Spectrum Estimation Research Articles

Computed tomography data based X-ray spectrum estimation method

X-ray spectrum plays an important role in computed tomography (CT) beam hardening correction, dual spectral X-ray CT imaging, and radiation dose calculation. The commonly used method to estimate X-ray spectrum is to estimate the spectra indirectly by using the attenuation data of X-ray passing through the phantoms with different thickness. Since the problem is seriously ill-conditioned, how to choose a suitable mold, establish scanning models and construct solving methods to improve the robustness and accuracy of energy spectrum estimation is the focus of this paper. In this work, in the absence scattering, we present a method to estimate the distribution of the X-ray spectrum by using CT scanning data. In this method, the mutual verification relationship between spectral estimation and image reconstruction is considered. That is, when the spectral estimation is correct, the spectral information can be used to construct a correction algorithm to remove hardening artifacts, and the image without hardening artifacts can be obtained. When the reconstructed image has no hardening artifact, it can indirectly prove that the estimated spectrum is accurate. For single-material molds, when there is no hardening artifact, CT images are fragmentation constant, which can be described by image total variation (TV) minimum. In this method, the mutual corroboration relationship is used to construct an optimization model, and then the X-ray spectrum is estimated and CT images without hardening artifacts are reconstructed through alternate iterative solutions. The characteristic of this method is that it does not necessitate obtaining the cross-line length of the measured mold with different thickness in advance, and it does not require high production precision of the said mold either. When there is a small amount of scattering in CT scanning data, the proposed method can also better estimate the energy spectrum, except for the large deviation in the high-energy part. However, as the scattering ratio increases, the high-energy portion of the energy spectrum will increase, resulting in the estimated spectrum differing greatly from the actual spectrum. Therefore, in the actual experiment, we add collimors in front of the X-ray source and detector to reduce the influence of scattering on the energy spectrum estimation. The numerical result and experimental result show that the proposed method can accurately and robustly estimate the X-ray energy spectrum.

Transport of Cosmic-Ray Electrons from 1 au to the Sun

Gamma rays are produced by cosmic-ray (CR) protons interacting with the particles at the solar photosphere and by CR electrons and positrons (CRes) via inverse Compton scattering of solar photons. The former comes from the solar disk while the latter extends beyond the disk. Evaluation of these emissions requires the flux and spectrum of CRs in the vicinity of the Sun, while most observations provide flux and spectra near the Earth, at around 1 au from the Sun. Past estimates of the quiet Sun gamma-ray emission use phenomenological modulation procedures to estimate spectra near the Sun. We show that CRe transport in the inner heliosphere requires a kinetic approach and use a novel approximation to determine the variation of CRe flux and spectrum from 1 au to the Sun including the effects of (1) the structure of the large-scale magnetic field, (2) small scale turbulence in the solar wind from several in situ measurements, in particular, those by Parker Solar Probe that extend this information to 0.1 au, and (3) most importantly, energy losses due to synchrotron and inverse Compton processes. We present results on the flux and spectrum variation of CRes from 1 au to the Sun for several transport models. In forthcoming papers we will use these results for a more accurate estimate of quiet Sun inverse Compton gamma-ray spectra, and, for the first time, the spectra of extreme ultraviolet to hard X-ray photons produced by synchrotron emission. These can be compared with the quiet Sun gamma-ray observation by the Fermi and X-ray upper limits set by RHESSI.

Analysis and Estimation of Power Spectrum by EEG During the Kraepelin Test with or Without Rhythm

In this paper, by measuring EEG during the Kraepelin test, it was clarified that the optimal rhythm which maximized the number of answers was found to be 1.2-times, when the rhythm answering without rhythm was set at 1.0-times. It was clarified that when the optimum rhythm was listened to, the ratio of power spectrum for relaxation/concentration was higher, the standard deviation of that ratio was smaller, and the activation of the left and right brains was not unbalanced. The power spectrum of the EEG while listening to the rhythm and answering the Kraepelin test was estimated from the EEG when answering without rhythm, and the error was less than 10%.

