A robust spatial filtering technique for multisource localization and geoacoustic inversion.

PubMed

Stotts, S A

2005-07-01

Geoacoustic inversion and source localization using beamformed data from a ship of opportunity has been demonstrated with a bottom-mounted array. An alternative approach, which lies within a class referred to as spatial filtering, transforms element level data into beam data, applies a bearing filter, and transforms back to element level data prior to performing inversions. Automation of this filtering approach is facilitated for broadband applications by restricting the inverse transform to the degrees of freedom of the array, i.e., the effective number of elements, for frequencies near or below the design frequency. A procedure is described for nonuniformly spaced elements that guarantees filter stability well above the design frequency. Monitoring energy conservation with respect to filter output confirms filter stability. Filter performance with both uniformly spaced and nonuniformly spaced array elements is discussed. Vertical (range and depth) and horizontal (range and bearing) ambiguity surfaces are constructed to examine filter performance. Examples that demonstrate this filtering technique with both synthetic data and real data are presented along with comparisons to inversion results using beamformed data. Examinations of cost functions calculated within a simulated annealing algorithm reveal the efficacy of the approach.

Optimization of CO2 Surface Flux using GOSAT Total Column CO2: First Results for 2009-2010

NASA Astrophysics Data System (ADS)

Basu, S.; Houweling, S.

2011-12-01

Constraining surface flux estimates of CO2 using satellite measurements has been one of the long-standing goals of the atmospheric inverse modeling community. We present the first results of inverting GOSAT total column CO2 measurements for obtaining global monthly CO2 flux maps over one year (June 2009 to May 2010). We use the SRON RemoTeC retrieval of CO2 for our inversions. The SRON retrieval has been shown to have no bias when compared to TCCON total column measurements, and latitudinal gradients of the retrieved CO2 are consistent with gradients deduced from the surface flask network [Butz et al, 2011]. This makes this retrieval an ideal candidate for atmospheric inversions, which are highly sensitive to spurious gradients. Our inversion system is analogous to the CarbonTracker (CT) data assimilation system; it is initialized with the prior CO2 fluxes of CT, and uses the same atmospheric transport model, i.e., TM5. The two major differences are (a) we add GOSAT CO2 data to the inversion in addition to flask data, and (b) we use a 4DVAR optimization system instead of a Kalman filter. We compare inversions using (a) only GOSAT total column CO2 measurements, (b) only surface flask CO2 measurements, and (c) the joint data set of GOSAT and surface flask measurements. We validate GOSAT-only inversions against the NOAA surface flask network and joint inversions against CONTRAIL and other aircraft campaigns. We see that inverted fluxes from a GOSAT-only inversion are consistent with fluxes from a stations-only inversion, reaffirming the low biases in SRON retrievals. From the joint inversion, we estimate the amount of added constraints upon adding GOSAT total column measurements to existing surface layer measurements.

Ozonometer M-124 calibration for the Ukrainian network: method and results

NASA Astrophysics Data System (ADS)

Grytsai, A.; Milinevsky, G.; Evtushevsky, O.; Sosonkin, M.; Kravchenko, V.; Danylevsky, V.

2016-12-01

M-124 filter ozonometers are used for total ozone measuring in Ukraine since 1970s. Recently the need to calibrate several M-124 instruments of the Ukrainian filter ozonometer network is raised to continue ozone observations. The calibration became possible owing to the accurate ozone measurements by Dobson spectrophotometer started in 2010 at the Kyiv-Goloseyev WMO station located at the Main Astronomical Observatory of National Academy of Sciences of Ukraine. For calibration purposes the simultaneous M-124 and Dobson Direct Sun measurements were carried out during the 2013-2016 period by researchers from Taras Shevchenko National University of Kyiv and Main Astronomical Observatory. The M-124 instrument has two spectral channels: first is 305 nm and second is 325 nm. Outgoing signal from M-124 is determined by transparency of the terrestrial atmosphere and filter characteristics. Theoretical description of the solar radiation propagation through the atmosphere is determined by the Bouguer-Lambert-Beer law taking into account ozone absorption, Rayleigh and aerosol scattering. Parameters of the aerosol scattering have been determined from observations with the CIMEL sunphotometer of Aerosol Robotic Network which is also located at the Kyiv-Goloseyev station. The ozonometers optical characteristics were studied after M-124 refurbishment and modernization at the Central Geophysical Observatory of Ukraine that includes a significant part of the whole calibration work. Knowing the spectral dependence of each filter is necessary to calculate signal ratios in two channels. This information allowed solving the inverse problem of determining total ozone content in the terrestrial atmosphere. Comparison of these results with Dobson spectrophotometer data shows their good quality even without an additional correction. These results open a possibility to calibrate M-124 filter ozonometers for future ozone measurements at the observation sites of the Ukraine ozonometer network.

Estimating uncertainty of Full Waveform Inversion with Ensemble-based methods

NASA Astrophysics Data System (ADS)

Thurin, J.; Brossier, R.; Métivier, L.

2017-12-01

Uncertainty estimation is one key feature of tomographic applications for robust interpretation. However, this information is often missing in the frame of large scale linearized inversions, and only the results at convergence are shown, despite the ill-posed nature of the problem. This issue is common in the Full Waveform Inversion community.While few methodologies have already been proposed in the literature, standard FWI workflows do not include any systematic uncertainty quantifications methods yet, but often try to assess the result's quality through cross-comparison with other results from seismic or comparison with other geophysical data. With the development of large seismic networks/surveys, the increase in computational power and the more and more systematic application of FWI, it is crucial to tackle this problem and to propose robust and affordable workflows, in order to address the uncertainty quantification problem faced for near surface targets, crustal exploration, as well as regional and global scales.In this work (Thurin et al., 2017a,b), we propose an approach which takes advantage of the Ensemble Transform Kalman Filter (ETKF) proposed by Bishop et al., (2001), in order to estimate a low-rank approximation of the posterior covariance matrix of the FWI problem, allowing us to evaluate some uncertainty information of the solution. Instead of solving the FWI problem through a Bayesian inversion with the ETKF, we chose to combine a conventional FWI, based on local optimization, and the ETKF strategies. This scheme allows combining the efficiency of local optimization for solving large scale inverse problems and make the sampling of the local solution space possible thanks to its embarrassingly parallel property. References:Bishop, C. H., Etherton, B. J. and Majumdar, S. J., 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Monthly weather review, 129(3), 420-436.Thurin, J., Brossier, R. and Métivier, L. 2017,a.: Ensemble-Based Uncertainty Estimation in Full Waveform Inversion. 79th EAGE Conference and Exhibition 2017, (12 - 15 June, 2017)Thurin, J., Brossier, R. and Métivier, L. 2017,b.: An Ensemble-Transform Kalman Filter - Full Waveform Inversion scheme for Uncertainty estimation; SEG Technical Program Expanded Abstracts 2012

Estimation of the full-field dynamic response of a floating bridge using Kalman-type filtering algorithms

NASA Astrophysics Data System (ADS)

Petersen, Ø. W.; Øiseth, O.; Nord, T. S.; Lourens, E.

2018-07-01

Numerical predictions of the dynamic response of complex structures are often uncertain due to uncertainties inherited from the assumed load effects. Inverse methods can estimate the true dynamic response of a structure through system inversion, combining measured acceleration data with a system model. This article presents a case study of full-field dynamic response estimation of a long-span floating bridge: the Bergøysund Bridge in Norway. This bridge is instrumented with a network of 14 triaxial accelerometers. The system model consists of 27 vibration modes with natural frequencies below 2 Hz, obtained from a tuned finite element model that takes the fluid-structure interaction with the surrounding water into account. Two methods, a joint input-state estimation algorithm and a dual Kalman filter, are applied to estimate the full-field response of the bridge. The results demonstrate that the displacements and the accelerations can be estimated at unmeasured locations with reasonable accuracy when the wave loads are the dominant source of excitation.

Study on geophone coupling and attenuating compensatory of low-depression velocity layer in desert area

USGS Publications Warehouse

Shi, Z.; Tian, G.; Dong, S.; Xia, J.; He, H.; ,

2004-01-01

In a desert area, it is difficult to couple geophones with dry sands. A low and depression velocity layer can seriously attenuate high frequency components of seismic data. Therefore, resolution and signal-to-noise (S/N) ratio of seismic data deteriorate. To enhance resolution and S/N ratio of seismic data, we designed a coupling compensatory inverse filter by using the single trace seismic data from Seismic Wave Detect System (SWDS) and common receivers on equal conditions. We designed an attenuating compensatory inverse filter by using seismic data from a microseismogram log. At last, in order to convert a shot gather from common receivers to a shot gather from SWDS, we applied the coupling compensatory inverse filter to the shot gather from common receivers. And then we applied the attenuating compensatory inverse filter to the coupling stacked seismic data to increase its resolution and S/N ratio. The results show that the resolution of seismic data from common receivers after processing by using the coupling compensatory inverse filter is nearly comparable with that of data from SWDS. It is also found that the resolution and S/N ratio have been enhanced after the use of attenuating compensatory inverse filter. From the results, we can conclude that the filters can compensate high frequencies of seismic data. Moreover, the low frequency changed nearly.

Microcomputer Simulation of a Fourier Approach to Optical Wave Propagation

DTIC Science & Technology

1992-06-01

and transformed input in transform domain). 44 Figure 21. SHFTOUTPUT1 ( inverse transform of product of Bessel filter and transformed input). . . . 44...Figure 22. SHFT OUTPUT2 ( inverse transform of product of ,derivative filter and transformed input).. 45 Figure 23. •tIFT OUTPUT (sum of SHFTOUTPUT1...52 Figure 33. SHFT OUTPUT1 at time slice 1 ( inverse transform of product of Bessel filter and transformed input) .... ............. ... 53

Counting Magnetic Bipoles on the Sun by Polarity Inversion

NASA Technical Reports Server (NTRS)

Jones, Harrison P.

2004-01-01

This paper presents a simple and efficient algorithm for deriving images of polarity inversion from NSO/Kitt Peak magnetograms without use of contouring routines and shows by example how these maps depend upon the spatial scale for filtering the raw data. Smaller filtering scales produce many localized closed contours in mixed polarity regions while supergranular and larger filtering scales produce more global patterns. The apparent continuity of an inversion line depends on how the spatial filtering is accomplished, but its shape depends only on scale. The total length of the magnetic polarity inversion contours varies as a power law of the filter scale with fractal dimension of order 1.9. The amplitude but nut the exponent of this power-law relation varies with solar activity. The results are compared to similar analyses of areal distributions of bipolar magnetic regions.

Bessel smoothing filter for spectral-element mesh

NASA Astrophysics Data System (ADS)

Trinh, P. T.; Brossier, R.; Métivier, L.; Virieux, J.; Wellington, P.

2017-06-01

Smoothing filters are extremely important tools in seismic imaging and inversion, such as for traveltime tomography, migration and waveform inversion. For efficiency, and as they can be used a number of times during inversion, it is important that these filters can easily incorporate prior information on the geological structure of the investigated medium, through variable coherent lengths and orientation. In this study, we promote the use of the Bessel filter to achieve these purposes. Instead of considering the direct application of the filter, we demonstrate that we can rely on the equation associated with its inverse filter, which amounts to the solution of an elliptic partial differential equation. This enhances the efficiency of the filter application, and also its flexibility. We apply this strategy within a spectral-element-based elastic full waveform inversion framework. Taking advantage of this formulation, we apply the Bessel filter by solving the associated partial differential equation directly on the spectral-element mesh through the standard weak formulation. This avoids cumbersome projection operators between the spectral-element mesh and a regular Cartesian grid, or expensive explicit windowed convolution on the finite-element mesh, which is often used for applying smoothing operators. The associated linear system is solved efficiently through a parallel conjugate gradient algorithm, in which the matrix vector product is factorized and highly optimized with vectorized computation. Significant scaling behaviour is obtained when comparing this strategy with the explicit convolution method. The theoretical numerical complexity of this approach increases linearly with the coherent length, whereas a sublinear relationship is observed practically. Numerical illustrations are provided here for schematic examples, and for a more realistic elastic full waveform inversion gradient smoothing on the SEAM II benchmark model. These examples illustrate well the efficiency and flexibility of the approach proposed.

The attitude inversion method of geostationary satellites based on unscented particle filter

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Wang, Yang; Hu, Heng; Gou, Ruixin; Liu, Hao

2018-04-01

The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.

Vibrato in Singing Voice: The Link between Source-Filter and Sinusoidal Models

NASA Astrophysics Data System (ADS)

Arroabarren, Ixone; Carlosena, Alfonso

2004-12-01

The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.

TOMOGRAPHY OF PLASMA FLOWS IN THE UPPER SOLAR CONVECTION ZONE USING TIME-DISTANCE INVERSION COMBINING RIDGE AND PHASE-SPEED FILTERING

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

Svanda, Michal, E-mail: michal@astronomie.cz; Astronomical Institute, Charles University in Prague, Faculty of Mathematics and Physics, V Holesovickach 2, CZ-18000 Prague 8

2013-09-20

The consistency of time-distance inversions for horizontal components of the plasma flow on supergranular scales in the upper solar convection zone is checked by comparing the results derived using two k-{omega} filtering procedures-ridge filtering and phase-speed filtering-commonly used in time-distance helioseismology. I show that both approaches result in similar flow estimates when finite-frequency sensitivity kernels are used. I further demonstrate that the performance of the inversion improves (in terms of a simultaneously better averaging kernel and a lower noise level) when the two approaches are combined together in one inversion. Using the combined inversion, I invert for horizontal flows inmore » the upper 10 Mm of the solar convection zone. The flows connected with supergranulation seem to be coherent only for the top {approx}5 Mm; deeper down there is a hint of change of the convection scales toward structures larger than supergranules.« less

Moment tensor inversions using strong motion waveforms of Taiwan TSMIP data, 1993–2009

USGS Publications Warehouse

Chang, Kaiwen; Chi, Wu-Cheng; Gung, Yuancheng; Dreger, Douglas; Lee, William H K.; Chiu, Hung-Chie

2011-01-01

Earthquake source parameters are important for earthquake studies and seismic hazard assessment. Moment tensors are among the most important earthquake source parameters, and are now routinely derived using modern broadband seismic networks around the world. Similar waveform inversion techniques can also apply to other available data, including strong-motion seismograms. Strong-motion waveforms are also broadband, and recorded in many regions since the 1980s. Thus, strong-motion data can be used to augment moment tensor catalogs with a much larger dataset than that available from the high-gain, broadband seismic networks. However, a systematic comparison between the moment tensors derived from strong motion waveforms and high-gain broadband waveforms has not been available. In this study, we inverted the source mechanisms of Taiwan earthquakes between 1993 and 2009 by using the regional moment tensor inversion method using digital data from several hundred stations in the Taiwan Strong Motion Instrumentation Program (TSMIP). By testing different velocity models and filter passbands, we were able to successfully derive moment tensor solutions for 107 earthquakes of Mw >= 4.8. The solutions for large events agree well with other available moment tensor catalogs derived from local and global broadband networks. However, for Mw = 5.0 or smaller events, we consistently over estimated the moment magnitudes by 0.5 to 1.0. We have tested accelerograms, and velocity waveforms integrated from accelerograms for the inversions, and found the results are similar. In addition, we used part of the catalogs to study important seismogenic structures in the area near Meishan Taiwan which was the site of a very damaging earthquake a century ago, and found that the structures were dominated by events with complex right-lateral strike-slip faulting during the recent decade. The procedures developed from this study may be applied to other strong-motion datasets to compliment or fill gaps in catalogs from regional broadband networks and teleseismic networks.

Seismic waveform inversion using neural networks

NASA Astrophysics Data System (ADS)

De Wit, R. W.; Trampert, J.

2012-12-01

Full waveform tomography aims to extract all available information on Earth structure and seismic sources from seismograms. The strongly non-linear nature of this inverse problem is often addressed through simplifying assumptions for the physical theory or data selection, thus potentially neglecting valuable information. Furthermore, the assessment of the quality of the inferred model is often lacking. This calls for the development of methods that fully appreciate the non-linear nature of the inverse problem, whilst providing a quantification of the uncertainties in the final model. We propose to invert seismic waveforms in a fully non-linear way by using artificial neural networks. Neural networks can be viewed as powerful and flexible non-linear filters. They are very common in speech, handwriting and pattern recognition. Mixture Density Networks (MDN) allow us to obtain marginal posterior probability density functions (pdfs) of all model parameters, conditioned on the data. An MDN can approximate an arbitrary conditional pdf as a linear combination of Gaussian kernels. Seismograms serve as input, Earth structure parameters are the so-called targets and network training aims to learn the relationship between input and targets. The network is trained on a large synthetic data set, which we construct by drawing many random Earth models from a prior model pdf and solving the forward problem for each of these models, thus generating synthetic seismograms. As a first step, we aim to construct a 1D Earth model. Training sets are constructed using the Mineos package, which computes synthetic seismograms in a spherically symmetric non-rotating Earth by summing normal modes. We train a network on the body waveforms present in these seismograms. Once the network has been trained, it can be presented with new unseen input data, in our case the body waves in real seismograms. We thus obtain the posterior pdf which represents our final state of knowledge given the information in the training set and the real data.

Filtering techniques for efficient inversion of two-dimensional Nuclear Magnetic Resonance data

NASA Astrophysics Data System (ADS)

Bortolotti, V.; Brizi, L.; Fantazzini, P.; Landi, G.; Zama, F.

2017-10-01

The inversion of two-dimensional Nuclear Magnetic Resonance (NMR) data requires the solution of a first kind Fredholm integral equation with a two-dimensional tensor product kernel and lower bound constraints. For the solution of this ill-posed inverse problem, the recently presented 2DUPEN algorithm [V. Bortolotti et al., Inverse Problems, 33(1), 2016] uses multiparameter Tikhonov regularization with automatic choice of the regularization parameters. In this work, I2DUPEN, an improved version of 2DUPEN that implements Mean Windowing and Singular Value Decomposition filters, is deeply tested. The reconstruction problem with filtered data is formulated as a compressed weighted least squares problem with multi-parameter Tikhonov regularization. Results on synthetic and real 2D NMR data are presented with the main purpose to deeper analyze the separate and combined effects of these filtering techniques on the reconstructed 2D distribution.

An OTA-C filter for ECG acquisition systems with highly linear range and less passband attenuation

NASA Astrophysics Data System (ADS)

Jihai, Duan; Chuang, Lan; Weilin, Xu; Baolin, Wei

2015-05-01

A fifth order operational transconductance amplifier-C (OTA-C) Butterworth type low-pass filter with highly linear range and less passband attenuation is presented for wearable bio-telemetry monitoring applications in a UWB wireless body area network. The source degeneration structure applied in typical small transconductance circuit is improved to provide a highly linear range for the OTA-C filter. Moreover, to reduce the passband attenuation of the filter, a cascode structure is employed as the output stage of the OTA. The OTA-based circuit is operated in weak inversion due to strict power limitation in the biomedical chip. The filter is fabricated in a SMIC 0.18-μm CMOS process. The measured results for the filter have shown a passband gain of -6.2 dB, while the -3-dB frequency is around 276 Hz. For the 0.8 VPP sinusoidal input at 100 Hz, a total harmonic distortion (THD) of -56.8 dB is obtained. An electrocardiogram signal with noise interference is fed into this chip to validate the function of the designed filter. Project supported by the National Natural Science Foundation of China (Nos. 61161003, 61264001, 61166004) and the Guangxi Natural Science Foundation (No. 2013GXNSFAA019333).

A feasibility study for long-path multiple detection using a neural network

NASA Technical Reports Server (NTRS)

Feuerbacher, G. A.; Moebes, T. A.

1994-01-01

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Use of switched capacitor filters to implement the discrete wavelet transform

NASA Technical Reports Server (NTRS)

Kaiser, Kraig E.; Peterson, James N.

1993-01-01

This paper analyzes the use of IIR switched capacitor filters to implement the discrete wavelet transform and the inverse transform, using quadrature mirror filters (QMF) which have the necessary symmetry for reconstruction of the data. This is done by examining the sensitivity of the QMF transforms to the manufacturing variance in the desired capacitances. The performance is evaluated at the outputs of the separate filter stages and the error in the reconstruction of the inverse transform is compared with the desired results.

Inverse halftoning via robust nonlinear filtering

NASA Astrophysics Data System (ADS)

Shen, Mei-Yin; Kuo, C.-C. Jay

1999-10-01

A new blind inverse halftoning algorithm based on a nonlinear filtering technique of low computational complexity and low memory requirement is proposed in this research. It is called blind since we do not require the knowledge of the halftone kernel. The proposed scheme performs nonlinear filtering in conjunction with edge enhancement to improve the quality of an inverse halftoned image. Distinct features of the proposed approach include: efficiently smoothing halftone patterns in large homogeneous areas, additional edge enhancement capability to recover the edge quality and an excellent PSNR performance with only local integer operations and a small memory buffer.

Comparing closed quotient in children singers' voices as measured by high-speed-imaging, electroglottography, and inverse filtering.

PubMed

Mecke, Ann-Christine; Sundberg, Johan; Granqvist, Svante; Echternach, Matthias

2012-01-01

The closed quotient, i.e., the ratio between the closed phase and the period, is commonly studied in voice research. However, the term may refer to measures derived from different methods, such as inverse filtering, electroglottography or high-speed digital imaging (HSDI). This investigation compares closed quotient data measured by these three methods in two boy singers. Each singer produced sustained tones on two different pitches and a glissando. Audio, electroglottographic signal (EGG), and HSDI were recorded simultaneously. The audio signal was inverse filtered by means of the decap program; the closed phase was defined as the flat minimum portion of the flow glottogram. Glottal area was automatically measured in the high speed images by the built-in camera software, and the closed phase was defined as the flat minimum portion of the area-signal. The EGG-signal was analyzed in four different ways using the matlab open quotient interface. The closed quotient data taken from the EGG were found to be considerably higher than those obtained from inverse filtering. Also, substantial differences were found between the closed quotient derived from HSDI and those derived from inverse filtering. The findings illustrate the importance of distinguishing between these quotients. © 2012 Acoustical Society of America.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

PubMed Central

Dura-Bernal, Salvador; Li, Kan; Neymotin, Samuel A.; Francis, Joseph T.; Principe, Jose C.; Lytton, William W.

2016-01-01

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors. PMID:26903796

The Ellipticity Filter-A Proposed Solution to the Mixed Event Problem in Nuclear Seismic Discrimination

DTIC Science & Technology

1974-09-07

ellipticity filter. The source waveforms are recreated by an inverse transform of those complex ampli- tudes associated with the same azimuth...terms of the three complex data points and the ellipticity. Having solved the equations for all frequency bins, the inverse transform of...Transform of those complex amplitudes associated with Source 1, yielding the signal a (t). Similarly, take the inverse Transform of all

Visualizing deep neural network by alternately image blurring and deblurring.

PubMed

Wang, Feng; Liu, Haijun; Cheng, Jian

2018-01-01

Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Surface wave tomography of Europe from ambient seismic noise

NASA Astrophysics Data System (ADS)

Lu, Yang; Stehly, Laurent; Paul, Anne

2017-04-01

We present a European scale high-resolution 3-D shear wave velocity model derived from ambient seismic noise tomography. In this study, we collect 4 years of continuous seismic recordings from 1293 stations across much of the European region (10˚W-35˚E, 30˚N-75˚N), which yields more than 0.8 million virtual station pairs. This data set compiles records from 67 seismic networks, both permanent and temporary from the EIDA (European Integrated Data Archive). Rayleigh wave group velocity are measured at each station pair using the multiple-filter analysis technique. Group velocity maps are estimated through a linearized tomographic inversion algorithm at period from 5s to 100s. Adaptive parameterization is used to accommodate heterogeneity in data coverage. We then apply a two-step data-driven inversion method to obtain the shear wave velocity model. The two steps refer to a Monte Carlo inversion to build the starting model, followed by a linearized inversion for further improvement. Finally, Moho depth (and its uncertainty) are determined over most of our study region by identifying and analysing sharp velocity discontinuities (and sharpness). The resulting velocity model shows good agreement with main geological features and previous geophyical studies. Moho depth coincides well with that obtained from active seismic experiments. A focus on the Greater Alpine region (covered by the AlpArray seismic network) displays a clear crustal thinning that follows the arcuate shape of the Alps from the southern French Massif Central to southern Germany.

Enhancing Spin Filters by Use of Bulk Inversion Asymmetry

NASA Technical Reports Server (NTRS)

Ting, David; Cartoixa,Xavier

2007-01-01

Theoretical calculations have shown that the degrees of spin polarization in proposed nonmagnetic semiconductor resonant tunneling spin filters could be increased through exploitation of bulk inversion asymmetry (BIA). These enhancements would be effected through suitable orientation of spin collectors (or spin-polarization- inducing lateral electric fields), as described below. Spin filters -- more precisely, sources of spin-polarized electron currents -- have been sought for research on, and development of, the emerging technological discipline of spintronics (spin-transport electronics). The proposed spin filters were to be based on the Rashba effect, which is an energy splitting of what would otherwise be degenerate quantum states, caused by a spinorbit interaction in conjunction with a structural-inversion asymmetry (SIA) in the presence of interfacial electric fields in a semiconductor heterostructure. The magnitude of the energy split is proportional to the electron wave number. In a spin filter, the spin-polarized currents produced by the Rashba effect would be extracted by quantum-mechanical resonant tunneling.

Microwave photonic filters with negative coefficients based on phase inversion in an electro-optic modulator.

PubMed

Capmany, José; Pastor, Daniel; Martinez, Alfonso; Ortega, Beatriz; Sales, Salvador

2003-08-15

We report on a novel technical approach to the implementation of photonic rf filters that is based on the pi phase inversion that a rf modulating signal suffers in an electro-optic Mach-Zehnder modulator, which depends on whether the positive or the negative linear slope of the signal's modulation transfer function is employed. Experimental evidence is provided of the implementation of filters with negative coefficients that shows excellent agreement with results predicted by the theory.

Joint Inversion of Borehole Strainmeter and GPS Time Series for Slip and Stress Distribution during Cascadian ETS

NASA Astrophysics Data System (ADS)

Benz, N.; Bartlow, N. M.

2017-12-01

The addition of borehole strainmeter (BSM) to cGPS time series inversions can yield more precise slip distributions at the subduction interface during episodic tremor and slip (ETS) events in the Cascadia subduction zone. Traditionally very noisy BSM data has not been easy to incorporate until recently, but developments in processing noise, re-orientation of strain components, removal of tidal, hydrologic, and atmospheric signals have made this additional source of data viable (Roeloffs, 2010). The major advantage with BSMs is their sensitivity to spatial derivatives in slip, which is valuable for investigating the ETS nucleation process and stress changes on the plate interface due to ETS. Taking advantage of this, we simultaneously invert PBO GPS and cleaned BSM time series with the Network Inversion Filter (Segall and Matthews, 1997) for slip distribution and slip rate during selected Cascadia ETS events. Stress distributions are also calculated for the plate interface using these inversion results to estimate the amount of stress change during an ETS event. These calculations are performed with and without the utilization of BSM time series, highlighting the role of BSM data in constraining slip and stress.

Source mechanism analysis of central Aceh earthquake July 2, 2013 Mw 6.2 using moment tensor inversion with BMKG waveform data

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

Prasetyo, Retno Agung, E-mail: prasetyo.agung@bmkg.go.id; Heryandoko, Nova; Afnimar

The source mechanism of earthquake on July 2, 2013 was investigated by using moment tensor inversion. The result also compared by the field observation. Five waveform data of BMKG’s seismic network used to estimate the mechanism of earthquake, namely ; KCSI, MLSI, LASI, TPTI and SNSI. Main shock data taken during 200 seconds and filtered by using Butterworth band pass method from 0.03 to 0.05 Hz of frequency. Moment tensor inversion method is applied based on the point source assumption. Furthermore, the Green function calculated using the extended reflectivity method which modified by Kohketsu. The inversion result showed a strike-slipmore » faulting, where the nodal plane strike/dip/rake (124/80.6/152.8) and minimum variance value 0.3285 at a depth of 6 km (centroid). It categorized as a shallow earthquake. Field observation indicated that the building orientated to the east. It can be related to the southwest of dip direction which has 152 degrees of slip. As conclusion, the Pressure (P) and Tension (T) axis described dominant compression is happen from the south which is caused by pressure of the Indo-Australian plate.« less

Lidar inversion of atmospheric backscatter and extinction-to-backscatter ratios by use of a Kalman filter.

PubMed

Rocadenbosch, F; Soriano, C; Comerón, A; Baldasano, J M

1999-05-20

A first inversion of the backscatter profile and extinction-to-backscatter ratio from pulsed elastic-backscatter lidar returns is treated by means of an extended Kalman filter (EKF). The EKF approach enables one to overcome the intrinsic limitations of standard straightforward nonmemory procedures such as the slope method, exponential curve fitting, and the backward inversion algorithm. Whereas those procedures are inherently not adaptable because independent inversions are performed for each return signal and neither the statistics of the signals nor a priori uncertainties (e.g., boundary calibrations) are taken into account, in the case of the Kalman filter the filter updates itself because it is weighted by the imbalance between the a priori estimates of the optical parameters (i.e., past inversions) and the new estimates based on a minimum-variance criterion, as long as there are different lidar returns. Calibration errors and initialization uncertainties can be assimilated also. The study begins with the formulation of the inversion problem and an appropriate atmospheric stochastic model. Based on extensive simulation and realistic conditions, it is shown that the EKF approach enables one to retrieve the optical parameters as time-range-dependent functions and hence to track the atmospheric evolution; the performance of this approach is limited only by the quality and availability of the a priori information and the accuracy of the atmospheric model used. The study ends with an encouraging practical inversion of a live scene measured at the Nd:YAG elastic-backscatter lidar station at our premises at the Polytechnic University of Catalonia, Barcelona.

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

NASA Technical Reports Server (NTRS)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

Robotic fish tracking method based on suboptimal interval Kalman filter

NASA Astrophysics Data System (ADS)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

Multiple estimation channel decoupling and optimization method based on inverse system

NASA Astrophysics Data System (ADS)

Wu, Peng; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

This paper addressed the intelligent autonomous navigation request of intelligent deformation missile, based on the intelligent deformation missile dynamics and kinematics modeling, navigation subsystem solution method and error modeling, and then focuses on the corresponding data fusion and decision fusion technology, decouples the sensitive channel of the filter input through the inverse system of design dynamics to reduce the influence of sudden change of the measurement information on the filter input. Then carrying out a series of simulation experiments, which verified the feasibility of the inverse system decoupling algorithm effectiveness.

In-Plane Structure of Underpotentially Deposited Copper on Gold (111) Determined by Surface EXAFS (Extended X-Ray Absorption Fine Structure).

DTIC Science & Technology

1988-01-28

EXAFS is the inverse transform of the two peaks in the RSF using a filtering a12 function to isolate the range between I and 4A. Both the frequency...backscattering of different neighbors. This inverse transform contains only one frequency and its envelope of intensity is the backscattering amplitude function...and the inverse transform of the RSF using a fourier filter between 1 and 4A (Solid line). Insert: Radial Structure Function (RSF) analyzed between

CMOS image sensor with contour enhancement

NASA Astrophysics Data System (ADS)

Meng, Liya; Lai, Xiaofeng; Chen, Kun; Yuan, Xianghui

2010-10-01

Imitating the signal acquisition and processing of vertebrate retina, a CMOS image sensor with bionic pre-processing circuit is designed. Integration of signal-process circuit on-chip can reduce the requirement of bandwidth and precision of the subsequent interface circuit, and simplify the design of the computer-vision system. This signal pre-processing circuit consists of adaptive photoreceptor, spatial filtering resistive network and Op-Amp calculation circuit. The adaptive photoreceptor unit with a dynamic range of approximately 100 dB has a good self-adaptability for the transient changes in light intensity instead of intensity level itself. Spatial low-pass filtering resistive network used to mimic the function of horizontal cell, is composed of the horizontal resistor (HRES) circuit and OTA (Operational Transconductance Amplifier) circuit. HRES circuit, imitating dendrite of the neuron cell, comprises of two series MOS transistors operated in weak inversion region. Appending two diode-connected n-channel transistors to a simple transconductance amplifier forms the OTA Op-Amp circuit, which provides stable bias voltage for the gate of MOS transistors in HRES circuit, while serves as an OTA voltage follower to provide input voltage for the network nodes. The Op-Amp calculation circuit with a simple two-stage Op-Amp achieves the image contour enhancing. By adjusting the bias voltage of the resistive network, the smoothing effect can be tuned to change the effect of image's contour enhancement. Simulations of cell circuit and 16×16 2D circuit array are implemented using CSMC 0.5μm DPTM CMOS process.

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.

An efficient implementation of a high-order filter for a cubed-sphere spectral element model

NASA Astrophysics Data System (ADS)

Kang, Hyun-Gyu; Cheong, Hyeong-Bin

2017-03-01

A parallel-scalable, isotropic, scale-selective spatial filter was developed for the cubed-sphere spectral element model on the sphere. The filter equation is a high-order elliptic (Helmholtz) equation based on the spherical Laplacian operator, which is transformed into cubed-sphere local coordinates. The Laplacian operator is discretized on the computational domain, i.e., on each cell, by the spectral element method with Gauss-Lobatto Lagrange interpolating polynomials (GLLIPs) as the orthogonal basis functions. On the global domain, the discrete filter equation yielded a linear system represented by a highly sparse matrix. The density of this matrix increases quadratically (linearly) with the order of GLLIP (order of the filter), and the linear system is solved in only O (Ng) operations, where Ng is the total number of grid points. The solution, obtained by a row reduction method, demonstrated the typical accuracy and convergence rate of the cubed-sphere spectral element method. To achieve computational efficiency on parallel computers, the linear system was treated by an inverse matrix method (a sparse matrix-vector multiplication). The density of the inverse matrix was lowered to only a few times of the original sparse matrix without degrading the accuracy of the solution. For better computational efficiency, a local-domain high-order filter was introduced: The filter equation is applied to multiple cells, and then the central cell was only used to reconstruct the filtered field. The parallel efficiency of applying the inverse matrix method to the global- and local-domain filter was evaluated by the scalability on a distributed-memory parallel computer. The scale-selective performance of the filter was demonstrated on Earth topography. The usefulness of the filter as a hyper-viscosity for the vorticity equation was also demonstrated.

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

NASA Astrophysics Data System (ADS)

Vackář, Jiří; Burjánek, Jan; Gallovič, František; Zahradník, Jiří; Clinton, John

2017-04-01

Focal mechanisms are important for understanding seismotectonics of a region, and they serve as a basic input for seismic hazard assessment. Usually, the point source approximation and the moment tensor (MT) are used. We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances and high signal-to-noise are rejected, and full-waveform inversion in a space-time grid around a provided hypocenter. The method is innovative in the following aspects: (i) The CMT inversion is fully automated, no user interaction is required, although the details of the process can be visually inspected latter on many figures which are automatically plotted.(ii) The automated process includes detection of disturbances based on MouseTrap code, so disturbed recordings do not affect inversion.(iii) A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequencies.(iv) Bayesian approach is used, so not only the best solution is obtained, but also the posterior probability density function.(v) A space-time grid search effectively combined with the least-squares inversion of moment tensor components speeds up the inversion and allows to obtain more accurate results compared to stochastic methods. The method has been tested on synthetic and observed data. It has been tested by comparison with manually processed moment tensors of all events greater than M≥3 in the Swiss catalogue over 16 years using data available at the Swiss data center (http://arclink.ethz.ch). The quality of the results of the presented automated process is comparable with careful manual processing of data. The software package programmed in Python has been designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large previously existing earthquake catalogues and data sets.

Correlation Filter Synthesis Using Neural Networks.

DTIC Science & Technology

1993-12-01

trained neural networks may be understood as "smart" data interpolators, the stored filter and the filter synthesis approaches have much in common: in...the former new filters are found by searching a data bank consisting of the filters themselves; in the latter filters are formed from a distributed... data bank that contains neural network interaction strengths or weights. 1.2 Key Results and Outputs Excellent computer simulation results were

Resonant TMR inversion in LiF/EuS based spin-filter tunnel junctions

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

Liu, Fen; Chen, Aixi; Institute for Quantum Computing & Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1 Canada

Resonant tunneling can lead to inverse tunnel magnetoresistance when impurity levels rather than direct tunneling dominate the transport process. We fabricated hybrid magnetic tunnel junctions of CoFe/LiF/EuS/Ti, with an epitaxial LiF energy barrier joined with a polycrystalline EuS spin-filter barrier. Due to the water solubility of LiF, the devices were fully packaged in situ. The devices showed sizeable positive TMR up to 16% at low bias voltages but clearly inverted TMR at higher bias voltages. The TMR inversion depends sensitively on the thickness of LiF, and the tendency of inversion disappears when LiF gets thick enough and recovers its intrinsicmore » properties.« less

Deconvolution using a neural network

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

Lehman, S.K.

1990-11-15

Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

Improving Estimates Of Phase Parameters When Amplitude Fluctuates

NASA Technical Reports Server (NTRS)

Vilnrotter, V. A.; Brown, D. H.; Hurd, W. J.

1989-01-01

Adaptive inverse filter applied to incoming signal and noise. Time-varying inverse-filtering technique developed to improve digital estimate of phase of received carrier signal. Intended for use where received signal fluctuates in amplitude as well as in phase and signal tracked by digital phase-locked loop that keeps its phase error much smaller than 1 radian. Useful in navigation systems, reception of time- and frequency-standard signals, and possibly spread-spectrum communication systems.

Design of order statistics filters using feedforward neural networks

NASA Astrophysics Data System (ADS)

Maslennikova, Yu. S.; Bochkarev, V. V.

2016-08-01

In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.

Method and system for determining induction motor speed

DOEpatents

Parlos, Alexander G.; Bharadwaj, Raj M.

2004-03-30

A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

Use of Whatman-41 filters in air quality sampling networks (with applications to elemental analysis)

NASA Technical Reports Server (NTRS)

Neustadter, H. E.; Sidik, S. M.; King, R. B.; Fordyce, J. S.; Burr, J. C.

1974-01-01

The operation of a 16-site parallel high volume air sampling network with glass fiber filters on one unit and Whatman-41 filters on the other is reported. The network data and data from several other experiments indicate that (1) Sampler-to-sampler and filter-to-filter variabilities are small; (2) hygroscopic affinity of Whatman-41 filters need not introduce errors; and (3) suspended particulate samples from glass fiber filters averaged slightly, but not statistically significantly, higher than from Whatman-41-filters. The results obtained demonstrate the practicability of Whatman-41 filters for air quality monitoring and elemental analysis.

Applications of Bayesian spectrum representation in acoustics

NASA Astrophysics Data System (ADS)

Botts, Jonathan M.

This dissertation utilizes a Bayesian inference framework to enhance the solution of inverse problems where the forward model maps to acoustic spectra. A Bayesian solution to filter design inverts a acoustic spectra to pole-zero locations of a discrete-time filter model. Spatial sound field analysis with a spherical microphone array is a data analysis problem that requires inversion of spatio-temporal spectra to directions of arrival. As with many inverse problems, a probabilistic analysis results in richer solutions than can be achieved with ad-hoc methods. In the filter design problem, the Bayesian inversion results in globally optimal coefficient estimates as well as an estimate the most concise filter capable of representing the given spectrum, within a single framework. This approach is demonstrated on synthetic spectra, head-related transfer function spectra, and measured acoustic reflection spectra. The Bayesian model-based analysis of spatial room impulse responses is presented as an analogous problem with equally rich solution. The model selection mechanism provides an estimate of the number of arrivals, which is necessary to properly infer the directions of simultaneous arrivals. Although, spectrum inversion problems are fairly ubiquitous, the scope of this dissertation has been limited to these two and derivative problems. The Bayesian approach to filter design is demonstrated on an artificial spectrum to illustrate the model comparison mechanism and then on measured head-related transfer functions to show the potential range of application. Coupled with sampling methods, the Bayesian approach is shown to outperform least-squares filter design methods commonly used in commercial software, confirming the need for a global search of the parameter space. The resulting designs are shown to be comparable to those that result from global optimization methods, but the Bayesian approach has the added advantage of a filter length estimate within the same unified framework. The application to reflection data is useful for representing frequency-dependent impedance boundaries in finite difference acoustic simulations. Furthermore, since the filter transfer function is a parametric model, it can be modified to incorporate arbitrary frequency weighting and account for the band-limited nature of measured reflection spectra. Finally, the model is modified to compensate for dispersive error in the finite difference simulation, from the filter design process. Stemming from the filter boundary problem, the implementation of pressure sources in finite difference simulation is addressed in order to assure that schemes properly converge. A class of parameterized source functions is proposed and shown to offer straightforward control of residual error in the simulation. Guided by the notion that the solution to be approximated affects the approximation error, sources are designed which reduce residual dispersive error to the size of round-off errors. The early part of a room impulse response can be characterized by a series of isolated plane waves. Measured with an array of microphones, plane waves map to a directional response of the array or spatial intensity map. Probabilistic inversion of this response results in estimates of the number and directions of image source arrivals. The model-based inversion is shown to avoid ambiguities associated with peak-finding or inspection of the spatial intensity map. For this problem, determining the number of arrivals in a given frame is critical for properly inferring the state of the sound field. This analysis is effectively compression of the spatial room response, which is useful for analysis or encoding of the spatial sound field. Parametric, model-based formulations of these problems enhance the solution in all cases, and a Bayesian interpretation provides a principled approach to model comparison and parameter estimation. v

A BASIC program for the removal of noise from reaction traces using Fourier filtering.

PubMed

Brittain, T

1989-04-01

Software for the removal of noise from reaction curves using the principle of Fourier filtering has been written in BASIC to execute on a PC. The program inputs reaction traces which are subjected to a rotation-inversion process, to produce functions suitable for Fourier analysis. Fourier transformation into the frequency domain is followed by multiplication of the transform by a rectangular filter function, to remove the noise frequencies. Inverse transformation then yields a noise-reduced reaction trace suitable for further analysis. The program is interactive at each stage and could easily be modified to remove noise from a range of input data types.

Microelectromechanical filter formed from parallel-connected lattice networks of contour-mode resonators

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

Wojciechowski, Kenneth E; Olsson, III, Roy H; Ziaei-Moayyed, Maryam

2013-07-30

A microelectromechanical (MEM) filter is disclosed which has a plurality of lattice networks formed on a substrate and electrically connected together in parallel. Each lattice network has a series resonant frequency and a shunt resonant frequency provided by one or more contour-mode resonators in the lattice network. Different types of contour-mode resonators including single input, single output resonators, differential resonators, balun resonators, and ring resonators can be used in MEM filter. The MEM filter can have a center frequency in the range of 10 MHz-10 GHz, with a filter bandwidth of up to about 1% when all of the latticemore » networks have the same series resonant frequency and the same shunt resonant frequency. The filter bandwidth can be increased up to about 5% by using unique series and shunt resonant frequencies for the lattice networks.« less

Automated Detection and Modeling of Slow Slip: Case Study of the Cascadia Subduction Zone

NASA Astrophysics Data System (ADS)

Crowell, B. W.; Bock, Y.; Liu, Z.

2012-12-01

The discovery of transient slow slip events over the past decade has changed our understanding of tectonic hazards and the earthquake cycle. Proper geodetic characterization of transient deformation is necessary for studies of regional interseismic, coseismic and postseismic tectonics, and miscalculations can affect our understanding of the regional stress field. We utilize two different methods to create a complete record of slow slip from continuous GPS stations in the Cascadia subduction zone between 1996 and 2012: spatiotemporal principal component analysis (PCA) and the relative strength index (RSI). The PCA is performed on 100 day windows of nearby stations to locate signals that exist across many stations in the network by looking at the ratio of the first two eigenvalues. The RSI is a financial momentum oscillator that looks for changes in individual time series with respect to previous epochs to locate rapid changes, indicative of transient deformation. Using both methods, we create a complete history of slow slip across the Cascadia subduction zone, fully characterizing the timing, progression, and magnitude of events. We inject the results from the automated transient detection into a time-dependent slip inversion and apply a Kalman filter based network inversion method to image the spatiotemporal variation of slip transients along the Cascadia margin.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

An Adaptive Model of Student Performance Using Inverse Bayes

ERIC Educational Resources Information Center

Lang, Charles

2014-01-01

This article proposes a coherent framework for the use of Inverse Bayesian estimation to summarize and make predictions about student behaviour in adaptive educational settings. The Inverse Bayes Filter utilizes Bayes theorem to estimate the relative impact of contextual factors and internal student factors on student performance using time series…

Comparisons of Spectral Aerosol Single Scattering Albedo in Seoul, South Korea

NASA Technical Reports Server (NTRS)

Mok, Jungbin; Krotkov, Nickolay A.; Torres, Omar; Jethva, Hiren; Loughman, Robert P.; Spinei, Elena; Campanelli, Monica; Li, Zhanqing; Go, Sujung; Labow, Gordon;

Imaging Faults in Carbonate Reservoir using Full Waveform Inversion and Reverse Time Migration of Walkaway VSP Data

NASA Astrophysics Data System (ADS)

Takam Takougang, E. M.; Bouzidi, Y.

