Spectral Filtering Techniques Research Articles

The South African land cover change detection derived from 2013_2014 and 2017_2018 land cover products

The appetite for up-to-date information about the earth’s surface is ever increasing, as such information provides a basis for a large number of applications. These include the earth’s resource detection and evaluation, land cover and land use change monitoring together with other vast environmental studies such as climate change assessment. Due to the advantages of repetitive data acquisition, the synoptic view, together with the varied spatial resolution it provides, and its available historically achieved dataset, remote sensing earth observation has become the major preferred data source for various earth studies. This study assesses land cover change detection of the land cover products (2013_2014 and 2017_2018) derived from earth observation.There are vast number of change detection methodologies and techniques with some still emerging. This study embarked on post classification change detection methodology which entailed morphological and spectral filtering techniques. The 10 land cover classes that were assessed for change detection are: natural wooded land, shrubland, grassland, waterbodies, wetlands, barren lands, cultivated, built-up, planted forest together with mines and quarries. The change detection accuracy result was 74.97%. Through the likelihood analysis, the likelihood for change to occur (e.g. cultivated to grassland) and unlikelihood of change to occur (e.g. built-up to planted forest), resulted in 72.2% areas of potential realistic change.The change detection results, further depend on the quality, compatibility and accuracy of the input land cover datasets. The application of different ancillary data together with different modelling techniques for land cover classification also affect the true reflectance of land cover change detection. Therefore extra caution should be exercised when analysing change detection so as to provide true and reliable changes.

WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions

Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions. Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods. Main results. WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods. Significance. WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available at https://github.com/gferrazzi/WHOCARES.

A novel grid regression demodulation method for radiographic grid artifact correction.

In x-ray radiography, the commonly used antiscatter grid for enhancing image quality causes artifacts in the form of periodic noises, such as shadows, cutoff, and Moiré fringes. Software degridding is traditionally performed via linear or homomorphic filtering in the spectral domain. These methods inevitably result in image blurring, information loss, and distortion, thus hindering detection and assessment of diseases. We seek effective and practical solutions for grid artifact correction based on spatial-domain analysis toward high-quality imaging. By analyzing the physical process of grid artifact formation, we track down the root of the problem associated with spectral filtering. We propose the grid regression demodulation (GRD). The degridding cost is forged as a functional of the latent x-ray photon image and parametric grid model characterizing grid transmission property. Regularization on the grid spectra is incorporated. We devise optimization algorithms for artifact correction and grid pattern estimation. GRD decouples the partially overlapped spectra of the grid and anatomy, and removes the artifacts independently, thus restoring the underlying clinically relevant data. Method efficacy is demonstrated using simulated and real data. GRD effectively preserves image edges, textures, and patterns while removing grid artifacts. For the known ground truth setting, GRD gives a near-perfect correction. For real data, GRD is capable of correcting not only the primary grid artifacts, but also the higher grid harmonic artifacts while keeping image content unaltered, which is unachievable by the other methods. Our method has low residual errors and exhibits a successful demodulation effect without introducing additional artifacts, while ringing or cilia artifacts are present in the others. The proposed method outperforms the prevalent transform techniques for correcting grid artifacts in digital radiography. It is self-sustained and self-adaptive to a range of targets and beam quality. Our approach is advantageous in restoring the latent image while suppressing grid noises. It retrieves the true scale factor of the degridded data, which is unattainable via any spectral filtering techniques. This work unlocks a promising venue to improve and upgrade low-dose medical radiographic imaging technology.

Local current analysis on defective zigzag graphene nanoribbons devices for biosensor material applications.

In this contribution, we aim at investigating the mechanism of biosensing in graphene-based materials from first principles. Inspired by recent experiments, we construct an atomistic model composed of a pyrene molecule serving as a linker fragment, which is used in experiment to attach certain aptamers, and a defective zigzag graphene nanoribbons (ZGNRs). Density functional theory including dispersive interaction is employed to study the energetics of the linker absorption on the defective ZGNRs. Combining non-equilibrium Green's function and the Landauer formalism, the total current-bias voltage dependence through the device is evaluated. Modifying the distance between the linker molecule and the nanojunction plane reveals a quantitative change in the total current-bias voltage dependence, which correlates to the experimental measurements. In order to illuminate the geometric origin of these variation observed in the considered systems, the local currents through the device are investigated using the method originally introduced by Evers and co-workers. In our new implementation, the numerical efficiency is improved by applying sparse matrix storage and spectral filtering techniques, without compromising the resolution of the local currents. Local current density maps qualitatively demonstrate the local variation of the interference between the linker molecule and the nanojunction plane.