Изменения в липидном и фосфолипидном спектре сыворотки крови при бактериальных кишечных инфекциях как предикторы развития синдрома раздраженного кишечника

Some patients develop irritable bowel syndrome (IBS) after acute diarrhea of bacterial etiology (ADBE). There are isolated works that present data concerning the parameters of total lipids and phospholipids in intestinal infections. The features of changes in lipid and phospholipid spectra in relation to the prognosis of IBS development in ADBE has not yet been evaluated. Objective. To evaluate serum lipid and phospholipid spectrum in patients with ADBE and to determine its significance in the development of IBS. Materials and methods. The study was performed in a group of patients with ADBE aged 18–65 years (n = 50) who received inpatient treatment in 2021–2022 at the Adygea Republican Infectious Diseases Hospital. For laboratory verification of the diagnosis, a bacteriological method and polymerase chain reaction (PCR) with a reagent kit “AmpliSens® AII screen-FL” were used. Blood sampling for lipid spectrum estimation was done on day 2–3 of the disease. The levels of triglycerides (TG), total cholesterol, high-density lipoproteins (HDL) and low-density lipoproteins (LDL), apolipoprotein A1 (APO-A1) and apolipoprotein-B (APO-B) were determined by enzymatic colorimetric method. Serum phospholipids were also isolated and fractionated into lysophosphatidylcholine, sphingomyelin, phosphatidylcholine, phosphatidylethanolamine by a unified thin-layer chromatography using Sorbfil TLC plates and Sorbfil TLC densitometer. SPSS Statistics 26.0 program was used to process the obtained results. ROC analysis was used to estimate the probability of IBS development. Results. The study of total lipid spectrum in patients in the acute period of the disease revealed hypertriglyceridemia of 2.2 mmol/L (95% CI: 1.9–2.5), which was observed in 62% of patients. An increase in the level of phosphatidylcholine was observed in 90% of patients, lysophosphatidylcholine – in 56%, a decrease in the level of phosphatidylethanolamine – in 72%, and sphingomyelin – in 24%. Subsequently, within a month after convalescence, 16 (32%) patients developed IBS. For the parameters that were altered in most patients with IBS (cholesterol, TG, phosphatidylcholine, phosphatidylethanolamine), ROC analysis was performed to assess the risk of developing post-infectious IBS. At a cholesterol level of 3.75 mmol/L and higher (AUC = 0.716 ± 0.086; p = 0.019), blood triglycerides 2.115 mmol/L and higher (AUC = 0.889 ± 0.051; p < 0.001), phosphatidylcholine 63.8% and higher (AUC = 0.827 ± 0.058; p < 0.001), phosphatidylethanolamine 14.3% and lower (AUC = 0.853 ± 0.055; p < 0.001), a high risk of developing IBS was predicted. Conclusion. Based on the increase in the level of cholesterol, triglycerides, phosphatidylcholine above or decrease in the level of phosphatidylethanolamine below the threshold values obtained by ROC analysis, it is possible to predict a high or low risk of IBS development in ADBE patients and to determine indications for timely preventive therapy of this pathology. Key words: cholesterol, triglycerides, lipids, bacterial intestinal infections, irritable bowel syndrome

CFAR based NOMP for Line Spectral Estimation and Detection

The line spectrum estimation and detection problem are considered in this paper. We propose a CFAR-based Newtonized OMP (NOMP-CFAR) method which can maintain a desired false alarm rate without the knowledge of the noise variance. The NOMP-CFAR consists of two steps, namely, an initialization step and a detection step. In the initialization step, NOMP is employed to obtain candidate sinusoidal components. In the detection step, CFAR detector is applied to detect each candidate frequency, and provides the “soft” thresholds. After removing the most unlikely target, the Newton refinements are used to refine the remaining parameters. The relationship between the false alarm rate and the required threshold is established. Compared with NOMP, NOMP-CFAR has only 1 dB performance loss in additive white Gaussian noise scenario with false alarm probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-2}$</tex-math></inline-formula> and detection probability 0.8 without knowledge of noise variance. For varied noise variance scenario, NOMP-CFAR still preserves its CFAR property, while NOMP violates the CFAR. Besides, real experiments are also conducted to demonstrate the detection performance of NOMP-CFAR, compared to CFAR and NOMP.

Power spectrum analysis method of coherent Doppler lidar based on linear prediction spectrum estimation

Power spectrum analysis method of coherent Doppler lidar based on linear prediction spectrum estimation

A Proposed Quantum Framework for Low-Complexity Quantum Simulation and Spectrum Estimation of Hankel-Patterned Systems

A Proposed Quantum Framework for Low-Complexity Quantum Simulation and Spectrum Estimation of Hankel-Patterned Systems

Pattern-Coupled Baseline Correction Method for Near-Infrared Spectroscopy Multivariate Modeling