2016-12-01

Multi-offset Vertical Seismic Profile (walkaway VSP) data were collected in an oil field located in a shallow water environment dominated by carbonate rocks, offshore the United Arab Emirates. The purpose of the survey was to provide structural information of the reservoir, around and away from the borehole. Five parallel lines were collected using an air gun at 25 m shot interval and 4 m source depth. A typical recording tool with 20 receivers spaced every 15.1 m, and located in a deviated borehole with an angle varying between 0 and 24 degree from the vertical direction, was used to record the data. The recording tool was deployed at different depths for each line, from 521 m to 2742 m depth. Smaller offsets were used for shallow receivers and larger offsets for deeper receivers. The lines merged to form the input dataset for waveform tomography. The total length of the combined lines was 9 km, containing 1344 shots and 100 receivers in the borehole located half-way down. Acoustic full waveform inversion was applied in the frequency domain to derive a high resolution velocity model. The final velocity model derived after the inversion using the frequencies 5-40 Hz, showed good correlation with velocities estimated from vertical incidence VSP and sonic log, confirming the success of the inversion. The velocity model showed anomalous low values in areas that correlate with known location of hydrocarbon reservoir. Pre-stack depth Reverse time migration was then applied using the final velocity model from waveform inversion and the up-going wavefield from the input data. The final estimated source signature from waveform inversion was used as input source for reverse time migration. To save computational memory and time, every 3 shots were used during reverse time migration and the data were low-pass filtered to 30 Hz. Migration artifacts were attenuated using a second order derivative filter. The final migration image shows a good correlation with the waveform tomography velocity model, and highlights a complex network of faults in the reservoir, that could be useful in understanding fluid and hydrocarbon movements. This study shows that the combination of full waveform tomography and reverse time migration can provide high resolution images that can enhance interpretation and characterization of oil reservoirs.

Nonlinear adaptive inverse control via the unified model neural network

NASA Astrophysics Data System (ADS)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Resilient filtering for time-varying stochastic coupling networks under the event-triggering scheduling

NASA Astrophysics Data System (ADS)

Wang, Fan; Liang, Jinling; Dobaie, Abdullah M.

2018-07-01

The resilient filtering problem is considered for a class of time-varying networks with stochastic coupling strengths. An event-triggered strategy is adopted to save the network resources by scheduling the signal transmission from the sensors to the filters based on certain prescribed rules. Moreover, the filter parameters to be designed are subject to gain perturbations. The primary aim of the addressed problem is to determine a resilient filter that ensures an acceptable filtering performance for the considered network with event-triggering scheduling. To handle such an issue, an upper bound on the estimation error variance is established for each node according to the stochastic analysis. Subsequently, the resilient filter is designed by locally minimizing the derived upper bound at each iteration. Moreover, rigorous analysis shows the monotonicity of the minimal upper bound regarding the triggering threshold. Finally, a simulation example is presented to show effectiveness of the established filter scheme.

JPRS Report Science & Technology Japan 16th International Congress of the International Society for Photogrammetry and Remote Sensing Volume 1

DTIC Science & Technology

1989-01-24

coherent noise . To overcome this disadvantages, a new holographic inverse filtering system has been developed by the authors. The inverse filter is...beam is blocked. The deblurred aerial image is formed in the image plane IP (the back focal plane of L2). The frequency of the grating used in this... impulse response of the optical system. For certain types of blurs, which include linear motion of the camera under the assumption that the picture

Recursive inverse kinematics for robot arms via Kalman filtering and Bryson-Frazier smoothing

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1987-01-01

This paper applies linear filtering and smoothing theory to solve recursively the inverse kinematics problem for serial multilink manipulators. This problem is to find a set of joint angles that achieve a prescribed tip position and/or orientation. A widely applicable numerical search solution is presented. The approach finds the minimum of a generalized distance between the desired and the actual manipulator tip position and/or orientation. Both a first-order steepest-descent gradient search and a second-order Newton-Raphson search are developed. The optimal relaxation factor required for the steepest descent method is computed recursively using an outward/inward procedure similar to those used typically for recursive inverse dynamics calculations. The second-order search requires evaluation of a gradient and an approximate Hessian. A Gauss-Markov approach is used to approximate the Hessian matrix in terms of products of first-order derivatives. This matrix is inverted recursively using a two-stage process of inward Kalman filtering followed by outward smoothing. This two-stage process is analogous to that recently developed by the author to solve by means of spatial filtering and smoothing the forward dynamics problem for serial manipulators.

Comparison of weighting techniques for acoustic full waveform inversion

NASA Astrophysics Data System (ADS)

Jeong, Gangwon; Hwang, Jongha; Min, Dong-Joo

2017-12-01

To reconstruct long-wavelength structures in full waveform inversion (FWI), the wavefield-damping and weighting techniques have been used to synthesize and emphasize low-frequency data components in frequency-domain FWI. However, these methods have some weak points. The application of wavefield-damping method on filtered data fails to synthesize reliable low-frequency data; the optimization formula obtained introducing the weighting technique is not theoretically complete, because it is not directly derived from the objective function. In this study, we address these weak points and present how to overcome them. We demonstrate that the source estimation in FWI using damped wavefields fails when the data used in the FWI process does not satisfy the causality condition. This phenomenon occurs when a non-causal filter is applied to data. We overcome this limitation by designing a causal filter. Also we modify the conventional weighting technique so that its optimization formula is directly derived from the objective function, retaining its original characteristic of emphasizing the low-frequency data components. Numerical results show that the newly designed causal filter enables to recover long-wavelength structures using low-frequency data components synthesized by damping wavefields in frequency-domain FWI, and the proposed weighting technique enhances the inversion results.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

NASA Astrophysics Data System (ADS)

Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

2016-06-01

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Flatness-based model inverse for feed-forward braking control

NASA Astrophysics Data System (ADS)

de Vries, Edwin; Fehn, Achim; Rixen, Daniel

2010-12-01

For modern cars an increasing number of driver assistance systems have been developed. Some of these systems interfere/assist with the braking of a car. Here, a brake actuation algorithm for each individual wheel that can respond to both driver inputs and artificial vehicle deceleration set points is developed. The algorithm consists of a feed-forward control that ensures, within the modelled system plant, the optimal behaviour of the vehicle. For the quarter-car model with LuGre-tyre behavioural model, an inverse model can be derived using v x as the 'flat output', that is, the input for the inverse model. A number of time derivatives of the flat output are required to calculate the model input, brake torque. Polynomial trajectory planning provides the needed time derivatives of the deceleration request. The transition time of the planning can be adjusted to meet actuator constraints. It is shown that the output of the trajectory planning would ripple and introduce a time delay when a gradual continuous increase of deceleration is requested by the driver. Derivative filters are then considered: the Bessel filter provides the best symmetry in its step response. A filter of same order and with negative real-poles is also used, exhibiting no overshoot nor ringing. For these reasons, the 'real-poles' filter would be preferred over the Bessel filter. The half-car model can be used to predict the change in normal load on the front and rear axle due to the pitching of the vehicle. The anticipated dynamic variation of the wheel load can be included in the inverse model, even though it is based on a quarter-car. Brake force distribution proportional to normal load is established. It provides more natural and simpler equations than a fixed force ratio strategy.

Verification of the helioseismology travel-time measurement technique and the inversion procedure for sound speed using artificial data

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

Parchevsky, K. V.; Zhao, J.; Hartlep, T.

We performed three-dimensional numerical simulations of the solar surface acoustic wave field for the quiet Sun and for three models with different localized sound-speed perturbations in the interior with deep, shallow, and two-layer structures. We used the simulated data generated by two solar acoustics codes that employ the same standard solar model as a background model, but utilize different integration techniques and different models of stochastic wave excitation. Acoustic travel times were measured using a time-distance helioseismology technique, and compared with predictions from ray theory frequently used for helioseismic travel-time inversions. It is found that the measured travel-time shifts agreemore » well with the helioseismic theory for sound-speed perturbations, and for the measurement procedure with and without phase-speed filtering of the oscillation signals. This testing verifies the whole measuring-filtering-inversion procedure for static sound-speed anomalies with small amplitude inside the Sun outside regions of strong magnetic field. It is shown that the phase-speed filtering, frequently used to extract specific wave packets and improve the signal-to-noise ratio, does not introduce significant systematic errors. Results of the sound-speed inversion procedure show good agreement with the perturbation models in all cases. Due to its smoothing nature, the inversion procedure may overestimate sound-speed variations in regions with sharp gradients of the sound-speed profile.« less

Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms

DTIC Science & Technology

2004-08-01

inverse transform process. 2. BACKGROUND The image processing research conducted at the AFRL/IFTA Reconfigurable Computing Laboratory has been...coefficients from the wavelet domain back into the original signal domain. In other words, the inverse transform produces the original signal x(t) from the...coefficients for an inverse wavelet transform, such that the MSE of images reconstructed by this inverse transform is significantly less than the mean squared

Three-dimensional inversion for Network-Magnetotelluric data

NASA Astrophysics Data System (ADS)

Siripunvaraporn, W.; Uyeshima, M.; Egbert, G.

2004-09-01

Three-dimensional inversion of Network-Magnetotelluric (MT) data has been implemented. The program is based on a conventional 3-D MT inversion code (Siripunvaraporn et al., 2004), which is a data space variant of the OCCAM approach. In addition to modifications required for computing Network-MT responses and sensitivities, the program makes use of Massage Passing Interface (MPI) software, with allowing computations for each period to be run on separate CPU nodes. Here, we consider inversion of synthetic data generated from simple models consisting of a 1 W-m conductive block buried at varying depths in a 100 W-m background. We focus in particular on inversion of long period (320-40,960 seconds) data, because Network-MT data usually have high coherency in these period ranges. Even with only long period data the inversion recovers shallow and deep structures, as long as these are large enough to affect the data significantly. However, resolution of the inversion depends greatly on the geometry of the dipole network, the range of periods used, and the horizontal size of the conductive anomaly.

Inverse kinematics problem in robotics using neural networks

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.; Lawrence, Charles

1992-01-01

In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.

Optical restoration of images blurred by atmospheric turbulence using optimum filter theory.

PubMed

Horner, J L

1970-01-01

The results of optimum filtering from communications theory have been applied to an image restoration problem. Photographic film imagery, degraded by long-term artificial atmospheric turbulence, has been restored by spatial filters placed in the Fourier transform plane. The time-averaged point spread function was measured and used in designing the filters. Both the simple inverse filter and the optimum least-mean-square filters were used in the restoration experiments. The superiority of the latter is conclusively demonstrated. An optical analog processor was used for the restoration.

NUCLEAR RADIATION DOSIMETER USING COMPOSITE FILTER AND A SINGLE ELEMENT FILTER

DOEpatents

Storm, E.; Shlaer, S.

1964-04-21

A nuclear radiation dosimeter is described that uses, in combination, a composite filter and a single element filter. The composite filter contains a plurality of comminuted metals having K-edges evenly distributed over the energy range of interest and the quantity of each of the metals is selected to result in filtering in an amount inversely proportional to the sensitivity of the film in the range over l00 kev. A copper filter is used that has a thickness to contribute the necessary additional correction in the interval between 40 and 100 kev. (AEC)

Potential of European 14CO2 observation network to estimate the fossil fuel CO2 emissions via atmospheric inversions

NASA Astrophysics Data System (ADS)

Wang, Yilong; Broquet, Grégoire; Ciais, Philippe; Chevallier, Frédéric; Vogel, Felix; Wu, Lin; Yin, Yi; Wang, Rong; Tao, Shu

2018-03-01

Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75° × 2.5° resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as posterior uncertainty, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called prior uncertainty). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic true emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and true estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

NASA Astrophysics Data System (ADS)

Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng

2018-05-01

This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.

Effectiveness of compressed sensing and transmission in wireless sensor networks for structural health monitoring

NASA Astrophysics Data System (ADS)

Fujiwara, Takahiro; Uchiito, Haruki; Tokairin, Tomoya; Kawai, Hiroyuki

2017-04-01

Regarding Structural Health Monitoring (SHM) for seismic acceleration, Wireless Sensor Networks (WSN) is a promising tool for low-cost monitoring. Compressed sensing and transmission schemes have been drawing attention to achieve effective data collection in WSN. Especially, SHM systems installing massive nodes of WSN require efficient data transmission due to restricted communications capability. The dominant frequency band of seismic acceleration is occupied within 100 Hz or less. In addition, the response motions on upper floors of a structure are activated at a natural frequency, resulting in induced shaking at the specified narrow band. Focusing on the vibration characteristics of structures, we introduce data compression techniques for seismic acceleration monitoring in order to reduce the amount of transmission data. We carry out a compressed sensing and transmission scheme by band pass filtering for seismic acceleration data. The algorithm executes the discrete Fourier transform for the frequency domain and band path filtering for the compressed transmission. Assuming that the compressed data is transmitted through computer networks, restoration of the data is performed by the inverse Fourier transform in the receiving node. This paper discusses the evaluation of the compressed sensing for seismic acceleration by way of an average error. The results present the average error was 0.06 or less for the horizontal acceleration, in conditions where the acceleration was compressed into 1/32. Especially, the average error on the 4th floor achieved a small error of 0.02. Those results indicate that compressed sensing and transmission technique is effective to reduce the amount of data with maintaining the small average error.

Deciphering the role of fluids in early stage rifting from full moment tensor inversion of East African earthquakes

NASA Astrophysics Data System (ADS)

Oliva, S. J. C.; Ebinger, C. J.; Keir, D.; Shillington, D. J.; Chindandali, P. R. N.

2016-12-01

The East African Rift splits around the Archaean Tanzania craton into the magmatic Eastern branch and the mostly amagmatic Western branch, which continues south of the craton. Temporary seismic networks recently deployed in three rift sectors allow for comparison and insights into the early stages of rifting, including areas with lower crustal earthquakes. We analyze earthquakes with ML > 3.5 in the area recorded by CRAFTI (northern Tanzania/Kenya), TANGA (Tanganyika rift), and/or SEGMeNT (Malawi rift) networks. For events not well enclosed by these arrays, nearby permanent stations are used to improve azimuthal coverage when possible. We present source mechanisms as well as better-constrained source depth estimates from moment tensor inversion using Dreger and Ford TDMT algorithm (Dreger, 2003; Minson & Dreger, 2008). Data and synthetic waveforms are bandpass filtered between 0.02 to 0.10 Hz, or a narrower frequency band within this range, depending on lake noise, which can interfere strongly on the lower end of this frequency range. Results suggest local stress reorientations as well as significant dilatation components on some events within magmatic rift sectors. The implications of these results for crustal rheology and magmatic modification will be discussed in light of the growing complementary data sets from the three projects to inform our understanding of early rifting as a whole.

A Novel Modulation Classification Approach Using Gabor Filter Network

PubMed Central

Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

2014-01-01

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

Large-scale shear velocity structure of the upper mantle beneath Africa and surrounding regions

NASA Astrophysics Data System (ADS)

Legendre, Cédric; Meier, Thomas; Lebedev, Sergei; Friederich, Wolfgang; Egelados Working Group

2010-05-01

The automated multimode waveform inversion technique developed by Lebedev et al. (2005) was applied to available data of broadband stations in Africa and surrounding regions. It performs a fitting of the complete waveform starting from the S-wave onset to the surface wave. Assuming the location and focal mechanism of a considered earthquake as known, the first basic step is to consider each available seismogram separately and to find the velocity perturbations that can explain the filtered seismogram best. In a second step, each velocity perturbations serves as a linear constraint in an inversion for a 3D S-wave velocity model of the upper mantle. We collected data for the years from 1990 to 2006 from all permanent stations for which data were available via the data centers of ORFEUS, GEOFON and IRIS, and from others that build the Virtual European Seismological Network (VEBSN) as well as all available African stations. Just recently we were also able to add the data recorded by the temporary broadband EGELADOS network in the southern Aegean. This represents a huge dataset with all available stations in Africa and surroundings regions. The resulting models exhibit an overwhelming structural detail in relation to the size of the region considered in the inversion. They are to our knowledge the most detailed models of shear wave velocity currently available for the African upper mantle and surroundings. Most prominent features are an extremely sharp demarcation of the Dead Sea Rift System. Narrow high velocity regions follow the Hellenic arc and the Ionian trench toward the north. Low velocity zones are found at depths around 150 km in the Middle East region. The hotspots in North Africa are also clearly imaged.

High Efficiency, Transparent, Reusable, and Active PM2.5 Filters by Hierarchical Ag Nanowire Percolation Network.

PubMed

Jeong, Seongmin; Cho, Hyunmin; Han, Seonggeun; Won, Phillip; Lee, Habeom; Hong, Sukjoon; Yeo, Junyeob; Kwon, Jinhyeong; Ko, Seung Hwan

2017-07-12

Air quality has become a major public health issue in Asia including China, Korea, and India. Particulate matters are the major concern in air quality. We present the first environmental application demonstration of Ag nanowire percolation network for a novel, electrical type transparent, reusable, and active PM2.5 air filter although the Ag nanowire percolation network has been studied as a very promising transparent conductor in optoelectronics. Compared with previous particulate matter air filter study using relatively weaker short-range intermolecular force in polar polymeric nanofiber, Ag nanowire percolation network filters use stronger long-range electrostatic force to capture PM2.5, and they are highly efficient (>99.99%), transparent, working on an active mode, low power consumption, antibacterial, and reusable after simple washing. The proposed new particulate matter filter can be applied for a highly efficient, reusable, active and energy efficient filter for wearable electronics application.

Inversion of surface parameters using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Olvera, J.; Fung, A. K.; Manry, M. T.

1992-01-01

A neural network approach to the inversion of surface scattering parameters is presented. Simulated data sets based on a surface scattering model are used so that the data may be viewed as taken from a completely known randomly rough surface. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) are tested on the simulated backscattering data. The RMS error of training the FL network is found to be less than one half the error of the BP network while requiring one to two orders of magnitude less CPU time. When applied to inversion of parameters from a statistically rough surface, the FL method is successful at recovering the surface permittivity, the surface correlation length, and the RMS surface height in less time and with less error than the BP network. Further applications of the FL neural network to the inversion of parameters from backscatter measurements of an inhomogeneous layer above a half space are shown.

United States Air Force Summer Faculty Research Program (1983). Technical Report. Volume 2

DTIC Science & Technology

1983-12-01

filters are given below: (1) Inverse filter - Based on the model given in Eq. (2) and the criterion of minimizing the norm (i.e., power ) of the...and compared based on their performances In machine classification under a variety of blur and noise conditions. These filters are analyzed to...criteria based on various assumptions of the Image models* In practice filter performance varies with the type of image, the blur and the noise conditions

Least squares restoration of multi-channel images

NASA Technical Reports Server (NTRS)

Chin, Roland T.; Galatsanos, Nikolas P.

1989-01-01

In this paper, a least squares filter for the restoration of multichannel imagery is presented. The restoration filter is based on a linear, space-invariant imaging model and makes use of an iterative matrix inversion algorithm. The restoration utilizes both within-channel (spatial) and cross-channel information as constraints. Experiments using color images (three-channel imagery with red, green, and blue components) were performed to evaluate the filter's performance and to compare it with other monochrome and multichannel filters.

Fetal head detection and measurement in ultrasound images by a direct inverse randomized Hough transform

NASA Astrophysics Data System (ADS)

Lu, Wei; Tan, Jinglu; Floyd, Randall C.

2005-04-01

Object detection in ultrasound fetal images is a challenging task for the relatively low resolution and low signal-to-noise ratio. A direct inverse randomized Hough transform (DIRHT) is developed for filtering and detecting incomplete curves in images with strong noise. The DIRHT combines the advantages of both the inverse and the randomized Hough transforms. In the reverse image, curves are highlighted while a large number of unrelated pixels are removed, demonstrating a "curve-pass filtering" effect. Curves are detected by iteratively applying the DIRHT to the filtered image. The DIRHT was applied to head detection and measurement of the biparietal diameter (BPD) and head circumference (HC). No user input or geometric properties of the head were required for the detection. The detection and measurement took 2 seconds for each image on a PC. The inter-run variations and the differences between the automatic measurements and sonographers" manual measurements were small compared with published inter-observer variations. The results demonstrated that the automatic measurements were consistent and accurate. This method provides a valuable tool for fetal examinations.

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

PubMed Central

Bashir, Ali Kashif; Lim, Se-Jung; Hussain, Chauhdary Sajjad; Park, Myong-Soon

2011-01-01

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes. PMID:22163999

Removing tidal-period variations from time-series data using low-pass digital filters

USGS Publications Warehouse

Walters, Roy A.; Heston, Cynthia

1982-01-01

Several low-pass, digital filters are examined for their ability to remove tidal Period Variations from a time-series of water surface elevation for San Francisco Bay. The most efficient filter is the one which is applied to the Fourier coefficients of the transformed data, and the filtered data recovered through an inverse transform. The ability of the filters to remove the tidal components increased in the following order: 1) cosine-Lanczos filter, 2) cosine-Lanczos squared filter; 3) Godin filter; and 4) a transform fitter. The Godin fitter is not sufficiently sharp to prevent severe attenuation of 2–3 day variations in surface elevation resulting from weather events.

Real-time digital filtering, event triggering, and tomographic reconstruction of JET soft x-ray data (abstract)

NASA Astrophysics Data System (ADS)

Edwards, A. W.; Blackler, K.; Gill, R. D.; van der Goot, E.; Holm, J.

1990-10-01

Based upon the experience gained with the present soft x-ray data acquisition system, new techniques are being developed which make extensive use of digital signal processors (DSPs). Digital filters make 13 further frequencies available in real time from the input sampling frequency of 200 kHz. In parallel, various algorithms running on further DSPs generate triggers in response to a range of events in the plasma. The sawtooth crash can be detected, for example, with a delay of only 50 μs from the onset of the collapse. The trigger processor interacts with the digital filter boards to ensure data of the appropriate frequency is recorded throughout a plasma discharge. An independent link is used to pass 780 and 24 Hz filtered data to a network of transputers. A full tomographic inversion and display of the 24 Hz data is carried out in real time using this 15 transputer array. The 780 Hz data are stored for immediate detailed playback following the pulse. Such a system could considerably improve the quality of present plasma diagnostic data which is, in general, sampled at one fixed frequency throughout a discharge. Further, it should provide valuable information towards designing diagnostic data acquisition systems for future long pulse operation machines when a high degree of real-time processing will be required, while retaining the ability to detect, record, and analyze events of interest within such long plasma discharges.

Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms

DTIC Science & Technology

2004-08-06

wavelet transforms. Whereas the term “evolved” pertains only to the altered wavelet coefficients used during the inverse transform process. 2...words, the inverse transform produces the original signal x(t) from the wavelet and scaling coefficients. )()( ,, tdtx nk n nk k ψ...reconstruct the original signal as accurately as possible. The inverse transform reconstructs an approximation of the original signal (Burrus

Recursive inversion of externally defined linear systems by FIR filters

NASA Technical Reports Server (NTRS)

Bach, Ralph E., Jr.; Baram, Yoram

1989-01-01

The approximate inversion of an internally unknown linear system, given by its impulse response sequence, by an inverse system having a finite impulse response, is considered. The recursive least-squares procedure is shown to have an exact initialization, based on the triangular Toeplitz structure of the matrix involved. The proposed approach also suggests solutions to the problem of system identification and compensation.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recurrent Neural Network for Computing the Drazin Inverse.

PubMed

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

GARCH modelling of covariance in dynamical estimation of inverse solutions

NASA Astrophysics Data System (ADS)

Galka, Andreas; Yamashita, Okito; Ozaki, Tohru

2004-12-01

The problem of estimating unobserved states of spatially extended dynamical systems poses an inverse problem, which can be solved approximately by a recently developed variant of Kalman filtering; in order to provide the model of the dynamics with more flexibility with respect to space and time, we suggest to combine the concept of GARCH modelling of covariance, well known in econometrics, with Kalman filtering. We formulate this algorithm for spatiotemporal systems governed by stochastic diffusion equations and demonstrate its feasibility by presenting a numerical simulation designed to imitate the situation of the generation of electroencephalographic recordings by the human cortex.

Mini-batch optimized full waveform inversion with geological constrained gradient filtering

NASA Astrophysics Data System (ADS)

Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai

2018-05-01

High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.

Filters | CTIO

Science.gov Websites

Visitor's Computer Guidelines Network Connection Request Instruments Instruments by Telescope IR Instruments MOSAIC Filters Hydra Filters IR Filters ANDICAM Filters Y4KCam filters CTIO Various Filters Filters for 5.75X5.75-inch Filters MOSAIC Filters Hydra Filters IR Filters ANDICAM Filters Y4KCam filters CTIO Various

Optical determination of material abundances by using neural networks for the derivation of spectral filters

NASA Astrophysics Data System (ADS)

Krippner, Wolfgang; Wagner, Felix; Bauer, Sebastian; Puente León, Fernando

2017-06-01

Using appropriately designed spectral filters allows to optically determine material abundances. While an infinite number of possibilities exist for determining spectral filters, we take advantage of using neural networks to derive spectral filters leading to precise estimations. To overcome some drawbacks that regularly influence the determination of material abundances using hyperspectral data, we incorporate the spectral variability of the raw materials into the training of the considered neural networks. As a main result, we successfully classify quantized material abundances optically. Thus, the main part of the high computational load, which belongs to the use of neural networks, is avoided. In addition, the derived material abundances become invariant against spatially varying illumination intensity as a remarkable benefit in comparison with spectral filters based on the Moore-Penrose pseudoinverse, for instance.

WFC3/UVIS Updated 2017 Chip-Dependent Inverse Sensitivity Values

NASA Astrophysics Data System (ADS)

Deustua, S. E.; Mack, J.; Bajaj, V.; Khandrika, H.

2017-06-01

We present chip-dependent inverse sensitivity values recomputed for the 42 full frame filters based on the analysis of standard star observations with the WFC3/UVIS imager obtained between 2009 and 2015. Chip-dependent inverse sensitivities reported in the image header are now for the 'infinite' aperture, which is defined to have a radius of 6 arcseconds (151 pixels), and supercede the 2016 photometry header keyword values (PHOTFLAM, PHTFLAM1, PHTFLAM2), which correspond to a 0.3962 arcsecond (10 pixel) aperture. These new values are implemented in the June 2017 IMPHTTAB delivery and are concordant with the current synthetic photometry tables in the reference file database (CRDS). Since approximately 90% of the light is enclosed within 10 pixels, the new keyword values are 10% smaller. We also compute inverse sensitivities for an aperture with radius of 0.3962 arcseconds. Compared to the 2016 implementation, these new inverse sensitivity values differ by less than 0.5%, on average, for the same aperture. Values for the filters F200LP, F350LP, F600LP and F487N changed by more than 1% for UVIS1. UVIS2 values that changed by more than 1% are for the filters F350LP, F600LP, F850LP, F487N, and F814W. The 2017 VEGAmag zeropoint values in the UV change by up to 0.1 mag compared to 2016 and are calculated using the CALPSEC STIS spectrum for Vega. In 2016, the zeropoints were calculated with the CALSPEC Vega model.

Identifing Atmospheric Pollutant Sources Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Paes, F. F.; Campos, H. F.; Luz, E. P.; Carvalho, A. R.

2008-05-01

The estimation of the area source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion. In the inverse analysis, an area source domain is considered, where the strength of such area source term is assumed unknown. The inverse problem is solved by using a supervised artificial neural network: multi-layer perceptron. The conection weights of the neural network are computed from delta rule - learning process. The neural network inversion is compared with results from standard inverse analysis (regularized inverse solution). In the regularization method, the inverse problem is formulated as a non-linear optimization approach, whose the objective function is given by the square difference between the measured pollutant concentration and the mathematical models, associated with a regularization operator. In our numerical experiments, the forward problem is addressed by a source-receptor scheme, where a regressive Lagrangian model is applied to compute the transition matrix. The second order maximum entropy regularization is used, and the regularization parameter is calculated by the L-curve technique. The objective function is minimized employing a deterministic scheme (a quasi-Newton algorithm) [1] and a stochastic technique (PSO: particle swarm optimization) [2]. The inverse problem methodology is tested with synthetic observational data, from six measurement points in the physical domain. The best inverse solutions were obtained with neural networks. References: [1] D. R. Roberti, D. Anfossi, H. F. Campos Velho, G. A. Degrazia (2005): Estimating Emission Rate and Pollutant Source Location, Ciencia e Natura, p. 131-134. [2] E.F.P. da Luz, H.F. de Campos Velho, J.C. Becceneri, D.R. Roberti (2007): Estimating Atmospheric Area Source Strength Through Particle Swarm Optimization. Inverse Problems, Desing and Optimization Symposium IPDO-2007, April 16-18, Miami (FL), USA, vol 1, p. 354-359.

A key heterogeneous structure of fractal networks based on inverse renormalization scheme

NASA Astrophysics Data System (ADS)

Bai, Yanan; Huang, Ning; Sun, Lina

2018-06-01

Self-similarity property of complex networks was found by the application of renormalization group theory. Based on this theory, network topologies can be classified into universality classes in the space of configurations. In return, through inverse renormalization scheme, a given primitive structure can grow into a pure fractal network, then adding different types of shortcuts, it exhibits different characteristics of complex networks. However, the effect of primitive structure on networks structural property has received less attention. In this paper, we introduce a degree variance index to measure the dispersion of nodes degree in the primitive structure, and investigate the effect of the primitive structure on network structural property quantified by network efficiency. Numerical simulations and theoretical analysis show a primitive structure is a key heterogeneous structure of generated networks based on inverse renormalization scheme, whether or not adding shortcuts, and the network efficiency is positively correlated with degree variance of the primitive structure.

3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Huang, Jingyi; McBratney, Alex B.; Minasny, Budiman; Triantafilis, John

2017-06-01

Mapping and immediate forecasting of soil water content (θ) and its movement can be challenging. Although inversion of apparent electrical conductivity (ECa) measured by electromagnetic induction to calculate depth-specific electrical conductivity (σ) has been used, it is difficult to apply it across a field. In this paper we use a calibration established along a transect, across a 3.94-ha field with varying soil texture, using an ensemble Kalman filter (EnKF) to monitor and nowcast the 3-dimensional θ dynamics on 16 separate days over a period of 38 days. The EnKF combined a physical model fitted with θ measured by soil moisture sensors and an Artificial Neural Network model comprising σ generated by quasi-3d inversions of DUALEM-421S ECa data. Results showed that the distribution of θ was controlled by soil texture, topography, and vegetation. Soil water dried fastest at the beginning after the initial irrigation event and decreased with time and soil depth, which was consistent with classical soil drying theory and experiments. It was also found that the soil dried fastest in the loamy and duplex soils present in the field, which was attributable to deep drainage and preferential flow. It was concluded that the EnKF approach can be used to improve the irrigation efficiency by applying variable irrigation rates across the field. In addition, soil water status can be nowcasted across large spatial extents using this method with weather forecast information, which will provide guidance to farmers for real-time irrigation management.

Global surface mass time variations by using a two-step inversion for cumulating daily satellite gravity information

NASA Astrophysics Data System (ADS)

Ramillien, Guillaume; Frappart, Frappart; Seoane, Lucia

2015-04-01

We propose a new method to produce time series of global maps of surface mass variations by progressive integration of daily geopotential variations measured by orbiting satellites. In the case of the GRACE mission (2002 - 2012), these geopotential variations can be determined from very accurate inter-satellite K-Band Range Rate (KBRR) measurements of 5-second daily orbits. In particular, the along-track gravity contribution of hydrology is extracted by removing de-aliasing models for static field, atmosphere, oceans mass variations (including periodical tides), as well as polar movements. Our determination of surface mass sources consists of two successive dependent Kalman filter stages. The first one consists of reducing the satellite-based potential anomalies by adjusting the longest spatial wavelengths (i.e., low-degree spherical harmonics less than 5-6). In the second stage, the residual potential anomalies from the previous stage are used to recover surface mass density changes - in terms of Equivalent-Water Height (EWH) - over a global network of juxtaposed triangular elements. These surface tiles of ~40,000 km x km are imposed to be identical and homogeneously-distributed over the terrestrial sphere, however they can be adapted to the local geometry of the surface mass. Our global approach was tested by inverting simulated hydrology-related geopotential data, and successfully applied to estimate time-varying surface mass densities from real GRACE-based residuals. This strategy of combined Kalman filter-type inversions can also be useful for exploring the possibility of reaching better time and space resolutions for hydrology, that would be hopefully brought by future low altitude geodetic missions.

Developing a Method for Resolving NOx Emission Inventory Biases Using Discrete Kalman Filter Inversion, Direct Sensitivities, and Satellite-Based Columns

EPA Science Inventory

An inverse method was developed to integrate satellite observations of atmospheric pollutant column concentrations and direct sensitivities predicted by a regional air quality model in order to discern biases in the emissions of the pollutant precursors.

CONSISTENT USE OF THE KALMAN FILTER IN CHEMICAL TRANSPORT MODELS (CTMS) FOR DEDUCING EMISSIONS

EPA Science Inventory

Past research has shown that emissions can be deduced using observed concentrations of a chemical, a Chemical Transport Model (CTM), and the Kalman filter in an inverse modeling application. An expression was derived for the relationship between the "observable" (i.e., the con...

An Active Filter Primer, Mod 1.

DTIC Science & Technology

1983-02-01

D-Element Phase Shifting Super-Capacitor Delay Network Frequency Domain Filtering 20...Response A-44 O-OF-3 Notch Response A-45 1-Pole 1-Zero All-Pass Response A-46 2-Pole 2-Zero 90°-Phase-Difference Network Response. . . . A-47 A-13...Delagrange, "A Useful Filter Family," NSWC WOL TR 75-170. 7. E. A. Guillemin, Synthesis of Passive Networks , Wiley, 1957. 8. Harry Y-F. Lam, Analog and

Adaptive Filtering Using Recurrent Neural Networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Rayleigh wave group-velocity across the Dominican Republic and Puerto Rico from ambient noise tomography

NASA Astrophysics Data System (ADS)

Quiros, D.; Pulliam, J.; Polanco Rivera, E.; Huerfano Moreno, V. A.

2017-12-01

The eastern North America-Caribbean (NA-CAR) plate boundary near the islands of Hispaniola (which is comprised of the Dominican Republic and Haiti) and Puerto Rico is a complex transition zone in which strain is accommodated by two transform fault systems and oblique subduction. In 2013, scientists from Baylor University, the Autonomous University of Santo Domingo, and the Puerto Rico Seismic Network deployed 16 broadband stations on the Dominican Republic to expand the local permanent network. The goal of the Greater Antilles Seismic Program (GrASP) is to combine its data with that from permanent networks in Puerto Rico, Haiti, Cuba, the Cayman Islands, and Jamaica to develop a better understanding of the crust and upper mantle structure in the Northeastern Caribbean (Greater Antilles). One important goal of GrASP is to develop robust velocity models that can be used to improve earthquake location and seismic hazard efforts. In this study, we focus on obtaining Rayleigh wave group velocity maps from ambient noise tomography. By cross-correlating ambient seismic noise recorded at 53 stations between 2010 to present, we obtain Green's functions between 1165 pairs of stations. From these, we obtain dispersion curves by the application of FTAN methods with phase-matched filtering. Selection criteria depend on the signal-to-noise ratio and seasonal variability, with further filtering done by rejecting velocities incompatible with maps produced from overdamped tomographic inversions. Preliminary dispersion maps show strong correlations with large-scale geological and tectonic features for periods between 5 - 20 s, such as the Cordillera Central in both the Dominican Republic and Puerto Rico, the Mona Passage, and the NA-CAR subduction zone. Ongoing efforts focus on including shorter periods in Puerto Rico as its denser station distribution could allow us to retrieve higher resolution group velocity maps.

Direct determination of geocenter motion by combining SLR, VLBI, GNSS, and DORIS time series

NASA Astrophysics Data System (ADS)

Wu, X.; Abbondanza, C.; Altamimi, Z.; Chin, T. M.; Collilieux, X.; Gross, R. S.; Heflin, M. B.; Jiang, Y.; Parker, J. W.

2013-12-01

The longest-wavelength surface mass transport includes three degree-one spherical harmonic components involving hemispherical mass exchanges. The mass load causes geocenter motion between the center-of-mass of the total Earth system (CM) and the center-of-figure of the solid Earth surface (CF), and deforms the solid Earth. Estimation of the degree-1 surface mass changes through CM-CF and degree-1 deformation signatures from space geodetic techniques can thus complement GRACE's time-variable gravity data to form a complete change spectrum up to a high resolution. Currently, SLR is considered the most accurate technique for direct geocenter motion determination. By tracking satellite motion from ground stations, SLR determines the motion between CM and the geometric center of its ground network (CN). This motion is then used to approximate CM-CF and subsequently for deriving degree-1 mass changes. However, the SLR network is very sparse and uneven in global distribution. The average number of operational tracking stations is about 20 in recent years. The poor network geometry can have a large CN-CF motion and is not ideal for the determination of CM-CF motion and degree-1 mass changes. We recently realized an experimental Terrestrial Reference Frame (TRF) through station time series using the Kalman filter and the RTS smoother. The TRF has its origin defined at nearly instantaneous CM using weekly SLR measurement time series. VLBI, GNSS and DORIS time series are combined weekly with those of SLR and tied to the geocentric (CM) reference frame through local tie measurements and co-motion constraints on co-located geodetic stations. The unified geocentric time series of the four geodetic techniques provide a much better network geometry for direct geodetic determination of geocenter motion. Results from this direct approach using a 90-station network compares favorably with those obtained from joint inversions of GPS/GRACE data and ocean bottom pressure models. We will also show that a previously identified discrepancy in X-component between direct SLR orbit-tracking and inverse determined geocenter motions is largely reconciled with the new unified network.

Restoration of longitudinal images.

PubMed

Hu, Y; Frieden, B R

1988-01-15

In this paper, a method of restoring longitudinal images is developed. By using the transfer function for longitudinal objects, and inverse filtering, a longitudinal image may be restored. The Fourier theory and sampling theorems for transverse images cannot be used directly in the longitudinal case. A modification and reasonable approximation are introduced. We have numerically established a necessary relationship between just-resolved longitudinal separation (after inverse filtering), noise level, and the taking conditions of object distance and lens diameter. An empirical formula is also found to well-fit the computed results. This formula may be of use for designing optical systems which are to image longitudinal details, such as in robotics or microscopy.

A 2D Electron Density and Plasma Current Density Diagnostic for Opening Switches

DTIC Science & Technology

2006-02-01

x, y)) can be recovered by taking the inverse transform of C(f - f,, y), and calculating the inverse tangent of the ratio of its real and imaginary...parts, 27rfox + (x,y) = tan-1 [Re(IT)/Im(IT)], (7) where IT represents the inverse transform of C(f - fo, y). There are a number of options available...notch filtering around f, before the inverse transform is taken. However, since frequency space is discrete due to the discrete nature of the FFT, we

Role of Retinocortical Processing in Spatial Vision

DTIC Science & Technology

1989-06-01

its inverse transform . These are even- symmetric functions. Odd-symmetric Gabor functions would also be required for image coding (Daugman, 1987), but...spectrum square; thus its horizontal and vertical scale factors may differ by a power of 2. Since the inverse transform undoes this distor- tion, it has...FIGURE 3 STANDARD FORM OF EVEN GABOR FILTER 7 order to inverse - transform correctly. We used Gabor functions with the standard shape of Daugman’s "polar

Estimation of Uncertainties of Full Moment Tensors

DTIC Science & Technology

2017-10-06

Nevada Test Site (tab. 1 of Ford et al., 2009). Figure 1 shows the three regions and the stations used within the moment tensor inversions . For the...and additional bandpass filtering, were applied during the moment tensor inversions . We use high-frequency P waves for the Uturuncu and NTS events...reliable when we align the P waves on the observed P arrival time. 3.2 Methods Seismic moment tensor inversion requires specifying a misfit function

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

NASA Astrophysics Data System (ADS)

Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image.

PubMed

Kumar, M; Mishra, S K

2017-01-01

The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

A robust pseudo-inverse spectral filter applied to the Earth Radiation Budget Experiment (ERBE) scanning channels

NASA Technical Reports Server (NTRS)

Avis, L. M.; Green, R. N.; Suttles, J. T.; Gupta, S. K.

1984-01-01

Computer simulations of a least squares estimator operating on the ERBE scanning channels are discussed. The estimator is designed to minimize the errors produced by nonideal spectral response to spectrally varying and uncertain radiant input. The three ERBE scanning channels cover a shortwave band a longwave band and a ""total'' band from which the pseudo inverse spectral filter estimates the radiance components in the shortwave band and a longwave band. The radiance estimator draws on instantaneous field of view (IFOV) scene type information supplied by another algorithm of the ERBE software, and on a priori probabilistic models of the responses of the scanning channels to the IFOV scene types for given Sun scene spacecraft geometry. It is found that the pseudoinverse spectral filter is stable, tolerant of errors in scene identification and in channel response modeling, and, in the absence of such errors, yields minimum variance and essentially unbiased radiance estimates.

Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability

NASA Astrophysics Data System (ADS)

Kar, Soummya; Moura, José M. F.

2011-04-01

The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.

Unwinding the hairball graph: Pruning algorithms for weighted complex networks

NASA Astrophysics Data System (ADS)

Dianati, Navid

2016-01-01

Empirical networks of weighted dyadic relations often contain "noisy" edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges. Here, we focus on integer-weighted graphs commonly arising when weights count the occurrences of an "event" relating the nodes. We introduce a simple and intuitive null model related to the configuration model of network generation and derive two significance filters from it: the marginal likelihood filter (MLF) and the global likelihood filter (GLF). The former is a fast algorithm assigning a significance score to each edge based on the marginal distribution of edge weights, whereas the latter is an ensemble approach which takes into account the correlations among edges. We apply these filters to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph. Furthermore, compared with thresholding based on edge weight, we show that our filters extract a larger and significantly sparser giant component.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Recursive Inversion By Finite-Impulse-Response Filters

NASA Technical Reports Server (NTRS)

Bach, Ralph E., Jr.; Baram, Yoram

1991-01-01

Recursive approximation gives least-squares best fit to exact response. Algorithm yields finite-impulse-response approximation of unknown single-input/single-output, causal, time-invariant, linear, real system, response of which is sequence of impulses. Applicable to such system-inversion problems as suppression of echoes and identification of target from its scatter response to incident impulse.

A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1994-01-01

A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness. The synergistic benefits of the proposed architecture are demonstrated and discussed in the context of the noise-filtering of signals that are typically encountered in aerospace control systems. The synthesis of such a gain-scheduled neurofiltering provides the robustness of linear filtering, while preserving the nominal performance advantage of conventional nonlinear neurofiltering. Quantitative performance and robustness evaluations are provided for the signal processing of pitch rate responses to typical pilot command inputs for a modern fighter aircraft model.

Characterization of network structure in stereoEEG data using consensus-based partial coherence.

PubMed

Ter Wal, Marije; Cardellicchio, Pasquale; LoRusso, Giorgio; Pelliccia, Veronica; Avanzini, Pietro; Orban, Guy A; Tiesinga, Paul He

2018-06-06

Coherence is a widely used measure to determine the frequency-resolved functional connectivity between pairs of recording sites, but this measure is confounded by shared inputs to the pair. To remove shared inputs, the 'partial coherence' can be computed by conditioning the spectral matrices of the pair on all other recorded channels, which involves the calculation of a matrix (pseudo-) inverse. It has so far remained a challenge to use the time-resolved partial coherence to analyze intracranial recordings with a large number of recording sites. For instance, calculating the partial coherence using a pseudoinverse method produces a high number of false positives when it is applied to a large number of channels. To address this challenge, we developed a new method that randomly aggregated channels into a smaller number of effective channels on which the calculation of partial coherence was based. We obtained a 'consensus' partial coherence (cPCOH) by repeating this approach for several random aggregations of channels (permutations) and only accepting those activations in time and frequency with a high enough consensus. Using model data we show that the cPCOH method effectively filters out the effect of shared inputs and performs substantially better than the pseudo-inverse. We successfully applied the cPCOH procedure to human stereotactic EEG data and demonstrated three key advantages of this method relative to alternative procedures. First, it reduces the number of false positives relative to the pseudo-inverse method. Second, it allows for titration of the amount of false positives relative to the false negatives by adjusting the consensus threshold, thus allowing the data-analyst to prioritize one over the other to meet specific analysis demands. Third, it substantially reduced the number of identified interactions compared to coherence, providing a sparser network of connections from which clear spatial patterns emerged. These patterns can serve as a starting point of further analyses that provide insight into network dynamics during cognitive processes. These advantages likely generalize to other modalities in which shared inputs introduce confounds, such as electroencephalography (EEG) and magneto-encephalography (MEG). Copyright © 2018. Published by Elsevier Inc.

Computational time reduction for sequential batch solutions in GNSS precise point positioning technique

NASA Astrophysics Data System (ADS)

Martín Furones, Angel; Anquela Julián, Ana Belén; Dimas-Pages, Alejandro; Cos-Gayón, Fernando

2017-08-01

Precise point positioning (PPP) is a well established Global Navigation Satellite System (GNSS) technique that only requires information from the receiver (or rover) to obtain high-precision position coordinates. This is a very interesting and promising technique because eliminates the need for a reference station near the rover receiver or a network of reference stations, thus reducing the cost of a GNSS survey. From a computational perspective, there are two ways to solve the system of observation equations produced by static PPP either in a single step (so-called batch adjustment) or with a sequential adjustment/filter. The results of each should be the same if they are both well implemented. However, if a sequential solution (that is, not only the final coordinates, but also those observed in previous GNSS epochs), is needed, as for convergence studies, finding a batch solution becomes a very time consuming task owing to the need for matrix inversion that accumulates with each consecutive epoch. This is not a problem for the filter solution, which uses information computed in the previous epoch for the solution of the current epoch. Thus filter implementations need extra considerations of user dynamics and parameter state variations between observation epochs with appropriate stochastic update parameter variances from epoch to epoch. These filtering considerations are not needed in batch adjustment, which makes it attractive. The main objective of this research is to significantly reduce the computation time required to obtain sequential results using batch adjustment. The new method we implemented in the adjustment process led to a mean reduction in computational time by 45%.

A stress-constrained geodetic inversion method for spatiotemporal slip of a slow slip event with earthquake swarm

NASA Astrophysics Data System (ADS)

Hirose, H.; Tanaka, T.

