Adaptive spatial filtering for daytime satellite quantum key distribution
NASA Astrophysics Data System (ADS)
Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.
2014-11-01
The rate of secure key generation (SKG) in quantum key distribution (QKD) is adversely affected by optical noise and loss in the quantum channel. In a free-space atmospheric channel, the scattering of sunlight into the channel can lead to quantum bit error ratios (QBERs) sufficiently large to preclude SKG. Furthermore, atmospheric turbulence limits the degree to which spatial filtering can reduce sky noise without introducing signal losses. A system simulation quantifies the potential benefit of tracking and higher-order adaptive optics (AO) technologies to SKG rates in a daytime satellite engagement scenario. The simulations are performed assuming propagation from a low-Earth orbit (LEO) satellite to a terrestrial receiver that includes an AO system comprised of a Shack-Hartmann wave-front sensor (SHWFS) and a continuous-face-sheet deformable mirror (DM). The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain waveoptics hardware emulator. Secure key generation rates are then calculated for the decoy state QKD protocol as a function of the receiver field of view (FOV) for various pointing angles. The results show that at FOVs smaller than previously considered, AO technologies can enhance SKG rates in daylight and even enable SKG where it would otherwise be prohibited as a consequence of either background optical noise or signal loss due to turbulence effects.
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
NASA Astrophysics Data System (ADS)
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
NASA Astrophysics Data System (ADS)
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
Prototype adaptive bow-tie filter based on spatial exposure time modulation
NASA Astrophysics Data System (ADS)
Badal, Andreu
2016-03-01
In recent years, there has been an increased interest in the development of dynamic bow-tie filters that are able to provide patient-specific x-ray beam shaping. We introduce the first physical prototype of a new adaptive bow-tie filter design based on the concept of "spatial exposure time modulation." While most existing bow-tie filters operate by attenuating the radiation beam differently in different locations using partially attenuating objects, the presented filter shapes the radiation field using two movable completely radio-opaque collimators. The aperture and speed of the collimators is modulated in synchrony with the x-ray exposure to selectively block the radiation emitted to different parts of the object. This mode of operation does not allow the reproduction of every possible attenuation profile, but it can reproduce the profile of any object with an attenuation profile monotonically decreasing from the center to the periphery, such as an object with an elliptical cross section. Therefore, the new adaptive filter provides the same advantages as the currently existing static bow-tie filters, which are typically designed to work for a pre-determined cylindrical object at a fixed distance from the source, and provides the additional capability to adapt its performance at image acquisition time to better compensate for the actual diameter and location of the imaged object. A detailed description of the prototype filter, the implemented control methods, and a preliminary experimental validation of its performance are presented.
Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin
2015-01-01
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis. PMID:25619991
Spatial adaptive upsampling filter for HDR image based on multiple luminance range
NASA Astrophysics Data System (ADS)
Chen, Qian; Su, Guan-ming; Peng, Yin
2014-03-01
In this paper, we propose an adaptive upsampling filter to spatially upscale HDR image based on luminance range of the HDR picture in each color channel. It first searches for the optimal luminance range values to partition an HDR image to three different parts: dark, mid-tone and highlight. Then we derive the optimal set of filter coefficients both vertically and horizontally for each part. When the HDR pixel is within the dark area, we apply one set of filter coefficients to vertically upsample the pixel. If the HDR pixel falls in mid-tone area, we apply another set of filter for vertical upsampling. Otherwise the HDR pixel is in highlight area, another set of filter will be applied for vertical upsampling. Horizontal upsampling will be carried out likewise based on its luminance. The inherent idea to partition HDR image to different luminance areas is based on the fact that most HDR images are created from multiple exposures. Different exposures usually demonstrate slight variation in captured signal statistics, such as noise level, subtle misalignment etc. Hence, to group different regions to three luminance partitions actually helps to eliminate the variation between signals, and to derive optimal filter for each group with signals of lesser variation is certainly more efficient than for the entire HDR image. Experimental results show that the proposed adaptive upsampling filter based on luminance ranges outperforms the optimal upsampling filter around 0.57dB for R channel, 0.44dB for G channel and 0.31dB for B channel.
NASA Astrophysics Data System (ADS)
Yushkov, Konstantin B.; Molchanov, Vladimir Y.; Belousov, Pavel V.; Abrosimov, Aleksander Y.
2016-01-01
We report a method for edge enhancement in the images of transparent samples using analog image processing in coherent light. The experimental technique is based on adaptive spatial filtering with an acousto-optic tunable filter in a telecentric optical system. We demonstrate processing of microscopic images of unstained and stained histological sections of human thyroid tumor with improved contrast.
Longmire, M S; Milton, A F; Takken, E H
1982-11-01
Several 1-D signal processing techniques have been evaluated by simulation with a digital computer using high-spatial-resolution (0.15 mrad) noise data gathered from back-lit clouds and uniform sky with a scanning data collection system operating in the 4.0-4.8-microm spectral band. Two ordinary bandpass filters and a least-mean-square (LMS) spatial filter were evaluated in combination with a fixed or adaptive threshold algorithm. The combination of a 1-D LMS filter and a 1-D adaptive threshold sensor was shown to reject extreme cloud clutter effectively and to provide nearly equal signal detection in a clear and cluttered sky, at least in systems whose NEI (noise equivalent irradiance) exceeds 1.5 x 10(-13) W/cm(2) and whose spatial resolution is better than 0.15 x 0.36 mrad. A summary gives highlights of the work, key numerical results, and conclusions.
Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter
2014-01-01
Background In magnetic resonance electrical impedance tomography (MREIT), we reconstruct conductivity images using magnetic flux density data induced by externally injected currents. Since we extract magnetic flux density data from acquired MR phase images, the amount of measurement noise increases in regions of weak MR signals. Especially for local regions of MR signal void, there may occur excessive amounts of noise to deteriorate the quality of reconstructed conductivity images. In this paper, we propose a new conductivity image enhancement method as a postprocessing technique to improve the image quality. Methods Within a magnetic flux density image, the amount of noise varies depending on the position-dependent MR signal intensity. Using the MR magnitude image which is always available in MREIT, we estimate noise levels of measured magnetic flux density data in local regions. Based on the noise estimates, we adjust the window size and weights of a spatial averaging filter, which is applied to reconstructed conductivity images. Without relying on a partial differential equation, the new method is fast and can be easily implemented. Results Applying the novel conductivity image enhancement method to experimental data, we could improve the image quality to better distinguish local regions with different conductivity contrasts. From phantom experiments, the estimated conductivity values had 80% less variations inside regions of homogeneous objects. Reconstructed conductivity images from upper and lower abdominal regions of animals showed much less artifacts in local regions of weak MR signals. Conclusion We developed the fast and simple method to enhance the conductivity image quality by adaptively adjusting the weights and window size of the spatial averaging filter using MR magnitude images. Since the new method is implemented as a postprocessing step, we suggest adopting it without or with other preprocessing methods for application studies where conductivity
NASA Astrophysics Data System (ADS)
Mohamed, Khaled M.; Hardie, Russell C.
2015-12-01
We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel's spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.
Murray, J.E.; Estabrook, K.G.; Milam, D.; Sell, W.D.; Van Wonterghem, R.M.; Feil, M.D.; Rubenchick, A.M.
1996-12-09
Experiments and calculations indicate that the threshold pressure in spatial filters for distortion of a transmitted pulse scales approximately as I{sup O.2} and (F{number_sign}){sup 2} over the intensity range from 10{sup 14} to 2xlO{sup 15} W/CM{sup 2} . We also demonstrated an interferometric diagnostic that will be used to measure the scaling relationships governing pinhole closure in spatial filters.
Spatial filtering with photonic crystals
Maigyte, Lina; Staliunas, Kestutis
2015-03-15
Photonic crystals are well known for their celebrated photonic band-gaps—the forbidden frequency ranges, for which the light waves cannot propagate through the structure. The frequency (or chromatic) band-gaps of photonic crystals can be utilized for frequency filtering. In analogy to the chromatic band-gaps and the frequency filtering, the angular band-gaps and the angular (spatial) filtering are also possible in photonic crystals. In this article, we review the recent advances of the spatial filtering using the photonic crystals in different propagation regimes and for different geometries. We review the most evident configuration of filtering in Bragg regime (with the back-reflection—i.e., in the configuration with band-gaps) as well as in Laue regime (with forward deflection—i.e., in the configuration without band-gaps). We explore the spatial filtering in crystals with different symmetries, including axisymmetric crystals; we discuss the role of chirping, i.e., the dependence of the longitudinal period along the structure. We also review the experimental techniques to fabricate the photonic crystals and numerical techniques to explore the spatial filtering. Finally, we discuss several implementations of such filters for intracavity spatial filtering.
Spatial filtering through elementary examples
NASA Astrophysics Data System (ADS)
Gluskin, Emanuel
2004-05-01
The spatial filtering features of resistive grids have become important in microelectronics in the last two decades, in particular because of the current interest in the design of 'vision chips.' However, these features of the grids are unexpected for many who received a basic physics or electrical engineering education. The author's opinion is that the concept of spatial filtering is important in itself, and should be introduced and separately considered at an early educational stage. We thus discuss some simple examples, of both continuous and discrete systems in which spatial filtering may be observed, using only basic physics concepts.
Correia, Carlos M; Teixeira, Joel
2014-12-01
Computationally efficient wave-front reconstruction techniques for astronomical adaptive-optics (AO) systems have seen great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered much attention, especially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl ratio) and further develop formulae for the anti-aliasing (AA) Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e., discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise, and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performance results as a function of an ensemble of forward models for the Shack-Hartmann wave-front sensor (using sparse and nonsparse representations) and compute point-spread-function raw intensities. We find that for a 32×32 single-conjugated AOs system the aliasing propagation coefficient is roughly 60% of the least-squares filters, whereas the noise propagation is around 80%. Contrast improvements of factors of up to 2 are achievable across the field in the H band. For current and next-generation high-contrast imagers, despite better aliasing mitigation, AA Wiener filtering cannot be used as a standalone method and must therefore be used in combination with optical spatial filters deployed before image formation actually takes place.
Whittemore, Stephen Richard
2013-09-10
Imaging systems include a detector and a spatial light modulator (SLM) that is coupled so as to control image intensity at the detector based on predetermined detector limits. By iteratively adjusting SLM element values, image intensity at one or all detector elements or portions of an imaging detector can be controlled to be within limits. The SLM can be secured to the detector at a spacing such that the SLM is effectively at an image focal plane. In some applications, the SLM can be adjusted to impart visible or hidden watermarks to images or to reduce image intensity at one or a selected set of detector elements so as to reduce detector blooming
Pixelated filters for spatial imaging
NASA Astrophysics Data System (ADS)
Mathieu, Karine; Lequime, Michel; Lumeau, Julien; Abel-Tiberini, Laetitia; Savin De Larclause, Isabelle; Berthon, Jacques
2015-10-01
Small satellites are often used by spatial agencies to meet scientific spatial mission requirements. Their payloads are composed of various instruments collecting an increasing amount of data, as well as respecting the growing constraints relative to volume and mass; So small-sized integrated camera have taken a favored place among these instruments. To ensure scene specific color information sensing, pixelated filters seem to be more attractive than filter wheels. The work presented here, in collaboration with Institut Fresnel, deals with the manufacturing of this kind of component, based on thin film technologies and photolithography processes. CCD detectors with a pixel pitch about 30 μm were considered. In the configuration where the matrix filters are positioned the closest to the detector, the matrix filters are composed of 2x2 macro pixels (e.g. 4 filters). These 4 filters have a bandwidth about 40 nm and are respectively centered at 550, 700, 770 and 840 nm with a specific rejection rate defined on the visible spectral range [500 - 900 nm]. After an intense design step, 4 thin-film structures have been elaborated with a maximum thickness of 5 μm. A run of tests has allowed us to choose the optimal micro-structuration parameters. The 100x100 matrix filters prototypes have been successfully manufactured with lift-off and ion assisted deposition processes. High spatial and spectral characterization, with a dedicated metrology bench, showed that initial specifications and simulations were globally met. These excellent performances knock down the technological barriers for high-end integrated specific multi spectral imaging.
Spatial filters for high average power lasers
Erlandson, Alvin C
2012-11-27
A spatial filter includes a first filter element and a second filter element overlapping with the first filter element. The first filter element includes a first pair of cylindrical lenses separated by a first distance. Each of the first pair of cylindrical lenses has a first focal length. The first filter element also includes a first slit filter positioned between the first pair of cylindrical lenses. The second filter element includes a second pair of cylindrical lenses separated by a second distance. Each of the second pair of cylindrical lenses has a second focal length. The second filter element also includes a second slit filter positioned between the second pair of cylindrical lenses.
Frequency domain FIR and IIR adaptive filters
NASA Technical Reports Server (NTRS)
Lynn, D. W.
1990-01-01
A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented.
Adaptive filtering image preprocessing for smart FPA technology
NASA Astrophysics Data System (ADS)
Brooks, Geoffrey W.
1995-05-01
This paper discusses two applications of adaptive filters for image processing on parallel architectures. The first, based on the results of previously accomplished work, summarizes the analyses of various adaptive filters implemented for pixel-level image prediction. FIR filters, fixed and adaptive IIR filters, and various variable step size algorithms were compared with a focus on algorithm complexity against the ability to predict future pixel values. A gaussian smoothing operation with varying spatial and temporal constants were also applied for comparisons of random noise reductions. The second application is a suggestion to use memory-adaptive IIR filters for detecting and tracking motion within an image. Objects within an image are made of edges, or segments, with varying degrees of motion. An application has been previously published that describes FIR filters connecting pixels and using correlations to determine motion and direction. This implementation seems limited to detecting motion coinciding with FIR filter operation rate and the associated harmonics. Upgrading the FIR structures with adaptive IIR structures can eliminate these limitations. These and any other pixel-level adaptive filtering application require data memory for filter parameters and some basic computational capability. Tradeoffs have to be made between chip real estate and these desired features. System tradeoffs will also have to be made as to where it makes the most sense to do which level of processing. Although smart pixels may not be ready to implement adaptive filters, applications such as these should give the smart pixel designer some long range goals.
Split quaternion nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2010-04-01
A split quaternion learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of three- and four-dimensional signals is proposed. The derivation takes into account the non-commutativity of the quaternion product, an aspect neglected in the derivation of the existing learning algorithms. It is shown that the additional information taken into account by a rigorous treatment of quaternion algebra provides improved performance on hypercomplex processes. A rigorous analysis of the convergence of the proposed algorithms is also provided. Simulations on both benchmark and real-world signals support the approach.
Spatial filters for shape control
NASA Technical Reports Server (NTRS)
Lindner, Douglas K.; Reichard, Karl M.
1992-01-01
Recently there has emerged a new class of sensors, called spatial filters, for structures which respond over a significant gauge length. Examples include piezoelectric laminate PVDF film, modal domain optical fiber sensors, and holographic sensors. These sensors have a unique capability in that they can be fabricated to locally alter their sensitivity to the measurand. In this paper we discuss how these sensors can be used for the implementation of control algorithms for the suppression of acoustic radiation from flexible structures. Based on this relationship between the total power radiated to the far field to the modal velocities of the structure, we show how the sensor placement to optimize the control algorithm to suppress the radiated power.
Spatial filters for high power lasers
Erlandson, Alvin Charles; Bayramian, Andrew James
2014-12-02
A spatial filter includes a first filter element and a second filter element overlapping with the first filter element. The first filter element includes a first pair of cylindrical lenses separated by a first distance. Each of the first pair of cylindrical lenses has a first focal length. The first filter element also includes a first longitudinal slit filter positioned between the first pair of cylindrical lenses. The second filter element includes a second pair of cylindrical lenses separated by a second distance. Each of the second pair of cylindrical lenses has a second focal length. The second filter element also includes a second longitudinal slit filter positioned between the second pair of cylindrical lenses.
Adaptive Mallow's optimization for weighted median filters
NASA Astrophysics Data System (ADS)
Rachuri, Raghu; Rao, Sathyanarayana S.
2002-05-01
This work extends the idea of spectral optimization for the design of Weighted Median filters and employ adaptive filtering that updates the coefficients of the FIR filter from which the weights of the median filters are derived. Mallows' theory of non-linear smoothers [1] has proven to be of great theoretical significance providing simple design guidelines for non-linear smoothers. It allows us to find a set of positive weights for a WM filter whose sample selection probabilities (SSP's) are as close as possible to a SSP set predetermined by Mallow's. Sample selection probabilities have been used as a basis for designing stack smoothers as they give a measure of the filter's detail preserving ability and give non-negative filter weights. We will extend this idea to design weighted median filters admitting negative weights. The new method first finds the linear FIR filter coefficients adaptively, which are then used to determine the weights of the median filter. WM filters can be designed to have band-pass, high-pass as well as low-pass frequency characteristics. Unlike the linear filters, however, the weighted median filters are robust in the presence of impulsive noise, as shown by the simulation results.
Adaptable Iterative and Recursive Kalman Filter Schemes
NASA Technical Reports Server (NTRS)
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
Adaptive filtering in biological signal processing.
Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A
1990-01-01
The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.
Neural nets for adaptive filtering and adaptive pattern recognition
Widrow, B.; Winter, R.
1988-03-01
The fields of adaptive signal processing and adaptive neural networks have been developing independently but have that adaptive linear combiner (ALC) in common. With its inputs connected to a tapped delay line, the ALC becomes a key component of an adaptive filter. With its output connected to a quantizer, the ALC becomes an adaptive threshold element of adaptive neuron. Adaptive threshold elements, on the other hand, are the building blocks of neural networks. Today neural nets are the focus of widespread research interest. Areas of investigation include pattern recognition and trainable logic. Neural network systems have not yet had the commercial impact of adaptive filtering. The commonality of the ALC to adaptive signal processing and adaptive neural networks suggests the two fields have much to share with each other. This article describes practical applications of the ALC in signal processing and pattern recognition.
Enhancement of Electrolaryngeal Speech by Adaptive Filtering.
ERIC Educational Resources Information Center
Espy-Wilson, Carol Y.; Chari, Venkatesh R.; MacAuslan, Joel M.; Huang, Caroline B.; Walsh, Michael J.
1998-01-01
A study tested the quality and intelligibility, as judged by several listeners, of four users' electrolaryngeal speech, with and without filtering to compensate for perceptually objectionable acoustic characteristics. Results indicated that an adaptive filtering technique produced a noticeable improvement in the quality of the Transcutaneous…
Spatial Region Filtering in IRAF/PROS
NASA Astrophysics Data System (ADS)
Mandel, E.; Roll, J.; Schmidt, D.; Vanhilst, M.; Burg, R.
1993-01-01
We describe a spatial region filtering scheme in IRAF that allows users to specify one or more geometric shapes to be included or excluded when masking spatial data. These shapes can be combined using Boolean algebra to create the complex masks required for X-ray analysis.
Development of a spatial filtering apparatus
NASA Astrophysics Data System (ADS)
Wilson, Nicolle
This thesis contains a discussion of the theoretical background for Fourier spatial filtering and a description of the design and construction of a portable in-class spatial filtering apparatus. A portable, in-class spatial filtering demonstration apparatus was designed and built. This apparatus uses liquid crystal display (LCD) panels from two projectors as the object and filter masks. The blue LCD panel from the first projector serves as the object mask, and the red panel from the second projector serves as the filter mask. The panels were extracted from their projectors and mounted onto aluminum blocks which are held in place by optical component mounts. Images are written to the LCD panels via custom open source software developed for this apparatus which writes independent monochromatic images to the video signal. The software has two monochromatic image windows, basic image manipulation tools, and two video feed input display windows. Two complementary metal-oxide semiconductor (CMOS) sensors are positioned to record the reconstructed image of the object mask and the diffraction pattern created by the object mask. The object and filter mask can be digitally changed and the effects on the filtered image and diffraction pattern can be observed in real-time. The entire apparatus is assembled onto a rolling cart which allows it to be easily taken into classrooms.
Matched filter based iterative adaptive approach
NASA Astrophysics Data System (ADS)
Nepal, Ramesh; Zhang, Yan Rockee; Li, Zhengzheng; Blake, William
2016-05-01
Matched Filter sidelobes from diversified LPI waveform design and sensor resolution are two important considerations in radars and active sensors in general. Matched Filter sidelobes can potentially mask weaker targets, and low sensor resolution not only causes a high margin of error but also limits sensing in target-rich environment/ sector. The improvement in those factors, in part, concern with the transmitted waveform and consequently pulse compression techniques. An adaptive pulse compression algorithm is hence desired that can mitigate the aforementioned limitations. A new Matched Filter based Iterative Adaptive Approach, MF-IAA, as an extension to traditional Iterative Adaptive Approach, IAA, has been developed. MF-IAA takes its input as the Matched Filter output. The motivation here is to facilitate implementation of Iterative Adaptive Approach without disrupting the processing chain of traditional Matched Filter. Similar to IAA, MF-IAA is a user parameter free, iterative, weighted least square based spectral identification algorithm. This work focuses on the implementation of MF-IAA. The feasibility of MF-IAA is studied using a realistic airborne radar simulator as well as actual measured airborne radar data. The performance of MF-IAA is measured with different test waveforms, and different Signal-to-Noise (SNR) levels. In addition, Range-Doppler super-resolution using MF-IAA is investigated. Sidelobe reduction as well as super-resolution enhancement is validated. The robustness of MF-IAA with respect to different LPI waveforms and SNR levels is also demonstrated.
Spatial filtering in ambient noise interferometry.
Carrière, Olivier; Gerstoft, Peter; Hodgkiss, William S
2014-03-01
Theoretically, the empirical Green's function between a pair of receivers can be extracted from the cross correlation of the received diffuse noise. The diffuse noise condition rarely is met in the ocean and directional sources may bias the Green's function. Here matrix-based spatial filters are used for removing unwanted contributions in the cross correlations. Two methods are used for solving the matrix filter design problem. First a matrix least-square problem is solved with a low-rank approximation of the pseudo-inverse, here, derived for linear and planar arrays. Second, a convex optimization approach is used to solve the design problem reformulated with ad hoc constraints. The spatial filter is applied to real-data cross correlations of elements from a linear array to attenuate the contribution of a discrete interferer. In the case of a planar array and simulated data, a spatial filter enables a passive upgoing/downgoing wavefield separation along with an efficient rejection of horizontally propagating noise. The impact of array size and frequency band on the filtered cross correlations is discussed.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
VSP wave separation by adaptive masking filters
NASA Astrophysics Data System (ADS)
Rao, Ying; Wang, Yanghua
2016-06-01
In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.
Adaptive wavelet Wiener filtering of ECG signals.
Smital, Lukáš; Vítek, Martin; Kozumplík, Jiří; Provazník, Ivo
2013-02-01
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.
Adaptive wavelet Wiener filtering of ECG signals.
Smital, Lukáš; Vítek, Martin; Kozumplík, Jiří; Provazník, Ivo
2013-02-01
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter. PMID:23192472
Kalman filter based control for Adaptive Optics
NASA Astrophysics Data System (ADS)
Petit, Cyril; Quiros-Pacheco, Fernando; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François; Fusco, Thierry
2004-12-01
Classical Adaptive Optics suffer from a limitation of the corrected Field Of View. This drawback has lead to the development of MultiConjugated Adaptive Optics. While the first MCAO experimental set-ups are presently under construction, little attention has been paid to the control loop. This is however a key element in the optimization process especially for MCAO systems. Different approaches have been proposed in recent articles for astronomical applications : simple integrator, Optimized Modal Gain Integrator and Kalman filtering. We study here Kalman filtering which seems a very promising solution. Following the work of Brice Leroux, we focus on a frequential characterization of kalman filters, computing a transfer matrix. The result brings much information about their behaviour and allows comparisons with classical controllers. It also appears that straightforward improvements of the system models can lead to static aberrations and vibrations filtering. Simulation results are proposed and analysed thanks to our frequential characterization. Related problems such as model errors, aliasing effect reduction or experimental implementation and testing of Kalman filter control loop on a simplified MCAO experimental set-up could be then discussed.
Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids
NASA Astrophysics Data System (ADS)
Lombard, Anthony; Reindl, Klaus; Kellermann, Walter
2009-12-01
We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.
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.
Adaptation Driven by Spatial Heterogeneities
NASA Astrophysics Data System (ADS)
Hermsen, Rutger
2011-03-01
Biological evolution and ecology are intimately linked, because the reproductive success or ``fitness'' of an organism depends crucially on its ecosystem. Yet, most models of evolution (or population genetics) consider homogeneous, fixed-size populations subjected to a constant selection pressure. To move one step beyond such ``mean field'' descriptions, we discuss stochastic models of evolution driven by spatial heterogeneity. We imagine a population whose range is limited by a spatially varying environmental parameter, such as a temperature or the concentration of an antibiotic drug. Individuals in the population replicate, die and migrate stochastically. Also, by mutation, they can adapt to the environmental stress and expand their range. This way, adaptation and niche expansion go hand in hand. This mode of evolution is qualitatively different from the usual notion of a population climbing a fitness gradient. We analytically calculate the rate of adaptation by solving a first passage time problem. Interestingly, the joint effects of reproduction, death, mutation and migration result in two distinct parameter regimes depending on the relative time scales of mutation and migration. We argue that the proposed scenario may be relevant for the rapid evolution of antibiotic resistance. This work was supported by the Center for Theoretical Biological Physics sponsored by the National Science Foundation (NSF) (Grant PHY-0822283).
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach. PMID:21712159
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach.
Spatial region filtering in IRAF/PROS
NASA Technical Reports Server (NTRS)
Mandel, Eric; Roll, John; Schmidt, Dennis; Vanhilst, Mike; Burg, Richard
1992-01-01
In order to analyze x ray data, it is nearly always necessary to extract source and background events from a data set. Typically, this is done by defining geometric spatial regions of the data set to describe the source and background. For example, one might wish to extract source events from a circular or elliptical region centered at a particular pixel, and background events from a circular or elliptical annulus whose inner radius matches the source region. At the same time, it might be necessary to exclude one or more nearby sources from the source or background region in question. Thus, it might be necessary to define a pie-shaped region or even an entirely irregularly-shaped region to exclude. A spatial filtering scheme called REGIONS was implemented in IRAF/PROS to support these and other types of spatial region extraction. It allows users to create a spatial mask by specifying one or more ASCII geometric shape descriptors (box, circle, ellipse, pie, point, annulus, and polygon) as regions to be included or excluded in the mask. In addition, two or more shapes can be combined using Boolean algebra to create an infinite variety of sophisticated regions. Each geometric shape has a specific set of parameters that describe that shape. For example, a circle is described by a center and a radius, while a box is described by a center, length, width, and rotation angle. These quantities can be specified in units of pixels or, in cases where the target image contains world coordinate system information, they can be described in units such as RA and Dec. Users can create region mask files by feeding an ASCII region descriptor to the IRAF/PROS plcreate task. Temporary masks can also be created from ASCII region descriptors by individual applications that call the routines in the region creation library. This library implements a yacc-based region parser that compiles the ASCII descriptors into 'software CPU' instructions which are then executed to create the mask. The
Precise adaptive photonic rf filters realized with adaptive Bragg gratings
NASA Astrophysics Data System (ADS)
Wickham, Michael G.; Upton, Eric L.
2000-09-01
The demand for higher data capacity and reduced levels of interference in the communications arena are driving dtat links toward high carrier frequencies and wider modulation bandwidths. Circuitry for performing intermediate frequency processing over these more demanding ranges is needed to provide complex signal processing. We have demonstrated photonics technologies utilizing Bragg Grating Signal Processing (BGSP), which can be used to perform a variety of RF filter functions. The desirable benefits of multiple-tap adaptive finite impulse response (FIR) filters, infinite impulse response (IIR) filters, and equalizers are well known; however, they are usually the province of digital signal processing and demand preprocessor sample rates that require high system power consumption. BGSPs provide these functions with discrete optical taps and digital controls while only requiring bandwidths easily provided by conventional RF circuitry. This is because the actual signal processing of the large information bandwidths is performed in the optical regime, while control functions are performed at RF frequencies compatible with integrated circuit technologies. To realize the performance benefits of photonic processing, the Bragg grating reflectors must be stabilized against environmental without unduly taxing the RF control circuitry. We have implemented a orthogonally coded tap modulation technique which stabilizes the transfer function of the signal processor and enables significant adaptive IF signal processing to be obtained with very low size, weight, and power. Our demonstration of a photonic proof-of-concept architecture is a reconfigurable, multiple-tap FIR filter that is dynamically controlled to implement low-pass, high-pass, band-pass, band-stop, and tunable filters operating over bandwidths of 3 Ghz.
Adaptation and the temporal delay filter of fly motion detectors.
Harris, R A; O'Carroll, D C; Laughlin, S B
1999-08-01
Recent accounts attribute motion adaptation to a shortening of the delay filter in elementary motion detectors (EMDs). Using computer modelling and recordings from HS neurons in the drone-fly Eristalis tenax, we present evidence that challenges this theory. (i) Previous evidence for a change in the delay filter comes from 'image step' (or 'velocity impulse') experiments. We note a large discrepancy between the temporal frequency tuning predicted from these experiments and the observed tuning of motion sensitive cells. (ii) The results of image step experiments are highly sensitive to the experimental method used. (iii) An apparent motion stimulus reveals a much shorter EMD delay than suggested by previous 'image step' experiments. This short delay agrees with the observed temporal frequency sensitivity of the unadapted cell. (iv) A key prediction of a shortening delay filter is that the temporal frequency optimum of the cell should show a large shift to higher temporal frequencies after motion adaptation. We show little change in the temporal or spatial frequency (and hence velocity) optima following adaptation.
Spectral analysis and filtering techniques in digital spatial data processing
Pan, Jeng-Jong
1989-01-01
A filter toolbox has been developed at the EROS Data Center, US Geological Survey, for retrieving or removing specified frequency information from two-dimensional digital spatial data. This filter toolbox provides capabilities to compute the power spectrum of a given data and to design various filters in the frequency domain. Three types of filters are available in the toolbox: point filter, line filter, and area filter. Both the point and line filters employ Gaussian-type notch filters, and the area filter includes the capabilities to perform high-pass, band-pass, low-pass, and wedge filtering techniques. These filters are applied for analyzing satellite multispectral scanner data, airborne visible and infrared imaging spectrometer (AVIRIS) data, gravity data, and the digital elevation models (DEM) data. -from Author
Infrared Target Acquisition Using An Adaptive Difference-Of-Boxes Filter
NASA Astrophysics Data System (ADS)
Guissin, Rami
1990-01-01
A variety of spatial filters have been previously proposed as detection filters for automated target acquisition. One class of filters, namely the matched filter, is designed for maximimum signal to noise response at true target locations. The filter design is a function of target dimensions and intensity distributions, and of the corresponding background spectrum. The filter sensitivity to target dimensions may be overcome by adapting the filter's dimensions to the incoming image signal, or by the economical use of (at least) two filters, designed separately for small and large targets. The robustness of the Difference-of-Boxes (DOB) filter is established for a class of targets having smooth, 2nd order intensity distributions, in the presence of both white noise and cluttered backgrounds.
Adaptive filters for detection of gravitational waves from coalescing binaries
Eleuteri, Antonio; Milano, Leopoldo; De Rosa, Rosario; Garufi, Fabio; Acernese, Fausto; Barone, Fabrizio; Giordano, Lara; Pardi, Silvio
2006-06-15
In this work we propose use of infinite impulse response adaptive line enhancer (IIR ALE) filters for detection of gravitational waves from coalescing binaries. We extend our previous work and define an adaptive matched filter structure. Filter performance is analyzed in terms of the tracking capability and determination of filter parameters. Furthermore, following the Neyman-Pearson strategy, receiver operating characteristics are derived, with closedform expressions for detection threshold, false alarm, and detection probability. Extensive tests demonstrate the effectiveness of adaptive filters both in terms of small computational cost and robustness.
Multimodal Medical Image Fusion by Adaptive Manifold Filter.
Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images. PMID:26664494
Adaptive filtering of Echelle spectra of distant Quasars
NASA Technical Reports Server (NTRS)
Priebe, A.; Liebscher, D.-E.; Lorenz, H.; Richter, G.-M.
1992-01-01
The study of the Ly alpha - forest of distant (approximately greater than 3) Quasars is an important tool in obtaining a more detailed picture of the distribution of matter along the line of sight and thus of the general distribution of matter in the Universe and is therefore of important cosmological significance. Obviously, this is one of the tasks where spectral resolution plays an important role. The spectra used were obtained with the EFOSC at the ESO 3.6m telescope. Applying for the data reduction the standard Echelle procedure, as it is implemented for instance in the MIDAS-package, one uses stationary filters (e.g. median) for noise and cosmic particle event reduction in the 2-dimensional Echelle image. These filters are useful if the spatial spectrum of the noise reaches essentially higher frequencies then the highest resolution features in the image. Otherwise the resolution in the data will be degraded and the spectral lines smoothed. However, in the Echelle spectra the highest resolution is already in the range of one or a few pixels and therefore stationary filtering means always a loss of resolution. An Echelle reduction procedure on the basis of a space variable filter described which recognizes the local resolution in the presence of noise and adapts to it is developed. It was shown that this technique leads to an improvement in resolution by a factor of 2 with respect to standard procedures.
Statistical-uncertainty-based adaptive filtering of lidar signals
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-02-10
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H{sub 2}O and the N{sub 2} photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America.
Statistical-uncertainty-based adaptive filtering of lidar signals.
Fuehrer, P L; Friehe, C A; Hristov, T S; Cooper, D I; Eichinger, W E
2000-02-10
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H(2)O and the N(2) photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds (δi) and adaptive disturbance attenuation (γ), which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter. PMID:25244587
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-09-19
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter. PMID:25244587
Improved Spatial Filter for high power Lasers
Estabrook, Kent G.; Celliers, Peter M.; Murray, James E.; DaSilva, Luiz; MacGowan, Brian J.; Rubenchik, Alexander M.; Manes, Kenneth R.; Drake, Robert P.; Afeyan, Bedros
1998-06-01
A new pinhole architecture incorporates features intended to reduce the rate of plasma generation in a spatial filter for high-energy laser pulse beams. An elongated pinhole aperture is provided in an apertured body for rejecting off-axis rays of the laser pulse beam. The internal surface of the elongated aperture has a diameter which progressively tapers from a larger entrance cross-sectional area at an inlet to a smaller output cross-sectional area at an outlet. The tapered internal surface causes off-axis rays to be refracted in a low density plasma layer that forms on the internal surface or specularly reflected at grazing incidence from the internal surface. Off-axis rays of the high-energy pulse beam are rejected by this design. The external surface of the apertured body adjacent to the larger entrance cross-sectional area at the inlet to the elongated aperture is angled obliquely with respect to the to direction of the path of the high-energy laser pulse beam to backscatter off-axis rays away from the high-energy pulse beam. The aperture is formed as a truncated cone or alternatively with a tapered square cross-section. The internal surface of the aperture is coated with an ablative material, preferably high-density material which can be deposited with an exploding wire.
Spatial adaptation on video display terminals
Greenhouse, D.S.; Bailey, I.L.; Howarth, P.A.; Berman, S.M.
1989-01-01
Spatial adaptation, in the form of a frequency-specific reduction in contrast sensitivity, can occur when the visual system is exposed to certain stimuli. We employed vertical sinusoidal test gratings to investigate adaptation to the horizontal structure of text presented on a standard video display terminal. The parameters of the contrast sensitivity test were selected on the basis of waveform analysis of spatial luminance scans of the text stimulus. We found that subjects exhibited a small, but significant, frequency-specific adaptation consistent with the spatial frequency spectrum of the stimulus. Theoretical and practical significance of this finding are discussed. 6 refs., 4 figs.
Diagnostic analysis of vibration signals using adaptive digital filtering techniques
NASA Technical Reports Server (NTRS)
Jewell, R. E.; Jones, J. H.; Paul, J. E.
1983-01-01
Signal enhancement techniques are described using recently developed digital adaptive filtering equipment. Adaptive filtering concepts are not new; however, as a result of recent advances in microprocessor-based electronics, hardware has been developed that has stable characteristics and of a size exceeding 1000th order. Selected data processing examples are presented illustrating spectral line enhancement, adaptive noise cancellation, and transfer function estimation in the presence of corrupting noise.
Novel spatially distributed porous Si optical bandpass filters
NASA Astrophysics Data System (ADS)
Tokranova, N.; Levitsky, I.; Gracias, A.; Xu, B.; Castracane, J.
2006-02-01
To assist the growth of the telecommunication sector, new types of optical components such as those based on optical interference filter technology are critical. Existing technologies based on thin-film processing for production of optical communications filters have rapidly advanced. Although the Fabry-Perot bandpass filters made by deposition of alternate layers with high- and low- refractive index have a broad rejection band and a narrow passband, this technique does not allow for the control of filter parameters such as specification and adjustment of the transmitted wavelength at any place across the surface of the filter. The new approach discussed in the paper is directed toward the anodization of silicon to fabricate not only multilayer optical filters with a uniform passband across the field of view but also specially designed passbands at any single point in the field of view of the optical system. In particular, the realization and characterization of spatially distributed filters made of porous silicon are presented. These filters are able to select various passbands in the visible and IR regions. The filters were fabricated on p + and p - type doped substrates. By varying the electrode configuration on the backside of wafer and the applied potential during electrochemical etching, the desired spatially distributed filter can be formed. The impact of wafer resistivity on filter parameters is discussed.
A model for radar images and its application to adaptive digital filtering of multiplicative noise.
Frost, V S; Stiles, J A; Shanmugan, K S; Holtzman, J C
1982-02-01
Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.
Filter. Remix. Make.: Cultivating Adaptability through Multimodality
ERIC Educational Resources Information Center
Dusenberry, Lisa; Hutter, Liz; Robinson, Joy
2015-01-01
This article establishes traits of adaptable communicators in the 21st century, explains why adaptability should be a goal of technical communication educators, and shows how multimodal pedagogy supports adaptability. Three examples of scalable, multimodal assignments (infographics, research interviews, and software demonstrations) that evidence…
Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.
ERIC Educational Resources Information Center
Butler, Ronald W.
The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…
Adaptive median filtering for preprocessing of time series measurements
NASA Technical Reports Server (NTRS)
Paunonen, Matti
1993-01-01
A median (L1-norm) filtering program using polynomials was developed. This program was used in automatic recycling data screening. Additionally, a special adaptive program to work with asymmetric distributions was developed. Examples of adaptive median filtering of satellite laser range observations and TV satellite time measurements are given. The program proved to be versatile and time saving in data screening of time series measurements.
