit Fast Fourier transforms (FFT's) for both P- and S- waves on analog seismic data after it has been sampled at a given rate. The measured parameter data from each channel are then reviewed for event validity by a central controlling microprocessor and if determined by preset criteria to constitute a valid event, the parameter data are passed to an analysis computer for calculation of hypocenter location, running b-values, source parameters, event count, P- wave polarities, moment-tensor inversion, and Vp/Vs ratios. The in-field real-time analysis of data maximizes the efficiency of microearthquake surveys allowing flexibility in experimental procedures, with a minimum of traditional labor-intensive postprocessing. A unique consequence of the system is that none of the original data (i.e., the sensor analog output signals) are necessarily saved after computation, but rather, the numerical parameters generated by the automatic analysis are the sole output of the automated seismic processor.

Muscle activity characterization by laser Doppler Myography

NASA Astrophysics Data System (ADS)

Scalise, Lorenzo; Casaccia, Sara; Marchionni, Paolo; Ercoli, Ilaria; Primo Tomasini, Enrico

2013-09-01

Electromiography (EMG) is the gold-standard technique used for the evaluation of muscle activity. This technique is used in biomechanics, sport medicine, neurology and rehabilitation therapy and it provides the electrical activity produced by skeletal muscles. Among the parameters measured with EMG, two very important quantities are: signal amplitude and duration of muscle contraction, muscle fatigue and maximum muscle power. Recently, a new measurement procedure, named Laser Doppler Myography (LDMi), for the non contact assessment of muscle activity has been proposed to measure the vibro-mechanical behaviour of the muscle. The aim of this study is to present the LDMi technique and to evaluate its capacity to measure some characteristic features proper of the muscle. In this paper LDMi is compared with standard superficial EMG (sEMG) requiring the application of sensors on the skin of each patient. sEMG and LDMi signals have been simultaneously acquired and processed to test correlations. Three parameters has been analyzed to compare these techniques: Muscle activation timing, signal amplitude and muscle fatigue. LDMi appears to be a reliable and promising measurement technique allowing the measurements without contact with the patient skin.

Equivalence of linear canonical transform domains to fractional Fourier domains and the bicanonical width product: a generalization of the space-bandwidth product.

PubMed

Oktem, Figen S; Ozaktas, Haldun M

2010-08-01

Linear canonical transforms (LCTs) form a three-parameter family of integral transforms with wide application in optics. We show that LCT domains correspond to scaled fractional Fourier domains and thus to scaled oblique axes in the space-frequency plane. This allows LCT domains to be labeled and ordered by the corresponding fractional order parameter and provides insight into the evolution of light through an optical system modeled by LCTs. If a set of signals is highly confined to finite intervals in two arbitrary LCT domains, the space-frequency (phase space) support is a parallelogram. The number of degrees of freedom of this set of signals is given by the area of this parallelogram, which is equal to the bicanonical width product but usually smaller than the conventional space-bandwidth product. The bicanonical width product, which is a generalization of the space-bandwidth product, can provide a tighter measure of the actual number of degrees of freedom, and allows us to represent and process signals with fewer samples.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Condition monitoring of turning process using infrared thermography technique - An experimental approach

NASA Astrophysics Data System (ADS)

Prasad, Balla Srinivasa; Prabha, K. Aruna; Kumar, P. V. S. Ganesh

2017-03-01

In metal cutting machining, major factors that affect the cutting tool life are machine tool vibrations, tool tip/chip temperature and surface roughness along with machining parameters like cutting speed, feed rate, depth of cut, tool geometry, etc., so it becomes important for the manufacturing industry to find the suitable levels of process parameters for obtaining maintaining tool life. Heat generation in cutting was always a main topic to be studied in machining. Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for development of the effective monitoring of tool condition monitoring systems with improved accuracy. From a process improvement point of view, it is definitely more advantageous to proactively monitor quality directly in the process instead of the product, so that the consequences of a defective part can be minimized or even eliminated. In the present work, a real time process monitoring method is explored using multiple sensors. It focuses on the development of a test bed for monitoring the tool condition in turning of AISI 316L steel by using both coated and uncoated carbide inserts. Proposed tool condition monitoring (TCM) is evaluated in the high speed turning using multiple sensors such as Laser Doppler vibrometer and infrared thermography technique. The results indicate the feasibility of using the dominant frequency of the vibration signals for the monitoring of high speed turning operations along with temperatures gradient. A possible correlation is identified in both regular and irregular cutting tool wear. While cutting speed and feed rate proved to be influential parameter on the depicted temperatures and depth of cut to be less influential. Generally, it is observed that lower heat and temperatures are generated when coated inserts are employed. It is found that cutting temperatures are gradually increased as edge wear and deformation developed.

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.

Weighted parallel contributions of binocular correlation and match signals to conscious perception of depth

PubMed Central

2016-01-01

Binocular disparity is detected in the primary visual cortex by a process similar to calculation of local cross-correlation between left and right retinal images. As a consequence, correlation-based neural signals convey information about false disparities as well as the true disparity. The false responses in the initial disparity detectors are eliminated at later stages in order to encode only disparities of the features correctly matched between the two eyes. For a simple stimulus configuration, a feed-forward nonlinear process can transform the correlation signal into the match signal. For human observers, depth judgement is determined by a weighted sum of the correlation and match signals rather than depending solely on the latter. The relative weight changes with spatial and temporal parameters of the stimuli, allowing adaptive recruitment of the two computations under different visual circumstances. A full transformation from correlation-based to match-based representation occurs at the neuronal population level in cortical area V4 and manifests in single-neuron responses of inferior temporal and posterior parietal cortices. Neurons in area V5/MT represent disparity in a manner intermediate between the correlation and match signals. We propose that the correlation and match signals in these areas contribute to depth perception in a weighted, parallel manner. This article is part of the themed issue ‘Vision in our three-dimensional world’. PMID:27269600

Development of a Multi-Channel Piezoelectric Acoustic Sensor Based on an Artificial Basilar Membrane

PubMed Central

Jung, Youngdo; Kwak, Jun-Hyuk; Lee, Young Hwa; Kim, Wan Doo; Hur, Shin

2014-01-01

In this research, we have developed a multi-channel piezoelectric acoustic sensor (McPAS) that mimics the function of the natural basilar membrane capable of separating incoming acoustic signals mechanically by their frequency and generating corresponding electrical signals. The McPAS operates without an external energy source and signal processing unit with a vibrating piezoelectric thin film membrane. The shape of the vibrating membrane was chosen to be trapezoidal such that different locations of membrane have different local resonance frequencies. The length of the membrane is 28 mm and the width of the membrane varies from 1 mm to 8 mm. Multiphysics finite element analysis (FEA) was carried out to predict and design the mechanical behaviors and piezoelectric response of the McPAS model. The designed McPAS was fabricated with a MEMS fabrication process based on the simulated results. The fabricated device was tested with a mouth simulator to measure its mechanical and piezoelectrical frequency response with a laser Doppler vibrometer and acoustic signal analyzer. The experimental results show that the as fabricated McPAS can successfully separate incoming acoustic signals within the 2.5 kHz–13.5 kHz range and the maximum electrical signal output upon acoustic signal input of 94 dBSPL was 6.33 mVpp. The performance of the fabricated McPAS coincided well with the designed parameters. PMID:24361926

Weighted parallel contributions of binocular correlation and match signals to conscious perception of depth.