2017-12-01

Geodetic inversions have been performed by using GNSS data and/or tiltmeter data in order to estimate spatio-temporal fault slip distributions. They have been applied for slow slip events (SSEs), which are episodic fault slip lasting for days to years (e.g., Ozawa et al., 2001; Hirose et al., 2014). Although their slip distributions are important information in terms of inferring strain budget and frictional characteristics on a subduction plate interface, inhomogeneous station coverage generally yields spatially non-uniform slip resolution, and in a worse case, a slip distribution can not be recovered. It is known that an SSE which accompanies an earthquake swarm around the SSE slip area, such as the Boso Peninsula SSEs (e.g., Hirose et al., 2014). Some researchers hypothesize that these earthquakes are triggered by a stress change caused by the accompanying SSE (e.g., Segall et al., 2006). Based on this assumption, it is possible that a conventional geodetic inversion which impose a constraint on the stress change that promotes earthquake activities may improve the resolution of the slip distribution. Here we develop an inversion method based on the Network Inversion Filter technique (Segall and Matthews, 1997), incorporating a constraint on a positive change in Coulomb failure stress (Delta-CFS) at the accompanied earthquakes. In addition, we apply this new method to synthetic data in order to check the effectiveness of the method and the characteristics of the inverted slip distributions. The results show that there is a case in which the reproduction of a slip distribution is better with earthquake information than without it. That is, it is possible to improve the reproducibility of a slip distribution of an SSE with this new inversion method if an earthquake catalog for the accompanying earthquake activity can be used when available geodetic data are insufficient.

Inversion of atmospheric optical parameters from elastic-backscatter lidar returns using a Kalman filter

NASA Astrophysics Data System (ADS)

Rocadenbosch, Francesc; Comeron, Adolfo; Vazquez, Gregori; Rodriguez-Gomez, Alejandro; Soriano, Cecilia; Baldasano, Jose M.

1998-12-01

Up to now, retrieval of the atmospheric extinction and backscatter has mainly relied on standard straightforward non-memory procedures such as slope-method, exponential- curve fitting and Klett's method. Yet, their performance becomes ultimately limited by the inherent lack of adaptability as they only work with present returns and neither past estimations, nor the statistics of the signals or a prior uncertainties are taken into account. In this work, a first inversion of the backscatter and extinction- to-backscatter ratio from pulsed elastic-backscatter lidar returns is tackled by means of an extended Kalman filter (EKF), which overcomes these limitations. Thus, as long as different return signals income,the filter updates itself weighted by the unbalance between the a priori estimates of the optical parameters and the new ones based on a minimum variance criterion. Calibration errors or initialization uncertainties can be assimilated also. The study begins with the formulation of the inversion problem and an appropriate stochastic model. Based on extensive simulation and realistic conditions, it is shown that the EKF approach enables to retrieve the sought-after optical parameters as time-range-dependent functions and hence, to track the atmospheric evolution, its performance being only limited by the quality and availability of the 'a priori' information and the accuracy of the atmospheric model assumed. The study ends with an encouraging practical inversion of a live-scene measured with the Nd:YAG elastic-backscatter lidar station at our premises in Barcelona.

Mathematics of Computed Tomography

NASA Astrophysics Data System (ADS)

Hawkins, William Grant

A review of the applications of the Radon transform is presented, with emphasis on emission computed tomography and transmission computed tomography. The theory of the 2D and 3D Radon transforms, and the effects of attenuation for emission computed tomography are presented. The algebraic iterative methods, their importance and limitations are reviewed. Analytic solutions of the 2D problem the convolution and frequency filtering methods based on linear shift invariant theory, and the solution of the circular harmonic decomposition by integral transform theory--are reviewed. The relation between the invisible kernels, the inverse circular harmonic transform, and the consistency conditions are demonstrated. The discussion and review are extended to the 3D problem-convolution, frequency filtering, spherical harmonic transform solutions, and consistency conditions. The Cormack algorithm based on reconstruction with Zernike polynomials is reviewed. An analogous algorithm and set of reconstruction polynomials is developed for the spherical harmonic transform. The relations between the consistency conditions, boundary conditions and orthogonal basis functions for the 2D projection harmonics are delineated and extended to the 3D case. The equivalence of the inverse circular harmonic transform, the inverse Radon transform, and the inverse Cormack transform is presented. The use of the number of nodes of a projection harmonic as a filter is discussed. Numerical methods for the efficient implementation of angular harmonic algorithms based on orthogonal functions and stable recursion are presented. The derivation of a lower bound for the signal-to-noise ratio of the Cormack algorithm is derived.

Neural correlates of cognitive aging during the perception of facial age: the role of relatively distant and local texture information

PubMed Central

Komes, Jessica; Schweinberger, Stefan R.; Wiese, Holger

2015-01-01

Previous event-related potential (ERP) research revealed that older relative to younger adults show reduced inversion effects in the N170 (with more negative amplitudes for inverted than upright faces), suggestive of impairments in face perception. However, as these studies used young to middle-aged faces only, this finding may reflect preferential processing of own- relative to other-age faces rather than age-related decline. We conducted an ERP study in which young and older participants categorized young and old upright or inverted faces by age. Stimuli were presented either unfiltered or low-pass filtered at 30, 20, or 10 cycles per image (CPI). Response times revealed larger inversion effects, with slower responses for inverted faces, for young faces in young participants. Older participants did not show a corresponding effect. ERPs yielded a trend toward reduced N170 inversion effects in older relative to younger adults independent of face age. Moreover, larger inversion effects for young relative to old faces were detected, and filtering resulted in smaller N170 amplitudes. The reduced N170 inversion effect in older adults may reflect age-related changes in neural correlates of face perception. A smaller N170 inversion effect for old faces may indicate that facial changes with age hamper early face perception stages. PMID:26441790

A-law/Mu-law Dynamic Range Compression Deconvolution (Preprint)

DTIC Science & Technology

2008-02-04

noise filtering via the spectrum proportionality filter, and second the signal deblurring via the inverse filter. In this process for regions when...is the joint image of motion impulse response and the noisy blurred image with signal to noise ratio 5, 6(A’) is the gray level recovered image...joint image of motion impulse response and the noisy blurred image with signal to noise ratio 5, (A’) the gray level recovered image using the A-law

Simultaneous signal reconstruction from both superficial and deep tissue for fNIRS using depth-selective filtering method

NASA Astrophysics Data System (ADS)

Fujii, M.

2017-07-01

Two variations of a depth-selective back-projection filter for functional near-infrared spectroscopy (fNIRS) systems are introduced. The filter comprises a depth-selective algorithm that uses inverse problems applied to an optically diffusive multilayer medium. In this study, simultaneous signal reconstruction of both superficial and deep tissue from fNIRS experiments of the human forehead using a prototype of a CW-NIRS system is demonstrated.

Optical UWB pulse generator using an N tap microwave photonic filter and phase inversion adaptable to different pulse modulation formats.

PubMed

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

2009-03-30

We propose theoretically and demonstrate experimentally an optical architecture for flexible Ultra-Wideband pulse generation. It is based on an N-tap reconfigurable microwave photonic filter fed by a laser array by using phase inversion in a Mach-Zehnder modulator. Since a large number of positive and negative coefficients can be easily implemented, UWB pulses fitted to the FCC mask requirements can be generated. As an example, a four tap pulse generator is experimentally demonstrated which complies with the FCC regulation. The proposed pulse generator allows different pulse modulation formats since the amplitude, polarity and time delay of generated pulse is controlled.

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

The neural network approximation method for solving multidimensional nonlinear inverse problems of geophysics

NASA Astrophysics Data System (ADS)

Shimelevich, M. I.; Obornev, E. A.; Obornev, I. E.; Rodionov, E. A.

2017-07-01

The iterative approximation neural network method for solving conditionally well-posed nonlinear inverse problems of geophysics is presented. The method is based on the neural network approximation of the inverse operator. The inverse problem is solved in the class of grid (block) models of the medium on a regularized parameterization grid. The construction principle of this grid relies on using the calculated values of the continuity modulus of the inverse operator and its modifications determining the degree of ambiguity of the solutions. The method provides approximate solutions of inverse problems with the maximal degree of detail given the specified degree of ambiguity with the total number of the sought parameters n × 103 of the medium. The a priori and a posteriori estimates of the degree of ambiguity of the approximated solutions are calculated. The work of the method is illustrated by the example of the three-dimensional (3D) inversion of the synthesized 2D areal geoelectrical (audio magnetotelluric sounding, AMTS) data corresponding to the schematic model of a kimberlite pipe.

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy

NASA Astrophysics Data System (ADS)

Gabbard, Hunter; Williams, Michael; Hayes, Fergus; Messenger, Chris

2018-04-01

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy.

PubMed

Gabbard, Hunter; Williams, Michael; Hayes, Fergus; Messenger, Chris

2018-04-06

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.

Going beyond the reflectance limit of cholesteric liquid crystals

NASA Astrophysics Data System (ADS)

Mitov, Michel; Dessaud, Nathalie

2006-05-01

Cholesteric liquid-crystalline states of matter are abundant in nature: atherosclerosis, arthropod cuticles, condensed phases of DNA, plant cell walls, human compact bone osteon, and chiral biopolymers. The self-organized helical structure produces unique optical properties. Light is reflected when the wavelength matches the pitch (twice periodicity); cholesteric liquid crystals are not only coloured filters, but also reflectors and polarizers. But, in theory, the reflectance is limited to 50% of the ambient (unpolarized) light because circularly polarized light of the same handedness as the helix is reflected. Here we give details of a cholesteric medium for which the reflectance limit is exceeded. Photopolymerizable monomers are introduced into a cholesteric medium exhibiting a thermally induced helicity inversion, and the blend is then cured with ultraviolet light when the helix is right-handed. Because of memory effects attributable to the polymer network, the reflectance exceeds 50% when measured at the temperature assigned for a cholesteric helix with the same pitch but a left-handed sense before the reaction. As cholesteric materials are used as tunable bandpass filters, reflectors or polarizers and temperature or pressure sensors, novel opportunities to modulate the reflection over the whole light flux range, instead of only 50%, are offered.

Filter frequency response of time dependent signal using Laplace transform

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

Shestakov, Aleksei I.

We analyze the effect a filter has on a time dependent signal x(t). If X(s) is the Laplace transform of x and H (s) is the filter Transfer function, the response in frequency space is X (s) H (s). Consequently, in real space, the response is the convolution (x*h) (t), where hi is the Laplace inverse of H. Effects are analyzed and analytically for functions such as (t/t c) 2 e -t/tmore » $$_c$$, where t c = const. We consider lowpass, highpass and bandpass filters.« less

Time-Dependent Stochastic Inversion in Acoustic Tomography of the Atmosphere with Reciprocal Sound Transmission

DTIC Science & Technology

2008-10-17

R E 1960 A new approach to linear filtering and prediction problems Trans. ASME D 82 35–45 [23] Brown R G and Hwang Y C 1992 Introduction to Random...vector Wiener filter [21]. TDSI is also somewhat similar to the Kalman filter [22, 23] which is applied in many areas including tomography [24–27]. The...453–76 [21] Links J M, Prince J L and Gupta S N 1996 A vector Wiener filter for dual-radionuclide imaging IEEE Trans. Med. Imaging 15 700–9 [22] Kalman

Kalman filter for statistical monitoring of forest cover across sub-continental regions [Symposium

Treesearch

Raymond L. Czaplewski

1991-01-01

The Kalman filter is a generalization of the composite estimator. The univariate composite estimate combines 2 prior estimates of population parameter with a weighted average where the scalar weight is inversely proportional to the variances. The composite estimator is a minimum variance estimator that requires no distributional assumptions other than estimates of the...

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

2018-01-01

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

Resonator memories and optical novelty filters

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.; Erle, Marie C.

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive materials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydreaming" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

Resonator Memories And Optical Novelty Filters

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.; Erie, Marie C.

1987-05-01

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content-addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive ma-terials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydream-ing" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

Multiple Scattering Effects in EXAFS (Extended X-Ray Absorption Fine Structure): Comparison between Theory and Experiment for Pt Metal.

DTIC Science & Technology

1984-05-29

radial distribution and inverse transform of this range. The region commonly used for the inverse transform usually corresponds to the first or second...r-space and comparing it with the corresponding model function. Figure 5 shows the filtered spectra (k weighting), where the pe. 48 allowed inverse ... transform range was successively increased from the radial distance corresponding to the first shell to all the first four shells. There are no multiple

A Geophysical Inversion Model Enhancement Technique Based on the Blind Deconvolution

NASA Astrophysics Data System (ADS)

Zuo, B.; Hu, X.; Li, H.

2011-12-01

A model-enhancement technique is proposed to enhance the geophysical inversion model edges and details without introducing any additional information. Firstly, the theoretic correctness of the proposed geophysical inversion model-enhancement technique is discussed. An inversion MRM (model resolution matrix) convolution approximating PSF (Point Spread Function) method is designed to demonstrate the correctness of the deconvolution model enhancement method. Then, a total-variation regularization blind deconvolution geophysical inversion model-enhancement algorithm is proposed. In previous research, Oldenburg et al. demonstrate the connection between the PSF and the geophysical inverse solution. Alumbaugh et al. propose that more information could be provided by the PSF if we return to the idea of it behaving as an averaging or low pass filter. We consider the PSF as a low pass filter to enhance the inversion model basis on the theory of the PSF convolution approximation. Both the 1D linear and the 2D magnetotelluric inversion examples are used to analyze the validity of the theory and the algorithm. To prove the proposed PSF convolution approximation theory, the 1D linear inversion problem is considered. It shows the ratio of convolution approximation error is only 0.15%. The 2D synthetic model enhancement experiment is presented. After the deconvolution enhancement, the edges of the conductive prism and the resistive host become sharper, and the enhancement result is closer to the actual model than the original inversion model according the numerical statistic analysis. Moreover, the artifacts in the inversion model are suppressed. The overall precision of model increases 75%. All of the experiments show that the structure details and the numerical precision of inversion model are significantly improved, especially in the anomalous region. The correlation coefficient between the enhanced inversion model and the actual model are shown in Fig. 1. The figure illustrates that more information and details structure of the actual model are enhanced through the proposed enhancement algorithm. Using the proposed enhancement method can help us gain a clearer insight into the results of the inversions and help make better informed decisions.

Accurate Ultrasonic Measurement of Surface Profile Using Phase Shift of Echo and Inverse Filtering

NASA Astrophysics Data System (ADS)

Arihara, Chihiro; Hasegawa, Hideyuki; Kanai, Hiroshi

2006-05-01

Atherosclerosis is the main cause of circulatory diseases such as myocardial infarction and cerebral infarction, and it is very important to diagnose atherosclerosis in its early stage. In the early stage of atherosclerosis, the luminal surface of an arterial wall becomes rough because of the injury of the endothelium [R. Ross: New Engl. J. Med. 340 (2004) 115]. Conventional ultrasonic diagnostic equipments cannot detect such roughness on the order of micrometer because of their low resolution of approximately 0.1 mm. In this study, for the accurate detection of surface roughness, an ultrasonic beam was scanned in the direction that is parallel to the surface of an object. When there is a gap on the surface, the phase of the echo from the surface changes because the distance between the probe and the surface changes during the scanning. Therefore, surface roughness can be assessed by estimating the phase shift of echoes obtained during the beam scanning. Furthermore, lateral resolution, which is deteriorated by a finite diameter of the ultrasound beam, was improved by an inverse filter. By using the proposed method, the surface profile of a phantom, which had surface roughness on the micrometer order, was detected, and the estimated surface profiles became more precise by applying the inverse filter.

Temperature profile retrievals with extended Kalman-Bucy filters

NASA Technical Reports Server (NTRS)

Ledsham, W. H.; Staelin, D. H.

1979-01-01

The Extended Kalman-Bucy Filter is a powerful technique for estimating non-stationary random parameters in situations where the received signal is a noisy non-linear function of those parameters. A practical causal filter for retrieving atmospheric temperature profiles from radiances observed at a single scan angle by the Scanning Microwave Spectrometer (SCAMS) carried on the Nimbus 6 satellite typically shows approximately a 10-30% reduction in rms error about the mean at almost all levels below 70 mb when compared with a regression inversion.

Estimation of Atmospheric Methane Surface Fluxes Using a Global 3-D Chemical Transport Model

NASA Astrophysics Data System (ADS)

Chen, Y.; Prinn, R.

2003-12-01

Accurate determination of atmospheric methane surface fluxes is an important and challenging problem in global biogeochemical cycles. We use inverse modeling to estimate annual, seasonal, and interannual CH4 fluxes between 1996 and 2001. The fluxes include 7 time-varying seasonal (3 wetland, rice, and 3 biomass burning) and 3 steady aseasonal (animals/waste, coal, and gas) global processes. To simulate atmospheric methane, we use the 3-D chemical transport model MATCH driven by NCEP reanalyzed observed winds at a resolution of T42 ( ˜2.8° x 2.8° ) in the horizontal and 28 levels (1000 - 3 mb) in the vertical. By combining existing datasets of individual processes, we construct a reference emissions field that represents our prior guess of the total CH4 surface flux. For the methane sink, we use a prescribed, annually-repeating OH field scaled to fit methyl chloroform observations. MATCH is used to produce both the reference run from the reference emissions, and the time-dependent sensitivities that relate individual emission processes to observations. The observational data include CH4 time-series from ˜15 high-frequency (in-situ) and ˜50 low-frequency (flask) observing sites. Most of the high-frequency data, at a time resolution of 40-60 minutes, have not previously been used in global scale inversions. In the inversion, the high-frequency data generally have greater weight than the weekly flask data because they better define the observational monthly means. The Kalman Filter is used as the optimal inversion technique to solve for emissions between 1996-2001. At each step in the inversion, new monthly observations are utilized and new emissions estimates are produced. The optimized emissions represent deviations from the reference emissions that lead to a better fit to the observations. The seasonal processes are optimized for each month, and contain the methane seasonality and interannual variability. The aseasonal processes, which are less variable, are solved as constant emissions over the entire time period. The Kalman Filter also produces emission uncertainties which quantify the ability of the observing network to constrain different processes. The sensitivity of the inversion to different observing sites and model sampling strategies is also tested. In general, the inversion reduces coal and gas emissions, and increases rice and biomass burning emissions relative to the reference case. Increases in both tropical and northern wetland emissions are found to have dominated the strong atmospheric methane increase in 1998. Northern wetlands are the best constrained processes, while tropical regions are poorly constrained and will require additional observations in the future for significant uncertainty reduction. The results of this study also suggest that interannual varying transport like NCEP and high-frequency measurements should be used when solving for methane emissions at monthly time resolution. Better estimates of global OH fluctuations are also necessary to fully describe the interannual behavior of methane observations.

Global surface density of water mass variations by using a two-step inversion by cumulating daily satellite gravity information

NASA Astrophysics Data System (ADS)

Ramillien, Guillaume; Frappart, Frédéric; Seoane, Lucia

2016-04-01

We propose a new method to produce time series of global maps of surface mass variations by progressive integration of daily geopotential variations measured by orbiting satellites. In the case of the GRACE mission, these geopotential variations can be determined from very accurate inter-satellite K-Band Range Rate (KBRR) measurements of 5-second daily orbits. In particular, the along-track gravity contribution of hydrological mass changes is extracted by removing de-aliasing models for static field, atmosphere, oceans mass variations (including periodical tides), as well as polar movements. Our determination of surface mass sources is composed of two successive dependent Kalman filter stages. The first one consists of reducing the satellite-based potential anomalies by adjusting the longest spatial wavelengths (i.e., low-degree spherical harmonics lower than 2). In the second stage, the residual potential anomalies from the previous stage are used to recover surface mass density changes - in terms of Equivalent-Water Height (EWH) - over a global network of juxtaposed triangular elements. These surface tiles of ~100,000 km x km (or equivalently 330 km by 330 km) are defined to be of equal areas over the terrestrial sphere. However they can be adapted to the local geometry of the surface mass. Our global approach was tested by inverting geopotential data, and successfully applied to estimate time-varying surface mass densities from real GRACE-based residuals. This strategy of combined Kalman filter-type inversions can also be useful for exploring the possibility of improving time and space resolutions for ocean and land studies that would be hopefully brought by future low altitude geodetic missions.

Synthesis of highly integrated optical network based on microdisk-resonator add-drop filters in silicon-on-insulator technology

NASA Astrophysics Data System (ADS)

Kaźmierczak, Andrzej; Dortu, Fabian; Giannone, Domenico; Bogaerts, Wim; Drouard, Emmanuel; Rojo-Romeo, Pedro; Gaffiot, Frederic

2009-10-01

We analyze a highly compact optical add-drop filter topology based on a pair of microdisk resonators and a bus waveguide intersection. The filter is further assessed on an integrated optical 4×4 network for optical on-chip communication. The proposed network structure, as compact as 50×50 μm, is fabricated in a CMOS-compatible process on a silicon-on-insulator (SOI) substrate. Finally, the experimental results demonstrate the proper operation of the fabricated devices.

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.

PubMed

Quevedo-Tumailli, Viviana F; Ortega-Tenezaca, Bernabé; González-Díaz, Humbert

2018-03-02

The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans, and many more are organized into larger clusters. This raises intriguing questions previously asked by different authors. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? We define a new kind of network coined as the gene orientation inversion network (GOIN). GOIN's complex network encodes short- and long-range patterns of inversion of the orientation of pairs of gene in the chromosome. We selected Plasmodium falciparum as a case of study due to the high relevance of this parasite to public health (causal agent of malaria). We constructed here for the first time all of the GOINs for the genome of this parasite. These networks have an average of 383 nodes (genes in one chromosome) and 1314 links (pairs of gene with inverse orientation). We calculated node centralities and other parameters of these networks. These numerical parameters were used to study different properties of gene inversion patterns, for example, distribution, local communities, similarity to Erdös-Rényi random networks, randomness, and so on. We find clues that seem to indicate that gene orientation inversion does not follow a random pattern. We noted that some gene communities in the GOINs tend to group genes encoding for RIFIN-related proteins in the proteome of the parasite. RIFIN-like proteins are a second family of clonally variant proteins expressed on the surface of red cells infected with Plasmodium falciparum. Consequently, we used these centralities as input of machine learning (ML) models to predict the RIFIN-like activity of 5365 proteins in the proteome of Plasmodium sp. The best linear ML model found discriminates RIFIN-like from other proteins with sensitivity and specificity 70-80% in training and external validation series. All of these results may point to a possible biological relevance of gene orientation inversion not directly dependent on genetic sequence information. This work opens the gate to the use of GOINs as a tool for the study of the structure of chromosomes and the study of protein function in proteome research.

Nanophotonic particle simulation and inverse design using artificial neural networks.

PubMed

Peurifoy, John; Shen, Yichen; Jing, Li; Yang, Yi; Cano-Renteria, Fidel; DeLacy, Brendan G; Joannopoulos, John D; Tegmark, Max; Soljačić, Marin

2018-06-01

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

Identification of Correlated GRACE Monthly Harmonic Coefficients Using Pattern Recognition and Neural Networks

NASA Astrophysics Data System (ADS)

Piretzidis, D.; Sra, G.; Sideris, M. G.

2016-12-01

This study explores new methods for identifying correlation errors in harmonic coefficients derived from monthly solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission using pattern recognition and neural network algorithms. These correlation errors are evidenced in the differences between monthly solutions and can be suppressed using a de-correlation filter. In all studies so far, the implementation of the de-correlation filter starts from a specific minimum order (i.e., 11 for RL04 and 38 for RL05) until the maximum order of the monthly solution examined. This implementation method has two disadvantages, namely, the omission of filtering correlated coefficients of order less than the minimum order and the filtering of uncorrelated coefficients of order higher than the minimum order. In the first case, the filtered solution is not completely free of correlated errors, whereas the second case results in a monthly solution that suffers from loss of geophysical signal. In the present study, a new method of implementing the de-correlation filter is suggested, by identifying and filtering only the coefficients that show indications of high correlation. Several numerical and geometric properties of the harmonic coefficient series of all orders are examined. Extreme cases of both correlated and uncorrelated coefficients are selected, and their corresponding properties are used to train a two-layer feed-forward neural network. The objective of the neural network is to identify and quantify the correlation by providing the probability of an order of coefficients to be correlated. Results show good performance of the neural network, both in the validation stage of the training procedure and in the subsequent use of the trained network to classify independent coefficients. The neural network is also capable of identifying correlated coefficients even when a small number of training samples and neurons are used (e.g.,100 and 10, respectively).

WE-D-204-02: Novel Method for Correcting Degradation of Sharpness of Liquid-Crystal Display Based On Modulation Transfer Function

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

Tokurei, S; Department of Radiology, Yamaguchi University Hospital, Ube, Yamaguchi; Morishita, J

2015-06-15

Purpose: To develop a method for improving sharpness of images reproduced on liquid-crystal displays (LCDs) by compensating for the degradation of modulation transfer function (MTF) of the LCD. Methods: The inherent MTF of a color LCD (display MTF) was measured using a commercially available color digital camera. The frequency responses necessary to compensate for the resolution property of the LCD were calculated from the inverses of the display MTFs in both the horizontal and vertical directions. In addition, the inverses of the display MTFs were combined with the response of the human eye. The finite impulse response (FIR) filters weremore » computed by taking the inverse Fourier transform of the frequency responses, and the effects of the FIR filtering on both the resolution and noise properties of the displayed images were verified by measuring the MTF and Wiener spectrum (WS), respectively. The FIR filtering was then applied to the representation of digital bone and chest radiographs. Results: The FIR filtering improved the MTF values by up to almost 1.0 or greater over the frequency range of interest, while it minimally increased the WS values. Combining the inverses of the display MTFs with the response of the human eye led to further refinement of the MTF. Our method was successfully and beneficially applied to the image interpretation of bone radiographs. The resolution enhancement of chest radiographs, which include larger scattered radiation than bone radiographs, was easily perceived by incorporating the response of the human eye. In addition, no artifacts were observed on the processed images. Conclusion: Our proposed method to compensate for the degradation of the resolution properties of LCDs has the potential to improve the observer performance of radiologists when reading digital radiographs. This work was supported in part by grant from EIZO Corporation.« less

Finessing filter scarcity problem in face recognition via multi-fold filter convolution

NASA Astrophysics Data System (ADS)

Low, Cheng-Yaw; Teoh, Andrew Beng-Jin

2017-06-01

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

Joint Inversion of 1-Hz GPS Data and Strong Motion Records for the Rupture Process of the 2008 Iwate-Miyagi Nairiku Earthquake: Objectively Determining Relative Weighting

NASA Astrophysics Data System (ADS)

Wang, Z.; Kato, T.; Wang, Y.

2015-12-01

The spatiotemporal fault slip history of the 2008 Iwate-Miyagi Nairiku earthquake, Japan, is obtained by the joint inversion of 1-Hz GPS waveforms and near-field strong motion records. 1-Hz GPS data from GEONET is processed by GAMIT/GLOBK and then a low-pass filter of 0.05 Hz is applied. The ground surface strong motion records from stations of K-NET and Kik-Net are band-pass filtered for the range of 0.05 ~ 0.3 Hz and integrated once to obtain velocity. The joint inversion exploits a broader frequency band for near-field ground motions, which provides excellent constraints for both the detailed slip history and slip distribution. A fully Bayesian inversion method is performed to simultaneously and objectively determine the rupture model, the unknown relative weighting of multiple data sets and the unknown smoothing hyperparameters. The preferred rupture model is stable for different choices of velocity structure model and station distribution, with maximum slip of ~ 8.0 m and seismic moment of 2.9 × 1019 Nm (Mw 6.9). By comparison with the single inversion of strong motion records, the cumulative slip distribution of joint inversion shows sparser slip distribution with two slip asperities. One common slip asperity extends from the hypocenter southeastward to the ground surface of breakage; another slip asperity, which is unique for joint inversion contributed by 1-Hz GPS waveforms, appears in the deep part of fault where very few aftershocks are occurring. The differential moment rate function of joint and single inversions obviously indicates that rich high frequency waves are radiated in the first three seconds but few low frequency waves.

Applicability of Neural Networks to Etalon Fringe Filtering in Laser Spectrometers

NASA Technical Reports Server (NTRS)

Nicely, J. M.; Hanisco, T. F.; Riris, H.

2018-01-01

We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.

Applicability of neural networks to etalon fringe filtering in laser spectrometers

NASA Astrophysics Data System (ADS)

Nicely, J. M.; Hanisco, T. F.; Riris, H.

2018-05-01

We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.

Low Complexity Track Initialization and Fusion for Multi-Modal Sensor Networks

DTIC Science & Technology

2012-11-08

feature was demonstrated via the simulations. Aerospace 2011work further documents our investigation of multiple target tracking filters in...bounds that determine how well a sensor network can resolve and localize multiple targets as a function of the operating parameters such as sensor...probability density (PHD) filter for binary measurements using proximity sensors. 15. SUBJECT TERMS proximity sensors, PHD filter, multiple

Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

NASA Astrophysics Data System (ADS)

Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong

2017-08-01

Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.

Inverse design of high-Q wave filters in two-dimensional phononic crystals by topology optimization.

PubMed

Dong, Hao-Wen; Wang, Yue-Sheng; Zhang, Chuanzeng

2017-04-01

Topology optimization of a waveguide-cavity structure in phononic crystals for designing narrow band filters under the given operating frequencies is presented in this paper. We show that it is possible to obtain an ultra-high-Q filter by only optimizing the cavity topology without introducing any other coupling medium. The optimized cavity with highly symmetric resonance can be utilized as the multi-channel filter, raising filter and T-splitter. In addition, most optimized high-Q filters have the Fano resonances near the resonant frequencies. Furthermore, our filter optimization based on the waveguide and cavity, and our simple illustration of a computational approach to wave control in phononic crystals can be extended and applied to design other acoustic devices or even opto-mechanical devices. Copyright © 2016 Elsevier B.V. All rights reserved.

Sequential Geoacoustic Filtering and Geoacoustic Inversion

DTIC Science & Technology

2015-09-30

and online algorithms. We show here that CS obtains higher resolution than MVDR, even in scenarios, which favor classical high-resolution methods...windows actually performs better than conventional beamforming and MVDR/ MUSIC (see Figs. 1-2). Compressive geoacoustic inversion Geoacoustic...histograms based on 100 Monte Carlo simulations, and c)(CS, exhaustive-search, CBF, MVDR, and MUSIC performance versus SNR. The true source positions

[Research on the method of interference correction for nondispersive infrared multi-component gas analysis].

PubMed

Sun, You-Wen; Liu, Wen-Qing; Wang, Shi-Mei; Huang, Shu-Hua; Yu, Xiao-Man

2011-10-01

A method of interference correction for nondispersive infrared multi-component gas analysis was described. According to the successive integral gas absorption models and methods, the influence of temperature and air pressure on the integral line strengths and linetype was considered, and based on Lorentz detuning linetypes, the absorption cross sections and response coefficients of H2O, CO2, CO, and NO on each filter channel were obtained. The four dimension linear regression equations for interference correction were established by response coefficients, the absorption cross interference was corrected by solving the multi-dimensional linear regression equations, and after interference correction, the pure absorbance signal on each filter channel was only controlled by the corresponding target gas concentration. When the sample cell was filled with gas mixture with a certain concentration proportion of CO, NO and CO2, the pure absorbance after interference correction was used for concentration inversion, the inversion concentration error for CO2 is 2.0%, the inversion concentration error for CO is 1.6%, and the inversion concentration error for NO is 1.7%. Both the theory and experiment prove that the interference correction method proposed for NDIR multi-component gas analysis is feasible.

Generalized filtering of laser fields in optimal control theory: application to symmetry filtering of quantum gate operations

NASA Astrophysics Data System (ADS)

Schröder, Markus; Brown, Alex

2009-10-01

We present a modified version of a previously published algorithm (Gollub et al 2008 Phys. Rev. Lett.101 073002) for obtaining an optimized laser field with more general restrictions on the search space of the optimal field. The modification leads to enforcement of the constraints on the optimal field while maintaining good convergence behaviour in most cases. We demonstrate the general applicability of the algorithm by imposing constraints on the temporal symmetry of the optimal fields. The temporal symmetry is used to reduce the number of transitions that have to be optimized for quantum gate operations that involve inversion (NOT gate) or partial inversion (Hadamard gate) of the qubits in a three-dimensional model of ammonia.

MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

NASA Astrophysics Data System (ADS)

Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

Enhanced online convolutional neural networks for object tracking

NASA Astrophysics Data System (ADS)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

A regional high-resolution carbon flux inversion of North America for 2004

NASA Astrophysics Data System (ADS)

Schuh, A. E.; Denning, A. S.; Corbin, K. D.; Baker, I. T.; Uliasz, M.; Parazoo, N.; Andrews, A. E.; Worthy, D. E. J.

2010-05-01

Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Current operational systems like CarbonTracker as well as many previous global inversions including the Transcom suite of inversions have utilized inversion regions formed by collapsing biome-similar grid cells into larger aggregated regions. An extreme example of this might be where corrections to NEE imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO2 mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from the RAMS and the Parameterized Chemical Transport Model (PCTM) models is used to forge a connection between upwind biosphere fluxes and downwind observed CO2 mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO2. RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a mean estimate 0.57 Pg/yr sink in North America. We perform the inversion with two independently derived boundary inflow conditions and calculate jackknife-based statistics to test the robustness of the model results. We then compare final results to estimates obtained from the CarbonTracker inversion system and at the Southern Great Plains flux site. Results are promising, showing the ability to correct carbon fluxes from the biosphere models over annual and seasonal time scales, as well as over the different GPP and ER components. Additionally, the correlation of an estimated sink of carbon in the South Central United States with regional anomalously high precipitation in an area of managed agricultural and forest lands provides interesting hypotheses for future work.

Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Zovi, Francesco; Camporese, Matteo; Hendricks Franssen, Harrie-Jan; Huisman, Johan Alexander; Salandin, Paolo

2017-05-01

Alluvial aquifers are often characterized by the presence of braided high-permeable paleo-riverbeds, which constitute an interconnected preferential flow network whose localization is of fundamental importance to predict flow and transport dynamics. Classic geostatistical approaches based on two-point correlation (i.e., the variogram) cannot describe such particular shapes. In contrast, multiple point geostatistics can describe almost any kind of shape using the empirical probability distribution derived from a training image. However, even with a correct training image the exact positions of the channels are uncertain. State information like groundwater levels can constrain the channel positions using inverse modeling or data assimilation, but the method should be able to handle non-Gaussianity of the parameter distribution. Here the normal score ensemble Kalman filter (NS-EnKF) was chosen as the inverse conditioning algorithm to tackle this issue. Multiple point geostatistics and NS-EnKF have already been tested in synthetic examples, but in this study they are used for the first time in a real-world case study. The test site is an alluvial unconfined aquifer in northeastern Italy with an extension of approximately 3 km2. A satellite training image showing the braid shapes of the nearby river and electrical resistivity tomography (ERT) images were used as conditioning data to provide information on channel shape, size, and position. Measured groundwater levels were assimilated with the NS-EnKF to update the spatially distributed groundwater parameters (hydraulic conductivity and storage coefficients). Results from the study show that the inversion based on multiple point geostatistics does not outperform the one with a multiGaussian model and that the information from the ERT images did not improve site characterization. These results were further evaluated with a synthetic study that mimics the experimental site. The synthetic results showed that only for a much larger number of conditioning piezometric heads, multiple point geostatistics and ERT could improve aquifer characterization. This shows that state of the art stochastic methods need to be supported by abundant and high-quality subsurface data.

Decomposition of ECG by linear filtering.

PubMed

Murthy, I S; Niranjan, U C

1992-01-01

A simple method is developed for the delineation of a given electrocardiogram (ECG) signal into its component waves. The properties of discrete cosine transform (DCT) are exploited for the purpose. The transformed signal is convolved with appropriate filters and the component waves are obtained by computing the inverse transform (IDCT) of the filtered signals. The filters are derived from the time signal itself. Analysis of continuous strips of ECG signals with various arrhythmias showed that the performance of the method is satisfactory both qualitatively and quantitatively. The small amplitude P wave usually had a high percentage rms difference (PRD) compared to the other large component waves.

[Investigation of fast filter of ECG signals with lifting wavelet and smooth filter].

PubMed

Li, Xuefei; Mao, Yuxing; He, Wei; Yang, Fan; Zhou, Liang

2008-02-01

The lifting wavelet is used to decompose the original ECG signals and separate them into the approach signals with low frequency and the detail signals with high frequency, based on frequency characteristic. Parts of the detail signals are ignored according to the frequency characteristic. To avoid the distortion of QRS Complexes, the approach signals are filtered by an adaptive smooth filter with a proper threshold value. Through the inverse transform of the lifting wavelet, the reserved approach signals are reconstructed, and the three primary kinds of noise are limited effectively. In addition, the method is fast and there is no time delay between input and output.

Nanophotonic particle simulation and inverse design using artificial neural networks

PubMed Central

Peurifoy, John; Shen, Yichen; Jing, Li; Cano-Renteria, Fidel; DeLacy, Brendan G.; Joannopoulos, John D.; Tegmark, Max

2018-01-01

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical. PMID:29868640

Context-based retrieval of functional modules in protein-protein interaction networks.

PubMed

Dobay, Maria Pamela; Stertz, Silke; Delorenzi, Mauro

2017-03-27

Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Communication Policies in Knowledge Networks

NASA Astrophysics Data System (ADS)

Ioannidis, Evangelos; Varsakelis, Nikos; Antoniou, Ioannis

2018-02-01

Faster knowledge attainment within organizations leads to improved innovation, and therefore competitive advantage. Interventions on the organizational network may be risky or costly or time-demanding. We investigate several communication policies in knowledge networks, which reduce the knowledge attainment time without interventions. We examine the resulting knowledge dynamics for real organizational networks, as well as for artificial networks. More specifically, we investigate the dependence of knowledge dynamics on: (1) the Selection Rule of agents for knowledge acquisition, and (2) the Order of implementation of "Selection" and "Filtering". Significant decrease of the knowledge attainment time (up to -74%) can be achieved by: (1) selecting agents of both high knowledge level and high knowledge transfer efficiency, and (2) implementing "Selection" after "Filtering" in contrast to the converse, implicitly assumed, conventional prioritization. The Non-Commutativity of "Selection" and "Filtering", reveals a Non-Boolean Logic of the Network Operations. The results demonstrate that significant improvement of knowledge dynamics can be achieved by implementing "fruitful" communication policies, by raising the awareness of agents, without any intervention on the network structure.

Filtering in Hybrid Dynamic Bayesian Networks

NASA Technical Reports Server (NTRS)

Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin

2000-01-01

We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).

A parallel VLSI architecture for a digital filter using a number theoretic transform

NASA Technical Reports Server (NTRS)

Truong, T. K.; Reed, I. S.; Yeh, C. S.; Shao, H. M.

1983-01-01

The advantages of a very large scalee integration (VLSI) architecture for implementing a digital filter using fermat number transforms (FNT) are the following: It requires no multiplication. Only additions and bit rotations are needed. It alleviates the usual dynamic range limitation for long sequence FNT's. It utilizes the FNT and inverse FNT circuits 100% of the time. The lengths of the input data and filter sequences can be arbitraty and different. It is regular, simple, and expandable, and as a consequence suitable for VLSI implementation.

Iterative Inverse Modeling for Reconciliation of Emission Inventories during the 2006 TexAQS Intensive Field Campaign

NASA Astrophysics Data System (ADS)

Xiao, X.; Cohan, D. S.

2009-12-01

Substantial uncertainties in current emission inventories have been detected by the Texas Air Quality Study 2006 (TexAQS 2006) intensive field program. These emission uncertainties have caused large inaccuracies in model simulations of air quality and its responses to management strategies. To improve the quantitative understanding of the temporal, spatial, and categorized distributions of primary pollutant emissions by utilizing the corresponding measurements collected during TexAQS 2006, we implemented both the recursive Kalman filter and a batch matrix inversion 4-D data assimilation (FDDA) method in an iterative inverse modeling framework of the CMAQ-DDM model. Equipped with the decoupled direct method, CMAQ-DDM enables simultaneous calculation of the sensitivity coefficients of pollutant concentrations to emissions to be used in the inversions. Primary pollutant concentrations measured by the multiple platforms (TCEQ ground-based, NOAA WP-3D aircraft and Ronald H. Brown vessel, and UH Moody Tower) during TexAQS 2006 have been integrated for the use in the inverse modeling. Firstly pseudo-data analyses have been conducted to assess the two methods, taking a coarse spatial resolution emission inventory as a case. Model base case concentrations of isoprene and ozone at arbitrarily selected ground grid cells were perturbed to generate pseudo measurements with different assumed Gaussian uncertainties expressed by 1-sigma standard deviations. Single-species inversions have been conducted with both methods for isoprene and NOx surface emissions from eight states in the Southeastern United States by using the pseudo measurements of isoprene and ozone, respectively. Utilization of ozone pseudo data to invert for NOx emissions serves only for the purpose of method assessment. Both the Kalman filter and FDDA methods show good performance in tuning arbitrarily shifted a priori emissions to the base case “true” values within 3-4 iterations even for the nonlinear responses of ozone to NOx emissions. While the Kalman filter has better performance under the situation of very large observational uncertainties, the batch matrix FDDA method is better suited for incorporating temporally and spatially irregular data such as those measured by NOAA aircraft and ship. After validating the methods with the pseudo data, the inverse technique is applied to improve emission estimates of NOx from different source sectors and regions in the Houston metropolitan area by using NOx measurements during TexAQS 2006. EPA NEI2005-based and Texas-specified Emission Inventories for 2006 are used as the a priori emission estimates before optimization. The inversion results will be presented and discussed. Future work will conduct inverse modeling for additional species, and then perform a multi-species inversion for emissions consistency and reconciliation with secondary pollutants such as ozone.

Structure of solar coronal streamers

NASA Astrophysics Data System (ADS)

Schultz, C. G.

The present, direct method for the solution of generalized potential problems works outward from an O-point, under an assumption of the existence of flux surfaces. At each flux surface, a Fourier filter is used for the flux surface length variable to prevent numerical error amplifications, and the value of the inverse curvature radius and the normal direction are filtered to avoid the effects of local wrinkles.

Systolic Algorithms for Imaging from Space

DTIC Science & Technology

1989-07-31

on a keystone or trapezoidal grid [ Arikan & Munson, 1987]. The image reconstruction algorithm then simply applies an inverse 2-D FFT to the stored...rithm composed of groups of point targets, and we determined the effects of windowing and incor- poration of a Jacobian weighting factor [ Arikan ...the impulse response of the desired filter [ Arikan & Munson, 1989]. The necessary filtering is then accomplished through the physical mechanism of the

Modeling and control of magnetorheological fluid dampers using neural networks

NASA Astrophysics Data System (ADS)

Wang, D. H.; Liao, W. H.

2005-02-01

Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

Configuration and Management of Wireless Sensor Networks

DTIC Science & Technology

2005-12-01

monitor network status. B. CONCLUSIONS AND FUTURE WORK WSNs are an exciting and useful technology which will be used in various areas in the...int h = getSize().height; Image resizedImage = null; ImageFilter replicate = new ReplicateScaleFilter(w, h); ImageProducer prod = new

Porosity Estimation By Artificial Neural Networks Inversion . Application to Algerian South Field

NASA Astrophysics Data System (ADS)

Eladj, Said; Aliouane, Leila; Ouadfeul, Sid-Ali

2017-04-01

One of the main geophysicist's current challenge is the discovery and the study of stratigraphic traps, this last is a difficult task and requires a very fine analysis of the seismic data. The seismic data inversion allows obtaining lithological and stratigraphic information for the reservoir characterization . However, when solving the inverse problem we encounter difficult problems such as: Non-existence and non-uniqueness of the solution add to this the instability of the processing algorithm. Therefore, uncertainties in the data and the non-linearity of the relationship between the data and the parameters must be taken seriously. In this case, the artificial intelligence techniques such as Artificial Neural Networks(ANN) is used to resolve this ambiguity, this can be done by integrating different physical properties data which requires a supervised learning methods. In this work, we invert the acoustic impedance 3D seismic cube using the colored inversion method, then, the introduction of the acoustic impedance volume resulting from the first step as an input of based model inversion method allows to calculate the Porosity volume using the Multilayer Perceptron Artificial Neural Network. Application to an Algerian South hydrocarbon field clearly demonstrate the power of the proposed processing technique to predict the porosity for seismic data, obtained results can be used for reserves estimation, permeability prediction, recovery factor and reservoir monitoring. Keywords: Artificial Neural Networks, inversion, non-uniqueness , nonlinear, 3D porosity volume, reservoir characterization .

Neural network explanation using inversion.

PubMed

Saad, Emad W; Wunsch, Donald C

2007-01-01

An important drawback of many artificial neural networks (ANN) is their lack of explanation capability [Andrews, R., Diederich, J., & Tickle, A. B. (1996). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8, 373-389]. This paper starts with a survey of algorithms which attempt to explain the ANN output. We then present HYPINV, a new explanation algorithm which relies on network inversion; i.e. calculating the ANN input which produces a desired output. HYPINV is a pedagogical algorithm, that extracts rules, in the form of hyperplanes. It is able to generate rules with arbitrarily desired fidelity, maintaining a fidelity-complexity tradeoff. To our knowledge, HYPINV is the only pedagogical rule extraction method, which extracts hyperplane rules from continuous or binary attribute neural networks. Different network inversion techniques, involving gradient descent as well as an evolutionary algorithm, are presented. An information theoretic treatment of rule extraction is presented. HYPINV is applied to example synthetic problems, to a real aerospace problem, and compared with similar algorithms using benchmark problems.

Methods to assess carbonaceous aerosol sampling artifacts for IMPROVE and other long-term networks.