Adaptive Control of Flexible Structures Using Residual Mode Filters
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Frost, Susan
2010-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter. We have proposed a modified adaptive controller with a residual mode filter. The RMF is used to accommodate troublesome modes in the system that might otherwise inhibit the adaptive controller, in particular the ASPR condition. This new theory accounts for leakage of the disturbance term into the Q modes. A simple three-mode example shows that the RMF can restore stability to an otherwise unstable adaptively controlled system. This is done without modifying the adaptive controller design.
Eigenvector spatial filtering for image analysis: An efficient algorithm
NASA Astrophysics Data System (ADS)
Rura, Melissa J.
Eigenvector Spatial Filtering (ESF) is an established method in social science literature for incorporating spatial information in model specifications. ESF computes spatial eigenvectors, which are defined by the spatial structure associated with a variable. One important limitation of this technique is that it becomes computationally intensive in image analysis because of the massive number of image pixels. This research develops an algorithm, which makes ESF more efficient, by using the analytical solution for the eigenvalues and spatial eigenvectors, which are essentially a series of orthogonal, uncorrelated map patterns that describe positively spatial autocorrelated patterns through negatively spatially autocorrelated patterns, and global, regional, and local patterns of spatial dependencies in a surface. A reformulation of the analytical solution reduces the required computations and allows the eigenvectors to be computed sequentially. Finally, a series of sampling methods are explored. This algorithm is applied to three example multispectral images of different sizes: small (i.e., ˜200,000 pixels), medium (i.e., ˜1,000,000 pixels) and large (i.e., ˜110,000,000 pixels) and is evaluated in terms of output for each sampling technique and the complete spectral information. The output spatial filters of these sampling techniques compare to the filter generated with the complete spectral information. In terms of efficiency evaluation, the time is required to construct filters through sampling versus through analysis of the complete image surface is evaluated and the complexity of set-up and execution of the sampled and distributed algorithms are assessed.
A hybrid method for optimization of the adaptive Goldstein filter
NASA Astrophysics Data System (ADS)
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J
2014-05-01
In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation
NASA Technical Reports Server (NTRS)
Galante, Joseph M.; Sanner, Robert M.
2012-01-01
Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.
Analysis on Influence Factors of Adaptive Filter Acting on ANC
NASA Astrophysics Data System (ADS)
Zhang, Xiuqun; Zou, Liang; Ni, Guangkui; Wang, Xiaojun; Han, Tao; Zhao, Quanfu
The noise problem has become more and more serious in recent years. The adaptive filter theory which is applied in ANC [1] (active noise control) has also attracted more and more attention. In this article, the basic principle and algorithm of adaptive theory are both researched. And then the influence factor that affects its covergence rate and noise reduction is also simulated.
Spatial and temporal filtering technique for processing lidar photocount data.
Gardner, C S; Shelton, J D
1981-04-01
Shot noise places a practical limit on the spatial and temporal resolution of lidar photocount data. A 2-D signal-processing technique that utilizes spatial and temporal filtering to reduce shot noise and increase resolution is described. The technique is applied to sodium lidar data collected during the fall of 1979 over Urbana, Illinois. Temporal filtering is shown to enhance the spatial resolution of the sodium profiles significantly by reducing shot noise by more than 10 dB. The signal-processing technique is applicable to a wide variety of lidar data.
Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering.
Nan, Zhixiong; Wei, Ping; Xu, Linhai; Zheng, Nanning
2016-01-01
Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters-the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)-are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle. PMID:27529248
An adaptive filter bank for motor imagery based Brain Computer Interface.
Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A
2008-01-01
Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
NASA Technical Reports Server (NTRS)
Frost, Susan A.; Balas, Mark J.
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.
Sidelobe reduction via adaptive FIR filtering in SAR imagery.
Degraaf, S R
1994-01-01
The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.
Time-Domain Filtering for Spatial Large-Eddy Simulation
NASA Technical Reports Server (NTRS)
Pruett, C. David
1997-01-01
An approach to large-eddy simulation (LES) is developed whose subgrid-scale model incorporates filtering in the time domain, in contrast to conventional approaches, which exploit spatial filtering. The method is demonstrated in the simulation of a heated, compressible, axisymmetric jet, and results are compared with those obtained from fully resolved direct numerical simulation. The present approach was, in fact, motivated by the jet-flow problem and the desire to manipulate the flow by localized (point) sources for the purposes of noise suppression. Time-domain filtering appears to be more consistent with the modeling of point sources; moreover, time-domain filtering may resolve some fundamental inconsistencies associated with conventional space-filtered LES approaches.
Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering
Nan, Zhixiong; Wei, Ping; Xu, Linhai; Zheng, Nanning
2016-01-01
Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle. PMID:27529248
Adaptive filtering for ECG rejection from surface EMG recordings.
Marque, C; Bisch, C; Dantas, R; Elayoubi, S; Brosse, V; Pérot, C
2005-06-01
Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.
Adaptive texture filtering for defect inspection in ultrasound images
NASA Astrophysics Data System (ADS)
Zmola, Carl; Segal, Andrew C.; Lovewell, Brian; Nash, Charles
1993-05-01
The use of ultrasonic imaging to analyze defects and characterize materials is critical in the development of non-destructive testing and non-destructive evaluation (NDT/NDE) tools for manufacturing. To develop better quality control and reliability in the manufacturing environment advanced image processing techniques are useful. For example, through the use of texture filtering on ultrasound images, we have been able to filter characteristic textures from highly-textured C-scan images of materials. The materials have highly regular characteristic textures which are of the same resolution and dynamic range as other important features within the image. By applying texture filters and adaptively modifying their filter response, we have examined a family of filters for removing these textures.
Robust Wiener filtering for Adaptive Optics
Poyneer, L A
2004-06-17
In many applications of optical systems, the observed field in the pupil plane has a non-uniform phase component. This deviation of the phase of the field from uniform is called a phase aberration. In imaging systems this aberration will degrade the quality of the images. In the case of a large astronomical telescope, random fluctuations in the atmosphere lead to significant distortion. These time-varying distortions can be corrected using an Adaptive Optics (AO) system, which is a real-time control system composed of optical, mechanical and computational parts. Adaptive optics is also applicable to problems in vision science, laser propagation and communication. For a high-level overview, consult this web site. For an in-depth treatment of the astronomical case, consult these books.
ADAPTIVE LAPLACIAN FILTERING FOR SENSORIMOTOR RHYTHM-BASED BRAIN-COMPUTER INTERFACES
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-01-01
Objective Sensorimotor rhythms (SMRs) are 8–30 Hz oscillations in the EEG recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two, or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-ratio of EEG. Here we test the hypothesis that a new filter design, called an “adaptive Laplacian (ALAP) filter,” can provide better performance for SMR-based BCIs. Approach An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights, and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing leave-one-out cross-validation error through a gradient descent method, and is computationally feasible. Main results Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filter. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy as well as in mean squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs. PMID:23220879
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented. PMID:19923047
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.
High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property
2016-01-01
Cortical dipole imaging has been developed to visualize brain electrical activity in high spatial resolution. It is necessary to solve an inverse problem to estimate the cortical dipole distribution from the scalp potentials. In the present study, the accuracy of cortical dipole imaging was improved by focusing on filtering property of the spatial inverse filter. We proposed an inverse filter that optimizes filtering property using a sigmoid function. The ability of the proposed method was compared with the traditional inverse techniques, such as Tikhonov regularization, truncated singular value decomposition (TSVD), and truncated total least squares (TTLS), in a computer simulation. The proposed method was applied to human experimental data of visual evoked potentials. As a result, the estimation accuracy was improved and the localized dipole distribution was obtained with less noise. PMID:27688747
High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property
2016-01-01
Cortical dipole imaging has been developed to visualize brain electrical activity in high spatial resolution. It is necessary to solve an inverse problem to estimate the cortical dipole distribution from the scalp potentials. In the present study, the accuracy of cortical dipole imaging was improved by focusing on filtering property of the spatial inverse filter. We proposed an inverse filter that optimizes filtering property using a sigmoid function. The ability of the proposed method was compared with the traditional inverse techniques, such as Tikhonov regularization, truncated singular value decomposition (TSVD), and truncated total least squares (TTLS), in a computer simulation. The proposed method was applied to human experimental data of visual evoked potentials. As a result, the estimation accuracy was improved and the localized dipole distribution was obtained with less noise.
A New Method to Cancel RFI---The Adaptive Filter
NASA Astrophysics Data System (ADS)
Bradley, R.; Barnbaum, C.
1996-12-01
An increasing amount of precious radio frequency spectrum in the VHF, UHF, and microwave bands is being utilized each year to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Some radio spectral lines of astronomical interest occur outside the protected radio astronomy bands and are unobservable due to heavy interference. Conventional approaches to deal with RFI include legislation, notch filters, RF shielding, and post-processing techniques. Although these techniques are somewhat successful, each suffers from insufficient interference cancellation. One concept of interference excision that has not been used before in radio astronomy is adaptive interference cancellation. The concept of adaptive interference canceling was first introduced in the mid-1970s as a way to reduce unwanted noise in low frequency (audio) systems. Examples of such systems include the canceling of maternal ECG in fetal electrocardiography and the reduction of engine noise in the passenger compartment of automobiles. Only recently have high-speed digital filter chips made adaptive filtering possible in a bandwidth as large a few megahertz, finally opening the door to astronomical uses. The system consists of two receivers: the main beam of the radio telescope receives the desired signal corrupted by RFI coming in the sidelobes, and the reference antenna receives only the RFI. The reference antenna is processed using a digital adaptive filter and then subtracted from the signal in the main beam, thus producing the system output. The weights of the digital filter are adjusted by way of an algorithm that minimizes, in a least-squares sense, the power output of the system. Through an adaptive-iterative process, the interference canceler will lock onto the RFI and the filter will adjust itself to minimize the effect of the RFI at the system output. We are building a prototype 100 MHz receiver and will measure the cancellation
Spatial adaptive sampling in multiscale simulation
NASA Astrophysics Data System (ADS)
Rouet-Leduc, Bertrand; Barros, Kipton; Cieren, Emmanuel; Elango, Venmugil; Junghans, Christoph; Lookman, Turab; Mohd-Yusof, Jamaludin; Pavel, Robert S.; Rivera, Axel Y.; Roehm, Dominic; McPherson, Allen L.; Germann, Timothy C.
2014-07-01
In a common approach to multiscale simulation, an incomplete set of macroscale equations must be supplemented with constitutive data provided by fine-scale simulation. Collecting statistics from these fine-scale simulations is typically the overwhelming computational cost. We reduce this cost by interpolating the results of fine-scale simulation over the spatial domain of the macro-solver. Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation. Our approach is local in space and time, avoids the need for a central database, and is designed to parallelize well on large computer clusters. To demonstrate our method, we simulate one-dimensional elastodynamic shock propagation using the Heterogeneous Multiscale Method (HMM); we find that spatial adaptive sampling requires only ≈50×N0.14 fine-scale simulations to reconstruct the stress field at all N grid points. Related multiscale approaches, such as Equation Free methods, may also benefit from spatial adaptive sampling.
Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca
2011-11-15
Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold
Measurement of Spatial Filtering Capabilities of Single Mode Infrared Fibers
NASA Technical Reports Server (NTRS)
Ksendzov, Alexander; Bloemhof, E.; White, V.; Wallace, J. K.; Gappinger, R. O.; Sanghera, J. S.; Busse, L. E.; Kim, W. J.; Pureza, P. C.; Nguyen, V. Q.; Aggarwal, I. D.; Shalem, S.; Katzi, A.
2006-01-01
Spatial filtering is necessary to achieve deep nulls in optical interferometer and single mode infrared fibers can serve as spatial filters. The filtering function is based on the ability of these devices to perform the mode-cleaning function: only the component of the input field that is coupled to the single bound (fundamental) mode of the device propagates to the output without substantial loss. In practical fiber devices, there are leakage channels that cause light not coupled into the fundamental mode to propagate to the output. These include propagation through the fiber cladding and by means of a leaky mode. We propose a technique for measuring the magnitude of this leakage and apply it to infrared fibers made at the Naval Research Laboratory and at Tel Aviv University.
Asymmetric 2D spatial beam filtering by photonic crystals
NASA Astrophysics Data System (ADS)
Gailevicius, D.; Purlys, V.; Maigyte, L.; Gaizauskas, E.; Peckus, M.; Gadonas, R.; Staliunas, K.
2016-04-01
Spatial filtering techniques are important for improving the spatial quality of light beams. Photonic crystals (PhCs) with a selective spatial (angular) transmittance can also provide spatial filtering with the added benefit transversal symmetries, submillimeter dimensions and monolithic integration in other devices, such as micro-lasers or semiconductor lasers. Workable bandgap PhC configurations require a modulated refractive index with period lengths that are approximately less than the wavelength of radiation. This imposes technical limitations, whereby the available direct laser write (DLW) fabrication techniques are limited in resolution and refractive index depth. If, however, a deflection mechanism is chosen instead, a functional filter PhC can be produced that is operational in the visible wavelength regime. For deflection based PhCs glass is an attractive choice as it is highly stable medium. 2D and 3D PhC filter variations have already been produced on soda-lime glass. However, little is known about how to control the scattering of PhCs when approaching the smallest period values. Here we look into the internal structure of the initially symmetric geometry 2D PhCs and associating it with the resulting transmittance spectra. By varying the DLW fabrication beam parameters and scanning algorithms, we show that such PhCs contain layers that are comprised of semi-tilted structure voxels. We show the appearance of asymmetry can be compensated in order to circumvent some negative effects at the cost of potentially maximum scattering efficiency.
The Classification of Chinese Characters by Spatial Filtering Techniques.
ERIC Educational Resources Information Center
Ankeney, Lawrence Arthur
A method is proposed in which nondefined Chinese characters may be uniquely classified thus making them compatible for machine translation. An optical-digital device is used to locate nondefined geometric shapes within Chinese characters via spatial filtering techniques and cyclic cross-correlation. Seventeen nondefined geometric shapes are found…
Spatial arrangement of color filter array for multispectral image acquisition
NASA Astrophysics Data System (ADS)
Shrestha, Raju; Hardeberg, Jon Y.; Khan, Rahat
2011-03-01
In the past few years there has been a significant volume of research work carried out in the field of multispectral image acquisition. The focus of most of these has been to facilitate a type of multispectral image acquisition systems that usually requires multiple subsequent shots (e.g. systems based on filter wheels, liquid crystal tunable filters, or active lighting). Recently, an alternative approach for one-shot multispectral image acquisition has been proposed; based on an extension of the color filter array (CFA) standard to produce more than three channels. We can thus introduce the concept of multispectral color filter array (MCFA). But this field has not been much explored, particularly little focus has been given in developing systems which focuses on the reconstruction of scene spectral reflectance. In this paper, we have explored how the spatial arrangement of multispectral color filter array affects the acquisition accuracy with the construction of MCFAs of different sizes. We have simulated acquisitions of several spectral scenes using different number of filters/channels, and compared the results with those obtained by the conventional regular MCFA arrangement, evaluating the precision of the reconstructed scene spectral reflectance in terms of spectral RMS error, and colorimetric ▵E*ab color differences. It has been found that the precision and the the quality of the reconstructed images are significantly influenced by the spatial arrangement of the MCFA and the effect will be more and more prominent with the increase in the number of channels. We believe that MCFA-based systems can be a viable alternative for affordable acquisition of multispectral color images, in particular for applications where spatial resolution can be traded off for spectral resolution. We have shown that the spatial arrangement of the array is an important design issue.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance. PMID:25321679
Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces
NASA Astrophysics Data System (ADS)
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-02-01
Objective. Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an ‘adaptive Laplacian (ALAP) filter’, can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.
Hardware implementation of a discrete-time analog adaptive filter
Donohoe, G.W.
1981-01-01
This paper describes a hardware implementation of a discrete-time adaptive filter using a bucket-brigade device (BBD) tapped analog delay line, analog voltage multipliers and operational amplifier integrators and summing circuits. Some design considerations for this class of circuits are discussed.
Low-pass spatial filtering of satellite radar data
NASA Technical Reports Server (NTRS)
Mueller, Paul W.; Hoffer, Roger N.
1989-01-01
Thirty-four low-pass spatial filter treatments were applied to a multi-angle SIR-B data set to reduce speckle effects and improve classification performance. These treatments were based on four algorithms: square mean, separable mean, square median, and separable recursive median. The filtered images were evaluated using both quantitative and qualitative techniques. It was determined that the square median algorithm implemented at two iterations with a window size of 3 by 3 produced the best overall results with the 28.5-m SIR-B data.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Extended adaptive filtering for wide-angle SAR image formation
NASA Astrophysics Data System (ADS)
Wang, Yanwei; Roberts, William; Li, Jian
2005-05-01
For two-dimensional (2-D) spectral analysis, the adaptive filtering based technologies, such as CAPON and APES (Amplitude and Phase EStimation), are developed under the implicit assumption that the data sets are rectangular. However, in real SAR applications, especially for the wide-angle cases, the collected data sets are always non-rectangular. This raises the problem of how to extend the original adaptive filtering based algorithms for such kind of scenarios. In this paper, we propose an extended adaptive filtering (EAF) approach, which includes Extended APES (E-APES) and Extended CAPON (E-CAPON), for arbitrarily shaped 2-D data. The EAF algorithms adopt a missing-data approach where the unavailable data samples close to the collected data set are assumed missing. Using a group of filter-banks with varying sizes, these algorithms are non-iterative and do not require the estimation of the unavailable samples. The improved imaging results of the proposed algorithms are demonstrated by applying them to two different SAR data sets.
NASA Astrophysics Data System (ADS)
Chen, Yangkang
2016-07-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only needs to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
NASA Astrophysics Data System (ADS)
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (
NASA Astrophysics Data System (ADS)
Tian, Yuexin; Liu, Yinghui; Gao, Kun; Shu, Yuwen; Ni, Guoqiang
2014-11-01
A temporal-spatial filtering algorithm based on kernel density estimation structure is presented for background suppression in this paper. The algorithm can be divided into spatial filtering and temporal filtering. Smoothing process is applied to the background of an infrared image sequence by using the kernel density estimation algorithm in spatial filtering. The probability density of the image gray values after spatial filtering is calculated with the kernel density estimation algorithm in temporal filtering. The background residual and blind pixels are picked out based on their gray values, and are further filtered. The algorithm is validated with a real infrared image sequence. The image sequence is processed by using Fuller kernel filter, Uniform kernel filter and high-pass filter. Quantitatively analysis shows that the temporal-spatial filtering algorithm based on the nonparametric method is a satisfactory way to suppress background clutter in infrared images. The SNR is significantly improved as well.
Adaptive nonlocal means filtering based on local noise level for CT denoising
Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando; Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H.
2014-01-15
Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the
Spatial filter system as an optical relay line
Hunt, John T.; Renard, Paul A.
1979-01-01
A system consisting of a set of spatial filters that are used to optically relay a laser beam from one position to a downstream position with minimal nonlinear phase distortion and beam intensity variation. The use of the device will result in a reduction of deleterious beam self-focusing and produce a significant increase in neutron yield from the implosion of targets caused by their irradiation with multi-beam glass laser systems.
Adaptive gain and filtering circuit for a sound reproduction system
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor)
1998-01-01
Adaptive compressive gain and level dependent spectral shaping circuitry for a hearing aid include a microphone to produce an input signal and a plurality of channels connected to a common circuit output. Each channel has a preset frequency response. Each channel includes a filter with a preset frequency response to receive the input signal and to produce a filtered signal, a channel amplifier to amplify the filtered signal to produce a channel output signal, a threshold register to establish a channel threshold level, and a gain circuit. The gain circuit increases the gain of the channel amplifier when the channel output signal falls below the channel threshold level and decreases the gain of the channel amplifier when the channel output signal rises above the channel threshold level. A transducer produces sound in response to the signal passed by the common circuit output.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures was studied. Lattice filters are used widely in the areas of speech and signal processing. Herein, they are used to identify the structural dynamics model of the flexible structures. This identified model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures control is engaged. This type of validation scheme prevents instability when the overall loop is closed. The results obtained from simulation were compared to those obtained from experiments. In this regard, the flexible beam and grid apparatus at the Aerospace Control Research Lab (ACRL) of NASA Langley Research Center were used as the principal candidates for carrying out the above tasks. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods.
Spatial filtering velocimeter for vehicle navigation with extended measurement range
NASA Astrophysics Data System (ADS)
He, Xin; Zhou, Jian; Nie, Xiaoming; Long, Xingwu
2015-05-01
The idea of using spatial filtering velocimeter is proposed to provide accurate velocity information for vehicle autonomous navigation system. The presented spatial filtering velocimeter is based on a CMOS linear image sensor. The limited frame rate restricts high speed measurement of the vehicle. To extend measurement range of the velocimeter, a method of frequency shifting is put forward. Theoretical analysis shows that the frequency of output signal can be reduced and the measurement range can be doubled by this method when the shifting direction is set the same with that of image velocity. The approach of fast Fourier transform (FFT) is employed to obtain the power spectra of the spatially filtered signals. Because of limited frequency resolution of FFT, a frequency spectrum correction algorithm, called energy centrobaric correction, is used to improve the frequency resolution. The correction accuracy energy centrobaric correction is analyzed. Experiments are carried out to measure the moving surface of a conveyor belt. The experimental results show that the maximum measurable velocity is about 800deg/s without frequency shifting, 1600deg/s with frequency shifting, when the frame rate of the image is about 8117 Hz. Therefore, the measurement range is doubled by the method of frequency shifting. Furthermore, experiments were carried out to measure the vehicle velocity simultaneously using both the designed SFV and a laser Doppler velocimeter (LDV). The measurement results of the presented SFV are coincident with that of the LDV, but with bigger fluctuation. Therefore, it has the potential of application to vehicular autonomous navigation.
Mid-infrared spatial filter fabrication using laser chemical etching
NASA Astrophysics Data System (ADS)
Drouet D'Aubigny, Christian Y.; Walker, Christopher K.; Golish, Dathon R.
2004-10-01
Feedhorns like those commonly used in radio-telescope and radio communication equipment couple very efficiently (>98%) to the fundamental Gaussian mode (TEM00). High order modes are not propagated through a single-mode hollow metallic waveguides. It follows that a back to back feedhorn design joined with a small length of single-mode waveguide can be used as a very high throughput spatial filter. Laser micro machining provides a mean of scaling successful waveguide and quasi-optical components to far and mid infrared wavelengths. A laser micro machining system optimized for THz and far IR applications has been in operation at Steward Observatory for several years and produced devices designed to operate at λ=60μm. A new laser micromachining system capable of producing mid-infrared devices will soon be operational. These proceedings review metallic hollow waveguide spatial filtering theory, feedhorn designs as well as laser chemical etching and the design of a new high-NA UV laser etcher capable of sub-micron resolution to fabricate spatial filters for use in the mid-infrared.
Infinite impulse response modal filtering in visible adaptive optics
NASA Astrophysics Data System (ADS)
Agapito, G.; Arcidiacono, C.; Quirós-Pacheco, F.; Puglisi, A.; Esposito, S.
2012-07-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Adaptive bilateral filter for sharpness enhancement and noise removal.
Zhang, Buyue; Allebach, Jan P
2008-05-01
In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images. PMID:18390373
Model Adaptation for Prognostics in a Particle Filtering Framework
NASA Technical Reports Server (NTRS)
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Adaptive, template moderated, spatially varying statistical classification.
Warfield, S K; Kaus, M; Jolesz, F A; Kikinis, R
2000-03-01
A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy from medical images. The new algorithm is a form of spatially varying statistical classification, in which an explicit anatomical template is used to moderate the segmentation obtained by statistical classification. The algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which forms an adaptive, template moderated (ATM), spatially varying statistical classification (SVC). Classification methods and nonlinear registration methods are often complementary, both in the tasks where they succeed and in the tasks where they fail. By integrating these approaches the new algorithm avoids many of the disadvantages of each approach alone while exploiting the combination. The ATM SVC algorithm was applied to several segmentation problems, involving different image contrast mechanisms and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of neonates) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumors, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone. PMID:10972320
Speckle reduction in ultrasound images using nonisotropic adaptive filtering.
Eom, Kie B
2011-10-01
In this article, a speckle reduction approach for ultrasound imaging that preserves important features such as edges, corners and point targets is presented. Speckle reduction is an important problem in coherent imaging, such as ultrasound imaging or synthetic aperture radar, and many speckle reduction algorithms have been developed. Speckle is a non-additive and non-white process and the reduction of speckle without blurring sharp features is known to be difficult. The new speckle reduction algorithm presented in this article utilizes a nonhomogeneous filter that adapts to the proximity and direction of the nearest important features. To remove speckle without blurring important features, the location and direction of edges in the image are estimated. Then for each pixel in the image, the distance and angle to the nearest edge are efficiently computed by a two-pass algorithm and stored in distance and angle maps. Finally for each pixel, an adaptive directional filter aligned to the nearest edge is applied. The shape and orientation of the adaptive filter are determined from the distance and angle maps. The new speckle reduction algorithm is tested with both synthesized and real ultrasound images. The performance of the new algorithm is also compared with those of other speckle reduction approaches and it is shown that the new algorithm performs favorably in reducing speckle without blurring important features.
Frequency-shift low-pass filtering and least mean square adaptive filtering for ultrasound imaging
NASA Astrophysics Data System (ADS)
Wang, Shanshan; Li, Chunyu; Ding, Mingyue; Yuchi, Ming
2016-04-01
Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
A New Adaptive Framework for Collaborative Filtering Prediction.
Almosallam, Ibrahim A; Shang, Yi
2008-06-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.
A New Adaptive Framework for Collaborative Filtering Prediction
Almosallam, Ibrahim A.; Shang, Yi
2010-01-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix’s system. PMID:21572924
Switched Band-Pass Filters for Adaptive Transceivers
NASA Technical Reports Server (NTRS)
Wang, Ray
2007-01-01
Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.
First-moment filters for spatial independent cluster processes
NASA Astrophysics Data System (ADS)
Swain, Anthony; Clark, Daniel E.
2010-04-01
A group target is a collection of individual targets which are, for example, part of a convoy of articulated vehicles or a crowd of football supporters and can be represented mathematically as a spatial cluster process. The process of detecting, tracking and identifying group targets requires the estimation of the evolution of such a dynamic spatial cluster process in time based on a sequence of partial observation sets. A suitable generalisation of the Bayes filter for this system would provide us with an optimal (but computationally intractable) estimate of a multi-group multi-object state based on measurements received up to the current time-step. In this paper, we derive the first-moment approximation of the multi-group multi-target Bayes filter, inspired by the first-moment multi-object Bayes filter derived by Mahler. Such approximations are Bayes optimal and provide estimates for the number of clusters (groups) and their positions in the group state-space, as well as estimates for the number of cluster components (object targets) and their positions in target state-space.
Spatial perception and adaptive sonar behavior.
Aytekin, Murat; Mao, Beatrice; Moss, Cynthia F
2010-12-01
Bat echolocation is a dynamic behavior that allows for real-time adaptations in the timing and spectro-temporal design of sonar signals in response to a particular task and environment. To enable detailed, quantitative analyses of adaptive sonar behavior, echolocation call design was investigated in big brown bats, trained to rest on a stationary platform and track a tethered mealworm that approached from a starting distance of about 170 cm in the presence of a stationary sonar distracter. The distracter was presented at different angular offsets and distances from the bat. The results of this study show that the distance and the angular offset of the distracter influence sonar vocalization parameters of the big brown bat, Eptesicus fuscus. Specifically, the bat adjusted its call duration to the closer of two objects, distracter or insect target, and the magnitude of the adjustment depended on the angular offset of the distracter. In contrast, the bat consistently adjusted its call rate to the distance of the insect, even when this target was positioned behind the distracter. The results hold implications for understanding spatial information processing and perception by echolocation.
Adaptive filters and internal models: multilevel description of cerebellar function.
Porrill, John; Dean, Paul; Anderson, Sean R
2013-11-01
Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles.
NASA Astrophysics Data System (ADS)
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Robust myelin water quantification: averaging vs. spatial filtering.
Jones, Craig K; Whittall, Kenneth P; MacKay, Alex L
2003-07-01
The myelin water fraction is calculated, voxel-by-voxel, by fitting decay curves from a multi-echo data acquisition. Curve-fitting algorithms require a high signal-to-noise ratio to separate T(2) components in the T(2) distribution. This work compared the effect of averaging, during acquisition, to data postprocessed with a noise reduction filter. Forty regions, from five volunteers, were analyzed. A consistent decrease in the myelin water fraction variability with no bias in the mean was found for all 40 regions. Images of the myelin water fraction of white matter were more contiguous and had fewer "holes" than images of myelin water fractions from unfiltered echoes. Spatial filtering was effective for decreasing the variability in myelin water fraction calculated from 4-average multi-echo data.
Performance of a simplified slit spatial filter for large laser systems.
Xiong, Han; Yuan, Xiao; Zhang, Xiang; Zou, Kuaisheng
2014-09-01
A new-type slit spatial filter system with three lenses was proposed, in which the focal spot was turned into focal line by adding cylindrical lenses to increase focal area and then lower the focal intensity. Its performances on image relay, aperture matching and spatial filtering are comprehended by detailed theoretical calculations and numerical simulation. According to transmission spatial filter in national ignition facility, we present a replaceable slit spatial filter, which can reduce the overall length of laser system, improve the beam quality and suppress or even avoid the pinhole (slit) closure in the spatial filter. PMID:25321597
Projective filtering of the fundamental eigenmode from spatially multimode radiation
NASA Astrophysics Data System (ADS)
Pérez, A. M.; Sharapova, P. R.; Straupe, S. S.; Miatto, F. M.; Tikhonova, O. V.; Leuchs, G.; Chekhova, M. V.
2015-11-01
Lossless filtering of a single coherent (Schmidt) mode from spatially multimode radiation is a problem crucial for optics in general and for quantum optics in particular. It becomes especially important in the case of nonclassical light that is fragile to optical losses. An example is bright squeezed vacuum generated via high-gain parametric down conversion or four-wave mixing. Its highly multiphoton and multimode structure offers a huge increase in the information capacity provided that each mode can be addressed separately. However, the nonclassical signature of bright squeezed vacuum, photon-number correlations, are highly susceptible to losses. Here we demonstrate lossless filtering of a single spatial Schmidt mode by projecting the spatial spectrum of bright squeezed vacuum on the eigenmode of a single-mode fiber. Moreover, we show that the first Schmidt mode can be captured by simply maximizing the fiber-coupled intensity. Importantly, the projection operation does not affect the targeted mode and leaves it usable for further applications.
Filter bank common spatial patterns in mental workload estimation.
Arvaneh, Mahnaz; Umilta, Alberto; Robertson, Ian H
2015-01-01
EEG-based workload estimation technology provides a real time means of assessing mental workload. Such technology can effectively enhance the performance of the human-machine interaction and the learning process. When designing workload estimation algorithms, a crucial signal processing component is the feature extraction step. Despite several studies on this field, the spatial properties of the EEG signals were mostly neglected. Since EEG inherently has a poor spacial resolution, features extracted individually from each EEG channel may not be sufficiently efficient. This problem becomes more pronounced when we use low-cost but convenient EEG sensors with limited stability which is the case in practical scenarios. To address this issue, in this paper, we introduce a filter bank common spatial patterns algorithm combined with a feature selection method to extract spatio-spectral features discriminating different mental workload levels. To evaluate the proposed algorithm, we carry out a comparative analysis between two representative types of working memory tasks using data recorded from an Emotiv EPOC headset which is a mobile low-cost EEG recording device. The experimental results showed that the proposed spatial filtering algorithm outperformed the state-of-the algorithms in terms of the classification accuracy.
An Adaptive Multipath Mitigation Filter for GNSS Applications
NASA Astrophysics Data System (ADS)
Chang, Chung-Liang; Juang, Jyh-Ching
2008-12-01
Global navigation satellite system (GNSS) is designed to serve both civilian and military applications. However, the GNSS performance suffers from several errors, such as ionosphere delay, troposphere delay, ephemeris error, and receiver noise and multipath. Among these errors, the multipath is one of the most unpredictable error sources in high-accuracy navigation. This paper applies a modified adaptive filter to reduce code and carrier multipath errors in GPS. The filter employs a tap-delay line with an Adaline network to estimate the direction and the delayed-signal parameters. Then, the multipath effect is mitigated by subtracting the estimated multipath effects from the processed correlation function. The hardware complexity of the method is also compared with other existing methods. Simulation results show that the proposed method using field data has a significant reduction in multipath error especially in short-delay multipath scenarios.
Fast Source Camera Identification Using Content Adaptive Guided Image Filter.
Zeng, Hui; Kang, Xiangui
2016-03-01
Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy. PMID:27404627
Bistatic passive radar simulator with spatial filtering subsystem
NASA Astrophysics Data System (ADS)
Hossa, Robert; Szlachetko, Boguslaw; Lewandowski, Andrzej; Górski, Maksymilian
2009-06-01
The purpose of this paper is to briefly introduce the structure and features of the developed virtual passive FM radar implemented in Matlab system of numerical computations and to present many alternative ways of its performance. An idea of the proposed solution is based on analytic representation of transmitted direct signals and reflected echo signals. As a spatial filtering subsystem a beamforming network of ULA and UCA dipole configuration dedicated to bistatic radar concept is considered and computationally efficient procedures are presented in details. Finally, exemplary results of the computer simulations of the elaborated virtual simulator are provided and discussed.
Demultiplexing, orientation selectivity, and spatial filters in color vision
NASA Astrophysics Data System (ADS)
Martinez-Uriegas, Eugenio
1993-09-01
Chromatic-achromatic demultiplexing is the only model that merges three neurophysiological characteristics found only after precortical levels of vision processing in primates: (1) orientation selectivity, (2) interaction of on and off cells, and (3) color decoding. For example, a demultiplexing cortical unit is selective to purely achromatic changes only when they take place at its preferred orientation, but its signal is chromatic-achromatic ambiguous for any other angle; this hypothetical unit is fed with outputs from alternate rows of on and off color- opponent neurons of the lateral geniculate nucleus (LGN). It has a spatial sensitivity profile well described by either difference-of-Gaussian models, Gabor-like models or n-derivative-of- Gaussian models that include orientation tuning. In consequence, current models of spatial filtering and orientation tuning of cortical neurons can be consistently connected with the chromatic-achromatic dimensions through the multiplexing model.
Attitude determination using an adaptive multiple model filtering Scheme
NASA Astrophysics Data System (ADS)
Lam, Quang; Ray, Surendra N.
1995-05-01
Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown
Lu, Jun; Xie, Kan; McFarland, Dennis J
2014-07-01
Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.
Adaptive non-local means filtering based on local noise level for CT denoising
NASA Astrophysics Data System (ADS)
Li, Zhoubo; Yu, Lifeng; Trzasko, Joshua D.; Fletcher, Joel G.; McCollough, Cynthia H.; Manduca, Armando
2012-03-01
Radiation dose from CT scans is an increasing health concern in the practice of radiology. Higher dose scans can produce clearer images with high diagnostic quality, but may increase the potential risk of radiation-induced cancer or other side effects. Lowering radiation dose alone generally produces a noisier image and may degrade diagnostic performance. Recently, CT dose reduction based on non-local means (NLM) filtering for noise reduction has yielded promising results. However, traditional NLM denoising operates under the assumption that image noise is spatially uniform noise, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT data using a global filtering strength cannot achieve optimal denoising performance. In this work, we have developed a technique for efficiently estimating the local noise level for CT images, and have modified the NLM algorithm to adapt to local variations in noise level. The local noise level estimation technique matches the true noise distribution determined from multiple repetitive scans of a phantom object very well. The modified NLM algorithm provides more effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with the clinical workflow.
Extensions to polar formatting with spatially variant post-filtering
NASA Astrophysics Data System (ADS)
Garber, Wendy L.; Hawley, Robert W.
2011-06-01
The polar format algorithm (PFA) is computationally faster than back projection for producing spotlight mode synthetic aperture radar (SAR). This is very important in applications such as video SAR for persistent surveillance, as images may need to be produced in real time. PFA's speed is largely due to making a planar wavefront assumption and forming the image onto a regular grid of pixels lying in a plane. Unfortunately, both assumptions cause loss of focus in airborne persistent surveillance applications. The planar wavefront assumption causes a loss of focus in the scene for pixels that are far from scene center. The planar grid of image pixels causes loss of the depth of focus for conic flight geometries. In this paper, we present a method to compensate for the loss of depth of focus while warping the image onto a terrain map to produce orthorectified imagery. This technique applies a spatially variant post-filter and resampling to correct the defocus while dewarping the image. This work builds on spatially variant post-filtering techniques previously developed at Sandia National Laboratories in that it incorporates corrections for terrain height and circular flight paths. This approach produces high quality SAR images many times faster than back projection.
Hyperspectral Region Classification Using Three-Dimensional Spectral/Spatial Gabor Filters
NASA Astrophysics Data System (ADS)
Bau, Tien Cheng
A three-dimensional (3D) spectral/spatial DFT can be used to represent a hyperspectral image region using a dense sampling in the frequency domain. In many cases, a more compact frequency-domain representation that preserves the three-dimensional structure of the data can be exploited. For this purpose, we have developed a new model for spectral/spatial information based on 3D Gabor filters. These filters capture specific orientation, scale, and wavelength-dependent properties of hyperspectral image data and provide an efficient means of sampling a three-dimensional frequency-domain representation. Since 3D Gabor filters allow for a large number of spectral/spatial features to be used to represent an image region, the performance and efficiency of algorithms that use this representation can be further improved if methods are available to reduce the size of the model. Thus, we have derived methods for selecting features that emphasize the most significant spectral/spatial differences for a set of classes. In addition, the orientation and scale selective properties of the filters allow the development of new algorithms that are invariant to rotation and scale. The new approach can also adapt to changes in the environmental conditions. The analysis of 3D textures under changing environmental conditions is addressed using an invariant recognition algorithm. The new features are compared against pure spectral features and multiband generalizations of gray-level co-occurrence matrix (GLCM) features using both synthesized and real-world data. We have demonstrated that the 3D Gabor features can be used to improve the classification of hyperspectral regions over using only spectral features.
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-05-15
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-01-01
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661
Controller-structure interaction compensation using adaptive residual mode filters
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1990-01-01
It is not feasible to construct controllers for large space structures or large scale systems (LSS's) which are of the same order as the structures. The complexity of the dynamics of these systems is such that full knowledge of its behavior cannot by processed by today's controller design methods. The controller for system performance of such a system is therefore based on a much smaller reduced-order model (ROM). Unfortunately, the interaction between the LSS and the ROM-based controller can produce instabilities in the closed-loop system due to the unmodeled dynamics of the LSS. Residual mode filters (RMF's) allow the systematic removal of these instabilities in a matter which does not require a redesign of the controller. In addition RMF's have a strong theoretical basis. As simple first- or second-order filters, the RMF CSI compensation technique is at once modular, simple and highly effective. RMF compensation requires knowledge of the dynamics of the system modes which resulted in the previous closed-loop instabilities (the residual modes), but this information is sometimes known imperfectly. An adaptive, self-tuning RMF design, which compensates for uncertainty in the frequency of the residual mode, has been simulated using continuous-time and discrete-time models of a flexible robot manipulator. Work has also been completed on the discrete-time experimental implementation on the Martin Marietta flexible robot manipulator experiment. This paper will present the results of that work on adaptive, self-tuning RMF's, and will clearly show the advantage of this adaptive compensation technique for controller-structure interaction (CSI) instabilities in actively-controlled LSS's.
