Spectrum Estimation Research Articles

Time-domain slicing optical frequency domain reflectometry

A time-domain slicing (TDS) optical frequency domain reflectometry is proposed for large strain sensing with better spatial resolution. Compared with the conventional frequency domain slicing (FDS) method, the TDS with a Burg spectrum estimation is capable of enhancing the similarity of a local spectrum under large strain and mostly suppressing the fake peaks during the strain resolving. The experimental results demonstrated that it enables measurements of strain ranging from 600 to 4200 µε with a spatial resolution of 2.4 mm and a narrow optical frequency scanning range of only 10 nm. Moreover, the measurement accuracy is improved by six times by decreasing the root mean square error (RMSE) from 8.6611 to 1.3396 µε without any hardware modification.

The Atacama Cosmology Telescope: Mitigating the Impact of Extragalactic Foregrounds for the DR6 Cosmic Microwave Background Lensing Analysis

We investigate the impact and mitigation of extragalactic foregrounds for the cosmic microwave background (CMB) lensing power spectrum analysis of Atacama Cosmology Telescope (ACT) data release 6 (DR6) data. Two independent microwave sky simulations are used to test a range of mitigation strategies. We demonstrate that finding and then subtracting point sources, finding and then subtracting models of clusters, and using a profile bias-hardened lensing estimator together reduce the fractional biases to well below statistical uncertainties, with the inferred lensing amplitude, A lens, biased by less than 0.2σ. We also show that another method where a model for the cosmic infrared background (CIB) contribution is deprojected and high-frequency data from Planck is included has similar performance. Other frequency-cleaned options do not perform as well, either incurring a large noise cost or resulting in biased recovery of the lensing spectrum. In addition to these simulation-based tests, we also present null tests on the ACT DR6 data for sensitivity of our lensing spectrum estimation to differences in foreground levels between the two ACT frequencies used, while nulling the CMB lensing signal. These tests pass whether the nulling is performed at the map or bandpower level. The CIB-deprojected measurement performed on the DR6 data is consistent with our baseline measurement, implying that contamination from the CIB is unlikely to significantly bias the DR6 lensing spectrum. This collection of tests gives confidence that the ACT DR6 lensing measurements and cosmological constraints presented in companion papers to this work are robust to extragalactic foregrounds.

Insights on the effect of process conditions on the optical properties of silver ion exchanged soda-lime silicate glass.

The effects of ion exchange time and temperature on the optical properties and plasmonic response of silver ion exchanged soda-lime silicate glass were investigated using scanning electron microscopy (SEM) in energy dispersive spectrometry (EDS) configuration, m-lines spectroscopy, photoluminescence (PL) spectroscopy, and UV-visible absorption spectroscopy. SEM analyses in EDS mode provided profiles of silver oxide molar concentration. These profiles were directly correlated to the silver diffusion coefficient using an adjustment procedure. The effective indices of ion exchanged glasses measured by the standard prism coupling technique (m-lines) allowed access to refractive index distributions in ion exchange regions. These ion-exchanged glasses underwent evaluation to determine their potential suitability for use in multimode planar systems. The PL results acquired after ion exchange demonstrated that the creation of Ag0atoms from Ag+ions was responsible for the decline and quenching of PL intensity at ion exchange times and temperatures increase. Silver nanoparticles were generated in the samples subjected to ion exchange at 480 °C without the need for post-exchange treatments. The emergence of the surface plasmon resonance band around 427 nm in the optical absorption spectra confirmed the formation of Ag nanoparticles in annealed glasses. Estimates of the UV-visible absorption spectra indicated an average size of silver nanoparticles ranging from 1.8 to 2.4 nm.

Improved Vibration Localization Algorithm for Multiple Intrusions Based on Phase Spectrum Estimation in Distributed Mach–Zender/Sagnac Optical Fiber Sensing System

Improved Vibration Localization Algorithm for Multiple Intrusions Based on Phase Spectrum Estimation in Distributed Mach–Zender/Sagnac Optical Fiber Sensing System

Projection domain decomposition denoising algorithm based on low rank and similarity-based regularization.

Projection Domain Decomposition (PDD) is a dual energy reconstruction method which implements the decomposition process before image reconstruction. The advantage of PDD is that it can alleviate beam hardening artifacts and metal artifacts effectively as energy spectra estimation is considered in PDD. However, noise amplification occurs during the decomposition process, which significantly impacts the accuracy of effective atomic number and electron density. Therefore, effective noise reduction techniques are required in PDD. This study aims to develop a new algorithm capable of minimizing noise while simultaneously preserving edges and fine details. In this study, a denoising algorithm based on low rank and similarity-based regularization (LRSBR) is presented. This algorithm incorporates the low-rank characteristic of tensors into similarity-based regularization (SBR) framework. This method effectively addresses the issue of instability in edge pixels within the SBR algorithm and enhances the structural consistency of dual-energy images. A series of simulation and practical experiments were conducted on a dual-layer dual-energy CT system. Experiments demonstrate that the proposed method outperforms exiting noise removal methods in Peak Signal-to-noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity (SSIM). Meanwhile, there has been a notable enhancement in the visual quality of CT images. The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications.

Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

The accurate reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of its power spectrum are crucial for studying the early universe. In this paper, we implement a convolutional neural network to apply the Wiener Filter to CMB temperature maps, and use it intensively to compute an optimal quadratic estimation of the power spectrum. Our neural network has a UNet architecture as that implemented in WienerNet, but with novel aspects such as being written in python 3 and TensorFlow 2. It also includes an extra channel for the noise variance map, to account for inhomogeneous noise, and a channel for the mask. The network is very efficient, overcoming the bottleneck that is typically found in standard methods to compute the Wiener Filter, such as those that apply the conjugate gradient. It scales efficiently with the size of the map, making it a useful tool to include in CMB data analysis. The accuracy of the Wiener Filter reconstruction is satisfactory, as compared with the standard method. We heavily use this approach to efficiently estimate the power spectrum, by performing a simulation-based analysis of the optimal quadratic estimator. We further evaluate the quality of the reconstructed maps in terms of the power spectrum and find that we can properly recover the statistical properties of the signal. We find that the proposed architecture can account for inhomogeneous noise efficiently. Furthermore, increasing the complexity of the variance map presents a more significant challenge for the convergence of the network than the noise level does.

Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, these signals are often contaminated by motion artifacts, resulting in low-quality noise-enriched signals. Therefore, it is crucial to ensure high-quality signals to extract accurate cardiorespiratory information. Although several rule-based and Machine-Learning (ML) - based approaches exist for PPG signal quality estimation but their effectiveness is questionable during evaluation in diverse datasets. This study proposes a lightweight, slim CNN architecture that utilizes a novel Quantum Pattern Recognition (QPR) technique for signal quality assessment. The proposed algorithm is initially validated on the University of Queensland database (clinical) and a noisy PPG database (non-clinical) collected from the ‘Welltory app’. A total of 28366, 5 s signal segments are preprocessed and transformed into image files of 20 × 500 pixels. The image files and heart rate obtained from the PPG signals are treated as input to the 2D slim CNN architecture. The developed model classifies the PPG signal as ‘good’ or ‘bad’ with an accuracy of 98.5%, 99.2% sensitivity, 97.5% specificity, and 99% F1-score. The classified database is further used for cuffless Blood Pressure estimation, and estimation error is reduced ≥ 1 mmHg w.r.t. reference BP signals. Finally, the proposed scheme is validated in STM32 series board for a resource-constrained wearable implementation. The computational load of the algorithm is reduced by network pruning and quantization for edge-device implementation, and it achieves an accuracy of 94.3%. This extensive analysis concludes that a slim architecture, along with a novel spatio–temporal pattern recognition technique, improves the system’s performance.

A de-noising method of magnetotelluric signals based on the generalized S-transform

The generalized S-transform has excellent time-frequency resolution capability. It can effectively demonstrate the non-stationary characteristics of signals and the dynamic changes in energy. This provides the conditions for denoising in the time-frequency domain. The magnetotelluric (MT) survey method is often affected by various artificial noises, and these noises are very prominent in certain frequency bands. To address this problem, a method based on the generalized S transform is proposed to de-noise MT data. The generalized S transform is used to improve the time-frequency localization of the different signal components, meanwhile the electromagnetic noise can be conveniently suppressed in the time-frequency domain. Simulated signals and measured MT signals contaminated by common sinusoidal and triangular wave interference are processed. The results were compared with those of the Wavelet threshold method. Results show that the presented method is more conducive to the identification and suppression of electromagnetic noise and shows better de-noising results than the Wavelet threshold method. After de-noising, the useful information of the MT signal affected by strong interference is highlighted, which provides favorable conditions for power spectrum and impedance estimation in the future. Further, the response parameter curve calculated from the de-noised data becomes stable. The proposed method provides a new alternative for de-noising of MT signals, especially those with low signal-to-noise ratios.

BERTground: A Transformer-Based Model of Background Spectra on the ISS-Based NICER Space Telescope

The Neutron star Interior Composition Explorer (NICER) is an International Space Station (ISS)-based Space Telescope developed by NASA and devoted to the study of high-energy X-Ray sources in the universe, including but not limited to neutron stars, pulsars, and black holes in stellar systems and active galactic nuclei (AGN). One prominent problem with NICER observations is the highly variable background spectra, obscuring actual signals of astrophysical sources and negatively affecting scientific analysis of the targets. Therefore, obtaining accurate estimations of the background spectra is crucial to filter the noise and facilitate better scientific discoveries of new astronomical objects. In this paper, we propose the very first Deep Neural Network architecture to model the NICER background spectra variation using information about the spacecraft and telescope associated with each observation. In particular, we develop a BERT-based architecture with tokenizers applied to different groups of features in our tabular dataset. We also introduce an adapted Tabular Deep Residual Network architecture as the predictor following the Transformer modules in our network. We show that our model outperforms the current state-of-the-art background model developed by the NICER team in most evaluation metrics. Finally, we discuss pathways and future work for the deployment of this model on NASA’s next versions of HEASARC Software packages.

A new method for the estimation of the Rayleigh wave group-velocity spectrum

S-wave velocity is commonly used to understand the characteristics of the subsurface, which can be retrieved by inverting the phase or group velocity in Rayleigh wave surveys. Now, many studies have shown the advantage of analyzing group velocity over phase velocity to obtain the S-wave velocity. However, previous methods often suffer from poor resolution in estimating group velocity. To improve the resolution of the group velocity and reduce uncertainty in extracting dispersion curves, we develop a new method for the estimation of the Rayleigh wave group-velocity spectrum based on the slant stack and an adaptive generalized S-transform (APST) with energy reassignment. This method incorporates the APST and the energy reassignment technique to enhance the time-frequency representation and then calculates the group velocity at each frequency using the slant stack. By conducting experiments with synthetic seismic records, it is evident that the new method has high resolution and accurate multimode group-velocity calculations. Furthermore, when applied to a field data set and a synthetic record with noise, the method shows better results and noise robustness. This confirms the superior performance of the method at generating high-resolution dispersion images of the group velocity compared with previous approaches. The method will become an efficient tool in Rayleigh wave exploration.

Higher order statistics-driven magnitude and phase spectrum estimation for speech enhancement

Higher order statistics (HOS), can be effectively employed for noise suppression, provided the noise follows a Gaussian distribution. Since most of the noises are distributed normally, HOS can be effectively used for speech enhancement in noisy environments. In the current work, HOS-based parametric modelling for magnitude spectrum estimation is proposed to improve the SNR under noisy conditions. To establish this, a non-Gaussian reduced ARMA model formulated using third order cumulant sequences (Giannakis, 1990) is used. Here, the AR and MA model orders, p and q, are dynamically estimated by the well-established periodicity estimation technique under noisy conditions namely the Ramanujan Filter Bank (RFB) approach. The AR coefficients estimated from the reduced ARMA model are used to obtain the partially enhanced speech output, whose magnitude spectrum is then subjected to second-level enhancement using log MMSE with modified speech presence uncertainty (SPU) estimation technique. The refined magnitude spectrum, is combined with the phase spectrum extracted using proposed bicoherence-based phase compensation (BPC) technique, to estimate the enhanced speech output. The HOS-driven speech enhancement technique proposed in the current work is observed to be efficient for white, pink, babble and buccaneer noises. The objective measures, PESQ and STOI, indicate that the proposed method works well under all the noise conditions considered for evaluation.

Field Test of an Autonomous Observing System Prototype for Measuring Oceanographic Parameters from Ships

A prototype of an autonomous system for the retrieval of oceanographic, wave, and meteorologic data was installed and tested in May 2021 on a Portuguese research vessel navigating on the Atlantic Ocean. The system was designed to be installed in fishing vessels that could operate as a distributed network of ocean data collection. It consists of an automatic weather station, a ferrybox with a water pumping system, an inertial measurement unit, a GNSS unit, an onboard desktop computer, and a wave estimator algorithm for wave spectra estimation. Among several parameters collected by this system’s sensors are the air temperature, barometric pressure, humidity, wind speed and direction, sea water temperature, pH, dissolved oxygen, salinity, chlorophyll-a, roll, pitch, heave, true heading, and geolocation of the ship. This paper’s objectives are the following: (1) describe the autonomous prototype; and (2) present the data obtained during a full-scale trial; (3) discuss the results, advantages, and limitations of the system and future developments. Meteorologic measurements were validated by a second weather station onboard. The estimated wave parameters and wave spectra showed good agreement with forecasted data from the Copernicus database. The results are promising, and the system can be a cost-effective solution for voluntary observing ships.

Unravelling the excitation mechanism of very long-period (VLP) tremors in the Gulf of Guinea: evidence for vibrations of thin surface crustal plates

SUMMARY The Gulf of Guinea exhibits a continuous emission of narrow-band and long-period signals (16, 26 and 27 s) on teleseismic records, yet the underlying excitation mechanism remains unclear. This study establishes a connection between these tremors and the vibration of thin, decoupled crustal plates at unexplored volcanoes in the gulf. We first formulate the damped plate oscillation equation, by incorporating the vibration of the thin surface crustal plate and magma flow in the subsurface sill. The findings reveal that a fundamental-mode vibration with a period of several dozen seconds can be induced by a crustal plate that is less than 1.0 km thick but extends over tens of kilometres in both length and width, given a subsurface sill depth exceeding 10.0 cm. The thin plate hypothesis also allows for excitation of a few overtone modes, but such waves in higher frequencies diminish over long distances, leaving only the monotonous fundamental-mode vibration at teleseismic stations. The long duration of Guinea tremors at each recurrence is attributed to the presence of low viscosity basaltic magma, which influences the damping factor. Direct wave loads at the shallow gulf serve as the primary vibration source, accounting for seasonal variations and recurring patterns. Sporadic energy bursts may also occur due to large storms. Radiation patterns of Guinea tremors are linked to the geometric structure of the thin plate. Our theoretical estimates of tremor spectra closely align with observed data, confirming the model’s accuracy in capturing reported Guinea tremor characteristics. This study provides valuable insights into the origins of very long-period tremors at continental volcanoes.

Compressed covariance sensing for blade tip timing measurement

Due to harsh working conditions, rotating blades are prone to failures. Thus, it is urgently needed to monitor blade health. Blade tip timing (BTT) is a promising non-contact vibration technique for rotating blades owing to its high efficiency and long service time. However, due to severe undersampling, traditional spectrum analysis methods are ineffective in identifying characteristic parameters and recovering the power spectrum of vibrations for condition monitoring. To address this issue, this paper proposes a compressed covariance sensing-based BTT (CCS-BTT) method for power spectrum estimation. Specifically, we build a compressed covariance sensing representation model for BTT signal, where the covariance representation can be non-underdetermined owing to the Hermitian Toeplitz structure of the covariance matrix. Then, a universal covariance sampling pattern (UCSP) is derived to ensure the complete covariance information can be efficiently recovered without sparsity constraint. By utilizing the inherent periodicity, the derived UCSP significantly improves the efficiency of BTT measurement. Moreover, for practicality, we present a series of optimized layouts based on UCSP that cover almost all possible cases of probe numbers in BTT measurement. Finally, extensive simulations and experiments validate the effectiveness and performance of CCS-BTT. By shifting the perspective from the signal itself to its covariance, CCS-BTT opens many avenues for the development of BTT signal processing. In addition, the proposed method is expected to be used in deterministic compressive sampling of wide-sense stationary signals.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave (FMCW) radar is an advantageous sensor scheme for target estimation and environmental perception. However, existing algorithms based on discrete Fourier transform (DFT), multiple signal classification (MUSIC) and compressed sensing, etc., cannot achieve both low complexity and high resolution simultaneously. This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar. Firstly, we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy. Secondly, we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data, which improves the efficiency of continuous target tracking. These two approaches have improved the algorithm efficiency by nearly 2–4 orders of magnitude without losing accuracy and resolution. Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS.

We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second-order random walk (RW2D) priors are assumed on each of the time-varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time-varying spectra. Posterior distributions of the time-varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large-sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Materials (Li, Yue and Bruce, 2023a), and R code (Li, Yue and Bruce, 2023b) for this article are also available.

High resolution spectrum estimation of virtual array based on FIM

Underwater small aperture sonar spectral estimation methods are limited by physical dimensions, resulting in insufficient azimuthal resolving power. In this paper, we propose that based on the Fourier Integral Method(FIM) equivalent to a virtual array of extended aperture, after using subarray smoothing, combined with the spectral estimation method, we get the improved FIM-CBF, FIM-Capon and FIM-MUSIC, and give the applicable conditions of these three improved methods, and use the virtual aperture expansion and noise averaging suppression, to achieve the improvement of the azimuthal resolving power of the spectral estimation method under the low SNR.

A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling

With the growing scarcity of spectrum resources, wideband spectrum sensing is necessary to process a large volume of data at a high sampling rate. For some applications, only second-order statistics are required for spectrum estimation. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. The solution involves the inherent structure of the sensing vector to reconstruct the autocorrelation sequence of inputs from sub-Nyquist samples, which requires only parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time without the need to pre-estimate the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, allowing for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, thus making it a more practical solution for real-time and distributed wideband spectrum sensing applications.

[Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment].

The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.

Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia

Precise time–frequency (TF) analysis of electroencephalogram (EEG) signals is critical in evaluating cortical responses of patients with cervical dystonia (CD). Traditional methods are faced with challenges of constrained time–frequency resolution and accuracy, limiting the application of EEG in CD patients. This study introduces a novel adaptive basis function-based for TF representation method to meet the challenge. The methodology begins by identifying the kernel function center through an adaptive clustering technique. Then, the optimum structures and scales of the kernel function are determined by the improved genetic algorithm, which enable more precise tracking of EEG signals. Finally, accurately estimated parameters are converted to high-resolution TF images using a parameter spectrum estimation method, providing more detailed information of the EEG data. Leveraging the insights from the TF images, a regression model correlating TF features with clinical scores was developed to assess severity of CD patients. Simulation results show that the proposed method has superior tracking capabilities and a higher time–frequency resolution than current state-of-the-art methods. In the analysis of real EEG signals, we observed a notable elevation in gamma band power within the C3 and P3 channels, significantly differing from healthy individuals (p < 0.05), however, which cannot be found by other methods. This indicates distinctive high-frequency cortical activation associated with CD. Moreover, the regression model reaches a correlation coefficient above 0.82, suggesting its potential for objectively assessing severity of CD patients. Collectively, this study provides a robust tool for EEG signal analysis, and the analysis result will contribute to clinic treatment.