PubMed

Watson, John G; Chow, Judith C; Chen, L W Antony; Frank, Neil H

2009-08-01

Volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) adsorb to quartz fiber filters during fine and coarse particulate matter (PM2.5 and PM10, respectively) sampling for thermal/optical carbon analysis that measures organic carbon (OC) and elemental carbon (EC). Particulate SVOCs can evaporate after collection, with a small portion adsorbed within the filter. Adsorbed organic gases are measured as particulate OC, so passive field blanks, backup filters, prefilter organic denuders, and regression methods have been applied to compensate for positive OC artifacts in several long-term chemical speciation networks. Average backup filter OC levels from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network were approximately 19% higher than field blank values. This difference is within the standard deviation of the average and likely results from low SVOC concentrations in the rural to remote environments of most IMPROVE sites. Backup filters from an urban (Fort Meade, MD) site showed twice the OC levels of field blanks. Sectioning backup filters from top to bottom showed nonuniform OC densities within the filter, contrary to the assumption that VOCs and SVOCs on a backup filter equal those on the front filter. This nonuniformity may be partially explained by evaporation and readsorption of vapors in different parts of the front and backup quartz fiber filter owing to temperature, relative humidity, and ambient concentration changes throughout a 24-hr sample duration. OC-PM2.5 regression analysis and organic denuder approaches demonstrate negative sampling artifact from both Teflon membrane and quartz fiber filters.

Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

NASA Astrophysics Data System (ADS)

Mao, Deqing; Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu

2018-01-01

Doppler beam sharpening (DBS) is a critical technology for airborne radar ground mapping in forward-squint region. In conventional DBS technology, the narrow-band Doppler filter groups formed by fast Fourier transform (FFT) method suffer from low spectral resolution and high side lobe levels. The iterative adaptive approach (IAA), based on the weighted least squares (WLS), is applied to the DBS imaging applications, forming narrower Doppler filter groups than the FFT with lower side lobe levels. Regrettably, the IAA is iterative, and requires matrix multiplication and inverse operation when forming the covariance matrix, its inverse and traversing the WLS estimate for each sampling point, resulting in a notably high computational complexity for cubic time. We propose a fast IAA (FIAA)-based super-resolution DBS imaging method, taking advantage of the rich matrix structures of the classical narrow-band filtering. First, we formulate the covariance matrix via the FFT instead of the conventional matrix multiplication operation, based on the typical Fourier structure of the steering matrix. Then, by exploiting the Gohberg-Semencul representation, the inverse of the Toeplitz covariance matrix is computed by the celebrated Levinson-Durbin (LD) and Toeplitz-vector algorithm. Finally, the FFT and fast Toeplitz-vector algorithm are further used to traverse the WLS estimates based on the data-dependent trigonometric polynomials. The method uses the Hermitian feature of the echo autocorrelation matrix R to achieve its fast solution and uses the Toeplitz structure of R to realize its fast inversion. The proposed method enjoys a lower computational complexity without performance loss compared with the conventional IAA-based super-resolution DBS imaging method. The results based on simulations and measured data verify the imaging performance and the operational efficiency.

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.

W phase source inversion for moderate to large earthquakes (1990-2010)

USGS Publications Warehouse

Duputel, Zacharie; Rivera, Luis; Kanamori, Hiroo; Hayes, Gavin P.

2012-01-01

Rapid characterization of the earthquake source and of its effects is a growing field of interest. Until recently, it still took several hours to determine the first-order attributes of a great earthquake (e.g. Mw≥ 7.5), even in a well-instrumented region. The main limiting factors were data saturation, the interference of different phases and the time duration and spatial extent of the source rupture. To accelerate centroid moment tensor (CMT) determinations, we have developed a source inversion algorithm based on modelling of the W phase, a very long period phase (100–1000 s) arriving at the same time as the P wave. The purpose of this work is to finely tune and validate the algorithm for large-to-moderate-sized earthquakes using three components of W phase ground motion at teleseismic distances. To that end, the point source parameters of all Mw≥ 6.5 earthquakes that occurred between 1990 and 2010 (815 events) are determined using Federation of Digital Seismograph Networks, Global Seismographic Network broad-band stations and STS1 global virtual networks of the Incorporated Research Institutions for Seismology Data Management Center. For each event, a preliminary magnitude obtained from W phase amplitudes is used to estimate the initial moment rate function half duration and to define the corner frequencies of the passband filter that will be applied to the waveforms. Starting from these initial parameters, the seismic moment tensor is calculated using a preliminary location as a first approximation of the centroid. A full CMT inversion is then conducted for centroid timing and location determination. Comparisons with Harvard and Global CMT solutions highlight the robustness of W phase CMT solutions at teleseismic distances. The differences in Mw rarely exceed 0.2 and the source mechanisms are very similar to one another. Difficulties arise when a target earthquake is shortly (e.g. within 10 hr) preceded by another large earthquake, which disturbs the waveforms of the target event. To deal with such difficult situations, we remove the perturbation caused by earlier disturbing events by subtracting the corresponding synthetics from the data. The CMT parameters for the disturbed event can then be retrieved using the residual seismograms. We also explore the feasibility of obtaining source parameters of smaller earthquakes in the range 6.0 ≤Mw w= 6 or larger.

Molecular surface mesh generation by filtering electron density map.

PubMed

Giard, Joachim; Macq, Benoît

2010-01-01

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Efficient implementations of a pseudodynamical stochastic filtering strategy for static elastography.

PubMed

Banerjee, Biswanath; Roy, Debasish; Vasu, Ram Mohan

2009-08-01

A computationally efficient pseudodynamical filtering setup is established for elasticity imaging (i.e., reconstruction of shear modulus distribution) in soft-tissue organs given statically recorded and partially measured displacement data. Unlike a regularized quasi-Newton method (QNM) that needs inversion of ill-conditioned matrices, the authors explore pseudodynamic extended and ensemble Kalman filters (PD-EKF and PD-EnKF) that use a parsimonious representation of states and bypass explicit regularization by recursion over pseudotime. Numerical experiments with QNM and the two filters suggest that the PD-EnKF is the most robust performer as it exhibits no sensitivity to process noise covariance and yields good reconstruction even with small ensemble sizes.

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

PubMed

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

2015-10-14

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

Parameter Estimation for the Blind Restoration of Blurred Imagery.

DTIC Science & Technology

1986-09-01

17 Noise Process .... ............. 23 Restoration Methods .... .......... 26 Inverse Filter .... ........... 26 Wiener Filter...of Eq. (155) ....... .................... ... 64 Table 2 Restored Pictures and Noise Variances ........ . 69 v 5𔃼 5- viq °,. r -’ .’S’ .N’% N...restoration system. g(x,y) Degraded image. G(u,v) Discrete Fourier Transform of the degraded image. n(x,y) Noise . N(u,v) Discrete Fourier transform of n

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Decoupling control of vehicle chassis system based on neural network inverse system

NASA Astrophysics Data System (ADS)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

Scene-based nonuniformity correction for focal plane arrays by the method of the inverse covariance form.

PubMed

Torres, Sergio N; Pezoa, Jorge E; Hayat, Majeed M

2003-10-10

What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and the bias of each detector in the focal-plane array are assumed constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they are allowed to randomly drift from one sequence to another following a discrete-time Gauss-Markov process. The inverse covariance form filter estimates the gain and the bias of each detector in the focal-plane array and optimally updates them as they drift in time. The estimation is performed with considerably higher computational efficiency than the equivalent KF. The ability of the algorithm in compensating for fixed-pattern noise in infrared imagery and in reducing the computational complexity is demonstrated by use of both simulated and real data.

Sensitivity of Inverse Estimation of 2004 Elemental Carbon Emissions Inventory in the United States to the Choice of Observational Networks

EPA Science Inventory

Choice of observational networks used for inverse re-estimation of elemental (or black) carbon (EC) emissions in the United States impacts results. We convert the Thermal Optical Transmittance (TOT) EC measurements to the Thermal Optical Reflectance (TOR) equivalent to make full...

A Structural and Content-Based Analysis for Web Filtering.

ERIC Educational Resources Information Center

Lee, P. Y.; Hui, S. C.; Fong, A. C. M.

2003-01-01

Presents an analysis of the distinguishing features of pornographic Web pages so that effective filtering techniques can be developed. Surveys the existing techniques for Web content filtering and describes the implementation of a Web content filtering system that uses an artificial neural network. (Author/LRW)

Detection of anomalous signals in temporally correlated data (Invited)

NASA Astrophysics Data System (ADS)

Langbein, J. O.

2010-12-01

Detection of transient tectonic signals in data obtained from large geodetic networks requires the ability to detect signals that are both temporally and spatially coherent. In this report I will describe a modification to an existing method that estimates both the coefficients of temporally correlated noise model and an efficient filter based on the noise model. This filter, when applied to the original time-series, effectively whitens (or flattens) the power spectrum. The filtered data provide the means to calculate running averages which are then used to detect deviations from the background trends. For large networks, time-series of signal-to-noise ratio (SNR) can be easily constructed since, by filtering, each of the original time-series has been transformed into one that is closer to having a Gaussian distribution with a variance of 1.0. Anomalous intervals may be identified by counting the number of GPS sites for which the SNR exceeds a specified value. For example, during one time interval, if there were 5 out of 20 time-series with SNR>2, this would be considered anomalous; typically, one would expect at 95% confidence that there would be at least 1 out of 20 time-series with an SNR>2. For time intervals with an anomalously large number of high SNR, the spatial distribution of the SNR is mapped to identify the location of the anomalous signal(s) and their degree of spatial clustering. Estimating the filter that should be used to whiten the data requires modification of the existing methods that employ maximum likelihood estimation to determine the temporal covariance of the data. In these methods, it is assumed that the noise components in the data are a combination of white, flicker and random-walk processes and that they are derived from three different and independent sources. Instead, in this new method, the covariance matrix is constructed assuming that only one source is responsible for the noise and that source can be represented as a white-noise random-number generator convolved with a filter whose spectral properties are frequency (f) independent at its highest frequencies, 1/f at the middle frequencies, and 1/f2 at the lowest frequencies. For data sets with no gaps in their time-series, construction of covariance and inverse covariance matrices is extremely efficient. Application of the above algorithm to real data potentially involves several iterations as small, tectonic signals of interest are often indistinguishable from background noise. Consequently, simply plotting the time-series of each GPS site is used to identify the largest outliers and signals independent of their cause. Any analysis of the background noise levels must factor in these other signals while the gross outliers need to be removed.

Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

2016-09-01

The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey

2015-12-01

The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.

Preconditioner-free Wiener filtering with a dense noise matrix

NASA Astrophysics Data System (ADS)

Huffenberger, Kevin M.

2018-05-01

This work extends the Elsner & Wandelt (2013) iterative method for efficient, preconditioner-free Wiener filtering to cases in which the noise covariance matrix is dense, but can be decomposed into a sum whose parts are sparse in convenient bases. The new method, which uses multiple messenger fields, reproduces Wiener-filter solutions for test problems, and we apply it to a case beyond the reach of the Elsner & Wandelt (2013) method. We compute the Wiener-filter solution for a simulated Cosmic Microwave Background (CMB) map that contains spatially varying, uncorrelated noise, isotropic 1/f noise, and large-scale horizontal stripes (like those caused by atmospheric noise). We discuss simple extensions that can filter contaminated modes or inverse-noise-filter the data. These techniques help to address complications in the noise properties of maps from current and future generations of ground-based Microwave Background experiments, like Advanced ACTPol, Simons Observatory, and CMB-S4.

Fault Isolation Filter for Networked Control System with Event-Triggered Sampling Scheme

PubMed Central

Li, Shanbin; Sauter, Dominique; Xu, Bugong

2011-01-01

In this paper, the sensor data is transmitted only when the absolute value of difference between the current sensor value and the previously transmitted one is greater than the given threshold value. Based on this send-on-delta scheme which is one of the event-triggered sampling strategies, a modified fault isolation filter for a discrete-time networked control system with multiple faults is then implemented by a particular form of the Kalman filter. The proposed fault isolation filter improves the resource utilization with graceful fault estimation performance degradation. An illustrative example is given to show the efficiency of the proposed method. PMID:22346590

On the role of dimensionality and sample size for unstructured and structured covariance matrix estimation

NASA Technical Reports Server (NTRS)

Morgera, S. D.; Cooper, D. B.

1976-01-01

The experimental observation that a surprisingly small sample size vis-a-vis dimension is needed to achieve good signal-to-interference ratio (SIR) performance with an adaptive predetection filter is explained. The adaptive filter requires estimates as obtained by a recursive stochastic algorithm of the inverse of the filter input data covariance matrix. The SIR performance with sample size is compared for the situations where the covariance matrix estimates are of unstructured (generalized) form and of structured (finite Toeplitz) form; the latter case is consistent with weak stationarity of the input data stochastic process.

Transforming War Fighting through the Use of Service Based Architecture (SBA) Technology

DTIC Science & Technology

2006-05-04

near-real-time video & telemetry to users on network using standard web-based protocols – Provides web-based access to archived video files MTI...Target Tracks Service Capabilities – Disseminates near-real-time MTI and Target Tracks to users on network based on consumer specified geographic...filter IBS SIGINT Service Capabilities – Disseminates near-real-time IBS SIGINT data to users on network based on consumer specified geographic filter

Correlation filtering in financial time series (Invited Paper)

NASA Astrophysics Data System (ADS)

Aste, T.; Di Matteo, Tiziana; Tumminello, M.; Mantegna, R. N.

2005-05-01

We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al.,1 we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).

Using Deep Slow Slip in New Zealand to Constrain Slip Partitioning

NASA Astrophysics Data System (ADS)

Bartlow, N. M.; Wallace, L. M.

2016-12-01

Underneath New Zealand's North Island, the Pacific plate subducts obliquely beneath the Australian plate. Just to the south, subduction ceases and the plate boundary transitions to the mainly strike-slip, steeply dipping Alpine fault that runs along the South Island. In the region of the southern North Island, the relative plate motion has significant components of both convergence and along strike motion, and slip is partitioned between the main Hikurangi subduction interface and a series of shallower strike-slip faults running thurough the North Island (Wallace and Beavan, GRL, 2010). This region also hosts deep ( 50 km), long duration ( 1 year) slow slip events (SSEs). From early 2013 to early 2016, continuous GPS stations maintained by GeoNet in this region recorded two such deep SSEs on the Hikurangi megathrust. The first SSE occurred on the Kapiti patch, just southwest of the North Island coast. SSEs previous occurred here in 2003 and 2008 (Wallace and Beavan, JGR, 2010). The 2014 Kapiti SSE is unique because it was rapidly decelerated following increased normal stress (clamping) caused by a nearby M 6.3 earthquake (Wallace et al., GRL, 2014). However, GPS data indicates that slip did not stop entirely, and soon after the Manawatu slow slip patch just to the northeast ruptured in another SSE. This patch previously had large SSEs in 2004/2005 and 2010/2011. Given the previous repeat interval of 5.5 years, the 2014/2015 Manawatu SSE is early; however, the record is very short. Here we show Network Inversion Filter derived models of slow slip for the various phases of the Kapiti and Manawatu SSEs, which indicate a possible continuous migration of slip from the Kapiti SSE patch to the Manawatu SSE patch, and we quantify the shear stress increase on the Manawatu patch after the Kapiti SSE. Additionally, we explore allowing the Network Inversion Filter to vary the direction of slip on the plate interface to better fit the data. We estimate how much of the strike-slip and dip-slip components of the relative plate motion are being accommodated by the main thrust interface, and infer how much slip is being accommodated by the strike-slip faults and forearc rotation. We compare our results to those from prior block models of inter-SSE data (Wallace et al., G3, 2009) and explore the implications for seismic hazard assessment in this region.

Radiated emissions comparison of seven-stage modal filter constructions for Ethernet 100Base-T network protection

NASA Astrophysics Data System (ADS)

Khazhibekov, R. R.; Zabolotsky, A. M.

2018-05-01

The authors consider Ethernet protection devices based on modal filtering. Radiated emission measurement results for three modal filter constructions are presented. It is shown that the improved construction of a non-resistive filter has lower emission levels than the original one.

Phase Inversion: Inferring Solar Subphotospheric Flow and Other Asphericity from the Distortion of Acoustic Waves

NASA Technical Reports Server (NTRS)

Gough, Douglas; Merryfield, William J.; Toomre, Juri

1998-01-01

A method is proposed for analyzing an almost monochromatic train of waves propagating in a single direction in an inhomogeneous medium that is not otherwise changing in time. An effective phase is defined in terms of the Hilbert transform of the wave function, which is related, via the JWKB approximation, to the spatial variation of the background state against which the wave is propagating. The contaminating effect of interference between the truly monochromatic components of the train is eliminated using its propagation properties. Measurement errors, provided they are uncorrelated, are manifest as rapidly varying noise; although that noise can dominate the raw phase-processed signal, it can largely be removed by low-pass filtering. The intended purpose of the analysis is to determine the distortion of solar oscillations induced by horizontal structural variation and material flow. It should be possible to apply the method directly to sectoral modes. The horizontal phase distortion provides a measure of longitudinally averaged properties of the Sun in the vicinity of the equator, averaged also in radius down to the depth to which the modes penetrate. By combining such averages from different modes, the two-dimensional variation can be inferred by standard inversion techniques. After taking due account of horizontal refraction, it should be possible to apply the technique also to locally sectoral modes that propagate obliquely to the equator and thereby build a network of lateral averages at each radius, from which the full three-dimensional structure of the Sun can, in principle, be determined as an inverse Radon transform.

Seismic tomography of the southern California crust based on spectral-element and adjoint methods

NASA Astrophysics Data System (ADS)

Tape, Carl; Liu, Qinya; Maggi, Alessia; Tromp, Jeroen

2010-01-01

We iteratively improve a 3-D tomographic model of the southern California crust using numerical simulations of seismic wave propagation based on a spectral-element method (SEM) in combination with an adjoint method. The initial 3-D model is provided by the Southern California Earthquake Center. The data set comprises three-component seismic waveforms (i.e. both body and surface waves), filtered over the period range 2-30 s, from 143 local earthquakes recorded by a network of 203 stations. Time windows for measurements are automatically selected by the FLEXWIN algorithm. The misfit function in the tomographic inversion is based on frequency-dependent multitaper traveltime differences. The gradient of the misfit function and related finite-frequency sensitivity kernels for each earthquake are computed using an adjoint technique. The kernels are combined using a source subspace projection method to compute a model update at each iteration of a gradient-based minimization algorithm. The inversion involved 16 iterations, which required 6800 wavefield simulations. The new crustal model, m16, is described in terms of independent shear (VS) and bulk-sound (VB) wave speed variations. It exhibits strong heterogeneity, including local changes of +/-30 per cent with respect to the initial 3-D model. The model reveals several features that relate to geological observations, such as sedimentary basins, exhumed batholiths, and contrasting lithologies across faults. The quality of the new model is validated by quantifying waveform misfits of full-length seismograms from 91 earthquakes that were not used in the tomographic inversion. The new model provides more accurate synthetic seismograms that will benefit seismic hazard assessment.

Adjoint Tomography of the Southern California Crust (Invited) (Invited)

NASA Astrophysics Data System (ADS)

Tape, C.; Liu, Q.; Maggi, A.; Tromp, J.

2009-12-01

We iteratively improve a three-dimensional tomographic model of the southern California crust using numerical simulations of seismic wave propagation based on a spectral-element method (SEM) in combination with an adjoint method. The initial 3D model is provided by the Southern California Earthquake Center. The dataset comprises three-component seismic waveforms (i.e. both body and surface waves), filtered over the period range 2-30 s, from 143 local earthquakes recorded by a network of 203 stations. Time windows for measurements are automatically selected by the FLEXWIN algorithm. The misfit function in the tomographic inversion is based on frequency-dependent multitaper traveltime differences. The gradient of the misfit function and related finite-frequency sensitivity kernels for each earthquake are computed using an adjoint technique. The kernels are combined using a source subspace projection method to compute a model update at each iteration of a gradient-based minimization algorithm. The inversion involved 16 iterations, which required 6800 wavefield simulations and a total of 0.8 million CPU hours. The new crustal model, m16, is described in terms of independent shear (Vs) and bulk-sound (Vb) wavespeed variations. It exhibits strong heterogeneity, including local changes of ±30% with respect to the initial 3D model. The model reveals several features that relate to geologic observations, such as sedimentary basins, exhumed batholiths, and contrasting lithologies across faults. The quality of the new model is validated by quantifying waveform misfits of full-length seismograms from 91 earthquakes that were not used in the tomographic inversion. The new model provides more accurate synthetic seismograms that will benefit seismic hazard assessment.

Multichannel Networked Phasemeter Readout and Analysis

NASA Technical Reports Server (NTRS)

Edmonds, Karina

2008-01-01

Netmeter software reads a data stream from up to 250 networked phasemeters, synchronizes the data, saves the reduced data to disk (after applying a low-pass filter), and provides a Web server interface for remote control. Unlike older phasemeter software that requires a special, real-time operating system, this program can run on any general-purpose computer. It needs about five percent of the CPU (central processing unit) to process 20 channels because it adds built-in data logging and network-based GUIs (graphical user interfaces) that are implemented in Scalable Vector Graphics (SVG). Netmeter runs on Linux and Windows. It displays the instantaneous displacements measured by several phasemeters at a user-selectable rate, up to 1 kHz. The program monitors the measure and reference channel frequencies. For ease of use, levels of status in Netmeter are color coded: green for normal operation, yellow for network errors, and red for optical misalignment problems. Netmeter includes user-selectable filters up to 4 k samples, and user-selectable averaging windows (after filtering). Before filtering, the program saves raw data to disk using a burst-write technique.

Integrating image processing and classification technology into automated polarizing film defect inspection

NASA Astrophysics Data System (ADS)

Kuo, Chung-Feng Jeffrey; Lai, Chun-Yu; Kao, Chih-Hsiang; Chiu, Chin-Hsun

2018-05-01

In order to improve the current manual inspection and classification process for polarizing film on production lines, this study proposes a high precision automated inspection and classification system for polarizing film, which is used for recognition and classification of four common defects: dent, foreign material, bright spot, and scratch. First, the median filter is used to remove the impulse noise in the defect image of polarizing film. The random noise in the background is smoothed by the improved anisotropic diffusion, while the edge detail of the defect region is sharpened. Next, the defect image is transformed by Fourier transform to the frequency domain, combined with a Butterworth high pass filter to sharpen the edge detail of the defect region, and brought back by inverse Fourier transform to the spatial domain to complete the image enhancement process. For image segmentation, the edge of the defect region is found by Canny edge detector, and then the complete defect region is obtained by two-stage morphology processing. For defect classification, the feature values, including maximum gray level, eccentricity, the contrast, and homogeneity of gray level co-occurrence matrix (GLCM) extracted from the images, are used as the input of the radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) classifier, 96 defect images are then used as training samples, and 84 defect images are used as testing samples to validate the classification effect. The result shows that the classification accuracy by using RBFNN is 98.9%. Thus, our proposed system can be used by manufacturing companies for a higher yield rate and lower cost. The processing time of one single image is 2.57 seconds, thus meeting the practical application requirement of an industrial production line.

Inverse problem of radiofrequency sounding of ionosphere

NASA Astrophysics Data System (ADS)

Velichko, E. N.; Yu. Grishentsev, A.; Korobeynikov, A. G.

2016-01-01

An algorithm for the solution of the inverse problem of vertical ionosphere sounding and a mathematical model of noise filtering are presented. An automated system for processing and analysis of spectrograms of vertical ionosphere sounding based on our algorithm is described. It is shown that the algorithm we suggest has a rather high efficiency. This is supported by the data obtained at the ionospheric stations of the so-called “AIS-M” type.

An Efficient Conflict Detection Algorithm for Packet Filters

NASA Astrophysics Data System (ADS)

Lee, Chun-Liang; Lin, Guan-Yu; Chen, Yaw-Chung

Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.

Recursive inverse factorization.

PubMed

Rubensson, Emanuel H; Bock, Nicolas; Holmström, Erik; Niklasson, Anders M N

2008-03-14

A recursive algorithm for the inverse factorization S(-1)=ZZ(*) of Hermitian positive definite matrices S is proposed. The inverse factorization is based on iterative refinement [A.M.N. Niklasson, Phys. Rev. B 70, 193102 (2004)] combined with a recursive decomposition of S. As the computational kernel is matrix-matrix multiplication, the algorithm can be parallelized and the computational effort increases linearly with system size for systems with sufficiently sparse matrices. Recent advances in network theory are used to find appropriate recursive decompositions. We show that optimization of the so-called network modularity results in an improved partitioning compared to other approaches. In particular, when the recursive inverse factorization is applied to overlap matrices of irregularly structured three-dimensional molecules.

The simulation of magnetic resonance elastography through atherosclerosis.

PubMed

Thomas-Seale, L E J; Hollis, L; Klatt, D; Sack, I; Roberts, N; Pankaj, P; Hoskins, P R

2016-06-14

The clinical diagnosis of atherosclerosis via the measurement of stenosis size is widely acknowledged as an imperfect criterion. The vulnerability of an atherosclerotic plaque to rupture is associated with its mechanical properties. The potential to image these mechanical properties using magnetic resonance elastography (MRE) was investigated through synthetic datasets. An image of the steady state wave propagation, equivalent to the first harmonic, can be extracted directly from finite element analysis. Inversion of this displacement data yields a map of the shear modulus, known as an elastogram. The variation of plaque composition, stenosis size, Gaussian noise, filter thresholds and excitation frequency were explored. A decreasing mean shear modulus with an increasing lipid composition was identified through all stenosis sizes. However the inversion algorithm showed sensitivity to parameter variation leading to artefacts which disrupted both the elastograms and quantitative trends. As noise was increased up to a realistic level, the contrast was maintained between the fully fibrous and lipid plaques but lost between the interim compositions. Although incorporating a Butterworth filter improved the performance of the algorithm, restrictive filter thresholds resulted in a reduction of the sensitivity of the algorithm to composition and noise variation. Increasing the excitation frequency improved the techniques ability to image the magnitude of the shear modulus and identify a contrast between compositions. In conclusion, whilst the technique has the potential to image the shear modulus of atherosclerotic plaques, future research will require the integration of a heterogeneous inversion algorithm. Copyright © 2016 Elsevier Ltd. All rights reserved.

Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and $l_{2}$ - $l_{\\infty }$ Performances.

PubMed

Choi, Hyun Duck; Ahn, Choon Ki; Karimi, Hamid Reza; Lim, Myo Taeg

2017-10-01

This paper studies delay-dependent exponential dissipative and l 2 - l ∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l 2 - l ∞ senses. The design of the desired exponential dissipative and l 2 - l ∞ filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

PubMed

Sokoloski, Sacha

2017-09-01

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

Calculation method of water injection forward modeling and inversion process in oilfield water injection network

NASA Astrophysics Data System (ADS)

Liu, Long; Liu, Wei

2018-04-01

A forward modeling and inversion algorithm is adopted in order to determine the water injection plan in the oilfield water injection network. The main idea of the algorithm is shown as follows: firstly, the oilfield water injection network is inversely calculated. The pumping station demand flow is calculated. Then, forward modeling calculation is carried out for judging whether all water injection wells meet the requirements of injection allocation or not. If all water injection wells meet the requirements of injection allocation, calculation is stopped, otherwise the demand injection allocation flow rate of certain step size is reduced aiming at water injection wells which do not meet requirements, and next iterative operation is started. It is not necessary to list the algorithm into water injection network system algorithm, which can be realized easily. Iterative method is used, which is suitable for computer programming. Experimental result shows that the algorithm is fast and accurate.

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

NASA Astrophysics Data System (ADS)

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

2017-03-01

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

Computed inverse resonance imaging for magnetic susceptibility map reconstruction.

PubMed

Chen, Zikuan; Calhoun, Vince

2012-01-01

This article reports a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a 2-step computational approach. The forward T2*-weighted MRI (T2*MRI) process is broken down into 2 steps: (1) from magnetic susceptibility source to field map establishment via magnetization in the main field and (2) from field map to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes 2 inverse steps to reverse the T2*MRI procedure: field map calculation from MR-phase image and susceptibility source calculation from the field map. The inverse step from field map to susceptibility map is a 3-dimensional ill-posed deconvolution problem, which can be solved with 3 kinds of approaches: the Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from an MR-phase image with high fidelity (spatial correlation ≈ 0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by 2 computational steps: calculating the field map from the phase image and reconstructing the susceptibility map from the field map. The crux of CIMRI lies in an ill-posed 3-dimensional deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm.

Computed inverse MRI for magnetic susceptibility map reconstruction

PubMed Central

Chen, Zikuan; Calhoun, Vince

2015-01-01

Objective This paper reports on a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a two-step computational approach. Methods The forward T2*-weighted MRI (T2*MRI) process is decomposed into two steps: 1) from magnetic susceptibility source to fieldmap establishment via magnetization in a main field, and 2) from fieldmap to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes two inverse steps to reverse the T2*MRI procedure: fieldmap calculation from MR phase image and susceptibility source calculation from the fieldmap. The inverse step from fieldmap to susceptibility map is a 3D ill-posed deconvolution problem, which can be solved by three kinds of approaches: Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. Results Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from a MR phase image with high fidelity (spatial correlation≈0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. Conclusions The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by two computational steps: calculating the fieldmap from the phase image and reconstructing the susceptibility map from the fieldmap. The crux of CIMRI lies in an ill-posed 3D deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm. PMID:22446372

Lithospheric layering in the North American craton revealed by including Short Period Constraints in Full Waveform Tomography

NASA Astrophysics Data System (ADS)

Roy, C.; Calo, M.; Bodin, T.; Romanowicz, B. A.

2017-12-01

Recent receiver function studies of the North American craton suggest the presence of significant layering within the cratonic lithosphere, with significant lateral variations in the depth of the velocity discontinuities. These structural boundaries have been confirmed recently using a transdimensional Markov Chain Monte Carlo approach (TMCMC), inverting surface wave dispersion data and converted phases simultaneously (Calò et al., 2016; Roy and Romanowicz 2017). The lateral resolution of upper mantle structure can be improved with a high density of broadband seismic stations, or with a sparse network using full waveform inversion based on numerical wavefield computation methods such as the Spectral Element Method (SEM). However, inverting for discontinuities with strong topography such as MLDS's or LAB, presents challenges in an inversion framework, both computationally, due to the short periods required, and from the point of view of stability of the inversion. To overcome these limitations, and to improve resolution of layering in the upper mantle, we are developing a methodology that combines full waveform inversion tomography and information provided by short period seismic observables. We have extended the 30 1D radially anisotropic shear velocity profiles of Calò et al. 2016 to several other stations, for which we used a recent shear velocity model (Clouzet et al., 2017) as constraint in the modeling. These 1D profiles, including both isotropic and anisotropic discontinuities in the upper mantle (above 300 km depth) are then used to build a 3D starting model for the full waveform tomographic inversion. This model is built after 1) homogenization of the layered 1D models and 2) interpolation between the 1D smooth profiles and the model of Clouzet et al. 2017, resulting in a smooth 3D starting model. Waveforms used in the inversion are filtered at periods longer than 30s. We use the SEM code "RegSEM" for forward computations and a quasi-Newton inversion approach in which kernels are computed using normal mode perturbation theory. The resulting volumetric velocity perturbations around the homogenized starting model are then added to the discontinuous 3D starting model by dehomogenizing the model. We present here the first results of such an approach for refining structure in the North American continent.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

PubMed

Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

2016-11-01

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

An accurate nonlinear stochastic model for MEMS-based inertial sensor error with wavelet networks

NASA Astrophysics Data System (ADS)

El-Diasty, Mohammed; El-Rabbany, Ahmed; Pagiatakis, Spiros

2007-12-01

The integration of Global Positioning System (GPS) with Inertial Navigation System (INS) has been widely used in many applications for positioning and orientation purposes. Traditionally, random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in classical Kalman filters. The main disadvantage of classical Kalman filter is the potentially unstable linearization of the nonlinear dynamic system. Consequently, a nonlinear stochastic model is not optimal in derivative-based filters due to the expected linearization error. With a derivativeless-based filter such as the unscented Kalman filter or the divided difference filter, the filtering process of a complicated highly nonlinear dynamic system is possible without linearization error. This paper develops a novel nonlinear stochastic model for inertial sensor error using a wavelet network (WN). A wavelet network is a highly nonlinear model, which has recently been introduced as a powerful tool for modelling and prediction. Static and kinematic data sets are collected using a MEMS-based IMU (DQI-100) to develop the stochastic model in the static mode and then implement it in the kinematic mode. The derivativeless-based filtering method using GM, AR, and the proposed WN-based processes are used to validate the new model. It is shown that the first-order WN-based nonlinear stochastic model gives superior positioning results to the first-order GM and AR models with an overall improvement of 30% when 30 and 60 seconds GPS outages are introduced.

Application of neural networks with orthogonal activation functions in control of dynamical systems

NASA Astrophysics Data System (ADS)

Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

2016-04-01

In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

Echo state networks with filter neurons and a delay&sum readout.

PubMed

Holzmann, Georg; Hauser, Helmut

2010-03-01

Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It has been demonstrated that ESNs outperform other methods on a number of benchmark tasks. Although the approach is appealing, there are still some inherent limitations in the original formulation. Here we suggest two enhancements of this network model. First, the previously proposed idea of filters in neurons is extended to arbitrary infinite impulse response (IIR) filter neurons. This enables such networks to learn multiple attractors and signals at different timescales, which is especially important for modeling real-world time series. Second, a delay&sum readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks. It is shown in commonly used benchmark tasks and real-world examples, that this new structure is able to significantly outperform standard ESNs and other state-of-the-art models for nonlinear dynamical system modeling. Copyright 2009 Elsevier Ltd. All rights reserved.

78 FR 57648 - Notice of Issuance of Final Determination Concerning Video Teleconferencing Server

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-19

... the Chinese- origin Video Board and the Filter Board, impart the essential character to the video... includes the codec; a network filter electronic circuit board (``Filter Board''); a housing case; a power... (``Linux software''). The Linux software allows the Filter Board to inspect each Ethernet packet of...

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

NASA Astrophysics Data System (ADS)

Wutsqa, D. U.; Marwah, M.

2017-06-01

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

An exact algorithm for optimal MAE stack filter design.

PubMed

Dellamonica, Domingos; Silva, Paulo J S; Humes, Carlos; Hirata, Nina S T; Barrera, Junior

2007-02-01

We propose a new algorithm for optimal MAE stack filter design. It is based on three main ingredients. First, we show that the dual of the integer programming formulation of the filter design problem is a minimum cost network flow problem. Next, we present a decomposition principle that can be used to break this dual problem into smaller subproblems. Finally, we propose a specialization of the network Simplex algorithm based on column generation to solve these smaller subproblems. Using our method, we were able to efficiently solve instances of the filter problem with window size up to 25 pixels. To the best of our knowledge, this is the largest dimension for which this problem was ever solved exactly.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

PubMed

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

PubMed

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Effects of the measurement configuration in GPR prospecting

NASA Astrophysics Data System (ADS)

Persico, Raffaele; Soldovieri, Francesco

2017-04-01

The measurement configuration is an issue of great interest in problems of inverse scattering in general, and in particular in problems regarding GPR data. In particular, the measurement configuration has an influence on the amount of retrievable information [1-2] and can be a way to achieve an intrinsic two dimensional filtering of the data [3], possibly accounting for the characteristics of the exploited antennas too [4]. However, no filter is able to erase exactly the undesired contribution to the comprehensive signal while leaving unperturbed the useful part of the gathered datum. In other word, any filtering of the data (included those implicitly imposed through the measurement configuration) has some price in terms of loss or distortion of the received information, and therefore it has to be applied only when needed and only at the right degree of intensity. In particular, differential measurement configurations have been introduced in the last few years, especially with interest in the field of detection of UXO [5-6]. The filtering effects in some differential configuration are not immediately understood, but need some deep reasoning. In particular, the theory of the diffraction tomography, allows to quantify the retrievable spatial frequencies under the measurement configuration at hand, and so allows to quantify the filtering effect of the differential configurations. Examples will be shown at the conference, regarding both a horizontal and a vertical differential configuration. References [1] R. Persico, R. Bernini, F. Soldovieri, "The role of the measurement configuration in inverse scattering from buried objects under the Born approximation", IEEE Trans. On Antennas and Prop., vol. 53, n. 6, pp. 1875-1886, June 2005. [2] R. Persico, "On the role of measurement Configuration in Contactless GPR data Processing by Means of Linear Inverse Scattering, IEEE Trans. On Antennas and Prop AP, Vol. 54 n. 7 p. 2062-2071, July 2006. [3] R. Persico, F. Soldovieri, Effects of the Background Removal in Linear Inverse Scattering, IEEE Trans. on Geos. and Rem. Sens., vol. 46, n. 4, pp. 1104-1114, April 2008. [4] F. Soldovieri, R. Persico and G. Leone, "Effect of source and receiver radiation characteristics in subsurface prospecting within the DBA", Radio Science, vol. 40, RS3006, May 2005. [5] R. Persico, F. Soldovieri, A Microwave Tomography approach for a Differential Configuration in GPR Prospecting, IEEE Trans. On Antennas and Prop AP, vol. 54, n. 11, pp. 3541-3548, November 2006. [6] R. Persico, G. Pochanin, V. Ruban, I. Catapano, F. Soldovieri, Performances of a Microwave Tomographic Algorithm for GPR Systems Working in Differential Configuration, IEEE Jstars, vol. 9, n. 4, pp. 1343-1356, April 2016.

Network Visualization Project (NVP)

DTIC Science & Technology

2016-07-01

network visualization, network traffic analysis, network forensics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...shell, is a command-line framework used for network forensic analysis. Dshell processes existing pcap files and filters output information based on

Gravity effects on information filtering and network evolving.

PubMed

Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

2014-01-01

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.

Study on spin filtering and switching action in a double-triangular network chain

NASA Astrophysics Data System (ADS)

Zhang, Yongmei

2018-04-01

Spin transport properties of a double-triangular quantum network with local magnetic moment on backbones and magnetic flux penetrating the network plane are studied. Numerical simulation results show that such a quantum network will be a good candidate for spin filter and spin switch. Local dispersion and density of states are considered in the framework of tight-binding approximation. Transmission coefficients are calculated by the method of transfer matrix. Spin transmission is regulated by substrate magnetic moment and magnetic flux piercing those triangles. Experimental realization of such theoretical research will be conducive to designing of new spintronic devices.

Gravity Effects on Information Filtering and Network Evolving

PubMed Central

Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

2014-01-01

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model. PMID:24622162

Linear-phase delay filters for ultra-low-power signal processing in neural recording implants.

PubMed

Gosselin, Benoit; Sawan, Mohamad; Kerherve, Eric

2010-06-01

We present the design and implementation of linear-phase delay filters for ultra-low-power signal processing in neural recording implants. We use these filters as low-distortion delay elements along with an automatic biopotential detector to perform integral waveform extraction and efficient power management. The presented delay elements are realized employing continuous-time OTA-C filters featuring 9th-order equiripple transfer functions with constant group delay. Such analog delay enables processing neural waveforms with reduced overhead compared to a digital delay since it does not requires sampling and digitization. It uses an allpass transfer function for achieving wider constant-delay bandwidth than all-pole does. Two filters realizations are compared for implementing the delay element: the Cascaded structure and the Inverse follow-the-leader feedback filter. Their respective strengths and drawbacks are assessed by modeling parasitics and non-idealities of OTAs, and by transistor-level simulations. A budget of 200 nA is used in both filters. Experimental measurements with the chosen filter topology are presented and discussed.

Class network routing

DOEpatents

Bhanot, Gyan [Princeton, NJ; Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2009-09-08

Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.

Intra-Inversion Filtering for Use of Magnetic Fields to Locate and Characterize Magnetic Dipoles for Unexploded Ordnance (UXO) Cleanup

DTIC Science & Technology

2007-02-26

IIGE Intra-Inversion Gradient Estimation JPG Jefferson Proving Ground (Indiana); www.jpgbrac.com MTADS Multi- sensor Towed Array Detection...wherein the Statement of Need sought development of algorithms to exploit data from current state-of-the-art geophysical sensors and advanced sensors ...profile direction using an array of magnetometers as in the Multi- sensor Towed Array Detection System (MTADS). In most instances, such data may be

Performance comparisons on spatial lattice algorithm and direct matrix inverse method with application to adaptive arrays processing

NASA Technical Reports Server (NTRS)

An, S. H.; Yao, K.

1986-01-01

Lattice algorithm has been employed in numerous adaptive filtering applications such as speech analysis/synthesis, noise canceling, spectral analysis, and channel equalization. In this paper the application to adaptive-array processing is discussed. The advantages are fast convergence rate as well as computational accuracy independent of the noise and interference conditions. The results produced by this technique are compared to those obtained by the direct matrix inverse method.

Variability of the 2014-present inflation source at Mauna Loa volcano revealed using time-dependent modeling

NASA Astrophysics Data System (ADS)

Johanson, I. A.; Miklius, A.; Okubo, P.; Montgomery-Brown, E. K.

2017-12-01

Mauna Loa volcano is the largest active volcano on earth and in the 20thcentury produced roughly one eruption every seven years. The 33-year quiescence since its last eruption 1984 has been punctuated by three inflation episodes where magma likely entered the shallow plumbing system, but was not erupted. The most recent began in 2014 and is ongoing. Unlike prior inflation episodes, the current one is accompanied by a significant increase in shallow seismicity, a pattern that is similar to earlier pre-eruptive periods. We apply the Kalman filter based Network Inversion Filter (NIF) to the 2014-present inflation episode using data from a 27 station continuous GPS network on Mauna Loa. The model geometry consists of a point volume source and tabular, dike-like body, which have previously been shown to provide a good fit to deformation data from a 2004-2009 inflation episode. The tabular body is discretized into 1km x 1km segments. For each day, the NIF solves for the rates of opening on the tabular body segments (subject to smoothing and positivity constraints), volume change rate in the point source, and slip rate on a deep décollement fault surface, which is constrained to a constant (no transient slip allowed). The Kalman filter in the NIF provides for smoothing both forwards and backwards in time. The model shows that the 2014-present inflation episode occurred as several sub-events, rather than steady inflation. It shows some spatial variability in the location of the inflation sub-events. In the model, opening in the tabular body is initially concentrated below the volcano's summit, in an area roughly outlined by shallow seismicity. In October, 2015 opening in the tabular body shifts to be centered beneath the southwest portion of the summit and seismicity becomes concentrated in this area. By late 2016, the opening rate on the tabular body decreases and is once again under the central part of summit. This modeling approach has allowed us to track these features on a daily basis and capture the evolution of the inflation episode as it occurs.

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We also demonstrate that differences between the modeled and observed meteorology can be used to predict uncertainties associated with atmospheric transport and dispersion modeling which can help improve the skill of an inversion at urban scales.

Maximally Informative Statistics for Localization and Mapping

NASA Technical Reports Server (NTRS)

Deans, Matthew C.

2001-01-01

This paper presents an algorithm for localization and mapping for a mobile robot using monocular vision and odometry as its means of sensing. The approach uses the Variable State Dimension filtering (VSDF) framework to combine aspects of Extended Kalman filtering and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use an interpolation scheme based on Gaussian quadrature to linearize measurements rather than relying on analytic Jacobians. The second is to replace the inverse covariance matrix in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the problem of localization and mapping with omnidirectional vision are presented.

Training of Attentional Filtering, but Not of Memory Storage, Enhances Working Memory Efficiency by Strengthening the Neuronal Gatekeeper Network.

PubMed

Schmicker, Marlen; Schwefel, Melanie; Vellage, Anne-Katrin; Müller, Notger G

2016-04-01

Memory training (MT) in older adults with memory deficits often leads to frustration and, therefore, is usually not recommended. Here, we pursued an alternative approach and looked for transfer effects of 1-week attentional filter training (FT) on working memory performance and its neuronal correlates in young healthy humans. The FT effects were compared with pure MT, which lacked the necessity to filter out irrelevant information. Before and after training, all participants performed an fMRI experiment that included a combined task in which stimuli had to be both filtered based on color and stored in memory. We found that training induced processing changes by biasing either filtering or storage. FT induced larger transfer effects on the untrained cognitive function than MT. FT increased neuronal activity in frontal parts of the neuronal gatekeeper network, which is proposed to hinder irrelevant information from being unnecessarily stored in memory. MT decreased neuronal activity in the BG part of the gatekeeper network but enhanced activity in the parietal storage node. We take these findings as evidence that FT renders working memory more efficient by strengthening the BG-prefrontal gatekeeper network. MT, on the other hand, simply stimulates storage of any kind of information. These findings illustrate a tight connection between working memory and attention, and they may open up new avenues for ameliorating memory deficits in patients with cognitive impairments.

Neural network modeling of a dolphin's sonar discrimination capabilities.

PubMed

Au, W W; Andersen, L N; Rasmussen, A R; Roitblat, H L; Nachtigall, P E

1995-07-01

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information from the echoes. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters and the energy from each filter was computed in time increments of 1/bandwidth. Echo features of the standard and each comparison target were analyzed in pairs by a counterpropagation neural network, a backpropagation neural network, and a model using Euclidean distance measures. The backpropagation network performed better than both the counterpropagation network, and the Euclidean model, using either spectral-only features or combined temporal and spectral features. All models performed better using features containing both temporal and spectral information. The backpropagation network was able to perform better than the dolphins for noise-free echoes with Q values as low as 2 and 3. For a Q of 2, only temporal information was available. However, with noisy data, the network required a Q of 8 in order to perform as well as the dolphin.