In near-infrared (NIR) modeling, the baseline drift tends to affect the qualitative and quantitative performance of the multivariate calibration model. Baseline correction method based on the sparse representation of independent wavelength is a recent method for pure spectra estimation. In fact, the measured NIR generally exhibit local wavelength coupling sparse properties, and the existing baseline correction methods suffer from performance degradation due to ignoring the wavelength coupling effect on baseline correction. In this paper, the pattern-coupled learning framework considering the local coupling property is proposed to achieve pure spectrum fitting and baseline correction simultaneously. Specifically, the pattern-coupled hierarchical Gaussian prior model is introduced to characterize the local sparse structure corresponding to characteristic peaks with wavelength coupling. The adaptive coupling method is further proposed to achieve robust learning of NIR under different noise interference. Unlike conventional frameworks where wavelength or hyperparameters are learned independently, the proposed mode coupling framework enables accurate baseline estimation without prior knowledge of spectral structure by characterizing wavelength coupling properties. Compared with the state-of-the-art methods, the RMSE and R2 of the proposed method are reduced and increased by 14.41% and 1.53%, 14.12% and 2.01%, 12.86% and 12.93% in three measured NIR datasets, respectively.

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot and sparse signals, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrificing the range and/or azimuth resolution, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high; thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis for evenly distributed antenna array case are discussed in this paper. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect.

Broadband Spatial Spectrum Estimation Based on Space-Time Minimum Variance Distortionless Response and Frequency Derivative Constraints

The two-dimensional optimization capability of the space-time minimum variance distortionless response (STMVDR) can improve the spatial spectrum estimation performance of the broadband beamformer with limited samples, but its high sensitivity to frequency errors may lead to the suppression of some narrowband signals not at the grid point of the frequency response calculation. In order to reduce the sensitivity of STMVDR to frequency errors, a broadband spatial spectrum estimation method based on STMVDR and frequency derivative constraints is proposed in this paper. First, based on the beam response of the STMVDR beamformer, an arbitrary order frequency derivative constraint is derived. Second, combining the gain constraint and frequency derivative constraint, the linear constrained minimum variance problem is established to solve the optimal weight of the beamformer. Finally, the calculation method of the broadband spatial spectrum is derived. Numerical simulations show that the proposed method not only solves the problem of frequency mismatch caused by the insufficient number of points for calculating frequency response, but also does not cause the spatial response related peaks to deviate from the true direction due to the broadening of the main lobe of frequency response. In addition, the proposed method can reduce the computational complexity of the algorithm by more than half by increasing the interval of the frequency calculation grid points, and does not significantly loss in the angular resolution capability and estimation accuracy of the algorithm.

Recent Advances in Attenuation Estimation.

This chapter reviews some of the recent advances in the estimation of the local and the total attenuation, with an emphasis on reducing the bias and variance of the estimates. A special focus is put on describing the effect of power spectrum estimation on bias and variance, the introduction of regularization strategies, as well as on eliminating the need to use reference phantoms for compensating for system dependent effects.

FEATURES OF OXIDATIVE METABOLISM AND GENETIC DISORDERS IN PERIPHERAL BLOOD LYMPHOCYTES OF PATIENTS WITH PRIMARY CERVICAL CANCER.

Thecombination of chemo- and radiotherapy used as main treatment of locally advanced cervical cancer (CC) may lead to side effects in healthy cells, which undermine theeffectiveness of treatment and quality of life. Theassessment of damage level in healthy radiosensitive cells from thetumor environment before thetreatment is important in order to predict and prevent remote side effects of radiation. To study theoxidative metabolism and genetic disorders in peripheral blood lymphocytes (PBL) of primary CC patients in order to evaluate thepossibilities of predicting radiation complications based on themolecular and biological properties of PBL. Peripheral blood samples were collected from 13primary CC patients T1-4N0-1M0-1, and PBL were routinely isolated. Theoxidative metabolism (mitochondrial trans-membrane potential, superoxide anion radical (О2•) generation, reactive oxygen species (ROS) production in PBL as well as thelevel of SH-groups in plasma and pro/antioxidant ratio in hemolysates were examined. Thedevelopment of genetic instability was determined by estimation of DNA double-strand breaks (DNA-DSB), frequency and spectrum of chromosome aberrations and apoptosis. Themarked increase in theintensity of О2• generation in PBL (1.5-fold), depletion of SH-groups content (1.6-fold) and a shift in thepro-antioxidant balance (1.4-fold) towards its prooxidant component were observed in theblood of primary CC patients as compared to healthy individuals. These oxidative stress related events were accompanied by an increase in thelevel of DNA-DSB (2.1-fold), apoptosis (3.5-fold) and frequency of cells with chromosome aberrations (3.9-fold). On thecontrary, significant decrease in mitochondrial trans-membrane potential (2.0-fold) and ROS generation in PBL (4.0-fold) were detected. Preliminary data indicate a violation of redox processes regulation, a shift in thepro-antioxidant balance towards its pro-oxidant component, accompanied by an increase in thelevel of DNA damage, development of genetic instability and apoptotic death of blood lymphocytes in primary CC patients.