Molecule mapping of HR8799b using OSIRIS on Keck

Context. In 2015, Barman et al. (ApJ, 804, 61) presented detections of absorption from water, carbon monoxide, and methane in the atmosphere of the directly imaged exoplanet HR8799b using integral field spectroscopy (IFS) with OSIRIS on the Keck II telescope. We recently devised a new method to analyse IFU data, called molecule mapping, searching for high-frequency signatures of particular molecules in an IFU data cube. Aims. The aim of this paper is to use the molecule mapping technique to search for the previously detected spectral signatures in HR8799b using the same data, allowing a comparison of molecule mapping with previous methods. Methods. The medium-resolution H- and K-band pipeline-reduced archival data were retrieved from the Keck archive facility. Telluric and stellar lines were removed from each spectrum in the data cube, after which the residuals were cross-correlated with model spectra of carbon monoxide, water, and methane. Results. Both carbon monoxide and water are clearly detected at high signal-to-noise, however, methane is not retrieved. Conclusions. Molecule mapping works very well on the OSIRIS data of exoplanet HR8799b. However, it is not evident why methane is detected in the original analysis, but not with the molecule mapping technique. Possible causes could be the presence of telluric residuals, different spectral filtering techniques, or the use of different methane models. We do note that in the original analysis methane was only detected in the K-band, while the H-band methane signal could be expected to be comparably strong. More sensitive observations with the JWST will be capable of confirming or disproving the presence of methane in this planet at high confidence.

Correction of Wavefront Tilt Caused by Atmospheric Turbulence Using Quadrant Detectors for Enabling Fast Free-Space Quantum Communications in Daylight

Low photon-level signals used in most free-space quantum communication systems require a narrow field of view in the receiver to minimize the amount of background noise coupled into the single photon detectors. This can be achieved through beam tracking techniques, which compensate atmospheric effects, such as beam wander, in the receiver, reducing the long-term beam area. However, reducing the diameter of this area below a few microns, typically necessary to achieve a low level of solar background noise and successful daylight quantum transmission, require fine tracking precision and diffraction-limited optics. We demonstrate that this can be done with standard voice-coil fast steering mirrors and cheap commercially-available quadrant detectors. Two correcting strategies (open and closed loop) are experimentally tested and analyzed for their applicability in metropolitan (~km range) free-space quantum communications. The area containing the random fluctuations of the beam centroid caused by atmospheric turbulence at the focal plane of the receiver was reduced by a factor of 4 with an open-loop configuration, and up to a factor of nine with a closed loop configuration. This is equivalent to a reduction in the quantum bit error rate caused by background solar noise of up to one order of magnitude, which, combined with spectral filtering techniques, enable the possibility of fast daylight quantum key distribution.

Hilbert Transform, Spectral Filtering and Option Pricing

We show how spectral filtering techniques can improve the convergence of numerical schemes which use discrete Hilbert transforms based on a sinc function expansion, and thus ultimately on the fast Fourier transform. This is relevant, for example, for the computation of fluctuation identities, which give the distribution of the maximum or the minimum of a random path, or the joint distribution at maturity with the extrema staying below or above barriers. We use as examples the methods by Feng and Linetsky (2008) and Fusai, Germano and Marazzina (2016) to price discretely monitored barrier options where the underlying asset price is modelled by a Levy process. Both methods show exponential convergence with respect to the number of grid points in most cases, but are limited to polynomial convergence under certain conditions. We relate these rates of convergence to the Gibbs phenomenon for Fourier transforms and achieve improved results with spectral filtering.

Reflection Enhancement Processing in Ultra-Shallow Seismic Data

The lack of standard procedure for processing shallow reflection seismic data is an impediment to the use of reflection seismic data for near surface applications. The wave field of ultra-shallow reflection data is often complicated by low frequency high amplitude events in the same time window with primary reflections. The standard method for processing exploration-scale seismic data proved unsatisfactory for ultra-shallow reflection seismic data: scaling down the acquisition geometry is inadequate because the frequency bandwidth of the data recorded does not scale up in the same proportion. In this study, data-driven workflow of specialised techniques is used to preserve the low-amplitude energies reflected by shallow geological layers. Surgical top and end muting; cone and polygonal muting;  spectral-filtering techniques and careful validation of coherent events, by synthetic modelling, were combined to degrade noise and other non-reflection events. A combination of constant velocity gather, Semblance Plot, and Constant Velocity Stack techniques was applied to obtain the optimum move-out velocity. Applied to an ultrashallow seismic data from Southeast Spain, the workflow improved the signal-to-noise ratio of processed data. The CDP–stacked image constructed for the survey reveals some shallow vertical faults suspected to be associating with the main Carbonera Faults. These faults are comparable to faults projections constructed from a field based geological mapping undertaken many years before the seismic survey.