Imaging flow cytometer using computation and spatially coded filter
NASA Astrophysics Data System (ADS)
Han, Yuanyuan; Lo, Yu-Hwa
2016-03-01
Flow cytometry analyzes multiple physical characteristics of a large population of single cells as cells flow in a fluid stream through an excitation light beam. Flow cytometers measure fluorescence and light scattering from which information about the biological and physical properties of individual cells are obtained. Although flow cytometers have massive statistical power due to their single cell resolution and high throughput, they produce no information about cell morphology or spatial resolution offered by microscopy, which is a much wanted feature missing in almost all flow cytometers. In this paper, we invent a method of spatial-temporal transformation to provide flow cytometers with cell imaging capabilities. The method uses mathematical algorithms and a specially designed spatial filter as the only hardware needed to give flow cytometers imaging capabilities. Instead of CCDs or any megapixel cameras found in any imaging systems, we obtain high quality image of fast moving cells in a flow cytometer using photomultiplier tube (PMT) detectors, thus obtaining high throughput in manners fully compatible with existing cytometers. In fact our approach can be applied to retrofit traditional flow cytometers to become imaging flow cytometers at a minimum cost. To prove the concept, we demonstrate cell imaging for cells travelling at a velocity of 0.2 m/s in a microfluidic channel, corresponding to a throughput of approximately 1,000 cells per second.
Spatial grid services for adaptive spatial query optimization
NASA Astrophysics Data System (ADS)
Gao, Bingbo; Xie, Chuanjie; Sheng, Wentao
2008-10-01
Spatial information sharing and integration has now become an important issue of Geographical Information Science (GIS). Web Service technologies provide a easy and standard way to share spatial resources over network, and grid technologies which aim at sharing resources such as data, storage, and computational powers can help the sharing go deeper. However, the dynamic characteristic of grid brings complexity to spatial query optimization which is more stressed in GIS domain because spatial operations are both CPU intensive and data intensive. To address this problem, a new grid framework is employed to provide standard spatial services which can also manage and report their state information to the coordinator which is responsible for distributed spatial query optimization.
Spatial-filter models to describe IC lithographic behavior
NASA Astrophysics Data System (ADS)
Stirniman, John P.; Rieger, Michael L.
1997-07-01
Proximity correction systems require an accurate, fast way to predict how a pattern configuration will transfer to the wafer. In this paper we present an efficient method for modeling the pattern transfer process based on Dennis Gabor's `theory of communication'. This method is based on a `convolution form' where any 2D transfer process can be modeled with a set of linear, 2D spatial filters, even when the transfer process is non-linear. We will show that this form is a general case from which other well-known process simulation models can be derived. Furthermore, we will demonstrate that the convolution form can be used to model observed phenomena, even when the physical mechanisms involved are unknown.
Patterned wafer inspection using spatial filtering for the cluster environment.
Taubenblatt, M A; Batchelder, J S
1992-06-10
Automated-process tool clusters are becoming increasingly prevalent in advanced semiconductor manufacturing plants, necessitating integrated inspection of patterned semiconductor wafers for defects and particulates. Integrated inspection tools must be small, sensitive, inexpensive, and fast in order to be compatible with the cluster environment. We show that intensity spatial filtering, with some refinements, can provide the required sensitivity and speed in a small, inexpensive package. By using dark-field illumination and a nonrectangular azimuthal orientation (e.g., 45 degrees ) to the primarily rectangular pattern, we show that the strongest diffraction from the pattern can be made to bypass the optical system entirely. This technique alleviates stringent scatter and antireflection requirements on the optics, and it permits the use of off-the-shelf components.
Applications Of A Spatial Filtering Detector To Dynamic Interferometry
NASA Astrophysics Data System (ADS)
Yamaguchi, Ichirou
1987-01-01
Interferometry has recently shown great advances in practical applications owing to progress and utility of electrooptic devices and computers. For objects of interferometry it is now strongly desired to measure such dynamic quantities as displacement, vibration, strain, and temperature. In this case rapid movement of interference fringes or speckle patterns has to be detected. However, the conventional image processing techniques using digital computers are not quick enough for this purpose. For reducing computation time it is necessary to endow the detector with a preprocessing function. One of the solutions is a spatial filtering detector with electronic scanning facility which has been used for three dimensional displacement meter [1] and for accerelating a laser speckle strain gauge [2]. This detector, which consists of a photodiode array and its control circuit, delivers a voltage that is proportional to speckle displacement normal to the array. This paper reports applications of this detector to catch the movement of speckles and interference fringes obtained from optical fiber interferometers.
Spatially Distributed Dendritic Resonance Selectively Filters Synaptic Input
Segev, Idan; Shamma, Shihab
2014-01-01
An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs. PMID:25144440
Simple method for adaptive filtering of motion artifacts in E-textile wearable ECG sensors.
Alkhidir, Tamador; Sluzek, Andrzej; Yapici, Murat Kaya
2015-08-01
In this paper, we have developed a simple method for adaptive out-filtering of the motion artifact from the electrocardiogram (ECG) obtained by using conductive textile electrodes. The textile electrodes were placed on the left and the right wrist to measure ECG through lead-1 configuration. The motion artifact was induced by simple hand movements. The reference signal for adaptive filtering was obtained by placing additional electrodes at one hand to capture the motion of the hand. The adaptive filtering was compared to independent component analysis (ICA) algorithm. The signal-to-noise ratio (SNR) for the adaptive filtering approach was higher than independent component analysis in most cases.
Adaptive noise cancellation based on beehive pattern evolutionary digital filter
NASA Astrophysics Data System (ADS)
Zhou, Xiaojun; Shao, Yimin
2014-01-01
Evolutionary digital filtering (EDF) exhibits the advantage of avoiding the local optimum problem by using cloning and mating searching rules in an adaptive noise cancellation system. However, convergence performance is restricted by the large population of individuals and the low level of information communication among them. The special beehive structure enables the individuals on neighbour beehive nodes to communicate with each other and thus enhance the information spread and random search ability of the algorithm. By introducing the beehive pattern evolutionary rules into the original EDF, this paper proposes an improved beehive pattern evolutionary digital filter (BP-EDF) to overcome the defects of the original EDF. In the proposed algorithm, a new evolutionary rule which combines competing cloning, complete cloning and assistance mating methods is constructed to enable the individuals distributed on the beehive to communicate with their neighbours. Simulation results are used to demonstrate the improved performance of the proposed algorithm in terms of convergence speed to the global optimum compared with the original methods. Experimental results also verify the effectiveness of the proposed algorithm in extracting feature signals that are contaminated by significant amounts of noise during the fault diagnosis task.
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting
NASA Astrophysics Data System (ADS)
Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.
2014-12-01
Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
Adaptive data filtering of inertial sensors with variable bandwidth.
Alam, Mushfiqul; Rohac, Jan
2015-02-02
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.
Filter ensemble regularized common spatial pattern for EEG classification
NASA Astrophysics Data System (ADS)
Su, Yuxi; Li, Yali; Wang, Shengjin
2015-07-01
Common Spatial Pattern (CSP) is one of the most effective feature extraction algorithm for Brain-Computer Interfaces (BCI). Despite its advantages of wide versatility and high efficiency, CSP is shown to be non-robust to noise and prone to over fitting when training sample number is limited. In order to overcome these problems, Regularized Common Spatial Pattern (RCSP) is further proposed. RCSP regularized covariance matrix estimation by two parameters, which reduces the estimation difference and improves the stationarity under small sample condition. However, RCSP does not make full use of the frequency information. In this paper, we presents a filter ensemble technique for RCSP (FERCSP) to further extract frequency information and aggregate all the RCSPs efficiently to get an ensemble-based solution. The performance of the proposed algorithm is evaluated on data set IVa of BCI Competition III against other five RCSPbased algorithms. The experimental results show that FERCSP significantly outperforms those of the existing methods in classification accuracy. The FERCSP outperforms the CSP algorithm and R-CSP-A algorithm in all five subjects with an average improvement of 6% in accuracy.
High efficient superresolution combination filter with twin LCD spatial light modulators.
Gundu, Phanindra; Hack, Erwin; Rastogi, Pramod
2005-04-18
A comparative study of pupil filters for transverse superresolution is presented in this article. We propose to combine the advantages of amplitude and phase filters in one complex filter that performs better than either phase or amplitude filters designed so far. The performance here refers to having a smaller spot size along with higher peak to side lobe intensity ratio. Using numerical simulation the limitations of phase and amplitude filters are assessed. The experimental verification of the designed combination filter is performed with two LCD spatial light modulators used for displaying separately the phase and amplitude part of the filter. Results obtained from this setup confirm the simulation.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-02-06
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349
Generation of spatial filters by ICA for detecting motor-related oscillatory EEG.
Kanoh, Shin'ichiro; Miyamoto, Ko-ichiro; Yoshinobu, Tatsuo
2012-01-01
To detect the imagined limb movement from EEG for the use in BCI, the increase (ERS) and decrease (ERD) of the band power of the EEG originated from the sensorimotor cortex are commonly used. A spatial filter using neighboring channels is generally applied to the measured EEG for detecting such brain activity related to the motor imagery. However, the configuration and location of the spatial filter have been selected by the empirical method on trial-and-error basis. In this study, we recorded the EEG during motor imagery of left hand, right hand and feet from five subjects, and the ICA (independent component analysis) was applied to discover the spatial filters for extracting event-related EEG components of the motor imagery. It was suggested that the application of ICA might offer the experimenters appropriate local spatial filters, or at least, the "initial guess" for designing or selecting custom local spatial filters. PMID:23366237
NASA Astrophysics Data System (ADS)
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
An online novel adaptive filter for denoising time series measurements.
Willis, Andrew J
2006-04-01
A nonstationary form of the Wiener filter based on a principal components analysis is described for filtering time series data possibly derived from noisy instrumentation. The theory of the filter is developed, implementation details are presented and two examples are given. The filter operates online, approximating the maximum a posteriori optimal Bayes reconstruction of a signal with arbitrarily distributed and non stationary statistics. PMID:16649562
An Adaptive Fourier Filter for Relaxing Time Stepping Constraints for Explicit Solvers
Gelb, Anne; Archibald, Richard K
2015-01-01
Filtering is necessary to stabilize piecewise smooth solutions. The resulting diffusion stabilizes the method, but may fail to resolve the solution near discontinuities. Moreover, high order filtering still requires cost prohibitive time stepping. This paper introduces an adaptive filter that controls spurious modes of the solution, but is not unnecessarily diffusive. Consequently we are able to stabilize the solution with larger time steps, but also take advantage of the accuracy of a high order filter.
Microscopy with spatial filtering for monitoring subcellular morphology
NASA Astrophysics Data System (ADS)
Zheng, Jing-Yi
Dynamic alteration in organelle morphology is an important indicator of cellular function and many efforts have been made to monitor the subcellular morphology. Optical scatter imaging (OSI), which combines light scattering spectroscopy with microscopic imaging, was developed to non-invasively track real-time changes in particle morphology in situ. Using a variable diameter iris as a Fourier spatial filter, the technique consisted of collecting images that encoded the intensity ratio of wide-to-narrow angle scatter (OSIR, optical scatter imaging ratio) at each pixel in the full field of view. For spherical particles, the OSIR was shown to decrease monotonically with diameter. In living cells, we reported this technique is able to detect mitochondrial morphological alterations, which were mediated by the Bcl- xL transmembrane domain, but could not be observed by fluorescence or DIC images1. However, the initial design was based on Mie theory of scattering by spheres, and hence only adequate for measuring spherical particles. This limits the applicability of OSI to cellular functional studies involving organelles, which are naturally non-spherical. In this project, we aim to enhance the current capability of the existing optical scatter microscope to assess size and shape information for both spherical and non-spherical particles, and eventually apply this technique for monitoring and quantifying subcellular morphology within living cells. To reach this goal, we developed an improved system, in which the variable diameter iris is replaced with a digital micromirror device and adopted the concept of Gabor filtering to extend our assessment of morphology to the characterization of particle shape and orientation. Using bacteria and polystyrene spheres, we show how this system can be used to assess particle aspect ratio even when imaged at low resolution. We also show the feasibility of detecting alterations in organelle aspect ratio in situ within living cells. This
Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array
Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine
2016-01-01
In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme. PMID:27721506
Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array
NASA Astrophysics Data System (ADS)
Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine
2016-10-01
In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme.
Burst noise reduction of image by decimation and adaptive weighted median filter
NASA Astrophysics Data System (ADS)
Nakayama, Fumitaka; Meguro, Mitsuhiko; Hamada, Nozomu
2000-12-01
The removal of noise in image is one of the important issues, and useful as a preprocessing for edge detection, motion estimation and so on. Recently, many studies on the nonlinear digital filter for impulsive noise reduction have been reported. The median filter, the representative of the nonlinear filters, is very effective for removing impulsive noise and preserving sharp edge. In some cases, burst (i.e., successive) impulsive noise is added to image, and this type of noise is difficult to remove by using the median filter. In this paper, we propose an Adaptive Weighted Median (AWM) filter with Decimation (AWM-D filter) for burst noise reduction. This method can also be applied to recover large destructive regions, such as blotch and scratch. The proposed filter is an extension of the Decimated Median (DM) filter, which is useful for reducing successive impulsive noise. The DM filter can split long impulsive noise sequences into short ones, and remove burst noise in spite of the short filter window. Nevertheless, the DM filter also has two disadvantages. One is that the signals without added noise is unnecessary filtered. The other is that the position information in the window is not considered in the weight determinative process, as common in the median type filter. To improve detail-preserving property of the DM filter, we use the noise detection procedure and the AWM-D filter, which can be tuned by Least Mean Absolute (LMA) algorithm. The AWM-D filter preserves details more precisely than the median-type filter, because the AWM-D filter has the weights that can control the filter output. Through some simulations, the higher performance of the proposed filter is shown compared with the simple median, the WM filter, and the DM filter.
Research of fetal ECG extraction using wavelet analysis and adaptive filtering.
Wu, Shuicai; Shen, Yanni; Zhou, Zhuhuang; Lin, Lan; Zeng, Yanjun; Gao, Xiaofeng
2013-10-01
Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.
Non-adaptive robust filters for speckle noise reduction
NASA Astrophysics Data System (ADS)
Frery, Alejandro C.; Santanna, Sidnei J. S.
1993-06-01
After briefly reviewing some classical filters for speckle removal, five filters with characteristics of robustness, suitable for speckle noise reduction, are derived and implemented. These filters are the ones based on the trimmed maximum likelihood and moments estimators, the ones based on the median, on the inter-quartile range, and on the median absolute deviation. Assuming that observations within a synthetic aperture radar image are outcomes of independent Rayleigh random variables, these filters exhibit a good performance from both the signal-to-noise reduction and from the edge preserving criteria. The problem of filtering in an image is posed as an estimation problem.
Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems
NASA Astrophysics Data System (ADS)
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou; Li, Tianrui
2013-01-01
This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter.
A high-power spatial filter for Thomson scattering stray light reduction
NASA Astrophysics Data System (ADS)
Levesque, J. P.; Litzner, K. D.; Mauel, M. E.; Maurer, D. A.; Navratil, G. A.; Pedersen, T. S.
2011-03-01
The Thomson scattering diagnostic on the High Beta Tokamak-Extended Pulse (HBT-EP) is routinely used to measure electron temperature and density during plasma discharges. Avalanche photodiodes in a five-channel interference filter polychromator measure scattered light from a 6 ns, 800 mJ, 1064 nm Nd:YAG laser pulse. A low cost, high-power spatial filter was designed, tested, and added to the laser beamline in order to reduce stray laser light to levels which are acceptable for accurate Rayleigh calibration. A detailed analysis of the spatial filter design and performance is given. The spatial filter can be easily implemented in an existing Thomson scattering system without the need to disturb the vacuum chamber or significantly change the beamline. Although apertures in the spatial filter suffer substantial damage from the focused beam, with proper design they can last long enough to permit absolute calibration.
NASA Technical Reports Server (NTRS)
Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.
2012-01-01
A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.
Jarman, Nicholas; Trengove, Chris; Steur, Erik; Tyukin, Ivan; van Leeuwen, Cees
2014-12-01
A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.
Adaptation of Gabor filters for simulation of human preattentive mechanism for a mobile robot
NASA Astrophysics Data System (ADS)
Kulkarni, Naren; Naghdy, Golshah A.
1993-08-01
Vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanism can be adapted [Nag90]. During the preattentive search the scene is analyzed rapidly but in sufficient detail for the attention to be focused on the `area of interest.' The `area of interest' can further be scrutinized in more detail for recognition purposes. This `area of interest' can be a text message to facilitate navigation. Gabor filters and an automated turning mechanism are used to isolate the `area of interest.' These regions are subsequently processed with optimal spatial resolution for perception tasks. This method has clear advantages over the global operators in that, after an initial search, it scans each region of interest with optimum resolution. This reduces the volume of information for recognition stages and ensures that no region is over or under estimated.
Adaptive filters for monitoring localized brain activity from surface potential time series
Spencer, M.E. . Signal and Image Processing Inst. TRW, Inc., Redondo Beach, CA ); Leahy, R.M. . Signal and Image Processing Inst.); Mosher, J.C. . Signal and Image Processing Inst. Lo
1992-01-01
We address the problem of processing electroencephalographic (EEG) data to monitor the time series of the components of a current dipole source vector at a given location in the head. This is the spatial filtering problem for vector sources in a lossy, three dimensional, zero delay medium. Dipolar and distributed sources at other than the desired location are canceled or attenuated with an adaptive linearly constrained minimum variance (LCMV) beamformer. Actual EEG data acquired from a human subject serves as the interference in a case where the desired source is simulated and superimposed on the actual data. It is shown that the LCMV beamformer extracts the desired dipole time series while effectively canceling the subjects interference.
Adaptive filters for monitoring localized brain activity from surface potential time series
Spencer, M.E. |; Leahy, R.M.; Mosher, J.C. |; Lewis, P.S.
1992-12-01
We address the problem of processing electroencephalographic (EEG) data to monitor the time series of the components of a current dipole source vector at a given location in the head. This is the spatial filtering problem for vector sources in a lossy, three dimensional, zero delay medium. Dipolar and distributed sources at other than the desired location are canceled or attenuated with an adaptive linearly constrained minimum variance (LCMV) beamformer. Actual EEG data acquired from a human subject serves as the interference in a case where the desired source is simulated and superimposed on the actual data. It is shown that the LCMV beamformer extracts the desired dipole time series while effectively canceling the subjects interference.
Short spatial filters with spherical lenses for high-power pulsed lasers
Burdonov, K F; Soloviev, A A; Shaikin, A A; Potemkin, A K; Egorov, A S
2013-11-30
We report possible employment of short spatial filters based on spherical lenses in a pulsed laser source (neodymium glass, 300 J, 1 ns). The influence of the spherical aberration on the quality of output radiation and coefficient of conversion to the second harmonics is studied. The ultra-short aberration spatial filter of length 1.9 m with an aperture of 122 mm is experimentally tested. A considerable shortening of multi-cascade pump lasers for modern petawatt laser systems is demonstrated by the employment of short spatial filters without expensive aspherical optics. (elements of laser systems)
Mode discrimination of unstable resonators with spatial filters and by phase modification.
Southwell, W H
1979-07-01
The effects of an intracavity spatial filter in a half-symmetric unstable bare cavity resonator have been studied using iterative propagation techniques to obtain pure l-mode resonator solutions. The results indicate that the mode-loss difference is highest when the spatial-filter radius is at the first or third dark ring of the Airy pattern at the spatial filter. Furthermore, the results are not directly dependent on the resonator-equivalent Fresnel number. Also presented are results indicating that aspherizing the feedback mirror can be done in such a way as to increase mode discrimination. PMID:19687846
Adaptive filter for mine detection and classification in side-scan sonar imagery
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Antoni, Diana; Fernandez, Manuel F.; Dobeck, Gerald J.
1995-06-01
A need exists to develop robust automatic techniques for discriminating between minelike target and clutter returns in sonar imagery. To address this need, an adaptive clutter suppression linear FIR filtering technique has been developed and applied to side scan sonar imagery data. The adaptive filtering procedure consists of four stages. First, a normalized average target signature (shape) within the filter window is computed using training set data. Second, the background clutter covariance matrix is computed by scanning the filter window over the data. Third, following substitutions of the average target signature and covariance expressions into a set of normal equations, an adaptive filter is computed which simultaneously suppresses the background clutter while preserving the peak of the average target signature. Finally, the data is filtered using the 2D adaptive range-crossrange filter. The overall mine detection processing string includes automatic gain control, data decimation, adaptive clutter filtering (ACF), 2D normalization, thresholding, exceedance clustering, limiting the number of exceedances and secondary thresholding processing blocks. The utility of the ACF processing string was demonstrated with three side scan sonar datasets. The ACF algorithm provided average probability of detection and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.
Marathe, A R.; Taylor, D M
2013-01-01
Objective Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys. Approach Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intracortical microelectrodes had previously been implanted and removed leaving focal cortical damage but no lasting motor deficits. Spatial filters tested included: no filtering, common average referencing (CAR), principle component analysis (PCA), and eight novel modifications of the common spatial pattern (CSP) algorithm. The spatial filtering method and decoder combination that performed the best offline was then used online where monkeys controlled cursor velocity using continuous wrist position decoded from epidural field potentials in real time. Main results Optimized CSP methods improved continuous wrist position decoding accuracy by 69% over CAR and by 80% compared to no filtering. Kalman decoders performed better than linear regression decoders and benefitted from including more spatially-filtered signals but not from pre-smoothing the calculated power spectra. Conversely, linear regression decoders required fewer spatially-filtered signals and were improved by pre-smoothing the power values. The ‘position-to-velocity’ transformation used during online control enabled the animals to generate smooth closed-loop movement trajectories using the somewhat limited position information available in the epidural signals. The monkeys’ online performance significantly improved across days of closed-loop training. Significance Most published BMI studies that use electrocortographic signals to decode continuous limb movements either use no spatial filtering or CAR. This study suggests a
NASA Astrophysics Data System (ADS)
Marathe, A. R.; Taylor, D. M.
2013-06-01
Objective. Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys. Approach. Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intracortical microelectrodes had previously been implanted and removed leaving focal cortical damage but no lasting motor deficits. Spatial filters tested included: no filtering, common average referencing (CAR), principle component analysis, and eight novel modifications of the common spatial pattern (CSP) algorithm. The spatial filtering method and decoder combination that performed the best offline was then used online where monkeys controlled cursor velocity using continuous wrist position decoded from epidural field potentials in real time. Main results. Optimized CSP methods improved continuous wrist position decoding accuracy by 69% over CAR and by 80% compared to no filtering. Kalman decoders performed better than linear regression decoders and benefitted from including more spatially-filtered signals but not from pre-smoothing the calculated power spectra. Conversely, linear regression decoders required fewer spatially-filtered signals and were improved by pre-smoothing the power values. The ‘position-to-velocity’ transformation used during online control enabled the animals to generate smooth closed-loop movement trajectories using the somewhat limited position information available in the epidural signals. The monkeys’ online performance significantly improved across days of closed-loop training. Significance. Most published BMI studies that use electrocorticographic signals to decode continuous limb movements either use no spatial filtering or CAR. This study suggests a
Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing
2016-01-01
The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method.
Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing
2016-01-01
The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method. PMID:26253754
Adaptive multidirectional frequency domain filter for noise removal in wrapped phase patterns.
Liu, Guixiong; Chen, Dongxue; Peng, Yanhua; Zeng, Qilin
2016-08-01
In order to avoid the detrimental effects of excessive noise in the phase fringe patterns of a laser digital interferometer over the accuracy of phase unwrapping and the successful detection of mechanical fatigue defects, an effective method of adaptive multidirectional frequency domain filtering is introduced based on the characteristics of the energy spectrum of localized wrapped phase patterns. Not only can this method automatically set the cutoff frequency, but it can also effectively filter out noise while preserving the image edge information. Compared with the sine and cosine transform filtering and the multidirectional frequency domain filtering, the experimental results demonstrate that the image filtered by our method has the fewest number of residues and is the closest to the noise-free image, compared to the two aforementioned methods, demonstrating the effectiveness of this adaptive multidirectional frequency domain filter. PMID:27505376
An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise
NASA Astrophysics Data System (ADS)
Nair, Madhu S.; Ameera Mol, P. M.
2014-09-01
Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.
Fernández-Corazza, M; von Ellenrieder, N; Muravchik, C H
2015-02-01
We localize dynamic electrical conductivity changes and reconstruct their time evolution introducing the spatial filtering technique to electrical impedance tomography (EIT). More precisely, we use the unit-noise-gain constrained variation of the distortionless-response linearly constrained minimum variance spatial filter. We address the effects of interference and the use of zero gain constraints. The approach is successfully tested in simulated and real tank phantoms. We compute the position error and resolution to compare the localization performance of the proposed method with the one-step Gauss-Newton reconstruction with Laplacian prior. We also study the effects of sensor position errors. Our results show that EIT spatial filtering is useful for localizing conductivity changes of relatively small size and for estimating their time-courses. Some potential dynamic EIT applications such as acute ischemic stroke detection and neuronal activity localization may benefit from the higher resolution of spatial filters as compared to conventional tomographic reconstruction algorithms.
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
NASA Astrophysics Data System (ADS)
Chang, John
Wireless communication has experienced an explosion of growth, especially in the past half- decade, due to the ubiquity of wireless devices, such as tablets, WiFi-enabled devices, and especially smartphones. Proliferation of smartphones with powerful processors and graphic chips have given an increasing amount of people the ability to access anything from anywhere. Unfortunately, this ease of access has greatly increased mobile wireless bandwidth and have begun to stress carrier networks and spectra. Wireless interference cancellation will play a big role alongside the popularity of wire- less communication. In this thesis, we will investigate optical signal processing methods for wireless interference cancellation methods. Optics provide the perfect backdrop for interference cancellation. Mobile wireless data is already aggregated and transported through fiber backhaul networks in practice. By sandwiching the signal processing stage between the receiver and the fiber backhaul, processing can easily be done locally in one location. Further, optics offers the advantages of being instantaneously broadband and size, weight, and power (SWAP). We are primarily concerned with two methods for interference cancellation, based on microwave photonic filters, in this thesis. The first application is for a co-channel situation, in which a transmitter and receiver are co-located and transmitting at the same frequency. A novel analog optical technique extended for multipath interference cancellation of broadband signals is proposed and experimentally demonstrated in this thesis. The proposed architecture was able to achieve a maximum of 40 dB of cancellation over 200 MHz and 50 dB of cancellation over 10 MHz. The broadband nature of the cancellation, along with its depth, demonstrates both the precision of the optical components and the validity of the architecture. Next, we are interested in a scenario with dynamically changing interference, which requires an adaptive photonic
Impulse radar imaging for dispersive concrete using inverse adaptive filtering techniques
Arellano, J.; Hernandez, J.M.; Brase, J.
1993-05-01
This publication addresses applications of a delayed inverse model adaptive filter for modeled data obtained from short-pulse radar reflectometry. To determine the integrity of concrete, a digital adaptive filter was used, which allows compensation of dispersion and clutter generated by the concrete. A standard set of weights produced by an adaptive filter are used on modeled data to obtain the inverse-impulse response of the concrete. The data for this report include: Multiple target, nondispersive data; single-target, variable-size dispersive data; single-target, variable-depth dispersive data; and single-target, variable transmitted-pulse-width dispersive data. Results of this simulation indicate that data generated by the weights of the adaptive filter, coupled with a two-dimensional, synthetic-aperture focusing technique, successfully generate two-dimensional images of targets within the concrete from modeled data.
Adaptive Filtering for Large Space Structures: A Closed-Form Solution
NASA Technical Reports Server (NTRS)
Rauch, H. E.; Schaechter, D. B.
1985-01-01
In a previous paper Schaechter proposes using an extended Kalman filter to estimate adaptively the (slowly varying) frequencies and damping ratios of a large space structure. The time varying gains for estimating the frequencies and damping ratios can be determined in closed form so it is not necessary to integrate the matrix Riccati equations. After certain approximations, the time varying adaptive gain can be written as the product of a constant matrix times a matrix derived from the components of the estimated state vector. This is an important savings of computer resources and allows the adaptive filter to be implemented with approximately the same effort as the nonadaptive filter. The success of this new approach for adaptive filtering was demonstrated using synthetic data from a two mode system.
The effect of spatial luminance distribution on dark adaptation.
Stokkermans, Mariska G M; Vogels, Ingrid M L C; Heynderickx, Ingrid E J
2016-06-01
Recent studies show that dark adaptation in the visual system depends on local luminance levels surrounding the viewing direction. These studies, however, do not explain to what extent veiling luminance is responsible for the outcome. To address the latter, in this study dark adaptation was measured for three different spatial luminance distributions surrounding a target to be detected, while keeping the veiling luminance at the location of the target equivalent. The results show that a background with bright areas close to the viewing direction yields longer adaptation times than a background with bright areas at a larger visual angle. Therefore, we conclude that dark adaptation is affected to a great extent by local luminance, even when controlling for veiling luminance. Based on our results, a simple but adequate model is proposed to predict the adaptation luminance threshold for backgrounds having a nonuniform luminance distribution.
NASA Technical Reports Server (NTRS)
Lindner, Douglas K.; Reichard, Karl M.
1992-01-01
Distributed-effect sensors, which respond to spatially distributed inputs over a significant gauge length, encompass piezoelectric laminate films, modal-domain optical fiber sensors, and holographic sensors; they can be fabricated with spatially varying sensitivity to a distributed measurand for spatial filtering. Such spatial filters are configurable to extract various structural parameters from distributed measurements that cannot be directly measured by sensors. A modeling is presently conducted for distributed-effect sensors' integration into state-space structural models, noting the effects of fabrication errors on sensor operation.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y; Fernández, Eduardo
2010-01-01
In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579
Adaptive Kalman-Bucy filter for differential absorption lidar time series data.
Warren, R E
1987-11-15
An extension of the Kalman-Bucy algorithm for on-line estimation of multimaterial path-integrated concentration from multiwavelength differential absorption lidar time series data is presented in which the system model covariance is adaptively estimated from the input data. Performance of the filter is compared with that of a nonadaptive Kalman-Bucy filter using synthetic and actual lidar data.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y.; Fernández, Eduardo
2010-01-01
In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579
Adaptive box filters for removal of random noise from digital images
Eliason, E.M.; McEwen, A.S.
1990-01-01
We have developed adaptive box-filtering algorithms to (1) remove random bit errors (pixel values with no relation to the image scene) and (2) smooth noisy data (pixels related to the image scene but with an additive or multiplicative component of noise). For both procedures, we use the standard deviation (??) of those pixels within a local box surrounding each pixel, hence they are adaptive filters. This technique effectively reduces speckle in radar images without eliminating fine details. -from Authors
Adaptive alpha-trimmed mean filters under deviations from assumed noise model.
Oten, Remzi; de Figueiredo, Rui J P
2004-05-01
Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non-Gaussian noise. They are especially preferred if the underlying noise deviates from Gaussian with the impulsive noise components. The key design issue of these filters is to select its only parameter, alpha, optimally for a given noise type. In image restoration, adaptive filters utilize the flexibility of selecting alpha according to some local noise statistics. In the present paper, we first review the existing adaptive alpha-trimmed mean filter schemes. We then analyze the performance of these filters when the underlying noise distribution deviates from the Gaussian and does not satisfy the assumptions such as symmetry. Specifically, the clipping effect and the mixed noise cases are analyzed. We also present a new adaptive alpha-trimmed filter implementation that detects the nonsymmetry points locally and applies alpha-trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision. Comparisons of the speed and filtering performances under deviations from symmetry and Gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes. PMID:15376595
Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter
NASA Astrophysics Data System (ADS)
Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio
2012-01-01
Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.
NASA Technical Reports Server (NTRS)
Rajan, P. K.; Khan, Ajmal
1993-01-01
Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter.
Efficient array beam forming by spatial filtering for ultrasound B-mode imaging
Kim, Kang-Sik; Liu, Jie; Insana, Michael F.
2009-01-01
This paper proposes an efficient array beam-forming method using spatial matched filtering (SMF) for ultrasonic imaging. In the proposed method, ultrasonic waves are transmitted from an array subaperture with fixed transmit focus as in conventional array imaging. At receive, radio frequency echo signals from each receive channel are passed through a spatial matched filter that is constructed based on the system transmit-receive spatial impulse response. The filtered echo signals are then summed without time delays. The filter concentrates and spatially registers the echo energy from each element so that the pulse-echo impulse response of the summed output is focused with acceptably low side lobes. Analytical beam pattern analysis and simulation results using a linear array show that this spatial filtering method can improve lateral resolution and contrast-to-noise ratio as compared with conventional dynamic receive focusing (DRF) methods. Experimental results with a linear array are consistent but point out the need to address additional practical issues. Spatial filtering is equivalent to synthetic aperture methods that dynamically focus on both transmit and receive throughout the field of view. In one common example of phase aberrations, the SMF method was degraded to a degree comparable to conventional DRF methods. PMID:16938973
Stabilizing the thermal lattice Boltzmann method by spatial filtering
NASA Astrophysics Data System (ADS)
Gillissen, J. J. J.
2016-10-01
We propose to stabilize the thermal lattice Boltzmann method by filtering the second- and third-order moments of the collision operator. By means of the Chapman-Enskog expansion, we show that the additional numerical diffusivity diminishes in the low-wavnumber limit. To demonstrate the enhanced stability, we consider a three-dimensional thermal lattice Boltzmann system involving 33 discrete velocities. Filtering extends the linear stability of this thermal lattice Boltzmann method to 10-fold smaller transport coefficients. We further demonstrate that the filtering does not compromise the accuracy of the hydrodynamics by comparing simulation results to reference solutions for a number of standardized test cases, including natural convection in two dimensions.
Male superiority in spatial navigation: adaptation or side effect?
Clint, Edward K; Sober, Elliott; Garland, Theodore; Rhodes, Justin S
2012-12-01
In the past few decades, sex differences in spatial cognition have often been attributed to adaptation in response to natural selection. A common explanation is that home range size differences between the sexes created different cognitive demands pertinent to wayfinding in each sex and resulted in the evolution of sex differences in spatial navigational ability in both humans and nonhuman mammals. However, the assumption of adaptation as the appropriate mode of explanation was nearly simultaneous with the discovery and subsequent verification of the male superiority effect, even without any substantive evidence establishing a causal role for adaptation. An alternate possibility that the sex difference in cognition is a genetic or hormonal side effect has not been rigorously tested using the comparative method. The present study directly evaluates how well the range hypothesis fits the available data on species differences in spatial ability by use of a phylogenetically based, cross-species, comparative analysis. We find no support for the hypothesis that species differences in home range size dimorphism are positively associated with parallel differences in spatial navigation abilities. The alternative hypothesis that sex differences in spatial cognition result as a hormonal side effect is better supported by the data.
Eulerian Time-Domain Filtering for Spatial LES
NASA Technical Reports Server (NTRS)
Pruett, C. David
1997-01-01
Eulerian time-domain filtering seems to be appropriate for LES (large eddy simulation) of flows whose large coherent structures convect approximately at a common characteristic velocity; e.g., mixing layers, jets, and wakes. For these flows, we develop an approach to LES based on an explicit second-order digital Butterworth filter, which is applied in,the time domain in an Eulerian context. The approach is validated through a priori and a posteriori analyses of the simulated flow of a heated, subsonic, axisymmetric jet.
Infrared moving point target detection based on spatial-temporal local contrast filter
NASA Astrophysics Data System (ADS)
Deng, Lizhen; Zhu, Hu; Tao, Chao; Wei, Yantao
2016-05-01
Infrared moving point target detection is a challenging task. In this paper, we define a novel spatial local contrast (SLC) and a novel temporal local contrast (TLC) to enhance the target's contrast. Based on the defined spatial local contrast and temporal local contrast, we propose a simple but powerful spatial-temporal local contrast filter (STLCF) to detect moving point target from infrared image sequences. In order to verify the performance of spatial-temporal local contrast filter on detecting moving point target, different detection methods are used to detect the target from several infrared image sequences for comparison. The experimental results show that the proposed spatial-temporal local contrast filter has great superiority in moving point target detection.
NASA Astrophysics Data System (ADS)
Rodríguez-Caballero, E.; Afana, A.; Chamizo, S.; Solé-Benet, A.; Canton, Y.
2016-07-01
Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies.
2014-01-01
Background This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets. Methods In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs. Results The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the
Least-mean-square spatial filter for IR sensors.
Takken, E H; Friedman, D; Milton, A F; Nitzberg, R
1979-12-15
A new least-mean-square filter is defined for signal-detection problems. The technique is proposed for scanning IR surveillance systems operating in poorly characterized but primarily low-frequency clutter interference. Near-optimal detection of point-source targets is predicted both for continuous-time and sampled-data systems.
Adaptive filter for reconstruction of stereo audio signals
NASA Astrophysics Data System (ADS)
Cisowski, Krzysztof
2004-05-01
The paper presents a new approach to reconstruction of impulsively disturbed stereo audio signals. The problems of restoration of large blocks of missing samples are outlined. Present methods of removing of covariance defect are discussed. Model of stereophonic signal is defined and Kalman filter appropriate for this model is introduced. Modifications of the filter directing to the new method of reconstruction of block of missing samples are discussed. Projection based algorithm allows to recover samples of left (or right) stereo channel using additional information included in undistorted samples from the other channel.
Adaptive box filters for removal of random noise from digital images
NASA Technical Reports Server (NTRS)
Eliason, Eric M.; Mcewen, Alfred S.
1990-01-01
Adaptive box-filtering algorithms to remove random bit errors and to smooth noisy data have been developed. For both procedures, the standard deviation of those pixels within a local box surrounding each pixel is used. A series of two or three filters with decreasing box sizes can be run to clean up extremely noisy images and to remove bit errors near sharp edges. The second filter, for noise smoothing, is similar to the 'sigma filter' of Lee (1983). The technique effectively reduces speckle in radar images without eliminating fine details.