Wave shadowing effects on phase resolved wave spectra estimated from RADAR backscatter: Part 1: An investigation of the errors due to wave shadowing in the spectral averaging approach

This is the first part of a short two part series concerned which is acknowledged as the primary source of error involved in the estimation of phase resolved sea wave spectra from RADAR backscatter measurement. A mathematical model is produced of what is termed here as spectral averaging which is the most commonly used technique for such estimation purposes. It is found that the spectral averaging technique can be described by a wave-number domain convolution integral. The kernel of the convolution must be a good approximation to a delta function for good estimates to be obtained. Using simulations of the wave shadowing process it is shown that good approximations can be obtained to the delta function requirement using approximately one tenth of the data employed in typical implementations of spectral averaging. This large data requirement is due to the need to impose the wave dispersion relationship as a constraint during the estimation process. Two new estimation methods are briefly introduced to resolve this situation and can operate on small data sets, one of which, termed conflation, is developed in detail in Part 2.

Wave shadowing effects on phase resolved wave spectra estimated from RADAR backscatter: Part 2: The conflation technique and the deconvolution techniques for the mitigation of wave shadowing effects

This is the second part of a short two series concerned with wave shadowing which is acknowledged as the primary source of error involved in the estimation of phase resolved sea wave spectra from RADAR backscatter measurement. A new technique termed conflation is introduced that uses multiple RADAR scan data sets to build a multi-segment data set together with a corresponding theoretic model. Using simulation the conflation methodology is compared against the so called spectral averaging technique that is the standard approach for estimating phase resolved wave spectra. Conflation is found to provide superior estimates, in particular making it possible to avoid the need to impose the wave dispersion relationship though an additional computational step as is necessary in spectral averaging. A second technique based upon deconvolution is also introduced for mitigating wave shadowing errors. However because deconvolution is a very mature process well covered in the signal process and linear system theory literature the discussion of this second method is very brief.

Performance analysis on the speed spectrum estimation based on dual-HFM waveform

Real-time Doppler estimation of sonar signals is of great significance to underwater communication and detection. The Hyperbolic-Frequency-Modulated (HFM) waveform-based speed spectrum estimation is one of the modern methods for underwater Doppler estimation, and this paper presents a detailed analyses of the derivation and the performance of this method. The analyzing results show the robustness of the method on multipath channels and multi-highlight targets and provide the main lobe width of the speed spectrum. Moreover, the paper examines the spectral properties of the HFM signal from the frequency-domain Doppler invariance property, which could provide insights for better understanding and utilization of this Doppler invariant waveform.

Angular Spectrum of Acoustic Pulses at Long Ranges

Long-range propagation of sound pulses in the deep ocean is considered. A new method for the estimation of the pulse angular spectrum is presented. The method is based on the Husimi transform of a wave field and can be realized with a short vertical array of nondirectional hydrophones. As a result, one obtains a diagram of the arrival pattern in the time–angle plane. The method is applied to a model of the underwater sound channel in the Sea of Japan. Special attention is paid to sound scattering on a cold synoptic eddy along the waveguide. It is shown that the synoptic eddy leads to a splitting of the individual ray’s arrivals into clusters with close angles and times. The random sound-speed perturbation induced by internal waves blurs these clusters into a fuzzy background and simultaneously broaden the angular spectrum of pulses. Nevertheless, it is found that the latter effect is relatively weak for short vertical arrays. In particular, it is shown that increasing the array length from 10 to 30 m results in the separation of the arrivals with opposite angles.

Pulse sequence induced variability combined with multivariate analysis as a potential tool for 13C solid-state NMR signals separation, quantification, and classification

Multivariate Curve Resolution (MCR) is a multivariate analysis procedure commonly used to analyze spectroscopic data providing the number of components coexisting in a chemical system, the pure spectra of the components as well as their concentration profiles. Usually, this procedure relies on the existence of distinct systematic variability among spectra of the different samples, which is provided by different sources of variation associated to differences in samples origin, composition, physical chemical treatment, etc. In solid-state NMR, MCR has been also used as a post-processing method for spectral denoising or editing based on a given NMR property. In this type of use, the variability is induced by the incrementation of a given parameter in the pulse-sequence, which encodes the separation property in the acquired spectra. In this article we further explore the idea of using a specific pulse sequence to induce a controlled variability in the 13C solid-state NMR spectra and then apply MCR to separate the pure spectra of the components according to the properties associated to the induced variability. We build upon a previous study of sugarcane bagasse where a series of 13C solid-state NMR spectra acquired with the Torchia-T1 CPMAS pulse sequence, with varying relaxation periods, was combined with different sample treatments, to estimate individual 13C solid-state NMR spectra of different molecular components (cellulose, xylan and lignin). Using the same pulse sequence, we show other application examples to demonstrate the potentiality, parameter optimization and/or establish the limitations of the procedure. As a first proof of principle, we apply the approach to commercial semicrystalline medium density polyethylene (MDPE) and polyether ether ketone (PEEK) providing the estimation of the individual 13C ssNMR spectra of the polymer chains in the amorphous (short T1) and crystalline (long T1) domains. The analysis also provided the relative intensities of each estimated pure spectra, which are related to the characteristic T1 decays of the amorphous and crystalline domain fractions. We also apply the analysis to isotactic poly (1-butene) (iPB-I) as an example in which the induced T1 variability occurs due to the mobility difference between the polymer backbone and side-chains. A jack-knifing procedure and a student t text allow us to stablish the minimum number of spectra and the range of relaxation periods that need to be used to achieve a precise estimation of the individual pure spectra and their relative intensities. A detail discussion about possible drawbacks, applications to more complex systems, and potential extensions to other type of induced variability are also presented.

Improved Speech Spatial Covariance Matrix Estimation for Online Multi-Microphone Speech Enhancement.

Online multi-microphone speech enhancement aims to extract target speech from multiple noisy inputs by exploiting the spatial information as well as the spectro-temporal characteristics with low latency. Acoustic parameters such as the acoustic transfer function and speech and noise spatial covariance matrices (SCMs) should be estimated in a causal manner to enable the online estimation of the clean speech spectra. In this paper, we propose an improved estimator for the speech SCM, which can be parameterized with the speech power spectral density (PSD) and relative transfer function (RTF). Specifically, we adopt the temporal cepstrum smoothing (TCS) scheme to estimate the speech PSD, which is conventionally estimated with temporal smoothing. Furthermore, we propose a novel RTF estimator based on a time difference of arrival (TDoA) estimate obtained by the cross-correlation method. Furthermore, we propose refining the initial estimate of speech SCM by utilizing the estimates for the clean speech spectrum and clean speech power spectrum. The proposed approach showed superior performance in terms of the perceptual evaluation of speech quality (PESQ) scores, extended short-time objective intelligibility (eSTOI), and scale-invariant signal-to-distortion ratio (SISDR) in our experiments on the CHiME-4 database.

Modulation of the symbionts light environment in hospite in scleractinian corals

The upregulation of animal chromoproteins (CPs) during thermal stress produces “colorful” bleached corals that facilitate coral recovery after bleaching. In situ measurements indicate that animal CPs present in coral tissues reduce the elevated internal light environment of the remaining symbionts in bleached or low-pigmented stressed corals. However, there is still a lack of understanding regarding the extent to which animal CPs contribute to modifying the internal light environment of the symbionts in hospite. In this study, we evaluate the effect of three animal CPs on the optical properties of the coral tissue and their internal light environment using a numerical model. The model allows estimations of the absorbance spectra of corals as a function of changes in symbiont and animal pigmentation, as well as descriptions of the light environment in hospite of the symbionts. These descriptions were derived from the quantification of the contribution of each pigment component to light absorption, together with the contribution of the coral skeleton’s reflectance. Simulations indicate that animal CPs upregulation modifies the spectral distribution and the intensity of the internal light field. Animal CPs can reduce up to 11% of the light intensity in hospite when present individually, and up to 24% when present in combination. Such reduction may play a critical role in preventing the full development of the bleached phenotype when irradiance rises to excessive levels at low coral pigmentation, facilitating coral recovery and symbiont tissue re-colonization after bleaching. Accordingly, coral’s CPs components need to also be considered when selecting coral species for future restoration efforts.

Area and Spectrum Estimates for Stable Minimal Surfaces

This paper mainly concerns the area growth and bottom spectrum of complete stable minimal surfaces in a three-dimensional manifold with scalar curvature bounded from below. When the ambient manifold is the Euclidean space, by an elementary argument, it is shown directly from the stability inequality that the area of such minimal surfaces grows exactly as the Euclidean plane. Consequently, such minimal surfaces must be flat, a well-known result due to Fisher-Colbrie and Schoen as well as do Carmo and Peng. In the case of general ambient manifold, explicit area growth estimate is also derived. For the bottom spectrum, a self-contained argument involving positive Green’s function is provided for its upper bound estimates. The argument extends to stable minimal hypersurfaces in a complete manifold of dimension up to six with sectional curvature bounded from below.