Noise Spectrum Research Articles

Steady spectra of supreme resolution and lowest noise in high-order optimized derivative fast Fourier transform for ovarian NMR spectroscopy

The optimized derivative fast Fourier transform (dFFT) simultaneously increases resolution and reduces noise in spectra reconstructed from encoded time signals. The pertinent applications have recently been published for time signals encoded with and without water suppression by in vitro and in vivo magnetic resonance spectroscopy (MRS). Even with the employed lower derivative orders, genuine resonances were narrowed, their intensities enhanced and the background baselines flattened. This unequivocally separated many overlapped peaks that are the thorniest problem in data analysis by signal processing. However, it has been common knowledge that higher-order derivative spectra quickly deteriorate with the increased derivative order. The optimized dFFT can challenge such findings. An unprecedented resilience of this processor to derivative-induced distortions is presently demonstrated for high derivative orders (up to 20). The salient illustrations are given for the water residual, lactate quartet and lactate doublet alongside their close surroundings. These applications of diagnostic relevance for patients with cancer are reported for time signals encoded with water suppression by in vitro proton MRS of human ovary.

Implications of vertical stability control on the SPARC tokamak

To achieve its performance goals, SPARC plans to operate in equilibrium configurations with a strong elongation of κareal∼1.75 , which in turn will destabilize the n = 0 vertical instability. However, SPARC also features a relatively thick conducting wall that is designed to withstand disruption forces, leading to lower vertical instability growth rates than usually encountered. In this work, we use the TokSyS framework to survey families of accessible shapes near the SPARC baseline configuration, finding maximum growth rates in the range of γ≲100 s−1. The addition of steel vertical stability plates has only a modest ( ∼25% ) effect on reducing the vertical growth rate and almost no effect on the plasma controllability when the full vertical stability system is taken into account, providing flexibility in the plate conductivity in the SPARC design. Analysis of the maximum controllable displacement on SPARC is used to inform the power supply voltage and current limit requirements needed to control an initial vertical displacement of 5% of the minor radius. From the expected spectra of plasma disturbances and diagnostic noise, requirements for filter latency and vertical stability coil heating tolerances are also obtained. Small modifications to the outboard limiter location are suggested to allow for an unmitigated vertical disturbance as large as 5% of the minor radius without allowing the plasma to become limited. Further, investigations with the 3D COMSOL code reveal that strategic inclusion of insulating structures within the VSC supports are needed to maintain sufficient magnetic response. The workflows presented here help to establish a model for the integrated predictive design for future devices by coupling engineering decisions with physics needs.

P-wave Pairing Near a Spin-Split Josephson Junction

Superconductivity and magnetism are competing effects that can coexist in certain regimes. Their co-existence leads to unexpected new behaviors that include the onset of exotic electron pair mechanisms and topological phases. In this work, we study the properties of a Josephson junction between two spin-split superconductors. The spin-splitting in the superconductors can arise from either the coupling to a ferromagnetic material or an external magnetic field. The properties of the junction are dominated by the Andreev bound states that are also split. One of these states can cross the superconductor’s Fermi level, leading to a ground-state transition characterized by a suppressed supercurrent. We interpret the supercurrent blockade as coming from a dominance of p-wave pairing close to the junction, where the electrons are at both sides. To support this interpretation, we analyze the different pairing channels and show that p-wave pairing is favored in the case where the magnetization of the two superconductors is parallel and suppressed in the anti-parallel case. We also analyze the noise spectrum that shows signatures of the ground-state transition in the form of an elevated zero-frequency noise.

Intermittent Shot Noise Generating 1/f Fluctuations

When the rate of shot noise is controlled by on–off states we speak of intermittent shot noise. The on–off states lead to alternately occurring clusters of events and intermissions, respectively. We derive the power spectrum of the intermittent shot noise by applying the Wiener-Khinchin theorem. Besides reduced shot noise, we obtain excess noise, which depends on the parameters of the on–off states. We calculate the excess noise for power-law distributed on-states; within the scaling region, the excess noise is excellently approximated by [Formula: see text]. The behavior of the slope b and of the amplitude C in dependence of the on–off times is investigated. For large scaling regions, we find a preference for a pure 1/f shape. Finally, we regard the variance of events occurring within a time interval. In the presence of 1/f fluctuations, the variance of counts attains extreme values which are accompanied by an extreme property of slope b.