Real-time 3D adaptive filtering for portable imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often not able to run with sufficient performance on a portable platform. In recent years, advanced multicore DSPs have been introduced that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms like 3D adaptive filtering, improving the image quality of portable medical imaging devices. In this study, the performance of a 3D adaptive filtering algorithm on a digital signal processor (DSP) is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec.
Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes.
Forester, Brenna R; Jones, Matthew R; Joost, Stéphane; Landguth, Erin L; Lasky, Jesse R
2016-01-01
The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.
Prism adaptation for spatial neglect after stroke: translational practice gaps
Barrett, A. M.; Goedert, Kelly M.; Basso, Julia C.
2012-01-01
Spatial neglect increases hospital morbidity and costs in around 50% of the 795,000 people per year in the USA who survive stroke, and an urgent need exists to reduce the care burden of this condition. However, effective acute treatment for neglect has been elusive. In this article, we review 48 studies of a treatment of intense neuroscience interest: prism adaptation training. Due to its effects on spatial motor ‘aiming’, prism adaptation training may act to reduce neglect-related disability. However, research failed, first, to suggest methods to identify the 50–75% of patients who respond to treatment; second, to measure short-term and long-term outcomes in both mechanism-specific and functionally valid ways; third, to confirm treatment utility during the critical first 8 weeks poststroke; and last, to base treatment protocols on systematic dose–response data. Thus, considerable investment in prism adaptation research has not yet touched the fundamentals needed for clinical implementation. We suggest improved standards and better spatial motor models for further research, so as to clarify when, how and for whom prism adaptation should be applied. PMID:22926312
Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering
NASA Technical Reports Server (NTRS)
Klimesh, M.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices
ERIC Educational Resources Information Center
Hsu, Ching-Kun
2015-01-01
The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…
Study on GPS attitude determination system aided INS using adaptive Kalman filter
NASA Astrophysics Data System (ADS)
Bian, Hongwei; Jin, Zhihua; Tian, Weifeng
2005-10-01
A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
An Adaptive Kalman Filter using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
Radiotherapy Adapted to Spatial and Temporal Variability in Tumor Hypoxia
Sovik, Aste; Malinen, Eirik . E-mail: emalinen@fys.uio.no; Skogmo, Hege K.; Bentzen, Soren M.; Bruland, Oyvind S.; Olsen, Dag Rune
2007-08-01
Purpose: To explore the feasibility and clinical potential of adapting radiotherapy to temporal and spatial variations in tumor oxygenation. Methods and Materials: Repeated dynamic contrast enhanced magnetic resonance (DCEMR) images were taken of a canine sarcoma during the course of fractionated radiation therapy. The tumor contrast enhancement was assumed to represent the oxygen distribution. The IMRT plans were retrospectively adapted to the DCEMR images by employing tumor dose redistribution. Optimized nonuniform tumor dose distributions were calculated and compared with a uniform dose distribution delivering the same integral dose to the tumor. Clinical outcome was estimated from tumor control probability (TCP) and normal tissue complication probability (NTCP) modeling. Results: The biologically adapted treatment was found to give a substantial increase in TCP compared with conventional radiotherapy, even when only pretreatment images were used as basis for the treatment planning. The TCP was further increased by repeated replanning during the course of treatment, and replanning twice a week was found to give near optimal TCP. Random errors in patient positioning were found to give a small decrease in TCP, whereas systematic errors were found to reduce TCP substantially. NTCP for the adapted treatment was similar to or lower than for the conventional treatment, both for parallel and serial normal tissue structures. Conclusion: Biologically adapted radiotherapy is estimated to improve treatment outcome of tumors having spatial and temporal variations in radiosensitivity.
Photonic lantern adaptive spatial mode control in LMA fiber amplifiers.
Montoya, Juan; Aleshire, Chris; Hwang, Christopher; Fontaine, Nicolas K; Velázquez-Benítez, Amado; Martz, Dale H; Fan, T Y; Ripin, Dan
2016-02-22
We demonstrate adaptive-spatial mode control (ASMC) in few-moded double-clad large mode area (LMA) fiber amplifiers by using an all-fiber-based photonic lantern. Three single-mode fiber inputs are used to adaptively inject the appropriate superposition of input modes in a multimode gain fiber to achieve the desired mode at the output. By actively adjusting the relative phase of the single-mode inputs, near-unity coherent combination resulting in a single fundamental mode at the output is achieved.
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
NASA Astrophysics Data System (ADS)
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
Design of a composite filter realizable on practical spatial light modulators
NASA Technical Reports Server (NTRS)
Rajan, P. K.; Ramakrishnan, Ramachandran
1994-01-01
Hybrid optical correlator systems use two spatial light modulators (SLM's), one at the input plane and the other at the filter plane. Currently available SLM's such as the deformable mirror device (DMD) and liquid crystal television (LCTV) SLM's exhibit arbitrarily constrained operating characteristics. The pattern recognition filters designed with the assumption that the SLM's have ideal operating characteristic may not behave as expected when implemented on the DMD or LCTV SLM's. Therefore it is necessary to incorporate the SLM constraints in the design of the filters. In this report, an iterative method is developed for the design of an unconstrained minimum average correlation energy (MACE) filter. Then using this algorithm a new approach for the design of a SLM constrained distortion invariant filter in the presence of input SLM is developed. Two different optimization algorithms are used to maximize the objective function during filter synthesis, one based on the simplex method and the other based on the Hooke and Jeeves method. Also, the simulated annealing based filter design algorithm proposed by Khan and Rajan is refined and improved. The performance of the filter is evaluated in terms of its recognition/discrimination capabilities using computer simulations and the results are compared with a simulated annealing optimization based MACE filter. The filters are designed for different LCTV SLM's operating characteristics and the correlation responses are compared. The distortion tolerance and the false class image discrimination qualities of the filter are comparable to those of the simulated annealing based filter but the new filter design takes about 1/6 of the computer time taken by the simulated annealing filter design.
Stent enhancement in digital x-ray fluoroscopy using an adaptive feature enhancement filter
NASA Astrophysics Data System (ADS)
Jiang, Yuhao; Zachary, Josey
2016-03-01
Fluoroscopic images belong to the classes of low contrast and high noise. Simply lowering radiation dose will render the images unreadable. Feature enhancement filters can reduce patient dose by acquiring images at low dose settings and then digitally restoring them to the original quality. In this study, a stent contrast enhancement filter is developed to selectively improve the contrast of stent contour without dramatically boosting the image noise including quantum noise and clinical background noise. Gabor directional filter banks are implemented to detect the edges and orientations of the stent. A high orientation resolution of 9° is used. To optimize the use of the information obtained from Gabor filters, a computerized Monte Carlo simulation followed by ROC study is used to find the best nonlinear operator. The next stage of filtering process is to extract symmetrical parts in the stent. The global and local symmetry measures are used. The information gathered from previous two filter stages are used to generate a stent contour map. The contour map is then scaled and added back to the original image to get a contrast enhanced stent image. We also apply a spatio-temporal channelized Hotelling observer model and other numerical measures to characterize the response of the filters and contour map to optimize the selections of parameters for image quality. The results are compared to those filtered by an adaptive unsharp masking filter previously developed. It is shown that stent enhancement filter can effectively improve the stent detection and differentiation in the interventional fluoroscopy.
Independent motion detection with a rival penalized adaptive particle filter
NASA Astrophysics Data System (ADS)
Becker, Stefan; Hübner, Wolfgang; Arens, Michael
2014-10-01
Aggregation of pixel based motion detection into regions of interest, which include views of single moving objects in a scene is an essential pre-processing step in many vision systems. Motion events of this type provide significant information about the object type or build the basis for action recognition. Further, motion is an essential saliency measure, which is able to effectively support high level image analysis. When applied to static cameras, background subtraction methods achieve good results. On the other hand, motion aggregation on freely moving cameras is still a widely unsolved problem. The image flow, measured on a freely moving camera is the result from two major motion types. First the ego-motion of the camera and second object motion, that is independent from the camera motion. When capturing a scene with a camera these two motion types are adverse blended together. In this paper, we propose an approach to detect multiple moving objects from a mobile monocular camera system in an outdoor environment. The overall processing pipeline consists of a fast ego-motion compensation algorithm in the preprocessing stage. Real-time performance is achieved by using a sparse optical flow algorithm as an initial processing stage and a densely applied probabilistic filter in the post-processing stage. Thereby, we follow the idea proposed by Jung and Sukhatme. Normalized intensity differences originating from a sequence of ego-motion compensated difference images represent the probability of moving objects. Noise and registration artefacts are filtered out, using a Bayesian formulation. The resulting a posteriori distribution is located on image regions, showing strong amplitudes in the difference image which are in accordance with the motion prediction. In order to effectively estimate the a posteriori distribution, a particle filter is used. In addition to the fast ego-motion compensation, the main contribution of this paper is the design of the probabilistic
Temporal dark adaptation to spatially complex backgrounds: effect of an additional light source.
Stokkermans, M G M; Heynderickx, I E J
2014-07-01
Visual adaptation (and especially dark adaptation) has been studied extensively in the past, however, mainly addressing adaptation to fully dark backgrounds. At this stage, it is unclear whether these results are not too simple to be applied to complex situations, such as predicting adaptation of a motorist driving at night. To fill this gap we set up a study investigating how spatially complex backgrounds influence temporal dark adaptation. Our results showed that dark adaptation to spatially complex backgrounds leads to much longer adaptation times than dark adaptation to spatially uniform backgrounds. We conclude therefore that the adaptation models based on past studies overestimate the visual system's sensitivity to detect luminance variations in spatially complex environments. Our results also showed large variations in adaptation times when varying the degree of spatial complexity of the background. Hence, we may conclude that it is important to take into account models that are based on spatially complex backgrounds when predicting dark adaptation for complex environments.
Interocular transfer of spatial adaptation is weak at low spatial frequencies.
Baker, Daniel H; Meese, Tim S
2012-06-15
Adapting one eye to a high contrast grating reduces sensitivity to similar target gratings shown to the same eye, and also to those shown to the opposite eye. According to the textbook account, interocular transfer (IOT) of adaptation is around 60% of the within-eye effect. However, most previous studies on this were limited to using high spatial frequencies, sustained presentation, and criterion-dependent methods for assessing threshold. Here, we measure IOT across a wide range of spatiotemporal frequencies, using a criterion-free 2AFC method. We find little or no IOT at low spatial frequencies, consistent with other recent observations. At higher spatial frequencies, IOT was present, but weaker than previously reported (around 35%, on average, at 8c/deg). Across all conditions, monocular adaptation raised thresholds by around a factor of 2, and observers showed normal binocular summation, demonstrating that they were not binocularly compromised. These findings prompt a reassessment of our understanding of the binocular architecture implied by interocular adaptation. In particular, the output of monocular channels may be available to perceptual decision making at low spatial frequencies.
Spatial join optimization among WFSs based on recursive partitioning and filtering rate estimation
NASA Astrophysics Data System (ADS)
Lan, Guiwen; Wu, Congcong; Shi, Guangyi; Chen, Qi; Yang, Zhao
2015-12-01
Spatial join among Web Feature Services (WFS) is time-consuming for most of non-candidate spatial objects may be encoded by GML and transferred to client side. In this paper, an optimization strategy is proposed to enhance performance of these joins by filtering non-candidate spatial objects as many as possible. By recursive partitioning, the data skew of sub-areas is facilitated to reduce data transmission using spatial semi-join. Moreover filtering rate is used to determine whether a spatial semi-join for a sub-area is profitable and choose a suitable execution plan for it. The experimental results show that the proposed strategy is feasible under most circumstances.
Holliday, I E; Ruddock, K H
1983-01-01
We have studied visual detection of a circular target moving across a spatially and/or temporally modulated background. Illumination, It, for threshold detection of the target has been measured as a function of background modulation frequency and changes in It associated with background modulation provide a means of determining the frequency response characteristics of visual channels. Temporal frequency responses obtained with temporally modulated, spatially uniform backgrounds have pass-band characteristics and the temporal frequency for peak response increases with increase in mean background illumination. These temporal frequency responses resemble those of the de Lange (1954) filter, but the latter incorporates the incremental thresholds for steady backgrounds. The amplitude of this temporal response saturates at low (approximately 40%) background modulation, decreases to zero as the target velocity falls to zero, and is maximum for a circular target of diameter 2 degrees. The spatial characteristics of this temporal filter were measured with a background field consisting of alternate steady and flickering bars. The resulting spatial frequency curve peaks at 1 cycle deg-1 for all background illuminations and is independent of the background grating orientation. This spatial response differs significantly from the IMG spatial functions observed with a background grating (Barbur and Ruddock, 1980). The spatial and temporal responses reviewed above exhibit similar parametric variations and we therefore associate them with a single spatio-temporal filter, ST2. A second temporal response, with low-pass frequency characteristics, was observed with a background field consisting of two matched gratings, presented in spatial and temporal antiphase. This response has parametric properties similar to those of the IMG spatial response described previously by Barbur and Ruddock (1980), thus we associated the two sets of data with a single spatio-temporal filter, ST1. We
NIF Inert Gas/Vacuum Management Prestart Review Phase 3 - Permit Spatial Filter Vacuum
Williams, J; Beavers, T; Bryan, S; Hermes, G; Patton, H
2001-03-01
A Management Prestart Review (MPR) for the National Ignition Facility (NIF) vacuum testing of spatial filters, the Cavity Spatial Filter (CSF) and the Transport Spatial Filter (TSF), was conducted during March 2001. The review was performed to determine the readiness of the Beamline Infrastucture System (BIS) team and the Integration Management and Installation (IMI) contractor to start the vacuum testing of the components and assemblies that constitute the four CSF clusters and four TSF clusters in the NIF laser. This review assures that appropriate engineering, planning and management is in place to start this testing. Completion and acceptance of this report satisfies the LLNL requirement for MPRs to be conducted whenever a significant new risk is introduced into a project and is an essential part of the ISM work authorization process.
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.
Adaptive filters for suppressing irregular hostile jamming in direct sequence spread-spectrum system
NASA Astrophysics Data System (ADS)
Lee, Jung Hoon; Lee, Choong Woong
A stable and high-performance adaptive filter for suppressing irregular hostile jamming in direct-sequence (DS) spread-spectrum systems is designed. A gradient-search fast converging algorithm (GFC) is suggested. For the case of a sudden parameter jump or incoming of an interference, the transient behaviors of the receiver using a GFC adaptive filter are investigated and compared with those of the receiver using a least-mean-square (LMS) or a lattice adaptive filter. The results are shown in the response graphs of the simulated receiver during the short period when the characteristic of a jammer is suddenly changed. Steady-state performances of those receivers are also evaluated in the sense of the excess mean-square error over that of an optimum receiver for suppressing stationary interferences.
A particle filtering approach for spatial arrival time tracking in ocean acoustics.
Jain, Rashi; Michalopoulou, Zoi-Heleni
2011-06-01
The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers. The comparison demonstrates a significant advantage in using the particle filter. It is also shown that posterior probability density functions of times and amplitudes become readily available with particle filtering. PMID:21682358
A particle filtering approach for spatial arrival time tracking in ocean acoustics.
Jain, Rashi; Michalopoulou, Zoi-Heleni
2011-06-01
The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers. The comparison demonstrates a significant advantage in using the particle filter. It is also shown that posterior probability density functions of times and amplitudes become readily available with particle filtering.
NASA Astrophysics Data System (ADS)
Li, Wei; Haese-Coat, Veronique; Ronsin, Joseph
1996-03-01
An adaptive GA scheme is adopted for the optimal morphological filter design problem. The adaptive crossover and mutation rate which make the GA avoid premature and at the same time assure convergence of the program are successfully used in optimal morphological filter design procedure. In the string coding step, each string (chromosome) is composed of a structuring element coding chain concatenated with a filter sequence coding chain. In decoding step, each string is divided into 3 chains which then are decoded respectively into one structuring element with a size inferior to 5 by 5 and two concatenating morphological filter operators. The fitness function in GA is based on the mean-square-error (MSE) criterion. In string selection step, a stochastic tournament procedure is used to replace the simple roulette wheel program in order to accelerate the convergence. The final convergence of our algorithm is reached by a two step converging strategy. In presented applications of noise removal from texture images, it is found that with the optimized morphological filter sequences, the obtained MSE values are smaller than those using corresponding non-adaptive morphological filters, and the optimized shapes and orientations of structuring elements take approximately the same shapes and orientations as those of the image textons.
Sudeep, P V; Issac Niwas, S; Palanisamy, P; Rajan, Jeny; Xiaojun, Yu; Wang, Xianghong; Luo, Yuemei; Liu, Linbo
2016-04-01
Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. PMID:26907572
An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.
Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W
2015-01-01
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.
An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.
Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W
2015-01-01
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance. PMID:26451817
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.
Beam control and diagnostic functions in the NIF transport spatial filter
Holdener, F.R.; Ables, E.; Bliss, E.S.
1996-10-01
Beam control and diagnostic systems are required to align the National Ignition Facility (NIF) laser prior to a shot as well as to provide diagnostics on 192 beam lines at shot time. A design that allows each beam`s large spatial filter lenses to also serve as objective lenses for beam control and diagnostic sensor packages helps to accomplish the task at a reasonable cost. However, this approach also causes a high concentration of small optics near the pinhole plane of the transport spatial filter (TSF) at the output of each beam. This paper describes the optomechanical design in and near the central vacuum vessel of the TSF.
Stent enhancement using a locally adaptive unsharp masking filter in digital x-ray fluoroscopy
NASA Astrophysics Data System (ADS)
Jiang, Yuhao; Ekanayake, Eranda
2014-03-01
Low exposure X-ray fluoroscopy is used to guide some complicate interventional procedures. Due to the inherent high levels of noise, improving the visibility of some interventional devices such as stent will greatly benefit those interventional procedures. Stent, which is made up of tiny steel wires, is also suffered from contrast dilutions of large flat panel detector pixels. A novel adaptive unsharp masking filter has been developed to improve stent contrast in real-time applications. In unsharp masking processing, the background is estimated and subtracted from the original input image to create a foreground image containing objects of interest. A background estimator is therefore critical in the unsharp masking processing. In this specific study, orientation filter kernels are used as the background estimator. To make the process simple and fast, the kernels average along a line of pixels. A high orientation resolution of 18° is used. A nonlinear operator is then used to combine the information from the images generated from convolving the original background and noise only images with orientation filters. A computerized Monte Carlo simulation followed by ROC study is used to identify the best nonlinear operator. We then apply the unsharp masking filter to the images with stents present. It is shown that the locally adaptive unsharp making filter is an effective filter for improving stent visibility in the interventional fluoroscopy. We also apply a spatio-temporal channelized human observer model to quantitatively optimize and evaluate the filter.
Adaptive identification and control of structural dynamics systems using recursive lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Montgomery, R. C.; Williams, J. P.
1985-01-01
A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.
NASA Astrophysics Data System (ADS)
Öhberg, Fredrik; Lundström, Ronnie; Grip, Helena
2013-08-01
For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ˜2-3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers.
Spatial coherence of the generalized diffraction-filtered resonator copper vapor laser.
Prakash, O; Shukla, P K; Dixit, S K; Chatterjee, S; Vora, H S; Bhatnagar, R
1998-11-20
The results of a study on the spatial coherence of a generalized diffraction-filtered resonator (GDFR) copper vapor laser (CVL) for various magnifications are presented. The coherence width and output power are compared with that of unstable resonators (UR's) of equivalent magnifications. It is established, by use of reversal shear interferometry, that the GDFR CVL beam has better spatial coherence and average power characteristics than the UR CVL beam for equivalent resonator magnifications. PMID:18301614
NASA Astrophysics Data System (ADS)
Abramovich, Iu. I.; Arov, D. Z.; Kachur, V. G.
1987-12-01
The paper considers the problem of finding the vector of an adaptive filter of stationary-noise compensation which corresponds to a Toeplitz correlation-matrix structure. The existence of a Toeplitz solution is demonstrated. Lower-bound estimates are obtained for the gain in noise-compensation efficiency using a priori information about the Toeplitz matrix structure. Constructive methods for obtaining adaptive solutions corresponding to these estimates are indicated.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
Evaluation of spatial filtering on the accuracy of wheat area estimate
NASA Technical Reports Server (NTRS)
Dejesusparada, N. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Delima, A. M.
1982-01-01
A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.
NASA Astrophysics Data System (ADS)
Zhang, Ying; Qi, Hong-Ji; Yi, Kui; Wang, Yan-Zhi; Sui, Zhan; Shao, Jian-Da
2015-10-01
The experiment setup of a reflecting combination device, which has more advantages than a transmitting combination device, is designed in this study. To achieve angular spectrum selectivity, only one type of reflective component is needed, so difficulties of design and preparation are reduced. A dielectric multilayer film is applied to the reflective component, and the long wave-pass coating stacks of the structure are designed. To achieve high stopband transmittance and reduce electric field intensity at a wavelength of 1053 nm, an objective function is proposed for designing an optimized coating. The final optimized coating has good spectral characteristics and a high laser-induced damage threshold. A dielectric multilayer film with high reflectance plays an important role in preparing and applying a dielectric multilayer film reflecting cutoff filter-combination device.
Yan, X.H.; Zheng, Q.; Ho, C.R. . Center for Remote Sensing); Tai, C.K.; Cheney, R.E. )
1994-05-01
A pattern recognition method and a spatial integration filtering method have been developed to analyze satellite altimeter sea surface elevation anomaly (SSEA) data for the tropical Pacific Ocean. The pattern recognition method treats SSEA as an independent variable of the ocean, and SSEA map, its two-dimensional distribution image, as similar to a normal satellite image. The results of the pattern recognition processing of the SSEA maps quantitatively reveal the information of the measurement of SSEA patterns. The spatial integration filtering method is used as low-pass filtering to detect the equatorial Kelvin waves from Geosat SSEA time series data. The wave patterns and the frequency spectra are derived from SSEA data. This article describes these two methods, including an overview of the algorithms used and the results derived. Further applications of the methods are then suggested.
ERIC Educational Resources Information Center
Walker, Jearl
1982-01-01
Spatial filtering, based on diffraction/interference of light waves, is a technique by which unwanted information in a picture ("noise") can be separated from wanted information. A series of experiments is described in which students can create a system that functions as an optical computer to create clearer pictures. (Author/JN)
NASA Technical Reports Server (NTRS)
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
NASA Astrophysics Data System (ADS)
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
Method for measuring the spatial variability of aerosol penetration through respirator filters.
Huang, C; Willeke, K; Qian, Y; Grinshpun, S; Ulevicius, V
1998-07-01
Fibrous filter media are widely used in respirators to remove airborne particulate matter from the inhaled airflow of workers. The N95 half-mask particulate respirator appears to be the most frequently used respirator under the new NIOSH regulation, 42 CFR 84. Considerable spatial variability in light penetration through the fibrous filter medium of an N95 respirator can be seen by visual observation when it is held to the light. This variability is due to the way in which the fibers are manufactured and laid down to form the filter medium. Similar spatial variability is expected in the aerosol penetration through the filters. Therefore, a test method has been developed for measuring the spatial variability in aerosol penetration. The main components of this method are an aerosol generator, a filter test stand with a movable sampling inlet, an aerosol size spectrometer, and an aerosol photometer. Measurements with the filter media of N95 respirators, tested at average filtration velocities corresponding to light, moderate, and heavy work loads, have shown spatial variations in aerosol penetration in excess of 100% relative to the average aerosol penetration for the entire respirator. N95 respirators are required to be at least 95% efficient (i.e., less than 5% penetrating) at the most penetrating particle size, when tested at 85 L/min. Tests with the new method have shown that the aerosol penetration of the most penetrating particles of about 0.1 micron diameter may locally be higher than 5%, while the average aerosol penetration of 0.1 micron particles is less than 5%. PMID:9697293
NASA Astrophysics Data System (ADS)
Hayes, Charles E.; McClellan, James H.; Scott, Waymond R.; Kerr, Andrew J.
2016-05-01
This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.
NASA Astrophysics Data System (ADS)
Liu, Delian; Li, Zhaohui; Wang, Xiaorui; Zhang, Jianqi
2015-11-01
Target detection is of great importance both in civil and military fields. Here a new moving target detection approach is proposed, which employs a nonlinear adaptive filter to remove large fluctuations on temporal profiles that are produced by evolving clutters. Initially, this paper discusses the temporal behaviors of different pixels in infrared sequences. Then, the new nonlinear adaptive filter that is a variation of the median-modified Wiener filter is given to extract pulse signals on temporal profiles that relate to moving targets. Next, the variance of each temporal profile is estimated by segmenting each temporal profile into several segments to normalize the amplitude of the pulse signals. Finally, the proposed approach is tested via two infrared image sequences and compared with several conventional target detection algorithms. The results show our approach has a high effectiveness in extracting target temporal profiles amidst heavy and slowly evolving clutters.
Noise filtering tradeoffs in spatial gradient sensing and cell polarization response
2011-01-01
Background Cells sense chemical spatial gradients and respond by polarizing internal components. This process can be disrupted by gradient noise caused by fluctuations in chemical concentration. Results We investigated how external gradient noise affects spatial sensing and response focusing on noise-filtering and the resultant tradeoffs. First, using a coarse-grained mathematical model of gradient-sensing and cell polarity, we characterized three negative consequences of noise: Inhibition of the extent of polarization, degradation of directional accuracy, and production of a noisy output polarization. Next, we explored filtering strategies and discovered that a combination of positive feedback, multiple signaling stages, and time-averaging produced good results. There was an important tradeoff, however, because filtering resulted in slower polarization. Simulations demonstrated that a two-stage filter-amplifier resulted in a balanced outcome. Then, we analyzed the effect of noise on a mechanistic model of yeast cell polarization in response to gradients of mating pheromone. This analysis showed that yeast cells likely also combine the above three filtering mechanisms into a filter-amplifier structure to achieve impressive spatial-noise tolerance, but with the consequence of a slow response time. Further investigation of the amplifier architecture revealed two positive feedback loops, a fast inner and a slow outer, both of which contributed to noise-tolerant polarization. This model also made specific predictions about how orientation performance depended upon the ratio between the gradient slope (signal) and the noise variance. To test these predictions, we performed microfluidics experiments measuring the ability of yeast cells to orient to shallow gradients of mating pheromone. The results of these experiments agreed well with the modeling predictions, demonstrating that yeast cells can sense gradients shallower than 0.1% μm-1, approximately a single receptor
A unified set-based test with adaptive filtering for gene-environment interaction analyses.
Liu, Qianying; Chen, Lin S; Nicolae, Dan L; Pierce, Brandon L
2016-06-01
In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate P-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
A unified set-based test with adaptive filtering for gene-environment interaction analyses
Liu, Qianying; Chen, Lin S.; Nicolae, Dan L.; Pierce, Brandon L.
2015-01-01
Summary In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate p-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
Zhang, Feihu; Buckl, Christian; Knoll, Alois
2014-01-01
This paper studies the problem of multiple vehicle cooperative localization with spatial registration in the formulation of the probability hypothesis density (PHD) filter. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors (with biases) to cooperatively localize positions, a simultaneous solution for joint spatial registration and state estimation is proposed. For this, we rely on the sequential Monte Carlo implementation of the PHD filtering. Compared to other methods, the concept of multiple vehicle cooperative localization with spatial registration is first proposed under Random Finite Set Theory. In addition, the proposed solution also addresses the challenges for multiple vehicle cooperative localization, e.g., the communication bandwidth issue and data association uncertainty. The simulation result demonstrates its reliability and feasibility in large-scale environments. PMID:24406860
Spatial compression impairs prism adaptation in healthy individuals.
Scriven, Rachel J; Newport, Roger
2013-01-01
Neglect patients typically present with gross inattention to one side of space following damage to the contralateral hemisphere. While prism-adaptation (PA) is effective in ameliorating some neglect behaviors, the mechanisms involved and their relationship to neglect remain unclear. Recent studies have shown that conscious strategic control (SC) processes in PA may be impaired in neglect patients, who are also reported to show extraordinarily long aftereffects compared to healthy participants. Determining the underlying cause of these effects may be the key to understanding therapeutic benefits. Alternative accounts suggest that reduced SC might result from a failure to detect prism-induced reaching errors properly either because (a) the size of the error is underestimated in compressed visual space or (b) pathologically increased error-detection thresholds reduce the requirement for error correction. The purpose of this study was to model these two alternatives in healthy participants and to examine whether SC and subsequent aftereffects were abnormal compared to standard PA. Each participant completed three PA procedures within a MIRAGE mediated reality environment with direction errors recorded before, during and after adaptation. During PA, visual feedback of the reach could be compressed, perturbed by noise, or represented veridically. Compressed visual space significantly reduced SC and aftereffects compared to control and noise conditions. These results support recent observations in neglect patients, suggesting that a distortion of spatial representation may successfully model neglect and explain neglect performance while adapting to prisms. PMID:23675332
Spatial structure enhanced cooperation in dissatisfied adaptive snowdrift game
NASA Astrophysics Data System (ADS)
Zhang, Wen; Xu, Chen; Hui, Pak Ming
2013-05-01
The dissatisfied adaptive snowdrift game (DASG) describes the adaptive actions driven by the level of dissatisfaction when two connected agents interact. We study the DASG in static networks both numerically and analytically. In a random network of uniform degree k, the system evolves into a homogeneous state consisting only of cooperators when the cost-to-benefit ratio r < r 0 and a mixed phase with the coexistence of cooperators and defectors when r > r 0, where r 0 is a threshold. For an infinite population, the large k limit corresponding to the well-mixed case is solved analytically. A theory is developed based on the pair approximation. It gives the frequency of cooperation f c and the densities of different pairs that are in good agreement with simulation results. The results revealed that f c is enhanced in networked populations with a finite k, when compared with the well-mixed case. The reasons that the theory works well for the present model are traced back to the weak spatial correlation implied by the random network and the fact that the adaptive actions in DASG are driven only by the strategy pairs. The results shed light on the class of models that the pair approximation is applicable.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.
Contribution of Cerebellar Sensorimotor Adaptation to Hippocampal Spatial Memory
Passot, Jean-Baptiste; Sheynikhovich, Denis; Duvelle, Éléonore; Arleo, Angelo
2012-01-01
Complementing its primary role in motor control, cerebellar learning has also a bottom-up influence on cognitive functions, where high-level representations build up from elementary sensorimotor memories. In this paper we examine the cerebellar contribution to both procedural and declarative components of spatial cognition. To do so, we model a functional interplay between the cerebellum and the hippocampal formation during goal-oriented navigation. We reinterpret and complete existing genetic behavioural observations by means of quantitative accounts that cross-link synaptic plasticity mechanisms, single cell and population coding properties, and behavioural responses. In contrast to earlier hypotheses positing only a purely procedural impact of cerebellar adaptation deficits, our results suggest a cerebellar involvement in high-level aspects of behaviour. In particular, we propose that cerebellar learning mechanisms may influence hippocampal place fields, by contributing to the path integration process. Our simulations predict differences in place-cell discharge properties between normal mice and L7-PKCI mutant mice lacking long-term depression at cerebellar parallel fibre-Purkinje cell synapses. On the behavioural level, these results suggest that, by influencing the accuracy of hippocampal spatial codes, cerebellar deficits may impact the exploration-exploitation balance during spatial navigation. PMID:22485133
Spüler, Martin; Walter, Armin; Rosenstiel, Wolfgang; Bogdan, Martin
2014-11-01
Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct spatial filters that increase classification accuracy for BCIs based on visual evoked potentials, we show in this paper, how CCA can also be used for spatial filtering of event-related potentials like P300. We also evaluate the use of CCA for spatial filtering on other data with evoked and event-related potentials and show that CCA performs consistently better than other standard spatial filtering methods.
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
NASA Astrophysics Data System (ADS)
Law, K. J. H.; Sanz-Alonso, D.; Shukla, A.; Stuart, A. M.
2016-06-01
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz '96 model is used to exemplify our findings. We consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recover the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.
Improvement of spatial resolution in confocal microscope with shifted-focus phase filter
NASA Astrophysics Data System (ADS)
Huang, Xiangdong; Xiang, Xiaoyan; Wang, Chongyang
2015-02-01
A spatial super-resolution method is proposed based on the multiplicative character of confocal microscope's amplitude point-spread functions. The axial resolution can be greatly improved by introducing a shifted-focus phase filters in illumination part of a confocal microscope. However, this improvement is accompanied by a decrease of transversal resolution. Thus, a super-Gaussian phase filter is optimized to control the focal shift and transversal intensity distribution in a confocal microscope. Numerical simulation results indicate that the proposed method is useful to obtain a significant improvement in the optical sectioning capacity.
NASA Technical Reports Server (NTRS)
Keel, Byron M.
1989-01-01
An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.
Spatial routing of optical beams through time-domain spatial-spectral filtering
NASA Astrophysics Data System (ADS)
Babbitt, W. R.; Mossberg, T. W.
1995-04-01
We propose a novel new method of temporal-waveform-controlled high-speed passive spatial routing of optical beams. The method provides for the redirection of optical signals contained within a single input beam into output directions that are specified entirely by temporal information encoded on the waveform of each incident signal. The routing is effected by means of deflection from spectrally structured spatial gratings that may be optically programmed into materials with or without intrinsic frequency selectivity.
An adaptive filter for studying the life cycle of optical rogue waves.
Liu, Chu; Rees, Eric J; Laurila, Toni; Jian, Shuisheng; Kaminski, Clemens F
2010-12-01
We present an adaptive numerical filter for analyzing fiber-length dependent properties of optical rogue waves, which are highly intense and extremely red-shifted solitons that arise during supercontinuum generation in photonic crystal fiber. We use this filter to study a data set of 1000 simulated supercontinuum pulses, produced from 5 ps pump pulses containing random noise. Optical rogue waves arise in different supercontinuum pulses at various positions along the fiber, and exhibit a lifecycle: their intensity peaks over a finite range of fiber length before declining slowly.
Cooperation of a Dissatisfied Adaptive Prisoner's Dilemma in Spatial Structures
NASA Astrophysics Data System (ADS)
Zhang, Wen; Li, Yao-Sheng; Du, Peng; Xu, Chen
2013-10-01
We study the cooperative behavior of a dissatisfied adaptive prisoner's dilemma via a pair updating rule. We compare two kinds of relationship among the competing agents, one is the well-mixed population and the other is the two-dimensional square lattice. It is found that the cooperation emerges in both the cases and the frequency of cooperation is enhanced in the square lattice. Though it is impossible for the cooperators to have a higher average payoff than that of the defectors in the well-mixed case, the cooperators in the spatial square lattice could have higher average payoffs in certain regions of the game parameters. We theoretically analyze the well-mixed case exactly and the square lattice by pair approximation. The theoretic results are in agreement with the simulation data.
Adaptive spatially dependent weighting scheme for tomosynthesis reconstruction
NASA Astrophysics Data System (ADS)
Levakhina, Yulia; Duschka, Robert; Vogt, Florian; Barkhausen, JOErg; Buzug, Thorsten M.
2012-03-01
Digital Tomosynthesis (DT) is an x-ray limited-angle imaging technique. An accurate image reconstruction in tomosynthesis is a challenging task due to the violation of the tomographic sufficiency conditions. A classical "shift-and-add" algorithm (or simple backprojection) suffers from blurring artifacts, produced by structures located above and below the plane of interest. The artifact problem becomes even more prominent in the presence of materials and tissues with a high x-ray attenuation, such as bones, microcalcifications or metal. The focus of the current work is on reduction of ghosting artifacts produced by bones in the musculoskeletal tomosynthesis. A novel dissimilarity concept and a modified backprojection with an adaptive spatially dependent weighting scheme (ωBP) are proposed. Simulated data of software phantom, a structured hardware phantom and a human hand raw-data acquired with a Siemens Mammomat Inspiration tomosynthesis system were reconstructed using conventional backprojection algorithm and the new ωBP-algorithm. The comparison of the results to the non-weighted case demonstrates the potential of the proposed weighted backprojection to reduce the blurring artifacts in musculoskeletal DT. The proposed weighting scheme is not limited to the tomosynthesis limitedangle geometry. It can also be adapted for Computed Tomography (CT) and included in iterative reconstruction algorithms (e.g. SART).
Filter multiplexing by use of spatial Code Division Multiple Access approach.
Solomon, Jonathan; Zalevsky, Zeev; Mendlovic, David; Monreal, Javier Garcia
2003-02-10
The increasing popularity of optical communication has also brought a demand for a broader bandwidth. The trend, naturally, was to implement methods from traditional electronic communication. One of the most effective traditional methods is Code Division Multiple Access. In this research, we suggest the use of this approach for spatial coding applied to images. The approach is to multiplex several filters into one plane while keeping their mutual orthogonality. It is shown that if the filters are limited by their bandwidth, the output of all the filters can be sampled in the original image resolution and fully recovered through an all-optical setup. The theoretical analysis of such a setup is verified in an experimental demonstration.
Design and fabrication of microlens and spatial filter array by self-alignment
NASA Astrophysics Data System (ADS)
Yang, Ren; Chan, Kin Foong; Feng, Zhiqiang; Mei, Wenhui
2003-01-01
For typically small volume production of MEMS, MOEMS, fine feature PCB, high density chip packaging and display panels, especially for lab tests, low cost and the capability to change the original design easily and quickly are very important for customers and researchers. BALL Semiconductor Inc.'s Maskless Lithography Systems (MLS) feature the Digital Mirror Device (DMD) as the pattern generator to replace photo-masks. This can remove masks from UV lithography, and dramatically reduce the running cost and save time for lab tests and small volume production. At Ball Semiconductor Inc, 1.5μm line/space, 10μm line/space, and 20μm line/space Maskless Lithography Systems were developed. In our MLS, an 848×600 microlens and spatial filter array (MLSFA) was used to focus the light and to filter the noise. In order to produce smaller line-space than 16μm the MLSFA was used to get smaller UV light pad (compared with the SVGA DMD"s micro-mirror: 17μm×17μm) and to filter the noise produced from the DMD, optical lens system, and micro lens array. This MLSFA is one of the key devices for our Maskless Lithography System, and determines the resolution and quality of maskless lithography. A novel design and fabrication process of a single-package MLSFA for our Maskless Lithography System will be introduced. To avoid problems produced by misalignment between a two-piece spatial filter and microlens array, MEMS processing is used to integrate the microlens array with the spatial filter array. In this paper, the self-alignment method used to fabricate exactly matched MLSFA will be presented.
Effect of spatial filtering on crosstalk reduction in surface EMG recordings.
