Deconvolution Matrix Research Articles

Estimation of Synthetic Unit Hydrographs’ Parameter Values: The Case of Wadi Al-Badan Sub-Catchment, West Bank, Palestine

Developing unit hydrographs is critical in gauged catchments. Due to the lack of local data, engineers should strive to estimate discharges based on empirical (synthetic) methods in ungauged catchments. Synthetic unit hydrograph methods, such as the Snyder and Soil Conservation Service are popular and play an essential role in hydrology. These methods are simple, requiring only catchment characteristics. Therefore, these methods are valuable tools for simulating runoff in ungauged catchments. This research paper focuses on the Al-Badan sub-catchment, which is located in the northeastern part of the West Bank in Palestine with an area of 83 km2. The study used a de-convolution matrix approach to develop an average one-hour unit hydrograph by selecting four significant rainfall events in the period (2005-2007), and the direct runoff hydrographs which are measured by Al-Badan flume located in the outlet of the sub-catchment. By computing the characteristics of the average one-hour UH with a peak discharge of 4.52 m3/sec and time to peak of 5 hours, Snyder and SCS UHs were developed to suit Al-Badan sub-catchment physical characteristics. The parameters were determined for the Snyder; peaking coefficient depends on the storage capacity of the catchment Cp = 0.88 and the non-dimensional regional coefficient representing catchment storage effects and slope Ct = 1.26 and for the SCS; Cp = 1.90 and the coefficient depending on the total runoff volume occur before peak discharge C = 2.92. These synthetic one-hour UHs were examined for two selected rainfall events that occurred in the period (2017–2019) by comparing observed and simulated direct runoff hydrographs. The performance was tested statistically. Results showed that the synthetic UHs are suitable for application and that the SCS method is more applicable.

Optimal estimation of the binned mask-free power spectrum, bispectrum, and trispectrum on the full sky: Scalar edition

We derive optimal estimators for the two-, three-, and four-point correlators of statistically isotropic scalar fields defined on the sphere, such as the cosmic microwave background temperature fluctuations, allowing for arbitrary (linear) masking and inpainting schemes. In each case, we give the optimal unwindowed estimator (obtained via a maximum-likelihood prescription, with an associated Fisher deconvolution matrix), and an idealized form, and pay close attention to their efficient computation. For the trispectrum, we include both parity-even and parity-odd contributions, as allowed by symmetry. The estimators can include arbitrary weighting of the data (and remain unbiased), but are shown to be optimal in the limit of inverse-covariance weighting and Gaussian statistics. The normalization of the estimators is computed via Monte Carlo methods, with the rate-limiting steps (involving spherical harmonic transforms) scaling linearly with the number of bins. An accompanying code package, polybin, implements these estimators in python, and we demonstrate the estimators' efficacy via a suite of validation tests.

SARS-CoV-2 infection dynamics and genomic surveillance to detect variants in wastewater - a longitudinal study in Bengaluru, India.

Environmental surveillance (ES) of a pathogen is crucial for understanding the community load of disease. As an early warning system, ES for SARS-CoV-2 has complemented routine diagnostic surveillance by capturing near real-time virus circulation at a population level. In this longitudinal study conducted between January 2022 and June 2022 in 28 sewershed sites in Bengaluru city (∼11 million inhabitants), we quantified weekly SARS-CoV-2 RNA concentrations to track infection dynamics and provide evidence of change in the relative abundance of emerging variants. We describe an early warning system using the exponentially weighted moving average control chart and demonstrate how SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 8-14 days earlier in wastewater than in clinical data. This was further corroborated by showing that the estimated number of infections is strongly correlated with SARS-CoV-2 RNA copies detected in the wastewater. Using a deconvolution matrix, we detected emerging variants of concern up to two months earlier in wastewater samples. In addition, we found a huge diversity in variants detected in wastewater compared to clinical samples. The findings from this study have been discussed regularly with local authorities to inform policy-making decisions. Our study highlights that quantifying viral titre, correlating it with a known number of cases in the area, and combined with genomic surveillance helps in tracking variants of concern (VOC) over time and space, enabling timely and making informed policy decisions. This work has been supported by funding from the Rockefeller Foundation grant to National Centre for Biological Sciences, TIFR) and the Indian Council of Medical Research grant to (FI) Tata Institute for Genetics and Society and Tata Trusts.

Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework

A method is developed that removes a priori information from remotely sensed atmospheric state profiles. This consists of a Wiener deconvolution, whereby the required cost function is obtained from the complete data fusion framework. Asserting that the deconvoluted averaging kernel matrix has to equal the unit matrix, results in an iterative process for determining a profile-specific deconvolution matrix. In contrast with previous deconvolution approaches, only the dimensions of this matrix have to be fixed beforehand, while the iteration process optimizes the vertical grid. This method is applied to ozone profile retrievals from simulated and real measurements co-located with the Izaña ground station. Individual profile deconvolutions yield strong outliers, including negative ozone concentration values, but their spatiotemporal averaging results in prior-free atmospheric state representations that correspond to the initial retrievals within their uncertainty. Averaging deconvoluted profiles thus looks like a viable alternative in the creation of harmonized Level-3 data, avoiding vertical smoothing difference errors and the difficulties that arise with averaged averaging kernels.