Passive laser power stabilization in a broadband noise spectrum via a second-harmonic generator.

An extremely conspicuous passive power noise stabilization is the first, to the best of our knowledge, discovered in a cavity-enhanced second-harmonic generation (SHG) process. Differing from the SHG as a buffer reservoir, the stronger strength of the nonlinear interaction pushes the power noise suppression level to a higher value and exhibits a broadband noise reduction performance due to the mechanism of dynamic pump suppression in the SHG process. The noise is suppressed to near shot noise limit (SNL) among the kHz to MHz frequency range, accompanied by a maximum noise reduction of 35 dB. A comprehensive demonstration indicates that the nonlinear interaction has no function on the phase noise of fundamental and harmonic waves. A theoretical model is also established that is consistent well with the experimental results. The methodology is beneficial to multiple optical metrology experiments.

Interplay between magnetic gap and quasi-particle lifetime in topological insulator ferromagnet/f-wave superconductor junctions

Using the modified Blonder–Tinkham–Klapwijk (BTK) theory, the interplay between the lifetime of quasi particles and the magnetic gap in a topological insulator-based ferromagnet/f-wave superconductor (TI-based FM/f–wave SC) tunnel structure is theoretically studied. Two symmetries of f 1 and f 2 waves are considered for superconducting pairing states. The results indicate that reducing the finite quasi-particle lifetime will induce a transformation of energy-gap peaks into a zero-bias peak in tunneling conductance spectrum, as well as a transformation of energy-gap dips into a zero-bias dip in shot noise spectrum, ultimately resulting in the smoothing of the zero-bias conductance peak and the zero-bias shot noise dip. An increase in magnetic gap will suppress the tunnel conductance and shot noise when the conventional Andreev retro-reflection dominates, but will enhance them when the specular Andreev reflection is dominant. Both specular Andreev reflection and conventional Andreev retro-reflection will be enhanced as the quasi-particle lifetime increases. When Fermi energy equals the magnetic gap, shot noise and tunneling conductance vanish across all energy ranges. These findings not only contribute to a better understanding of specular Andreev reflection in the FM/f–wave SC junction based on TIs but also provide insights for experimentally determining the f-wave pairing symmetry.

Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms

In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.

Full-Permutation Dynamical Decoupling in Triple-Quantum-Dot Spin Qubits

Dynamical decoupling of spin qubits in silicon can increase fidelity and can be used to extract the frequency spectra of noise processes. We demonstrate a full-permutation dynamical decoupling technique that cyclically exchanges the spins in a triple-quantum-dot qubit. This sequence not only suppresses both low-frequency charge-noise-induced and magnetic-noise-induced errors; it also refocuses leakage errors to first order, which is particularly interesting for encoded exchange-only qubits. For a specific construction, which we call “NZ1y,” the qubit is isolated from error sources to such a degree that we measure a remarkable exchange pulse error of 2.8×10−5. This sequence maintains a quantum state for roughly 18,000 exchange pulses, extending the qubit coherence from T2∗=2μs to T2=720μs. We experimentally validate an error model that includes 1/f charge noise and 1/f magnetic noise in two ways: by direct exchange-qubit simulation and by integration of the assumed noise spectra with derived filter functions, both of which reproduce the measured error and leakage with respect to a change of the repetition rate. Published by the American Physical Society 2024

Delay Segmented Tristable Stochastic Resonance System Driven by Non-Gaussian Colored Noise and Its Application in Bearing Fault Detection