Mesin, Luca; Smith, Stuart; Hugo, Suzanne; Viljoen, Suretha; Hanekom, Tania
2009-04-01
Increasing the selectivity of the detection system in surface electromyography (EMG) is beneficial in the collection of information of a specific portion of the investigated muscle and to reduce the contribution of undesired components, such as non-propagating components (due to generation or end-of-fibre effects) or crosstalk from nearby muscles. A comparison of the ability of different spatial filters to reduce the amount of crosstalk in surface EMG measurements was conducted in this paper using simulated signals. It focused on the influence of different properties of the muscle anatomy (changing subcutaneous layer thickness, skin conductivity, fibre length) and detection system (single, double and normal double differential, with two inter-electrode distances - IED) on the amount of crosstalk present in the measurements. A cylindrical multilayer (skin, subcutaneous tissue, muscle, bone) analytical model was used to simulate single fibre action potentials (SFAPs). Fibres were grouped together in motor units (MUs) and motor unit action potentials (MUAPs) were obtained by adding the SFAPs of the corresponding fibres. Interference surface EMG signals were obtained, modelling the recruitment of MUs and rate coding. The average rectified value (ARV) and mean frequency (MNF) content of the EMG signals were studied and used as a basis for determining the selectivity of each spatial filter. From these results it was found that the selectivity of each spatial filter varies depending on the transversal location of the measurement electrodes and on the anatomy. An increase in skin conductivity favourably affects the selectivity of normal double differential filters as does an increase in subcutaneous layer thickness. An increase in IED decreases the selectivity of all the analysed filters.
NASA Astrophysics Data System (ADS)
Petrak, D.; Haedrich, T.
The paper presents a comparison between the fiber-optical spatial filter anemometry (FOA) and LDA for the particle velocity measurement in a two-phase flow. An LDA two beam anemometer and a differential-type optical fiber array spatial filter were used for the velocity measurements on glass particles with a mean diameter of 116 microns in a horizontal channel air flow. Two different probe pipe constructions were investigated. In general the results show that the FOA-probe signals have a low signal-to-noise ratio in comparison with the LDA-signals and that the mean FOA-particle velocity is smaller than the mean LDA-particle velocity. A FOA-system with a probe construction like a Pitot tube is preferred for the application.
NASA Astrophysics Data System (ADS)
Uno, Katsuhiro; Kihou, Takemune
2014-05-01
An inspection of mechanically damaged areas on manufactured metal parts is necessary to produce high-quality products. A scanning probe on a sample is necessary for a conventional surface inspection system, which is time-consuming. We propose a novel high-speed detection method for defects on metal surfaces with rolling indentations. To obtain a large field of view in a measurement, we used a laser sheet that was expanded with a laser line generator and also used an expanded collimated beam, rather than a small laser spot as used in conventional techniques. Furthermore, we used an obliquely incident laser beam to suppress the effect of the rolling indentations surrounding defects, and also applied spatial frequency filtering to extract only defects. The spatial frequency filtering under oblique incidence is theoretically explained and defect extraction was investigated in experiments.
ATLID receiving spatial and spectral filtering units: design and associated performances
NASA Astrophysics Data System (ADS)
Vaché, Maxime; de Saint Seine, Diego; Leblay, Pierrick; Hélière, Arnaud; Pereira Do Carmo, João.; Berlioz, Philippe; Archer, Julien
2015-09-01
ATLID (ATmospheric LIDar) is one of the four key instruments of EarthCARE (Earth Clouds, Aerosols and Radiations Explorer) satellite. It is a program of and funded by the European Space Agency and under prime contractorship of Airbus Defence and Space. ATLID is dedicated to the understanding of aerosols and clouds contribution to earth climate. It is an atmospheric LIDAR that measures the emitted 354.8nm ultraviolet laser which is backscattered by the atmosphere. The molecules and the particles have different optical signatures and can consequently be distinguished thanks to polarization analyses and spectral filtering of the backscattered signal. The following optical units of ATLID receiver chain directly contribute to this function : after ATLID afocal telescope, the CAS-OA, the Optical Assembly of the Co Alignment Sensor, samples and images the beam on the CAS sensor in order to optimize the alignment of transmitting and receiving telescopes. The beam goes through the BF sub-assemblies, the Blocking Filter which has two filtering functions: (1) spatial with the ERO-BF, which is a Kepler afocal spatial filtering module that defines the instrument field of view and blocks the background and straylight out of the useful field of view; (2) spectral with the ERO-EFO, the Entrance Filtering Optic, which is mainly composed of a very narrow bandpass filter with a high rejection factor. This filter rejects the background from the useful signal and contributes to increase the signal-to-noise ratio. The EFO also allows an on-ground adjustment of the orientation of the linear polarization of the input beam. After filtering and polarization adjustment, the beam is injected in several optical fibers and transported to the instrument detectors. This last transport function is done by the FCA, the Fiber Coupler Assembly. This paper presents the flight models of the previously described units, details the opto-mechanical design, and reviews the main achieved performances with a
Tone reproduction for high-dynamic range imaging based on adaptive filtering
NASA Astrophysics Data System (ADS)
Ha, Changwoo; Lee, Joohyun; Jeong, Jechang
2014-03-01
A tone reproduction algorithm with enhanced contrast of high-dynamic range images on conventional low-dynamic range display devices is presented. The proposed algorithm consists mainly of block-based parameter estimation, a characteristic-based luminance adjustment, and an adaptive Gaussian filter using minimum description length. Instead of relying only on the reduction of the dynamic range, a characteristic-based luminance adjustment process modifies the luminance values. The Gaussian-filtered luminance value is obtained from appropriate value of variance, and the contrast is then enhanced through the use of a relation between the adjusted luminance and Gaussian-filtered luminance values. In the final tone-reproduction process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. The experimental results demonstrate that the proposed algorithm achieves a good subjective quality while enhancing the contrast of the image details.
Moreno-Pino, Mario; De la Iglesia, Rodrigo; Valdivia, Nelson; Henríquez-Castilo, Carlos; Galán, Alexander; Díez, Beatriz; Trefault, Nicole
2016-07-01
Spatial environmental heterogeneity influences diversity of organisms at different scales. Environmental filtering suggests that local environmental conditions provide habitat-specific scenarios for niche requirements, ultimately determining the composition of local communities. In this work, we analyze the spatial variation of microbial communities across environmental gradients of sea surface temperature, salinity and photosynthetically active radiation and spatial distance in Fildes Bay, King George Island, Antarctica. We hypothesize that environmental filters are the main control of the spatial variation of these communities. Thus, strong relationships between community composition and environmental variation and weak relationships between community composition and spatial distance are expected. Combining physical characterization of the water column, cell counts by flow cytometry, small ribosomal subunit genes fingerprinting and next generation sequencing, we contrast the abundance and composition of photosynthetic eukaryotes and heterotrophic bacterial local communities at a submesoscale. Our results indicate that the strength of the environmental controls differed markedly between eukaryotes and bacterial communities. Whereas eukaryotic photosynthetic assemblages responded weakly to environmental variability, bacteria respond promptly to fine-scale environmental changes in this polar marine system. PMID:27127198
Moreno-Pino, Mario; De la Iglesia, Rodrigo; Valdivia, Nelson; Henríquez-Castilo, Carlos; Galán, Alexander; Díez, Beatriz; Trefault, Nicole
2016-07-01
Spatial environmental heterogeneity influences diversity of organisms at different scales. Environmental filtering suggests that local environmental conditions provide habitat-specific scenarios for niche requirements, ultimately determining the composition of local communities. In this work, we analyze the spatial variation of microbial communities across environmental gradients of sea surface temperature, salinity and photosynthetically active radiation and spatial distance in Fildes Bay, King George Island, Antarctica. We hypothesize that environmental filters are the main control of the spatial variation of these communities. Thus, strong relationships between community composition and environmental variation and weak relationships between community composition and spatial distance are expected. Combining physical characterization of the water column, cell counts by flow cytometry, small ribosomal subunit genes fingerprinting and next generation sequencing, we contrast the abundance and composition of photosynthetic eukaryotes and heterotrophic bacterial local communities at a submesoscale. Our results indicate that the strength of the environmental controls differed markedly between eukaryotes and bacterial communities. Whereas eukaryotic photosynthetic assemblages responded weakly to environmental variability, bacteria respond promptly to fine-scale environmental changes in this polar marine system.
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.
Multicomponent AM-FM demodulation based on energy separation and adaptive filtering
NASA Astrophysics Data System (ADS)
Qin, Yi
2013-07-01
Multicomponent AM-FM demodulation is an important tool in many engineering applications. To improve the demodulation accuracy of the commonly used methods, such as iterative Hilbert transform (IHT) and Hilbert-Huang transform (HHT), a new multicomponent AM-FM demodulation method is proposed in this paper. The proposed method achieves multicomponent demodulation by using an iteratively energy separation algorithm and adaptive low-pass filtering. Using the frequency spectra of instantaneous amplitude and frequency obtained by the energy separation algorithm at each level, the used filters are adaptively designed. In addition, this proposed method uses symmetric extension to solve the boundary effect in the estimation of instantaneous amplitudes and frequencies. The demodulation process is automatic for an arbitrary signal. Simulation and application results show that the proposed method has high demodulation accuracy than IHT, HHT and other typical methods, and it can be effectively applied to extracting weak fault feature from mechanical vibration signals.
NASA Technical Reports Server (NTRS)
Penland, Cecile; Ghil, Michael; Weickmann, Klaus M.
1991-01-01
The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum
Wilson, Emma D.; Assaf, Tareq; Pearson, Martin J.; Rossiter, Jonathan M.; Dean, Paul; Anderson, Sean R.; Porrill, John
2015-01-01
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks. PMID:26257638
NASA Astrophysics Data System (ADS)
Flad, David; Beck, Andrea; Munz, Claus-Dieter
2016-05-01
Scale-resolving simulations of turbulent flows in complex domains demand accurate and efficient numerical schemes, as well as geometrical flexibility. For underresolved situations, the avoidance of aliasing errors is a strong demand for stability. For continuous and discontinuous Galerkin schemes, an effective way to prevent aliasing errors is to increase the quadrature precision of the projection operator to account for the non-linearity of the operands (polynomial dealiasing, overintegration). But this increases the computational costs extensively. In this work, we present a novel spatially and temporally adaptive dealiasing strategy by projection filtering. We show this to be more efficient for underresolved turbulence than the classical overintegration strategy. For this novel approach, we discuss the implementation strategy and the indicator details, show its accuracy and efficiency for a decaying homogeneous isotropic turbulence and the transitional Taylor-Green vortex and compare it to the original overintegration approach and a state of the art variational multi-scale eddy viscosity formulation.
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system.
Design of adaptive control systems by means of self-adjusting transversal filters
NASA Technical Reports Server (NTRS)
Merhav, S. J.
1986-01-01
The design of closed-loop adaptive control systems based on nonparametric identification was addressed. Implementation is by self-adjusting Least Mean Square (LMS) transversal filters. The design concept is Model Reference Adaptive Control (MRAC). Major issues are to preserve the linearity of the error equations of each LMS filter, and to prevent estimation bias that is due to process or measurement noise, thus providing necessary conditions for the convergence and stability of the control system. The controlled element is assumed to be asymptotically stable and minimum phase. Because of the nonparametric Finite Impulse Response (FIR) estimates provided by the LMS filters, a-priori information on the plant model is needed only in broad terms. Following a survey of control system configurations and filter design considerations, system implementation is shown here in Single Input Single Output (SISO) format which is readily extendable to multivariable forms. In extensive computer simulation studies the controlled element is represented by a second-order system with widely varying damping, natural frequency, and relative degree.
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system. PMID:21193194
NASA Astrophysics Data System (ADS)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called "curse of dimensionality". Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the "restart" scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost.
Subotić, Miško; Šarić, Zoran; Jovičić, Slobodan T
2012-03-01
Transient otoacoustic emission (TEOAE) is a method widely used in clinical practice for assessment of hearing quality. The main problem in TEOAE detection is its much lower level than the level of environmental and biological noise. While the environmental noise level can be controlled, the biological noise can be only reduced by appropriate signal processing. This paper presents a new two-probe preprocessing TEOAE system for suppression of the biological noise by adaptive filtering. The system records biological noises in both ears and applies a specific adaptive filtering approach for suppression of biological noise in the ear canal with TEOAE. The adaptive filtering approach includes robust sign error LMS algorithm, stimuli response summation according to the derived non-linear response (DNLR) technique, subtraction of the estimated TEOAE signal and residual noise suppression. The proposed TEOAE detection system is tested by three quality measures: signal-to-noise ratio (S/N), reproducibility of TEOAE, and measurement time. The maximal TEOAE detection improvement is dependent on the coherence function between biological noise in left and right ears. The experimental results show maximal improvement of 7 dB in S/N, improvement in reproducibility near 40% and reduction in duration of TEOAE measurement of over 30%.
Ship detection for high resolution optical imagery with adaptive target filter
NASA Astrophysics Data System (ADS)
Ju, Hongbin
2015-10-01
Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.
Experimental evidence of the spatial coherence moiré and the filtering of classes of radiator pairs.
Castaneda, Roman; Usuga-Castaneda, Mario; Herrera-Ramírez, Jorge
2007-08-01
Evidence of the physical existence of the spatial coherence moiré is obtained by confronting numerical results with experimental results of spatially partial interference. Although it was performed for two particular cases, the results reveal a general behavior of the optical fields in any state of spatial coherence. Moreover, the study of the spatial coherence moiré deals with a new type of filtering, named filtering of classes of radiator pairs, which allows changing the power spectrum at the observation plane by modulating the complex degree of spatial coherence, without altering the power distribution at the aperture plane or introducing conventional spatial filters. This new procedure can optimize some technological applications of actual interest, as the beam shaping for instance.
Cecotti, Hubert; Eckstein, Miguel P; Giesbrecht, Barry
2014-11-01
Accurate detection of single-trial event-related potentials (ERPs) in the electroencephalogram (EEG) is a difficult problem that requires efficient signal processing and machine learning techniques. Supervised spatial filtering methods that enhance the discriminative information in EEG data are commonly used to improve single-trial ERP detection. We propose a convolutional neural network (CNN) with a layer dedicated to spatial filtering for the detection of ERPs and with training based on the maximization of the area under the receiver operating characteristic curve (AUC). The CNN is compared with three common classifiers: 1) Bayesian linear discriminant analysis; 2) multilayer perceptron (MLP); and 3) support vector machines. Prior to classification, the data were spatially filtered with xDAWN (for the maximization of the signal-to-signal-plus-noise ratio), common spatial pattern, or not spatially filtered. The 12 analytical techniques were tested on EEG data recorded in three rapid serial visual presentation experiments that required the observer to discriminate rare target stimuli from frequent nontarget stimuli. Classification performance discriminating targets from nontargets depended on both the spatial filtering method and the classifier. In addition, the nonlinear classifier MLP outperformed the linear methods. Finally, training based AUC maximization provided better performance than training based on the minimization of the mean square error. The results support the conclusion that the choice of the systems architecture is critical and both spatial filtering and classification must be considered together.
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
Stuart, Andrew M.; Shukla, Abhishek; Sanz-Alonso, Daniel; Law, K. J. H.
2016-02-23
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz ’96 model is used to exemplify our findings. Here, we consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recovermore » the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.« less
Crosstalk elimination in the detection of dual-beam optical tweezers by spatial filtering
Ott, Dino; Oddershede, Lene B.; Reihani, S. Nader S.
2014-05-15
In dual-beam optical tweezers, the accuracy of position and force measurements is often compromised by crosstalk between the two detected signals, this crosstalk leading to systematic and significant errors on the measured forces and distances. This is true both for dual-beam optical traps where the splitting of the two traps is done by polarization optics and for dual optical traps constructed by other methods, e.g., holographic tweezers. If the two traps are orthogonally polarized, most often crosstalk is minimized by inserting polarization optics in front of the detector; however, this method is not perfect because of the de-polarization of the trapping beam introduced by the required high numerical aperture optics. Here we present a simple and easy-to-implement method to efficiently eliminate crosstalk. The method is based on spatial filtering by simply inserting a pinhole at the correct position and is highly compatible with standard back focal plane photodiode based detection of position and force. Our spatial filtering method reduces crosstalk up to five times better than polarization filtering alone. The effectiveness is dependent on pinhole size and distance between the traps and is here quantified experimentally and reproduced by theoretical modeling. The method here proposed will improve the accuracy of force-distance measurements, e.g., of single molecules, performed by dual-beam optical traps and hence give much more scientific value for the experimental efforts.
NASA Astrophysics Data System (ADS)
Jang, Jae-Young; Cho, Myungjin
2015-12-01
In this paper, we propose a new fast computational integral imaging reconstruction (CIIR) scheme without the deterioration of the spatial filtering effect by combined use of spatial filtering and rearrangement of elemental image pixels. In the proposed scheme, the elemental image array (EIA) recorded by lenslet array is spatially filtered through the convolution of depth-dependent delta function array for a given depth. Then, the spatially filtered EIA is reconstructed as the 3D slice image using pixels of the elemental image rearrangement technique. Our scheme provides both the fast calculation with the same properties of the conventional CIIR and the improved visual quality of the reconstructed 3D slice image. To verify our scheme, we perform preliminary experiments and compare other techniques.
NASA Astrophysics Data System (ADS)
Sartori, Pablo; Tu, Yuhai
2011-04-01
Two distinct mechanisms for filtering noise in an input signal are identified in a class of adaptive sensory networks. We find that the high-frequency noise is filtered by the output degradation process through time-averaging; while the low-frequency noise is damped by adaptation through negative feedback. Both filtering processes themselves introduce intrinsic noises, which are found to be unfiltered and can thus amount to a significant internal noise floor even without signaling. These results are applied to E. coli chemotaxis. We show unambiguously that the molecular mechanism for the Berg-Purcell time-averaging scheme is the dephosphorylation of the response regulator CheY-P, not the receptor adaptation process as previously suggested. The high-frequency noise due to the stochastic ligand binding-unbinding events and the random ligand molecule diffusion is averaged by the CheY-P dephosphorylation process to a negligible level in E. coli. We identify a previously unstudied noise source caused by the random motion of the cell in a ligand gradient. We show that this random walk induced signal noise has a divergent low-frequency component, which is only rendered finite by the receptor adaptation process. For gradients within the E. coli sensing range, this dominant external noise can be comparable to the significant intrinsic noise in the system. The dependence of the response and its fluctuations on the key time scales of the system are studied systematically. We show that the chemotaxis pathway may have evolved to optimize gradient sensing, strong response, and noise control in different time scales.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Microscopy with spatial filtering for sorting particles and monitoring subcellular morphology
NASA Astrophysics Data System (ADS)
Zheng, Jing-Yi; Qian, Zhen; Pasternack, Robert M.; Boustany, Nada N.
2009-02-01
Optical scatter imaging (OSI) was developed to non-invasively track real-time changes in particle morphology with submicron sensitivity in situ without exogenous labeling, cell fixing, or organelle isolation. For spherical particles, the intensity ratio of wide-to-narrow angle scatter (OSIR, Optical Scatter Image Ratio) was shown to decrease monotonically with diameter and agree with Mie theory. In living cells, we recently reported this technique is able to detect mitochondrial morphological alterations, which were mediated by the Bcl-xL transmembrane domain, and could not be observed by fluorescence or differential interference contrast images. Here we further extend the ability of morphology assessment by adopting a digital micromirror device (DMD) for Fourier filtering. When placed in the Fourier plane the DMD can be used to select scattering intensities at desired combination of scattering angles. We designed an optical filter bank consisting of Gabor-like filters with various scales and rotations based on Gabor filters, which have been widely used for localization of spatial and frequency information in digital images and texture analysis. Using a model system consisting of mixtures of polystyrene spheres and bacteria, we show how this system can be used to sort particles on a microscopic slide based on their size, orientation and aspect ratio. We are currently applying this technique to characterize the morphology of subcellular organelles to help understand fundamental biological processes.
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory. PMID:22315542
Locally adaptive regression filter-based infrared focal plane array non-uniformity correction
NASA Astrophysics Data System (ADS)
Li, Jia; Qin, Hanlin; Yan, Xiang; Huang, He; Zhao, Yingjuan; Zhou, Huixin
2015-10-01
Due to the limitations of the manufacturing technology, the response rates to the same infrared radiation intensity in each infrared detector unit are not identical. As a result, the non-uniformity of infrared focal plane array, also known as fixed pattern noise (FPN), is generated. To solve this problem, correcting the non-uniformity in infrared image is a promising approach, and many non-uniformity correction (NUC) methods have been proposed. However, they have some defects such as slow convergence, ghosting and scene degradation. To overcome these defects, a novel non-uniformity correction method based on locally adaptive regression filter is proposed. First, locally adaptive regression method is used to separate the infrared image into base layer containing main scene information and the detail layer containing detailed scene with FPN. Then, the detail layer sequence is filtered by non-linear temporal filter to obtain the non-uniformity. Finally, the high quality infrared image is obtained by subtracting non-uniformity component from original image. The experimental results show that the proposed method can significantly eliminate the ghosting and the scene degradation. The results of correction are superior to the THPF-NUC and NN-NUC in the aspects of subjective visual and objective evaluation index.
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-01-01
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively. PMID:25502124
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-12-09
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.
High performance 3D adaptive filtering for DSP based portable medical imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.
Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter
NASA Astrophysics Data System (ADS)
Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi
2013-03-01
Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.
Two-Dimensional Planar Lightwave Circuit Integrated Spatial Filter Array and Method of Use Thereof
NASA Technical Reports Server (NTRS)
Ai, Jun (Inventor); Dimov, Fedor (Inventor)
2015-01-01
A large coherent two-dimensional (2D) spatial filter array (SFA), 30 by 30 or larger, is produced by coupling a 2D planar lightwave circuit (PLC) array with a pair of lenslet arrays at the input and output side. The 2D PLC array is produced by stacking a plurality of chips, each chip with a plural number of straight PLC waveguides. A pupil array is coated onto the focal plane of the lenslet array. The PLC waveguides are produced by deposition of a plural number of silica layers on the silicon wafer, followed by photolithography and reactive ion etching (RIE) processes. A plural number of mode filters are included in the silica-on-silicon waveguide such that the PLC waveguide is transparent to the fundamental mode but higher order modes are attenuated by 40 dB or more.
Ray, Jaideep; Lefantzi, Sophia; Najm, Habib N.; Kennedy, Christopher A.
2006-01-01
Block-structured adaptively refined meshes (SAMR) strive for efficient resolution of partial differential equations (PDEs) solved on large computational domains by clustering mesh points only where required by large gradients. Previous work has indicated that fourth-order convergence can be achieved on such meshes by using a suitable combination of high-order discretizations, interpolations, and filters and can deliver significant computational savings over conventional second-order methods at engineering error tolerances. In this paper, we explore the interactions between the errors introduced by discretizations, interpolations and filters. We develop general expressions for high-order discretizations, interpolations, and filters, in multiple dimensions, using a Fourier approach, facilitating the high-order SAMR implementation. We derive a formulation for the necessary interpolation order for given discretization and derivative orders. We also illustrate this order relationship empirically using one and two-dimensional model problems on refined meshes. We study the observed increase in accuracy with increasing interpolation order. We also examine the empirically observed order of convergence, as the effective resolution of the mesh is increased by successively adding levels of refinement, with different orders of discretization, interpolation, or filtering.
Gershgorin, B.; Harlim, J. Majda, A.J.
2010-01-01
The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates
Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter
NASA Astrophysics Data System (ADS)
Wang, Yong; Tan, Yihua; Tian, Jinwen
2010-07-01
We present a new scheme based on multiple-cue integration for visual tracking within a Gaussian particle filter framework. The proposed method integrates the color, shape, and texture cues of an object to construct a hybrid likelihood model. During the measurement step, the likelihood model can be switched adaptively according to environmental changes, which improves the object representation to deal with the complex disturbances, such as appearance changes, partial occlusions, and significant clutter. Moreover, the confidence weights of the cues are adjusted online through the estimation using a particle filter, which ensures the tracking accuracy and reliability. Experiments are conducted on several real video sequences, and the results demonstrate that the proposed method can effectively track objects in complex scenarios. Compared with previous similar approaches through some quantitative and qualitative evaluations, the proposed method performs better in terms of tracking robustness and precision.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF. PMID:27475606
NASA Astrophysics Data System (ADS)
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Rusconi, Maria Luisa; Carelli, Laura
2012-01-01
This study describes the long-term effectiveness on spatial neglect recovery of a 2-week treatment based on prism adaptation (PA). Seven right-brain-damaged patients affected by chronic neglect were evaluated before, after two weeks of the PA treatment and at a follow-up (variable between 8 and 30 months after the end of PA). Neglect evaluation was performed by means of BIT (conventional and behavioral), Fluff Test, and Comb and Razor Test. The results highlight an improvement, after the PA training, in both tasks performed using the hand trained in PA treatment and in behavioral tasks not requiring a manual motor response. Such effects extend, even if not significantly, to all BIT subtests. These results support previous findings, showing that PA improves neglect also on imagery tasks with no manual component, and provide further evidence for long-lasting efficacy of PA training. Dissociations have been found with regard to PA efficacy on peripersonal, personal, and representational neglect, visuospatial agraphia and neglect dyslexia. In particular, we found no significant differences between the pre-training and post-training PA session in personal neglect measures, and a poor recovery of neglect dyslexia after PA treatment. The recruitment of a larger sample could help to confirm the effectiveness of the prismatic lenses with regard to the different clinical manifestations of spatial neglect.
Damage and fracture in large aperture, fused silica, vacuum spatial filter lenses
Campbell, J.H.; Edwards, G.J.; Marion, J.E.
1995-07-07
Optical damage that results in large scale fracture has been observed in the large, high-fluence, fused-silica, spatial filter lenses on the Nova and Beamlet lasers. In nearly all cases damage occurs on the vacuum side of the lenses and because the vacuum side of the lens is under tensile stress this damage can lead to catastrophic crack growth if the flaw (damage) size exceeds the critical flaw size for SiO{sub 2}. The damaged 52 cm Nova lenses fracture into two and sometimes three large pieces. Although under full vacuum load at the time they fracture, the Nova lenses do not implode. Rather the authors have observed that the pieces lock together and air slowly leaks into the vacuum spatial filter housing through the lens cracks. The Beamlet lenses have a larger aspect ratio and peak tensile stress than Nova. The peak tensile stress at the center of the output surface of the Beamlet lens is 1,490 psi versus 810 psi for Nova. During a recent Beamlet high energy shot, a damage spot on the lens grew to the critical flaw size and the lens imploded. Post shot data indicate the lens probably fractured into 5 to 7 pieces, however, unlike Nova, these pieces did not lock together. Analysis shows that the likely source of damage is contamination from pinhole blow-off or out-gassing of volatile materials within the spatial filter. Contamination degrades the antireflection properties of the sol-gel coating and reduces its damage threshold. By changing the design of the Beamlet lens it may be possible to insure that it fails safe by locking up in much that same manner as the Nova lens.
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.
Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation
NASA Astrophysics Data System (ADS)
Xu, Xiangbo; Chen, Shao; Zhang, Yanan
2016-07-01
To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.
Flicker adaptation or superimposition raises the apparent spatial frequency of coarse test gratings.
Kaneko, Sae; Giaschi, Deborah; Anstis, Stuart
2015-03-01
Independent channels respond to both the spatial and temporal characteristics of visual stimuli. Gratings <3 cycles per degree (cpd) are sensed by transient channels that prefer intermittent stimulation, while gratings >3 cpd are sensed by sustained channels that prefer steady stimulation. From this we predict that adaptation to a spatially uniform flickering field will selectively adapt the transient channels and raise the apparent spatial frequency of coarse sinusoidal gratings. Observers adapted to a spatially uniform field whose upper or lower half was steady and whose other half was flickering. They then adjusted the spatial frequency of a stationary test (matching) grating on the previously unmodulated half field until it matched the apparent spatial frequency of a grating falling on the previously flickering half field. The adapting field flickered at 8 Hz and the spatial frequency of the gratings was varied in octave steps from 0.25 to 16 cpd. As predicted, adapting to flicker raised the apparent spatial frequency of the test gratings. The aftereffect reached a peak of 11% between 0.5 and 1 cpd and disappeared above 4 cpd. We also observed that superimposed 10 Hz luminance flicker raised the apparent spatial frequency of 0.5 cpd test gratings. The effect was not seen with slower flicker or finer test gratings. Altogether, our study suggests that apparent spatial frequency is determined by the balance between transient and sustained channels and that an imbalance between the channels caused by flicker can alter spatial frequency perception.
Mesin, Luca; Tizzani, Francesca; Farina, Dario
2006-10-01
Muscle fiber conduction velocity (CV) can be estimated by the application of a pair of spatial filters to surface electromagnetic (EMG) signals and compensation of the spatial filter transfer function with equivalent temporal filters. This method integrates the selection of the spatial filters for signal detection to the estimation of CV. Using this approach, in this paper, we propose a novel technique for signal-based selection of the spatial filter pair that minimizes the effect of nonpropagating signal components (end-of-fiber effects) on CV estimates (optimal filters). The technique is applicable to signals with one propagating and one nonpropagating component, such as single motor unit action potentials. It is shown that the determination of the optimal filters also allows the identification of the propagating and nonpropagating signal components. The new method was applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were recorded from the abductor pollicis brevis with a linear array of 16 electrodes. In the simulations, the proposed approach provided CV estimates with lower bias due to nonpropagating signal components than previously proposed methods based on the entire signal waveform. In the experimental signals, the technique separated propagating and nonpropagating signal components with an average reconstruction error of 2.9 +/- 0.9% of the signal energy. The technique may find application in single motor unit studies for decreasing the variability and bias of CV estimates due to the presence and different weights of the nonpropagating components.
Liu, Yan; Pecht, Michael G
2006-01-01
The effectiveness of electrocardiogram (ECG) monitors can be significantly impaired by motion artifacts which can cause misdiagnoses, lead to inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a significant source of motion artifacts in current ECG monitoring. In this study, motion artifacts are adaptively filtered by using skin strain as the reference variable. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The results demonstrate that this device and method can significantly reduce skin strain induced ECG artifacts.
Evaluation of an adaptive filtering algorithm for CT cardiac imaging with EKG modulated tube current
NASA Astrophysics Data System (ADS)
Li, Jianying; Hsieh, Jiang; Mohr, Kelly; Okerlund, Darin
2005-04-01
We have developed an adaptive filtering algorithm for cardiac CT scans with EKG-modulated tube current to optimize resolution and noise for different cardiac phases and to provide safety net for cases where end-systole phase is used for coronary imaging. This algorithm has been evaluated using patient cardiac CT scans where lower tube currents are used for the systolic phases. In this paper, we present the evaluation results. The results demonstrated that with the use of the proposed algorithm, we could improve image quality for all cardiac phases, while providing greater noise and streak artifact reduction for systole phases where lower CT dose were used.
Forward scattering detection of a submerged moving target based on adaptive filtering technique.
He, Chuanlin; Yang, Kunde; Lei, Bo; Ma, Yuanliang
2015-09-01
Forward scattered waves are always overwhelmed by severely intense direct blasts when a submerged target crosses the source-receiver line. A processing scheme called direct blast suppression based on adaptive filtering (DBS-AF) is proposed to suppress such blasts. A verification experiment was conducted in a lake with a vertical hydrophone array and 10 kHz CW impulses. Processing results show that the direct blast is suppressed in a single channel, and an intruding target is identified by the lobes in the detection curve. The detection performance is improved by adopting a time-delay beam-former on the array as a pre-processing technique. PMID:26428829
Cornelis, Bram; Moonen, Marc; Wouters, Jan
2012-06-01
This paper evaluates noise reduction techniques in bilateral and binaural hearing aids. Adaptive implementations (on a real-time test platform) of the bilateral and binaural speech distortion weighted multichannel Wiener filter (SDW-MWF) and a competing bilateral fixed beamformer are evaluated. As the SDW-MWF relies on a voice activity detector (VAD), a realistic binaural VAD is also included. The test subjects (both normal hearing subjects and hearing aid users) are tested by an adaptive speech reception threshold (SRT) test in different spatial scenarios, including a realistic cafeteria scenario with nonstationary noise. The main conclusions are: (a) The binaural SDW-MWF can further improve the SRT (up to 2 dB) over the improvements achieved by bilateral algorithms, although a significant difference is only achievable if the binaural SDW-MWF uses a perfect VAD. However, in the cafeteria scenario only the binaural SDW-MWF achieves a significant SRT improvement (2.6 dB with perfect VAD, 2.2 dB with real VAD), for the group of hearing aid users. (b) There is no significant degradation when using a real VAD at the input signal-to-noise ratio (SNR) levels where the hearing aid users reach their SRT. (c) The bilateral SDW-MWF achieves no SRT improvements compared to the bilateral fixed beamformer.
Rohr, Michaela; Wentura, Dirk
2014-10-01
High and low spatial frequency information has been shown to contribute differently to the processing of emotional information. In three priming studies using spatial frequency filtered emotional face primes, emotional face targets, and an emotion categorization task, we investigated this issue further. Differences in the pattern of results between short and masked, and short and long unmasked presentation conditions emerged. Given long and unmasked prime presentation, high and low frequency primes triggered emotion-specific priming effects. Given brief and masked prime presentation in Experiment 2, we found a dissociation: High frequency primes caused a valence priming effect, whereas low frequency primes yielded a differentiation between low and high arousing information within the negative domain. Brief and unmasked prime presentation in Experiment 3 revealed that subliminal processing of primes was responsible for the pattern observed in Experiment 2. The implications of these findings for theories of early emotional information processing are discussed. PMID:25286124
NASA Astrophysics Data System (ADS)
Koga, Takanori; Suetake, Noriaki
2015-02-01
This paper describes the detail-preserving impulse noise removal performance of a one-dimensional (1-D) switching median filter (SMF) applied along an adaptive space-filling curve. Usually, a SMF with a two-dimensional (2-D) filter window is widely used for impulse noise removal while still preserving detailed parts in an input image. However, the noise detector of the 2-D filter does not always distinguish between the original pixels and the noise-corrupted ones perfectly. In particular, pixels constituting thin lines in an input image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered regardless of the necessity of the filtering. To cope with this problem, we propose a new impulse noise removal method based on a 1-D SMF and a space-filling curve which is adaptively drawn using a minimum spanning tree reflecting structural context of an input image.
Sadjadi, Firooz A; Mahalanobis, Abhijit
2006-05-01
We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states. By designing a maximum average correlation height filter, we derive a target versus clutter distance measure as a function of four transmit and receive polarization state angles. The results of applying this method on real synthetic aperture radar imagery indicate a set of four transmit and receive angles that lead to maximum target versus clutter discrimination. These optimum angles are different for different targets. Hence, by adaptive control of the state of polarization of polarimetric radar, one can noticeably improve the discrimination of targets from clutter.
High dynamic range image rendering with a Retinex-based adaptive filter.
Meylan, Laurence; Süsstrunk, Sabine
2006-09-01
We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art. PMID:16948325
Application of speed-enhanced spatial domain correlation filters for real-time security monitoring
NASA Astrophysics Data System (ADS)
Gardezi, Akber; Bangalore, Nagachetan; Al-Kandri, Ahmed; Birch, Philip; Young, Rupert; Chatwin, Chris
2011-11-01
A speed enhanced space variant correlation filer which has been designed to be invariant to change in orientation and scale of the target object but also to be spatially variant, i.e. the filter function becoming dependant on local clutter conditions within the image. The speed enhancement of the filter is due to the use of optimization techniques employing low-pass filtering to restrict kernel movement to be within regions of interest. The detection and subsequent identification capability of the two-stage process has been evaluated in highly cluttered backgrounds using both visible and thermal imagery acquired from civil and defense domains along with associated training data sets for target detection and classification. In this paper a series of tests have been conducted in multiple scenarios relating to situations that pose a security threat. Performance matrices comprised of peak-to-correlation energy (PCE) and peak-to-side lobe ratio (PSR) measurements of the correlation output have been calculated to allow the definition of a recognition criterion. The hardware implementation of the system has been discussed in terms of Field Programmable Gate Array (FPGA) chipsets with implementation bottle necks and their solution being considered.
Full complex spatial filtering with a phase mostly DMD. [Deformable Mirror Device
NASA Technical Reports Server (NTRS)
Florence, James M.; Juday, Richard D.
1991-01-01
A new technique for implementing fully complex spatial filters with a phase mostly deformable mirror device (DMD) light modulator is described. The technique combines two or more phase-modulating flexure-beam mirror elements into a single macro-pixel. By manipulating the relative phases of the individual sub-pixels within the macro-pixel, the amplitude and the phase can be independently set for this filtering element. The combination of DMD sub-pixels into a macro-pixel is accomplished by adjusting the optical system resolution, thereby trading off system space bandwidth product for increased filtering flexibility. Volume in the larger dimensioned space, space bandwidth-complex axes count, is conserved. Experimental results are presented mapping out the coupled amplitude and phase characteristics of the individual flexure-beam DMD elements and demonstrating the independent control of amplitude and phase in a combined macro-pixel. This technique is generally applicable for implementation with any type of phase modulating light modulator.
Accounting for spatial correlations of the observation errors with Ensemble Kalman filters
NASA Astrophysics Data System (ADS)
Cosme, Emmanuel; Jean-Michel, Brankart; Clément, Ubelmann; Jacques, Verron; Pierre, Brasseur
2013-04-01
The standard Kalman filter observational update requires the inversion of the innovation error covariance matrix, what is often impractical. Most implementations of the Ensemble Kalman filter circumvent this difficulty assuming the diagonality of the observation error covariance matrix, what makes the analysis calculation numerically tractable. However, when observation errors are actually correlated spatially, such hypothesis leads to an inappropriate use of observations. Experiments show that the analysis state error variances yielded by the Ensemble Kalman filter can be severely underestimated. In this presentation, we describe a parameterization of the observation error covariance matrix which preserves its diagonal shape, but represents a simple first order autoregressive correlation structure of the observation errors. This parameterization is based upon an augmentation of the observation vector with gradients of observations. Numerical applications to ocean altimetry show the detrimental effects of specifying a diagonal matrix when observations errors are correlated, and how the new parameterization not only removes the detrimental effects of correlations, but also makes use of these correlations to improve the data assimilation products.
Baresová, E; Grieszbach, G; Schack, B; Vilser, W; Bräuer-Burchardt, C; Senff, I
This study deals with methods focused on estimating blood velocity. The estimation of the linear trend function of a non-stationary signal based on the adaptive recursive estimation of the mean value function is used for the determination of the time delay of two indicator dilution curves. The filter property of this trend operator depends on the choice of a constant parameter c, the so-called adaptation factor. The functional connection between the filter property and the adaptation factor is considered in such a way that an objective calculation of arterial blood velocity in retinal vessels is possible.
Spatial filter based light-sheet laser interference technique for three-dimensional nanolithography
Mohan, Kavya; Mondal, Partha Pratim
2015-02-23
We propose a laser interference technique for the fabrication of 3D nano-structures. This is possible with the introduction of specialized spatial filter in a 2π cylindrical lens system (consists of two opposing cylindrical lens sharing a common geometrical focus). The spatial filter at the back-aperture of a cylindrical lens gives rise to multiple light-sheet patterns. Two such interfering counter-propagating light-sheet pattern result in periodic 3D nano-pillar structure. This technique overcomes the existing slow point-by-point scanning, and has the ability to pattern selectively over a large volume. The proposed technique allows large-scale fabrication of periodic structures. Computational study shows a field-of-view (patterning volume) of approximately 12.2 mm{sup 3} with the pillar-size of 80 nm and inter-pillar separation of 180 nm. Applications are in nano-waveguides, 3D nano-electronics, photonic crystals, and optical microscopy.
NASA Astrophysics Data System (ADS)
Hicks, Brian A.; Chakrabarti, Supriya; Cook, Timothy A.
2015-01-01
We explore the use of hexagonal segment tip-tilt-piston deformable mirrors alone and paired with pinhole spatial filter arrays for high-order wavefront correction of nulling interferometers used for visible light study of exoplanetary systems at 107 to 1010 contrast within regions extending ˜0.1 to 6 arc s from a parent star. A similar system has been proposed using a single-mode fiber array as an alternative to using multiple deformable mirrors to correct both phase aberrations and balance electric field amplitude, the benefit being drastically reduced component and control complexity. Performance is compared using measured deformable mirror data for hexagonal arrays consisting of a number of rings NR=2 to 18, emphasizing the trade between throughput and the additional contrast gained from suppressing wavefront errors introduced by the deformable mirror at spatial frequencies Λ≥NR that are otherwise present in the image at corresponding field locations. Taking into account effects of loss of throughput and vignetting, the nulled signal-to-noise ratio is shown to improve for filtered systems in the outer portion of the field of view. Modeled performance shows no significant change in signal-to-noise in the inner field of view.
Transmission Phase Holography: Spatial-Mode Filter Design for Quantum Information Applications
NASA Astrophysics Data System (ADS)
Hillmer, Rachel; Barreiro, Julio; Kwiat, Paul
2007-03-01
Photon spatial modes offer access to promising new applications in quantum information because they provide a higher-dimensional basis set than the usual two-dimensional one associated with polarization. Downconversion experiments have demonstrated spatial-mode entanglement [1], and even hyperentanglement in polarization and spatial mode [2]. However optical elements currently lack the refinement necessary to perform efficient, high-fidelity operations using spatial modes. Holographic filters for Laguerre-Gaussian and Hermite-Gaussian laser modes can act as modes converters, and have long been studied (under the terms ``modans'' and ``kinoforms'') for use in electrical engineering applications [3,4]. Her we present analytical refinements and optimizations of these techniques, with predicted mode fidelities over 95% and diffraction efficiencies up to 98%. Results of our experimental implementions of these solutions are presented. [1] Walborn, S.P, et al, ``Entanglement and conservation of orbital angular momentum in spontaneous parametric down-conversion,'' Phys. Rev. A 69, 023811 (2004); [2] Barreiro, J.T. et al, ``Generation of Hyperentangled Photon Pairs,'' Phys. Rev. Lett. 95, 260501 (2005); [3] Soifer, V.A., ``Methods of Computer Design of Diffractive Optical Elements,'' John Wiley & Sons, Inc., 2002; [4] Golub, M. and Soifer, V., ``Laser Beam Mode Selection by Computer Generated Holograms,'' CRC Press, Inc., 1994.
NASA Astrophysics Data System (ADS)
Bergeler, S.; Ewald, H.; Krambeer, H.; Kubota, E.
2005-06-01
Conventional humanitarian mine detectors based on magnetic and magneto-inductive procedures are able to detect very small metal pieces in the ground. These evaluation methods however result in a high rate of false alarm; the presence of metallic parts detected which are not to be assigned as mines. If you want to classify the metal piece in the ground (e.g. the shape) you have to measure the electro-magnetic field at different positions. Therefore the actual position must be known for each measuring point. By use of the optical spatial filtering method we are able to measure the velocity vector. With the sample time we get the required x-y-position. In our approach we use structured photo detectors as a filter grating and as a detector too. This technique for position determination possesses some interesting advantages such as the use of incoherent light and simplicity of the optical and mechanical set up. New two-dimensional CMOS sensor arrays with direct pixel access allow a fast read out of sub frames. A disadvantage is the slow signal to noise ratio and the price of industrial CMOS cameras that facilitate frame grabbing. The use of simple CCD web cameras limit the maximum measurable velocity, having a read out time of 60 Hz (max), but the price decrease extreme. Early tests using structured photo detectors and spatial filtering methods for position determination show very good results for velocities from 0 to 250 mm/s. A local resolution of 1 mm can be achieved. Tests have also been performed using an ordinary optical mouse as the position determination system.
Effects of prism adaptation on motor-intentional spatial bias in neglect
Fortis, Paola; Chen, Peii; Goedert, Kelly M.; Barrett, Anna M.
2011-01-01
Prism adaptation may alleviate some symptoms of spatial neglect. However, the mechanism through which this technique works is still unclear. The current study investigated whether prism adaptation differentially affects dysfunction in perceptual-attentional “where” versus motor-intentional “aiming” bias. Five neglect patients performed a line bisection task in which lines were viewed under both normal and right-left reversed viewing conditions, allowing for the fractionation of “where” and “aiming” spatial bias components. Following two consecutive days of prism adaptation, participants demonstrated a significant improvement in “aiming” spatial bias, with no effect on “where” spatial bias. These findings suggest that prism adaptation may primarily affect motor-intentional “aiming” bias in post-stroke spatial neglect patients. PMID:21817924
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-01-01
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal. PMID:26512665
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-10-23
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-01-01
A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn’s disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLac) analysis was applied for feature extraction from the HAF-filtered images. The resulted scheme, namely HAF-DLac, incorporates support vector machines for robust lesion recognition performance. For the training and testing of HAF-DLac, an 800-image database was used, acquired from 13 patients who undertook WCE examinations, where the abnormal cases were grouped into mild and severe, according to the severity of the depicted lesion, for a more extensive evaluation of the performance. Experimental results, along with comparison with other related efforts, have shown that the HAF-DLac approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). The promising performance of HAF-DLac paves the way for a complete computer-aided diagnosis system that could support physicians’ clinical practice.
NASA Astrophysics Data System (ADS)
Dong, Gangqi; Zhu, Zheng H.
2016-05-01
This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.
Bai, Mingsian R; Chi, Li-Wen; Liang, Li-Huang; Lo, Yi-Yang
2016-02-01
In this paper, an evolutionary exposition is given in regard to the enhancing strategies for acoustic echo cancellers (AECs). A fixed beamformer (FBF) is utilized to focus on the near-end speaker while suppressing the echo from the far end. In reality, the array steering vector could differ considerably from the ideal freefield plane wave model. Therefore, an experimental procedure is developed to interpolate a practical array model from the measured frequency responses. Subband (SB) filtering with polyphase implementation is exploited to accelerate the cancellation process. Generalized sidelobe canceller (GSC) composed of an FBF and an adaptive blocking module is combined with AEC to maximize cancellation performance. Another enhancement is an internal iteration (IIT) procedure that enables efficient convergence in the adaptive SB filters within a sample time. Objective tests in terms of echo return loss enhancement (ERLE), perceptual evaluation of speech quality (PESQ), word recognition rate for automatic speech recognition (ASR), and subjective listening tests are conducted to validate the proposed AEC approaches. The results show that the GSC-SB-AEC-IIT approach has attained the highest ERLE without speech quality degradation, even in double-talk scenarios. PMID:26936567
Adaptive filtering of ECG interference on surface EEnGs based on signal averaging.
Garcia-Casado, Javier; Martinez-de-Juan, Jose L; Ponce, Jose L
2006-06-01
An external electroenterogram (EEnG) is the recording of the small bowel myoelectrical signal using contact electrodes placed on the abdominal surface. It is a weak signal affected by possible movements and by the interferences of respiration and, principally, of the cardiac signal. In this paper an adaptive filtering technique was proposed to identify and subsequently cancel ECG interference on canine surface EEnGs by means of a signal averaging process time-locked with the R-wave. Twelve recording sessions were carried out on six conscious dogs in the fasting state. The adaptive filtering technique used increases the signal-to-interference ratio of the raw surface EEnG from 16.7 +/- 6.5 dB up to 31.9 +/- 4.0 dB. In addition to removing ECG interference, this technique has been proven to respect intestinal SB activity, i.e. the EEnG component associated with bowel contractions, despite the fact that they overlap in the frequency domain. In this way, more robust non-invasive intestinal motility indicators can be obtained with correlation coefficients of 0.68 +/- 0.09 with internal intestinal activity. The method proposed here may also be applied to other biological recordings affected by cardiac interference and could be a very helpful tool for future applications of non-invasive recordings of gastrointestinal signals.
Crowder, S.V.; Eshleman, L.
1998-08-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards white building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper the authors address the issue of low volume statistical process control. They investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. The authors develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, they study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. They show that far fewer data values are needed than is typically recommended for process control applications. And they demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
CROWDER, STEPHEN V.
1999-09-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards while building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper we address the issue of low volume statistical process control. We investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. We develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, we study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. We show that far fewer data values are needed than is typically recommended for process control applications. We also demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
Johansson, A Torbjorn; White, Paul R
2011-08-01
This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances. PMID:21877804
NASA Astrophysics Data System (ADS)
Goovaerts, P.; Jacquez, G. M.; Marcus, A. W.
2004-12-01
Spatial data are periodically collected and processed to monitor, analyze and interpret developments in our changing environment. Remote sensing is a modern way of data collecting and has seen an enormous growth since launching of modern satellites and development of airborne sensors. In particular, the recent availability of high spatial resolution hyperspectral imagery (spatial resolution of less than 5 meters and including data collected over 64 or more bands of electromagnetic radiation for each pixel offers a great potential to significantly enhance environmental mapping and our ability to model spatial systems. High spatial resolution imagery contains a remarkable quantity of information that could be used to analyze spatial breaks (boundaries), areas of similarity (clusters), and spatial autocorrelation (associations) across the landscape. This paper addresses the specific issue of soil disturbance detection, which could indicate the presence of land mines or recent movements of troop and heavy equipment. A challenge presented by soil detection is to retain the measurement of fine-scale features (i.e. mineral soil changes, organic content changes, vegetation disturbance related changes, aspect changes) while still covering proportionally large spatial areas. An additional difficulty is that no ground data might be available for the calibration of spectral signatures, and little might be known about the size of patches of disturbed soils to be detected. This paper describes a new technique for automatic target detection which capitalizes on both spatial and across spectral bands correlation, does not require any a priori information on the target spectral signature but does not allow discrimination between targets. This approach involves successively a multivariate statistical analysis (principal component analysis) of all spectral bands, a geostatistical filtering of noise and regional background in the first principal components using factorial kriging, and
Carmena, Jose M.
2016-01-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to
Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M
2016-04-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters. PMID:26451391
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters.
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters. PMID:26451391
Mazumder, Ria; Clymer, Bradley D; Mo, Xiaokui; White, Richard D; Kolipaka, Arunark
2016-06-01
Diffusion tensor imaging (DTI) is used to quantify myocardial fiber orientation based on helical angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, we propose to reduce TA by implementing a 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivo healthy and infarcted porcine hearts. DTI was performed on ex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering (AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering (AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure. PMID:26843150
Energy-filtering TEM at high magnification: spatial resolution and detection limits.
Grogger, Werner; Schaffer, Bernhard; Krishnan, Kannan M; Hofer, Ferdinand
2003-09-01
Energy-filtering TEM (EFTEM) has turned out to be a very efficient and rapid tool for the chemical characterization of a specimen on a nanometer and even subnanometer length scale. Especially, the detection and measurement of very thin layers has become a great application of this technique in many materials science fields, e.g. semiconductors and hard disk technology. There, the reliability of compositional profiles is an important issue. However, the experimentally obtainable spatial resolution strongly influences the appearance of a thin layer in an EFTEM image, when dimensions reach subnanometer levels, which mainly leads to a broadening of the layer in the image. This fact has to be taken into account, when measuring the thickness of such a thin layer. Additionally, the convolution decreases contrast which makes the layer less visible in the image and finally determines the detection limit. In this work we present a systematic study on specifically designed Mn/PdMn multilayer test specimens to explore the practical aspects of spatial resolution and detection limits in EFTEM. Although specific to the ionization edges used, we will present general conclusions about the practical limitations in terms of EFTEM spatial resolution. Additionally, work will be shown about low energy-loss imaging of thin oxide layers, where delocalization is the main factor responsible for broadening. PMID:12871810
How does spatial dispersal network affect the evolution of parasite local adaptation?
Vogwill, Tom; Fenton, Andy; Brockhurst, Michael A
2010-06-01
Studying patterns of parasite local adaptation can provide insights into the spatiotemporal dynamics of host-parasite coevolution. Many factors, both biotic and abiotic, have been identified that influence parasite local adaptation. In particular, dispersal and population structuring are considered important determinants of local adaptation. We investigated how the shape of the spatial dispersal network within experimental landscapes affected local adaptation of a bacteriophage parasite to its bacterial host. Regardless of landscape topology, dispersal always led to the evolution of phages with broader infectivity range. However, when the spatial dispersal network resulted in spatial variation in the breadth of phage infectivity range, significant levels of parasite local adaptation and local maladaptation were detected within the same landscape using the local versus foreign definition of local adaptation. By contrast, local adaptation was not detected using the home versus away or local versus global definitions of local adaptation. This suggests that spatial dispersal networks may play an important role in driving parasite local adaptation, particularly when the shape of the dispersal network generates nonuniform levels of host resistance or parasite infectivity throughout a species' range. PMID:20050909
Orientation and spatial frequency selectivity of adaptation to color and luminance gratings.
Bradley, A; Switkes, E; De Valois, K
1988-01-01
Prolonged viewing of sinusoidal luminance gratings produces elevated contrast detection thresholds for test gratings that are similar in spatial frequency and orientation to the adaptation stimulus. We have used this technique to investigate orientation and spatial frequency selectivity in the processing of color contrast information. Adaptation to isoluminant red-green gratings produces elevated color contrast thresholds that are selective for grating orientation and spatial frequency. Only small elevations in color contrast thresholds occur after adaptation to luminance gratings, and vice versa. Although the color adaptation effects appear slightly less selective than those for luminance, our results suggest similar spatial processing of color and luminance contrast patterns by early stages of the human visual system.
Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P. A.; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel
2014-01-01
Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes
NASA Astrophysics Data System (ADS)
Ando, S.; Nara, T.; Kurihara, T.
2014-08-01
Spatial filtering velocimetry was proposed in 1963 by Ator as a velocity-sensing technique for aerial camera-control systems. The total intensity of a moving surface is observed through a set of parallel-slit reticles, resulting in a narrow-band temporal signal whose frequency is directly proportional to the image velocity. However, even despite its historical importance and inherent technical advantages, the mathematical formulation of this technique is only valid when infinite-length observation in both space and time is possible, which causes significant errors in most applications where a small receptive window and high resolution in both axes are desired. In this study, we apply a novel mathematical technique, the weighted integral method, to solve this problem, and obtain exact sensing schemes and algorithms for finite (arbitrarily small but non-zero) size reticles and short-time estimation. Practical considerations for utilizing these schemes are also explored both theoretically and experimentally.
NASA Astrophysics Data System (ADS)
Gan, Qifeng; Seoud, Lama; Ben Tahar, Houssem; Langlois, J. M. Pierre
2016-04-01
Spatial Averaging Filters (SAF) are extensively used in image processing for image smoothing and denoising. Their latest implementations have already achieved constant time computational complexity regardless of kernel size. However, all the existing O(1) algorithms require additional memory for temporary data storage. In order to minimize memory usage in embedded systems, we introduce a new two-dimensional recursive SAF. It uses previous resultant pixel values along both rows and columns to calculate the current one. It can achieve constant time computational complexity without using any additional memory usage. Experimental comparisons with previous SAF implementations shows that the proposed 2D-Recursive SAF does not require any additional memory while offering a computational time similar to the most efficient existing SAF algorithm. These features make it especially suitable for embedded systems with limited memory capacity.
Zaitcev, Aleksandr; Cook, Greg; Wei Liu; Paley, Martyn; Milne, Elizabeth
2015-08-01
Brain-Computer Interfaces (BCIs) provide means for communication and control without muscular movement and, therefore, can offer significant clinical benefits. Electrical brain activity recorded by electroencephalography (EEG) can be interpreted into software commands by various classification algorithms according to the descriptive features of the signal. In this paper we propose a novel EEG BCI feature extraction method employing EEG source reconstruction and Filter Bank Common Spatial Patterns (FBCSP) based on Joint Approximate Diagonalization (JAD). The proposed method is evaluated by the commonly used reference EEG dataset yielding an average classification accuracy of 77.1 ± 10.1 %. It is shown that FBCSP feature extraction applied to reconstructed source components outperforms conventional CSP and FBCSP feature extraction methods applied to signals in the sensor domain.
Relationships between observer and Kalman Filter models for human dynamic spatial orientation.
Selva, Pierre; Oman, Charles M
2012-01-01
How does the central nervous system (CNS) combine sensory information from semicircular canal, otolith, and visual systems into perceptions of rotation, translation and tilt? Over the past four decades, a variety of input-output ("black box") mathematical models have been proposed to predict human dynamic spatial orientation perception and eye movements. The models have proved useful in vestibular diagnosis, aircraft accident investigation, and flight simulator design. Experimental refinement continues. This paper briefly reviews the history of two widely known model families, the linear "Kalman Filter" and the nonlinear "Observer". Recent physiologic data supports the internal model assumptions common to both. We derive simple 1-D and 3-D examples of each model for vestibular inputs, and show why - despite apparently different structure and assumptions - the linearized model predictions are dynamically equivalent when the four free model parameters are adjusted to fit the same empirical data, and perceived head orientation remains near upright. We argue that the motion disturbance and sensor noise spectra employed in the Kalman Filter formulation may reflect normal movements in daily life and perceptual thresholds, and thus justify the interpretation that the CNS cue blending scheme may well minimize least squares angular velocity perceptual errors.
Inostroza, Luis; Palme, Massimo; de la Barrera, Francisco
2016-01-01
Climate change will worsen the high levels of urban vulnerability in Latin American cities due to specific environmental stressors. Some impacts of climate change, such as high temperatures in urban environments, have not yet been addressed through adaptation strategies, which are based on poorly supported data. These impacts remain outside the scope of urban planning. New spatially explicit approaches that identify highly vulnerable urban areas and include specific adaptation requirements are needed in current urban planning practices to cope with heat hazards. In this paper, a heat vulnerability index is proposed for Santiago, Chile. The index was created using a GIS-based spatial information system and was constructed from spatially explicit indexes for exposure, sensitivity and adaptive capacity levels derived from remote sensing data and socio-economic information assessed via principal component analysis (PCA). The objective of this study is to determine the levels of heat vulnerability at local scales by providing insights into these indexes at the intra city scale. The results reveal a spatial pattern of heat vulnerability with strong variations among individual spatial indexes. While exposure and adaptive capacities depict a clear spatial pattern, sensitivity follows a complex spatial distribution. These conditions change when examining PCA results, showing that sensitivity is more robust than exposure and adaptive capacity. These indexes can be used both for urban planning purposes and for proposing specific policies and measures that can help minimize heat hazards in highly dynamic urban areas. The proposed methodology can be applied to other Latin American cities to support policy making.
Piaggi, Paolo; Menicucci, Danilo; Gentili, Claudio; Handjaras, Giacomo; Gemignani, Angelo; Landi, Alberto
2014-05-01
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.
Song, Hoon; Sung, Geeyoung; Choi, Sujin; Won, Kanghee; Lee, Hong-Seok; Kim, Hwi
2012-12-31
We propose an optical system for synthesizing double-phase complex computer-generated holograms using a phase-only spatial light modulator and a phase grating filter. Two separated areas of the phase-only spatial light modulator are optically superposed by 4-f configuration with an optimally designed grating filter to synthesize arbitrary complex optical field distributions. The tolerances related to misalignment factors are analyzed, and the optimal synthesis method of double-phase computer-generated holograms is described. PMID:23388811
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Singh, Omkar; Sunkaria, Ramesh Kumar
2015-01-01
Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods. PMID:25412942
Color filter array demosaicing: an adaptive progressive interpolation based on the edge type
NASA Astrophysics Data System (ADS)
Dong, Qiqi; Liu, Zhaohui
2015-10-01
Color filter array (CFA) is one of the key points for single-sensor digital cameras to produce color images. Bayer CFA is the most commonly used pattern. In this array structure, the sampling frequency of green is two times of red or blue, which is consistent with the sensitivity of human eyes to colors. However, each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to CFA demosaicing, is required to estimate the other two missing color values at each pixel. In this paper, we explore an adaptive progressive interpolation based on the edge type algorithm. The proposed demosaicing method consists of two successive steps: an interpolation step that estimates missing color values according to various edges and a post-processing step by iterative interpolation.
NASA Astrophysics Data System (ADS)
Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.
2005-03-01
A blood perfusion and oxygenation sensor has been developed for in situ monitoring of transplanted organs. In processing in situ data, motion artifacts due to increased perfusion can create invalid oxygenation saturation values. In order to remove the unwanted artifacts from the pulsatile signal, adaptive filtering was employed using a third wavelength source centered at 810nm as a reference signal. The 810 nm source resides approximately at the isosbestic point in the hemoglobin absorption curve where the absorbance of light is nearly equal for oxygenated and deoxygenated hemoglobin. Using an autocorrelation based algorithm oxygenation saturation values can be obtained without the need for large sampling data sets allowing for near real-time processing. This technique has been shown to be more reliable than traditional techniques and proven to adequately improve the measurement of oxygenation values in varying perfusion states.
Ko, Byung-hoon; Lee, Takhyung; Choi, Changmok; Kim, Youn-ho; Park, Gunguk; Kang, KyoungHo; Bae, Sang Kon; Shin, Kunsoo
2012-01-01
The electrocardiogram (ECG) is the main measurement parameter for effectively diagnosing chronic disease and guiding cardio-fitness therapy. ECGs contaminated by noise or artifacts disrupt the normal functioning of the automatic analysis algorithm. The objective of this study is to evaluate a method of measuring the HCP variation in motion artifacts through direct monitoring. The proposed wearable sensing device has two channels. One channel is used to measure the ECG through a differential amplifier. The other is for monitoring motion artifacts using the modified electrode and the same differential amplifier. Noise reduction was performed using adaptive filtering, based on a reference signal highly correlated with it. Direct measurement of HCP variations can eliminate the need for additional sensors. PMID:23366209
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers.
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers. PMID:26736389
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
Local stimulus disambiguation with global motion filters predicts adaptive surround modulation.
Dellen, Babette; Torras, Carme
2013-10-01
Humans have no problem segmenting different motion stimuli despite the ambiguity of local motion signals. Adaptive surround modulation, i.e., the apparent switching between integrative and antagonistic modes, is assumed to play a crucial role in this process. However, so far motion processing models based on local integration have not been able to provide a unifying explanation for this phenomenon. This motivated us to investigate the problem of local stimulus disambiguation in an alternative and fundamentally distinct motion-processing model which uses global motion filters for velocity computation. Local information is reconstructed at the end of the processing stream through the constructive interference of global signals, i.e., inverse transformations. We show that in this model local stimulus disambiguation can be achieved by means of a novel filter embedded in this architecture. This gives rise to both integrative and antagonistic effects which are in agreement with those observed in psychophysical experiments with humans, providing a functional explanation for effects of motion repulsion.
Adaptive Filter-bank Approach to Restoration and Spectral Analysis of Gapped Data
NASA Astrophysics Data System (ADS)
Stoica, Petre; Larsson, Erik G.; Li, Jian
2000-10-01
The main topic of this paper is the nonparametric estimation of complex (both amplitude and phase) spectra from gapped data, as well as the restoration of such data. The focus is on the extension of the APES (amplitude and phase estimation) approach to data sequences with gaps. APES, which is one of the most successful existing nonparametric approaches to the spectral analysis of full data sequences, uses a bank of narrowband adaptive (both frequency and data dependent) filters to estimate the spectrum. A recent interpretation of this approach showed that the filterbank used by APES and the resulting spectrum minimize a least-squares (LS) fitting criterion between the filtered sequence and its spectral decomposition. The extended approach, which is called GAPES for somewhat obvious reasons, capitalizes on the aforementioned interpretation: it minimizes the APES-LS fitting criterion with respect to the missing data as well. This should be a sensible thing to do whenever the full data sequence is stationary, and hence the missing data have the same spectral content as the available data. We use both simulated and real data examples to show that GAPES estimated spectra and interpolated data sequences have excellent accuracy. We also show the performance gain achieved by GAPES over two of the most commonly used approaches for gapped-data spectral analysis, viz., the periodogram and the parametric CLEAN method. This work was partly supported by the Swedish Foundation for Strategic Research.
A waveguide invariant adaptive matched filter for active sonar target depth classification.
Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey
2011-04-01
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions. PMID:21476638
Local stimulus disambiguation with global motion filters predicts adaptive surround modulation.
Dellen, Babette; Torras, Carme
2013-10-01
Humans have no problem segmenting different motion stimuli despite the ambiguity of local motion signals. Adaptive surround modulation, i.e., the apparent switching between integrative and antagonistic modes, is assumed to play a crucial role in this process. However, so far motion processing models based on local integration have not been able to provide a unifying explanation for this phenomenon. This motivated us to investigate the problem of local stimulus disambiguation in an alternative and fundamentally distinct motion-processing model which uses global motion filters for velocity computation. Local information is reconstructed at the end of the processing stream through the constructive interference of global signals, i.e., inverse transformations. We show that in this model local stimulus disambiguation can be achieved by means of a novel filter embedded in this architecture. This gives rise to both integrative and antagonistic effects which are in agreement with those observed in psychophysical experiments with humans, providing a functional explanation for effects of motion repulsion. PMID:23685285
A waveguide invariant adaptive matched filter for active sonar target depth classification.
Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey
2011-04-01
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions.
NASA Technical Reports Server (NTRS)
Wang, Ray (Inventor)
2009-01-01
A method and system for spatial data manipulation input and distribution via an adaptive wireless transceiver. The method and system include a wireless transceiver for automatically and adaptively controlling wireless transmissions using a Waveform-DNA method. The wireless transceiver can operate simultaneously over both the short and long distances. The wireless transceiver is automatically adaptive and wireless devices can send and receive wireless digital and analog data from various sources rapidly in real-time via available networks and network services.
Development of Climate Change Adaptation Platform using Spatial Information
NASA Astrophysics Data System (ADS)
Lee, J.; Oh, K. Y.; Lee, M. J.; Han, W. J.
2014-12-01
Climate change adaptation has attracted growing attention with the recent extreme weather conditions that affect people around the world. More and more countries, including the Republic of Korea, have begun to hatch adaptation plan to resolve these matters of great concern. They all, meanwhile, have mentioned that it should come first to integrate climate information in all analysed areas. That's because climate information is not independently made through one source; that is to say, the climate information is connected one another in a complicated way. That is the reason why we have to promote integrated climate change adaptation platform before setting up climate change adaptation plan. Therefore, the large-scaled project has been actively launched and worked on. To date, we researched 620 literatures and interviewed 51 government organizations. Based on the results of the researches and interviews, we obtained 2,725 impacts about vulnerability assessment information such as Monitoring and Forecasting, Health, Disaster, Agriculture, Forest, Water Management, Ecosystem, Ocean/Fisheries, Industry/Energy. Among 2,725 impacts, 995 impacts are made into a database until now. This database is made up 3 sub categories like Climate-Exposure, Sensitivity, Adaptive capacity, presented by IPCC. Based on the constructed database, vulnerability assessments were carried out in order to evaluate climate change capacity of local governments all over the country. These assessments were conducted by using web-based vulnerability assessment tool which was newly developed through this project. These results have shown that, metropolitan areas like Seoul, Pusan, Inchon, and so on have high risks more than twice than rural areas. Acknowledgements: The authors appreciate the support that this study has received from "Development of integrated model for climate change impact and vulnerability assessment and strengthening the framework for model implementation ", an initiative of the
Aging and luminance-adaptation effects on spatial contrast sensitivity.
Sloane, M E; Owsley, C; Jackson, C A
1988-12-01
Contrast sensitivity as a function of target luminance for four spatial frequencies (0.5, 2, 4, and 8 cycles/deg) was measured in younger (n = 12; age range, 19-35 years) and older (n = 11; age range, 68-79 years) adults in order to examine the feasibility of optical and neural explanations for the impairment of contrast sensitivity in older adults. All subjects were free from identifiable ocular disease and had good acuity. Sensitivity for each spatial frequency was measured at eight luminance levels spanning 3.5 log units in the photopic-mesopic range. When gratings were flickered at 0.5 Hz, functions for older adults were displaced downward on the sensitivity axis across all luminance levels, and the slopes of these functions were steeper than those for younger adults, suggesting that optical mechanisms alone cannot account for the vision loss in older adults. Further measurements, in which spatial targets were flickered at 7.5 Hz, indicated that this faster temporal modulation affected sensitivity as a function of luminance differentially in younger and older adults. These data imply that the neural mechanisms subserving human spatial vision undergo significant changes during adulthood.
Ally, Dilara; Wiss, Valorie R.; Deckert, Gail E.; Green, Danielle; Roychoudhury, Pavitra; Wichman, Holly A.; Brown, Celeste J.; Krone, Stephen M.
2014-01-01
Background Most clinical and natural microbial communities live and evolve in spatially structured environments. When changes in environmental conditions trigger evolutionary responses, spatial structure can impact the types of adaptive response and the extent to which they spread. In particular, localized competition in a spatial landscape can lead to the emergence of a larger number of different adaptive trajectories than would be found in well-mixed populations. Our goal was to determine how two levels of spatial structure affect genomic diversity in a population and how this diversity is manifested spatially. Methodology/Principal Findings We serially transferred bacteriophage populations growing at high temperatures (40°C) on agar plates for 550 generations at two levels of spatial structure. The level of spatial structure was determined by whether the physical locations of the phage subsamples were preserved or disrupted at each passage to fresh bacterial host populations. When spatial structure of the phage populations was preserved, there was significantly greater diversity on a global scale with restricted and patchy distribution. When spatial structure was disrupted with passaging to fresh hosts, beneficial mutants were spread across the entire plate. This resulted in reduced diversity, possibly due to clonal interference as the most fit mutants entered into competition on a global scale. Almost all substitutions present at the end of the adaptation in the populations with disrupted spatial structure were also present in the populations with structure preserved. Conclusions/Significance Our results are consistent with the patchy nature of the spread of adaptive mutants in a spatial landscape. Spatial structure enhances diversity and slows fixation of beneficial mutants. This added diversity could be beneficial in fluctuating environments. We also connect observed substitutions and their effects on fitness to aspects of phage biology, and we provide
Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.
Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid
2015-11-01
Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2).
Dose convolution filter: Incorporating spatial dose information into tissue response modeling
Huang Yimei; Joiner, Michael; Zhao Bo; Liao Yixiang; Burmeister, Jay
2010-03-15
Purpose: A model is introduced to integrate biological factors such as cell migration and bystander effects into physical dose distributions, and to incorporate spatial dose information in plan analysis and optimization. Methods: The model consists of a dose convolution filter (DCF) with single parameter {sigma}. Tissue response is calculated by an existing NTCP model with DCF-applied dose distribution as input. The authors determined {sigma} of rat spinal cord from published data. The authors also simulated the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying the DCF, the NTCP model successfully fits the rat spinal cord data with a predicted value of {sigma}=2.6{+-}0.5 mm, consistent with 2 mm migration distances of remyelinating cells. Moreover, it enables the appropriate prediction of a high relative seriality for spinal cord. The model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to {sigma}. Conclusions: The DCF model incorporates spatial dose information and offers an improved way to estimate tissue response from complex radiotherapy dose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance in small or highly nonuniform fields.
Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih; Wu, Jay; Chang, Shu-Jun
2014-08-15
Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. Methods: The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.
Savin, Douglas N.; Tseng, Shih-Chiao; Whitall, Jill; Morton, Susanne M.
2015-01-01
Background Persons with stroke and hemiparesis walk with a characteristic pattern of spatial and temporal asymmetry that is resistant to most traditional interventions. It was recently shown in nondisabled persons that the degree of walking symmetry can be readily altered via locomotor adaptation. However, it is unclear whether stroke-related brain damage affects the ability to adapt spatial or temporal gait symmetry. Objective Determine whether locomotor adaptation to a novel swing phase perturbation is impaired in persons with chronic stroke and hemiparesis. Methods Participants with ischemic stroke (14) and nondisabled controls (12) walked on a treadmill before, during, and after adaptation to a unilateral perturbing weight that resisted forward leg movement. Leg kinematics were measured bilaterally, including step length and single-limb support (SLS) time symmetry, limb angle center of oscillation, and interlimb phasing, and magnitude of “initial” and “late” locomotor adaptation rates were determined. Results All participants had similar magnitudes of adaptation and similar initial adaptation rates both spatially and temporally. All 14 participants with stroke and baseline asymmetry temporarily walked with improved SLS time symmetry after adaptation. However, late adaptation rates poststroke were decreased (took more strides to achieve adaptation) compared with controls. Conclusions Mild to moderate hemiparesis does not interfere with the initial acquisition of novel symmetrical gait patterns in both the spatial and temporal domains, though it does disrupt the rate at which “late” adaptive changes are produced. Impairment of the late, slow phase of learning may be an important rehabilitation consideration in this patient population. PMID:22367915
Preflight Adaptation Training for Spatial Orientation and Space Motion Sickness
NASA Technical Reports Server (NTRS)
Harm, Deborah L.; Parker, Donald E.
1994-01-01
Two part-task preflight adaptation trainers (PATs) are being developed at the NASA Johnson Space Center to preadapt astronauts to novel sensory stimulus conditions similar to those present in microgravity to facilitate adaptation to microgravity and readaptation to Earth. This activity is a major component of a general effort to develop countermeasures aimed at minimizing sensory and sensorimotor disturbances and Space Motion Sickness (SMS) associated with adaptation to microgravity and readaptation to Earth. Design principles for the development of the two trainers are discussed, along with a detailed description of both devices. In addition, a summary of four ground-based investigations using one of the trainers to determine the extent to which various novel sensory stimulus conditions produce changes in compensatory eye movement responses, postural equilibrium, motion sickness symptoms, and electrogastric responses are presented. Finally, a brief description of the general concept of dual-adopted states that underly the development of the PATs, and ongoing and future operational and basic research activities are presented.
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng
2015-01-01
Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems. PMID:26089975
Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus
MacLeod, Katrina M.; Lubejko, Susan T.; Steinberg, Louisa J.; Köppl, Christine; Peña, Jose L.
2014-01-01
In the visual, auditory, and electrosensory modalities, stimuli are defined by first- and second-order attributes. The fast time-pressure signal of a sound, a first-order attribute, is important, for instance, in sound localization and pitch perception, while its slow amplitude-modulated envelope, a second-order attribute, can be used for sound recognition. Ascending the auditory pathway from ear to midbrain, neurons increasingly show a preference for the envelope and are most sensitive to particular envelope modulation frequencies, a tuning considered important for encoding sound identity. The level at which this tuning property emerges along the pathway varies across species, and the mechanism of how this occurs is a matter of debate. In this paper, we target the transition between auditory nerve fibers and the cochlear nucleus angularis (NA). While the owl's auditory nerve fibers simultaneously encode the fast and slow attributes of a sound, one synapse further, NA neurons encode the envelope more efficiently than the auditory nerve. Using in vivo and in vitro electrophysiology and computational analysis, we show that a single-cell mechanism inducing spike threshold adaptation can explain the difference in neural filtering between the two areas. We show that spike threshold adaptation can explain the increased selectivity to modulation frequency, as input level increases in NA. These results demonstrate that a spike generation nonlinearity can modulate the tuning to second-order stimulus features, without invoking network or synaptic mechanisms. PMID:24790170
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou
2011-09-01
To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.
Low spatial frequency filtering modulates early brain processing of affective complex pictures.
Alorda, Catalina; Serrano-Pedraza, Ignacio; Campos-Bueno, J Javier; Sierra-Vázquez, Vicente; Montoya, Pedro
2007-11-01
Recent research on affective processing has suggested that low spatial frequency information of fearful faces provide rapid emotional cues to the amygdala, whereas high spatial frequencies convey fine-grained information to the fusiform gyrus, regardless of emotional expression. In the present experiment, we examined the effects of low (LSF, <15 cycles/image width) and high spatial frequency filtering (HSF, >25 cycles/image width) on brain processing of complex pictures depicting pleasant, unpleasant, and neutral scenes. Event-related potentials (ERP), percentage of recognized stimuli and response times were recorded in 19 healthy volunteers. Behavioral results indicated faster reaction times in response to unpleasant LSF than to unpleasant HSF pictures. Unpleasant LSF pictures and pleasant unfiltered pictures also elicited significant enhancements of P1 amplitudes at occipital electrodes as compared to neutral LSF and unfiltered pictures, respectively; whereas no significant effects of affective modulation were found for HSF pictures. Moreover, mean ERP amplitudes in the time between 200 and 500ms post-stimulus were significantly greater for affective (pleasant and unpleasant) than for neutral unfiltered pictures; whereas no significant affective modulation was found for HSF or LSF pictures at those latencies. The fact that affective LSF pictures elicited an enhancement of brain responses at early, but not at later latencies, suggests the existence of a rapid and preattentive neural mechanism for the processing of motivationally relevant stimuli, which could be driven by LSF cues. Our findings confirm thus previous results showing differences on brain processing of affective LSF and HSF faces, and extend these results to more complex and social affective pictures.
Saccadic latency is modulated by emotional content of spatially filtered face stimuli.
Bannerman, Rachel L; Hibbard, Paul B; Chalmers, Kirsty; Sahraie, Arash
2012-12-01
Models of attention and emotion assign a special status to the processing of threat. While evidence for threat-related attentional bias in highly anxious individuals is robust, effects in the normal population are mixed. An important explanation for the absence of threat-related attentional bias in nonanxious individuals may relate to the spatial frequency components of stimuli. Here we report behavioral data from two experiments examining the relationship between spatial frequency components of emotional and neutral faces and fast saccadic orienting behavior. In Experiment 1 participants had to saccade toward a single face, filtered to include mostly low, high or broad spatial frequencies (LSF, HSF or BSF), posing a fearful, happy or neutral expression presented for 20 ms in the periphery. At BSF a general emotional effect was found whereby saccadic responses were faster for fearful and happy faces relative to neutral, with no significant differences between fearful and happy faces. At LSF both fearful and happy faces had shorter saccadic latencies in comparison to neutral, demonstrating an emotional bias consistent with the BSF data. However, at LSF fearful faces resulted in significantly faster saccades than happy faces indicating that this bias was stronger for threat-related faces. There was no difference in saccadic responses between any emotions at HSF. Experiment 2 showed that the emotional bias diminished for inverted stimuli suggesting that the results were not attributable to low-level image properties. The findings suggest an overall advantage in the oculomotor system for orientation to emotional stimuli and at LSF in particular, a significantly faster localization of threat conveyed by the face stimuli in all individuals.
A tunable electrochromic fabry-perot filter for adaptive optics applications.
Blaich, Jonathan David; Kammler, Daniel R.; Ambrosini, Andrea; Sweatt, William C.; Verley, Jason C.; Heller, Edwin J.; Yelton, William Graham
2006-10-01
The potential for electrochromic (EC) materials to be incorporated into a Fabry-Perot (FP) filter to allow modest amounts of tuning was evaluated by both experimental methods and modeling. A combination of chemical vapor deposition (CVD), physical vapor deposition (PVD), and electrochemical methods was used to produce an ECFP film stack consisting of an EC WO{sub 3}/Ta{sub 2}O{sub 5}/NiO{sub x}H{sub y} film stack (with indium-tin-oxide electrodes) sandwiched between two Si{sub 3}N{sub 4}/SiO{sub 2} dielectric reflector stacks. A process to produce a NiO{sub x}H{sub y} charge storage layer that freed the EC stack from dependence on atmospheric humidity and allowed construction of this complex EC-FP stack was developed. The refractive index (n) and extinction coefficient (k) for each layer in the EC-FP film stack was measured between 300 and 1700 nm. A prototype EC-FP filter was produced that had a transmission at 500 nm of 36%, and a FWHM of 10 nm. A general modeling approach that takes into account the desired pass band location, pass band width, required transmission and EC optical constants in order to estimate the maximum tuning from an EC-FP filter was developed. Modeling shows that minor thickness changes in the prototype stack developed in this project should yield a filter with a transmission at 600 nm of 33% and a FWHM of 9.6 nm, which could be tuned to 598 nm with a FWHM of 12.1 nm and a transmission of 16%. Additional modeling shows that if the EC WO{sub 3} absorption centers were optimized, then a shift from 600 nm to 598 nm could be made with a FWHM of 11.3 nm and a transmission of 20%. If (at 600 nm) the FWHM is decreased to 1 nm and transmission maintained at a reasonable level (e.g. 30%), only fractions of a nm of tuning would be possible with the film stack considered in this study. These tradeoffs may improve at other wavelengths or with EC materials different than those considered here. Finally, based on our limited investigation and material set
Oka, Masayoshi; Wong, David W S
2016-06-01
Area-based measures of neighborhood characteristics simply derived from enumeration units (e.g., census tracts or block groups) ignore the potential of spatial spillover effects, and thus incorporating such measures into multilevel regression models may underestimate the neighborhood effects on health. To overcome this limitation, we describe the concept and method of areal median filtering to spatialize area-based measures of neighborhood characteristics for multilevel regression analyses. The areal median filtering approach provides a means to specify or formulate "neighborhoods" as meaningful geographic entities by removing enumeration unit boundaries as the absolute barriers and by pooling information from the neighboring enumeration units. This spatializing process takes into account for the potential of spatial spillover effects and also converts aspatial measures of neighborhood characteristics into spatial measures. From a conceptual and methodological standpoint, incorporating the derived spatial measures into multilevel regression analyses allows us to more accurately examine the relationships between neighborhood characteristics and health. To promote and set the stage for informative research in the future, we provide a few important conceptual and methodological remarks, and discuss possible applications, inherent limitations, and practical solutions for using the areal median filtering approach in the study of neighborhood effects on health.
Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Frost, Susan
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter.
Goldsworthy, Raymond L.; Delhorne, Lorraine A.; Desloge, Joseph G.; Braida, Louis D.
2014-01-01
This article introduces and provides an assessment of a spatial-filtering algorithm based on two closely-spaced (∼1 cm) microphones in a behind-the-ear shell. The evaluated spatial-filtering algorithm used fast (∼10 ms) temporal-spectral analysis to determine the location of incoming sounds and to enhance sounds arriving from straight ahead of the listener. Speech reception thresholds (SRTs) were measured for eight cochlear implant (CI) users using consonant and vowel materials under three processing conditions: An omni-directional response, a dipole-directional response, and the spatial-filtering algorithm. The background noise condition used three simultaneous time-reversed speech signals as interferers located at 90°, 180°, and 270°. Results indicated that the spatial-filtering algorithm can provide speech reception benefits of 5.8 to 10.7 dB SRT compared to an omni-directional response in a reverberant room with multiple noise sources. Given the observed SRT benefits, coupled with an efficient design, the proposed algorithm is promising as a CI noise-reduction solution. PMID:25096120
A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter
NASA Technical Reports Server (NTRS)
Krasowski, M. J.; Dickens, D. E.
1992-01-01
A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.
NASA Technical Reports Server (NTRS)
Toldalagi, P. M.
1980-01-01
A review is made of recursive statistical regression techniques incorporating past or past and future observations through smoothing and Kalman filtering, respectively; with results for the cases of the Tiros-N/MSU and Nimbus-6/Scams remote sensing satellite experiments. In response to the lack of a satisfactory model for the medium sounded, which is presently a major limitation on retrieval technique performance, a novel, global approach is proposed which casts the retrieval problem into the framework of adaptive filtering. A numerical implementation of such an adaptive system is presented, with a multilayer, semi-spectral general circulation model for the atmosphere being used to fine-tune the sensor as well as the dynamical equations of a Kalman filter. It is shown that the assimilation of radiometric data becomes a straightforward subproblem.
Goedert, Kelly M.; Chen, Peii; Boston, Raymond C.; Foundas, Anne L.; Barrett, A. M.
2013-01-01
Spatial neglect is a debilitating disorder for which there is no agreed upon course of rehabilitation. The lack of consensus on treatment may result from systematic differences in the syndromes’ characteristics, with spatial cognitive deficits potentially affecting perceptual-attentional Where or motor-intentional Aiming spatial processing. Heterogeneity of response to treatment might be explained by different treatment impact on these dissociated deficits: prism adaptation, for example, might reduce Aiming deficits without affecting Where spatial deficits. Here, we tested the hypothesis that classifying patients by their profile of Where-vs-Aiming spatial deficit would predict response to prism adaptation, and specifically that patients with Aiming bias would have better recovery than those with isolated Where bias. We classified the spatial errors of 24 sub-acute right-stroke survivors with left spatial neglect as: 1) isolated Where bias, 2) isolated Aiming bias or 3) both. Participants then completed two weeks of prism adaptation treatment. They also completed the Behavioral Inattention Test (BIT) and Catherine Bergego Scale (CBS) tests of neglect recovery weekly for six weeks. As hypothesized, participants with only Aiming deficits improved on the CBS, whereas, those with only Where deficits did not improve. Participants with both deficits demonstrated intermediate improvement. These results support behavioral classification of spatial neglect patients as a potential valuable tool for assigning targeted, effective early rehabilitation. PMID:24376064
Smart adaptive optic systems using spatial light modulators.
Clark, N; Banish, M; Ranganath, H S
1999-01-01
Many factors contribute to the aberrations induced in an optical system. Atmospheric turbulence between the object and the imaging system, physical or thermal perturbations in optical elements degrade the system's point spread function, and misaligned optics are the primary sources of aberrations that affect image quality. The design of a nonconventional real-time adaptive optic system using a micro-mirror device for wavefront correction is presented. The unconventional compensated imaging system presented offers advantages in speed, cost, power consumption, and weight. A pulsed-coupled neural network is used to as a preprocessor to enhance the performance of the wavefront sensor for low-light applications. Modeling results that characterize the system performance are presented. PMID:18252558
NASA Astrophysics Data System (ADS)
Kiani, Maryam; Pourtakdoust, Seid H.
2014-12-01
A novel algorithm is presented in this study for estimation of spacecraft's attitudes and angular rates from vector observations. In this regard, a new cubature-quadrature particle filter (CQPF) is initially developed that uses the Square-Root Cubature-Quadrature Kalman Filter (SR-CQKF) to generate the importance proposal distribution. The developed CQPF scheme avoids the basic limitation of particle filter (PF) with regards to counting the new measurements. Subsequently, CQPF is enhanced to adjust the sample size at every time step utilizing the idea of confidence intervals, thus improving the efficiency and accuracy of the newly proposed adaptive CQPF (ACQPF). In addition, application of the q-method for filter initialization has intensified the computation burden as well. The current study also applies ACQPF to the problem of attitude estimation of a low Earth orbit (LEO) satellite. For this purpose, the undertaken satellite is equipped with a three-axis magnetometer (TAM) as well as a sun sensor pack that provide noisy geomagnetic field data and Sun direction measurements, respectively. The results and performance of the proposed filter are investigated and compared with those of the extended Kalman filter (EKF) and the standard particle filter (PF) utilizing a Monte Carlo simulation. The comparison demonstrates the viability and the accuracy of the proposed nonlinear estimator.
NASA Astrophysics Data System (ADS)
Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar
2011-12-01
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
Spatial filtering self-velocimeter for vehicle application using a CMOS linear image sensor
NASA Astrophysics Data System (ADS)
He, Xin; Zhou, Jian; Nie, Xiaoming; Long, Xingwu
2015-03-01
The idea of using a spatial filtering velocimeter (SFV) to measure the velocity of a vehicle for an inertial navigation system is put forward. The presented SFV is based on a CMOS linear image sensor with a high-speed data rate, large pixel size, and built-in timing generator. These advantages make the image sensor suitable to measure vehicle velocity. The power spectrum of the output signal is obtained by fast Fourier transform and is corrected by a frequency spectrum correction algorithm. This velocimeter was used to measure the velocity of a conveyor belt driven by a rotary table and the measurement uncertainty is ˜0.54%. Furthermore, it was also installed on a vehicle together with a laser Doppler velocimeter (LDV) to measure self-velocity. The measurement result of the designed SFV is compared with that of the LDV. It is shown that the measurement result of the SFV is coincident with that of the LDV. Therefore, the designed SFV is suitable for a vehicle self-contained inertial navigation system.
Using Gabor filter banks and temporal-spatial constraints to compute 3D myocardium strain.
Chen, Ting; Axel, Leon
2006-01-01
In this paper, we describe a new approach for reconstructing 3D strains in the myocardium using tagged MR images. We first segment the myocardium using a 3D deformable model driven by image gradients and Gabor filter responses. Tags are automatically detected and tracked as deformable thin plates during systole and early diastole. To keep the tracking results more stable and consistent, we use a combination of gradient information, an intensity probabilistic model, the phase information, and a temporal-spatial smoothness constraint. Based on the tag deformation, we compute a dense displacement in the myocardium around both ventricles. The displacements in x-, y-, and z- directions are calculated separately and are combined to form the final displacement maps. We do not use the information outside the segmented surface of the myocardium to avoid displacement errors caused by noises, artifacts, and correlations between different regions in the myocardium. The strain in the myocardium during the heart cycle is derived from the displacement. This method accepts images of either a tag grid or separate horizontal and vertical tag lines as its input. Experimental results on phantom and real data demonstrate good performance of this method in calculating the myocardial strain.
Assessment of damage localization based on spatial filters using numerical crack propagation models
NASA Astrophysics Data System (ADS)
Deraemaeker, Arnaud
2011-07-01
This paper is concerned with vibration based structural health monitoring with a focus on non-model based damage localization. The type of damage investigated is cracking of concrete structures due to the loss of prestress. In previous works, an automated method based on spatial filtering techniques applied to large dynamic strain sensor networks has been proposed and tested using data from numerical simulations. In the simulations, simplified representations of cracks (such as a reduced Young's modulus) have been used. While this gives the general trend for global properties such as eigen frequencies, the change of more local features, such as strains, is not adequately represented. Instead, crack propagation models should be used. In this study, a first attempt is made in this direction for concrete structures (quasi brittle material with softening laws) using crack-band models implemented in the commercial software DIANA. The strategy consists in performing a non-linear computation which leads to cracking of the concrete, followed by a dynamic analysis. The dynamic response is then used as the input to the previously designed damage localization system in order to assess its performances. The approach is illustrated on a simply supported beam modeled with 2D plane stress elements.
Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors
Luessi, Martin; Babacan, S. Derin; Molina, Rafael; Booth, James R.; Katsaggelos, Aggelos K.
2011-01-01
In this paper, we propose a novel symmetrical EEG/fMRI fusion method which combines EEG and fMRI by means of a common generative model. We use a total variation (TV) prior to model the spatial distribution of the cortical current responses and hemodynamic response functions, and utilize spatially adaptive temporal priors to model their temporal shapes. The spatial adaptivity of the prior model allows for adaptation to the local characteristics of the estimated responses and leads to high estimation performance for the cortical current distribution and the hemodynamic response functions. We utilize a Bayesian formulation with a variational Bayesian framework and obtain a fully automatic fusion algorithm. Simulations with synthetic data and experiments with real data from a multimodal study on face perception demonstrate the performance of the proposed method. PMID:21130173
Adaptive spatial combining for passive time-reversed communications.
Gomes, João; Silva, António; Jesus, Sérgio
2008-08-01
Passive time reversal has aroused considerable interest in underwater communications as a computationally inexpensive means of mitigating the intersymbol interference introduced by the channel using a receiver array. In this paper the basic technique is extended by adaptively weighting sensor contributions to partially compensate for degraded focusing due to mismatch between the assumed and actual medium impulse responses. Two algorithms are proposed, one of which restores constructive interference between sensors, and the other one minimizes the output residual as in widely used equalization schemes. These are compared with plain time reversal and variants that employ postequalization and channel tracking. They are shown to improve the residual error and temporal stability of basic time reversal with very little added complexity. Results are presented for data collected in a passive time-reversal experiment that was conducted during the MREA'04 sea trial. In that experiment a single acoustic projector generated a 24-PSK (phase-shift keyed) stream at 200400 baud, modulated at 3.6 kHz, and received at a range of about 2 km on a sparse vertical array with eight hydrophones. The data were found to exhibit significant Doppler scaling, and a resampling-based preprocessing method is also proposed here to compensate for that scaling.
NASA Technical Reports Server (NTRS)
Kayanickupuram, A. J.; Ramos, K. A.; Cordova, M. L.; Wood, S. J.
2009-01-01
The need to resolve new patterns of sensory feedback in altered gravitoinertial environments requires cognitive processes to develop appropriate reference frames for spatial orientation awareness. The purpose of this study was to examine deficits in spatial cognitive performance during adaptation to conflicting tilt-translation stimuli. Fourteen subjects were tilted within a lighted enclosure that simultaneously translated at one of 3 frequencies. Tilt and translation motion was synchronized to maintain the resultant gravitoinertial force aligned with the longitudinal body axis, resulting in a mismatch analogous to spaceflight in which the canals and vision signal tilt while the otoliths do not. Changes in performance on different spatial cognitive tasks were compared 1) without motion, 2) with tilt motion alone (pitch at 0.15, 0.3 and 0.6 Hz or roll at 0.3 Hz), and 3) with conflicting tilt-translation motion. The adaptation paradigm was continued for up to 30 min or until the onset of nausea. The order of the adaptation conditions were counter-balanced across 4 different test sessions. There was a significant effect of stimulus frequency on both motion sickness and spatial cognitive performance. Only 3 of 14 were able to complete the full 30 min protocol at 0.15 Hz, while 7 of 14 completed 0.3 Hz and 13 of 14 completed 0.6 Hz. There were no changes in simple visual-spatial cognitive tests, e.g., mental rotation or match-to-sample. There were significant deficits during 0.15 Hz adaptation in both accuracy and reaction time during a spatial reference task in which subjects are asked to identify a match of a 3D reoriented cube assemblage. Our results are consistent with antidotal reports of cognitive impairment that are common during sensorimotor adaptation with G-transitions. We conclude that these cognitive deficits stem from the ambiguity of spatial reference frames for central processing of inertial motion cues.
Palme, Massimo; de la Barrera, Francisco
2016-01-01
Climate change will worsen the high levels of urban vulnerability in Latin American cities due to specific environmental stressors. Some impacts of climate change, such as high temperatures in urban environments, have not yet been addressed through adaptation strategies, which are based on poorly supported data. These impacts remain outside the scope of urban planning. New spatially explicit approaches that identify highly vulnerable urban areas and include specific adaptation requirements are needed in current urban planning practices to cope with heat hazards. In this paper, a heat vulnerability index is proposed for Santiago, Chile. The index was created using a GIS-based spatial information system and was constructed from spatially explicit indexes for exposure, sensitivity and adaptive capacity levels derived from remote sensing data and socio-economic information assessed via principal component analysis (PCA). The objective of this study is to determine the levels of heat vulnerability at local scales by providing insights into these indexes at the intra city scale. The results reveal a spatial pattern of heat vulnerability with strong variations among individual spatial indexes. While exposure and adaptive capacities depict a clear spatial pattern, sensitivity follows a complex spatial distribution. These conditions change when examining PCA results, showing that sensitivity is more robust than exposure and adaptive capacity. These indexes can be used both for urban planning purposes and for proposing specific policies and measures that can help minimize heat hazards in highly dynamic urban areas. The proposed methodology can be applied to other Latin American cities to support policy making. PMID:27606592
Inostroza, Luis; Palme, Massimo; de la Barrera, Francisco
2016-01-01
Climate change will worsen the high levels of urban vulnerability in Latin American cities due to specific environmental stressors. Some impacts of climate change, such as high temperatures in urban environments, have not yet been addressed through adaptation strategies, which are based on poorly supported data. These impacts remain outside the scope of urban planning. New spatially explicit approaches that identify highly vulnerable urban areas and include specific adaptation requirements are needed in current urban planning practices to cope with heat hazards. In this paper, a heat vulnerability index is proposed for Santiago, Chile. The index was created using a GIS-based spatial information system and was constructed from spatially explicit indexes for exposure, sensitivity and adaptive capacity levels derived from remote sensing data and socio-economic information assessed via principal component analysis (PCA). The objective of this study is to determine the levels of heat vulnerability at local scales by providing insights into these indexes at the intra city scale. The results reveal a spatial pattern of heat vulnerability with strong variations among individual spatial indexes. While exposure and adaptive capacities depict a clear spatial pattern, sensitivity follows a complex spatial distribution. These conditions change when examining PCA results, showing that sensitivity is more robust than exposure and adaptive capacity. These indexes can be used both for urban planning purposes and for proposing specific policies and measures that can help minimize heat hazards in highly dynamic urban areas. The proposed methodology can be applied to other Latin American cities to support policy making. PMID:27606592
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
NASA Astrophysics Data System (ADS)
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
A piezo-shunted kirigami auxetic lattice for adaptive elastic wave filtering
NASA Astrophysics Data System (ADS)
Ouisse, Morvan; Collet, Manuel; Scarpa, Fabrizio
2016-11-01
Tailoring the dynamical behavior of wave-guide structures can provide an efficient and physically elegant approach for optimizing mechanical components with regards to vibroacoustic propagation. Architectured materials as pyramidal core kirigami cells combined with smart systems may represent a promising way to improve the vibroacoustic quality of structural components. This paper describes the design and modeling of a pyramidal core with auxetic (negative Poisson’s ratio) characteristics and distributed shunted piezoelectric patches that allow for wave propagation control. The core is produced using a kirigami technique, inspired by the cutting/folding processes of the ancient Japanese art. The kirigami structure has a pyramidal unit cell shape that creates an in-plane negative Poisson’s ratio macroscopic behavior. This structure exhibits in-plane elastic properties (Young’s and shear modulus) which are higher than the out-of-plane ones, and hence this lattice has very specific properties in terms of wave propagation that are investigated in this work. The short-circuited configuration is first analyzed, before using negative capacitance and resistance as a shunt which provides impressive band gaps in the low frequency range. All configurations are investigated by using a full analysis of the Brillouin zone, rendering possible the deep understanding of the dynamical properties of the smart lattice. The results are presented in terms of dispersion and directivity diagrams, and the smart lattice shows quite interesting properties for the adaptive filtering of elastic waves at low frequencies bandwidths.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Seismic random noise attenuation based on adaptive time-frequency peak filtering
NASA Astrophysics Data System (ADS)
Deng, Xinhuan; Ma, Haitao; Li, Yue; Zeng, Qian
2015-02-01
Time-frequency peak filtering (TFPF) method uses a specific window with fixed length to recover band-limited signal in stationary random noise. However, the derivatives of signal such as seismic wavelets may change rapidly in some short time intervals. In this case, TFPF equipped with fixed window length will not provide an optimal solution. In this letter, we present an adaptive version of TFPF for seismic random noise attenuation. In our version, the improved intersection of confidence intervals combined with short-time energy criterion is used to preprocess the noisy signal. And then, we choose an appropriate threshold to divide the noisy signal into signal, buffer and noise. Different optimal window lengths are used in each type of segments. We test the proposed method on both synthetic and field seismic data. The experimental results illustrate that the proposed method makes the degree of amplitude preservation raise more than 10% and signal-to-noise (SNR) improve 2-4 dB compared with the original algorithm.
NASA Astrophysics Data System (ADS)
Wells, Gregg B.; Ricci, Anthony J.
2011-11-01
In the auditory system, mechanotransduction occurs in the hair cell sensory hair bundle and is the first major step in the translation of mechanical energy into electrical. Tonotopic variations in the activation kinetics of this process are posited to provide a low pass filter to the input. An adaptation process, also associated with mechanotransduction, is postulated to provide a high pass filter to the input in a tonotopic manner. Together a bandpass filter is created at the hair cell input. Corresponding mechanical components to both activation and adaptation are also suggested to be involved in generating cochlear amplification. A paradox to this story is that hair cells where the mechanotransduction properties are most robust possess an intrinsic electrical resonance mechanism proposed to account for all required tuning and amplification. A simple Hodgkin-Huxley type model is presented to attempt to determine the role of the activation and adaptation kinetics in further tuning hair cells that exhibit electrical resonance. Results further support that steady state mechanotransduction properties are critical for setting the resting potential of the hair cell while the kinetics of activation and adaptation are important for sharpening tuning around the characteristic frequency of the hair cell.
NASA Astrophysics Data System (ADS)
Wilhelmi, O.; Hayden, M.; Harlan, S.; Ruddell, D.; Komatsu, K.; England, B.; Uejio, C.
2008-12-01
Changing climate is predicted to increase the intensity and impacts of heat waves prompting the need to develop preparedness and adaptation strategies that reduce societal vulnerability. Central to understanding societal vulnerability, is adaptive capacity, the potential of a system or population to modify its features/behaviors so as to better cope with existing and anticipated stresses and fluctuations. Adaptive capacity influences adaptation, the actual adjustments made to cope with the impacts from current and future hazardous heat events. Understanding societal risks, vulnerabilities and adaptive capacity to extreme heat events and climate change requires an interdisciplinary approach that includes information about weather and climate, the natural and built environment, social processes and characteristics, interactions with the stakeholders, and an assessment of community vulnerability. This project presents a framework for an interdisciplinary approach and a case study that explore linkages between quantitative and qualitative data for a more comprehensive understanding of local level vulnerability and adaptive capacity to extreme heat events in Phoenix, Arizona. In this talk, we will present a methodological framework for conducting collaborative research on societal vulnerability and adaptive capacity on a local level that includes integration of household surveys into a quantitative spatial assessment of societal vulnerability. We highlight a collaborative partnership among researchers, community leaders and public health officials. Linkages between assessment of local adaptive capacity and development of regional climate change adaptation strategies will be discussed.
Cavalheri, Hamanda; Both, Camila; Martins, Marcio
2015-01-01
Both habitat filters and spatial processes can influence community structure. Space alone affects species immigration from the regional species pool, whereas habitat filters affect species distribution and inter-specific interactions. This study aimed to understand how the interplay between environmental and geographical processes influenced the structure of Neotropical snake communities in different habitat types. We selected six studies that sampled snakes in forests, four conducted in savannas and two in grasslands (the latter two are grouped in a non-forest category). We used the net relatedness and nearest taxon indices to assess phylogenetic structure within forest and non-forest areas. We also used the phylogenetic fuzzy-weighting algorithm to characterize phylogenetic structure across communities and the relation of phylogenetic composition patterns to habitat type, structure, and latitude. Finally, we tested for morphological trait convergence and phylogenetic niche conservatism using four forest and four non-forest areas for which morphological data were available. Community phylogenetic composition changed across forest and non-forest areas suggesting that environmental filtering influences community structure. Species traits were affected by habitat type, indicating convergence at the metacommunity level. Tail length, robustness, and number of ventral scales maximized community convergence among forest and non-forest areas. The observed patterns suggested environmental filtering, indicating that less vertically structured habitats represent a strong filter. Despite the fact that phylogenetic structure was not detected individually for each community, we observed a trend towards communities composed by more closely related species in higher latitudes and more overdispersed compositions in lower latitudes. Such pattern suggests that the limited distribution of major snake lineages constrained species distributions. Structure indices for each community
Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo
2013-01-01
Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.
Suyama, Takayuki
2016-01-01
This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a “bottom-up” manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives. PMID:27597862
Suyama, Takayuki
2016-01-01
This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a “bottom-up” manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives.
Yano, Ken; Suyama, Takayuki
2016-01-01
This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a "bottom-up" manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives. PMID:27597862
Adaptive Fraunhofer diffraction particle sizing instrument using a spatial light modulator.
Hirleman, E D; Dellenback, P A
1989-11-15
Integration of a magnetooptic spatial light modulator into a Fraunhofer diffraction particle sizing instrument is proposed and demonstrated theoretically and experimentally. The concept gives the instrument the ability to reconfigure a detector array on-line and thereby adapt to the measurement context.
Adaptive Fraunhofer diffraction particle sizing instrument using a spatial light modulator.
Hirleman, E D; Dellenback, P A
1989-11-15
Integration of a magnetooptic spatial light modulator into a Fraunhofer diffraction particle sizing instrument is proposed and demonstrated theoretically and experimentally. The concept gives the instrument the ability to reconfigure a detector array on-line and thereby adapt to the measurement context. PMID:20555963
Manosueb, Anchalee; Koseeyaporn, Jeerasuda; Wardkein, Paramote
2014-01-01
This paper presents a technique for finding the optimal initial weight for adaptive filter by using difference equation. The obtained analytical response of the system identifies the appropriate weights for the system and shows that the MSE depends on the initial weight. The proposed technique is applied to eliminate the known frequency power line interference (PLI) signal in the electrocardiogram (ECG) signal. The PLI signal is considered as a combination of cosine and sine signals. The adaptive filter, therefore, attempts to adjust the amplitude of cosine and sine signals to synthesize a reference signal very similar to the contaminated PLI signal. To compare the potential of the proposed technique to other techniques, the system is simulated by using the Matlab program and the TMS320C6713 digital board. The simulation results demonstrate that the proposed technique enables the system to eliminate the PLI signal with the fastest time and gains the superior results of the recovered ECG signal.
Tankanag, Arina V; Chemeris, Nikolay K
2009-10-01
The paper describes an original method for analysis of the peripheral blood flow oscillations measured with the laser Doppler flowmetry (LDF) technique. The method is based on the continuous wavelet transform and adaptive wavelet theory and applies an adaptive wavelet filtering to the LDF data. The method developed allows one to examine the dynamics of amplitude oscillations in a wide frequency range (from 0.007 to 2 Hz) and to process both stationary and non-stationary short (6 min) signals. The capabilities of the method have been demonstrated by analyzing LDF signals registered in the state of rest and upon humeral occlusion. The paper shows the main advantage of the method proposed, which is the significant reduction of 'border effects', as compared to the traditional wavelet analysis. It was found that the low-frequency amplitudes obtained by adaptive wavelets are significantly higher than those obtained by non-adaptive ones. The method suggested would be useful for the analysis of low-frequency components of the short-living transitional processes under the conditions of functional tests. The method of adaptive wavelet filtering can be used to process stationary and non-stationary biomedical signals (cardiograms, encephalograms, myograms, etc), as well as signals studied in the other fields of science and engineering.
Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
NASA Astrophysics Data System (ADS)
Gao, Zhen; Dai, Linglong; Wang, Zhaocheng; Chen, Sheng
2015-12-01
This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.
NASA Astrophysics Data System (ADS)
Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Ninomiya, Yuuji; Tomoshige, Yukihiro; Kurokawa, Takehiro; Ono, Yutaka; Nakamura, Yuko; Suzuki, Masayuki
2008-03-01
Recently, several kinds of post-processing image filters which reduce the noise of computed tomography (CT) images have been proposed. However, these image filters are mostly for adults. Because these are not very effective in small (< 20 cm) display fields of view (FOV), we cannot use them for pediatric body images (e.g., premature babies and infant children). We have developed a new noise reduction filter algorithm for pediatric body CT images. This algorithm is based on a 3D post-processing in which the output pixel values are calculated by nonlinear interpolation in z-directions on original volumetric-data-sets. This algorithm does not need the in-plane (axial plane) processing, so the spatial resolution does not change. From the phantom studies, our algorithm could reduce SD up to 40% without affecting the spatial resolution of x-y plane and z-axis, and improved the CNR up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of the pediatric body CT images.
Schwarz, Andreas; Scherer, Reinhold; Steyrl, David; Faller, Josef; Muller-Putz, Gernot R
2015-08-01
Sensorimotor rhythm (SMR) based Brain-Computer Interfaces (BCI) typically require lengthy user training. This can be exhausting and fatiguing for the user as data collection may be monotonous and typically without any feedback for user motivation. Hence new ways to reduce user training and improve performance are needed. We recently introduced a two class motor imagery BCI system which continuously adapted with increasing run-time to the brain patterns of the user. The system was designed to provide visual feedback to the user after just five minutes. The aim of the current work was to improve user-specific online adaptation, which was expected to lead to higher performances. To maximize SMR discrimination, the method of filter-bank common spatial patterns (fbCSP) and Random Forest (RF) classifier were combined. In a supporting online study, all volunteers performed significantly better than chance. Overall peak accuracy of 88.6 ± 6.1 (SD) % was reached, which significantly exceeded the performance of our previous system by 13%. Therefore, we consider this system the next step towards fully auto-calibrating motor imagery BCIs. PMID:26736445
Dąbrowski, M.; Chrapkiewicz, R.; Wasilewski, W.
2016-01-01
Warm atomic vapor quantum memories are simple and robust, yet suffer from a number of parasitic processes which produce excess noise. For operating in a single-photon regime precise filtering of the output light is essential. Here, we report a combination of magnetically tuned absorption and Faraday filters, both light–direction insensitive, which stop the driving lasers and attenuate spurious fluorescence and four-wave mixing while transmitting narrowband Stokes and anti-Stokes photons generated in write-in and readout processes. We characterize both filters with respect to adjustable working parameters. We demonstrate a significant increase in the signal-to-noise ratio upon applying the filters seen qualitatively in measurements of correlation between the Raman scattered photons.
NASA Astrophysics Data System (ADS)
Dąbrowski, M.; Chrapkiewicz, R.; Wasilewski, W.
2016-11-01
Warm atomic vapor quantum memories are simple and robust, yet suffer from a number of parasitic processes which produce excess noise. For operating in a single-photon regime precise filtering of the output light is essential. Here, we report a combination of magnetically tuned absorption and Faraday filters, both light-direction insensitive, which stop the driving lasers and attenuate spurious fluorescence and four-wave mixing while transmitting narrowband Stokes and anti-Stokes photons generated in write-in and readout processes. We characterize both filters with respect to adjustable working parameters. We demonstrate a significant increase in the signal-to-noise ratio upon applying the filters seen qualitatively in measurements of correlation between the Raman scattered photons.
Filtering of matter-wave vibrational states via spatial adiabatic passage
Loiko, Yu.; Ahufinger, V.; Corbalan, R.; Mompart, J.; Birkl, G.
2011-03-15
We discuss the filtering of the vibrational states of a cold atom in an optical trap by chaining this trap with two empty ones and adiabatically controlling the tunneling. Matter-wave filtering is performed by selectively transferring the population of the highest populated vibrational state to the most distant trap while the population of the rest of the states remains in the initial trap. Analytical conditions for two-state filtering are derived and then applied to an arbitrary number of populated bound states. Realistic numerical simulations close to state-of-the-art experimental arrangements are performed by modeling the triple well with time-dependent Poeschl-Teller potentials. In addition to filtering of vibrational states, we discuss applications for quantum tomography of the initial population distribution and engineering of atomic Fock states that, eventually, could be used for tunneling-assisted evaporative cooling.
Tanaka, Hirokazu; Sejnowski, Terrence J
2015-02-15
The brain processes sensory and motor information in a wide range of coordinate systems, ranging from retinal coordinates in vision to body-centered coordinates in areas that control musculature. Here we focus on the coordinate system used in the motor cortex to guide actions and examine physiological and psychophysical evidence for an allocentric reference frame based on spatial coordinates. When the equations of motion governing reaching dynamics are expressed as spatial vectors, each term is a vector cross product between a limb-segment position and a velocity or acceleration. We extend this computational framework to motor adaptation, in which the cross-product terms form adaptive bases for canceling imposed perturbations. Coefficients of the velocity- and acceleration-dependent cross products are assumed to undergo plastic changes to compensate the force-field or visuomotor perturbations. Consistent with experimental findings, each of the cross products had a distinct reference frame, which predicted how an acquired remapping generalized to untrained location in the workspace. In response to force field or visual rotation, mainly the coefficients of the velocity- or acceleration-dependent cross products adapted, leading to transfer in an intrinsic or extrinsic reference frame, respectively. The model further predicted that remapping of visuomotor rotation should under- or overgeneralize in a distal or proximal workspace. The cross-product bases can explain the distinct patterns of generalization in visuomotor and force-field adaptation in a unified way, showing that kinematic and dynamic motor adaptation need not arise through separate neural substrates.
Deployment of spatial attention without moving the eyes is boosted by oculomotor adaptation
Habchi, Ouazna; Rey, Elodie; Mathieu, Romain; Urquizar, Christian; Farnè, Alessandro; Pélisson, Denis
2015-01-01
Vertebrates developed sophisticated solutions to select environmental visual information, being capable of moving attention without moving the eyes. A large body of behavioral and neuroimaging studies indicate a tight coupling between eye movements and spatial attention. The nature of this link, however, remains highly debated. Here, we demonstrate that deployment of human covert attention, measured in stationary eye conditions, can be boosted across space by changing the size of ocular saccades to a single position via a specific adaptation paradigm. These findings indicate that spatial attention is more widely affected by oculomotor plasticity than previously thought. PMID:26300755
NASA Astrophysics Data System (ADS)
Ding, Quanxin; Guo, Chunjie; Cai, Meng; Liu, Hua
2007-12-01
Adaptive Optics Expand System is a kind of new concept spatial equipment, which concerns system, cybernetics and informatics deeply, and is key way to improve advanced sensors ability. Traditional Zernike Phase Contrast Method is developed, and Accelerated High-level Phase Contrast Theory is established. Integration theory and mathematical simulation is achieved. Such Equipment, which is based on some crucial components, such as, core optical system, multi mode wavefront sensor and so on, is established for AOES advantageous configuration and global design. Studies on Complicated Spatial Multisensor System Integratation and measurement Analysis including error analysis are carried out.
Allen, R; Fioratou, E; McGeorge, P
2011-02-01
This commentary considers the paper by Furley and Memmert (2010) who sought to test the respective validities of the specific processing and cognitive adaptation hypotheses. That they found no evidence of a difference between experienced basketball players and nonathletes on the Corsi block task, a measure of spatial memory, led them to infer support for the specific processing hypothesis, namely that differences between experts and novices manifest themselves only in processes related specifically to the domain of expertise. An alternative interpretation is offered, indicating possible confounds and referring to recent research that suggests Corsi block and dynamic spatial tasks depend upon different neuronal networks.
Zhang, Qian; Zhang, Hao Chi; Wu, Han; Cui, Tie Jun
2015-01-01
We propose a hybrid circuit for spoof surface plasmon polaritons (SPPs) and spatial waveguide modes to develop new microwave devices. The hybrid circuit includes a spoof SPP waveguide made of two anti-symmetric corrugated metallic strips and a traditional substrate integrated waveguide (SIW). From dispersion relations, we show that the electromagnetic waves only can propagate through the hybrid circuit when the operating frequency is less than the cut-off frequency of the SPP waveguide and greater than the cut-off frequency of SIW, generating efficient band-pass filters. We demonstrate that the pass band is controllable in a large range by designing the geometrical parameters of SPP waveguide and SIW. Full-wave simulations are provided to show the large adjustability of filters, including ultra wideband and narrowband filters. We fabricate a sample of the new hybrid device in the microwave frequencies, and measurement results have excellent agreements to numerical simulations, demonstrating excellent filtering characteristics such as low loss, high efficiency, and good square ratio. The proposed hybrid circuit gives important potential to accelerate the development of plasmonic integrated functional devices and circuits in both microwave and terahertz frequencies. PMID:26552584
Zhang, Qian; Zhang, Hao Chi; Wu, Han; Cui, Tie Jun
2015-11-10
We propose a hybrid circuit for spoof surface plasmon polaritons (SPPs) and spatial waveguide modes to develop new microwave devices. The hybrid circuit includes a spoof SPP waveguide made of two anti-symmetric corrugated metallic strips and a traditional substrate integrated waveguide (SIW). From dispersion relations, we show that the electromagnetic waves only can propagate through the hybrid circuit when the operating frequency is less than the cut-off frequency of the SPP waveguide and greater than the cut-off frequency of SIW, generating efficient band-pass filters. We demonstrate that the pass band is controllable in a large range by designing the geometrical parameters of SPP waveguide and SIW. Full-wave simulations are provided to show the large adjustability of filters, including ultra wideband and narrowband filters. We fabricate a sample of the new hybrid device in the microwave frequencies, and measurement results have excellent agreements to numerical simulations, demonstrating excellent filtering characteristics such as low loss, high efficiency, and good square ratio. The proposed hybrid circuit gives important potential to accelerate the development of plasmonic integrated functional devices and circuits in both microwave and terahertz frequencies.