PubMed

Fujita, Ichiro; Doi, Takahiro

2016-06-19

Binocular disparity is detected in the primary visual cortex by a process similar to calculation of local cross-correlation between left and right retinal images. As a consequence, correlation-based neural signals convey information about false disparities as well as the true disparity. The false responses in the initial disparity detectors are eliminated at later stages in order to encode only disparities of the features correctly matched between the two eyes. For a simple stimulus configuration, a feed-forward nonlinear process can transform the correlation signal into the match signal. For human observers, depth judgement is determined by a weighted sum of the correlation and match signals rather than depending solely on the latter. The relative weight changes with spatial and temporal parameters of the stimuli, allowing adaptive recruitment of the two computations under different visual circumstances. A full transformation from correlation-based to match-based representation occurs at the neuronal population level in cortical area V4 and manifests in single-neuron responses of inferior temporal and posterior parietal cortices. Neurons in area V5/MT represent disparity in a manner intermediate between the correlation and match signals. We propose that the correlation and match signals in these areas contribute to depth perception in a weighted, parallel manner.This article is part of the themed issue 'Vision in our three-dimensional world'. © 2016 The Author(s).

Simulation of optical signaling among nano-bio-sensors: enhancing of bioimaging contrast.

PubMed

SalmanOgli, A; Behzadi, S; Rostami, A

2014-09-01

In this article, the nanoparticle-dye systems is designed and simulated to illustrate the possibility of enhancement in optical imaging contrast. For this, the firefly optimization technique is used as an optical signaling mechanism among agents (nanoparticle-dye) because fireflies attract together due to their flashing light and optical signaling that is produced by a process of bioluminescence (also it has been investigated that other parameters such as neural response and brain function have essential role in attracting fireflies to each other). The first parameter is coincided with our work, because the nanoparticle-dye systems have ability to augment of received light and its amplification cause that the designed complex system act as a brightness particle. This induced behavior of nanoparticles can be considered as an optical communication and signaling. Indeed by functionalization of nanoparticles and then due to higher brightness of the tumor site because of active targeting, the other particles can be guided to reach toward the target point and the signaling among agents is done by optical relation similar to firefly nature. Moreover, the fundamental of this work is the use of surface plasmon resonance and plasmons hybridization, in which photonic signals can be manipulated on the nanoscale and can be used in biomedical applications such as electromagnetic field enhancement. Finally, it can be mentioned that by simultaneously using plasmon hybridization, near-field augmentation, and firefly algorithm, the optical imaging contrast can be impressively improved.

Proactive inhibitory control: A general biasing account☆

PubMed Central

Elchlepp, Heike; Lavric, Aureliu; Chambers, Christopher D.; Verbruggen, Frederick

2016-01-01

Flexible behavior requires a control system that can inhibit actions in response to changes in the environment. Recent studies suggest that people proactively adjust response parameters in anticipation of a stop signal. In three experiments, we tested the hypothesis that proactive inhibitory control involves adjusting both attentional and response settings, and we explored the relationship with other forms of proactive and anticipatory control. Subjects responded to the color of a stimulus. On some trials, an extra signal occurred. The response to this signal depended on the task context subjects were in: in the ‘ignore’ context, they ignored it; in the ‘stop’ context, they had to withhold their response; and in the ‘double-response’ context, they had to execute a secondary response. An analysis of event-related brain potentials for no-signal trials in the stop context revealed that proactive inhibitory control works by biasing the settings of lower-level systems that are involved in stimulus detection, action selection, and action execution. Furthermore, subjects made similar adjustments in the double-response and stop-signal contexts, indicating an overlap between various forms of proactive action control. The results of Experiment 1 also suggest an overlap between proactive inhibitory control and preparatory control in task-switching studies: both require reconfiguration of task-set parameters to bias or alter subordinate processes. We conclude that much of the top-down control in response inhibition tasks takes place before the inhibition signal is presented. PMID:26859519

Denoising of gravitational wave signals via dictionary learning algorithms

NASA Astrophysics Data System (ADS)

Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

2016-12-01

Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

High-Speed Data Acquisition and Digital Signal Processing System for PET Imaging Techniques Applied to Mammography

NASA Astrophysics Data System (ADS)

Martinez, J. D.; Benlloch, J. M.; Cerda, J.; Lerche, Ch. W.; Pavon, N.; Sebastia, A.

2004-06-01

This paper is framed into the Positron Emission Mammography (PEM) project, whose aim is to develop an innovative gamma ray sensor for early breast cancer diagnosis. Currently, breast cancer is detected using low-energy X-ray screening. However, functional imaging techniques such as PET/FDG could be employed to detect breast cancer and track disease changes with greater sensitivity. Furthermore, a small and less expensive PET camera can be utilized minimizing main problems of whole body PET. To accomplish these objectives, we are developing a new gamma ray sensor based on a newly released photodetector. However, a dedicated PEM detector requires an adequate data acquisition (DAQ) and processing system. The characterization of gamma events needs a free-running analog-to-digital converter (ADC) with sampling rates of more than 50 Ms/s and must achieve event count rates up to 10 MHz. Moreover, comprehensive data processing must be carried out to obtain event parameters necessary for performing the image reconstruction. A new generation digital signal processor (DSP) has been used to comply with these requirements. This device enables us to manage the DAQ system at up to 80 Ms/s and to execute intensive calculi over the detector signals. This paper describes our designed DAQ and processing architecture whose main features are: very high-speed data conversion, multichannel synchronized acquisition with zero dead time, a digital triggering scheme, and high throughput of data with an extensive optimization of the signal processing algorithms.

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

Jain, Abhilasha, E-mail: abhilasha.vnit@gmail.com; Kumar, Ashwini; Peshwe, D. R.

Rare earth activated hybrid phosphors have made significant progress in terms of better light output, color properties and potential for long life. All these features coupled with low cost production and reduced maintenance have offered phosphor converted LEDs for diverse optoelectronic applications including signal lighting in advanced aviation. The present paper explores the effect of various processing parameters on luminescent hybrid phosphors fabricated through combustion synthesis.

Definition and fabrication of an airborne scatterometer radar signal processor

NASA Technical Reports Server (NTRS)

1976-01-01

A hardware/software system which incorporates a microprocessor design and software for the calculation of normalized radar cross section in real time was developed. Interface is provided to decommutate the NASA ADAS data stream for aircraft parameters used in processing and to provide output in the form of strip chart and pcm compatible data recording.

Optimization and Analysis of Laser Beam Machining Parameters for Al7075-TiB2 In-situ Composite

NASA Astrophysics Data System (ADS)

Manjoth, S.; Keshavamurthy, R.; Pradeep Kumar, G. S.

2016-09-01

The paper focuses on laser beam machining (LBM) of In-situ synthesized Al7075-TiB2 metal matrix composite. Optimization and influence of laser machining process parameters on surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy of composites were studied. Al7075-TiB2 metal matrix composite was synthesized by in-situ reaction technique using stir casting process. Taguchi's L9 orthogonal array was used to design experimental trials. Standoff distance (SOD) (0.3 - 0.5mm), Cutting Speed (1000 - 1200 m/hr) and Gas pressure (0.5 - 0.7 bar) were considered as variable input parameters at three different levels, while power and nozzle diameter were maintained constant with air as assisting gas. Optimized process parameters for surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy were calculated by generating the main effects plot for signal noise ratio (S/N ratio) for surface roughness, VMRR and dimensional error using Minitab software (version 16). The Significant of standoff distance (SOD), cutting speed and gas pressure on surface roughness, volumetric material removal rate (VMRR) and dimensional error were calculated using analysis of variance (ANOVA) method. Results indicate that, for surface roughness, cutting speed (56.38%) is most significant parameter followed by standoff distance (41.03%) and gas pressure (2.6%). For volumetric material removal (VMRR), gas pressure (42.32%) is most significant parameter followed by cutting speed (33.60%) and standoff distance (24.06%). For dimensional error, Standoff distance (53.34%) is most significant parameter followed by cutting speed (34.12%) and gas pressure (12.53%). Further, verification experiments were carried out to confirm performance of optimized process parameters.

Exponential Modelling for Mutual-Cohering of Subband Radar Data

NASA Astrophysics Data System (ADS)

Siart, U.; Tejero, S.; Detlefsen, J.

2005-05-01

Increasing resolution and accuracy is an important issue in almost any type of radar sensor application. However, both resolution and accuracy are strongly related to the available signal bandwidth and energy that can be used. Nowadays, often several sensors operating in different frequency bands become available on a sensor platform. It is an attractive goal to use the potential of advanced signal modelling and optimization procedures by making proper use of information stemming from different frequency bands at the RF signal level. An important prerequisite for optimal use of signal energy is coherence between all contributing sensors. Coherent multi-sensor platforms are greatly expensive and are thus not available in general. This paper presents an approach for accurately estimating object radar responses using subband measurements at different RF frequencies. An exponential model approach allows to compensate for the lack of mutual coherence between independently operating sensors. Mutual coherence is recovered from the a-priori information that both sensors have common scattering centers in view. Minimizing the total squared deviation between measured data and a full-range exponential signal model leads to more accurate pole angles and pole magnitudes compared to single-band optimization. The model parameters (range and magnitude of point scatterers) after this full-range optimization process are also more accurate than the parameters obtained from a commonly used super-resolution procedure (root-MUSIC) applied to the non-coherent subband data.

Digital Detection and Processing of Multiple Quadrature Harmonics for EPR Spectroscopy

PubMed Central

Ahmad, R.; Som, S.; Kesselring, E.; Kuppusamy, P.; Zweier, J.L.; Potter, L.C.

2010-01-01

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration. PMID:20971667

Digital detection and processing of multiple quadrature harmonics for EPR spectroscopy.

PubMed

Ahmad, R; Som, S; Kesselring, E; Kuppusamy, P; Zweier, J L; Potter, L C

2010-12-01

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration. Copyright © 2010 Elsevier Inc. All rights reserved.

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Bypassing the Limits of Ll Regularization: Convex Sparse Signal Processing Using Non-Convex Regularization

NASA Astrophysics Data System (ADS)

Parekh, Ankit

Sparsity has become the basis of some important signal processing methods over the last ten years. Many signal processing problems (e.g., denoising, deconvolution, non-linear component analysis) can be expressed as inverse problems. Sparsity is invoked through the formulation of an inverse problem with suitably designed regularization terms. The regularization terms alone encode sparsity into the problem formulation. Often, the ℓ1 norm is used to induce sparsity, so much so that ℓ1 regularization is considered to be `modern least-squares'. The use of ℓ1 norm, as a sparsity-inducing regularizer, leads to a convex optimization problem, which has several benefits: the absence of extraneous local minima, well developed theory of globally convergent algorithms, even for large-scale problems. Convex regularization via the ℓ1 norm, however, tends to under-estimate the non-zero values of sparse signals. In order to estimate the non-zero values more accurately, non-convex regularization is often favored over convex regularization. However, non-convex regularization generally leads to non-convex optimization, which suffers from numerous issues: convergence may be guaranteed to only a stationary point, problem specific parameters may be difficult to set, and the solution is sensitive to the initialization of the algorithm. The first part of this thesis is aimed toward combining the benefits of non-convex regularization and convex optimization to estimate sparse signals more effectively. To this end, we propose to use parameterized non-convex regularizers with designated non-convexity and provide a range for the non-convex parameter so as to ensure that the objective function is strictly convex. By ensuring convexity of the objective function (sum of data-fidelity and non-convex regularizer), we can make use of a wide variety of convex optimization algorithms to obtain the unique global minimum reliably. The second part of this thesis proposes a non-linear signal decomposition technique for an important biomedical signal processing problem: the detection of sleep spindles and K-complexes in human sleep electroencephalography (EEG). We propose a non-linear model for the EEG consisting of three components: (1) a transient (sparse piecewise constant) component, (2) a low-frequency component, and (3) an oscillatory component. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, we propose a fast non-linear optimization algorithm to estimate the three components in the proposed signal model. The low-frequency and oscillatory components are then used to estimate the K-complexes and sleep spindles respectively. The proposed detection method is shown to outperform several state-of-the-art automated sleep spindles detection methods.

Optimization and Surface Modification of Al-6351 Alloy Using SiC-Cu Green Compact Electrode by Electro Discharge Coating Process

NASA Astrophysics Data System (ADS)

Chakraborty, Sujoy; Kar, Siddhartha; Dey, Vidyut; Ghosh, Subrata Kumar

2017-06-01

This paper introduces the surface modification of Al-6351 alloy by green compact SiC-Cu electrode using electro-discharge coating (EDC) process. A Taguchi L-16 orthogonal array is employed to investigate the process by varying tool parameters like composition and compaction load and electro-discharge machining (EDM) parameters like pulse-on time and peak current. Material deposition rate (MDR), tool wear rate (TWR) and surface roughness (SR) are measured on the coated specimens. An optimum condition is achieved by formulating overall evaluation criteria (OEC), which combines multi-objective task into a single index. The signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) is employed to investigate the effect of relevant process parameters. A confirmation test is conducted based on optimal process parameters and experimental results are provided to illustrate the effectiveness of this approach. The modified surface is characterized by optical microscope and X-ray diffraction (XRD) analysis. XRD analysis of the deposited layer confirmed the transfer of tool materials to the work surface and formation of inter-metallic phases. The micro-hardness of the resulting composite layer is also measured which is 1.5-3 times more than work material’s one and highest layer thickness (LT) of 83.644μm has been successfully achieved.

Dual learning processes underlying human decision-making in reversal learning tasks: functional significance and evidence from the model fit to human behavior

PubMed Central

Bai, Yu; Katahira, Kentaro; Ohira, Hideki

2014-01-01

Humans are capable of correcting their actions based on actions performed in the past, and this ability enables them to adapt to a changing environment. The computational field of reinforcement learning (RL) has provided a powerful explanation for understanding such processes. Recently, the dual learning system, modeled as a hybrid model that incorporates value update based on reward-prediction error and learning rate modulation based on the surprise signal, has gained attention as a model for explaining various neural signals. However, the functional significance of the hybrid model has not been established. In the present study, we used computer simulation in a reversal learning task to address functional significance in a probabilistic reversal learning task. The hybrid model was found to perform better than the standard RL model in a large parameter setting. These results suggest that the hybrid model is more robust against the mistuning of parameters compared with the standard RL model when decision-makers continue to learn stimulus-reward contingencies, which can create abrupt changes. The parameter fitting results also indicated that the hybrid model fit better than the standard RL model for more than 50% of the participants, which suggests that the hybrid model has more explanatory power for the behavioral data than the standard RL model. PMID:25161635

Carbon dioxide diffuse emission from the soil: ten years of observations at Vesuvio and Campi Flegrei (Pozzuoli), and linkages with volcanic activity

NASA Astrophysics Data System (ADS)

Granieri, D.; Avino, R.; Chiodini, G.

2010-01-01

Carbon dioxide flux from the soil is regularly monitored in selected areas of Vesuvio and Solfatara (Campi Flegrei, Pozzuoli) with the twofold aim of i) monitoring spatial and temporal variations of the degassing process and ii) investigating if the surface phenomena could provide information about the processes occurring at depth. At present, the surveyed areas include 15 fixed points around the rim of Vesuvio and 71 fixed points in the floor of Solfatara crater. Soil CO2 flux has been measured since 1998, at least once a month, in both areas. In addition, two automatic permanent stations, located at Vesuvio and Solfatara, measure the CO2 flux and some environmental parameters that can potentially influence the CO2 diffuse degassing. Series acquired by continuous stations are characterized by an annual periodicity that is related to the typical periodicities of some meteorological parameters. Conversely, series of CO2 flux data arising from periodic measurements over the arrays of Vesuvio and Solfatara are less dependent on external factors such as meteorological parameters, local soil properties (porosity, hydraulic conductivity) and topographic effects (high or low ground). Therefore we argue that the long-term trend of this signal contains the “best” possible representation of the endogenous signal related to the upflow of deep hydrothermal fluids.

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.

Electro-optic Mach-Zehnder Interferometer based Optical Digital Magnitude Comparator and 1's Complement Calculator

NASA Astrophysics Data System (ADS)

Kumar, Ajay; Raghuwanshi, Sanjeev Kumar

2016-06-01

The optical switching activity is one of the most essential phenomena in the optical domain. The electro-optic effect-based switching phenomena are applicable to generate some effective combinational and sequential logic circuits. The processing of digital computational technique in the optical domain includes some considerable advantages of optical communication technology, e.g. immunity to electro-magnetic interferences, compact size, signal security, parallel computing and larger bandwidth. The paper describes some efficient technique to implement single bit magnitude comparator and 1's complement calculator using the concepts of electro-optic effect. The proposed techniques are simulated on the MATLAB software. However, the suitability of the techniques is verified using the highly reliable Opti-BPM software. It is interesting to analyze the circuits in order to specify some optimized device parameter in order to optimize some performance affecting parameters, e.g. crosstalk, extinction ratio, signal losses through the curved and straight waveguide sections.

Cosmology and accelerator tests of strongly interacting dark matter

DOE PAGES

Berlin, Asher; Blinov, Nikita; Gori, Stefania; ...

2018-03-23

A natural possibility for dark matter is that it is composed of the stable pions of a QCD-like hidden sector. Existing literature largely assumes that pion self-interactions alone control the early universe cosmology. We point out that processes involving vector mesons typically dominate the physics of dark matter freeze-out and significantly widen the viable mass range for these models. The vector mesons also give rise to striking signals at accelerators. For example, in most of the cosmologically favored parameter space, the vector mesons are naturally long-lived and produce standard model particles in their decays. Electron and proton beam fixed-target experimentsmore » such as HPS, SeaQuest, and LDMX can exploit these signals to explore much of the viable parameter space. As a result, we also comment on dark matter decay inherent in a large class of previously considered models and explain how to ensure dark matter stability.« less

Cosmology and accelerator tests of strongly interacting dark matter

NASA Astrophysics Data System (ADS)

Berlin, Asher; Blinov, Nikita; Gori, Stefania; Schuster, Philip; Toro, Natalia

2018-03-01

A natural possibility for dark matter is that it is composed of the stable pions of a QCD-like hidden sector. Existing literature largely assumes that pion self-interactions alone control the early universe cosmology. We point out that processes involving vector mesons typically dominate the physics of dark matter freeze-out and significantly widen the viable mass range for these models. The vector mesons also give rise to striking signals at accelerators. For example, in most of the cosmologically favored parameter space, the vector mesons are naturally long-lived and produce standard model particles in their decays. Electron and proton beam fixed-target experiments such as HPS, SeaQuest, and LDMX can exploit these signals to explore much of the viable parameter space. We also comment on dark matter decay inherent in a large class of previously considered models and explain how to ensure dark matter stability.

Cosmology and accelerator tests of strongly interacting dark matter

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

Berlin, Asher; Blinov, Nikita; Gori, Stefania

A natural possibility for dark matter is that it is composed of the stable pions of a QCD-like hidden sector. Existing literature largely assumes that pion self-interactions alone control the early universe cosmology. We point out that processes involving vector mesons typically dominate the physics of dark matter freeze-out and significantly widen the viable mass range for these models. The vector mesons also give rise to striking signals at accelerators. For example, in most of the cosmologically favored parameter space, the vector mesons are naturally long-lived and produce standard model particles in their decays. Electron and proton beam fixed-target experimentsmore » such as HPS, SeaQuest, and LDMX can exploit these signals to explore much of the viable parameter space. As a result, we also comment on dark matter decay inherent in a large class of previously considered models and explain how to ensure dark matter stability.« less

Optimization of an angle-beam ultrasonic approach for characterization of impact damage in composites

NASA Astrophysics Data System (ADS)

Henry, Christine; Kramb, Victoria; Welter, John T.; Wertz, John N.; Lindgren, Eric A.; Aldrin, John C.; Zainey, David

2018-04-01

Advances in NDE method development are greatly improved through model-guided experimentation. In the case of ultrasonic inspections, models which provide insight into complex mode conversion processes and sound propagation paths are essential for understanding the experimental data and inverting the experimental data into relevant information. However, models must also be verified using experimental data obtained under well-documented and understood conditions. Ideally, researchers would utilize the model simulations and experimental approach to efficiently converge on the optimal solution. However, variability in experimental parameters introduce extraneous signals that are difficult to differentiate from the anticipated response. This paper discusses the results of an ultrasonic experiment designed to evaluate the effect of controllable variables on the anticipated signal, and the effect of unaccounted for experimental variables on the uncertainty in those results. Controlled experimental parameters include the transducer frequency, incidence beam angle and focal depth.

Digital simulation of a communication link for Pioneer Saturn Uranus atmospheric entry probe, part 1

NASA Technical Reports Server (NTRS)

Hinrichs, C. A.

1975-01-01

A digital simulation study is presented for a candidate modulator/demodulator design in an atmospheric scintillation environment with Doppler, Doppler rate, and signal attenuation typical of the conditions of an outer planet atmospheric probe. The simulation results indicate that the mean channel error rate with and without scintillation are similar to theoretical characterizations of the link. The simulation gives information for calculating other channel statistics and generates a quantized symbol stream on magnetic tape from which error correction decoding is analyzed. Results from the magnetic tape data analyses are also included. The receiver and bit synchronizer are modeled in the simulation at the level of hardware component parameters rather than at the loop equation level and individual hardware parameters are identified. The atmospheric scintillation amplitude and phase are modeled independently. Normal and log normal amplitude processes are studied. In each case the scintillations are low pass filtered. The receiver performance is given for a range of signal to noise ratios with and without the effects of scintillation. The performance is reviewed for critical reciever parameter variations.

Control and optimization system and method for chemical looping processes

DOEpatents

Lou, Xinsheng; Joshi, Abhinaya; Lei, Hao

2014-06-24

A control system for optimizing a chemical loop system includes one or more sensors for measuring one or more parameters in a chemical loop. The sensors are disposed on or in a conduit positioned in the chemical loop. The sensors generate one or more data signals representative of an amount of solids in the conduit. The control system includes a data acquisition system in communication with the sensors and a controller in communication with the data acquisition system. The data acquisition system receives the data signals and the controller generates the control signals. The controller is in communication with one or more valves positioned in the chemical loop. The valves are configured to regulate a flow of the solids through the chemical loop.

Control and optimization system and method for chemical looping processes

DOEpatents

Lou, Xinsheng; Joshi, Abhinaya; Lei, Hao

2015-02-17

A control system for optimizing a chemical loop system includes one or more sensors for measuring one or more parameters in a chemical loop. The sensors are disposed on or in a conduit positioned in the chemical loop. The sensors generate one or more data signals representative of an amount of solids in the conduit. The control system includes a data acquisition system in communication with the sensors and a controller in communication with the data acquisition system. The data acquisition system receives the data signals and the controller generates the control signals. The controller is in communication with one or more valves positioned in the chemical loop. The valves are configured to regulate a flow of the solids through the chemical loop.

Fiber-optic voltage measuring system

NASA Astrophysics Data System (ADS)

Ye, Miaoyuan; Nie, De-Xin; Li, Yan; Peng, Yu; Lin, Qi-Qing; Wang, Jing-Gang

1993-09-01

A new fibre optic voltage measuring system has been developed based on the electrooptic effect of bismuth germanium oxide (Bi4Ge3O12)crystal. It uses the LED as the light source. The light beam emitted from the light source is transmitted to the sensor through the optic fibre and the intensity of the output beam is changed by the applied voltage. This optic signal is transmitted to the PIN detector and converted to an electric signal which is processed by the electronic circuit and 8098 single chip microcomputer the output voltage signal obtained is directly proportional to the applied voltage. This paper describes the principle the configuration and the performance parameters of the system. Test results are evaluated and discussed.

Visual and acoustic communication in non-human animals: a comparison.

PubMed

Rosenthal, G G; Ryan, M J

2000-09-01

The visual and auditory systems are two major sensory modalities employed in communication. Although communication in these two sensory modalities can serve analogous functions and evolve in response to similar selection forces, the two systems also operate under different constraints imposed by the environment and the degree to which these sensory modalities are recruited for non-communication functions. Also, the research traditions in each tend to differ, with studies of mechanisms of acoustic communication tending to take a more reductionist tack often concentrating on single signal parameters, and studies of visual communication tending to be more concerned with multivariate signal arrays in natural environments and higher level processing of such signals. Each research tradition would benefit by being more expansive in its approach.

Research of pulse signal processing based on sleep-monitoring alarm system

NASA Astrophysics Data System (ADS)

Zhang, Kaisheng; Zeng, Yuan

2009-07-01

From pulse diagnosis of Chinese herbalist doctor to the research of cardiovascular system by modem iatrology,they all have showed and proved that human pulse has a good affinity with diseases,especially cardiovascular diseases. Human pulse contains much physical information, and it will be propitious to know the human healthy state early so as to get therapy and recovery early when pulse signal is often detected and analyzed. study how to use the embedded microcontroller to transmit physiological signal from human to personal computer by infrared communication, and the normal sphygmic parameter in one's sleeping is compared with the one measured in order to judge whether one's sleeping condition is normal, finally ascertain the best control plan.

Acoustic transducer for nuclear reactor monitoring

DOEpatents

Ahlgren, Frederic F.; Scott, Paul F.

1977-01-01

A transducer to monitor a parameter and produce an acoustic signal from which the monitored parameter can be recovered. The transducer comprises a modified Galton whistle which emits a narrow band acoustic signal having a frequency dependent upon the parameter being monitored, such as the temperature of the cooling media of a nuclear reactor. Multiple locations within a reactor are monitored simultaneously by a remote acoustic receiver by providing a plurality of transducers each designed so that the acoustic signal it emits has a frequency distinct from the frequencies of signals emitted by the other transducers, whereby each signal can be unambiguously related to a particular transducer.

Visual Image Sensor Organ Replacement: Implementation

NASA Technical Reports Server (NTRS)

Maluf, A. David (Inventor)

2011-01-01

Method and system for enhancing or extending visual representation of a selected region of a visual image, where visual representation is interfered with or distorted, by supplementing a visual signal with at least one audio signal having one or more audio signal parameters that represent one or more visual image parameters, such as vertical and/or horizontal location of the region; region brightness; dominant wavelength range of the region; change in a parameter value that characterizes the visual image, with respect to a reference parameter value; and time rate of change in a parameter value that characterizes the visual image. Region dimensions can be changed to emphasize change with time of a visual image parameter.

Temporal signal processing of dolphin biosonar echoes from salmon prey.

PubMed

Au, Whitlow W L; Ou, Hui Helen

2014-08-01

Killer whales project short broadband biosonar clicks. The broadband nature of the clicks provides good temporal resolution of echo highlights and allows for the discriminations of salmon prey. The echoes contain many highlights as the signals reflect off different surfaces and parts of the fish body and swim bladder. The temporal characteristics of echoes from salmon are highly aspect dependent and six temporal parameters were used in a support vector machine to discriminate between species. Results suggest that killer whales can classify salmon based on their echoes and provide some insight as to which features might enable the classification.

Identification of the structure parameters using short-time non-stationary stochastic excitation

NASA Astrophysics Data System (ADS)

Jarczewska, Kamila; Koszela, Piotr; Śniady, PaweŁ; Korzec, Aleksandra

2011-07-01

In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input-output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

Nondestructive online testing method for friction stir welding using acoustic emission

NASA Astrophysics Data System (ADS)

Levikhina, Anastasiya

2017-12-01

The paper reviews the possibility of applying the method of acoustic emission for online monitoring of the friction stir welding process. It is shown that acoustic emission allows the detection of weld defects and their location in real time. The energy of an acoustic signal and the median frequency are suggested to be used as informative parameters. The method of calculating the median frequency with the use of a short time Fourier transform is applied for the identification of correlations between the defective weld structure and properties of the acoustic emission signals received during welding.

The SKA1 LOW telescope: system architecture and design performance

NASA Astrophysics Data System (ADS)

Waterson, Mark F.; Labate, Maria Grazia; Schnetler, Hermine; Wagg, Jeff; Turner, Wallace; Dewdney, Peter

2016-07-01

The SKA1-LOW radio telescope will be a low-frequency (50-350 MHz) aperture array located in Western Australia. Its scientific objectives will prioritize studies of the Epoch of Reionization and pulsar physics. Development of the telescope has been allocated to consortia responsible for the aperture array front end, timing distribution, signal and data transport, correlation and beamforming signal processors, infrastructure, monitor and control systems, and science data processing. This paper will describe the system architectural design and key performance parameters of the telescope and summarize the high-level sub-system designs of the consortia.

TID measurement using oblique transmissions of HF pulses

NASA Astrophysics Data System (ADS)

Galkin, Ivan; Reinisch, Bodo; Huang, Xueqin; Paznukhov, Vadym; Hamel, Ryan; Kozlov, Alexander; Belehaki, Anna

2017-04-01

The Traveling Ionospheric Disturbance (TID), a wave-like signature of moving plasma density modulation in the ionosphere, is widely acknowledged for its utility in backtracking the anomalous events responsible for the TID generation, and as a major inconvenience to high-frequency (HF) operational systems because of its deleterious impact on the accuracy of navigation and geolocation. The pilot project "Net-TIDE" for the real-time detection and evaluation of TIDs began its operation in 2016 based on the remote-sensing data from synchronized, network-coordinated HF sounding between pairs of DPS4D ionosondes at five participating observatories in Europe. Measurement of all signal properties (Doppler frequency, angle of arrival, and time-of-flight from transmitter to receiver) proved to be instrumental in detecting the TID and deducing the TID parameters: amplitude, wavelength, phase velocity, and direction of propagation. Processing of the measured HF signal data required a specialized signal processing technique that is capable of consistently extracting different signals that have propagated along different ionospheric paths. The multi-path signal environment proved to be the greatest challenge for the reliable TID specification by Net-TIDE, demanding the development of an intelligent system for "signal tracking". The intelligent system is based on a neural network model of a pre-attentive vision capable of extracting continuous signal tracks from the multi-path signal ensemble. Specific examples of the Net-TIDE algorithm suite operation and its suitability for a fully automated TID warning service are discussed.

Study on acoustic-electric-heat effect of coal and rock failure processes under uniaxial compression

NASA Astrophysics Data System (ADS)

Li, Zhong-Hui; Lou, Quan; Wang, En-Yuan; Liu, Shuai-Jie; Niu, Yue

2018-02-01

In recent years, coal and rock dynamic disasters are becoming more and more severe, which seriously threatens the safety of coal mining. It is necessary to carry out an depth study on the various geophysical precursor information in the process of coal and rock failure. In this paper, with the established acoustic-electric-heat multi-parameter experimental system of coal and rock, the acoustic emission (AE), surface potential and thermal infrared radiation (TIR) signals were tested and analyzed in the failure processes of coal and rock under the uniaxial compression. The results show that: (1) AE, surface potential and TIR have different response characteristics to the failure process of the sample. AE and surface potential signals have the obvious responses to the occurrence, extension and coalescence of cracks. The abnormal TIR signals occur at the peak and valley points of the TIR temperature curve, and are coincident with the abnormities of AE and surface potential to a certain extent. (2) The damage precursor points and the critical precursor points were defined to analyze the precursor characteristics reflected by AE, surface potential and TIR signals, and the different signals have the different precursor characteristics. (3) The increment of the maximum TIR temperature after the main rupture of the sample is significantly higher than that of the average TIR temperature. Compared with the maximum TIR temperature, the average TIR temperature has significant hysteresis in reaching the first peak value after the main rapture. (4) The TIR temperature contour plots at different times well show the evolution process of the surface temperature field of the sample, and indicate that the sample failure originates from the local destruction.

Enhancing coronary Wave Intensity Analysis robustness by high order central finite differences.

PubMed

Rivolo, Simone; Asrress, Kaleab N; Chiribiri, Amedeo; Sammut, Eva; Wesolowski, Roman; Bloch, Lars Ø; Grøndal, Anne K; Hønge, Jesper L; Kim, Won Y; Marber, Michael; Redwood, Simon; Nagel, Eike; Smith, Nicolas P; Lee, Jack

2014-09-01

Coronary Wave Intensity Analysis (cWIA) is a technique capable of separating the effects of proximal arterial haemodynamics from cardiac mechanics. Studies have identified WIA-derived indices that are closely correlated with several disease processes and predictive of functional recovery following myocardial infarction. The cWIA clinical application has, however, been limited by technical challenges including a lack of standardization across different studies and the derived indices' sensitivity to the processing parameters. Specifically, a critical step in WIA is the noise removal for evaluation of derivatives of the acquired signals, typically performed by applying a Savitzky-Golay filter, to reduce the high frequency acquisition noise. The impact of the filter parameter selection on cWIA output, and on the derived clinical metrics (integral areas and peaks of the major waves), is first analysed. The sensitivity analysis is performed either by using the filter as a differentiator to calculate the signals' time derivative or by applying the filter to smooth the ensemble-averaged waveforms. Furthermore, the power-spectrum of the ensemble-averaged waveforms contains little high-frequency components, which motivated us to propose an alternative approach to compute the time derivatives of the acquired waveforms using a central finite difference scheme. The cWIA output and consequently the derived clinical metrics are significantly affected by the filter parameters, irrespective of its use as a smoothing filter or a differentiator. The proposed approach is parameter-free and, when applied to the 10 in-vivo human datasets and the 50 in-vivo animal datasets, enhances the cWIA robustness by significantly reducing the outcome variability (by 60%).

Data acquisition and analysis in the DOE/NASA Wind Energy Program

NASA Technical Reports Server (NTRS)

Neustadter, H. E.

1980-01-01

Four categories of data systems, each responding to a distinct information need are presented. The categories are: control, technology, engineering and performance. The focus is on the technology data system which consists of the following elements: sensors which measure critical parameters such as wind speed and direction, output power, blade loads and strains, and tower vibrations; remote multiplexing units (RMU) mounted on each wind turbine which frequency modulate, multiplex and transmit sensor outputs; the instrumentation available to record, process and display these signals; and centralized computer analysis of data. The RMU characteristics and multiplexing techniques are presented. Data processing is illustrated by following a typical signal through instruments such as the analog tape recorder, analog to digital converter, data compressor, digital tape recorder, video (CRT) display, and strip chart recorder.

Estimation of effect of hydrogen on the parameters of magnetoacoustic emission signals

NASA Astrophysics Data System (ADS)

Skalskyi, Valentyn; Stankevych, Olena; Dubytskyi, Olexandr

2018-05-01

The features of the magnetoacoustic emission (MAE) signals during magnetization of structural steels with the different degree of hydrogenating were investigated by the wavelet transform. The dominant frequency ranges of MAE signals for the different magnetic field strength were determined using Discrete Wavelet Transform (DWT), and the energy and spectral parameters of MAE signals were determined using Continuous Wavelet Transform (CWT). The characteristic differences of the local maximums of signals according to energy, bandwidth, duration and frequency were found. The methodology of estimation of state of local degradation of materials by parameters of wavelet transform of MAE signals was proposed. This methodology was approbated for investigate of state of long-time exploitations structural steels of oil and gas pipelines.

Functional relationship-based alarm processing system

DOEpatents

Corsberg, D.R.

1988-04-22

A functional relationship-based alarm processing system and method analyzes each alarm as it is activated and determines its relative importance with other currently activated alarms and signals in accordance with the functional relationships that the newly activated alarm has with other currently activated alarms. Once the initial level of importance of the alarm has been determined, that alarm is again evaluated if another related alarm is activated or deactivated. Thus, each alarm's importance is continuously updated as the state of the process changes during a scenario. Four hierarchical relationships are defined by this alarm filtering methodology: (1) level precursor (usually occurs when there are two alarm settings on the same parameter); (2) direct precursor (based on causal factors between two alarms); (3) required action (system response or action expected within a specified time following activation of an alarm or combination of alarms and process signals); and (4) blocking condition (alarms that are normally expected and are not considered important). The alarm processing system and method is sensitive to the dynamic nature of the process being monitored and is capable of changing the relative importance of each alarm as necessary. 12 figs.

Functional relationship-based alarm processing

DOEpatents

Corsberg, Daniel R.

1988-01-01

A functional relationship-based alarm processing system and method analyzes each alarm as it is activated and determines its relative importance with other currently activated alarms and signals in accordance with the relationships that the newly activated alarm has with other currently activated alarms. Once the initial level of importance of the alarm has been determined, that alarm is again evaluated if another related alarm is activated. Thus, each alarm's importance is continuously oupdated as the state of the process changes during a scenario. Four hierarchical relationships are defined by this alarm filtering methodology: (1) level precursor (usually occurs when there are two alarm settings on the same parameter); (2) direct precursor (based on caussal factors between two alarms); (3) required action (system response or action) expected within a specified time following activation of an alarm or combination of alarms and process signals); and (4) blocking condition (alarms that are normally expected and are not considered important). The alarm processing system and method is sensitive to the dynamic nature of the process being monitored and is capable of changing the relative importance of each alarm as necessary.

Functional relationship-based alarm processing system

DOEpatents

Corsberg, Daniel R.

1989-01-01

A functional relationship-based alarm processing system and method analyzes each alarm as it is activated and determines its relative importance with other currently activated alarms and signals in accordance with the functional relationships that the newly activated alarm has with other currently activated alarms. Once the initial level of importance of the alarm has been determined, that alarm is again evaluated if another related alarm is activated or deactivated. Thus, each alarm's importance is continuously updated as the state of the process changes during a scenario. Four hierarchical relationships are defined by this alarm filtering methodology: (1) level precursor (usually occurs when there are two alarm settings on the same parameter); (2) direct precursor (based on causal factors between two alarms); (3) required action (system response or action expected within a specified time following activation of an alarm or combination of alarms and process signals); and (4) blocking condition (alarms that are normally expected and are not considered important). The alarm processing system and method is sensitive to the dynamic nature of the process being monitored and is capable of changing the relative importance of each alarm as necessary.

Inflammable Gas Mixture Detection with a Single Catalytic Sensor Based on the Electric Field Effect

PubMed Central

Tong, Ziyuan; Tong, Min-Ming; Meng, Wen; Li, Meng

2014-01-01

This paper introduces a new way to analyze mixtures of inflammable gases with a single catalytic sensor. The analysis technology was based on a new finding that an electric field on the catalytic sensor can change the output sensitivity of the sensor. The analysis of mixed inflammable gases results from processing the output signals obtained by adjusting the electric field parameter of the catalytic sensor. For the signal process, we designed a group of equations based on the heat balance of catalytic sensor expressing the relationship between the output signals and the concentration of gases. With these equations and the outputs of different electric fields, the gas concentration in a mixture could be calculated. In experiments, a mixture of methane, butane and ethane was analyzed by this new method, and the results showed that the concentration of each gas in the mixture could be detected with a single catalytic sensor, and the maximum relative error was less than 5%. PMID:24717635

A robust nonlinear filter for image restoration.

PubMed

Koivunen, V

1995-01-01

A class of nonlinear regression filters based on robust estimation theory is introduced. The goal of the filtering is to recover a high-quality image from degraded observations. Models for desired image structures and contaminating processes are employed, but deviations from strict assumptions are allowed since the assumptions on signal and noise are typically only approximately true. The robustness of filters is usually addressed only in a distributional sense, i.e., the actual error distribution deviates from the nominal one. In this paper, the robustness is considered in a broad sense since the outliers may also be due to inappropriate signal model, or there may be more than one statistical population present in the processing window, causing biased estimates. Two filtering algorithms minimizing a least trimmed squares criterion are provided. The design of the filters is simple since no scale parameters or context-dependent threshold values are required. Experimental results using both real and simulated data are presented. The filters effectively attenuate both impulsive and nonimpulsive noise while recovering the signal structure and preserving interesting details.

PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

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

Sen, Satyabrata

We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratiomore » (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.« less

Analysis of EEG signals regularity in adults during video game play in 2D and 3D.

PubMed

Khairuddin, Hamizah R; Malik, Aamir S; Mumtaz, Wajid; Kamel, Nidal; Xia, Likun

2013-01-01

Video games have long been part of the entertainment industry. Nonetheless, it is not well known how video games can affect us with the advancement of 3D technology. The purpose of this study is to investigate the EEG signals regularity when playing video games in 2D and 3D modes. A total of 29 healthy subjects (24 male, 5 female) with mean age of 21.79 (1.63) years participated. Subjects were asked to play a car racing video game in three different modes (2D, 3D passive and 3D active). In 3D passive mode, subjects needed to wear a passive polarized glasses (cinema type) while for 3D active, an active shutter glasses was used. Scalp EEG data was recorded during game play using 19-channel EEG machine and linked ear was used as reference. After data were pre-processed, the signal irregularity for all conditions was computed. Two parameters were used to measure signal complexity for time series data: i) Hjorth-Complexity and ii) Composite Permutation Entropy Index (CPEI). Based on these two parameters, our results showed that the complexity level increased from eyes closed to eyes open condition; and further increased in the case of 3D as compared to 2D game play.

A new chaotic communication scheme based on adaptive synchronization.

PubMed

Xiang-Jun, Wu

2006-12-01

A new chaotic communication scheme using adaptive synchronization technique of two unified chaotic systems is proposed. Different from the existing secure communication methods, the transmitted signal is modulated into the parameter of chaotic systems. The adaptive synchronization technique is used to synchronize two identical chaotic systems embedded in the transmitter and the receiver. It is assumed that the parameter of the receiver system is unknown. Based on the Lyapunov stability theory, an adaptive control law is derived to make the states of two identical unified chaotic systems with unknown system parameters asymptotically synchronized; thus the parameter of the receiver system is identified. Then the recovery of the original information signal in the receiver is successfully achieved on the basis of the estimated parameter. It is noticed that the time required for recovering the information signal and the accuracy of the recovered signal very sensitively depends on the frequency of the information signal. Numerical results have verified the effectiveness of the proposed scheme.

Analyzing a stochastic time series obeying a second-order differential equation.

PubMed

Lehle, B; Peinke, J

2015-06-01

The stochastic properties of a Langevin-type Markov process can be extracted from a given time series by a Markov analysis. Also processes that obey a stochastically forced second-order differential equation can be analyzed this way by employing a particular embedding approach: To obtain a Markovian process in 2N dimensions from a non-Markovian signal in N dimensions, the system is described in a phase space that is extended by the temporal derivative of the signal. For a discrete time series, however, this derivative can only be calculated by a differencing scheme, which introduces an error. If the effects of this error are not accounted for, this leads to systematic errors in the estimation of the drift and diffusion functions of the process. In this paper we will analyze these errors and we will propose an approach that correctly accounts for them. This approach allows an accurate parameter estimation and, additionally, is able to cope with weak measurement noise, which may be superimposed to a given time series.

Reconstruction of ECG signals in presence of corruption.

PubMed

Ganeshapillai, Gartheeban; Liu, Jessica F; Guttag, John

2011-01-01

We present an approach to identifying and reconstructing corrupted regions in a multi-parameter physiological signal. The method, which uses information in correlated signals, is specifically designed to preserve clinically significant aspects of the signals. We use template matching to jointly segment the multi-parameter signal, morphological dissimilarity to estimate the quality of the signal segment, similarity search using features on a database of templates to find the closest match, and time-warping to reconstruct the corrupted segment with the matching template. In experiments carried out on the MIT-BIH Arrhythmia Database, a two-parameter database with many clinically significant arrhythmias, our method improved the classification accuracy of the beat type by more than 7 times on a signal corrupted with white Gaussian noise, and increased the similarity to the original signal, as measured by the normalized residual distance, by more than 2.5 times.

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.

Data mining spacecraft telemetry: towards generic solutions to automatic health monitoring and status characterisation

NASA Astrophysics Data System (ADS)

Royer, P.; De Ridder, J.; Vandenbussche, B.; Regibo, S.; Huygen, R.; De Meester, W.; Evans, D. J.; Martinez, J.; Korte-Stapff, M.

2016-07-01

We present the first results of a study aimed at finding new and efficient ways to automatically process spacecraft telemetry for automatic health monitoring. The goal is to reduce the load on the flight control team while extending the "checkability" to the entire telemetry database, and provide efficient, robust and more accurate detection of anomalies in near real time. We present a set of effective methods to (a) detect outliers in the telemetry or in its statistical properties, (b) uncover and visualise special properties of the telemetry and (c) detect new behavior. Our results are structured around two main families of solutions. For parameters visiting a restricted set of signal values, i.e. all status parameters and about one third of all the others, we focus on a transition analysis, exploiting properties of Poincare plots. For parameters with an arbitrarily high number of possible signal values, we describe the statistical properties of the signal via its Kernel Density Estimate. We demonstrate that this allows for a generic and dynamic approach of the soft-limit definition. Thanks to a much more accurate description of the signal and of its time evolution, we are more sensitive and more responsive to outliers than the traditional checks against hard limits. Our methods were validated on two years of Venus Express telemetry. They are generic for assisting in health monitoring of any complex system with large amounts of diagnostic sensor data. Not only spacecraft systems but also present-day astronomical observatories can benefit from them.

Detecting severity of delamination in a lap joint using S-parameters

NASA Astrophysics Data System (ADS)

Islam, M. M.; Huang, H.

2018-03-01

The scattering parameters (S-parameters) represent the frequency response of a two-port linear time-invariant network. Treating a lap joint structure instrumented with two piezoelectric wafer active transducers (PWaTs) as such a network, this paper investigates the application of the S-parameters for detecting the severity of delamination in the lap joint. The pulse-echo signal calculated from the reflection coefficients, namely the S 11 and S 22-parameters, can be divided into three signals, i.e. the excitation, resonant, and echo signals, based on their respective time spans. Analyzing the effects of the delamination on the resonant signal enables us to identify the resonance at which the resonant characteristics of the PWaTs are least sensitive to the delamination. Only at this resonance, we found that the reflection coefficients and the amplitude of the first arrival echo signal changed monotonously with the increase of the delamination length. This discovery is further validated by the time-domain pitch-catch signal calculated from the transmission coefficient (i.e. the S 21-parameter). In addition, comparing the pulse-echo signals obtained from both PWaTs enables us to determine the side of the lap joint that the delamination is located at. This work establishes the S-parameters as an effective tool to evaluate the effects of damage on the PWaT resonant characteristics, based on which the PWaT resonance can be selected judiciously for damage severity detection. Correlating the reflection and transmission coefficients also provide addition validations that increase the detection confidence.

Global scale stratospheric processes as measured by the infrasound IMS network

NASA Astrophysics Data System (ADS)

Le Pichon, A.; Ceranna, L.; Kechut, P.

2012-04-01

IMS infrasound array data are routinely processed at the International Data Center (IDC). The wave parameters of the detected signals are estimated with the Progressive Multi-Channel Correlation method (PMCC). This new implementation of the PMCC algorithm allows the full frequency range of interest (0.01-5 Hz) to be processed efficiently in a single computational run. We have processed continuous recordings from 41 certified IMS stations from 2005 to 2010. We show that microbaroms are the dominant source of signals and are near-continuously globally detected. The observed azimuthal seasonal trend correlates well with the variation of the effective sound speed ratio which is a proxy for the combined effects of refraction due to sound speed gradients and advection due to along-path wind on infrasound propagation. A general trend in signal backazimuth is observed between winter and summer, driven by the seasonal reversal of the stratospheric winds. Combined with propagation modeling, we show that such an analysis enables a characterization of the wind and temperature structure above the stratosphere and may provide detailed information on upper atmospheric processes (e.g., large-scale planetary waves, stratospheric warming effects). We correlate perturbations and deviations from the seasonal trend to short time-scale variability of the atmosphere. We discuss the potential benefit of long-term infrasound monitoring to infer stratospheric processes for the first time on a global scale.

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.

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters

PubMed Central

Nunez, Michael D.; Vandekerckhove, Joachim; Srinivasan, Ramesh

2016-01-01

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects. PMID:28435173

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters.

PubMed

Nunez, Michael D; Vandekerckhove, Joachim; Srinivasan, Ramesh

2017-02-01

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects.

LSST camera readout chip ASPIC: test tools

NASA Astrophysics Data System (ADS)

Antilogus, P.; Bailly, Ph; Jeglot, J.; Juramy, C.; Lebbolo, H.; Martin, D.; Moniez, M.; Tocut, V.; Wicek, F.

2012-02-01

The LSST camera will have more than 3000 video-processing channels. The readout of this large focal plane requires a very compact readout chain. The correlated ''Double Sampling technique'', which is generally used for the signal readout of CCDs, is also adopted for this application and implemented with the so called ''Dual Slope integrator'' method. We have designed and implemented an ASIC for LSST: the Analog Signal Processing asIC (ASPIC). The goal is to amplify the signal close to the output, in order to maximize signal to noise ratio, and to send differential outputs to the digitization. Others requirements are that each chip should process the output of half a CCD, that is 8 channels and should operate at 173 K. A specific Back End board has been designed especially for lab test purposes. It manages the clock signals, digitizes the analog differentials outputs of ASPIC and stores data into a memory. It contains 8 ADCs (18 bits), 512 kwords memory and an USB interface. An FPGA manages all signals from/to all components on board and generates the timing sequence for ASPIC. Its firmware is written in Verilog and VHDL languages. Internals registers permit to define various tests parameters of the ASPIC. A Labview GUI allows to load or update these registers and to check a proper operation. Several series of tests, including linearity, noise and crosstalk, have been performed over the past year to characterize the ASPIC at room and cold temperature. At present, the ASPIC, Back-End board and CCD detectors are being integrated to perform a characterization of the whole readout chain.

Continuous Odour Measurement with Chemosensor Systems

NASA Astrophysics Data System (ADS)

Boeker, Peter; Haas, T.; Diekmann, B.; Lammer, P. Schulze

2009-05-01

The continuous odour measurement is a challenging task for chemosensor systems. Firstly, a long term and stable measurement mode must be guaranteed in order to preserve the validity of the time consuming and expensive olfactometric calibration data. Secondly, a method is needed to deal with the incoming sensor data. The continuous online detection of signal patterns, the correlated gas emission and the assigned odour data is essential for the continuous odour measurement. Thirdly, a severe danger of over-fitting in the process of the odour calibration is present, because of the high measurement uncertainty of the olfactometry. In this contribution we present a technical solution for continuous measurements comprising of a hybrid QMB-sensor array and electrochemical cells. A set of software tools enables the efficient data processing and calibration and computes the calibration parameters. The internal software of the measurement systems microcontroller processes the calibration parameters online for the output of the desired odour information.

Coherence Measurements for Excited to Excited State Transitions in Barium

NASA Technical Reports Server (NTRS)

Trajmar, S.; Kanik, I.; Karaganov, V.; Zetner, P. W.; Csanak, G.

2000-01-01

Experimental studies concerning elastic and inelastic electron scattering by coherently ensembles of Ba (...6s6p (sub 1)P(sub 1)) atoms with various degrees of alignment will be described. An in-plane, linearly-polarized laser beam was utilized to prepare these target ensembles and the electron scattering signal as a function of polarization angle was measured for several laser geometries at fixed impact energies and scattering angles. From these measurements, we derived cross sections and electron-impact coherence parameters associated with the electron scattering process which is time reverse of the actual experimentally studied process. This interpretation of the experiment is based on the theory of Macek and Herte. The experimental results were also interpreted in terms of cross sections and collision parameters associated with the actual experimental processes. Results obtained so far will be presented and plans for further studies will be discussed.

Standardization of pitch-range settings in voice acoustic analysis.

PubMed

Vogel, Adam P; Maruff, Paul; Snyder, Peter J; Mundt, James C

2009-05-01

Voice acoustic analysis is typically a labor-intensive, time-consuming process that requires the application of idiosyncratic parameters tailored to individual aspects of the speech signal. Such processes limit the efficiency and utility of voice analysis in clinical practice as well as in applied research and development. In the present study, we analyzed 1,120 voice files, using standard techniques (case-by-case hand analysis), taking roughly 10 work weeks of personnel time to complete. The results were compared with the analytic output of several automated analysis scripts that made use of preset pitch-range parameters. After pitch windows were selected to appropriately account for sex differences, the automated analysis scripts reduced processing time of the 1,120 speech samples to less than 2.5 h and produced results comparable to those obtained with hand analysis. However, caution should be exercised when applying the suggested preset values to pathological voice populations.

Advanced control of dissolved oxygen concentration in fed batch cultures during recombinant protein production.

PubMed

Kuprijanov, A; Gnoth, S; Simutis, R; Lübbert, A

2009-02-01

Design and experimental validation of advanced pO(2) controllers for fermentation processes operated in the fed-batch mode are described. In most situations, the presented controllers are able to keep the pO(2) in fermentations for recombinant protein productions exactly on the desired value. The controllers are based on the gain-scheduling approach to parameter-adaptive proportional-integral controllers. In order to cope with the most often appearing distortions, the basic gain-scheduling feedback controller was complemented with a feedforward control component. This feedforward/feedback controller significantly improved pO(2) control. By means of numerical simulations, the controller behavior was tested and its parameters were determined. Validation runs were performed with three Escherichia coli strains producing different recombinant proteins. It is finally shown that the new controller leads to significant improvements in the signal-to-noise ratio of other key process variables and, thus, to a higher process quality.