Abstract This study investigates the Delayed Segmented Tristable Stochastic Resonance (DSTSR) system under the influence of additive non-Gaussian colored noise. The research employs an improved segmented tristable potential function, wherein the equilibrium points and barrier heights can be independently controlled by parameters. Simultaneously, the segmented function on both sides reduces the restrictions of higher-order terms on the walls of the potential wells. The equivalent Langevin equation for the DSTSR system is obtained using the path integral method, the unified colored noise approximation method, and the small-delay approximation. Subsequently, the theoretical expressions for the steady-state probability density, mean first passage time (MFPT), and Signal-to-Noise Ratio (SNR) are derived from the resulting equations, and the impact of variations in system parameters on these performance metrics is discussed. Additionally, Monte Carlo simulations for MFPT are conducted to verify the accuracy of the theoretical derivations. Combining the results from the theoretical section and the impact of parameters on system performance, the article employs an adaptive genetic algorithm to optimize system parameters. This algorithm is then applied to simulation experiments and bearing fault detection. In the simulation experiments, the DSTSR system is compared with other systems. The results indicate that the DSTSR system exhibits the highest SNR improvement. Furthermore, in bearing fault detection under non-Gaussian colored noise, the DSTSR system shows higher spectral amplitude and SNR at the fault frequency compared to the tristable stochastic resonance system and the segmented tristable stochastic resonance system without time delay feedback. This suggests that stochastic resonance can effectively detect weak signals in non-Gaussian non-white noise scenarios, and the introduction of time delay contributes to the occurrence of stochastic resonance to a certain extent.

A comparison between autocorrelation- and crosscorrelation-based coarse frame synchronization schemes for OFDM-PLC systems

Orthogonal frequency division multiplexing (OFDM)-based Power-Line Communications (PLC) systems are vulnerable to synchronization errors, and the particularities of the PLC channel affect the performance of the synchronization process. In this context, this work is focused on the initial pilot symbol detection, presenting a comparison between autocorrelation- (AC) and crosscorrelation (CC)-based timing metrics, which include the most common preamble-based synchronization strategies. Two robust timing metrics are defined and the effect of multipath, Colored Background Noise (CBN) and Impulsive Noise (IN) is theoretically evaluated on each. A detailed analysis of the probability distributions of the timing metrics is performed and primary detection thresholds and lower bounds for the symbol detection probability are derived. Simulations of the OFDM-PLC system are performed in order to validate the analysis, considering a statistic PLC channel model and different noise sources. Results show that the CBN and IN significantly degrade the timing metrics behavior, and that the CC-based timing metric is more robust to this worsening, thus achieving a better synchronization performance than the AC-based timing metric.

Improved UFIR Filter for Fusing Recent INS-assisted Visual Measurement under Colored Measurement Noise in UAV Landing

Improved UFIR Filter for Fusing Recent INS-assisted Visual Measurement under Colored Measurement Noise in UAV Landing

Improving the Quality and Performance of Underwater Image Classification using the CLAHE-CNN Method

Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolphins, sea rays, and whales in the underwater imagery dataset. The experiment was conducted using Python programming language on a computer that had 24GB RAM, Intel® Core™ i7-10510U CPU and hardware properties of Intel® HD Graphics graphics card. The results of this study show how CLAHE improved the CNN classification of underwater imagery by 0.91% in training data, 0.45% in validation data, and 2.02% in test data.

Finite element simulation analysis of vibration transmission characteristics of double shell based on ANSYS

Ship noise contains a large number of low-frequency line spectrum features. It is difficult to distinguish where they come from and even more difficult to identify the ship’s target types using the line spectrum. Therefore, research on ship targets’ vibration transmission characteristics and radiation noise is of great importance for underwater target recognition. From the perspective of underwater acoustic target recognition, this article establishes a simplified finite element model of the double shell using ANSYS, calculates the displacement response of the double shell under harmonic loads, and conducts modal analysis, harmonic response analysis, and transient dynamic analysis, which verifies the consistency of vibration between the inner shell and outer shell. Marine experimental data show that the low-frequency line spectrum of ship noise is related to the vibration frequency of ship main engines. It may be caused by diesel engine vibration transmitted through ship hull vibration and can be used as an auxiliary feature for underwater acoustic target recognition.