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved. PMID:27420062
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved. PMID:27420062
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-07-12
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
Adaptation of filtered back-projection to compton imaging with non-uniform azimuthal geometry
NASA Astrophysics Data System (ADS)
Lee, Hyounggun; Lee, Taewoong; Lee, Wonho
2016-05-01
For Compton image reconstruction, analytic reconstruction methods such as filtered backprojection have been used for real-time imaging. The conventional filtered back-projection method assumes a uniformly distributed azimuthal response in the detector system. In this study, we applied filtered back-projection to the experimental data from detector systems with limited azimuthal angle coverage ranges and estimated the limitations of the analytic reconstruction methods when applied to these systems. For the system with a uniform azimuthal response, the images reconstructed by using filtered back-projection showed better angular resolutions than the images obtained by using simple back-projection did. However, when filtered back-projection was applied to reconstruct Compton images based on measurements performed by using Compton cameras with limited response geometries, the reconstructed images exhibited artifacts caused by the geometrical limitations. Our proposed method employs the Compton camera's rotation to overcome the angular response limitations; when the rotation method was applied in this study, the artifacts in the reconstructed images caused by angular response limitations were minimized. With this method, filtered back-projection can be applied to reconstruct real-time Compton images even when the radiation measurements are performed by using Compton cameras with non-uniform azimuthal response geometries.
Mihajlovic, Vojkan; Patki, Shrishail; Grundlehner, Bernard
2014-01-01
Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.
NASA Astrophysics Data System (ADS)
Yano, Ken'ichi; Ohara, Eiichi; Horihata, Satoshi; Aoki, Takaaki; Nishimoto, Yutaka
A robot that supports independent living by assisting with eating and other activities which use the operator's own hand would be helpful for people suffering from tremors of the hand or any other body part. The proposed system using adaptive filter estimates tremor frequencies with a time-varying property and individual differences online. In this study, the estimated frequency is used to adjusting the tremor suppression filter which insulates the voluntary motion signal from the sensor signal containing tremor components. These system are integrated into the control system of the Meal-Assist Robot. As a result, the developed system makes it possible for the person with a tremor to manipulate the supporting robot without causing operability to deteriorate and without hazards due to improper operation.
NASA Astrophysics Data System (ADS)
Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Aldhaibani, Jaafar A.; Salman, M. K.; Abdullah, Farah Salwani
2015-05-01
For high data rate propagation in wireless ultra-wideband (UWB) communication systems, the inter-symbol interference (ISI), multiple-access interference (MAI), and multiple-users interference (MUI) are influencing the performance of the wireless systems. In this paper, the rake-receiver was presented with the spread signal by direct sequence spread spectrum (DS-SS) technique. The adaptive rake-receiver structure was shown with adjusting the receiver tap weights using least mean squares (LMS), normalized least mean squares (NLMS), and affine projection algorithms (APA) to support the weak signals by noise cancellation and mitigate the interferences. To minimize the data convergence speed and to reduce the computational complexity by the previous algorithms, a well-known approach of partial-updates (PU) adaptive filters were employed with algorithms, such as sequential-partial, periodic-partial, M-max-partial, and selective-partial updates (SPU) in the proposed system. The simulation results of bit error rate (BER) versus signal-to-noise ratio (SNR) are illustrated to show the performance of partial-update algorithms that have nearly comparable performance with the full update adaptive filters. Furthermore, the SPU-partial has closed performance to the full-NLMS and full-APA while the M-max-partial has closed performance to the full-LMS updates algorithms.
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Libera, Peter; Fernandez, Manuel F.; Dobeck, Gerald J.
1996-05-01
An automatic, robust, adaptive clutter suppression, mine detection and classification processing string has been developed and applied to side-scan sonar imagery data. The overall processing string includes data pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction, and classification processing blocks. The data pre-processing block contains automatic gain control and data decimation processing. The ACF technique designs a 2D adaptive range-crossrange linear FIR filter which is optimal in the Least Squares sense, simultaneously suppressing the background clutter while preserving an average peak target signature (normalized shape) computed a priori using training set data. A multiple reference ACF algorithm version was utilized to account for multiple target shapes (due to different mine types, multiple target aspect angles, etc.). The detection block consists of thresholding, clustering of exceedances and limiting their number, and a secondary thresholding process. Following feature extraction, the classification block applies a novel transformation to the data, which orthogonalizes the features and enables an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF/feature orthogonalization based LLRT mine classification processing string provided average probability of correct mine classification and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Fernandez, Manuel F.; Dobeck, Gerald J.
1997-07-01
An automatic, robust, adaptive clutter suppression, predetection level fusion, sea mine detection and classification processing string has been developed and applied to shallow water side-scan sonar imagery data. The overall processing string includes pre-processing string includes pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction and classification processing blocks. The pre-processing block contains automatic gain control, data decimation and data alignment processing. The ACF is a multi-dimensional adaptive linear FIR filter, optimal in the least squares sense, for simultaneous background clutter suppression and preservation of an average peak target signature. After data alignment, using a 3D ACF enables simultaneous multiple frequency data fusion and clutter suppression in the composite frequency-range-crossrange domain. Following 2D normalization, the detection consists of thresholding, clustering of exceedances and limiting their number. Finally, features are extracted and a orthogonalization transformation is applied to the data, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF, feature orthogonalization, LLRT-based classification processing string provided average probability of correct mine classification and false alarm rate performance exceeding the one obtained when utilizing an expert sonar operator. The overall processing string can be easily implemented in real-time using COTS technology.
NASA Astrophysics Data System (ADS)
Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian
2016-05-01
Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
NASA Astrophysics Data System (ADS)
Schaeper, M.; Schmidt, R.; Kostbade, R.; Damaschke, N.; Gimsa, J.
2016-07-01
Circular spatial filtering velocimetry (CSFV) was tested during the microscopic registration of the individual rotations of baker’s yeast cells. Their frequency-dependent rotation (electrorotation; ER) was induced in rotating electric fields, which were generated in a glass chip chamber with four electrodes (600 μm tip-to-tip distance). The electrodes were driven with sinusoidal quadrature signals of 5 or 8 V PP with frequencies up to 3 MHz. The observed cell rotation was of the order of 1–100 s per revolution. At each measuring frequency, the independent rotations of up to 20 cells were simultaneously recorded with a high-speed camera. CSFV was software-implemented using circular spatial filters with harmonic gratings. ER was proportional to the phase shift between the values of the spatial filtering signal of consecutive frames. ER spectra obtained by CSFV from the rotation velocities at different ER-field frequencies agreed well with manual measurements and theoretical spectra. Oscillations in the rotation velocity of a single cell in the elliptically polarized field near an electrode, which were resolved by CSFV, could not be visually discerned. ER step responses after field-on were recorded at 2500 frames per second. Analysis proved the high temporal resolution of CSFV and revealed a largely linear torque-friction relation during the acceleration phase of ER. Future applications of CSFV will allow for the simple and cheap automated high-resolution analysis of rotational movements where mechanical detection has too low a resolution or is not possible, e.g. in polluted environments or for gas and fluid vortices, microscopic objects, etc.
NASA Astrophysics Data System (ADS)
Schaeper, M.; Schmidt, R.; Kostbade, R.; Damaschke, N.; Gimsa, J.
2016-07-01
Circular spatial filtering velocimetry (CSFV) was tested during the microscopic registration of the individual rotations of baker’s yeast cells. Their frequency-dependent rotation (electrorotation; ER) was induced in rotating electric fields, which were generated in a glass chip chamber with four electrodes (600 μm tip-to-tip distance). The electrodes were driven with sinusoidal quadrature signals of 5 or 8 V PP with frequencies up to 3 MHz. The observed cell rotation was of the order of 1-100 s per revolution. At each measuring frequency, the independent rotations of up to 20 cells were simultaneously recorded with a high-speed camera. CSFV was software-implemented using circular spatial filters with harmonic gratings. ER was proportional to the phase shift between the values of the spatial filtering signal of consecutive frames. ER spectra obtained by CSFV from the rotation velocities at different ER-field frequencies agreed well with manual measurements and theoretical spectra. Oscillations in the rotation velocity of a single cell in the elliptically polarized field near an electrode, which were resolved by CSFV, could not be visually discerned. ER step responses after field-on were recorded at 2500 frames per second. Analysis proved the high temporal resolution of CSFV and revealed a largely linear torque-friction relation during the acceleration phase of ER. Future applications of CSFV will allow for the simple and cheap automated high-resolution analysis of rotational movements where mechanical detection has too low a resolution or is not possible, e.g. in polluted environments or for gas and fluid vortices, microscopic objects, etc.
Pre-Surgical fMRI Data Analysis Using a Spatially Adaptive Conditionally Autoregressive Model
Liu, Zhuqing; Berrocal, Veronica J.; Bartsch, Andreas J.; Johnson, Timothy D.
2015-01-01
Spatial smoothing is an essential step in the analysis of functional magnetic resonance imaging (fMRI) data. One standard smoothing method is to convolve the image data with a three-dimensional Gaussian kernel that applies a fixed amount of smoothing to the entire image. In pre-surgical brain image analysis where spatial accuracy is paramount, this method, however, is not reasonable as it can blur the boundaries between activated and deactivated regions of the brain. Moreover, while in a standard fMRI analysis strict false positive control is desired, for pre-surgical planning false negatives are of greater concern. To this end, we propose a novel spatially adaptive conditionally autoregressive model with variances in the full conditional of the means that are proportional to error variances, allowing the degree of smoothing to vary across the brain. Additionally, we present a new loss function that allows for the asymmetric treatment of false positives and false negatives. We compare our proposed model with two existing spatially adaptive conditionally autoregressive models. Simulation studies show that our model outperforms these other models; as a real model application, we apply the proposed model to the pre-surgical fMRI data of two patients to assess peri- and intra-tumoral brain activity. PMID:27042244
NASA Astrophysics Data System (ADS)
Xu, Shichao; Tan, Wenjiang; Si, Jinhai; Zhan, Pingping; Tong, Junyi; Hou, Xun
2014-09-01
We demonstrated two ballistic imaging for an object hidden behind turbid media using the optical Kerr gate (OKG) and spatial filtering (SF), respectively. The influence of the scattering parameters of the turbid media on the image contrast was investigated. The experimental results showed that the image contrast of the SF imaging decreased significantly with increasing optical density and scattering particle size of the turbid media. Compared to the SF imaging, the OKG imaging showed a higher and more stable image contrast as scattering photons in the optical gated imaging case were more effectively eliminated.
Gain Filtering for Single-Spatial-Mode Operation of Large-Mode-Area Fiber Amplifiers
Marciante, J.R.
2009-02-06
Gain filtering of higher order modes in large-mode-area fibers is an extremely robust method for providing diffraction-limited performance regardless of core diameter or input beam quality. Analytic calculations demonstrate that reducing the diameter of the gain dopants compared to the waveguide diameter produces differential gain that is higher for the fundamental mode than all other fiber modes at all saturation levels. Matching the gain dopant to the mode profile is not as beneficial as a simple step profile since the primarymechanism of gain filtering is to deny gain toward the edge of the waveguide where most of the higher order mode power is contained. Numerical simulations of multikilowatt fiber amplifiers with up to 100-μm-diameter cores show that gain filtering is extremely robust, providing 99% of the output power in the fundamental mode output with only 90% of the seed power in the fundamental mode. Even with poor seed launch with 50% of the power in the fundamental mode, gain filtering can provide up to 90% of the output power in the fundamental mode.
Switching among pulse-generation regimes in passively mode-locked fibre laser by adaptive filtering
NASA Astrophysics Data System (ADS)
Peng, Junsong; Boscolo, Sonia
2016-04-01
We show both numerically and experimentally that dispersion management can be realized by manipulating the dispersion of a filter in a passively mode-locked fibre laser. A programmable filter the dispersion of which can be software configured is employed in the laser. Solitons, stretched-pulses, and dissipative solitons can be targeted reliably by controlling the filter transmission function only, while the length of fibres is fixed in the laser. This technique shows remarkable advantages in controlling operation regimes in ultrafast fibre lasers, in contrast to the traditional technique in which dispersion management is achieved by optimizing the relative length of fibres with opposite-sign dispersion. Our versatile ultrafast fibre laser will be attractive for applications requiring different pulse profiles such as in optical signal processing and optical communications.
Sun, W Y
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050
McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.; Huynh, Timmy N.; Bhaduri, Budhendra L.
2015-02-03
Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less
Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050
McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.; Huynh, Timmy N.; Bhaduri, Budhendra L.
2015-02-03
Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.
Human Topological Task Adapted for Rats: Spatial Information Processes of the Parietal Cortex
Goodrich-Hunsaker, Naomi J.; Howard, Brian P.; Hunsaker, Michael R.; Kesner, Raymond P.
2008-01-01
Human research has shown that lesions of the parietal cortex disrupt spatial information processing, specifically topological information. Similar findings have been found in nonhumans. It has been difficult to determine homologies between human and non-human mnemonic mechanisms for spatial information processing because methodologies and neuropathology differ. The first objective of the present study was to adapt a previously established human task for rats. The second objective was to better characterize the role of parietal cortex (PC) and dorsal hippocampus (dHPC) for topological spatial information processing. Rats had to distinguish whether a ball inside a ring or a ball outside a ring was the correct, rewarded object. After rats reached criterion on the task (>95%) they were randomly assigned to a lesion group (control, PC, dHPC). Animals were then re-tested. Post-surgery data show that controls were 94% correct on average, dHPC rats were 89% correct on average, and PC rats were 56% correct on average. The results from the present study suggest that the parietal cortex, but not the dHPC processes topological spatial information. The present data are the first to support comparable topological spatial information processes of the parietal cortex in humans and rats. PMID:18571941
Locally adaptive, spatially explicit projection of US population for 2030 and 2050
McKee, Jacob J.; Rose, Amy N.; Bright, Edward A.; Huynh, Timmy; Bhaduri, Budhendra L.
2015-01-01
Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census’s projection methodology, with the US Census’s official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations. PMID:25605882
Dovlo, Edem; Lashkari, Bahman; Mandelis, Andreas; Shi, Wei; Liu, Fei-Fei
2015-01-01
Co-registered ultrasound (US) and frequency-domain photoacoustic radar (FD-PAR) imaging is reported for the first time in this paper. The merits of ultrasound and cross-correlation (radar) frequency-domain photoacoustic imaging are leveraged for accurate tumor detection. Commercial US imagers possess sophisticated, optimized software for rapid image acquisition that could dramatically speed-up PA imaging. The PAR image generated from the amplitude of the cross-correlation between detected and input signals was filtered by the standard deviation (SD) of the phase of the correlation signal, resulting in strong improvement of image spatial resolution, signal-to-noise ratio (SNR) and contrast. Application of phase-mediated image improvement is illustrated by imaging a cancer cell-injected mouse. A 14–15 dB SNR gain was recorded for the phase-filtered image compared to the amplitude and phase independently, while ~340 μm spatial resolution was seen for the phase PAR image compared to ~840 μm for the amplitude image. PMID:25798321
Terada, Y.; Tanida, H.; Uruga, T.; Takeuchi, A.; Suzuki, Y.; Goto, S.
2011-09-09
An x-ray microprobe system with total-reflection mirror optics for trace element analysis has been developed at beamline 37XU of SPring-8. To achieve sub-microprobe, a spatial filter has been installed downstream of a monochromator. Focusing tests have been performed in the x-ray energy range of 6-14 keV. A focused beam size of 0.83 {mu}m(V)x1.35 {mu}m(H) has been obtained at an x-ray energy of 10 keV, and using a spatial filter in the horizontal direction, the beam size is down to 0.84 {mu}m. Micro-x-ray absorption fine structure (XAFS) spectroscopy of submicrometer particles has been done by utilizing the total-reflection mirror optics. It was clearly observed from the nickel K-edge XAFS spectra that the oxidation state of nickel was a mixture of metal and oxide even in the single submicrometer particle.
Rassovsky, Yuri; Lee, Junghee; Nori, Poorang; Wu, Allan D; Iacoboni, Marco; Breitmeyer, Bruno G; Hellemann, Gerhard; Green, Michael F
2014-11-01
Schizophrenia patients have difficulty extracting emotional information from facial expressions. Perception of facial emotion can be examined by systematically altering the spatial frequency of stimuli and suppressing visual processing with temporal precision using transcranial magnetic stimulation (TMS). In the present study, we compared 25 schizophrenia patients and 27 healthy controls using a facial emotion identification task. Spatial processing was examined by presenting facial photographs that contained either high (HSF), low (LSF), or broadband/unfiltered (BSF) spatial frequencies. Temporal processing was manipulated using a single-pulse TMS delivered to the visual cortex either before (forward masking) or after (backward masking) photograph presentation. Consistent with previous studies, schizophrenia patients performed significantly below controls across all three spatial frequencies. A spatial frequency by forward/backward masking interaction effect demonstrated reduced performance in the forward masking component in the BSF condition and a reversed performance pattern in the HSF condition, with no significant differences between forward and backward masking in the LSF condition. However, the group by spatial frequency interaction was not significant. These findings indicate that manipulating visual suppression of emotional information at the level of the primary visual cortex results in comparable effects on both groups. This suggests that patients' deficits in facial emotion identification are not explained by low-level processes in the retino-geniculo-striate projection, but may rather depend on deficits of affect perception occurring at later integrative processing stages.
Prism adaptation and spatial neglect: the need for dose-finding studies
Goedert, Kelly M.; Zhang, Jeffrey Y.; Barrett, A. M.
2015-01-01
Spatial neglect is a devastating disorder in 50–70% of right-brain stroke survivors, who have problems attending to, or making movements towards, left-sided stimuli, and experience a high risk of chronic dependence. Prism adaptation is a promising treatment for neglect that involves brief, daily visuo-motor training sessions while wearing optical prisms. Its benefits extend to functional behaviors such as dressing, with effects lasting 6 months or longer. Because one to two sessions of prism adaptation induce adaptive changes in both spatial-motor behavior (Fortis et al., 2011) and brain function (Saj et al., 2013), it is possible stroke patients may benefit from treatment periods shorter than the standard, intensive protocol of ten sessions over two weeks—a protocol that is impractical for either US inpatient or outpatient rehabilitation. Demonstrating the effectiveness of a lower dose will maximize the availability of neglect treatment. We present preliminary data suggesting that four to six sessions of prism treatment may induce a large treatment effect, maintained three to four weeks post-treatment. We call for a systematic, randomized clinical trial to establish the minimal effective dose suitable for stroke intervention. PMID:25983688
Skretting, Arne
2010-01-01
When PET image volumes are reconstructed with ordered subset expectation-maximization (OSEM) and subjected to filtration with a 3D Gaussian filter the effective spatial resolution is a function of both the intrinsic scanner resolution and the user-selectable spatial width of the filter. A method was developed to derive the effective spatial resolution from such volumes obtained after acquisitions with a line source on a Siemens Biograph 64 PET/CT scanner. Assuming Gaussian distributions, the full widths at half maximum (FWHM) were derived from probit plots of cumulative spatial distributions across the line source. The effective FWHM values were also used to estimate the FWHM of the intrinsic resolution by extrapolation to a zero filter width.
Prismatic Adaptation Induces Plastic Changes onto Spatial and Temporal Domains in Near and Far Space
Patané, Ivan; Farnè, Alessandro; Frassinetti, Francesca
2016-01-01
A large literature has documented interactions between space and time suggesting that the two experiential domains may share a common format in a generalized magnitude system (ATOM theory). To further explore this hypothesis, here we measured the extent to which time and space are sensitive to the same sensorimotor plasticity processes, as induced by classical prismatic adaptation procedures (PA). We also exanimated whether spatial-attention shifts on time and space processing, produced through PA, extend to stimuli presented beyond the immediate near space. Results indicated that PA affected both temporal and spatial representations not only in the near space (i.e., the region within which the adaptation occurred), but also in the far space. In addition, both rightward and leftward PA directions caused opposite and symmetrical modulations on time processing, whereas only leftward PA biased space processing rightward. We discuss these findings within the ATOM framework and models that account for PA effects on space and time processing. We propose that the differential and asymmetrical effects following PA may suggest that temporal and spatial representations are not perfectly aligned. PMID:26981286
Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling
2016-05-01
Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. PMID:27058283
Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling
2016-05-01
Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches.
NASA Astrophysics Data System (ADS)
Yao, Jianjun; Di, Duotao; Jiang, Guilin; Gao, Shuang
2012-10-01
Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.
NASA Astrophysics Data System (ADS)
Tehsin, Sara; Rehman, Saad; Awan, Ahmad B.; Chaudry, Qaiser; Abbas, Muhammad; Young, Rupert; Asif, Afia
2016-04-01
Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.
Comparison of various schema of filter adaptivity for the tracking of maneuvering targets
NASA Astrophysics Data System (ADS)
Jouan, Alexandre; Bosse, Eloi; Simard, Marc-Alain; Shahbazian, Elisa
1998-09-01
Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (IMM) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters. However, the complexity of the IMM algorithm can prohibit its use in these applications of which similar algorithms cannot provide the necessary accuracy and which can ont afford the computational load of IMM algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a target maneuver threshold.
Adaptive multi-scale total variation minimization filter for low dose CT imaging
NASA Astrophysics Data System (ADS)
Zamyatin, Alexander; Katsevich, Gene; Krylov, Roman; Shi, Bibo; Yang, Zhi
2014-03-01
In this work we revisit TV filter and propose an improved version that is tailored to diagnostic CT purposes. We revise TV cost function, which results in symmetric gradient function that leads to more natural noise texture. We apply a multi-scale approach to resolve noise grain issue in CT images. We examine noise texture, granularity, and loss of low contrast in the test images. We also discuss potential acceleration by Nesterov and Conjugate Gradient methods.
Widenfalk, Lina A; Bengtsson, Jan; Berggren, Åsa; Zwiggelaar, Krista; Spijkman, Evelien; Huyer-Brugman, Florrie; Berg, Matty P
2015-10-01
Both the environment and the spatial configuration of habitat patches are important factors that shape community composition and affect species diversity patterns. Species have traits that allow them to respond to their environment. Our current knowledge on environment to species traits relationships is limited in spite of its potential importance for understanding community assembly and ecosystem function. The aim of our study was to examine the relative roles of environmental and spatial variables for the small-scale variation in Collembola (springtail) communities in a Dutch salt marsh. We used a trait-based approach in combination with spatial statistics and variance partitioning, between environmental and spatial variables, to examine the important ecological factors that drive community composition. Turnover of trait diversity across space was lower than for species diversity. Most of the variation in community composition was explained by small-scale spatial variation in topography, on a scale of 4-6 m, most likely because it determines the effect of inundation, which restricts where habitat generalists can persist. There were only small pure spatial effects on species and trait diversity, indicating that biotic interactions or dispersal limitation probably were less important for structuring the community at this scale. Our results suggest that for springtails, life form (i.e. whether they live in the soil or litter or on the surface/in vegetation) is an important and useful trait to understand community assembly. Hence, using traits in addition to species identity when analysing environment-organism relationships results in a better understanding of the factors affecting community composition. PMID:26001605
Highly efficient spatial data filtering in parallel using the opensource library CPPPO
NASA Astrophysics Data System (ADS)
Municchi, Federico; Goniva, Christoph; Radl, Stefan
2016-10-01
CPPPO is a compilation of parallel data processing routines developed with the aim to create a library for "scale bridging" (i.e. connecting different scales by mean of closure models) in a multi-scale approach. CPPPO features a number of parallel filtering algorithms designed for use with structured and unstructured Eulerian meshes, as well as Lagrangian data sets. In addition, data can be processed on the fly, allowing the collection of relevant statistics without saving individual snapshots of the simulation state. Our library is provided with an interface to the widely-used CFD solver OpenFOAM®, and can be easily connected to any other software package via interface modules. Also, we introduce a novel, extremely efficient approach to parallel data filtering, and show that our algorithms scale super-linearly on multi-core clusters. Furthermore, we provide a guideline for choosing the optimal Eulerian cell selection algorithm depending on the number of CPU cores used. Finally, we demonstrate the accuracy and the parallel scalability of CPPPO in a showcase focusing on heat and mass transfer from a dense bed of particles. Catalogue identifier: AFAQ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFAQ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Lesser General Public License, version 3 No. of lines in distributed program, including test data, etc.: 1043965 No. of bytes in distributed program, including test data, etc.: 11053655 Distribution format: tar.gz Programming language: C++, MPI, octave. Computer: Linux based clusters for HPC or workstations. Operating system: Linux based. Classification: 4.14, 6.5, 12. External routines: Qt5, hdf5-1.8.15, jsonlab, OpenFOAM/CFDEM, Octave/Matlab Nature of problem: Development of closure models for momentum, species transport and heat transfer in fluid and fluid-particle systems using purely Eulerian or Euler-Lagrange simulators. Solution
Spatial orientation, adaptation, and motion sickness in real and virtual environments
NASA Technical Reports Server (NTRS)
Dizio, Paul; Lackner, James R.
1992-01-01
Reason and Brand (1975) noted that motion sickness occurs in many situations involving either passive body motion or active interaction with the world via indirect sensorimotor interfaces (e.g., prism spectacles). As might be expected, motion sickness is being reported in VEs that involve apparent self-motion through space, the best known examples being flight simulators (Kennedy et al., 1990). The goals of this paper are to introduce the motion-sickness symptomatology; to outline some concepts that are central to theories of motion sickness, spatial orientation, and adaptation; and to discuss the implications of some trends in VE research and development.
NASA Astrophysics Data System (ADS)
Lee, Ping-Chang
2014-03-01
Computed tomography (CT) plays a key role in modern medical system, whether it be for diagnosis or therapy. As an increased risk of cancer development is associated with exposure to radiation, reducing radiation exposure in CT becomes an essential issue. Based on the compressive sensing (CS) theory, iterative based method with total variation (TV) minimization is proven to be a powerful framework for few-view tomographic image reconstruction. Multigrid method is an iterative method for solving both linear and nonlinear systems, especially when the system contains a huge number of components. In medical imaging, image background is often defined by zero intensity, thus attaining spatial support of the image, which is helpful for iterative reconstruction. In the proposed method, the image support is not considered as a priori knowledge. Rather, it evolves during the reconstruction process. Based on the CS framework, we proposed a multigrid method with adaptive spatial support constraint. The simultaneous algebraic reconstruction (SART) with TV minimization is implemented for comparison purpose. The numerical result shows: 1. Multigrid method has better performance while less than 60 views of projection data were used, 2. Spatial support highly improves the CS reconstruction, and 3. When few views of projection data were measured, our method performs better than the SART+TV method with spatial support constraint.
NASA Astrophysics Data System (ADS)
Songer, Jocelyn E.; Eatock, Ruth Anne
2011-11-01
The mammalian saccule detects head tilt and low-frequency head accelerations as well as higher-frequency bone vibrations and sounds. It has two different hair cell types, I and II, dispersed throughout two morphologically distinct regions, the striola and extrastriola. Afferents from the two zones have distinct response dynamics which may arise partly from zonal differences in hair cell properties. We find that type II hair cells in the rat saccular epithelium adapt with a time course appropriate for influencing afferent responses to head motions. Moreover, striolar type II hair cells adapted by a greater extent than extrastriolar type II hair cells and had greater phase leads in the mid-frequency range (5-50 Hz). These differences suggest that hair cell transduction may contribute to zonal differences in the adaptation of vestibular afferents to head motions.
NASA Astrophysics Data System (ADS)
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Limin; Gao, Feng; Zhao, Huijuan
2014-03-01
According to the morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic-rate images of fluorophore can provide diagnostic information for tumor differentiation, and especially have the potential for staging of tumors. In this paper, fluorescence diffuse optical tomography method is firstly used to acquire metabolism-related time-course images of the fluorophore concentration. Based on a two-compartment model comprised of plasma and extracelluar-extravascular space, we next propose an adaptive-EKF framework to estimate the pharmacokinetic-rate images. With the aid of a forgetting factor, the adaptive-EKF compensate the inaccuracy initial values and emphasize the effect of the current data in order to realize a better online estimation compared with the conventional EKF. We use simulate data to evaluate the performance of the proposed methodology. The results suggest that the adaptive-EKF can obtain preferable pharmacokinetic-rate images than the conventional EKF with higher quantitativeness and noise robustness.
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation
NASA Astrophysics Data System (ADS)
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile
NASA Astrophysics Data System (ADS)
von der Weid, Jean Pierre; Souto, Mario H.; Garcia, Joaquim D.; Amaral, Gustavo C.
2016-07-01
We present a novel methodology able to distinguish meaningful level shifts from typical signal fluctuations. A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm. The developed methodology demands low computational effort and can easily be embedded in a dedicated processing unit. Our case studies compare the new methodology with current available ones and show that it is the most adequate technique for fast detection of multiple unknown level-shifts in a noisy OTDR profile.
Mie light-scattering granulometer with adaptive numerical filtering. I. Theory.
Hespel, L; Delfour, A
2000-12-20
A search procedure based on a least-squares method including a regularization scheme constructed from numerical filtering is presented. This method, with the addition of a nephelometer, can be used to determine the particle-size distributions of various scattering media (aerosols, fogs, rocket exhausts, motor plumes) from angular static light-scattering measurements. For retrieval of the distribution function, the experimental data are matched with theoretical patterns derived from Mie theory. The method is numerically investigated with simulated data, and the performance of the inverse procedure is evaluated. The results show that the retrieved distribution function is quite reliable, even for strong levels of noise.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
NASA Astrophysics Data System (ADS)
Neuhäuser, Markus; Krackow, Sven
2007-02-01
The neonatal incidence rate of Down syndrome (DS) is well-known to accelerate strongly with maternal age. This non-linearity renders mere accumulation of defects at recombination during prolonged first meiotic prophase implausible as an explanation for DS rate increase with maternal age, but might be anticipated from chromosomal drive (CD) for trisomy 21. Alternatively, as there is selection against genetically disadvantaged embryos, the screening system that eliminates embryos with trisomy 21 might decay with maternal age. In this paper, we provide the first evidence for relaxed filtering stringency (RFS) to represent an adaptive maternal response that could explain accelerating DS rates with maternal age. Using historical data, we show that the proportion of aberrant live births decrease with increased family size in older mothers, that inter-birth intervals are longer before affected neonates than before normal ones, and that primiparae exhibit elevated levels of DS incidence at higher age. These findings are predicted by adaptive RFS but cannot be explained by the currently available alternative non-adaptive hypotheses, including CD. The identification of the relaxation control mechanism and therapeutic restoration of a stringent screen may have considerable medical implications.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-05-13
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
Optimal spatial filtering and transfer function for SAR ocean wave spectra
NASA Technical Reports Server (NTRS)
Beal, R. C.; Tilley, D. G.
1981-01-01
The impulse response of the SAR system is not a delta function and the spectra represent the product of the underlying image spectrum with the transform of the impulse response which must be removed. A digitally computed spectrum of SEASAT imagery of the Atlantic Ocean east of Cape Hatteras was smoothed with a 5 x 5 convolution filter and the trend was sampled in a direction normal to the predominant wave direction. This yielded a transform of a noise-like process. The smoothed value of this trend is the transform of the impulse response. This trend is fit with either a second- or fourth-order polynomial which is then used to correct the entire spectrum. A 16 x 16 smoothing of the spectrum shows the presence of two distinct swells. Correction of the effects of speckle is effected by the subtraction of a bias from the spectrum.
Spatial localization of the K+ channel selectivity filter by mutant cycle-based structure analysis.
Ranganathan, R; Lewis, J H; MacKinnon, R
1996-01-01
The structurally well-characterized scorpion toxin Agitoxin2 inhibits ion permeation through Shaker K+ channels by binding to the external pore entryway. Scanning mutagenesis identified a set of inhibitor residues critical for making energetic contacts with the channel. Using thermodynamic mutant cycle analysis, we have mapped channel residues relative to the known inhibitor structure. This study constrains the position of multiple channel residues within the pore-forming loops; in one stretch, we have been able to map five out of seven contiguous residues to the inhibitor interaction surface, including those involved in ion selectivity. One interaction in particular, that of K27M on the inhibitor with Y445F on the channel, is unique in that it depends on the K+ ion concentration. These results reveal a shallow vestibule formed by the pore loops at the K+ channel entryway. The selectivity filter is located at the center of the vestibule close to (approximately 5 A) the extracellular solution. PMID:8562077
Anomalous dispersion in atomic line filters applied for spatial frequency detection
Landolt, Andrin; Roesgen, Thomas
2009-11-01
The anomalous dispersion of an atomic line filter near a resonant transition is exploited for full-field frequency measurements. The influence of the line shape function on the dispersion in atomic vapors near resonance and the possibilities to increase sensitivity are discussed. From the model-calculated absorption of iodine vapor at frequency-doubled Nd:YAG laser wavelengths, the corresponding refractive index is obtained through the Kramers-Kronig relations. Both variables are used to assess the performance of a iodine vapor cell as a dispersive element in an interferometric setup for Doppler frequency shift detection. With good agreement, the predicted sensitivity of the setup is compared to an experimental calibration. Observed discrepancies are attributed to the assumption of a Gaussian line shape in the absorption model. The full-field Doppler frequency measurement capacity of the technique is demonstrated in a rotating disk experiment, and the measurement performance is assessed.
NASA Astrophysics Data System (ADS)
Capitán, José A.; Manrubia, Susanna
2015-12-01
The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.
Capitán, José A; Manrubia, Susanna
2015-12-01
The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.
Projection-based spatially adaptive reconstruction of block-transform compressed images.
Yang, Y; Galatsanos, N P; Katsaggelos, A K
1995-01-01
At the present time, block-transform coding is probably the most popular approach for image compression. For this approach, the compressed images are decoded using only the transmitted transform data. We formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. Numerical experiments are shown that demonstrate that the proposed algorithms work better than both the JPEG deblocking recommendation and our previous projection-based image decoding approach.
Capitán, José A; Manrubia, Susanna
2015-12-01
The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space. PMID:26764748
The role of motor learning in spatial adaptation near a tool.
Brown, Liana E; Doole, Robert; Malfait, Nicole
2011-01-01
Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented. PMID:22174944
Hatzis, C; Sweeney, P J; Srienc, F; Fredrickson, A G
1990-12-01
Suspension-feeding ciliates, either bacteriovorous or planktonic, are adapted to feed on particulate food matter of size much smaller than their own size. These microorganisms collect their prey by generating water currents that draw prey toward their capture surfaces. Under such conditions food particles are treated in bulk, and captures of individual food particles from a suspension by individual single-celled organisms are discrete events that occur at random intervals of time. Each such event is followed by a sequence of additional events that also occur at random intervals of time. This sequence culminates in the incorporation of the digestible portion of the food particle into the cell's cytoplasm and the expulsion of the indigestible portion from the cell. In theory, the rate of the overall ingestion-digestion process can be limited by the passage of particles through any stage of this sequence of events. In this paper, we assume that only the initial events in the sequence, those that occur in the oral region of the cell, limit the rate of the ingestion-digestion process, and we develop a discrete, stochastic model of filter feeding based on that assumption. We use the model to show how advanced instrumentation, such as flow cytometry, can be used to measure parameters of the model and also to answer a number of important questions about the mechanism of filter feeding. We show also how the model can be applied to nonhomogeneous cell populations for which parameters of the model are distributed.
Adaptive wiener image restoration kernel
Yuan, Ding
2007-06-05
A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.
Serebryannikov, Andriy E; Lalanne, Philippe; Petrov, Alexander Yu; Ozbay, Ekmel
2014-11-01
New diffractive optical elements offering a frequency tolerant, very efficient, high-pass and bandpass spatial filtering over a broad range of incidence angles are demonstrated by numerical simulations. The device operates in reflection mode owing to the (nearly) perfect blazing. It relies on two-dimensional square-lattice photonic crystals composed of dielectric rods with simple corrugations at the interface. Similar performance can be obtained with gratings composed of a single rod layer placed in the near field of a metal mirror, indicating a route to geometries that can be easily fabricated with modern nanotechnologies. Also equal splitting between zero and first negative orders can be obtained for incidence-angle variations that are wider than 60°. PMID:25361312
NASA Astrophysics Data System (ADS)
Ushenko, Yu A.
2012-11-01
The complex technique of concerted polarization-phase and spatial-frequency filtering of blood plasma laser images is suggested. The possibility of obtaining the coordinate distributions of phases of linearly and circularly birefringent protein networks of blood plasma separately is presented. The statistical (moments of the first to fourth orders) and scale self-similar (logarithmic dependences of power spectra) structure of phase maps of different types of birefringence of blood plasma of two groups of patients-healthy people (donors) and those suffering from rectal cancer-is investigated. The diagnostically sensitive parameters of a pathological change of the birefringence of blood plasma polycrystalline networks are determined. The effectiveness of this technique for detecting change in birefringence in the smears of other biological fluids in diagnosing the appearance of cholelithiasis (bile), operative differentiation of the acute and gangrenous appendicitis (exudate), and differentiation of inflammatory diseases of joints (synovial fluid) is shown.
Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong
2015-04-01
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.
Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong
2015-04-01
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness. PMID:25794375
An Adaptive Particle Filtering Approach to Tracking Modes in a Varying Shallow Ocean Environment
Candy, J V
2011-03-22
The shallow ocean environment is ever changing mostly due to temperature variations in its upper layers (< 100m) directly affecting sound propagation throughout. The need to develop processors that are capable of tracking these changes implies a stochastic as well as an 'adaptive' design. The stochastic requirement follows directly from the multitude of variations created by uncertain parameters and noise. Some work has been accomplished in this area, but the stochastic nature was constrained to Gaussian uncertainties. It has been clear for a long time that this constraint was not particularly realistic leading a Bayesian approach that enables the representation of any uncertainty distribution. Sequential Bayesian techniques enable a class of processors capable of performing in an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean. In this paper adaptive processors providing enhanced signals for acoustic hydrophonemeasurements on a vertical array as well as enhanced modal function estimates are developed. Synthetic data is provided to demonstrate that this approach is viable.
Goedert, Kelly M.; Shah, Priyanka; Foundas, Anne L.; Barrett, A. M.
2013-01-01
Prism adaptation treatment (PAT) is a promising rehabilitative method for functional recovery in persons with spatial neglect. Previous research suggests that PAT improves motor-intentional “aiming” deficits that frequently occur with frontal lesions. To test whether presence of frontal lesions predicted better improvement of spatial neglect after PAT, the current study evaluated neglect-specific improvement in functional activities (assessment with the Catherine Bergego Scale) over time in 21 right-brain-damaged stroke survivors with left-sided spatial neglect. The results demonstrated that neglect patients' functional activities improved after two weeks of PAT and continued improving for four weeks. Such functional improvement did not occur equally in all of the participants: Neglect patients with lesions involving the frontal cortex (n=13) experienced significantly better functional improvement than did those without frontal lesions (n=8). More importantly, voxel-based lesion-behavior mapping (VLBM) revealed that in comparison to the group of patients without frontal lesions, the frontal-lesioned neglect patients had intact regions in the medial temporal areas, the superior temporal areas, and the inferior longitudinal fasciculus. The medial cortical and subcortical areas in the temporal lobe were especially distinguished in the “frontal lesion” group. The findings suggest that the integrity of medial temporal structures may play an important role in supporting functional improvement after PAT. PMID:22941243