Development of a Comb Limiter Combiner with Sub band Known Interference Cancellation

DTIC Science & Technology

2017-10-17

Juarez, Head 55190 Networks Division ACRONYMS ABSF absorptive bandstop filters ATP applied thin films BAW bulk acoustic waves BPF bandpass filter ...BSF bandstop filters CW continuous wave CWSP Commercial Wideband Satellite Program DAC digital to analog converter DAC digital to analog converter...8 3.2 FREQUENCY AGILE ABSORPTIVE NOTCH FILTERS ................................................. 9 3.3 INTEGRATION OF

Multiple attenuation to reflection seismic data using Radon filter and Wave Equation Multiple Rejection (WEMR) method

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

Erlangga, Mokhammad Puput

Separation between signal and noise, incoherent or coherent, is important in seismic data processing. Although we have processed the seismic data, the coherent noise is still mixing with the primary signal. Multiple reflections are a kind of coherent noise. In this research, we processed seismic data to attenuate multiple reflections in the both synthetic and real seismic data of Mentawai. There are several methods to attenuate multiple reflection, one of them is Radon filter method that discriminates between primary reflection and multiple reflection in the τ-p domain based on move out difference between primary reflection and multiple reflection. However, inmore » case where the move out difference is too small, the Radon filter method is not enough to attenuate the multiple reflections. The Radon filter also produces the artifacts on the gathers data. Except the Radon filter method, we also use the Wave Equation Multiple Elimination (WEMR) method to attenuate the long period multiple reflection. The WEMR method can attenuate the long period multiple reflection based on wave equation inversion. Refer to the inversion of wave equation and the magnitude of the seismic wave amplitude that observed on the free surface, we get the water bottom reflectivity which is used to eliminate the multiple reflections. The WEMR method does not depend on the move out difference to attenuate the long period multiple reflection. Therefore, the WEMR method can be applied to the seismic data which has small move out difference as the Mentawai seismic data. The small move out difference on the Mentawai seismic data is caused by the restrictiveness of far offset, which is only 705 meter. We compared the real free multiple stacking data after processing with Radon filter and WEMR process. The conclusion is the WEMR method can more attenuate the long period multiple reflection than the Radon filter method on the real (Mentawai) seismic data.« less

Spectral Filtering Criteria for U-Band Test Light for In-Service Line Monitoring in Optical Fiber Networks

NASA Astrophysics Data System (ADS)

Honda, Nazuki; Izumita, Hisashi; Nakamura, Minoru

2006-06-01

In the fiber-to-the-home era, thousands of optical fibers will have to be accommodated in the central offices of optical access networks. To reduce maintenance costs and improve the service reliability of optical fiber networks, the authors must develop an optical fiber line testing system with a function for in-service line monitoring that uses a test light with a wavelength different from the communication light wavelength. To monitor an in-service line in an optical network, the effective rejection ratio of the test light must be taken into account. This ratio depends on the spectrum of the test light from the optical time-domain reflectometer and the rejection band of the filter in front of the optical network unit. The dependence of the effective rejection ratio as a function of the sideband suppression ratio (SBSR) and of the ratio of the rejection band to the bandwidth of the sideband noise d/D is clarified. When d/D =0.1 and the target effective rejection ratio of the filter is -40 dB, the SBSR and the filter loss of the termination cable must be -70 and -43 dB, respectively, or the SBSR must be -80 dB. When d/D < 0.5 and the target effective rejection ratio of the filter is -40 dB, the SBSR is also required to be -80 dB. In-service line monitoring for a 10-Gb/s transmission using a 1650-nm test light with an SBSR of -80 dB is also demonstrated.

An Efficient Index Dissemination in Unstructured Peer-to-Peer Networks

NASA Astrophysics Data System (ADS)

Takahashi, Yusuke; Izumi, Taisuke; Kakugawa, Hirotsugu; Masuzawa, Toshimitsu

Using Bloom filters is one of the most popular and efficient lookup methods in P2P networks. A Bloom filter is a representation of data item indices, which achieves small memory requirement by allowing one-sided errors (false positive). In the lookup scheme besed on the Bloom filter, each peer disseminates a Bloom filter representing indices of the data items it owns in advance. Using the information of disseminated Bloom filters as a clue, each query can find a short path to its destination. In this paper, we propose an efficient extension of the Bloom filter, called a Deterministic Decay Bloom Filter (DDBF) and an index dissemination method based on it. While the index dissemination based on a standard Bloom filter suffers performance degradation by containing information of too many data items when its dissemination radius is large, the DDBF can circumvent such degradation by limiting information according to the distance between the filter holder and the items holders, i. e., a DDBF contains less information for faraway items and more information for nearby items. Interestingly, the construction of DDBFs requires no extra cost above that of standard filters. We also show by simulation that our method can achieve better lookup performance than existing ones.

A recursive solution for a fading memory filter derived from Kalman filter theory

NASA Technical Reports Server (NTRS)

Statman, J. I.

1986-01-01

A simple recursive solution for a class of fading memory tracking filters is presented. A fading memory filter provides estimates of filter states based on past measurements, similar to a traditional Kalman filter. Unlike a Kalman filter, an exponentially decaying weight is applied to older measurements, discounting their effect on present state estimates. It is shown that Kalman filters and fading memory filters are closely related solutions to a general least squares estimator problem. Closed form filter transfer functions are derived for a time invariant, steady state, fading memory filter. These can be applied in loop filter implementation of the Deep Space Network (DSN) Advanced Receiver carrier phase locked loop (PLL).

A New Pipelined Systolic Array-Based Architecture for Matrix Inversion in FPGAs with Kalman Filter Case Study

NASA Astrophysics Data System (ADS)

Bigdeli, Abbas; Biglari-Abhari, Morteza; Salcic, Zoran; Tin Lai, Yat

2006-12-01

A new pipelined systolic array-based (PSA) architecture for matrix inversion is proposed. The pipelined systolic array (PSA) architecture is suitable for FPGA implementations as it efficiently uses available resources of an FPGA. It is scalable for different matrix size and as such allows employing parameterisation that makes it suitable for customisation for application-specific needs. This new architecture has an advantage of[InlineEquation not available: see fulltext.] processing element complexity, compared to the[InlineEquation not available: see fulltext.] in other systolic array structures, where the size of the input matrix is given by[InlineEquation not available: see fulltext.]. The use of the PSA architecture for Kalman filter as an implementation example, which requires different structures for different number of states, is illustrated. The resulting precision error is analysed and shown to be negligible.

Spatial-Heterodyne Interferometry For Reflection And Transm Ission (Shirt) Measurements

DOEpatents

Hanson, Gregory R [Clinton, TN; Bingham, Philip R [Knoxville, TN; Tobin, Ken W [Harriman, TN

2006-02-14

Systems and methods are described for spatial-heterodyne interferometry for reflection and transmission (SHIRT) measurements. A method includes digitally recording a first spatially-heterodyned hologram using a first reference beam and a first object beam; digitally recording a second spatially-heterodyned hologram using a second reference beam and a second object beam; Fourier analyzing the digitally recorded first spatially-heterodyned hologram to define a first analyzed image; Fourier analyzing the digitally recorded second spatially-heterodyned hologram to define a second analyzed image; digitally filtering the first analyzed image to define a first result; and digitally filtering the second analyzed image to define a second result; performing a first inverse Fourier transform on the first result, and performing a second inverse Fourier transform on the second result. The first object beam is transmitted through an object that is at least partially translucent, and the second object beam is reflected from the object.

Optimizing the performance of bandpass photon detectors for inverse photoemission: Transmission of alkaline earth fluoride window crystals

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

Thiede, Christian, E-mail: christian.thiede@uni-muenster.de; Schmidt, Anke B.; Donath, Markus

2015-08-15

Bandpass photon detectors are widely used in inverse photoemission in the isochromat mode at energies in the vacuum-ultraviolet spectral range. The energy bandpass of gas-filled counters is usually formed by the ionization threshold of the counting gas as high-pass filter and the transmission cutoff of an alkaline earth fluoride window as low-pass filter. The transmission characteristics of the window have, therefore, a crucial impact on the detector performance. We present transmission measurements in the vacuum-ultraviolet spectral range for alkaline earth fluoride window crystals in the vicinity of the transmission cutoff as a function of crystal purity, surface finish, surface contamination,more » temperature, and thickness. Our findings reveal that the transmission characteristics of the window crystal and, thus, the detector performance depend critically on these window parameters.« less

Observer efficiency in discrimination tasks simulating malignant and benign breast lesions imaged with ultrasound

PubMed Central

Abbey, Craig K.; Zemp, Roger J.; Liu, Jie; Lindfors, Karen K.; Insana, Michael F.

2009-01-01

We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio-frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image. PMID:16468454

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

NASA Astrophysics Data System (ADS)

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

2014-08-01

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

Delay decomposition approach to [Formula: see text] filtering analysis of genetic oscillator networks with time-varying delays.

PubMed

Revathi, V M; Balasubramaniam, P

2016-04-01

In this paper, the [Formula: see text] filtering problem is treated for N coupled genetic oscillator networks with time-varying delays and extrinsic molecular noises. Each individual genetic oscillator is a complex dynamical network that represents the genetic oscillations in terms of complicated biological functions with inner or outer couplings denote the biochemical interactions of mRNAs, proteins and other small molecules. Throughout the paper, first, by constructing appropriate delay decomposition dependent Lyapunov-Krasovskii functional combined with reciprocal convex approach, improved delay-dependent sufficient conditions are obtained to ensure the asymptotic stability of the filtering error system with a prescribed [Formula: see text] performance. Second, based on the above analysis, the existence of the designed [Formula: see text] filters are established in terms of linear matrix inequalities with Kronecker product. Finally, numerical examples including a coupled Goodwin oscillator model are inferred to illustrate the effectiveness and less conservatism of the proposed techniques.

Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

ERIC Educational Resources Information Center

Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

2001-01-01

Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

PubMed

Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

2007-05-01

This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks.

Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

NASA Astrophysics Data System (ADS)

Hinczewski, Michael; Thirumalai, D.

2014-10-01

Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.

The experimental identification of magnetorheological dampers and evaluation of their controllers

NASA Astrophysics Data System (ADS)

Metered, H.; Bonello, P.; Oyadiji, S. O.

2010-05-01

Magnetorheological (MR) fluid dampers are semi-active control devices that have been applied over a wide range of practical vibration control applications. This paper concerns the experimental identification of the dynamic behaviour of an MR damper and the use of the identified parameters in the control of such a damper. Feed-forward and recurrent neural networks are used to model both the direct and inverse dynamics of the damper. Training and validation of the proposed neural networks are achieved by using the data generated through dynamic tests with the damper mounted on a tensile testing machine. The validation test results clearly show that the proposed neural networks can reliably represent both the direct and inverse dynamic behaviours of an MR damper. The effect of the cylinder's surface temperature on both the direct and inverse dynamics of the damper is studied, and the neural network model is shown to be reasonably robust against significant temperature variation. The inverse recurrent neural network model is introduced as a damper controller and experimentally evaluated against alternative controllers proposed in the literature. The results reveal that the neural-based damper controller offers superior damper control. This observation and the added advantages of low-power requirement, extended service life of the damper and the minimal use of sensors, indicate that a neural-based damper controller potentially offers the most cost-effective vibration control solution among the controllers investigated.

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

NASA Technical Reports Server (NTRS)

Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

2006-01-01

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

Space-Time Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Shelton, Robert O.

1992-01-01

Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.

Direct and accelerated parameter mapping using the unscented Kalman filter.

PubMed

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

FAST TRACK PAPER: Non-iterative multiple-attenuation methods: linear inverse solutions to non-linear inverse problems - II. BMG approximation

NASA Astrophysics Data System (ADS)

Ikelle, Luc T.; Osen, Are; Amundsen, Lasse; Shen, Yunqing

2004-12-01

The classical linear solutions to the problem of multiple attenuation, like predictive deconvolution, τ-p filtering, or F-K filtering, are generally fast, stable, and robust compared to non-linear solutions, which are generally either iterative or in the form of a series with an infinite number of terms. These qualities have made the linear solutions more attractive to seismic data-processing practitioners. However, most linear solutions, including predictive deconvolution or F-K filtering, contain severe assumptions about the model of the subsurface and the class of free-surface multiples they can attenuate. These assumptions limit their usefulness. In a recent paper, we described an exception to this assertion for OBS data. We showed in that paper that a linear and non-iterative solution to the problem of attenuating free-surface multiples which is as accurate as iterative non-linear solutions can be constructed for OBS data. We here present a similar linear and non-iterative solution for attenuating free-surface multiples in towed-streamer data. For most practical purposes, this linear solution is as accurate as the non-linear ones.

A Topological Criterion for Filtering Information in Complex Brain Networks

PubMed Central

Latora, Vito; Chavez, Mario

2017-01-01

In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way. PMID:28076353

Recursive mass matrix factorization and inversion: An operator approach to open- and closed-chain multibody dynamics

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Kreutz, K.

1988-01-01

This report advances a linear operator approach for analyzing the dynamics of systems of joint-connected rigid bodies.It is established that the mass matrix M for such a system can be factored as M=(I+H phi L)D(I+H phi L) sup T. This yields an immediate inversion M sup -1=(I-H psi L) sup T D sup -1 (I-H psi L), where H and phi are given by known link geometric parameters, and L, psi and D are obtained recursively by a spatial discrete-step Kalman filter and by the corresponding Riccati equation associated with this filter. The factors (I+H phi L) and (I-H psi L) are lower triangular matrices which are inverses of each other, and D is a diagonal matrix. This factorization and inversion of the mass matrix leads to recursive algortihms for forward dynamics based on spatially recursive filtering and smoothing. The primary motivation for advancing the operator approach is to provide a better means to formulate, analyze and understand spatial recursions in multibody dynamics. This is achieved because the linear operator notation allows manipulation of the equations of motion using a very high-level analytical framework (a spatial operator algebra) that is easy to understand and use. Detailed lower-level recursive algorithms can readily be obtained for inspection from the expressions involving spatial operators. The report consists of two main sections. In Part 1, the problem of serial chain manipulators is analyzed and solved. Extensions to a closed-chain system formed by multiple manipulators moving a common task object are contained in Part 2. To retain ease of exposition in the report, only these two types of multibody systems are considered. However, the same methods can be easily applied to arbitrary multibody systems formed by a collection of joint-connected regid bodies.

Halogen Occultation Experiment (HALOE) optical filter characterization

NASA Technical Reports Server (NTRS)

Harvey, Gale A.

1989-01-01

The Halogen Occultation Experiment (HALOE) is a solar occultation experiment that will fly on the Upper Atmosphere Research Satellite to measure mixing ratio profiles of O3, H2O, NO2, NO, CH4, HCl, and HF. The inversion of the HALOE data will be critically dependent on a detailed knowledge of eight optical filters. A filter characterization program was undertaken to measure in-band transmissions, out-of-band transmissions, in-band transmission shifts with temperature, reflectivities, and age stability. Fourier Transform Infrared Spectrometers were used to perform measurements over the spectral interval 400/cm to 6300/cm (25 micrometers to 1.6 micrometers). Very high precision (0.1 percent T) in-band measurements and very high resolution (0.0001 percent T) out-of-band measurements have been made. The measurements revealed several conventional leaks at 0.01 percent transmission and greatly enhanced (1,000) leaks to the 2-element filters when placed in a Fabry-Perot cavity. Filter throughput changes by 5 percent for a 25 C change in filter temperature.

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

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

Li, Weixuan, E-mail: weixuan.li@usc.edu; Lin, Guang, E-mail: guang.lin@pnnl.gov; Zhang, Dongxiao, E-mail: dxz@pku.edu.cn

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functionsmore » is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and EnKF algorithms.« less

An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling

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

LI, Weixuan; Lin, Guang; Zhang, Dongxiao

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos bases in the expansion helps to capture uncertainty more accurately but increases computational cost. Bases selection is particularly importantmore » for high-dimensional stochastic problems because the number of polynomial chaos bases required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE bases are pre-set based on users’ experience. Also, for sequential data assimilation problems, the bases kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE bases for different problems and automatically adjusts the number of bases in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm is tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and EnKF algorithms.« less

Hybrid inversions of CO2 fluxes at regional scale applied to network design

NASA Astrophysics Data System (ADS)

Kountouris, Panagiotis; Gerbig, Christoph; -Thomas Koch, Frank

2013-04-01

Long term observations of atmospheric greenhouse gas measuring stations, located at representative regions over the continent, improve our understanding of greenhouse gas sources and sinks. These mixing ratio measurements can be linked to surface fluxes by atmospheric transport inversions. Within the upcoming years new stations are to be deployed, which requires decision making tools with respect to the location and the density of the network. We are developing a method to assess potential greenhouse gas observing networks in terms of their ability to recover specific target quantities. As target quantities we use CO2 fluxes aggregated to specific spatial and temporal scales. We introduce a high resolution inverse modeling framework, which attempts to combine advantages from pixel based inversions with those of a carbon cycle data assimilation system (CCDAS). The hybrid inversion system consists of the Lagrangian transport model STILT, the diagnostic biosphere model VPRM and a Bayesian inversion scheme. We aim to retrieve the spatiotemporal distribution of net ecosystem exchange (NEE) at a high spatial resolution (10 km x 10 km) by inverting for spatially and temporally varying scaling factors for gross ecosystem exchange (GEE) and respiration (R) rather than solving for the fluxes themselves. Thus the state space includes parameters for controlling photosynthesis and respiration, but unlike in a CCDAS it allows for spatial and temporal variations, which can be expressed as NEE(x,y,t) = λG(x,y,t) GEE(x,y,t) + λR(x,y,t) R(x,y,t) . We apply spatially and temporally correlated uncertainties by using error covariance matrices with non-zero off-diagonal elements. Synthetic experiments will test our system and select the optimal a priori error covariance by using different spatial and temporal correlation lengths on the error statistics of the a priori covariance and comparing the optimized fluxes against the 'known truth'. As 'known truth' we use independent fluxes generated from a different biosphere model (BIOME-BGC). Initially we perform single-station inversions for Ochsenkopf tall tower located in Germany. Further expansion of the inversion framework to multiple stations and its application to network design will address the questions of how well a set of network stations can constrain a given target quantity, and whether there are objective criteria to select an optimal configuration for new stations that maximizes the uncertainty reduction.

Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials

DOE PAGES

Na, Hyuntae; Lee, Seung-Yub; Üstündag, Ersan; ...

2013-01-01

This paper introduces a recent development and application of a noncommercial artificial neural network (ANN) simulator with graphical user interface (GUI) to assist in rapid data modeling and analysis in the engineering diffraction field. The real-time network training/simulation monitoring tool has been customized for the study of constitutive behavior of engineering materials, and it has improved data mining and forecasting capabilities of neural networks. This software has been used to train and simulate the finite element modeling (FEM) data for a fiber composite system, both forward and inverse. The forward neural network simulation precisely reduplicates FEM results several orders ofmore » magnitude faster than the slow original FEM. The inverse simulation is more challenging; yet, material parameters can be meaningfully determined with the aid of parameter sensitivity information. The simulator GUI also reveals that output node size for materials parameter and input normalization method for strain data are critical train conditions in inverse network. The successful use of ANN modeling and simulator GUI has been validated through engineering neutron diffraction experimental data by determining constitutive laws of the real fiber composite materials via a mathematically rigorous and physically meaningful parameter search process, once the networks are successfully trained from the FEM database.« less

Hydraulic tomography of discrete networks of conduits and fractures in a karstic aquifer by using a deterministic inversion algorithm

NASA Astrophysics Data System (ADS)

Fischer, P.; Jardani, A.; Lecoq, N.

2018-02-01

In this paper, we present a novel inverse modeling method called Discrete Network Deterministic Inversion (DNDI) for mapping the geometry and property of the discrete network of conduits and fractures in the karstified aquifers. The DNDI algorithm is based on a coupled discrete-continuum concept to simulate numerically water flows in a model and a deterministic optimization algorithm to invert a set of observed piezometric data recorded during multiple pumping tests. In this method, the model is partioned in subspaces piloted by a set of parameters (matrix transmissivity, and geometry and equivalent transmissivity of the conduits) that are considered as unknown. In this way, the deterministic optimization process can iteratively correct the geometry of the network and the values of the properties, until it converges to a global network geometry in a solution model able to reproduce the set of data. An uncertainty analysis of this result can be performed from the maps of posterior uncertainties on the network geometry or on the property values. This method has been successfully tested for three different theoretical and simplified study cases with hydraulic responses data generated from hypothetical karstic models with an increasing complexity of the network geometry, and of the matrix heterogeneity.

Constraining Swiss Methane Emissions from Atmospheric Observations: Sensitivities and Temporal Development

NASA Astrophysics Data System (ADS)

Henne, Stephan; Leuenberger, Markus; Steinbacher, Martin; Eugster, Werner; Meinhardt, Frank; Bergamaschi, Peter; Emmenegger, Lukas; Brunner, Dominik

2017-04-01

Similar to other Western European countries, agricultural sources dominate the methane (CH4) emission budget in Switzerland. 'Bottom-up' estimates of these emissions are still connected with relatively large uncertainties due to considerable variability and uncertainties in observed emission factors for the underlying processes (e.g., enteric fermentation, manure management). Here, we present a regional-scale (˜300 x 200 km2) atmospheric inversion study of CH4 emissions in Switzerland making use of the recently established CarboCount-CH network of four stations on the Swiss Plateau as well as the neighbouring mountain-top sites Jungfraujoch and Schauinsland (Germany). Continuous observations from all CarboCount-CH sites are available since 2013. We use a high-resolution (7 x 7 km2) Lagrangian particle dispersion model (FLEXPART-COSMO) in connection with two different inversion systems (Bayesian and extended Kalman filter) to estimate spatially and temporally resolved CH4 emissions for the Swiss domain in the period 2013 to 2016. An extensive set of sensitivity inversions is used to assess the overall uncertainty of our inverse approach. In general we find good agreement of the total Swiss CH4 emissions between our 'top-down' estimate and the national 'bottom-up' reporting. In addition, a robust emission seasonality, with reduced winter time values, can be seen in all years. No significant trend or year-to-year variability was observed for the analysed four-year period, again in agreement with a very small downward trend in the national 'bottom-up' reporting. Special attention is given to the influence of boundary conditions as taken from different global scale model simulations (TM5, FLEXPART) and remote observations. We find that uncertainties in the boundary conditions can induce large offsets in the national total emissions. However, spatial emission patterns are less sensitive to the choice of boundary condition. Furthermore and in order to demonstrate the validity of our approach, a series of inversion runs using synthetic observations, generated from 'true' emissions, in combination with various sources of uncertainty are presented.

Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter.

PubMed

Li, Liangping; Zhang, Meijing; Katzenstein, Kurt

2017-11-01

The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time-lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real-time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation. © 2017, National Ground Water Association.

Subglottal Impedance-Based Inverse Filtering of Voiced Sounds Using Neck Surface Acceleration

PubMed Central

Zañartu, Matías; Ho, Julio C.; Mehta, Daryush D.; Hillman, Robert E.; Wodicka, George R.

2014-01-01

A model-based inverse filtering scheme is proposed for an accurate, non-invasive estimation of the aerodynamic source of voiced sounds at the glottis. The approach, referred to as subglottal impedance-based inverse filtering (IBIF), takes as input the signal from a lightweight accelerometer placed on the skin over the extrathoracic trachea and yields estimates of glottal airflow and its time derivative, offering important advantages over traditional methods that deal with the supraglottal vocal tract. The proposed scheme is based on mechano-acoustic impedance representations from a physiologically-based transmission line model and a lumped skin surface representation. A subject-specific calibration protocol is used to account for individual adjustments of subglottal impedance parameters and mechanical properties of the skin. Preliminary results for sustained vowels with various voice qualities show that the subglottal IBIF scheme yields comparable estimates with respect to current aerodynamics-based methods of clinical vocal assessment. A mean absolute error of less than 10% was observed for two glottal airflow measures –maximum flow declination rate and amplitude of the modulation component– that have been associated with the pathophysiology of some common voice disorders caused by faulty and/or abusive patterns of vocal behavior (i.e., vocal hyperfunction). The proposed method further advances the ambulatory assessment of vocal function based on the neck acceleration signal, that previously have been limited to the estimation of phonation duration, loudness, and pitch. Subglottal IBIF is also suitable for other ambulatory applications in speech communication, in which further evaluation is underway. PMID:25400531

Spatial operator approach to flexible multibody system dynamics and control

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1991-01-01

The inverse and forward dynamics problems for flexible multibody systems were solved using the techniques of spatially recursive Kalman filtering and smoothing. These algorithms are easily developed using a set of identities associated with mass matrix factorization and inversion. These identities are easily derived using the spatial operator algebra developed by the author. Current work is aimed at computational experiments with the described algorithms and at modelling for control design of limber manipulator systems. It is also aimed at handling and manipulation of flexible objects.

Crustal and Upper Mantle Structure from Joint Inversion of Body Wave and Gravity Data

DTIC Science & Technology

2012-09-01

CRUSTAL AND UPPER MANTLE STRUCTURE FROM JOINT INVERSION OF BODY WAVE AND GRAVITY DATA Eric A. Bergman1, Charlotte Rowe2, and Monica Maceira2...for these events include many readings of direct crustal P and S phases, as well as regional (Pn and Sn) and teleseismic phases. These data have been...the usefulness of the gravity data, we apply high-pass filtering, yielding gravity anomalies that possess higher resolving power for crustal and

Neural joint control for Space Shuttle Remote Manipulator System

NASA Technical Reports Server (NTRS)

Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

1992-01-01

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

Vision-Based Position Estimation Utilizing an Extended Kalman Filter

DTIC Science & Technology

2016-12-01

POSITION ESTIMATION UTILIZING AN EXTENDED KALMAN FILTER by Joseph B. Testa III December 2016 Thesis Advisor: Vladimir Dobrokhodov Co...TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE VISION-BASED POSITION ESTIMATION UTILIZING AN EXTENDED KALMAN FILTER 5. FUNDING...spots” and network relay between the boarding team and ship. 14. SUBJECT TERMS UAV, ROS, extended Kalman filter , Matlab

Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters

PubMed Central

Sadri, Amir Reza; Zekri, Maryam; Sadri, Saeid; Gheissari, Niloofar

2012-01-01

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well. PMID:23493998

Interference Alignment With Partial CSI Feedback in MIMO Cellular Networks

NASA Astrophysics Data System (ADS)

Rao, Xiongbin; Lau, Vincent K. N.

2014-04-01

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. However, most existing IA designs require full channel state information (CSI) at the transmitters, which would lead to significant CSI signaling overhead. There are two techniques, namely CSI quantization and CSI feedback filtering, to reduce the CSI feedback overhead. In this paper, we consider IA processing with CSI feedback filtering in MIMO cellular networks. We introduce a novel metric, namely the feedback dimension, to quantify the first order CSI feedback cost associated with the CSI feedback filtering. The CSI feedback filtering poses several important challenges in IA processing. First, there is a hidden partial CSI knowledge constraint in IA precoder design which cannot be handled using conventional IA design methodology. Furthermore, existing results on the feasibility conditions of IA cannot be applied due to the partial CSI knowledge. Finally, it is very challenging to find out how much CSI feedback is actually needed to support IA processing. We shall address the above challenges and propose a new IA feasibility condition under partial CSIT knowledge in MIMO cellular networks. Based on this, we consider the CSI feedback profile design subject to the degrees of freedom requirements, and we derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks.

Comparison of magmatic and amagmatic rift zone kinematics using full moment tensor inversions of regional earthquakes

NASA Astrophysics Data System (ADS)

Jaye Oliva, Sarah; Ebinger, Cynthia; Shillington, Donna; Albaric, Julie; Deschamps, Anne; Keir, Derek; Drooff, Connor

2017-04-01

Temporary seismic networks deployed in the magmatic Eastern rift and the mostly amagmatic Western rift in East Africa present the opportunity to compare the depth distribution of strain, and fault kinematics in light of rift age and the presence or absence of surface magmatism. The largest events in local earthquake catalogs (ML > 3.5) are modeled using the Dreger and Ford full moment tensor algorithm (Dreger, 2003; Minson & Dreger, 2008) to better constrain source depth and to investigate non-double-couple components. A bandpass filter of 0.02 to 0.10 Hz is applied to the waveforms prior to inversion. Synthetics are based on 1D velocity models derived during seismic analysis and constrained by reflection and tomographic data where available. Results show significant compensated linear vector dipole (CLVD) and isotropic components for earthquakes in magmatic rift zones, whereas double-couple mechanisms predominate in weakly magmatic rift sectors. We interpret the isotropic components as evidence for fluid-involved faulting in the Eastern rift where volatile emissions are large, and dike intrusions well documented. Lower crustal earthquakes are found in both amagmatic and magmatic sectors. These results are discussed in the context of the growing database of complementary geophysical, geochemical, and geological studies in these regions as we seek to understand the role of magmatism and faulting in accommodating strain during early continental rifting.

Inverting Monotonic Nonlinearities by Entropy Maximization

PubMed Central

López-de-Ipiña Pena, Karmele; Caiafa, Cesar F.

2016-01-01

This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results. PMID:27780261

Inverting Monotonic Nonlinearities by Entropy Maximization.

PubMed

Solé-Casals, Jordi; López-de-Ipiña Pena, Karmele; Caiafa, Cesar F

2016-01-01

This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results.

Motion control of musculoskeletal systems with redundancy.

PubMed

Park, Hyunjoo; Durand, Dominique M

2008-12-01

Motion control of musculoskeletal systems for functional electrical stimulation (FES) is a challenging problem due to the inherent complexity of the systems. These include being highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant. The redundancy in particular makes it difficult to find an inverse model of the system for control purposes. We have developed a control system for multiple input multiple output (MIMO) redundant musculoskeletal systems with little prior information. The proposed method separates the steady-state properties from the dynamic properties. The dynamic control uses a steady-state inverse model and is implemented with both a PID controller for disturbance rejection and an artificial neural network (ANN) feedforward controller for fast trajectory tracking. A mechanism to control the sum of the muscle excitation levels is also included. To test the performance of the proposed control system, a two degree of freedom ankle-subtalar joint model with eight muscles was used. The simulation results show that separation of steady-state and dynamic control allow small output tracking errors for different reference trajectories such as pseudo-step, sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against system parameter and controller parameter variations. A possible application of this control algorithm is FES control using multiple contact cuff electrodes where mathematical modeling is not feasible and the redundancy makes the control of dynamic movement difficult.

Transfer functions of double- and multiple-cavity Fabry-Perot filters driven by Lorentzian sources.

PubMed

Marti, J; Capmany, J

1996-12-20

We derive expressions for the transfer functions of double- and multiple-cavity Fabry-Perot filters driven by laser sources with Lorentzian spectrum. These are of interest because of their applications in sensing and channel filtering in optical frequency-division multiplexing networks.

Transfer functions of double- and multiple-cavity Fabry Perot filters driven by Lorentzian sources

NASA Astrophysics Data System (ADS)

Marti, Javier; Capmany, Jose

1996-12-01

We derive expressions for the transfer functions of double- and multiple-cavity Fabry Perot filters driven by laser sources with Lorentzian spectrum. These are of interest because of their applications in sensing and channel filtering in optical frequency-division multiplexing networks.

EVALUATION OF THE FILTER PACK FOR LONG-DURATION SAMPLING OF AMBIENT AIR

EPA Science Inventory

A 14-week filter pack (FP) sampler evaluation field study was conducted at a site near Bondville, IL to investigate the impact of weekly sampling duration. Simultaneous samples were collected using collocated filter packs (FP) from two independent air quality monitoring networks...

NASA Tech Briefs, August 2004

NASA Technical Reports Server (NTRS)

2004-01-01

Topics covered include: Data Relay Board with Protocol for High-Speed, Free-Space Optical Communications; Software and Algorithms for Biomedical Image Data Processing and Visualization; Rapid Chemometric Filtering of Spectral Data; Prioritizing Scientific Data for Transmission; Determining Sizes of Particles in a Flow from DPIV Data; Faster Processing for Inverting GPS Occultation Data; FPGA-Based, Self-Checking, Fault-Tolerant Computers; Ultralow-Power Digital Correlator for Microwave Polarimetry; Grounding Headphones for Protection Against ESD; Lightweight Stacks of Direct Methanol Fuel Cells; Highly Efficient Vector-Inversion Pulse Generators; Estimating Basic Preliminary Design Performances of Aerospace Vehicles; Framework for Development of Object-Oriented Software; Analyzing Spacecraft Telecommunication Systems; Collaborative Planning of Robotic Exploration; Tools for Administration of a UNIX-Based Network; Preparing and Analyzing Iced Airfoils; Evaluating Performance of Components; Fuels Containing Methane of Natural Gas in Solution; Direct Electrolytic Deposition of Mats of MnxOy Nanowires; Bubble Eliminator Based on Centrifugal Flow; Inflatable Emergency Atmospheric-Entry Vehicles; Lightweight Deployable Mirrors with Tensegrity Supports; Centrifugal Adsorption Cartridge System; Ultrasonic Apparatus for Pulverizing Brittle Material; Transplanting Retinal Cells using Bucky Paper for Support; Using an Ultrasonic Instrument to Size Extravascular Bubbles; Coronagraphic Notch Filter for Raman Spectroscopy; On-the-Fly Mapping for Calibrating Directional Antennas; Working Fluids for Increasing Capacities of Heat Pipes; Computationally-Efficient Minimum-Time Aircraft Routes in the Presence of Winds; Liquid-Metal-Fed Pulsed Plasma Thrusters; Personal Radiation Protection System; and Attitude Control for a Solar-Sail Spacecraft.

High-performance implementation of Chebyshev filter diagonalization for interior eigenvalue computations

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

Pieper, Andreas; Kreutzer, Moritz; Alvermann, Andreas, E-mail: alvermann@physik.uni-greifswald.de

2016-11-15

We study Chebyshev filter diagonalization as a tool for the computation of many interior eigenvalues of very large sparse symmetric matrices. In this technique the subspace projection onto the target space of wanted eigenvectors is approximated with filter polynomials obtained from Chebyshev expansions of window functions. After the discussion of the conceptual foundations of Chebyshev filter diagonalization we analyze the impact of the choice of the damping kernel, search space size, and filter polynomial degree on the computational accuracy and effort, before we describe the necessary steps towards a parallel high-performance implementation. Because Chebyshev filter diagonalization avoids the need formore » matrix inversion it can deal with matrices and problem sizes that are presently not accessible with rational function methods based on direct or iterative linear solvers. To demonstrate the potential of Chebyshev filter diagonalization for large-scale problems of this kind we include as an example the computation of the 10{sup 2} innermost eigenpairs of a topological insulator matrix with dimension 10{sup 9} derived from quantum physics applications.« less

Extracting Low-Frequency Information from Time Attenuation in Elastic Waveform Inversion

NASA Astrophysics Data System (ADS)

Guo, Xuebao; Liu, Hong; Shi, Ying; Wang, Weihong

2017-03-01

Low-frequency information is crucial for recovering background velocity, but the lack of low-frequency information in field data makes inversion impractical without accurate initial models. Laplace-Fourier domain waveform inversion can recover a smooth model from real data without low-frequency information, which can be used for subsequent inversion as an ideal starting model. In general, it also starts with low frequencies and includes higher frequencies at later inversion stages, while the difference is that its ultralow frequency information comes from the Laplace-Fourier domain. Meanwhile, a direct implementation of the Laplace-transformed wavefield using frequency domain inversion is also very convenient. However, because broad frequency bands are often used in the pure time domain waveform inversion, it is difficult to extract the wavefields dominated by low frequencies in this case. In this paper, low-frequency components are constructed by introducing time attenuation into the recorded residuals, and the rest of the method is identical to the traditional time domain inversion. Time windowing and frequency filtering are also applied to mitigate the ambiguity of the inverse problem. Therefore, we can start at low frequencies and to move to higher frequencies. The experiment shows that the proposed method can achieve a good inversion result in the presence of a linear initial model and records without low-frequency information.

Inverse scattering theory: Inverse scattering series method for one dimensional non-compact support potential

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

Yao, Jie, E-mail: yjie2@uh.edu; Lesage, Anne-Cécile; Hussain, Fazle

2014-12-15

The reversion of the Born-Neumann series of the Lippmann-Schwinger equation is one of the standard ways to solve the inverse acoustic scattering problem. One limitation of the current inversion methods based on the reversion of the Born-Neumann series is that the velocity potential should have compact support. However, this assumption cannot be satisfied in certain cases, especially in seismic inversion. Based on the idea of distorted wave scattering, we explore an inverse scattering method for velocity potentials without compact support. The strategy is to decompose the actual medium as a known single interface reference medium, which has the same asymptoticmore » form as the actual medium and a perturbative scattering potential with compact support. After introducing the method to calculate the Green’s function for the known reference potential, the inverse scattering series and Volterra inverse scattering series are derived for the perturbative potential. Analytical and numerical examples demonstrate the feasibility and effectiveness of this method. Besides, to ensure stability of the numerical computation, the Lanczos averaging method is employed as a filter to reduce the Gibbs oscillations for the truncated discrete inverse Fourier transform of each order. Our method provides a rigorous mathematical framework for inverse acoustic scattering with a non-compact support velocity potential.« less

Neural nets for aligning optical components in harsh environments: Beam smoothing spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.

1991-01-01

The goal is to develop an approach to automating the alignment and adjustment of optical measurement, visualization, inspection, and control systems. Classical controls, expert systems, and neural networks are three approaches to automating the alignment of an optical system. Neural networks were chosen for this project and the judgements that led to this decision are presented. Neural networks were used to automate the alignment of the ubiquitous laser-beam-smoothing spatial filter. The results and future plans of the project are presented.

Design and Implementation of a High-Power Resonant DC-DC Converter Module for a Reduced-Scale Prototype Integrated Power System

DTIC Science & Technology

2001-09-01

damping RC network . The filter was designed to have a pole pair (~450 Hz) above the 360 Hz ripple of the six-pulse rectified DC supply but well below the...Circuit With Input Filtering Included. The damping network was designed using the guidance provided in reference [24] and its function is to lower the...converter as a linear network and estimated the spectrum envelope by multiplying the Fourier transform of the current waveform by the transfer

Multi-Beam Radio Frequency (RF) Aperture Arrays Using Multiplierless Approximate Fast Fourier Transform (FFT)

DTIC Science & Technology

2017-08-01

filtering, correlation and radio- astronomy . In this report approximate transforms that closely follow the DFT have been studied and found. The approximate...communications, data networks, sensor networks, cognitive radio, radar and beamforming, imaging, filtering, correlation and radio- astronomy . FFTs efficiently...public release; distribution is unlimited. 4.3 Digital Hardware and Design Architectures Collaboration for Astronomy Signal Processing and Electronics

Time-dependent inversions of slow slip at the Hikurangi subduction zone, New Zealand, using numerical Green's functions

NASA Astrophysics Data System (ADS)

Williams, C. A.; Wallace, L. M.; Bartlow, N. M.

2017-12-01

Slow slip events (SSEs) have been observed throughout the world, and the existence of these events has fundamentally altered our understanding of the possible ranges of slip behavior at subduction plate boundaries. In New Zealand, SSEs occur along the Hikurangi Margin, with shallower events in the north and deeper events to the south. In a recent study, Williams and Wallace (2015) found that static SSE inversions that consider elastic property variations provided significantly different results than those based on an elastic half-space. For deeper events, the heterogeneous models predicted smaller amounts of slip, while for shallower events the heterogeneous model predicted larger amounts of slip. In this study, we extend our initial work to examine the temporal variations in slip. We generate Green's functions using the PyLith finite element code (Aagaard et al., 2013) to allow consideration of elastic property variations provided by the New Zealand-wide seismic velocity model (Eberhart-Phillips et al., 2010). These Green's functions are then integrated to provide Green's functions compatible with the Network Inversion Filter (NIF, Segall and Matthews,1997; McGuire and Segall, 2003; Miyazaki et al.,2006). We examine 12 SSEs occurring along the Hikurangi Margin during 2010 and 2011, and compare the results using heterogeneous Green's functions with those of Bartlow et al. (2014), who examined the same set of SSEs with the NIF using a uniform elastic half-space model. The use of heterogeneous Green's functions should provide a more accurate picture of the slip distribution and evolution of the SSEs. This will aid in understanding the correlations between SSEs and seismicity and/or tremor and the role of SSEs in the accommodation of plate motion budgets in New Zealand.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Information filtering on coupled social networks.

PubMed

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.

The use of the multiwavelet transform for the estimation of surface wave group and phase velocities and their associated uncertainties

NASA Astrophysics Data System (ADS)

Poppeliers, C.; Preston, L. A.

2017-12-01

Measurements of seismic surface wave dispersion can be used to infer the structure of the Earth's subsurface. Typically, to identify group- and phase-velocity, a series of narrow-band filters are applied to surface wave seismograms. Frequency dependent arrival times of surface waves can then be identified from the resulting suite of narrow band seismograms. The frequency-dependent velocity estimates are then inverted for subsurface velocity structure. However, this technique has no method to estimate the uncertainty of the measured surface wave velocities, and subsequently there is no estimate of uncertainty on, for example, tomographic results. For the work here, we explore using the multiwavelet transform (MWT) as an alternate method to estimate surface wave speeds. The MWT decomposes a signal similarly to the conventional filter bank technique, but with two primary advantages: 1) the time-frequency localization is optimized in regard to the time-frequency tradeoff, and 2) we can use the MWT to estimate the uncertainty of the resulting surface wave group- and phase-velocities. The uncertainties of the surface wave speed measurements can then be propagated into tomographic inversions to provide uncertainties of resolved Earth structure. As proof-of-concept, we apply our technique to four seismic ambient noise correlograms that were collected from the University of Nevada Reno seismic network near the Nevada National Security Site. We invert the estimated group- and phase-velocities, as well the uncertainties, for 1-D Earth structure for each station pair. These preliminary results generally agree with 1-D velocities that are obtained from inverting dispersion curves estimated from a conventional Gaussian filter bank.

Reconstruction of stochastic temporal networks through diffusive arrival times

NASA Astrophysics Data System (ADS)

Li, Xun; Li, Xiang

2017-06-01

Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications.

Reconstruction of stochastic temporal networks through diffusive arrival times

PubMed Central

Li, Xun; Li, Xiang

2017-01-01

Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications. PMID:28604687

On absence of steady state in the Bouchaud-Mézard network model

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Serota, R. A.

2018-02-01

In the limit of infinite number of nodes (agents), the Itô-reduced Bouchaud-Mézard network model of economic exchange has a time-independent mean and a steady-state inverse gamma distribution. We show that for a finite number of nodes the mean is actually distributed as a time-dependent lognormal and inverse gamma is quasi-stationary, with the time-dependent scale parameter.

Methodology to estimate the relative pressure field from noisy experimental velocity data

NASA Astrophysics Data System (ADS)

Bolin, C. D.; Raguin, L. G.

2008-11-01

The determination of intravascular pressure fields is important to the characterization of cardiovascular pathology. We present a two-stage method that solves the inverse problem of estimating the relative pressure field from noisy velocity fields measured by phase contrast magnetic resonance imaging (PC-MRI) on an irregular domain with limited spatial resolution, and includes a filter for the experimental noise. For the pressure calculation, the Poisson pressure equation is solved by embedding the irregular flow domain into a regular domain. To lessen the propagation of the noise inherent to the velocity measurements, three filters - a median filter and two physics-based filters - are evaluated using a 2-D Couette flow. The two physics-based filters outperform the median filter for the estimation of the relative pressure field for realistic signal-to-noise ratios (SNR = 5 to 30). The most accurate pressure field results from a filter that applies in a least-squares sense three constraints simultaneously: consistency between measured and filtered velocity fields, divergence-free and additional smoothness conditions. This filter leads to a 5-fold gain in accuracy for the estimated relative pressure field compared to without noise filtering, in conditions consistent with PC-MRI of the carotid artery: SNR = 5, 20 x 20 discretized flow domain (25 X 25 computational domain).

Neural networks for data compression and invariant image recognition

NASA Technical Reports Server (NTRS)

Gardner, Sheldon

1989-01-01

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering

PubMed Central

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term. PMID:25343243

Signal propagation in cortical networks: a digital signal processing approach.

PubMed

Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

2009-01-01

This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion

NASA Astrophysics Data System (ADS)

Hansen, T. M.; Cordua, K. S.

2017-12-01

Probabilistically formulated inverse problems can be solved using Monte Carlo-based sampling methods. In principle, both advanced prior information, based on for example, complex geostatistical models and non-linear forward models can be considered using such methods. However, Monte Carlo methods may be associated with huge computational costs that, in practice, limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical forward response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival traveltime inversion of crosshole ground penetrating radar data. An accurate forward model, based on 2-D full-waveform modeling followed by automatic traveltime picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the accurate and computationally expensive forward model, and also considerably faster and more accurate (i.e. with better resolution), than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of non-linear and non-Gaussian inverse problems that have to be solved using Monte Carlo sampling techniques.

Moment-tensor solutions for the 24 November 1987 Superstition Hills, California, earthquakes

USGS Publications Warehouse

Sipkin, S.A.

1989-01-01

The teleseismic long-period waveforms recorded by the Global Digital Seismograph Network from the two largest Superstition Hills earthquakes are inverted using an algorithm based on optimal filter theory. These solutions differ slightly from those published in the Preliminary Determination of Epicenters Monthly Listing because a somewhat different, improved data set was used in the inversions and a time-dependent moment-tensor algorithm was used to investigate the complexity of the main shock. The foreshock (origin time 01:54:14.5, mb 5.7, Ms6.2) had a scalar moment of 2.3 ?? 1025 dyne-cm, a depth of 8km, and a mechanism of strike 217??, dip 79??, rake 4??. The main shock (origin time 13:15:56.4, mb 6.0, Ms6.6) was a complex event, consisting of at least two subevents, with a combined scalar moment of 1.0 ?? 1026 dyne-cm, a depth of 10km, and a mechanism of strike 303??, dip 89??, rake -180??. -Authors

Multiuser Transmit Beamforming for Maximum Sum Capacity in Tactical Wireless Multicast Networks

DTIC Science & Technology

2006-08-01

commonly used extended Kalman filter . See [2, 5, 6] for recent tutorial overviews. In particle filtering , continuous distributions are approximated by...signals (using and developing associated particle filtering tools). Our work on these topics has been reported in seven (IEEE, SIAM) journal papers and...multidimensional scaling, tracking, intercept, particle filters . 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT 18. SECURITY CLASSIFICATION OF

Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

PubMed

Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

2008-06-01

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

Crustal Structure of Indonesia from Seismic Ambient Noise Tomography

NASA Astrophysics Data System (ADS)

Saygin, E.; Cummins, P. R.; Suhardjono, S.; Nishida, K.