Masked Multiplexed Separations to Enhance Duty Cycle for Structures for Lossless Ion Manipulations.

The experimental paradigm of one ion packet release per spectrum severely hinders throughput in broadband ion mobility spectrometry (IMS) systems (e.g., drift tube and traveling wave systems). Ion trapping marginally mitigates this problem, but the duty cycle deficit is amplified when moving to high resolution, long pathlength systems. As a consequence, new multiplexing strategies that maximize throughput while preserving peak fidelity are essential for high-resolution IMS separations [e.g., structures for lossless ion manipulations (SLIMs) and multi-pass technologies]. Currently, broadly applicable deconvolution strategies for Hadamard-based ion multiplexing are limited to a narrow range of modulation sequences and do not fully maximize the ion signal generated during separation across an extended path length. Compared to prior Hadamard deconvolution errors that rely upon peak picking or discrete error classification, the masked deconvolution matrix technique exploits the knowledge that Hadamard transform artifacts are reflected about the central, primary signal [i.e., the true arrival time distribution (ATD)]. By randomly inducing mathematical artifacts, it is possible to identify spectral artifacts simply by their high degree of variability relative to the core ATD. It is important to note that the deweighting approach using the masked deconvolution matrix does not make any assumptions about the underlying transform and is applicable to any multiplexing strategy employing binary sequences. In addition to demonstrating a 100-fold increase in the total number of ions detected, the effective deconvolution of data from 5, 6, 7, and 8-bit pseudo-random sequences expands the utility and efficiency of the SLIM platform.

Measurement of prompt gamma field above the VR-1 water level

Pool-type reactors are an excellent source of high energy photons without neutrons because the water above fuel acts as an outstanding neutron shielding, whereas for high energy photons, it is nearly transparent. The absence of neutrons is a valuable property essential for testing semiconductor detectors because they are sensitive to neutrons, which might cause their damage. The high energy gammas are worth studying because they are responsible for radiation induced heating of reactor internal components in energy producing reactors, which might lead to their radiation induced damage by void swelling. At this effect, the helium produced in (n,α) reactions influenced by locally increased temperature clusters into bigger bubbles, which are increasing material volume, and therefore changing the geometry of internals. The referred method could be utilized e.g. for resource exploration by detecting prompt capture gammas. The prompt gamma field was measured at the VR-1 reactor with two different cores and water levels. The stilbene measurements showed relatively good agreement with the spectrum shape computed by MCNP6.2. An important result is a confirmation that the stilbene deconvolution matrix can be used with a rotated crystal. The testing measurement with HPGe detectors showed a possibility for measurement in such type of geometry.

Novel Method for Accurately Assessing Pull-up Artifacts in STR Analysis

OSIRIS is a mathematically-based software tool for Short Tandem Repeat (STR) and DNA fragment analysis (https://www.ncbi.nlm.nih.gov/osiris/). As part of its routine sample analyses, OSIRIS computes unique quality metrics that can be used for sample quality assessment. A common artifact of STR analysis is cross-channel pull-up or pull-down (negative pull-up). This occurs because of the spectral overlap between the dyes used with the marker set, and the failure of the color deconvolution matrix to isolate the colors in the dye set adequately. This paper describes a mathematical method for analyzing and quantifying the pull-up patterns across sample channels and effectively identifying and correcting the pull-up artifacts, as implemented in OSIRIS. Unlike approaches to pull-up that require a training set composed of previous samples, the algorithm described here uses a mathematical model of the underlying causes of pull-up. It is based solely on the information intrinsic to the sample it is analyzing and therefore incorporates the effects of the ambient conditions and the specific procedures used in creating the sample. These conditions are the physical determinants of the level of pull-up in the sample and are not likely to be represented in a training set consisting of past samples.

Evaluation of Lightning Current From Magnetic Field Based on Deconvolution Method

This article proposes the deconvolution method to evaluate the lightning channel-base current from the lightning magnetic field. We have verified the method using the magnetic field with/;without noises. The deconvolution results are both in good agreement with the simulated current. Also, we have discussed the deconvolution results when the condition number of the deconvolution matrix, $U_{{0}}$ , is different, and found that the deconvolution results will oscillate if the condition number of $U_{{0}}$ is very large. Note that the deconvolution method presented in this paper also works when using other engineering current models, such as transmission line (TL) model, and the modified TL model with exponential current decaying with height model, and so on. The deconvolution method is an efficient method to evaluate the lightning channel-base current from the electromagnetic field. This approach is also useful in research related to lightning characteristics and lightning protection.