Monitoring of Wine Process and Prediction of Its Parameters with Mid‐Infrared Spectroscopy

AbstractIt was aimed to predict the chemical (ethanol, glycerol, organic acids, titratable acidity, °Brix, sugars, total phenolic and anthocyanin content) and microbiological parameters of red, rose and white wines during their processing from must to bottling using mid‐infrared (IR) spectroscopy in combination with one of the multivariate statistical analysis techniques, partial least square (PLS) regression. Various spectral filtering techniques were employed before PLS regression analysis of mid‐IR data. The best results were obtained from the second‐order derivation for the chemical parameters except for alcohols. PLS models developed for the prediction of some of the chemical parameters have R2 values greater than 0.9, with low root mean square error values; however, prediction of microbial population from mid‐IR spectroscopy did not provide accurate results. IR spectroscopic and chemical–chromatographic data were also used to investigate the differences between processing steps, and principal component analysis allowed clear separation of the beginning of the process from the rest.Practical ApplicationsMonitoring of the wine process from must to final product is necessary for better control of the process and the quality. As a rapid and a minimum waste‐producing technique, mid‐IR spectroscopy in combination with chemometric methods could allow prediction of several chemical parameters simultaneously. Therefore, any problems that could be encountered during wine processing could be determined and interfered in a short time.

Theoretical analysis of direct and phase-filtered cross-correlation response to a sinusoidal displacement for PIV image processing

The response of PIV image processing routines adopting cross-correlation is commonly categorized as that of a moving average (MA) filter. This paper addresses the intrinsic response of the statistical operator to a sinusoidal displacement from a theoretical perspective. Evaluation of the derived analytical expressions for the correlation indicates the response not to behave as that of an MA filter contrary to the generally adopted simplification. Instead the inherent signal modulation is non-linear and is determined by the ratio between displacement amplitude and particle image diameter. This finding is expected to be of importance and have considerable implications for recursive image processing routines. In addition, readily available spectral filtering techniques are assessed in terms of effectiveness in minimization of correlation deterioration due to pixelization and displacement filtering inherent to particle image self-correlation. It is shown that the best possible correlation response in digital PIV is dictated by the convolution between particle image self-correlation and displacement distribution function, inevitably retaining particle image inherent amplitude modulation.

Determination of methyl C13–N15 dipolar couplings in peptides and proteins by three-dimensional and four-dimensional magic-angle spinning solid-state NMR spectroscopy

We describe three- and four-dimensional semiconstant-time transferred echo double resonance (SCT-TEDOR) magic-angle spinning solid-state nuclear magnetic resonance (NMR) experiments for the simultaneous measurement of multiple long-range (15)N-(13)C(methyl) dipolar couplings in uniformly (13)C, (15)N-enriched peptides and proteins with high resolution and sensitivity. The methods take advantage of (13)C spin topologies characteristic of the side-chain methyl groups in amino acids alanine, isoleucine, leucine, methionine, threonine, and valine to encode up to three distinct frequencies ((15)N-(13)C(methyl) dipolar coupling, (15)N chemical shift, and (13)C(methyl) chemical shift) within a single SCT evolution period of initial duration approximately 1(1)J(CC) (where (1)J(CC) approximately 35 Hz, is the one-bond (13)C(methyl)-(13)C J-coupling) while concurrently suppressing the modulation of NMR coherences due to (13)C-(13)C and (15)N-(13)C J-couplings and transverse relaxation. The SCT-TEDOR schemes offer several important advantages over previous methods of this type. First, significant (approximately twofold to threefold) gains in experimental sensitivity can be realized for weak (15)N-(13)C(methyl) dipolar couplings (corresponding to structurally interesting, approximately 3.5 A or longer, distances) and typical (13)C(methyl) transverse relaxation rates. Second, the entire SCT evolution period can be used for (13)C(methyl) and/or (15)N frequency encoding, leading to increased spectral resolution with minimal additional coherence decay. Third, the experiments are inherently "methyl selective," which results in simplified NMR spectra and obviates the use of frequency-selective pulses or other spectral filtering techniques. Finally, the (15)N-(13)C cross-peak buildup trajectories are purely dipolar in nature (i.e., not influenced by J-couplings or relaxation), which enables the straightforward extraction of (15)N-(13)C(methyl) distances using an analytical model. The SCT-TEDOR experiments are demonstrated on a uniformly (13)C, (15)N-labeled peptide, N-acetyl-valine, and a 56 amino acid protein, B1 immunoglobulin-binding domain of protein G (GB1), where the measured (15)N-(13)C(methyl) dipolar couplings provide site-specific information about side-chain dihedral angles and the packing of protein molecules in the crystal lattice.