2012-12-01

We image a region spanning from south Vietnam to north Australia using over 300 seismic stations by using ambient seismic noise cross-correlations. The backbone of the network is formed by using the broadband seismograph network of Indonesia with over 160 stations serving as mid-tie point in the region. The retrieved Green's functions from the cross-correlation of continuously recorded seismic ambient noise at the stations are used to perform surface wave dispersion analysis. We apply a multiple filter approach to measure the phase and group velocity dispersion of Rayleigh wave component of Green's functions. The traveltime information derived from the dispersion is then used in a nonlinear tomographic approach to map the velocity perturbation of the region. The forward problem for the tomographic imaging can accurately track the evolution of a wavefront in highly heterogeneous media. Therefore the highly complex velocity distribution of the region is accurately reflected into the forward calculations used in the inversion. In general, accretionary prisms in the region are marked with quite low group and phase velocities with perturbations up to 50%. Active volcanoes in Sumatra and Java islands are also marked with low velocities. Rajang delta in north-west Kalimantan and thick sediments in South China Sea are imaged with low velocities.

Focus-based filtering + clustering technique for power-law networks with small world phenomenon

NASA Astrophysics Data System (ADS)

Boutin, François; Thièvre, Jérôme; Hascoët, Mountaz

2006-01-01

Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.

Wavelength-tunable filter utilizing non-cyclic arrayed waveguide grating to create colorless, directionless, contentionless ROADMs

NASA Astrophysics Data System (ADS)

Niwa, Masaki; Takashina, Shoichi; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi; Watanabe, Toshio

2015-01-01

With the continuous increase in Internet traffic, reconfigurable optical add-drop multiplexers (ROADMs) have been widely adopted in the core and metro core networks. Current ROADMs, however, allow only static operation. To realize future dynamic optical-network services, and to minimize any human intervention in network operation, the optical signal add/drop part should have colorless/directionless/contentionless (C/D/C) capabilities. This is possible with matrix switches or a combination of splitter-switches and optical tunable filters. The scale of the matrix switch increases with the square of the number of supported channels, and hence, the matrix-switch-based architecture is not suitable for creating future large-scale ROADMs. In contrast, the numbers of splitter ports, switches, and tunable filters increase linearly with the number of supported channels, and hence the tunable-filter-based architecture will support all future traffic. So far, we have succeeded in fabricating a compact tunable filter that consists of multi-stage cyclic arrayed-waveguide gratings (AWGs) and switches by using planar-lightwave-circuit (PLC) technologies. However, this multistage configuration suffers from large insertion loss and filter narrowing. Moreover, power-consuming temperature control is necessary since it is difficult to make cyclic AWGs athermal. We propose here novel tunable-filter architecture that sandwiches a single-stage non-cyclic athermal AWG having flatter-topped passbands between small-scale switches. With this configuration, the optical tunable filter attains low insertion loss, large passband bandwidths, low power consumption, compactness, and high cost-effectiveness. A prototype is monolithically fabricated with PLC technologies and its excellent performance is experimentally confirmed utilizing 80-channel 30-GBaud dual-polarization quadrature phase-shift-keying (QPSK) signals.

Deep neural networks to enable real-time multimessenger astrophysics

NASA Astrophysics Data System (ADS)

George, Daniel; Huerta, E. A.

2018-02-01

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field of research, there is a pressing need to increase the depth and speed of the algorithms used to enable these ground-breaking discoveries. We introduce Deep Filtering—a new scalable machine learning method for end-to-end time-series signal processing. Deep Filtering is based on deep learning with two deep convolutional neural networks, which are designed for classification and regression, to detect gravitational wave signals in highly noisy time-series data streams and also estimate the parameters of their sources in real time. Acknowledging that some of the most sensitive algorithms for the detection of gravitational waves are based on implementations of matched filtering, and that a matched filter is the optimal linear filter in Gaussian noise, the application of Deep Filtering using whitened signals in Gaussian noise is investigated in this foundational article. The results indicate that Deep Filtering outperforms conventional machine learning techniques, achieves similar performance compared to matched filtering, while being several orders of magnitude faster, allowing real-time signal processing with minimal resources. Furthermore, we demonstrate that Deep Filtering can detect and characterize waveform signals emitted from new classes of eccentric or spin-precessing binary black holes, even when trained with data sets of only quasicircular binary black hole waveforms. The results presented in this article, and the recent use of deep neural networks for the identification of optical transients in telescope data, suggests that deep learning can facilitate real-time searches of gravitational wave sources and their electromagnetic and astroparticle counterparts. In the subsequent article, the framework introduced herein is directly applied to identify and characterize gravitational wave events in real LIGO data.

Non-linear Post Processing Image Enhancement

NASA Technical Reports Server (NTRS)

Hunt, Shawn; Lopez, Alex; Torres, Angel

1997-01-01

A non-linear filter for image post processing based on the feedforward Neural Network topology is presented. This study was undertaken to investigate the usefulness of "smart" filters in image post processing. The filter has shown to be useful in recovering high frequencies, such as those lost during the JPEG compression-decompression process. The filtered images have a higher signal to noise ratio, and a higher perceived image quality. Simulation studies comparing the proposed filter with the optimum mean square non-linear filter, showing examples of the high frequency recovery, and the statistical properties of the filter are given,

An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

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

Manoli, Gabriele, E-mail: manoli@dmsa.unipd.it; Nicholas School of the Environment, Duke University, Durham, NC 27708; Rossi, Matteo

The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequentialmore » inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.« less

Spectral filtering of gradient for l2-norm frequency-domain elastic waveform inversion

NASA Astrophysics Data System (ADS)

Oh, Ju-Won; Min, Dong-Joo

2013-05-01

To enhance the robustness of the l2-norm elastic full-waveform inversion (FWI), we propose a denoise function that is incorporated into single-frequency gradients. Because field data are noisy and modelled data are noise-free, the denoise function is designed based on the ratio of modelled data to field data summed over shots and receivers. We first take the sums of the modelled data and field data over shots, then take the sums of the absolute values of the resultant modelled data and field data over the receivers. Due to the monochromatic property of wavefields at each frequency, signals in both modelled and field data tend to be cancelled out or maintained, whereas certain types of noise, particularly random noise, can be amplified in field data. As a result, the spectral distribution of the denoise function is inversely proportional to the ratio of noise to signal at each frequency, which helps prevent the noise-dominant gradients from contributing to model parameter updates. Numerical examples show that the spectral distribution of the denoise function resembles a frequency filter that is determined by the spectrum of the signal-to-noise (S/N) ratio during the inversion process, with little human intervention. The denoise function is applied to the elastic FWI of synthetic data, with three types of random noise generated by the modified version of the Marmousi-2 model: white, low-frequency and high-frequency random noises. Based on the spectrum of S/N ratios at each frequency, the denoise function mainly suppresses noise-dominant single-frequency gradients, which improves the inversion results at the cost of spatial resolution.

Velocity Structure of the Iran Region Using Seismic and Gravity Observations

NASA Astrophysics Data System (ADS)

Syracuse, E. M.; Maceira, M.; Phillips, W. S.; Begnaud, M. L.; Nippress, S. E. J.; Bergman, E.; Zhang, H.

2015-12-01

We present a 3D Vp and Vs model of Iran generated using a joint inversion of body wave travel times, Rayleigh wave dispersion curves, and high-wavenumber filtered Bouguer gravity observations. Our work has two main goals: 1) To better understand the tectonics of a prominent example of continental collision, and 2) To assess the improvements in earthquake location possible as a result of joint inversion. The body wave dataset is mainly derived from previous work on location calibration and includes the first-arrival P and S phases of 2500 earthquakes whose initial locations qualify as GT25 or better. The surface wave dataset consists of Rayleigh wave group velocity measurements for regional earthquakes, which are inverted for a suite of period-dependent Rayleigh wave velocity maps prior to inclusion in the joint inversion for body wave velocities. We use gravity anomalies derived from the global gravity model EGM2008. To avoid mapping broad, possibly dynamic features in the gravity field intovariations in density and body wave velocity, we apply a high-pass wavenumber filter to the gravity measurements. We use a simple, approximate relationship between density and velocity so that the three datasets may be combined in a single inversion. The final optimized 3D Vp and Vs model allows us to explore how multi-parameter tomography addresses crustal heterogeneities in areas of limited coverage and improves travel time predictions. We compare earthquake locations from our models to independent locations obtained from InSAR analysis to assess the improvement in locations derived in a joint-inversion model in comparison to those derived in a more traditional body-wave-only velocity model.

Optimal application of Morrison's iterative noise removal for deconvolution. Appendices

NASA Technical Reports Server (NTRS)

Ioup, George E.; Ioup, Juliette W.

1987-01-01

Morrison's iterative method of noise removal, or Morrison's smoothing, is applied in a simulation to noise-added data sets of various noise levels to determine its optimum use. Morrison's smoothing is applied for noise removal alone, and for noise removal prior to deconvolution. For the latter, an accurate method is analyzed to provide confidence in the optimization. The method consists of convolving the data with an inverse filter calculated by taking the inverse discrete Fourier transform of the reciprocal of the transform of the response of the system. Various length filters are calculated for the narrow and wide Gaussian response functions used. Deconvolution of non-noisy data is performed, and the error in each deconvolution calculated. Plots are produced of error versus filter length; and from these plots the most accurate length filters determined. The statistical methodologies employed in the optimizations of Morrison's method are similar. A typical peak-type input is selected and convolved with the two response functions to produce the data sets to be analyzed. Both constant and ordinate-dependent Gaussian distributed noise is added to the data, where the noise levels of the data are characterized by their signal-to-noise ratios. The error measures employed in the optimizations are the L1 and L2 norms. Results of the optimizations for both Gaussians, both noise types, and both norms include figures of optimum iteration number and error improvement versus signal-to-noise ratio, and tables of results. The statistical variation of all quantities considered is also given.

Maximising information recovery from rank-order codes

NASA Astrophysics Data System (ADS)

Sen, B.; Furber, S.

2007-04-01

The central nervous system encodes information in sequences of asynchronously generated voltage spikes, but the precise details of this encoding are not well understood. Thorpe proposed rank-order codes as an explanation of the observed speed of information processing in the human visual system. The work described in this paper is inspired by the performance of SpikeNET, a biologically inspired neural architecture using rank-order codes for information processing, and is based on the retinal model developed by VanRullen and Thorpe. This model mimics retinal information processing by passing an input image through a bank of Difference of Gaussian (DoG) filters and then encoding the resulting coefficients in rank-order. To test the effectiveness of this encoding in capturing the information content of an image, the rank-order representation is decoded to reconstruct an image that can be compared with the original. The reconstruction uses a look-up table to infer the filter coefficients from their rank in the encoded image. Since the DoG filters are approximately orthogonal functions, they are treated as their own inverses in the reconstruction process. We obtained a quantitative measure of the perceptually important information retained in the reconstructed image relative to the original using a slightly modified version of an objective metric proposed by Petrovic. It is observed that around 75% of the perceptually important information is retained in the reconstruction. In the present work we reconstruct the input using a pseudo-inverse of the DoG filter-bank with the aim of improving the reconstruction and thereby extracting more information from the rank-order encoded stimulus. We observe that there is an increase of 10 - 15% in the information retrieved from a reconstructed stimulus as a result of inverting the filter-bank.

Tying the spectral ends of the M9.0 Tohoku-Oki earthquake sequence: High-frequency radiators and early quasi-static afterslip

NASA Astrophysics Data System (ADS)

Inbal, A.; Ampuero, J. P.; Avouac, J.; Lengliné, O.; Helmberger, D. V.

2012-12-01

The March 11, 2011 M9.0 Tohoku-Oki earthquake was recorded by dense seismological and geodetical networks deployed in Japan, as well as by a vast number of seismic stations worldwide. These observations allow us to study the properties of the subduction interface with unprecedented accuracy and resolution. Here we examine the spectral tails of the co- and post-seismic stages using local geodetic and seismological recordings. First, we study the details of high-frequency (HF) energy radiation during the rupture by using strong-motion recordings. Second, we jointly invert 1Hz GPS, ocean-bottom GPS and aftershock data for the spatio-temporal distribution of early afterslip. In order to constrain the spatial distribution of HF radiators we model waveform envelopes recorded by Kik-net borehole accelerometers located in northeastern Japan. We compute theoretical envelopes for waves traveling in a heterogeneous scattering medium, and invert for the location and amplitude of energy radiators for frequencies ranging from 1 to 16 Hz. Because the inversion is extremely sensitive to the response of individual sites, we adopt an empirical approach and iteratively separate the source and site terms from the stacked spectra of numerous events recorded by the network. The output response functions for each site are used to stabilize the inversion. Preliminary results are consistent with far-field observations and suggest that the HF energy emitted during the M9.0 event originated at the down-dip limit of the rupture zone. We apply waveform cross-correlation to identify repeating events within the aftershock sequence, and locate them by match-filtering their waveforms with known templates. Many of these events occur on seismic asperities loaded by the surrounding creep. We jointly invert the slip histories on these fault patches and the available GPS data for the spatio-temporal distribution of afterslip during the first few hours following the mainshock. We use the Principal Component Analysis Inversion Method to determine the time history of slip on the megathrust during seismic slip and aseismic afterslip. The eigenfunctions are constrained in an iterative process that incorporates the slip histories of seismic asperities. This approach allows documenting the seismic and aseismic phases in a self-consistent manner. The GPS-only inversion places most of the early afterslip east of the hypocenter up to the trench, an area that seemed to have undergone dynamic overshoot.

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas

2017-12-01

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

Capturing Postseismic Processes of the 2016 Mw 7.1 Kumamoto Earthquake, Japan, Using Dense, Continuous GPS and Short-repeat Time ALOS-2 InSAR Data: Implications for the Shallow Slip Deficit Problem

NASA Astrophysics Data System (ADS)

Milliner, C. W. D.; Burgmann, R.; Wang, T.; Inbal, A.; Bekaert, D. P.; Liang, C.; Fielding, E. J.

2017-12-01

Separating the contribution of shallow coseismic slip from rapidly decaying, postseismic afterslip in surface rupturing events has been difficult to resolve due to the typically sparse configuration of GPS networks and long-repeat time of InSAR acquisitions. Whether shallow fault motion along surface ruptures is a result of coseismic slip, or largely a product of rapid afterslip occurring within the first minutes to days, has significant implications for our understanding of the mechanics and frictional behavior of faulting in the shallow crust. To test this behavior in the case of a major surface rupturing event, we attempt to quantify the co- and postseismic slip of the 2016 Mw 7.1 Kumamoto earthquake sequence using a dense and continuous GPS network ( 10 km spacing), with short-repeat time, ALOS-2 InSAR data. Using the Network Inversion Filter method, we jointly invert the GPS and InSAR data to obtain a time history of afterslip in the first minutes to months following the mainshock. From our initial results, we find no clear evidence of significant shallow afterslip (i.e., no observable slip > 30 cm at depths of < 3 km, a minimum resolvable value), that could account for the 1 m of coseismic deficit of shallow slip inferred from our static finite-fault inversion. Our results show, aside from significant volumetric changes related to poroelastic processes, the majority of shallow fault slip was largely complete after rupture cessation. We also attempt to improve our coseismic slip model by implementing a method that inverts changes in seismicity rates for coseismic slip, helping constrain parts of the model space at depth where geodetic data loses resolving power. The use of geodetic data with the ability to resolve near-field, coseismic deformation and rapidly decaying postseismic processes will aid in our understanding of the frictional properties of shallow faulting, giving more reliable predictions for ground motion simulations and seismic hazard assessments.

Inversion of quasi-3D DC resistivity imaging data using artificial neural networks

NASA Astrophysics Data System (ADS)

Neyamadpour, Ahmad; Wan Abdullah, W. A. T.; Taib, Samsudin

2010-02-01

The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole-dipole array to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce synthetic data to train the artificial neural network was a homogeneous medium of 100Ωm resistivity with an embedded anomalous body of 1000Ωm resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another 11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network, with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure, and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole-dipole configuration both rapidly and accurately.

On the design of wave digital filters with low sensitivity properties.

NASA Technical Reports Server (NTRS)

Renner, K.; Gupta, S. C.

1973-01-01

The wave digital filter patterned after doubly terminated maximum available power (MAP) networks by means of the Richard's transformation has been shown to have low-coefficient-sensitivity properties. This paper examines the exact nature of the relationship between the wave-digital-filter structure and the MAP networks and how the sensitivity property arises, which permits implementation of the digital structure with a lower coefficient word length than that possible with the conventional structures. The proper design procedure is specified and the nature of the unique complementary outputs is discussed. Finally, an example is considered which illustrates the design, the conversion techniques, and the low sensitivity properties.

Universal filtered multi-carrier system for asynchronous uplink transmission in optical access network

NASA Astrophysics Data System (ADS)

Kang, Soo-Min; Kim, Chang-Hun; Han, Sang-Kook

2016-02-01

In passive optical network (PON), orthogonal frequency division multiplexing (OFDM) has been studied actively due to its advantages such as high spectra efficiency (SE), dynamic resource allocation in time or frequency domain, and dispersion robustness. However, orthogonal frequency division multiple access (OFDMA)-PON requires tight synchronization among multiple access signals. If not, frequency orthogonality could not be maintained. Also its sidelobe causes inter-channel interference (ICI) to adjacent channel. To prevent ICI caused by high sidelobes, guard band (GB) is usually used which degrades SE. Thus, OFDMA-PON is not suitable for asynchronous uplink transmission in optical access network. In this paper, we propose intensity modulation/direct detection (IM/DD) based universal filtered multi-carrier (UFMC) PON for asynchronous multiple access. The UFMC uses subband filtering to subsets of subcarriers. Since it reduces sidelobe of each subband by applying subband filtering, it could achieve better performance compared to OFDM. For the experimental demonstration, different sample delay was applied to subbands to implement asynchronous transmission condition. As a result, time synchronization robustness of UFMC was verified in asynchronous multiple access system.

Cross hole GPR traveltime inversion using a fast and accurate neural network as a forward model

NASA Astrophysics Data System (ADS)

Mejer Hansen, Thomas

2017-04-01

Probabilistic formulated inverse problems can be solved using Monte Carlo based sampling methods. In principle both advanced prior information, such as based on geostatistics, and complex non-linear forward physical models can be considered. However, in practice these methods can be associated with huge computational costs that in practice limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error, that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival travel time inversion of cross hole ground-penetrating radar (GPR) data. An accurate forward model, based on 2D full-waveform modeling followed by automatic travel time picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the full forward model, and considerably faster, and more accurate, than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of the types of inverse problems that can be solved using non-linear Monte Carlo sampling techniques.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

PubMed Central

Dülger, L. Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

PubMed

Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

PubMed

Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

2017-08-18

The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

Inverse targeting —An effective immunization strategy

NASA Astrophysics Data System (ADS)

Schneider, C. M.; Mihaljev, T.; Herrmann, H. J.

2012-05-01

We propose a new method to immunize populations or computer networks against epidemics which is more efficient than any continuous immunization method considered before. The novelty of our method resides in the way of determining the immunization targets. First we identify those individuals or computers that contribute the least to the disease spreading measured through their contribution to the size of the largest connected cluster in the social or a computer network. The immunization process follows the list of identified individuals or computers in inverse order, immunizing first those which are most relevant for the epidemic spreading. We have applied our immunization strategy to several model networks and two real networks, the Internet and the collaboration network of high-energy physicists. We find that our new immunization strategy is in the case of model networks up to 14%, and for real networks up to 33% more efficient than immunizing dynamically the most connected nodes in a network. Our strategy is also numerically efficient and can therefore be applied to large systems.

All-optical VPN utilizing DSP-based digital orthogonal filters access for PONs

NASA Astrophysics Data System (ADS)

Zhang, Xiaoling; Zhang, Chongfu; Chen, Chen; Jin, Wei; Qiu, Kun

2018-04-01

Utilizing digital filtering-enabled signal multiplexing and de-multiplexing, a cost-effective all-optical virtual private network (VPN) system is proposed, for the first time to our best knowledge, in digital filter multiple access passive optical networks (DFMA-PONs). Based on the DFMA technology, the proposed system can be easily designed to meet the requirements of next generation network's flexibility, elasticity, adaptability and compatibility. Through dynamic digital filter allocation and recycling, the proposed all-optical VPN system can provide dynamic establishments and cancellations of multiple VPN communications with arbitrary traffic volumes. More importantly, due to the employment of DFMA technology, the system is not limited to a fixed signal format and different signal formats such as pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM) and orthogonal frequency division multiplexing (OFDM) can be used. Moreover, one transceiver is sufficient to simultaneously transmit upstream (US)/VPN data to optical line terminal (OLT) or other VPN optical network units (ONUs), thus leading to great reduction in network constructions and operation expenditures. The proposed all-optical VPN system is demonstrated with the transceiver incorporating the formats of QAM and OFDM, which can be made transparent to downstream (DS), US and VPN communications. The bit error rates (BERs) of DS, US and VPN for OFDM signals are below the forward-error-correction (FEC) limit of 3 . 8 × 10-3 when the received optical powers are about -16.8 dBm, -14.5 dBm and -15.7 dBm, respectively.

Information Filtering on Coupled Social Networks

PubMed Central

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

The adaptive parallel UKF inversion method for the shape of space objects based on the ground-based photometric data

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Wang, Yang; Liu, Hao

2018-04-01

The space object in highly elliptical orbit is always presented as an image point on the ground-based imaging equipment so that it is difficult to resolve and identify the shape and attitude directly. In this paper a novel algorithm is presented for the estimation of spacecraft shape. The apparent magnitude model suitable for the inversion of object information such as shape and attitude is established based on the analysis of photometric characteristics. A parallel adaptive shape inversion algorithm based on UKF was designed after the achievement of dynamic equation of the nonlinear, Gaussian system involved with the influence of various dragging forces. The result of a simulation study demonstrate the viability and robustness of the new filter and its fast convergence rate. It realizes the inversion of combination shape with high accuracy, especially for the bus of cube and cylinder. Even though with sparse photometric data, it still can maintain a higher success rate of inversion.

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

PubMed

Bouchard, M

2001-01-01

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

PubMed

Zhao, Haiquan; Zhang, Jiashu

2009-12-01

To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

Kalman Filtering for Genetic Regulatory Networks with Missing Values

PubMed Central

Liu, Qiuhua; Lai, Tianyue; Wang, Wu

2017-01-01

The filter problem with missing value for genetic regulation networks (GRNs) is addressed, in which the noises exist in both the state dynamics and measurement equations; furthermore, the correlation between process noise and measurement noise is also taken into consideration. In order to deal with the filter problem, a class of discrete-time GRNs with missing value, noise correlation, and time delays is established. Then a new observation model is proposed to decrease the adverse effect caused by the missing value and to decouple the correlation between process noise and measurement noise in theory. Finally, a Kalman filtering is used to estimate the states of GRNs. Meanwhile, a typical example is provided to verify the effectiveness of the proposed method, and it turns out to be the case that the concentrations of mRNA and protein could be estimated accurately. PMID:28814967

Stochastic Prediction and Feedback Control of Router Queue Size in a Virtual Network Environment

DTIC Science & Technology

2014-09-18

predictor equations, while the update equations for measurement can be thought of as corrector equations. 11 2.3.1.1 Predict Equations In the... Adaptive Filters and Self -Learning Systems. Springer London, 2005. [11] Zarchan, P., and Musoff, H. Fundamentals of Kalman filtering: A Practical...iv AFIT-ENG-T-14-S-10 Abstract Modern congestion and routing management algorithms work well for networks with static topologies and moderate

An Algebraic Approach to Inference in Complex Networked Structures

DTIC Science & Technology

2015-07-09

44], [45],[46] where the shift is the elementary non-trivial filter that generates, under an appropriate notion of shift invariance, all linear ... elementary filter, and its output is a graph signal with the value at vertex n of the graph given approximately by a weighted linear combination of...AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07

Cellular traction force recovery: An optimal filtering approach in two-dimensional Fourier space.

PubMed

Huang, Jianyong; Qin, Lei; Peng, Xiaoling; Zhu, Tao; Xiong, Chunyang; Zhang, Youyi; Fang, Jing

2009-08-21

Quantitative estimation of cellular traction has significant physiological and clinical implications. As an inverse problem, traction force recovery is essentially susceptible to noise in the measured displacement data. For traditional procedure of Fourier transform traction cytometry (FTTC), noise amplification is accompanied in the force reconstruction and small tractions cannot be recovered from the displacement field with low signal-noise ratio (SNR). To improve the FTTC process, we develop an optimal filtering scheme to suppress the noise in the force reconstruction procedure. In the framework of the Wiener filtering theory, four filtering parameters are introduced in two-dimensional Fourier space and their analytical expressions are derived in terms of the minimum-mean-squared-error (MMSE) optimization criterion. The optimal filtering approach is validated with simulations and experimental data associated with the adhesion of single cardiac myocyte to elastic substrate. The results indicate that the proposed method can highly enhance SNR of the recovered forces to reveal tiny tractions in cell-substrate interaction.

Building a good initial model for full-waveform inversion using frequency shift filter

NASA Astrophysics Data System (ADS)

Wang, Guanchao; Wang, Shangxu; Yuan, Sanyi; Lian, Shijie

2018-05-01

Accurate initial model or available low-frequency data is an important factor in the success of full waveform inversion (FWI). The low-frequency helps determine the kinematical relevant components, low-wavenumber of the velocity model, which are in turn needed to avoid FWI trap in local minima or cycle-skipping. However, in the field, acquiring data that <5 Hz is a challenging and expensive task. We attempt to find the common point of low- and high-frequency signal, then utilize the high-frequency data to obtain the low-wavenumber velocity model. It is well known that the instantaneous amplitude envelope of a wavelet is invariant under frequency shift. This means that resolution is constant for a given frequency bandwidth, and independent of the actual values of the frequencies. Based on this property, we develop a frequency shift filter (FSF) to build the relationship between low- and high-frequency information with a constant frequency bandwidth. After that, we can use the high-frequency information to get a plausible recovery of the low-wavenumber velocity model. Numerical results using synthetic data from the Marmousi and layer model demonstrate that our proposed envelope misfit function based on the frequency shift filter can build an initial model with more accurate long-wavelength components, when low-frequency signals are absent in recorded data.

Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation

NASA Technical Reports Server (NTRS)

Rakoczy, John M.; Herren, Kenneth A.

2008-01-01

A technique for 6-degree-of-freedom (6DOF) pose estimation of space vehicles is being developed. This technique draws upon recent developments in implementing optical correlation measurements in a nonlinear estimator, which relates the optical correlation measurements to the pose states (orientation and position). For the optical correlator, the use of both conjugate filters and binary, phase-only filters in the design of synthetic discriminant function (SDF) filters is explored. A static neural network is trained a priori and used as the nonlinear estimator. New commercial animation and image rendering software is exploited to design the SDF filters and to generate a large filter set with which to train the neural network. The technique is applied to pose estimation for rendezvous and docking of free-flying spacecraft and to terrestrial surface mobility systems for NASA's Vision for Space Exploration. Quantitative pose estimation performance will be reported. Advantages and disadvantages of the implementation of this technique are discussed.

Analyses of integrated aircraft cabin contaminant monitoring network based on Kalman consensus filter.

PubMed

Wang, Rui; Li, Yanxiao; Sun, Hui; Chen, Zengqiang

2017-11-01

The modern civil aircrafts use air ventilation pressurized cabins subject to the limited space. In order to monitor multiple contaminants and overcome the hypersensitivity of the single sensor, the paper constructs an output correction integrated sensor configuration using sensors with different measurement theories after comparing to other two different configurations. This proposed configuration works as a node in the contaminant distributed wireless sensor monitoring network. The corresponding measurement error models of integrated sensors are also proposed by using the Kalman consensus filter to estimate states and conduct data fusion in order to regulate the single sensor measurement results. The paper develops the sufficient proof of the Kalman consensus filter stability when considering the system and the observation noises and compares the mean estimation and the mean consensus errors between Kalman consensus filter and local Kalman filter. The numerical example analyses show the effectiveness of the algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation

NASA Technical Reports Server (NTRS)

Rakoczy, John; Herren, Kenneth

2007-01-01

A technique for 6-degree-of-freedom (6DOF) pose estimation of space vehicles is being developed. This technique draws upon recent developments in implementing optical correlation measurements in a nonlinear estimator, which relates the optical correlation measurements to the pose states (orientation and position). For the optical correlator, the use of both conjugate filters and binary, phase-only filters in the design of synthetic discriminant function (SDF) filters is explored. A static neural network is trained a priori and used as the nonlinear estimator. New commercial animation and image rendering software is exploited to design the SDF filters and to generate a large filter set with which to train the neural network. The technique is applied to pose estimation for rendezvous and docking of free-flying spacecraft and to terrestrial surface mobility systems for NASA's Vision for Space Exploration. Quantitative pose estimation performance will be reported. Advantages and disadvantages of the implementation of this technique are discussed.

Simultaneous Faraday filtering of the Mollow triplet sidebands with the Cs-D1 clock transition.

PubMed

Portalupi, Simone Luca; Widmann, Matthias; Nawrath, Cornelius; Jetter, Michael; Michler, Peter; Wrachtrup, Jörg; Gerhardt, Ilja

2016-11-25

Hybrid quantum systems integrating semiconductor quantum dots (QDs) and atomic vapours become important building blocks for scalable quantum networks due to the complementary strengths of individual parts. QDs provide on-demand single-photon emission with near-unity indistinguishability comprising unprecedented brightness-while atomic vapour systems provide ultra-precise frequency standards and promise long coherence times for the storage of qubits. Spectral filtering is one of the key components for the successful link between QD photons and atoms. Here we present a tailored Faraday anomalous dispersion optical filter based on the caesium-D 1 transition for interfacing it with a resonantly pumped QD. The presented Faraday filter enables a narrow-bandwidth (Δω=2π × 1 GHz) simultaneous filtering of both Mollow triplet sidebands. This result opens the way to use QDs as sources of single as well as cascaded photons in photonic quantum networks aligned to the primary frequency standard of the caesium clock transition.

A game theory approach to target tracking in sensor networks.

PubMed

Gu, Dongbing

2011-02-01

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.

Optimization of the cleaning process on a pilot filtration setup for waste water treatment accompanied by flow visualization

NASA Astrophysics Data System (ADS)

Bílek, Petr; Hrůza, Jakub

2018-06-01

This paper deals with an optimization of the cleaning process on a liquid flat-sheet filter accompanied by visualization of the inlet side of a filter. The cleaning process has a crucial impact on the hydrodynamic properties of flat-sheet filters. Cleaning methods avoid depositing of particles on the filter surface and forming a filtration cake. Visualization significantly helps to optimize the cleaning methods, because it brings new overall view on the filtration process in time. The optical method, described in the article, enables to see flow behaviour in a thin laser sheet on the inlet side of a tested filter during the cleaning process. Visualization is a strong tool for investigation of the processes on filters in details and it is also possible to determine concentration of particles after an image analysis. The impact of air flow rate, inverse pressure drop and duration on the cleaning mechanism is investigated in the article. Images of the cleaning process are compared to the hydrodynamic data. The tests are carried out on a pilot filtration setup for waste water treatment.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

Inversion of parameters for semiarid regions by a neural network

NASA Technical Reports Server (NTRS)

Zurk, Lisa M.; Davis, Daniel; Njoku, Eni G.; Tsang, Leung; Hwang, Jenq-Neng

1992-01-01

Microwave brightness temperatures obtained from a passive radiative transfer model are inverted through use of a neural network. The model is applicable to semiarid regions and produces dual-polarized brightness temperatures for 6.6-, 10.7-, and 37-GHz frequencies. A range of temperatures is generated by varying three geophysical parameters over acceptable ranges: soil moisture, vegetation moisture, and soil temperature. A multilayered perceptron (MLP) neural network is trained with a subset of the generated temperatures, and the remaining temperatures are inverted using a backpropagation method. Several synthetic terrains are devised and inverted by the network under local constraints. All the inversions show good agreement with the original geophysical parameters, falling within 5 percent of the actual value of the parameter range.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Image feature based GPS trace filtering for road network generation and road segmentation

DOE PAGES

Yuan, Jiangye; Cheriyadat, Anil M.

2015-10-19

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

Image feature based GPS trace filtering for road network generation and road segmentation

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

Yuan, Jiangye; Cheriyadat, Anil M.

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

APPROXIMATION AND INVERSION OF A COMPLEX METEOROLOGICAL SYSTEM VIA LOCAL LINEAR FILTERS. (R825381)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

The performance of the spatiotemporal Kalman filter and LORETA in seizure onset localization.

PubMed

Hamid, Laith; Sarabi, Masoud; Japaridze, Natia; Wiegand, Gert; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Siniatchkin, Michael

2015-08-01

The assumption of spatial-smoothness is often used to solve the bioelectric inverse problem during electroencephalographic (EEG) source imaging, e.g., in low resolution electromagnetic tomography (LORETA). Since the EEG data show a temporal structure, the combination of the temporal-smoothness and the spatial-smoothness constraints may improve the solution of the EEG inverse problem. This study investigates the performance of the spatiotemporal Kalman filter (STKF) method, which is based on spatial and temporal smoothness, in the localization of a focal seizure's onset and compares its results to those of LORETA. The main finding of the study was that the STKF with an autoregressive model of order two significantly outperformed LORETA in the accuracy and consistency of the localization, provided that the source space consists of a whole-brain volumetric grid. In the future, these promising results will be confirmed using data from more patients and performing statistical analyses on the results. Furthermore, the effects of the temporal smoothness constraint will be studied using different types of focal seizures.

[Study on Differential Optical Absorption Spectroscopy Data Processing Based on Chirp-Z Transformation].

PubMed

Zheng, Hai-ming; Li, Guang-jie; Wu, Hao

2015-06-01

Differential optical absorption spectroscopy (DOAS) is a commonly used atmospheric pollution monitoring method. Denoising of monitoring spectral data will improve the inversion accuracy. Fourier transform filtering method is effectively capable of filtering out the noise in the spectral data. But the algorithm itself can introduce errors. In this paper, a chirp-z transform method is put forward. By means of the local thinning of Fourier transform spectrum, it can retain the denoising effect of Fourier transform and compensate the error of the algorithm, which will further improve the inversion accuracy. The paper study on the concentration retrieving of SO2 and NO2. The results show that simple division causes bigger error and is not very stable. Chirp-z transform is proved to be more accurate than Fourier transform. Results of the frequency spectrum analysis show that Fourier transform cannot solve the distortion and weakening problems of characteristic absorption spectrum. Chirp-z transform shows ability in fine refactoring of specific frequency spectrum.

Uniform apparent contrast noise: A picture of the noise of the visual contrast detection system

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.

1984-01-01

A picture which is a sample of random contrast noise is generated. The noise amplitude spectrum in each region of the picture is inversely proportional to spatial frequency contrast sensitivity for that region, assuming the observer fixates the center of the picture and is the appropriate distance from it. In this case, the picture appears to have approximately the same contrast everywhere. To the extent that contrast detection thresholds are determined by visual system noise, this picture can be regarded as a picture of the noise of that system. There is evidence that, at different eccentricities, contrast sensitivity functions differ only by a magnification factor. The picture was generated by filtering a sample of white noise with a filter whose frequency response is inversely proportional to foveal contrast sensitivity. It was then stretched by a space-varying magnification function. The picture summmarizes a noise linear model of detection and discrimination of contrast signals by referring the model noise to the input picture domain.

X-band preamplifier filter

NASA Technical Reports Server (NTRS)

Manshadi, F.

1986-01-01

A low-loss bandstop filter designed and developed for the Deep Space Network's 34-meter high-efficiency antennas is described. The filter is used for protection of the X-band traveling wave masers from the 20-kW transmitter signal. A combination of empirical and theoretical techniques was employed as well as computer simulation to verify the design before fabrication.

Metallic nanowire networks

DOEpatents

Song, Yujiang; Shelnutt, John A.

2012-11-06

A metallic nanowire network synthesized using chemical reduction of a metal ion source by a reducing agent in the presence of a soft template comprising a tubular inverse micellar network. The network of interconnected polycrystalline nanowires has a very high surface-area/volume ratio, which makes it highly suitable for use in catalytic applications.

A generalized model via random walks for information filtering

NASA Astrophysics Data System (ADS)

Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

2016-08-01

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation.

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

Adaptive Bloom Filter: A Space-Efficient Counting Algorithm for Unpredictable Network Traffic

NASA Astrophysics Data System (ADS)

Matsumoto, Yoshihide; Hazeyama, Hiroaki; Kadobayashi, Youki

The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.

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

Lucas, D. D.; Yver Kwok, C.; Cameron-Smith, P.

Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototypemore » network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH 2FCF 3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.« less

Virtual Seismic Observation (VSO) with Sparsity-Promotion Inversion

NASA Astrophysics Data System (ADS)

Tiezhao, B.; Ning, J.; Jianwei, M.

2017-12-01

Large station interval leads to low resolution images, sometimes prevents people from obtaining images in concerned regions. Sparsity-promotion inversion, a useful method to recover missing data in industrial field acquisition, can be lent to interpolate seismic data on none-sampled sites, forming Virtual Seismic Observation (VSO). Traditional sparsity-promotion inversion suffers when coming up with large time difference in adjacent sites, which we concern most and use shift method to improve it. The procedure of the interpolation is that we first employ low-pass filter to get long wavelength waveform data and shift the waveforms of the same wave in different seismograms to nearly same arrival time. Then we use wavelet-transform-based sparsity-promotion inversion to interpolate waveform data on none-sampled sites and filling a phase in each missing trace. Finally, we shift back the waveforms to their original arrival times. We call our method FSIS (Filtering, Shift, Interpolation, Shift) interpolation. By this way, we can insert different virtually observed seismic phases into none-sampled sites and get dense seismic observation data. For testing our method, we randomly hide the real data in a site and use the rest to interpolate the observation on that site, using direct interpolation or FSIS method. Compared with directly interpolated data, interpolated data with FSIS can keep amplitude better. Results also show that the arrival times and waveforms of those VSOs well express the real data, which convince us that our method to form VSOs are applicable. In this way, we can provide needed data for some advanced seismic technique like RTM to illuminate shallow structures.

Security Aspects of an Enterprise-Wide Network Architecture.

ERIC Educational Resources Information Center

Loew, Robert; Stengel, Ingo; Bleimann, Udo; McDonald, Aidan

1999-01-01

Presents an overview of two projects that concern local area networks and the common point between networks as they relate to network security. Discusses security architectures based on firewall components, packet filters, application gateways, security-management components, an intranet solution, user registration by Web form, and requests for…

Microcomputer-Based Digital Signal Processing Laboratory Experiments.

ERIC Educational Resources Information Center

Tinari, Jr., Rocco; Rao, S. Sathyanarayan

1985-01-01

Describes a system (Apple II microcomputer interfaced to flexible, custom-designed digital hardware) which can provide: (1) Fast Fourier Transform (FFT) computation on real-time data with a video display of spectrum; (2) frequency synthesis experiments using the inverse FFT; and (3) real-time digital filtering experiments. (JN)

Planar high temperature superconductor filters with backside coupling

NASA Technical Reports Server (NTRS)

Shen, Zhi-Yuan (Inventor)

1998-01-01

An improved high temperature superconducting planar filter wherein the coupling circuit or connecting network is located, in whole or in part, on the side of the substrate opposite the resonators and enables higher power handling capability.

Investigating the interaction between positions and signals of height-channel loudspeakers in reproducing immersive 3d sound

NASA Astrophysics Data System (ADS)

Karampourniotis, Antonios

Since transmission capacities have significantly increased over the past few years, researchers are now able to transmit a larger amount of data, namely multichannel audio content, in the consumer applications. What has not been investigated in a systematic way yet is how to deliver the multichannel content. Specifically, researchers' attention is focused on the quest of a standardized immersive reproduction format that incorporates height loudspeakers coupled with the new high-resolution and three-dimensional (3D) media content for a comprehensive 3D experience. To better understand and utilize the immersive audio reproduction, this research focused on the (1) interaction between the positioning of height loudspeakers and the signals fed to the loudspeakers, (2) investigation of the perceptual characteristics associated with the height ambiences, and (3) the influence of inverse filtering on perceived sound quality for the realistic 3D sound reproduction. The experiment utilized the existence of two layers of loudspeakers: horizontal layer following the ITU-R BS.775 five-channel loudspeaker configuration and height layer locating a total of twelve loudspeakers at the azimuth of +/-30°, +/-50°, +/-70°, +/-90°, +/-110° and +/-130° and elevation of 30°. Eight configurations were formed, each of which selected four height-loudspeakers from twelve. In the subjective evaluation, listeners compared, ranked and described the eight randomly presented configurations of 4-channel height ambiences. The stimuli for the experiment were four nine-channel (5 channels for the horizontal and 4 for the height loudspeakers) multichannel music. Moreover, an approach of Finite Impulse Response (FIR) inverse filtering was attempted, in order to remove the particular room's acoustic influence. Another set of trained professionals was informally asked to use descriptors to characterize the newly presented multichannel music with height ambiences rendered with inverse filtering. The experimental results indicate the significance of the positioning of the loudspeakers with respect to the signals being fed to those loudspeakers in delivering a 3D sound field. Furthermore, it has been revealed that the perceptual characteristics that listeners linked for multichannel music with height ambiences include envelopment, elevatedness and fullness. Last but not least, after applying the inverse filtering the subjective preference was not affected significantly. This allows for the author to believe that, in fact, the room's influence with respect to the subjective evaluation is not as important as the appropriate loudspeaker-positioning for the multichannel-reproduced music with height ambiences.

A Real-Time De-Noising Algorithm for E-Noses in a Wireless Sensor Network

PubMed Central

Qu, Jianfeng; Chai, Yi; Yang, Simon X.

2009-01-01

A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is an e-nose that includes four metal-oxide semiconductor (MOS) gas sensors. A modified Kalman filtering technique is proposed for collecting raw data and de-noising based on the output noise characteristics of those gas sensors. The measurement noise variance is obtained in real time by data analysis using the proposed slip windows average method. The optimal system noise variance of the filter is obtained by using the experiments data. The Kalman filter theory on how to acquire MOS gas sensors data is discussed. Simulation results demonstrate that the proposed method can adjust the Kalman filter parameters and significantly reduce the noise from the gas sensors. PMID:22399946

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Experimental demonstration of optical stealth transmission over wavelength-division multiplexing network.

PubMed

Zhu, Huatao; Wang, Rong; Pu, Tao; Fang, Tao; Xiang, Peng; Zheng, Jilin; Tang, Yeteng; Chen, Dalei

2016-08-10

We propose and experimentally demonstrate an optical stealth transmission system over a 200 GHz-grid wavelength-division multiplexing (WDM) network. The stealth signal is processed by spectral broadening, temporal spreading, and power equalizing. The public signal is suppressed by multiband notch filtering at the stealth channel receiver. The interaction between the public and stealth channels is investigated in terms of public-signal-to-stealth-signal ratio, data rate, notch-filter bandwidth, and public channel number. The stealth signal can transmit over 80 km single-mode fiber with no error. Our experimental results verify the feasibility of optical steganography used over the existing WDM-based optical network.

Estimating Transient Water Storage from Hurricane Harvey Using GPS Observations of Vertical Land Motion

NASA Astrophysics Data System (ADS)

Milliner, C. W. D.; Materna, K.; Burgmann, R.; Fu, Y.; Bekaert, D. P.; Moore, A. W.; Adhikari, S.

2017-12-01

The Global Positioning System (GPS) measures elastic ground motions due to variations in terrestrial water mass. Such measurements have been used to successfully study variations of hydrological loading over monthly-to-yearly timescales; e.g., seasonal changes in water storage in California (Argus et al., 2014), 3-year drought of Western US (Borsa et al., 2014) and monthly water storage change in the Pacific Northwest (Fu et al., 2015). However, inferring water storage variations from single loading events over daily-to-weekly timescales presents a major challenge, due to the relatively higher level of noise and systematic errors, such as common mode errors (CME). This makes geodetic investigations of transient hydrologic events, such as major hurricanes, particularly difficult. By using daily vertical GPS timeseries we resolve the spatial and temporal evolution of water loading from Hurricane Harvey across the Gulf coast by applying multiple network correction methods, which helps to isolate the hydrological loading signal. Using 340 GPS stations distributed across the southern US, we mitigate for the effects of spatially correlated CME by firstly removing vertical contributions from atmospheric and non-ocean tidal loading, and secondly correcting the residual positions for changes in translation, rotation and scaling using a Helmert transformation. Our results show a maximum subsidence of 1.8 cm occurring around Houston, and a clear migration of land subsidence from Corpus Christi to western Louisiana over a 7-day period, consistent with the movement of Harvey itself. We also present preliminary results using the Network Inversion Filter (Bekaert et al., 2016), in which we use a Kalman filter approach to describe the time-varying water mass in a stochastic sense. Although our results are preliminary, we find removal of systematic sources of noise can help reveal hydrological loading signals due to extreme, transient events, that would typically go missed by other spatially and temporally coarser methods (e.g., GRACE), providing valuable constraints on large and sudden changes to the hydrosphere.