Approaches to the formation of an image database in the explosive ordnance identification system for further training of a neural network

The use of unmanned aerial vehicles to detect explosive objects is a promising area of demining. Currently, a significant role in identifying detected objects remains with the operator (human). To solve the problem of identifying images of explosive objects, it is advisable to equip unmanned aerial vehicles or other robotic systems with appropriate software and hardware with built-in elements of artificial intelligence that would allow to recognize detected objects with a certain probability by their characteristic local features (shape/image, body material, body temperature) and classify them without operator participation. Recognition consists in calculating the relevance measure between the descriptions of the object and the reference, whose images are given as sets of descriptor vectors, and optimizing the values of this measure for the reference database. The article discusses the main algorithms (methods) for image identification and selects those that have higher performance and are built according to the binary type, which simplifies the calculation process and reduces costs. In general, the combination of ORB and BRISK descriptors gives better results, since it takes into account color distortion, poor lighting, noise, and different projections. The paper proposes approaches to forming a database of explosive ordnance images, which will further serve as an input layer for training a neural network to identify explosive ordnance. In the course of the work, a program code was written that makes it possible to test the performance of combined methods (algorithms) of image identification in terms of database formation, storage and testing.

On the aeroacoustic simulation and far-field noise modeling of a cylinder turbulent wake at the Reynolds number of 3900

The turbulent wake flows of circular cylinders have been under extensive study due to their strong fluctuating forces and noise levels, yet the relationship between them has rarely been examined. In this study, a high-fidelity numerical simulation is performed for a cylinder wake flow at the Reynolds number of 3900, aimed at far-field noise modeling based on the investigation of noise source characteristics. Despite irregular multi-scale turbulent wake eddies, the primary vortex shedding that resembles the Karman vortex shedding is observed, and the modes extracted by the dynamic mode decomposition technique at the primary shedding frequency and its higher harmonics appear as nearly two-dimensional spanwise structures. A non-permeable Ffowcs Williams and Hawkings (FW-H) approach is used to formulate the far-field noise spectra as the surface integral of unsteady flow samples on the cylinder surface and the volume integral of unsteady stress in the turbulent wake. The former can be modeled as the dipole sound of unsteady lift and drag, and the latter can be modeled as the tonal noise of discrete two-dimensional (i.e., spanwise-averaged) vortex modes. The results show that the vortex sound is extremely weak. Thus, the far-field noise can be modeled almost entirely by the aerodynamic forces, and the quantitative relationship between them is established by the convective FW-H solutions.

АНАЛІЗ МЕТОДІВ ЗНАХОДЖЕННЯ АНОМАЛІЙ У ЗОБРАЖЕННЯХ

Abstract. Automatic anomaly detection is of great importance in industry, remote sensing, and medicine. It is important to be able to automatically process large amounts of data to detect, for example, chemical objects in multispectral and hyperspectral satellite images, sea mines in side-scan sonar images, or defects in industrial monitoring applications. Automatic detection of anomalous structures on arbitrary images refers to the task of finding inappropriate patterns relative to the normal state of the image. This is a difficult task in computer vision, since there is no clear and straightforward definition of what is normal or not normal for a given arbitrary image. The practical importance is manifested in the development of algorithms and models that can automatically detect unusual or anomalous patterns in images. An analysis of methods for finding anomalies in images from the point of view of the possibility of application to arbitrary images has been carried out. The classification of anomaly detection methods according to the criteria of the involved approaches and models used for modeling the background is presented. Methods that use machine learning, such as one-class support vector method and variational autoencoder, nearest neighbor-based anomaly detection, clustering-based anomaly detection, statistical anomaly detection, spectral anomaly detection, anomaly detection using information theory are discussed. The main attention is paid to the methods classified according to the background modeling approach. Five categories of background modeling methods based on probability density function, global and local homogeneity, sparsity, and self-similarity are considered. For anomaly detection applications, it is recommended to use methods in which the background model best describes the expected anomaly-free background, as this generally results in the best performance. On the basis of research, it was established that an effective universal model for detecting anomalies in arbitrary images should: use only a self-similar or sparse background model; process the residual image as a stochastic process to detect anomalies, such as anomalies in color noise; preprocess the residual image before detecting the anomaly.

A learnable full-frequency transformer dual generative adversarial network for underwater image enhancement

Underwater applications present unique challenges such as color deviation, noise, and low contrast, which can degrade image quality. Addressing these issues, we propose a novel approach called the learnable full-frequency transformer dual generative adversarial network (LFT-DGAN). Our method comprises several key innovations. Firstly, we introduce a reversible convolution-based image decomposition technique. This method effectively separates underwater image information into low-, medium-, and high-frequency domains, enabling more thorough feature extraction. Secondly, we employ image channels and spatial similarity to construct a learnable full-frequency domain transformer. This transformer facilitates interaction between different branches of information, enhancing the overall image processing capabilities. Finally, we develop a robust dual-domain discriminator capable of learning spatial and frequency domain characteristics of underwater images. Extensive experimentation demonstrates the superiority of the LFT-DGAN method over state-of-the-art techniques across multiple underwater datasets. Our approach achieves significantly improved quality and evaluation metrics, showcasing its effectiveness in addressing the challenges posed by underwater imaging. The code can be found at https://github.com/zhengshijian1993/LFT-DGAN.

Modal Frequency Identification of Maize Picking Machine under Colored Noise Excitation

Modal Frequency Identification of Maize Picking Machine under Colored Noise Excitation

Colored noise in GRACE total water storage time series: Its impact on trend significance in the Türkiye region and major world river basins

Temporal correlations are prevalent in most geophysical time series data, leading to what is known as colored noise, which become more apparent in the frequency domain rather than time domain. Neglecting this type of noise in modeling can result in underestimated standard errors for the parameters of the model used to analyze the time series. As a result, statistical tests misinterpret the significance of these parameters, such as the overall trend rate, because the “signal to noise” ratio becomes incorrectly larger than it should be. In this study, we investigate the temporal correlations in the time series data from the Gravity Recovery and Climate Experiment (GRACE) mission. GRACE and its successor mission GRACE Follow-On (GRACE-FO) have been successfully employed to monitor global total water storage anomalies over two decades since its initial launch in 2002. We primarily utilize monthly GRACE mascon (mass concentration) solutions provided by NASA Goddard Space Flight Center (GSFC), examining both regional and global scales. The power spectrum density of the residuals, calculated after applying standard harmonic regression to each mascon time series on Earth’s land, reveals an average spectral index of κ = −1. This indicates the predominance of flicker noise, one of the most common forms of colored noise in geodetic time series. Notably, the noise becomes more Brownian (κ < −1) in regions with intense hydrological events, while it tends to be more white noise (κ → 0) in areas with less water circulation compared to other land regions. This observation suggests that unmodelled hydrological events, such as droughts, floods, and ones associated with ice-melting, contribute to the residuals as colored noise in the time series. To account for this noise in our time series modeling, we primarily consider autoregressive (AR) moving average (MA) process, commonly known as ARMA, in addition to the standard variance component estimation for noise amplitudes. We focus on individual mascon blocks in the Türkiye Region and mascon solutions for 24 world’s major river basins. Our findings demonstrate that an ARMA(1,1) model is sufficient for describing the noise in these regions. The results underscore the importance of considering colored noise, as it leads to approximately three times larger standard errors for the overall trend rate. This substantially impacts the significance of the trend rates.

Multi-path parallel enhancement of low-light images based on multiscale spatially aware Retinex decomposition

To resolve the issues of color distortion and detail blurring, this study proposes a multi-path parallel enhancement of low-light images (LLIs) based on multiscale spatially aware Retinex decomposition (MUSRetinex-Net). MUSRetinex-Net consists of three subnetworks. Firstly, MUSRetinex-Net inputs LLIs into an illumination level estimation image guided multiscale spatial perception decomposition network (MSPD-Net) which completes Retinex decomposition to retain richer color information and detail structure; then reflectance maps from LLIs are inputted into a three-pass parallel reflectance maps recovery network (TPR-Net) for more desirable noise removal and color recovery; illumination maps from LLIs are inputted into a multi-branch attention illumination maps adjustment network (MAA-Net) for adjustment to obtain a more accurate estimation of illumination levels. In this study, six publicly accessible datasets are utilized for experiments, two referenced evaluation metrics, and four non-referenced evaluation metrics are utilized for assessment and analysis. The experimental results show that the low-light image enhancement (LLIE) effects obtained by this study method are more consistent with the recognition level of the human eye and the structure of the natural environment. The method achieves the optimal referenced evaluation metrics score and the most comprehensive non-referenced evaluation metrics score on all six public datasets, with extremely high enhancement performance and generalization ability.