Measurement of neutron spectra in a silicon filtered neutron beam using stilbene detectors at the LVR-15 research reactor

A well-defined neutron spectrum is an essential tool for calibration and tests of spectrometry and dosimetry detectors, and evaluation methods for spectra processing. Many of the nowadays used neutron standards are calibrated against a fission spectrum which has a rather smooth energy dependence. In recent time, at the LVR-15 research reactor in Rez, an alternative approach was tested for the needs of fast neutron spectrometry detector calibration. This process comprises detector tests in a neutron beam, filtered by one meter of single-crystalline silicon, which contains several significant peaks in the fast neutron energy range. Tests in such neutron field can possibly reveal specific problems in the deconvolution matrix of the detection system, which may stay hidden in fields with a smooth structure and can provide a tool for a proper energy calibration. Test with several stilbene scintillator crystals in two different beam configurations supplemented by Monte-Carlo transport calculations have been carried out. The results have shown a high level of agreement between the experimental data and simulation, proving thus the accuracy of used deconvolution matrix. The chosen approach can, thus, provide a well-defined neutron reference field with a peaked structure for further tests of spectra evaluation methods and scintillation detector energy calibration.

Automated Ki67 Hotspot Detection For Breast Cancer Biopsies

Introduction/ Background The quantification of immunohistochemical Ki67 and the detection of active areas of tumor cell proliferation (hotspots) have a critical importance in the prognosis and treatment planning for breast cancer. Aims In this work an automated and robust method for the detection of hotspot areas in breast cancer biopsies is proposed with the aim of supporting the pathologists by highlighting hotspot areas. Methods The proposed method has been tested on one Ki67 stained image from Openslide [1] acquired at 40x with an Hamamatsu scanner and on 5 Ki67 stained images of breast cancer biopsies acquired at 40x with AperioCS. Each input image is divided in tiles, whose colors are deconvolved using the method of Ruifrok [2] to estimate the presence of Ki67. Tiles with an estimated Diaminobenzidine (DAB) positivity of more than 5% are considered as potential hotspots. Neighbouring positive tiles are merged to form a final hotspot area. The three hotspot areas with higher DAB positivity are also highlighted in the output. The hotspot areas are then written in an XML file which is read by a medical image viewer such as Aperio ImageScope. The proposed method has been implemented in Java using the BioFormats open source library [3]. Results The color deconvolution of each tile has been performed by applying the standard Hematoxylin/Diaminobenzidine (H/DAB) deconvolution matrix provided in Fiji [4]. The tests have been performed at different zoom levels resulting in similar, coherent outputs of hotspot areas. The resulting hotspot areas have been validated by visual comparison with the hotspot areas determined by experts, showing a significant superimposition as shown in Fig. 1. The proposed method has been compared with the ASH method [5] for the image acquired with Hamamatsu scanner, producing similar results in comparable time. In c o nclusion , a new fully automated method for the detection of Ki67 hotspot areas in breast cancer biopsies has been proposed to support the pathologist by highlighting different hotspot areas. It is able to process different medical images formats, making it more interoperable. The time performance is comparable with existing methods such as ASH [5]. The proposed method at low magnification such as 1x, 2x, and 4x produced varying results influenced by the size that each tile assumes at such magnification levels. Further extension of the proposed method will also include the MIB-1 estimation within the hotspot areas as well as extensive testing and validation. This work is partially founded by the EU FP7 program, grant number 612471.

Blind channel equalization using fourth-order cumulants and a neural network

This paper addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple recordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, withe fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.

ImagingP-to-Sconversions with broad-band seismic arrays using multichannel time-domain deconvolution

This paper describes a series of innovations in the problem of deconvolving forward scattered P-to-S conversions. We introduce a theoretical foundation for a recently developed multichannel stacking technique and show that this process is equivalent to a spatial convolution of the incident wavefield with the discretely sampled set of station locations. We then show that deconvolution of the stacked data is a form of multichannel deconvolution with a spatially variable set of weights equal to those used in stacking. This result is independent of the particular deconvolution method that is used. A second innovation focuses on the design of deconvolution operators that correctly account for the loss of high frequency components of P-to-S conversions caused by differential attenuation of P and S waves. We describe two complimentary methods to implement this: (1) through the use of a regularization operator that penalizes high frequencies and increases with P-to-S lag time, or (2) through the use of a quelling operator. For the latter, we introduce the use of a t* operator that is applied to the deconvolution matrix operator. The t* operator progressively filters the vertical component seismogram with increasing P-to-S lag time and is based on an earth model of body wave attenuation. Both techniques produce progressively smoother solutions for increasing P-to-S lag times. The quelling approach has two advantages: (1) it is based on the physical principle that this solution is designed to address, and (2) it provides a unified inversion framework for the combination of stacking and deconvolution. This combination may be interpreted as a three-dimensional quelling (smoothing) operator that is applied to the full wavefield to stabilize the inversion. Application of this procedure to synthetic data shows that while the addition of a time dependent component to the deconvolution tends to decrease the frequency content of the solution, the amplitude of background ringing is reduced and the input model is reliably recovered. Further tests with data from the Lodore broad-band array in Colorado and Wyoming show significant improvement over conventional time domain methods. We image lateral variations in Moho continuity and reflectivity across the array, with significant improvement in resolution in the first 10 seconds of data.