Designing optimal greenhouse gas observing networks that consider performance and cost

DOE PAGES

Lucas, D. D.; Yver Kwok, C.; Cameron-Smith, P.; ...

2015-06-16

Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototypemore » network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH 2FCF 3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.« less

Application of a moment tensor inversion code developed for mining-induced seismicity to fracture monitoring of civil engineering materials

NASA Astrophysics Data System (ADS)

Linzer, Lindsay; Mhamdi, Lassaad; Schumacher, Thomas

2015-01-01

A moment tensor inversion (MTI) code originally developed to compute source mechanisms from mining-induced seismicity data is now being used in the laboratory in a civil engineering research environment. Quantitative seismology methods designed for geological environments are being tested with the aim of developing techniques to assess and monitor fracture processes in structural concrete members such as bridge girders. In this paper, we highlight aspects of the MTI_Toolbox programme that make it applicable to performing inversions on acoustic emission (AE) data recorded by networks of uniaxial sensors. The influence of the configuration of a seismic network on the conditioning of the least-squares system and subsequent moment tensor results for a real, 3-D network are compared to a hypothetical 2-D version of the same network. This comparative analysis is undertaken for different cases: for networks consisting entirely of triaxial or uniaxial sensors; for both P and S-waves, and for P-waves only. The aim is to guide the optimal design of sensor configurations where only uniaxial sensors can be installed. Finally, the findings of recent laboratory experiments where the MTI_Toolbox has been applied to a concrete beam test are presented and discussed.

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

PubMed Central

Feng, Xiaoxue; Snoussi, Hichem; Liang, Yan; Jiao, Lianmeng

2014-01-01

Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint. PMID:25390408

Design of Provider-Provisioned Website Protection Scheme against Malware Distribution

NASA Astrophysics Data System (ADS)

Yagi, Takeshi; Tanimoto, Naoto; Hariu, Takeo; Itoh, Mitsutaka

Vulnerabilities in web applications expose computer networks to security threats, and many websites are used by attackers as hopping sites to attack other websites and user terminals. These incidents prevent service providers from constructing secure networking environments. To protect websites from attacks exploiting vulnerabilities in web applications, service providers use web application firewalls (WAFs). WAFs filter accesses from attackers by using signatures, which are generated based on the exploit codes of previous attacks. However, WAFs cannot filter unknown attacks because the signatures cannot reflect new types of attacks. In service provider environments, the number of exploit codes has recently increased rapidly because of the spread of vulnerable web applications that have been developed through cloud computing. Thus, generating signatures for all exploit codes is difficult. To solve these problems, our proposed scheme detects and filters malware downloads that are sent from websites which have already received exploit codes. In addition, to collect information for detecting malware downloads, web honeypots, which automatically extract the communication records of exploit codes, are used. According to the results of experiments using a prototype, our scheme can filter attacks automatically so that service providers can provide secure and cost-effective network environments.

Inverse Stochastic Resonance in Cerebellar Purkinje Cells

PubMed Central

Häusser, Michael; Gutkin, Boris S.; Roth, Arnd

2016-01-01

Purkinje neurons play an important role in cerebellar computation since their axons are the only projection from the cerebellar cortex to deeper cerebellar structures. They have complex internal dynamics, which allow them to fire spontaneously, display bistability, and also to be involved in network phenomena such as high frequency oscillations and travelling waves. Purkinje cells exhibit type II excitability, which can be revealed by a discontinuity in their f-I curves. We show that this excitability mechanism allows Purkinje cells to be efficiently inhibited by noise of a particular variance, a phenomenon known as inverse stochastic resonance (ISR). While ISR has been described in theoretical models of single neurons, here we provide the first experimental evidence for this effect. We find that an adaptive exponential integrate-and-fire model fitted to the basic Purkinje cell characteristics using a modified dynamic IV method displays ISR and bistability between the resting state and a repetitive activity limit cycle. ISR allows the Purkinje cell to operate in different functional regimes: the all-or-none toggle or the linear filter mode, depending on the variance of the synaptic input. We propose that synaptic noise allows Purkinje cells to quickly switch between these functional regimes. Using mutual information analysis, we demonstrate that ISR can lead to a locally optimal information transfer between the input and output spike train of the Purkinje cell. These results provide the first experimental evidence for ISR and suggest a functional role for ISR in cerebellar information processing. PMID:27541958

Neural network approach for the calculation of potential coefficients in quantum mechanics

NASA Astrophysics Data System (ADS)

Ossandón, Sebastián; Reyes, Camilo; Cumsille, Patricio; Reyes, Carlos M.

2017-05-01

A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct problem for different parametrizations of the chosen potential function. Then, using the attainable eigenvalues as a training set of the direct radial basis neural network a map of new eigenvalues was obtained. This relationship was later inverted and refined by training an inverse radial basis neural network, allowing the calculation of the unknown parameters and therefore estimating the potential function. Three numerical examples are presented in order to prove the effectiveness of the method. The results show that the method proposed has the advantage to use less computational resources without a significant accuracy loss.

Layer 1 VPN services in distributed next-generation SONET/SDH networks with inverse multiplexing

NASA Astrophysics Data System (ADS)

Ghani, N.; Muthalaly, M. V.; Benhaddou, D.; Alanqar, W.

2006-05-01

Advances in next-generation SONET/SDH along with GMPLS control architectures have enabled many new service provisioning capabilities. In particular, a key services paradigm is the emergent Layer 1 virtual private network (L1 VPN) framework, which allows multiple clients to utilize a common physical infrastructure and provision their own 'virtualized' circuit-switched networks. This precludes expensive infrastructure builds and increases resource utilization for carriers. Along these lines, a novel L1 VPN services resource management scheme for next-generation SONET/SDH networks is proposed that fully leverages advanced virtual concatenation and inverse multiplexing features. Additionally, both centralized and distributed GMPLS-based implementations are also tabled to support the proposed L1 VPN services model. Detailed performance analysis results are presented along with avenues for future research.

Sub-GHz-resolution C-band Nyquist-filtering interleaver on a high-index-contrast photonic integrated circuit.

PubMed

Zhuang, Leimeng; Zhu, Chen; Corcoran, Bill; Burla, Maurizio; Roeloffzen, Chris G H; Leinse, Arne; Schröder, Jochen; Lowery, Arthur J

2016-03-21

Modern optical communications rely on high-resolution, high-bandwidth filtering to maximize the data-carrying capacity of fiber-optic networks. Such filtering typically requires high-speed, power-hungry digital processes in the electrical domain. Passive optical filters currently provide high bandwidths with low power consumption, but at the expense of resolution. Here, we present a passive filter chip that functions as an optical Nyquist-filtering interleaver featuring sub-GHz resolution and a near-rectangular passband with 8% roll-off. This performance is highly promising for high-spectral-efficiency Nyquist wavelength division multiplexed (N-WDM) optical super-channels. The chip provides a simple two-ring-resonator-assisted Mach-Zehnder interferometer, which has a sub-cm² footprint owing to the high-index-contrast Si₃N₄/SiO₂ waveguide, while manifests low wavelength-dependency enabling C-band (> 4 THz) coverage with more than 160 effective free spectral ranges of 25 GHz. This device is anticipated to be a critical building block for spectrally-efficient, chip-scale transceivers and ROADMs for N-WDM super-channels in next-generation optical communication networks.

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.

Reverse engineering gene regulatory networks from measurement with missing values.

PubMed

Ogundijo, Oyetunji E; Elmas, Abdulkadir; Wang, Xiaodong

2016-12-01

Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values. We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with one-step or two-step missing measurements . The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeast Saccharomyces cerevisiae . PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when using the conventional Gaussian approximation (GA) filters ignoring the missing data points.

3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Huang, Jingyi; McBratney, Alex; Minasny, Budiman; Triantafilis, John

2017-04-01

Mapping and immediate forecasting of soil water content (θ) and its movement can be challenging. Although apparent electrical conductivity (ECa) measured by electromagnetic induction has been used, it is difficult to apply it along a transect or across a field. Across a 3.95-ha field with varying soil texture, an ensemble Kalman filter (EnFK) was used to monitor and nowcast θ dynamics in 2-d and 3-d over 16 days. The EnKF combined a physical model fitted with θ measured by soil moisture sensors and an Artificial Neural Network model comprising estimate of true electrical conductivity (σ) generated by inversions of DUALEM-421S ECa data. Results showed that the spatio-temporal variation in θ can be successfully modelled using the EnKF (Lin's concordance = 0.89). Soil water dried fast at the beginning of the irrigation and decreased with time and soil depth, which were consistent with the classical soil drying theory and experiments. It was also found that the soil dried fast in the loamy and duplex soils across the field, which was attributable to deep drainage and preferential flows. It was concluded that the EnKF approach can be used to better the irrigation practice so that variation in irrigation is minimised and irrigation efficiency is improved by applying variable rates of irrigation across the field. In addition, soil water status can be nowcasted using this method with weather forecast information, which will provide guidance to farmers for real-time irrigation management.

The Effects of Filter Cutoff Frequency on Musculoskeletal Simulations of High-Impact Movements.

PubMed

Tomescu, Sebastian; Bakker, Ryan; Beach, Tyson A C; Chandrashekar, Naveen

2018-02-12

Estimation of muscle forces through musculoskeletal simulation is important in understanding human movement and injury. Unmatched filter frequencies used to low-pass filter marker and force platform data can create artifacts during inverse dynamics analysis, but their effects on muscle force calculations are unknown. The objective of this study was to determine the effects of filter cutoff frequency on simulation parameters and magnitudes of lower extremity muscle and resultant joint contact forces during a high-impact maneuver. Eight participants performed a single leg jump-landing. Kinematics were captured with a 3D motion capture system and ground reaction forces were recorded with a force platform. The marker and force platform data were filtered using two matched filter frequencies (10-10Hz, 15-15Hz) and two unmatched frequencies (10-50Hz, 15-50Hz). Musculoskeletal simulations using Computed Muscle Control were performed in OpenSim. The results revealed significantly higher peak quadriceps (13%), hamstrings (48%), and gastrocnemius forces (69%) in the unmatched (10-50Hz, 15-50Hz) conditions than in the matched (10-10Hz, 15-15Hz) conditions (p<0.05). Resultant joint contact forces and reserve (non-physiologic) moments were similarly larger in the unmatched filter categories (p<0.05). This study demonstrated that artifacts created from filtering with unmatched filter cutoffs result in altered muscle forces and dynamics which are not physiologic.

Video-signal improvement using comb filtering techniques.

NASA Technical Reports Server (NTRS)

Arndt, G. D.; Stuber, F. M.; Panneton, R. J.

1973-01-01

Significant improvement in the signal-to-noise performance of television signals has been obtained through the application of comb filtering techniques. This improvement is achieved by removing the inherent redundancy in the television signal through linear prediction and by utilizing the unique noise-rejection characteristics of the receiver comb filter. Theoretical and experimental results describe the signal-to-noise ratio and picture-quality improvement obtained through the use of baseband comb filters and the implementation of a comb network as the loop filter in a phase-lock-loop demodulator. Attention is given to the fact that noise becomes correlated when processed by the receiver comb filter.

Source Localization in a Cognitive Radio Environment Consisting of Frequency and Spatial Mobility

DTIC Science & Technology

2011-12-01

are designed to track position over time using a wireless RF sensor network, such as Kalman filtering [13]. 74 THIS PAGE INTENTIONALLY LEFT BLANK...Radio,” Proceedings of the IEEE, vol. 97, no. 4, pp. 612–625, Apr. 2009. 80 [12] J. B. Bernthal, T. X. Brown , D. N. Hatfield, D. C. Sicker, P. A... Kalman Filtering in Wireless Sensor Networks,” IEEE Control Systems, vol. 30, no. 2, pp. 66–86, April 2010. [14] J. Nemeroff, L. Garcia, D

Improved source inversion from joint measurements of translational and rotational ground motions

NASA Astrophysics Data System (ADS)

Donner, S.; Bernauer, M.; Reinwald, M.; Hadziioannou, C.; Igel, H.

2017-12-01

Waveform inversion for seismic point (moment tensor) and kinematic sources is a standard procedure. However, especially in the local and regional distances a lack of appropriate velocity models, the sparsity of station networks, or a low signal-to-noise ratio combined with more complex waveforms hamper the successful retrieval of reliable source solutions. We assess the potential of rotational ground motion recordings to increase the resolution power and reduce non-uniquenesses for point and kinematic source solutions. Based on synthetic waveform data, we perform a Bayesian (i.e. probabilistic) inversion. Thus, we avoid the subjective selection of the most reliable solution according the lowest misfit or other constructed criterion. In addition, we obtain unbiased measures of resolution and possible trade-offs. Testing different earthquake mechanisms and scenarios, we can show that the resolution of the source solutions can be improved significantly. Especially depth dependent components show significant improvement. Next to synthetic data of station networks, we also tested sparse-network and single station cases.

Multiscale Embedded Gene Co-expression Network Analysis

PubMed Central

Song, Won-Min; Zhang, Bin

2015-01-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Topology reduction in deep convolutional feature extraction networks

NASA Astrophysics Data System (ADS)

Wiatowski, Thomas; Grohs, Philipp; Bölcskei, Helmut

2017-08-01

Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and 10,000s of nodes. Such network sizes entail significant computational complexity due to the large number of convolutions that need to be carried out; in addition, a large number of parameters needs to be learned and stored. Very deep and wide CNNs may therefore not be well suited to applications operating under severe resource constraints as is the case, e.g., in low-power embedded and mobile platforms. This paper aims at understanding the impact of CNN topology, specifically depth and width, on the network's feature extraction capabilities. We address this question for the class of scattering networks that employ either Weyl-Heisenberg filters or wavelets, the modulus non-linearity, and no pooling. The exponential feature map energy decay results in Wiatowski et al., 2017, are generalized to O(a-N), where an arbitrary decay factor a > 1 can be realized through suitable choice of the Weyl-Heisenberg prototype function or the mother wavelet. We then show how networks of fixed (possibly small) depth N can be designed to guarantee that ((1 - ɛ) · 100)% of the input signal's energy are contained in the feature vector. Based on the notion of operationally significant nodes, we characterize, partly rigorously and partly heuristically, the topology-reducing effects of (effectively) band-limited input signals, band-limited filters, and feature map symmetries. Finally, for networks based on Weyl-Heisenberg filters, we determine the prototype function bandwidth that minimizes - for fixed network depth N - the average number of operationally significant nodes per layer.

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

NASA Astrophysics Data System (ADS)

Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.

2017-12-01

Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

NASA Astrophysics Data System (ADS)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

QPA-CLIPS: A language and representation for process control

NASA Technical Reports Server (NTRS)

Freund, Thomas G.

1994-01-01

QPA-CLIPS is an extension of CLIPS oriented towards process control applications. Its constructs define a dependency network of process actions driven by sensor information. The language consists of three basic constructs: TASK, SENSOR, and FILTER. TASK's define the dependency network describing alternative state transitions for a process. SENSOR's and FILTER's define sensor information sources used to activate state transitions within the network. Deftemplate's define these constructs and their run-time environment is an interpreter knowledge base, performing pattern matching on sensor information and so activating TASK's in the dependency network. The pattern matching technique is based on the repeatable occurrence of a sensor data pattern. QPA-CIPS has been successfully tested on a SPARCStation providing supervisory control to an Allen-Bradley PLC 5 controller driving molding equipment.

Recoil Inversion in the Photodissociation of Carbonyl Sulfide near 234 nm.

PubMed

Sofikitis, Dimitris; Suarez, Jaime; Schmidt, Johan A; Rakitzis, T Peter; Farantos, Stavros C; Janssen, Maurice H M

2017-06-23

We report the observation of recoil inversion of the CO (v=0, J_{CO}=66) state in the UV dissociation of lab-frame oriented carbonyl sulfide (OCS). This state is ejected in the opposite direction with respect to all other (>30) states and in absence of any OCS rotation, thus resulting in spatial filtering of this particular high-J rovibrational state. This inversion is caused by resonances occurring in shallow local minima of the molecular potential, which bring the sulfur closer to the oxygen than the carbon atom, and is a striking example where such subtleties severely modify the photofragment trajectories. The resonant behavior is observed only in the photofragment trajectories and not in their population, showing that stereodynamic measurements from oriented molecules offer an indispensable probe for exploring energy landscapes.

Identifying Flow Networks in a Karstified Aquifer by Application of the Cellular Automata-Based Deterministic Inversion Method (Lez Aquifer, France)

NASA Astrophysics Data System (ADS)

Fischer, P.; Jardani, A.; Wang, X.; Jourde, H.; Lecoq, N.

2017-12-01

The distributed modeling of flow paths within karstic and fractured fields remains a complex task because of the high dependence of the hydraulic responses to the relative locations between observational boreholes and interconnected fractures and karstic conduits that control the main flow of the hydrosystem. The inverse problem in a distributed model is one alternative approach to interpret the hydraulic test data by mapping the karstic networks and fractured areas. In this work, we developed a Bayesian inversion approach, the Cellular Automata-based Deterministic Inversion (CADI) algorithm to infer the spatial distribution of hydraulic properties in a structurally constrained model. This method distributes hydraulic properties along linear structures (i.e., flow conduits) and iteratively modifies the structural geometry of this conduit network to progressively match the observed hydraulic data to the modeled ones. As a result, this method produces a conductivity model that is composed of a discrete conduit network embedded in the background matrix, capable of producing the same flow behavior as the investigated hydrologic system. The method is applied to invert a set of multiborehole hydraulic tests collected from a hydraulic tomography experiment conducted at the Terrieu field site in the Lez aquifer, Southern France. The emergent model shows a high consistency to field observation of hydraulic connections between boreholes. Furthermore, it provides a geologically realistic pattern of flow conduits. This method is therefore of considerable value toward an enhanced distributed modeling of the fractured and karstified aquifers.

Event-Triggered Fault Detection of Nonlinear Networked Systems.

PubMed

Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

2017-04-01

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

An Adaptive Jitter Mechanism for Reactive Route Discovery in Sensor Networks

PubMed Central

Cordero, Juan Antonio; Yi, Jiazi; Clausen, Thomas

2014-01-01

This paper analyses the impact of jitter when applied to route discovery in reactive (on-demand) routing protocols. In multi-hop non-synchronized wireless networks, jitter—a small, random variation in the timing of message emission—is commonly employed, as a means to avoid collisions of simultaneous transmissions by adjacent routers over the same channel. In a reactive routing protocol for sensor and ad hoc networks, jitter is recommended during the route discovery process, specifically, during the network-wide flooding of route request messages, in order to avoid collisions. Commonly, a simple uniform jitter is recommended. Alas, this is not without drawbacks: when applying uniform jitter to the route discovery process, an effect called delay inversion is observed. This paper, first, studies and quantifies this delay inversion effect. Second, this paper proposes an adaptive jitter mechanism, designed to alleviate the delay inversion effect and thereby to reduce the route discovery overhead and (ultimately) allow the routing protocol to find more optimal paths, as compared to uniform jitter. This paper presents both analytical and simulation studies, showing that the proposed adaptive jitter can effectively decrease the cost of route discovery and increase the path quality. PMID:25111238

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

Distributed Event-Based Set-Membership Filtering for a Class of Nonlinear Systems With Sensor Saturations Over Sensor Networks.

PubMed

Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos

2017-11-01

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

PubMed Central

Vargas-Meléndez, Leandro; Boada, Beatriz L.; Boada, María Jesús L.; Gauchía, Antonio; Díaz, Vicente

2016-01-01

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator. PMID:27589763

Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure.

PubMed

Kemppainen, Petri; Knight, Christopher G; Sarma, Devojit K; Hlaing, Thaung; Prakash, Anil; Maung Maung, Yan Naung; Somboon, Pradya; Mahanta, Jagadish; Walton, Catherine

2015-09-01

Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here, we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set from 21 three-spined stickleback individuals. In each case, we readily identified five distinct LD network clusters (single-outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so it is applicable to any population-genomic data set, making it especially valuable for nonmodel species. © 2015 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Generalization of the Lyot filter and its application to snapshot spectral imaging.

PubMed

Gorman, Alistair; Fletcher-Holmes, David William; Harvey, Andrew Robert

2010-03-15

A snapshot multi-spectral imaging technique is described which employs multiple cascaded birefringent interferometers to simultaneously spectrally filter and demultiplex multiple spectral images onto a single detector array. Spectral images are recorded directly without the need for inversion and without rejection of light and so the technique offers the potential for high signal-to-noise ratio. An example of an eight-band multi-spectral movie sequence is presented; we believe this is the first such demonstration of a technique able to record multi-spectral movie sequences without the need for computer reconstruction.

Frequency and time domain three-dimensional inversion of electromagnetic data for a grounded-wire source

NASA Astrophysics Data System (ADS)

Sasaki, Yutaka; Yi, Myeong-Jong; Choi, Jihyang; Son, Jeong-Sul

2015-01-01

We present frequency- and time-domain three-dimensional (3-D) inversion approaches that can be applied to transient electromagnetic (TEM) data from a grounded-wire source using a PC. In the direct time-domain approach, the forward solution and sensitivity were obtained in the frequency domain using a finite-difference technique, and the frequency response was then Fourier-transformed using a digital filter technique. In the frequency-domain approach, TEM data were Fourier-transformed using a smooth-spectrum inversion method, and the recovered frequency response was then inverted. The synthetic examples show that for the time derivative of magnetic field, frequency-domain inversion of TEM data performs almost as well as time-domain inversion, with a significant reduction in computational time. In our synthetic studies, we also compared the resolution capabilities of the ground and airborne TEM and controlled-source audio-frequency magnetotelluric (CSAMT) data resulting from a common grounded wire. An airborne TEM survey at 200-m elevation achieved a resolution for buried conductors almost comparable to that of the ground TEM method. It is also shown that the inversion of CSAMT data was able to detect a 3-D resistivity structure better than the TEM inversion, suggesting an advantage of electric-field measurements over magnetic-field-only measurements.

EDITORIAL: Inverse Problems in Engineering

NASA Astrophysics Data System (ADS)

West, Robert M.; Lesnic, Daniel

2007-01-01

Presented here are 11 noteworthy papers selected from the Fifth International Conference on Inverse Problems in Engineering: Theory and Practice held in Cambridge, UK during 11-15 July 2005. The papers have been peer-reviewed to the usual high standards of this journal and the contributions of reviewers are much appreciated. The conference featured a good balance of the fundamental mathematical concepts of inverse problems with a diverse range of important and interesting applications, which are represented here by the selected papers. Aspects of finite-element modelling and the performance of inverse algorithms are investigated by Autrique et al and Leduc et al. Statistical aspects are considered by Emery et al and Watzenig et al with regard to Bayesian parameter estimation and inversion using particle filters. Electrostatic applications are demonstrated by van Berkel and Lionheart and also Nakatani et al. Contributions to the applications of electrical techniques and specifically electrical tomographies are provided by Wakatsuki and Kagawa, Kim et al and Kortschak et al. Aspects of inversion in optical tomography are investigated by Wright et al and Douiri et al. The authors are representative of the worldwide interest in inverse problems relating to engineering applications and their efforts in producing these excellent papers will be appreciated by many readers of this journal.

Crustal Structure Beneath Taiwan Using Frequency-band Inversion of Receiver Function Waveforms

NASA Astrophysics Data System (ADS)

Tomfohrde, D. A.; Nowack, R. L.

Receiver function analysis is used to determine local crustal structure beneath Taiwan. We have performed preliminary data processing and polarization analysis for the selection of stations and events and to increase overall data quality. Receiver function analysis is then applied to data from the Taiwan Seismic Network to obtain radial and transverse receiver functions. Due to the limited azimuthal coverage, only the radial receiver functions are analyzed in terms of horizontally layered crustal structure for each station. In order to improve convergence of the receiver function inversion, frequency-band inversion (FBI) is implemented, in which an iterative inversion procedure with sequentially higher low-pass corner frequencies is used to stabilize the waveform inversion. Frequency-band inversion is applied to receiver functions at six stations of the Taiwan Seismic Network. Initial 20-layer crustal models are inverted for using prior tomographic results for the initial models. The resulting 20-1ayer models are then simplified to 4 to 5 layer models and input into an alternating depth and velocity frequency-band inversion. For the six stations investigated, the resulting simplified models provide an average estimate of 38 km for the Moho thickness surrounding the Central Range of Taiwan. Also, the individual station estimates compare well with the recent tomographic model of and the refraction results of Rau and Wu (1995) and the refraction results of Ma and Song (1997).

Filtered Push: Annotating Distributed Data for Quality Control and Fitness for Use Analysis

NASA Astrophysics Data System (ADS)

Morris, P. J.; Kelly, M. A.; Lowery, D. B.; Macklin, J. A.; Morris, R. A.; Tremonte, D.; Wang, Z.

2009-12-01

The single greatest problem with the federation of scientific data is the assessment of the quality and validity of the aggregated data in the context of particular research problems, that is, its fitness for use. There are three critical data quality issues in networks of distributed natural science collections data, as in all scientific data: identifying and correcting errors, maintaining currency, and assessing fitness for use. To this end, we have designed and implemented a prototype network in the domain of natural science collections. This prototype is built over the open source Map-Reduce platform Hadoop with a network client in the open source collections management system Specify 6. We call this network “Filtered Push” as, at its core, annotations are pushed from the network edges to relevant authoritative repositories, where humans and software filter the annotations before accepting them as changes to the authoritative data. The Filtered Push software is a domain-neutral framework for originating, distributing, and analyzing record-level annotations. Network participants can subscribe to notifications arising from ontology-based analyses of new annotations or of purpose-built queries against the network's global history of annotations. Quality and fitness for use of distributed natural science collections data can be addressed with Filtered Push software by implementing a network that allows data providers and consumers to define potential errors in data, develop metrics for those errors, specify workflows to analyze distributed data to detect potential errors, and to close the quality management cycle by providing a network architecture to pushing assertions about data quality such as corrections back to the curators of the participating data sets. Quality issues in distributed scientific data have several things in common: (1) Statements about data quality should be regarded as hypotheses about inconsistencies between perhaps several records, data sets, or practices of science. (2) Data quality problems often cannot be detected only from internal statistical correlations or logical analysis, but may need the application of defined workflows that signal illogical output. (3) Changes in scientific theory or practice over time can result in changes of what QC tests should be applied to legacy data. (4) The frequency of some classes of error in a data set may be identifiable without the ability to assert that a particular record is in error. To address these issues requires, as does science itself, framing QC hypotheses against data that may be anywhere and may arise at any time in the future. In short, QC for science data is a never ending process. It must provide for notice to an agent (human or software) that a given dataset supports a hypothesis of inconsistency with a current scientific resource or model, or with potential generalizations of the concepts in a metadata ontology. Like quality control in general, quality control of distributed data is a repeated cyclical process. In implementing a Filtered Push network for quality control, we have a model in which the cost of QC forever is not substantially greater than QC once.

Analysis of silicon on insulator (SOI) optical microring add-drop filter based on waveguide intersections

NASA Astrophysics Data System (ADS)

Kaźmierczak, Andrzej; Bogaerts, Wim; Van Thourhout, Dries; Drouard, Emmanuel; Rojo-Romeo, Pedro; Giannone, Domenico; Gaffiot, Frederic

2008-04-01

We present a compact passive optical add-drop filter which incorporates two microring resonators and a waveguide intersection in silicon-on-insulator (SOI) technology. Such a filter is a key element for designing simple layouts of highly integrated complex optical networks-on-chip. The filter occupies an area smaller than 10μm×10μm and exhibits relatively high quality factors (up to 4000) and efficient signal dropping capabilities. In the present work, the influence of filter parameters such as the microring-resonators radii and the coupling section shape are analyzed theoretically and experimentally

"Blocking" and "Filtering": a Commentary on Mobile Technology, Racism, and the Sexual Networks of Young Black MSM (YBMSM).

PubMed

Winder, Terrell J A; Lea, Charles H

2018-04-30

While research investigates the role and influence of geo-social networking (GSN) applications on HIV, less is known about the impact of GSN functions on disease transmission. In our formative research on young Black men who have sex with men's (YBMSM) technology use patterns and preferences for a smartphone-based HIV prevention intervention, we found that study participants used GSN "block" and "filter" functions as protective mechanisms against racism and racial sexual discrimination. Yet, we suggest that these functions may unintentionally create restrictive sexual networks that likely increase their risk for disease transmission. As such, we contend that attention to the unintended effects of these protective mechanisms against racism on GSN applications is fundamentally a public health issue that requires more research and explicit intervention. Ultimately, we use this work to hypothesize the role of blocking and filtering as a strategy to avoid racism on GSN applications that may partly explain HIV disparities among YBMSM.

Coarse cluster enhancing collaborative recommendation for social network systems

NASA Astrophysics Data System (ADS)

Zhao, Yao-Dong; Cai, Shi-Min; Tang, Ming; Shang, Min-Sheng

2017-10-01

Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90 % and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users' information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

PubMed

Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing

2017-08-01

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Fast, Nonlinear, Fully Probabilistic Inversion of Large Geophysical Problems

NASA Astrophysics Data System (ADS)

Curtis, A.; Shahraeeni, M.; Trampert, J.; Meier, U.; Cho, G.

2010-12-01

Almost all Geophysical inverse problems are in reality nonlinear. Fully nonlinear inversion including non-approximated physics, and solving for probability distribution functions (pdf’s) that describe the solution uncertainty, generally requires sampling-based Monte-Carlo style methods that are computationally intractable in most large problems. In order to solve such problems, physical relationships are usually linearized leading to efficiently-solved, (possibly iterated) linear inverse problems. However, it is well known that linearization can lead to erroneous solutions, and in particular to overly optimistic uncertainty estimates. What is needed across many Geophysical disciplines is a method to invert large inverse problems (or potentially tens of thousands of small inverse problems) fully probabilistically and without linearization. This talk shows how very large nonlinear inverse problems can be solved fully probabilistically and incorporating any available prior information using mixture density networks (driven by neural network banks), provided the problem can be decomposed into many small inverse problems. In this talk I will explain the methodology, compare multi-dimensional pdf inversion results to full Monte Carlo solutions, and illustrate the method with two applications: first, inverting surface wave group and phase velocities for a fully-probabilistic global tomography model of the Earth’s crust and mantle, and second inverting industrial 3D seismic data for petrophysical properties throughout and around a subsurface hydrocarbon reservoir. The latter problem is typically decomposed into 104 to 105 individual inverse problems, each solved fully probabilistically and without linearization. The results in both cases are sufficiently close to the Monte Carlo solution to exhibit realistic uncertainty, multimodality and bias. This provides far greater confidence in the results, and in decisions made on their basis.

Estimating regional CO2 and CH4 fluxes using GOSAT XCO2 and XCH4 observations

NASA Astrophysics Data System (ADS)

Fraser, A. C.; Palmer, P. I.; Feng, L.; Parker, R.; Boesch, H.; Cogan, A. J.

2012-12-01

We infer regional monthly surface flux estimates for CO2 and CH4, June 2009-December 2010, from proxy dry-air column-averaged mole fractions of CO2 and CH4 from the Greenhouse gases Observing SATellite (GOSAT) using an ensemble Kalman Filter combined with the GEOS-Chem chemistry transport model. We compare these flux estimates with estimates inferred from in situ surface mole fraction measurements and from combining in situ and GOSAT measurements in order to quantify the added value of GOSAT data above the conventional surface measurement network. We find that the error reduction, a measure of how much the posterior fluxes are being informed by the assimilated data, at least doubles when GOSAT measurements are used versus the surface only inversions, with the exception of regions that are well covered by the surface network at the spatial and temporal resolution of our flux estimation calculation. We have incorporated a new online bias correction scheme to account for GOSAT biases. We report global and regional flux estimates inferred from GOSAT and/or in situ measurements. While the global posterior fluxes from GOSAT and in situ measurements agree, we find significant differences in the regional fluxes, particularly over the tropics. We evaluate the posterior fluxes by comparing them against independent surface mole fraction, column, and aircraft measurements using the GEOS-Chem model as an intermediary.

A nonlinear Kalman filtering approach to embedded control of turbocharged diesel engines

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

2014-10-01

The development of efficient embedded control for turbocharged Diesel engines, requires the programming of elaborated nonlinear control and filtering methods. To this end, in this paper nonlinear control for turbocharged Diesel engines is developed with the use of Differential flatness theory and the Derivative-free nonlinear Kalman Filter. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances the Derivative-free nonlinear Kalman Filter is used and redesigned as a disturbance observer. The filter consists of the Kalman Filter recursion on the linearized equivalent of the Diesel engine model and of an inverse transformation based on differential flatness theory which enables to obtain estimates for the state variables of the initial nonlinear model. Once the disturbances variables are identified it is possible to compensate them by including an additional control term in the feedback loop. The efficiency of the proposed control method is tested through simulation experiments.

Image enhancement by spatial frequency post-processing of images obtained with pupil filters

NASA Astrophysics Data System (ADS)

Estévez, Irene; Escalera, Juan C.; Stefano, Quimey Pears; Iemmi, Claudio; Ledesma, Silvia; Yzuel, María J.; Campos, Juan

2016-12-01

The use of apodizing or superresolving filters improves the performance of an optical system in different frequency bands. This improvement can be seen as an increase in the OTF value compared to the OTF for the clear aperture. In this paper we propose a method to enhance the contrast of an image in both its low and its high frequencies. The method is based on the generation of a synthetic Optical Transfer Function, by multiplexing the OTFs given by the use of different non-uniform transmission filters on the pupil. We propose to capture three images, one obtained with a clear pupil, one obtained with an apodizing filter that enhances the low frequencies and another one taken with a superresolving filter that improves the high frequencies. In the Fourier domain the three spectra are combined by using smoothed passband filters, and then the inverse transform is performed. We show that we can create an enhanced image better than the image obtained with the clear aperture. To evaluate the performance of the method, bar tests (sinusoidal tests) with different frequency content are used. The results show that a contrast improvement in the high and low frequencies is obtained.

A filtering approach to edge preserving MAP estimation of images.

PubMed

Humphrey, David; Taubman, David

2011-05-01

The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the specific problem of demosaicing.

Distributed and decentralized state estimation in gas networks as distributed parameter systems.

PubMed

Ahmadian Behrooz, Hesam; Boozarjomehry, R Bozorgmehry

2015-09-01

In this paper, a framework for distributed and decentralized state estimation in high-pressure and long-distance gas transmission networks (GTNs) is proposed. The non-isothermal model of the plant including mass, momentum and energy balance equations are used to simulate the dynamic behavior. Due to several disadvantages of implementing a centralized Kalman filter for large-scale systems, the continuous/discrete form of extended Kalman filter for distributed and decentralized estimation (DDE) has been extended for these systems. Accordingly, the global model is decomposed into several subsystems, called local models. Some heuristic rules are suggested for system decomposition in gas pipeline networks. In the construction of local models, due to the existence of common states and interconnections among the subsystems, the assimilation and prediction steps of the Kalman filter are modified to take the overlapping and external states into account. However, dynamic Riccati equation for each subsystem is constructed based on the local model, which introduces a maximum error of 5% in the estimated standard deviation of the states in the benchmarks studied in this paper. The performance of the proposed methodology has been shown based on the comparison of its accuracy and computational demands against their counterparts in centralized Kalman filter for two viable benchmarks. In a real life network, it is shown that while the accuracy is not significantly decreased, the real-time factor of the state estimation is increased by a factor of 10. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Online particle detection with Neural Networks based on topological calorimetry information

NASA Astrophysics Data System (ADS)

Ciodaro, T.; Deva, D.; de Seixas, J. M.; Damazio, D.

2012-06-01

This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

Social networks and inflammatory markers in the Framingham Heart Study.

PubMed

Loucks, Eric B; Sullivan, Lisa M; D'Agostino, Ralph B; Larson, Martin G; Berkman, Lisa F; Benjamin, Emelia J

2006-11-01

Lack of social integration predicts coronary heart disease mortality in prospective studies; however, the biological pathways that may be responsible are poorly understood. The specific aims of this study were to examine whether social networks are associated with serum concentrations of the inflammatory markers interleukin-6 (IL-6), C-reactive protein (CRP), soluble intercellular adhesion molecule-1 (sICAM-1) and monocyte chemoattractant protein-1 (MCP-1). Participants in the Framingham Study attending examinations from 1998 to 2001 (n=3267) were eligible for inclusion in the study. Social networks were assessed using the Berkman-Syme Social Network Index (SNI). Concentrations of IL-6, CRP, sICAM-1 and MCP-1 were measured in fasting serum samples. Multivariable linear regression analyses were used to assess the association of social networks with inflammatory markers adjusting for potential confounders including age, smoking, blood pressure, total:HDL cholesterol ratio, body mass index, lipid-lowering and antihypertensive medication, diabetes, cardiovascular disease, depression and socioeconomic status. Results found that the SNI was significantly inversely associated with IL-6 in men (p=0.03) after adjusting for potential confounders. In age-adjusted analyses, social networks also were significantly inversely associated with IL-6 for women (p=0.03) and were marginally to modestly associated with CRP and sICAM-1 for men (p=0.08 and 0.02, respectively), but these associations were not significant in the multivariate analyses. In conclusion, social networks were found to be inversely associated with interleukin-6 levels in men. The possibility that inflammatory markers may be potential mediators between social integration and coronary heart disease merits further investigation.

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

This paper describes the performance of a simplified dynamic inversion controller with neural network supplementation. This 6 DOF (Degree-of-Freedom) simulation study focuses on the results with and without adaptation of neural networks using a simulation of the NASA modified F-15 which has canards. One area of interest is the performance of a simulated surface failure while attempting to minimize the inertial cross coupling effect of a [B] matrix failure (a control derivative anomaly associated with a jammed or missing control surface). Another area of interest and presented is simulated aerodynamic failures ([A] matrix) such as a canard failure. The controller uses explicit models to produce desired angular rate commands. The dynamic inversion calculates the necessary surface commands to achieve the desired rates. The simplified dynamic inversion uses approximate short period and roll axis dynamics. Initial results indicated that the transient response for a [B] matrix failure using a Neural Network (NN) improved the control behavior when compared to not using a neural network for a given failure, However, further evaluation of the controller was comparable, with objections io the cross coupling effects (after changes were made to the controller). This paper describes the methods employed to reduce the cross coupling effect and maintain adequate tracking errors. The IA] matrix failure results show that control of the aircraft without adaptation is more difficult [leas damped) than with active neural networks, Simulation results show Neural Network augmentation of the controller improves performance in terms of backing error and cross coupling reduction and improved performance with aerodynamic-type failures.

Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

NASA Astrophysics Data System (ADS)

Maurya, S. P.; Singh, K. H.; Singh, N. P.

2018-05-01

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

In-Network Processing of an Iceberg Join Query in Wireless Sensor Networks Based on 2-Way Fragment Semijoins

PubMed Central

Kang, Hyunchul

2015-01-01

We investigate the in-network processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result. Processing such a join involves the value matching for the join predicate as well as the checking of the cardinality constraint for the iceberg threshold. In the previous scheme, the value matching is carried out as the main task for filtering non-joinable tuples while the iceberg threshold is treated as an additional constraint. We take an alternative approach, meeting the cardinality constraint first and matching values next. In this approach, with a logical fragmentation of the join operand relations on the aggregate counts of the joining attribute values, the optimal sequence of 2-way fragment semijoins is generated, where each fragment semijoin employs a Bloom filter as a synopsis of the joining attribute values. This sequence filters non-joinable tuples in an energy-efficient way in WSNs. Through implementation and a set of detailed experiments, we show that our alternative approach considerably outperforms the previous one. PMID:25774710

Manifestation of remote response over the equatorial Pacific in a climate model

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu; Marx, L.

2007-10-01

In this paper we examine the simulations over the tropical Pacific Ocean from long-term simulations of two different versions of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled climate model that have a different global distribution of the inversion clouds. We find that subtle changes made to the numerics of an empirical parameterization of the inversion clouds can result in a significant change in the coupled climate of the equatorial Pacific Ocean. In one coupled simulation of this study we enforce a simple linear spatial filtering of the diagnostic inversion clouds to ameliorate its spatial incoherency (as a result of the Gibbs effect) while in the other we conduct no such filtering. It is found from the comparison of these two simulations that changing the distribution of the shallow inversion clouds prevalent in the subsidence region of the subtropical high over the eastern oceans in this manner has a direct bearing on the surface wind stress through surface pressure modifications. The SST in the warm pool region responds to this modulation of the wind stress, thus affecting the convective activity over the warm pool region and also the large-scale Walker and Hadley circulation. The interannual variability of SST in the eastern equatorial Pacific Ocean is also modulated by this change to the inversion clouds. Consequently, this sensitivity has a bearing on the midlatitude height response. The same set of two experiments were conducted with the respective versions of the atmosphere general circulation model uncoupled to the ocean general circulation model but forced with observed SST to demonstrate that this sensitivity of the mean climate of the equatorial Pacific Ocean is unique to the coupled climate model where atmosphere, ocean and land interact. Therefore a strong case is made for adopting coupled ocean-land-atmosphere framework to develop climate models as against the usual practice of developing component models independent of each other.

A Neural Network Aero Design System for Advanced Turbo-Engines

NASA Technical Reports Server (NTRS)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Uncertainty quantification of seabed parameters for large data volumes along survey tracks with a tempered particle filter

NASA Astrophysics Data System (ADS)

Dettmer, J.; Quijano, J. E.; Dosso, S. E.; Holland, C. W.; Mandolesi, E.

2016-12-01

Geophysical seabed properties are important for the detection and classification of unexploded ordnance. However, current surveying methods such as vertical seismic profiling, coring, or inversion are of limited use when surveying large areas with high spatial sampling density. We consider surveys based on a source and receiver array towed by an autonomous vehicle which produce large volumes of seabed reflectivity data that contain unprecedented and detailed seabed information. The data are analyzed with a particle filter, which requires efficient reflection-coefficient computation, efficient inversion algorithms and efficient use of computer resources. The filter quantifies information content of multiple sequential data sets by considering results from previous data along the survey track to inform the importance sampling at the current point. Challenges arise from environmental changes along the track where the number of sediment layers and their properties change. This is addressed by a trans-dimensional model in the filter which allows layering complexity to change along a track. Efficiency is improved by likelihood tempering of various particle subsets and including exchange moves (parallel tempering). The filter is implemented on a hybrid computer that combines central processing units (CPUs) and graphics processing units (GPUs) to exploit three levels of parallelism: (1) fine-grained parallel computation of spherical reflection coefficients with a GPU implementation of Levin integration; (2) updating particles by concurrent CPU processes which exchange information using automatic load balancing (coarse grained parallelism); (3) overlapping CPU-GPU communication (a major bottleneck) with GPU computation by staggering CPU access to the multiple GPUs. The algorithm is applied to spherical reflection coefficients for data sets along a 14-km track on the Malta Plateau, Mediterranean Sea. We demonstrate substantial efficiency gains over previous methods. [This research was supported in part by the U.S. Dept of Defense, thought the Strategic Environmental Research and Development Program (SERDP).

Neural control and transient analysis of the LCL-type resonant converter

NASA Astrophysics Data System (ADS)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Self-Powered Electrostatic Filter with Enhanced Photocatalytic Degradation of Formaldehyde Based on Built-in Triboelectric Nanogenerators.

PubMed

Feng, Yawei; Ling, Lili; Nie, Jinhui; Han, Kai; Chen, Xiangyu; Bian, Zhenfeng; Li, Hexing; Wang, Zhong Lin

2017-12-26

Recently, atmospheric pollution caused by particulate matter or volatile organic compounds (VOCs) has become a serious issue to threaten human health. Consequently, it is highly desirable to develop an efficient purifying technique with simple structure and low cost. In this study, by combining a triboelectric nanogenerator (TENG) and a photocatalysis technique, we demonstrated a concept of a self-powered filtering method for removing pollutants from indoor atmosphere. The photocatalyst P25 or Pt/P25 was embedded on the surface of polymer-coated stainless steel wires, and such steel wires were woven into a filtering network. A strong electric field can be induced on this filtering network by TENG, while both electrostatic adsorption effect and TENG-enhanced photocatalytic effect can be achieved. Rhodamine B (RhB) steam was selected as the pollutant for demonstration. The absorbed RhB on the filter network with TENG in 1 min was almost the same amount of absorption achieved in 15 min without using TENG. Meanwhile, the degradation of RhB was increased over 50% under the drive of TENG. Furthermore, such a device was applied for the degradation of formaldehyde, where degradation efficiency was doubled under the drive of TENG. This work extended the application for the TENG in self-powered electrochemistry, design and concept of which can be possibly applied in the field of haze governance, indoor air cleaning, and photocatalytic pollution removal for environmental protection.

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

Evaluation of deconvolution modelling applied to numerical combustion

NASA Astrophysics Data System (ADS)

Mehl, Cédric; Idier, Jérôme; Fiorina, Benoît

2018-01-01

A possible modelling approach in the large eddy simulation (LES) of reactive flows is to deconvolve resolved scalars. Indeed, by inverting the LES filter, scalars such as mass fractions are reconstructed. This information can be used to close budget terms of filtered species balance equations, such as the filtered reaction rate. Being ill-posed in the mathematical sense, the problem is very sensitive to any numerical perturbation. The objective of the present study is to assess the ability of this kind of methodology to capture the chemical structure of premixed flames. For that purpose, three deconvolution methods are tested on a one-dimensional filtered laminar premixed flame configuration: the approximate deconvolution method based on Van Cittert iterative deconvolution, a Taylor decomposition-based method, and the regularised deconvolution method based on the minimisation of a quadratic criterion. These methods are then extended to the reconstruction of subgrid scale profiles. Two methodologies are proposed: the first one relies on subgrid scale interpolation of deconvolved profiles and the second uses parametric functions to describe small scales. Conducted tests analyse the ability of the method to capture the chemical filtered flame structure and front propagation speed. Results show that the deconvolution model should include information about small scales in order to regularise the filter inversion. a priori and a posteriori tests showed that the filtered flame propagation speed and structure cannot be captured if the filter size is too large.

Artificial Neural Networks and Instructional Technology.

ERIC Educational Resources Information Center

Carlson, Patricia A.

1991-01-01

Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.

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

Ray, Jaideep; Lee, Jina; Lefantzi, Sophia

2013-09-01

The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. The limited nature of the measured data leads to a severely-underdetermined estimation problem. If the estimation is performed at fine spatial resolutions, it can also be computationally expensive. In order to enable such estimations, advances are needed in the spatial representation of ffCO2 emissions, scalable inversion algorithms and the identification of observables to measure. To that end, we investigate parsimonious spatial parameterizations of ffCO2 emissions whichmore » can be used in atmospheric inversions. We devise and test three random field models, based on wavelets, Gaussian kernels and covariance structures derived from easily-observed proxies of human activity. In doing so, we constructed a novel inversion algorithm, based on compressive sensing and sparse reconstruction, to perform the estimation. We also address scalable ensemble Kalman filters as an inversion mechanism and quantify the impact of Gaussian assumptions inherent in them. We find that the assumption does not impact the estimates of mean ffCO2 source strengths appreciably, but a comparison with Markov chain Monte Carlo estimates show significant differences in the variance of the source strengths. Finally, we study if the very different spatial natures of biogenic and ffCO2 emissions can be used to estimate them, in a disaggregated fashion, solely from CO2 concentration measurements, without extra information from products of incomplete combustion e.g., CO. We find that this is possible during the winter months, though the errors can be as large as 50%.« less

SU-E-J-174: Adaptive PET-Based Dose Painting with Tomotherapy

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

Darwish, N; Mackie, T; Thomadsen, B

2014-06-01

Purpose: PET imaging can be converted into dose prescription directly. Due to the variability of the intensity of PET the image, PET prescription maybe superior over uniform dose prescription. Furthermore, unlike the case in image reconstruction of not knowing the image solution in advance, the prescribed dose is known from a PET image a priori. Therefore, optimum beam orientations are derivable. Methods: We can assume the PET image to be the prescribed dose and invert it to determine the energy fluence. The same method used to reconstruct tissue images from projections could be used to solve the inverse problem ofmore » determining beam orientations and modulation patterns from a dose prescription [10]. Unlike standard tomographic reconstruction of images from measured projection profiles, the inversion of the prescribed dose results in photon fluence which may be negative and therefore unphysical. Two-dimensional modulated beams can be modelled in terms of the attenuated or exponential radon transform of the prescribed dose function (assumed to be the PET image in this case), an application of a Ram-Lak filter, and inversion by backprojection. Unlike the case in PET processing, however, the filtered beam obtained from the inversion represents a physical photon fluence. Therefore, a positivity constraint for the fluence (setting negative fluence to zero) must be applied (Brahme et al 1982, Bortfeld et al 1990) Results: Truncating the negative profiles from the PET data results in an approximation of the derivable energy fluence. Backprojection of the deliverable fluence is an approximation of the dose delivered. The deliverable dose is comparable to the original PET image and is similar to the PET image. Conclusion: It is possible to use the PET data or image as a direct indicator of deliverable fluence for cylindrical radiotherapy systems such as TomoTherapy.« less

Generalised filtering and stochastic DCM for fMRI.

PubMed

Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E; Penny, Will; Hu, Dewen; Friston, Karl

2011-09-15

This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and contrast deterministic and stochastic DCMs, which do and do not ignore random fluctuations or noise on hidden states. We then compare stochastic DCMs, which do and do not ignore conditional dependence between hidden states and model parameters (generalised filtering and dynamic expectation maximisation, respectively). We first characterise state-noise by comparing the log evidence of models with different a priori assumptions about its amplitude, form and smoothness. Face validity of the inversion scheme is then established using data simulated with and without state-noise to ensure that DCM can identify the parameters and model that generated the data. Finally, we address construct validity using real data from an fMRI study of internet addiction. Our analyses suggest the following. (i) The inversion of stochastic causal models is feasible, given typical fMRI data. (ii) State-noise has nontrivial amplitude and smoothness. (iii) Stochastic DCM has face validity, in the sense that Bayesian model comparison can distinguish between data that have been generated with high and low levels of physiological noise and model inversion provides veridical estimates of effective connectivity. (iv) Relaxing conditional independence assumptions can have greater construct validity, in terms of revealing group differences not disclosed by variational schemes. Finally, we note that the ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated. Copyright © 2011. Published by Elsevier Inc.

Airborne Network Optimization with Dynamic Network Update

DTIC Science & Technology

2015-03-26

Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University...Member Dr. Barry E. Mullins Member AFIT-ENG-MS-15-M-030 Abstract Modern networks employ congestion and routing management algorithms that can perform...airborne networks. Intelligent agents can make use of Kalman filter predictions to make informed decisions to manage communication in airborne networks. The

Spatial effects in real networks: Measures, null models, and applications

NASA Astrophysics Data System (ADS)

Ruzzenenti, Franco; Picciolo, Francesco; Basosi, Riccardo; Garlaschelli, Diego

2012-12-01

Spatially embedded networks are shaped by a combination of purely topological (space-independent) and space-dependent formation rules. While it is quite easy to artificially generate networks where the relative importance of these two factors can be varied arbitrarily, it is much more difficult to disentangle these two architectural effects in real networks. Here we propose a solution to this problem, by introducing global and local measures of spatial effects that, through a comparison with adequate null models, effectively filter out the spurious contribution of nonspatial constraints. Our filtering allows us to consistently compare different embedded networks or different historical snapshots of the same network. As a challenging application we analyze the World Trade Web, whose topology is known to depend on geographic distances but is also strongly determined by nonspatial constraints (degree sequence or gross domestic product). Remarkably, we are able to detect weak but significant spatial effects both locally and globally in the network, showing that our method succeeds in retrieving spatial information even when nonspatial factors dominate. We finally relate our results to the economic literature on gravity models and trade globalization.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Using an Extended Kalman Filter Learning Algorithm for Feed-Forward Neural Networks to Describe Tracer Correlations

NASA Technical Reports Server (NTRS)

Lary, David J.; Mussa, Yussuf

2004-01-01

In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

The use of filter media to determine filter cleanliness

NASA Astrophysics Data System (ADS)

Van Staden, S. J.; Haarhoff, J.

It is general believed that a sand filter starts its life with new, perfectly clean media, which becomes gradually clogged with each filtration cycle, eventually getting to a point where either head loss or filtrate quality starts to deteriorate. At this point the backwash cycle is initiated and, through the combined action of air and water, returns the media to its original perfectly clean state. Reality, however, dictates otherwise. Many treatment plants visited a decade or more after commissioning are found to have unacceptably dirty filter sand and backwash systems incapable of returning the filter media to a desired state of cleanliness. In some cases, these problems are common ones encountered in filtration plants but many reasons for media deterioration remain elusive, falling outside of these common problems. The South African conditions of highly eutrophic surface waters at high temperatures, however, exacerbate the problems with dirty filter media. Such conditions often lead to the formation of biofilm in the filter media, which is shown to inhibit the effective backwashing of sand and carbon filters. A systematic investigation into filter media cleanliness was therefore started in 2002, ending in 2005, at the University of Johannesburg (the then Rand Afrikaans University). This involved media from eight South African Water Treatment Plants, varying between sand and sand-anthracite combinations and raw water types from eutrophic through turbid to low-turbidity waters. Five states of cleanliness and four fractions of specific deposit were identified relating to in situ washing, column washing, cylinder inversion and acid-immersion techniques. These were measured and the results compared to acceptable limits for specific deposit, as determined in previous studies, though expressed in kg/m 3. These values were used to determine the state of the filters. In order to gain greater insight into the composition of the specific deposits stripped from the media, a four-point characterisation step was introduced for the resultant suspensions based on acid-solubility and volatility. Results showed that a reasonably effective backwash removed a median specific deposit of 0.89 kg/m 3. Further washing in a laboratory column removed a median specific deposit of 1.34 kg/m 3. Media subjected to a standardised cylinder inversion procedure removed a median specific deposit of 2.41 kg/m 3. Immersion in a strong acid removed a median specific deposit of 35.2 kg/m 3. The four-point characterisation step showed that the soluble-volatile fraction was consistently small in relation to the other fractions. The organic fraction was quite high at the RG treatment plant and the soluble-non-volatile fraction was particularly high at the BK treatment plant.

Spatial and temporal skin blood volume and saturation estimation using a multispectral snapshot imaging camera

NASA Astrophysics Data System (ADS)

Ewerlöf, Maria; Larsson, Marcus; Salerud, E. Göran

2017-02-01

Hyperspectral imaging (HSI) can estimate the spatial distribution of skin blood oxygenation, using visible to near-infrared light. HSI oximeters often use a liquid-crystal tunable filter, an acousto-optic tunable filter or mechanically adjustable filter wheels, which has too long response/switching times to monitor tissue hemodynamics. This work aims to evaluate a multispectral snapshot imaging system to estimate skin blood volume and oxygen saturation with high temporal and spatial resolution. We use a snapshot imager, the xiSpec camera (MQ022HG-IM-SM4X4-VIS, XIMEA), having 16 wavelength-specific Fabry-Perot filters overlaid on the custom CMOS-chip. The spectral distribution of the bands is however substantially overlapping, which needs to be taken into account for an accurate analysis. An inverse Monte Carlo analysis is performed using a two-layered skin tissue model, defined by epidermal thickness, haemoglobin concentration and oxygen saturation, melanin concentration and spectrally dependent reduced-scattering coefficient, all parameters relevant for human skin. The analysis takes into account the spectral detector response of the xiSpec camera. At each spatial location in the field-of-view, we compare the simulated output to the detected diffusively backscattered spectra to find the best fit. The imager is evaluated for spatial and temporal variations during arterial and venous occlusion protocols applied to the forearm. Estimated blood volume changes and oxygenation maps at 512x272 pixels show values that are comparable to reference measurements performed in contact with the skin tissue. We conclude that the snapshot xiSpec camera, paired with an inverse Monte Carlo algorithm, permits us to use this sensor for spatial and temporal measurement of varying physiological parameters, such as skin tissue blood volume and oxygenation.

Orbit Determination Using Vinti’s Solution

DTIC Science & Technology

2016-09-15

Surveillance Network STK Systems Tool Kit TBP Two Body Problem TLE Two-line Element Set xv Acronym Definition UKF Unscented Kalman Filter WPAFB Wright...simplicity, stability, and speed. On the other hand, Kalman filters would be best suited for sequential estimation of stochastic or random components of a...be likened to how an Unscented Kalman Filter samples a system’s nonlinearities directly, avoiding linearizing the dynamics in the partials matrices

A simple structure wavelet transform circuit employing function link neural networks and SI filters

NASA Astrophysics Data System (ADS)

Mu, Li; Yigang, He

2016-12-01

Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

Focal-Plane Alignment Sensing

DTIC Science & Technology

1993-02-01

amplification induced by the inverse filter. The problem of noise amplification that arises in conventional image deblurring problems has often been... noise sensitivity, and strategies for selecting a regularization parameter have been developed. The probability of convergence to within a prescribed...Strategies in Image Deblurring .................. 12 2.2.2 CLS Parameter Selection ........................... 14 2.2.3 Wiener Parameter Selection

Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.

PubMed

Hamid, Laith; Al Farawn, Ali; Merlet, Isabelle; Japaridze, Natia; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Wendling, Fabrice; Siniatchkin, Michael

2017-07-01

The clinical routine of non-invasive electroencephalography (EEG) is usually performed with 8-40 electrodes, especially in long-term monitoring, infants or emergency care. There is a need in clinical and scientific brain imaging to develop inverse solution methods that can reconstruct brain sources from these low-density EEG recordings. In this proof-of-principle paper we investigate the performance of the spatiotemporal Kalman filter (STKF) in EEG source reconstruction with 9-, 19- and 32- electrodes. We used simulated EEG data of epileptic spikes generated from lateral frontal and lateral temporal brain sources using state-of-the-art neuronal population models. For validation of source reconstruction, we compared STKF results to the location of the simulated source and to the results of low-resolution brain electromagnetic tomography (LORETA) standard inverse solution. STKF consistently showed less localization bias compared to LORETA, especially when the number of electrodes was decreased. The results encourage further research into the application of the STKF in source reconstruction of brain activity from low-density EEG recordings.

Improving GPR image resolution in lossy ground using dispersive migration

USGS Publications Warehouse

Oden, C.P.; Powers, M.H.; Wright, D.L.; Olhoeft, G.R.

2007-01-01

As a compact wave packet travels through a dispersive medium, it becomes dilated and distorted. As a result, ground-penetrating radar (GPR) surveys over conductive and/or lossy soils often result in poor image resolution. A dispersive migration method is presented that combines an inverse dispersion filter with frequency-domain migration. The method requires a fully characterized GPR system including the antenna response, which is a function of the local soil properties for ground-coupled antennas. The GPR system response spectrum is used to stabilize the inverse dispersion filter. Dispersive migration restores attenuated spectral components when the signal-to-noise ratio is adequate. Applying the algorithm to simulated data shows that the improved spatial resolution is significant when data are acquired with a GPR system having 120 dB or more of dynamic range, and when the medium has a loss tangent of 0.3 or more. Results also show that dispersive migration provides no significant advantage over conventional migration when the loss tangent is less than 0.3, or when using a GPR system with a small dynamic range. ?? 2007 IEEE.

Recent advances in polarized 3 He based neutron spin filter development

NASA Astrophysics Data System (ADS)

Chen, Wangchun; Gentile, Thomas; Erwin, Ross; Watson, Shannon; Krycka, Kathryn; Ye, Qiang; NCNR NIST Team; University of Maryland Team

2015-04-01

Polarized 3 He neutron spin filters (NSFs) are based on the strong spin-dependence of the neutron absorption cross section by 3 He. NSFs can polarize large area, widely divergent, and broadband neutron beams effectively and allow for combining a neutron polarizer and a spin flipper into a single polarizing device. The last capability utilizes 3 He spin inversion based on the adiabatic fast passage (AFP) nuclear magnetic resonance technique. Polarized 3 He NSFs are significantly expanding the polarized neutron measurement capabilities at the NIST Center for Neutron Research (NCNR). Here we present an overview of 3 He NSF applications to small-angle neutron scattering, thermal triple axis spectrometry, and wide-angle polarization analysis. We discuss a recent upgrade of our spin-exchange optical pumping (SEOP) systems that utilize chirped volume holographic gratings for spectral narrowing. The new capability allows us to polarize rubidium/potassium hybrid SEOP cells over a liter in volume within a day, with 3 He polarizations up to 88%, Finally we discuss how we can achieve nearly lossless 3 He polarization inversion with AFP.

The acetone bandpass detector for inverse photoemission: operation in proportional and Geiger–Müller modes

NASA Astrophysics Data System (ADS)

Thiede, Christian; Niehues, Iris; Schmidt, Anke B.; Donath, Markus

2018-06-01

Inverse photoemission is the most versatile experimental tool to study the unoccupied electronic structure at surfaces of solids. Typically, the experiments are performed in the isochromat mode with bandpass photon detectors. For gas-filled counters, the bandpass behavior is realized by the combination of the photoionization threshold of the counting gas as the high-pass filter and the ultraviolet transmission cutoff of an alkaline earth fluoride entrance window as the low-pass filter. The transmission characteristics of the entrance window determine the optical bandpass. The performance of the counter depends on the composition of the detection gas and the fill-gas pressure, the readout electronics and the counter geometry. For the well-known combination of acetone and CaF2, the detector can be operated in proportional and Geiger–Müller modes. In this work, we review aspects concerning the working principles, the counter construction and the read-out electronics. We identify optimum working parameters and provide a step-by-step recipe how to build, install and operate the device.

Experimental demonstration of wavelength domain rogue-free ONU based on wavelength-pairing for TDM/WDM optical access networks.

PubMed

Lee, Jie Hyun; Park, Heuk; Kang, Sae-Kyoung; Lee, Joon Ki; Chung, Hwan Seok

2015-11-30

In this study, we propose and experimentally demonstrate a wavelength domain rogue-free ONU based on wavelength-pairing of downstream and upstream signals for time/wavelength division-multiplexed optical access networks. The wavelength-pairing tunable filter is aligned to the upstream wavelength channel by aligning it to one of the downstream wavelength channels. Wavelength-pairing is implemented with a compact and cyclic Si-AWG integrated with a Ge-PD. The pairing filter covered four 100 GHz-spaced wavelength channels. The feasibility of the wavelength domain rogue-free operation is investigated by emulating malfunction of the misaligned laser. The wavelength-pairing tunable filter based on the Si-AWG blocks the upstream signal in the non-assigned wavelength channel before data collision with other ONUs.

Context-Based Tourism Information Filtering with a Semantic Rule Engine

PubMed Central

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction. PMID:22778584

Improving control and estimation for distributed parameter systems utilizing mobile actuator-sensor network.

PubMed

Mu, Wenying; Cui, Baotong; Li, Wen; Jiang, Zhengxian

2014-07-01

This paper proposes a scheme for non-collocated moving actuating and sensing devices which is unitized for improving performance in distributed parameter systems. By Lyapunov stability theorem, each moving actuator/sensor agent velocity is obtained. To enhance state estimation of a spatially distributes process, two kinds of filters with consensus terms which penalize the disagreement of the estimates are considered. Both filters can result in the well-posedness of the collective dynamics of state errors and can converge to the plant state. Numerical simulations demonstrate that the effectiveness of such a moving actuator-sensor network in enhancing system performance and the consensus filters converge faster to the plant state when consensus terms are included. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Context-based tourism information filtering with a semantic rule engine.

PubMed

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction.

Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

PubMed

Yasukawa, Shinsuke; Okuno, Hirotsugu; Ishii, Kazuo; Yagi, Tetsuya

2016-09-01

We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ultra-compact, flat-top demultiplexer using anti-reflection contra-directional couplers for CWDM networks on silicon.

PubMed

Shi, Wei; Yun, Han; Lin, Charlie; Greenberg, Mark; Wang, Xu; Wang, Yun; Fard, Sahba Talebi; Flueckiger, Jonas; Jaeger, Nicolas A F; Chrostowski, Lukas

2013-03-25

Wavelength-division-multiplexing (WDM) networks with wide channel grids and bandwidths are promising for low-cost, low-power optical interconnects. Wide-bandwidth, single-band (i.e., no free-spectral range) add-drop filters have been developed on silicon using anti-reflection contra-directional couplers with out-of-phase Bragg gratings. Using such filter components, we demonstrate a 4-channel, coarse-WDM demultiplexer with flat passbands of up to 13 nm and an ultra-compact size of 1.2 × 10(-3) mm(2).

Subband Approach to Bandlimited Crosstalk Cancellation System in Spatial Sound Reproduction

NASA Astrophysics Data System (ADS)

Bai, Mingsian R.; Lee, Chih-Chung

2006-12-01

Crosstalk cancellation system (CCS) plays a vital role in spatial sound reproduction using multichannel loudspeakers. However, this technique is still not of full-blown use in practical applications due to heavy computation loading. To reduce the computation loading, a bandlimited CCS is presented in this paper on the basis of subband filtering approach. A pseudoquadrature mirror filter (QMF) bank is employed in the implementation of CCS filters which are bandlimited to 6 kHz, where human's localization is the most sensitive. In addition, a frequency-dependent regularization scheme is adopted in designing the CCS inverse filters. To justify the proposed system, subjective listening experiments were undertaken in an anechoic room. The experiments include two parts: the source localization test and the sound quality test. Analysis of variance (ANOVA) is applied to process the data and assess statistical significance of subjective experiments. The results indicate that the bandlimited CCS performed comparably well as the fullband CCS, whereas the computation loading was reduced by approximately eighty percent.

Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography.

PubMed

Yang, Defu; Wang, Lin; Chen, Dongmei; Yan, Chenggang; He, Xiaowei; Liang, Jimin; Chen, Xueli

2018-05-17

The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SP N ) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment. Graphical abstract The filtered MLEM-based global reconstruction method for BLT.

Spectral information enhancement using wavelet-based iterative filtering for in vivo gamma spectrometry.

PubMed

Paul, Sabyasachi; Sarkar, P K

2013-04-01

Use of wavelet transformation in stationary signal processing has been demonstrated for denoising the measured spectra and characterisation of radionuclides in the in vivo monitoring analysis, where difficulties arise due to very low activity level to be estimated in biological systems. The large statistical fluctuations often make the identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet-based noise filtering methodology has been developed for better detection of gamma peaks in noisy data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for noise rejection and an inverse transform after soft 'thresholding' over the generated coefficients. Analyses of in vivo monitoring data of (235)U and (238)U were carried out using this method without disturbing the peak position and amplitude while achieving a 3-fold improvement in the signal-to-noise ratio, compared with the original measured spectrum. When compared with other data-filtering techniques, the wavelet-based method shows the best results.

Ultranarrow-bandwidth filter based on a thermal EIT medium.

PubMed

Wang, Gang; Wang, Yu-Sheng; Huang, Emily Kay; Hung, Weilun; Chao, Kai-Lin; Wu, Ping-Yeh; Chen, Yi-Hsin; Yu, Ite A

2018-05-21

We present high-contrast electromagnetically-induced-transparency (EIT) spectra in a heated vapor cell of single isotope 87 Rb atoms. The EIT spectrum has both high resonant transmission up to 67% and narrow linewidth of 1.1 MHz. We get rid of the possible amplification resulted from the effects of amplification without population inversion and four-wave mixing. Therefore, this high transmitted light is not artificial. The theoretical prediction of the probe transmission agrees well with the data and the experimental parameters can be derived reasonably from the model. Such narrow and high-contrast spectral profile can be employed as a high precision bandpass filter, which provides a significant advantage in terms of stability and tunability. The central frequency tuning range of the filter is larger than 100 MHz with out-of-band blocking ≥15 dB. This bandpass filter can effectively produce light fields with subnatural linewidth. Nonlinearity associating with the narrow-linewidth and high-contrast EIT profile can be very useful in the applications utilizing the EIT effect.

Tunable negative-tap photonic microwave filter based on a cladding-mode coupler and an optically injected laser of large detuning.

PubMed

Chan, Sze-Chun; Liu, Qing; Wang, Zhu; Chiang, Kin Seng

2011-06-20

A tunable negative-tap photonic microwave filter using a cladding-mode coupler together with optical injection locking of large wavelength detuning is demonstrated. Continuous and precise tunability of the filter is realized by physically sliding a pair of bare fibers inside the cladding-mode coupler. Signal inversion for the negative tap is achieved by optical injection locking of a single-mode semiconductor laser. To couple light into and out of the cladding-mode coupler, a pair of matching long-period fiber gratings is employed. The large bandwidth of the gratings requires injection locking of an exceptionally large wavelength detuning that has never been demonstrated before. Experimentally, injection locking with wavelength detuning as large as 27 nm was achieved, which corresponded to locking the 36-th side mode. Microwave filtering with a free-spectral range tunable from 88.6 MHz to 1.57 GHz and a notch depth larger than 35 dB was obtained.

Collaborative filtering on a family of biological targets.

PubMed

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

Filtering for networked control systems with single/multiple measurement packets subject to multiple-step measurement delays and multiple packet dropouts

NASA Astrophysics Data System (ADS)

Moayedi, Maryam; Foo, Yung Kuan; Chai Soh, Yeng

2011-03-01

The minimum-variance filtering problem in networked control systems, where both random measurement transmission delays and packet dropouts may occur, is investigated in this article. Instead of following the many existing results that solve the problem by using probabilistic approaches based on the probabilities of the uncertainties occurring between the sensor and the filter, we propose a non-probabilistic approach by time-stamping the measurement packets. Both single-measurement and multiple measurement packets are studied. We also consider the case of burst arrivals, where more than one packet may arrive between the receiver's previous and current sampling times; the scenario where the control input is non-zero and subject to delays and packet dropouts is examined as well. It is shown that, in such a situation, the optimal state estimate would generally be dependent on the possible control input. Simulations are presented to demonstrate the performance of the various proposed filters.

Multi-Antenna Data Collector for Smart Metering Networks with Integrated Source Separation by Spatial Filtering

NASA Astrophysics Data System (ADS)

Quednau, Philipp; Trommer, Ralph; Schmidt, Lorenz-Peter

2016-03-01

Wireless transmission systems in smart metering networks share the advantage of lower installation costs due to the expandability of separate infrastructure but suffer from transmission problems. In this paper the issue of interference of wireless transmitted smart meter data with third party systems and data from other meters is investigated and an approach for solving the problem is presented. A multi-channel wireless m-bus receiver was developed to separate the desired data from unwanted interferers by spatial filtering. The according algorithms are presented and the influence of different antenna types on the spatial filtering is investigated. The performance of the spatial filtering is evaluated by extensive measurements in a realistic surrounding with several hundreds of active wireless m-bus transponders. These measurements correspond to the future environment for data-collectors as they took place in rural and urban areas with smart gas meters equipped with wireless m-bus transponders installed in almost all surrounding buildings.

Exploring the Therapeutic Mechanism of Desmodium styracifolium on Oxalate Crystal-Induced Kidney Injuries Using Comprehensive Approaches Based on Proteomics and Network Pharmacology.

PubMed

Hou, Jiebin; Chen, Wei; Lu, Hongtao; Zhao, Hongxia; Gao, Songyan; Liu, Wenrui; Dong, Xin; Guo, Zhiyong

2018-01-01

Purpose: As a Chinese medicinal herb, Desmodium styracifolium (Osb.) Merr (DS) has been applied clinically to alleviate crystal-induced kidney injuries, but its effective components and their specific mechanisms still need further exploration. This research first combined the methods of network pharmacology and proteomics to explore the therapeutic protein targets of DS on oxalate crystal-induced kidney injuries to provide a reference for relevant clinical use. Methods: Oxalate-induced kidney injury mouse, rat, and HK-2 cell models were established. Proteins differentially expressed between the oxalate and control groups were respectively screened using iTRAQ combined with MALDI-TOF-MS. The common differential proteins of the three models were further analyzed by molecular docking with DS compounds to acquire differential targets. The inverse docking targets of DS were predicted through the platform of PharmMapper. The protein-protein interaction (PPI) relationship between the inverse docking targets and the differential proteins was established by STRING. Potential targets were further validated by western blot based on a mouse model with DS treatment. The effects of constituent compounds, including luteolin, apigenin, and genistein, were investigated based on an oxalate-stimulated HK-2 cell model. Results: Thirty-six common differentially expressed proteins were identified by proteomic analysis. According to previous research, the 3D structures of 15 major constituents of DS were acquired. Nineteen differential targets, including cathepsin D (CTSD), were found using molecular docking, and the component-differential target network was established. Inverse-docking targets including p38 MAPK and CDK-2 were found, and the network of component-reverse docking target was established. Through PPI analysis, 17 inverse-docking targets were linked to differential proteins. The combined network of component-inverse docking target-differential proteins was then constructed. The expressions of CTSD, p-p38 MAPK, and p-CDK-2 were shown to be increased in the oxalate group and decreased in kidney tissue by the DS treatment. Luteolin, apigenin, and genistein could protect oxalate-stimulated tubular cells as active components of DS. Conclusion: The potential targets including the CTSD, p38 MAPK, and CDK2 of DS in oxalate-induced kidney injuries and the active components (luteolin, apigenin, and genistein) of DS were successfully identified in this study by combining proteomics analysis, network pharmacology prediction, and experimental validation.

Mechanical and structural model of fractal networks of fat crystals at low deformations.

PubMed

Narine, S S; Marangoni, A G

1999-12-01

Fat-crystal networks demonstrate viscoelastic behavior at very small deformations. A structural model of these networks is described and supported by polarized light and atomic-force microscopy. A mechanical model is described which allows the shear elastic modulus (G') of the system to be correlated with forces acting within the network. The fractal arrangement of the network at certain length scales is taken into consideration. It is assumed that the forces acting are due to van der Waals forces. The final expression for G' is related to the volume fraction of solid fat (Phi) via the mass fractal dimension (D) of the network, which agrees with the experimental verification of the scaling behavior of fat-crystal networks [S. S. Narine and A. G. Marangoni, Phys. Rev. E 59, 1908 (1999)]. G' was also found to be inversely proportional to the diameter of the primary particles (sigma approximately equal to 6 microm) within the network (microstructural elements) as well as to the diameter of the microstructures (xi approximately equal to 100 microm) and inversely proportional to the cube of the intermicrostructural element distance (d(0)). This formulation of the elastic modulus agrees well with experimental observations.

CMOS-based Stochastically Spiking Neural Network for Optimization under Uncertainties

DTIC Science & Technology

2017-03-01

inverse tangent characteristics at varying input voltage (VIN) [Fig. 3], thereby it is suitable for Kernel function implementation. By varying bias...cost function/constraint variables are generated based on inverse transform on CDF. In Fig. 5, F-1(u) for uniformly distributed random number u [0, 1...extracts random samples of x varying with CDF of F(x). In Fig. 6, we present a successive approximation (SA) circuit to evaluate inverse

Local Estimators for Spacecraft Formation Flying

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Nabi, Marzieh

2011-01-01

A formation estimation architecture for formation flying builds upon the local information exchange among multiple local estimators. Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are needed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms should rely on a local information-exchange network, relaxing the assumptions on existing algorithms. In this research, it was shown that only local observability is required to design a formation estimator and control law. The approach relies on breaking up the overall information-exchange network into sequence of local subnetworks, and invoking an agreement-type filter to reach consensus among local estimators within each local network. State estimates were obtained by a set of local measurements that were passed through a set of communicating Kalman filters to reach an overall state estimation for the formation. An optimization approach was also presented by means of which diffused estimates over the network can be incorporated in the local estimates obtained by each estimator via local measurements. This approach compares favorably with that obtained by a centralized Kalman filter, which requires complete knowledge of the raw measurement available to each estimator.

Moment Tensor Inversions of the M1.7+ Earthquakes in Basel. Switzerland Reveal Predominant Shear Dislocations

NASA Astrophysics Data System (ADS)

Guilhem, A.; Walter, F. T.

2013-12-01

We investigate moment tensor solutions of nearly 30 magnitude (M) 1.7+ earthquakes that occurred in Basel, Switzerland during and after the simulation of the geothermal enhanced system between December 2nd and 8th 2006. In 2009, Deichmann and Ernst determined the focal mechanisms for these events using P-wave first-motions. They found clear evidence for double-couple mechanisms with no indications for substantial volumetric changes. This differs from evidences of composite type ruptures (i.e., shearing with isotropic motion) observed in other geothermal environments. Here, we use a similar approach for the computation of the moment tensor inversions to the one used by Guilhem et al. (2012) for M3 earthquakes in Geysers. We use a dataset from strong-motion stations located within 7 km from the epicenters, with data filtered between 0.5 and 3 Hz and integrated twice to displacement. The waveforms are inverted for both deviatoric and full moment tensor solutions. In addition, we perform a network sensitivity test (NSS) by computing 100 million random moment tensors for each event thus testing the sensitivity of the moment tensor solutions. Finally, because the injection of fluids in the ground can promote crack growth generating seismic events, we also compute a crack + double-couple inversion (Minson et al., 2007) for each of the studied earthquakes between December 2006 and May 2007. From this extensive search we find that the results of our different techniques converge. Moment tensor solutions are very similar to the first-motion focal mechanisms of Deichmann and Ernst (2009) and accordingly do not exhibit dominant volumetric changes except for a subset of events, which we discuss in some detail. References: Deichmann, N. and Ernst, J. (2009), Swiss J. Geosc. Guilhem, A., Dreger, D.S., Hutchings, L. J., and Johnson, L. (2012), AGU Fall meeting Minson, S. E., Dreger, D. S., Bürgmann, R., Kanamori, H., Larson, K. M. (2007), J. Geophys. Res.

Pipelined digital SAR azimuth correlator using hybrid FFT-transversal filter

NASA Technical Reports Server (NTRS)

Wu, C.; Liu, K. Y. (Inventor)

1984-01-01

A synthetic aperture radar system (SAR) having a range correlator is provided with a hybrid azimuth correlator which utilizes a block-pipe-lined fast Fourier transform (FFT). The correlator has a predetermined FFT transform size with delay elements for delaying SAR range correlated data so as to embed in the Fourier transform operation a corner-turning function as the range correlated SAR data is converted from the time domain to a frequency domain. The azimuth correlator is comprised of a transversal filter to receive the SAR data in the frequency domain, a generator for range migration compensation and azimuth reference functions, and an azimuth reference multiplier for correlation of the SAR data. Following the transversal filter is a block-pipelined inverse FFT used to restore azimuth correlated data in the frequency domain to the time domain for imaging.

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation.

PubMed

Bisconti, Cristian; Corallo, Angelo; Fortunato, Laura; Gentile, Antonio A; Massafra, Andrea; Pellè, Piergiuseppe

2015-01-01

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages.

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation

PubMed Central

Bisconti, Cristian; Corallo, Angelo; Fortunato, Laura; Gentile, Antonio A.; Massafra, Andrea; Pellè, Piergiuseppe

2015-01-01

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages. PMID:26617539

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

PubMed Central

Dimitriadis, Stavros I.; Salis, Christos; Tarnanas, Ioannis; Linden, David E.

2017-01-01

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects. PMID:28491032

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Analysing 21cm signal with artificial neural network

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; a Semelin, Benoit

2018-05-01

The 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

A Test Bed for Detection of Botnet Infections in Low Data Rate Tactical Networks

DTIC Science & Technology

2009-09-01

perimeter, their effectiveness in preventing further proliferation within a LAN is almost non-existent. Conficker disables antivirus and firewall software...enable: Yes Disable “strict” capture filtering: no Fencelist location: /etc/fencelist.txt (IP addresses and CIDR blocks Enable Fencelist filtering

Traffic sign recognition by color segmentation and neural network

NASA Astrophysics Data System (ADS)

Surinwarangkoon, Thongchai; Nitsuwat, Supot; Moore, Elvin J.

2011-12-01

An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

Distributed processing of a GPS receiver network for a regional ionosphere map

NASA Astrophysics Data System (ADS)

Choi, Kwang Ho; Hoo Lim, Joon; Yoo, Won Jae; Lee, Hyung Keun

2018-01-01

This paper proposes a distributed processing method applicable to GPS receivers in a network to generate a regional ionosphere map accurately and reliably. For accuracy, the proposed method is operated by multiple local Kalman filters and Kriging estimators. Each local Kalman filter is applied to a dual-frequency receiver to estimate the receiver’s differential code bias and vertical ionospheric delays (VIDs) at different ionospheric pierce points. The Kriging estimator selects and combines several VID estimates provided by the local Kalman filters to generate the VID estimate at each ionospheric grid point. For reliability, the proposed method uses receiver fault detectors and satellite fault detectors. Each receiver fault detector compares the VID estimates of the same local area provided by different local Kalman filters. Each satellite fault detector compares the VID estimate of each local area with that projected from the other local areas. Compared with the traditional centralized processing method, the proposed method is advantageous in that it considerably reduces the computational burden of each single Kalman filter and enables flexible fault detection, isolation, and reconfiguration capability. To evaluate the performance of the proposed method, several experiments with field collected measurements were performed.

Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

Time-Reversal Based Range Extension Technique for Ultra-wideband (UWB) Sensors and Applications in Tactical Communications and Networking

DTIC Science & Technology

2009-04-16

the transmitted waveform, then spectral mask, notch line of Arbitrary Notch Filter , the designed waveforms and multipath impulse response represented...400 Frequence (MHz) Figure 5.4: Spectral mask, notch line of Arbitrary Notch Filter , the designed waveforms and multipath impulse response...600 Frequence (MHz) Figure 5.7: Spectral mask, notch line of Arbitrary Notch Filter , the designed waveforms and multipath impulse response

Nonlinear filter based decision feedback equalizer for optical communication systems.

PubMed

Han, Xiaoqi; Cheng, Chi-Hao

2014-04-07

Nonlinear impairments in optical communication system have become a major concern of optical engineers. In this paper, we demonstrate that utilizing a nonlinear filter based Decision Feedback Equalizer (DFE) with error detection capability can deliver a better performance compared with the conventional linear filter based DFE. The proposed algorithms are tested in simulation using a coherent 100 Gb/sec 16-QAM optical communication system in a legacy optical network setting.

Novel Method for Detection of Air Pollution using Cellular Communication Networks

NASA Astrophysics Data System (ADS)

David, N.; Gao, O. H.

2016-12-01

Air pollution can lead to a wide spectrum of severe and chronic health impacts. Conventional tools for monitoring the phenomenon do not provide a sufficient monitoring solution in a global scale since they are, for example, not representative of the larger space or due to limited deployment as a result of practical limitations, such as: acquisition, installation, and ongoing maintenance costs. Near ground temperature inversions are directly identified with air pollution events since they suppress vertical atmospheric movement and trap pollutants near the ground. Wireless telecommunication links that comprise the data transfer infrastructure in cellular communication networks operate at frequencies of tens of GHz and are affected by different atmospheric phenomena. These systems are deployed near ground level across the globe, including in developing countries such as India, countries in Africa, etc. Many cellular providers routinely store data regarding the received signal levels in the network for quality assurance needs. Temperature inversions cause atmospheric layering, and change the refractive index of the air when compared to standard conditions. As a result, the ducts that are formed can operate, in essence, as atmospheric wave guides, and cause interference (signal amplification / attenuation) in the microwaves measured by the wireless network. Thus, this network is in effect, an existing system of environmental sensors for monitoring temperature inversions and the episodes of air pollution identified with them. This work presents the novel idea, and demonstrates it, in operation, over several events of air pollution which were detected by a standard cellular communication network during routine operation. Reference: David, N. and Gao, H.O. Using cellular communication networks to detect air pollution, Environmental Science & Technology, 2016 (accepted).

An advanced real-time digital signal processing system for linear systems emulation, with special emphasis on network and acoustic response characterization

NASA Astrophysics Data System (ADS)

Gaydecki, Patrick; Fernandes, Bosco

2003-11-01

A fast digital signal processing (DSP) system is described that can perform real-time emulation of a wide variety of linear audio-bandwidth systems and networks, such as reverberant spaces, musical instrument bodies and very high order filter networks. The hardware design is based upon a Motorola DSP56309 operating at 110 million multiplication-accumulations per second and a dual-channel 24 bit codec with a maximum sampling frequency of 192 kHz. High level software has been developed to express complex vector frequency responses as both infinite impulse response (IIR) and finite impulse response (FIR) coefficients, in a form suitable for real-time convolution by the firmware installed in the DSP system memory. An algorithm has also been devised to express IIR filters as equivalent FIR structures, thereby obviating the potential instabilities associated with recursive equations and negating the traditional deficiencies of FIR filters respecting equivalent analogue designs. The speed and dynamic range of the system is such that, when sampling at 48 kHz, the frequency response can be specified to a spectral precision of 22 Hz when sampling at 10 kHz, this resolution increases to 0.9 Hz. Moreover, it is also possible to control the phase of any frequency band with a theoretical precision of 10-5 degrees in all cases. The system has been applied in the study of analogue filter networks, real-time Hilbert transformation, phase-shift systems and musical instrument body emulation, where it is providing valuable new insights into the understanding of psychoacoustic mechanisms.

Emission computerized axial tomography from multiple gamma-camera views using frequency filtering.

PubMed

Pelletier, J L; Milan, C; Touzery, C; Coitoux, P; Gailliard, P; Budinger, T F

1980-01-01

Emission computerized axial tomography is achievable in any nuclear medicine department from multiple gamma camera views. Data are collected by rotating the patient in front of the camera. A simple fast algorithm is implemented, known as the convolution technique: first the projection data are Fourier transformed and then an original filter designed for optimizing resolution and noise suppression is applied; finally the inverse transform of the latter operation is back-projected. This program, which can also take into account the attenuation for single photon events, was executed with good results on phantoms and patients. We think that it can be easily implemented for specific diagnostic problems.

Improving information filtering via network manipulation

NASA Astrophysics Data System (ADS)

Zhang, Fuguo; Zeng, An

2012-12-01

The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.

Logical spin-filtering in a triangular network of quantum nanorings with a Rashba spin-orbit interaction

NASA Astrophysics Data System (ADS)

Dehghan, E.; Sanavi Khoshnoud, D.; Naeimi, A. S.

2018-01-01

The spin-resolved electron transport through a triangular network of quantum nanorings is studied in the presence of Rashba spin-orbit interaction (RSOI) and a magnetic flux using quantum waveguide theory. This study illustrates that, by tuning Rashba constant, magnetic flux and incoming electron energy, the triangular network of quantum rings can act as a perfect logical spin-filtering with high efficiency. By changing in the energy of incoming electron, at a proper value of the Rashba constant and magnetic flux, a reverse in the direction of spin can take place in the triangular network of quantum nanorings. Furthermore, the triangular network of quantum nanorings can be designed as a device and shows several simultaneous spintronic properties such as spin-splitter and spin-inverter. This spin-splitting is dependent on the energy of the incoming electron. Additionally, different polarizations can be achieved in the two outgoing leads from an originally incoming spin state that simulates a Stern-Gerlach apparatus.

An improved exact inversion formula for solenoidal fields in cone beam vector tomography

NASA Astrophysics Data System (ADS)

Katsevich, Alexander; Rothermel, Dimitri; Schuster, Thomas

2017-06-01

In this paper we present an improved inversion formula for the 3D cone beam transform of vector fields supported in the unit ball which is exact for solenoidal fields. It is well known that only the solenoidal part of a vector field can be determined from the longitudinal ray transform of a vector field in cone beam geometry. The inversion formula, as it was developed in Katsevich and Schuster (2013 An exact inversion formula for cone beam vector tomography Inverse Problems 29 065013), consists of two parts. The first part is of the filtered backprojection type, whereas the second part is a costly 4D integration and very inefficient. In this article we tackle this second term and obtain an improved formula, which is easy to implement and saves one order of integration. We also show that the first part contains all information about the curl of the field, whereas the second part has information about the boundary values. More precisely, the second part vanishes if the solenoidal part of the original field is tangential at the boundary. A number of numerical tests presented in the paper confirm the theoretical results and the exactness of the formula. Also, we obtain an inversion algorithm that works for general convex domains.

Bowhead whale localization using time-difference-of-arrival data from asynchronous recorders.

PubMed

Warner, Graham A; Dosso, Stan E; Hannay, David E

2017-03-01

This paper estimates bowhead whale locations and uncertainties using nonlinear Bayesian inversion of the time-difference-of-arrival (TDOA) of low-frequency whale calls recorded on onmi-directional asynchronous recorders in the shallow waters of the northeastern Chukchi Sea, Alaska. A Y-shaped cluster of seven autonomous ocean-bottom hydrophones, separated by 0.5-9.2 km, was deployed for several months over which time their clocks drifted out of synchronization. Hundreds of recorded whale calls are manually associated between recorders. The TDOA between hydrophone pairs are calculated from filtered waveform cross correlations and depend on the whale locations, hydrophone locations, relative recorder clock offsets, and effective waveguide sound speed. A nonlinear Bayesian inversion estimates all of these parameters and their uncertainties as well as data error statistics. The problem is highly nonlinear and a linearized inversion did not produce physically realistic results. Whale location uncertainties from nonlinear inversion can be low enough to allow accurate tracking of migrating whales that vocalize repeatedly over several minutes. Estimates of clock drift rates are obtained from inversions of TDOA data over two weeks and agree with corresponding estimates obtained from long-time averaged ambient noise cross correlations. The inversion is suitable for application to large data sets of manually or automatically detected whale calls.

Bandwidth variable transceivers with artificial neural network-aided provisioning and capacity improvement capabilities in meshed optical networks with cascaded ROADM filtering

NASA Astrophysics Data System (ADS)

Zhou, Xingyu; Zhuge, Qunbi; Qiu, Meng; Xiang, Meng; Zhang, Fangyuan; Wu, Baojian; Qiu, Kun; Plant, David V.

2018-02-01

We investigate the capacity improvement achieved by bandwidth variable transceivers (BVT) in meshed optical networks with cascaded ROADM filtering at fixed channel spacing, and then propose an artificial neural network (ANN)-aided provisioning scheme to select optimal symbol rate and modulation format for the BVTs in this scenario. Compared with a fixed symbol rate transceiver with standard QAMs, it is shown by both experiments and simulations that BVTs can increase the average capacity by more than 17%. The ANN-aided BVT provisioning method uses parameters monitored from a coherent receiver and then employs a trained ANN to transform these parameters into the desired configuration. It is verified by simulation that the BVT with the proposed provisioning method can approach the upper limit of the system capacity obtained by brute-force search under various degrees of flexibilities.

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Kalman and particle filtering methods for full vehicle and tyre identification

NASA Astrophysics Data System (ADS)

Bogdanski, Karol; Best, Matthew C.

2018-05-01

This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.

A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

PubMed

Suzuki, Kenji

2009-09-21

Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.

Solving inversion problems with neural networks

NASA Technical Reports Server (NTRS)

Kamgar-Parsi, Behzad; Gualtieri, J. A.

1990-01-01

A class of inverse problems in remote sensing can be characterized by Q = F(x), where F is a nonlinear and noninvertible (or hard to invert) operator, and the objective is to infer the unknowns, x, from the observed quantities, Q. Since the number of observations is usually greater than the number of unknowns, these problems are formulated as optimization problems, which can be solved by a variety of techniques. The feasibility of neural networks for solving such problems is presently investigated. As an example, the problem of finding the atmospheric ozone profile from measured ultraviolet radiances is studied.

Neural learning of constrained nonlinear transformations

NASA Technical Reports Server (NTRS)

Barhen, Jacob; Gulati, Sandeep; Zak, Michail

1989-01-01

Two issues that are fundamental to developing autonomous intelligent robots, namely, rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented.