Low-frequency Background Noise Research Articles

High Fidelity Full-Color Optical Sectioning Structured Illumination Microscopy by Fourier Domain Based Reconstruction

The natural color of biological specimens plays a crucial role in body protection, signaling, physiological adaptations, etc. Full-color optical sectioning structured illumination microscopy (OS-SIM) color is a promising approach that can reconstruct biological specimens in three-dimension meanwhile maintaining their natural color. Full-color OS-SIM takes the advantages of rapid imaging speed, compatibility with fluorescence and non-fluorescence samples, compact configuration, and low cost. However, the commonly used HSV-RMS reconstruction algorithm for full-color OS-SIM faces two issues to be improved. One is the RMS (root-mean-square) OS reconstruction algorithm is prone to background noise, and the other is the reconstruction is bound in RGB and HSV color spaces, consuming more reconstructing time. In this paper, we propose a full-color Fourier-OS-SIM method that allows for the OS reconstruction using the high-frequency spectrum of the sample and thus is immune to the low-frequency background noise. The full-color Fourier-OS-SIM directly runs in the RGB color space, providing an easy way to restore the color information. Simulation and experiments with various samples (pollen grains and tiny animals) demonstrate that the full-color Fourier-OS-SIM method is superior to the HSV-RMS method regarding background noise suppression. Moreover, benefiting from the background noise suppression merit, the quantitative morphological height map analysis with the full-color Fourier-OS-SIM method is more accurate. The proposed full-color Fourier-OS-SIM method is expected to find broad applications in biological and industrial fields where the 3D morphology and the color information of objects both need to be recovered.

Low-frequency background estimation and noise separation from high-frequency for background and noise subtraction.

In fluorescence microscopy, background blur and noise are two main factors preventing the achievement of high-signal-to-noise ratio (SNR) imaging. Background blur primarily emanates from inherent factors including the spontaneous fluorescence of biological samples and out-of-focus backgrounds, while noise encompasses Gaussian and Poisson noise components. To achieve background blur subtraction and denoising simultaneously, a pioneering algorithm based on low-frequency background estimation and noise separation from high-frequency (LBNH-BNS) is presented, which effectively disentangles noise from the desired signal. Furthermore, it seamlessly integrates low-frequency features derived from background blur estimation, leading to the effective elimination of noise and background blur in wide-field fluorescence images. In comparisons with other state-of-the-art background removal algorithms, LBNH-BNS demonstrates significant advantages in key quantitative metrics such as peak signal-to-noise ratio (PSNR) and manifests substantial visual enhancements. LBNH-BNS holds immense potential for advancing the overall performance and quality of wide-field fluorescence imaging techniques.

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.

Image improvement of temporal focusing multiphoton microscopy via superior spatial modulation excitation and Hilbert–Huang transform decomposition

Temporal focusing-based multiphoton excitation microscopy (TFMPEM) just provides the advantage of widefield optical sectioning ability with axial resolution of several micrometers. However, under the plane excitation, the photons emitted from the molecules in turbid tissues undergo scattering, resulting in complicated background noise and an impaired widefield image quality. Accordingly, this study constructs a general and comprehensive numerical model of TFMPEM utilizing Fourier optics and performs simulations to determine the superior spatial frequency and orientation of the structured pattern which maximize the axial excitation confinement. It is shown experimentally that the optimized pattern minimizes the intensity of the out-of-focus signal, and hence improves the quality of the image reconstructed using the Hilbert transform (HT). However, the square-like reflection components on digital micromirror device leads to pattern residuals in the demodulated image when applying high spatial frequency of structured pattern. Accordingly, the HT is replaced with Hilbert–Huang transform (HHT) in order to sift out the low-frequency background noise and pattern residuals in the demodulation process. The experimental results obtained using a kidney tissue sample show that the HHT yields a significant improvement in the TFMPEM image quality.

Study of weld pool monitoring system based on spatial filtering

The observable information of the welding process mainly includes the weld pool, weld seam, and arc length, which can be used to predict the penetration depth, perform weld tracking, and provide early warning of weld defects. Welding monitoring technology based on machine vision has been widely researched to improve the quality and efficiency of welding. This paper proposes a novel weld pool image acquisition system based on spatial filtering and Abbe’s theory of image formation. A high-pass spatial filter was designed to eliminate the low frequency background noise so that the contour of the weld pool, weld seam, and arc shape were acquired. Compared with the weld pool monitoring system based on an optical filter, this method more effectively solved the problem of the large radiation gradient in the weld pool area, and the weld pool could be more clearly observed. It provided a new method to image the weld pool image system with the advantages of low cost and ease of implementation.

DNLS: A Detection Method Based on Normalized Short-Time Fourier Transform-Radon Transform for Low Frequency Sonar Pulse Signal

Under low-frequency background noise environments, due to the characteristics of poor stability and many interference targets of noise, the detection of unknown low-frequency sonar signals faces huge challenge. And sonar pulse signal detection methods based on time domain or frequency domain have the limitation of insufficient detection Signal-to-Noise Ratio (SNR). In order to improve the detection capability of weak sonar pulse signal in low frequency background noise environments, a Detection method based on normalized short-time Fourier transform-Radon transform for Low frequency Sonar pulse signal (DNLS) is proposed, which is a constant false alarm detection method in normalized short-time Fourier transform-Radon transform domain. In DNLS method, after the normalized short-time Fourier transform-Radon transformation, low-frequency noise energy to be dispersed into the entire transformation domain, and the sonar pulse signal energy containing the Linear Frequency Modulation (LFM) component is concentrated at a specific target point in the normalized short-time Fourier transform-Radon transform transformation domain, which can obtain a higher local SNR than the time-domain SNR. Moreover, the specific target point is distinguishable from the background noise, and the impulse signal detection decision is completed by constructing hypothesis test statistics on the target point data. The DNLS method solves the detection problems of low-frequency background such as poor stability, large fluctuations, and more interference. And the method of obtaining the test statistics of the constant false alarm detection, estimating the background noise and calculating the detection threshold is given. Extensive simulation results and actual data processing show that, under the simulation condition, in the minimum detection SNR of LFM, Continuous Wave(CW)-LFM and pulse trains of frequency modulated pulse signals with the same pulse width, compared with the dual-threshold constant false alarm rate energy detection method, the DNLS method is improved by 15dB, 13dB and 4dB, respectively. Under actual data conditions, in the detection of CW-LFM pulse signals with the same pulse width, compared with the double-threshold constant false alarm rate energy detection method, the detection performance of the DNLS method in the case of no ship radiated noise interference and strong radiated noise interference improves by 5dB and 5.5dB, respectively. The data analysis results show that the DNLS method has very good detection performance for LFM, pulse trains of frequency modulated, CW-LFM and other sonar pulse signals at low SNR, and can effectively detect the sonar pulse signals under the background of strong ship radiated noise.

Distributed Optimization for Nonrigid Nano-Tomography

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist beam damage during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a joint solver for imaging samples at the nanoscale with projection alignment, unwarping and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. Applicability of the method is demonstrated on two large-scale nano-imaging experimental data sets.

Review of noise reduction methods of vibration signal for wind turbine bearings fault analysis

Considering that the vibration signals of wind turbines are interfered with by low-frequency noise and background noise at the same time, it is difficult to extract the characteristic frequencies of early weak faults. The research on wind turbine bearing vibration noise reduction method to found that the potential failure symptom as early as possible for reducing the failure rate and reduce the operation maintenance cost reduction and to strengthen the reliable operation of the wind turbine is of great significance. Based on the analysis and summary of vibration signal characteristics of wind turbine bearing, the fault signal analysis, and noise reduction method of wind turbine bearing is introduced in detail.

The gap prepulse inhibition of the acoustic startle (GPIAS) paradigm to assess auditory temporal processing: Monaural versus binaural presentation.

The Gap Prepulse Inhibition of the Acoustic Startle Reflex (GPIAS) is a paradigm used to assess auditory temporal processing in both animals and humans. It consists of the presentation of a silent gap embedded in noise and presented a few milliseconds before a startle sound. The silent gap produces the inhibition of the startle reflex, a phenomenon called gap-prepulse inhibition (GPI). This paradigm is also used to detect tinnitus in animal models. The lack of inhibition by the silent gaps is suggested to be indicative of the presence of tinnitus "filling-in" the gaps. The current research aims at improving the GPIAS technique by comparing the GPI produced by monaural versus binaural silent gaps in 29 normal-hearing subjects. Two gap durations (5 or 50ms), each embedded in two different frequency backgrounds (centered around 500 or 4 kHz). Both low- and high- frequency narrowband noises had a bandwidth of half an octave. Overall, the startle magnitude was greater for the binaural versus the monaural presentation, which might reflect binaural loudness summation. In addition, the GPI was similar between the monaural and the binaural presentations for the high-frequency background noise. However, the GPI was greater for the low-frequency background noise for the binaural, compared to the monaural, presentation. These findings suggest that monaural GPIAS might be more suited to detect tinnitus compared to the binaural presentation.

Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principal component analysis

Due to the severe interference of background noise, the signal-to-noise ratio of desert seismic data is extremely low. In addition, due to low-frequency characteristics of sand in the Tarim desert region, the background noise in desert seismic data is mainly distributed in low-frequency band, so that the frequency spectrum aliasing of effective signals and background noise is more serious than the general land seismic data. Thus, conventional filtering methods cannot effectively suppress background noise in desert seismic data and recover effective signals. In order to overcome the problem that low-frequency background noise in desert seismic data is hard to suppress, a new method called R-TNRD based on robust principal component analysis (RPCA) algorithm and trainable nonlinear reaction diffusion (TNRD) network is proposed in this paper. By using the good sparsity of RPCA, the input noisy desert seismic data are decomposed into a low-rank matrix and a sparse matrix, and these two matrices contain background noise and effective signals. Due to the serious spectrum aliasing of desert seismic data, conventional thresholds have been unable to extract effective signals from the two matrices obtained by RPCA effectively. Therefore, we introduce TNRD network into desert seismic data denoising. By network training with a low-frequency noise set, the optimisation of TNRD network can be achieved, so as to accurately extract the effective signals from the low-rank matrix and the sparse matrix. In the experimental part, we test the performance of R-TNRD on both synthetic and real seismic data. The results demonstrate that the proposed method can suppress background noise more effectively than conventional methods.

Singing under glass: rapid effects of anthropogenic habitat modification on song and response behaviours in an isolated house sparrow Passer domesticus population

Anthropogenic noise pollution and the introduction of novel infrastructure can impose strong selective pressures on avian communication by affecting the efficacy with which acoustic signals are transmitted and received. Many species have now been shown to sing at higher frequencies in noisy urban environments. However, few studies have investigated the effects of signal modification on the response behaviours of receivers, and fewer still have been able to indicate the timescale over which these changes in pitch have occurred. We compare vocal communication between house sparrows Passer domesticus that reside within the world's largest, single‐span glasshouse (completed in the year 2000), and house sparrows directly outside this glasshouse, in open farmland. The glasshouse contrasts both acoustically and physically with the external environment, low frequency background noise being significantly louder inside than outside. We show that minimum song frequency was significantly higher inside the glasshouse than in surrounding farm habitat. Using song playback, we also found that birds within the glasshouse reacted more strongly to playbacks from the glasshouse habitat than they did to playback of song from farm birds outside. The degree of difference in frequency is similar to that shown for other bird species between urban and rural environments, demonstrating that such behavioural differences may arise over a relatively short time period (14 yr in this case).

Comparative implementation of Handwritten and Machine written Gurmukhi text utilizing appropriate parameters

Optical character recognition is concerned with the recognition of optically processed characters. The recognition is done offline after the writing or printing has been completed, unlike online recognition where the computer has to recognize the characters instantly as they are drawn. The performance of character recognition depends upon the quality of scanned documents. The preprocessing steps are used for removing low-frequency background noise and normalizing the intensity of individual scanned documents. Several filters are used for reducing certain image details and enabling an easier or faster evaluation. The primary aim of the research work is to recognize handwritten and machine written characters and differentiate them. The language opted for the research work is Punjabi Gurmukhi and tool utilized is Matlab.

Dual multi-scale filter with SSS and GW for infrared small target detection

Multi-scale analysis is a powerful tool in the field of signal processing. In this paper, we propose an efficient small target detection algorithm that is mainly based on the dual multi-scale filters which work sequentially. The algorithm consists of two stages: at the first stage, Spectrum Scale-Space (SSS) is used as the pre-process procedure to obtain the multi-scale saliency maps, which can suppress the low frequency background noise and make the target region prominently at different scale levels. As a result, the more detail information and feature information can be exhibited in the different decomposition image level. After then, the least information entropy is used as the criterion to select the optimal salient map out; At the second stage, the Gabor wavelets (GW) algorithm is utilized to suppress the high frequency noise remained in the optimal salient map and match the feature of size and direction of small target at different scales and angles, and next, to ensure the robustness of the target detection, Non-negative Matrix Factorization (NMF) is applied to fuse all the GW multi-scale images into one optimal target image, which is the final output of the presented method. Experimental results show that, compared with the contrast method, the proposed algorithm has high SCRG and high correct target detection rate, and works well in different types of complex backgrounds.

Seasonal changes in atmospheric noise levels and the annual variation in pigeon homing performance.

Repeated releases of experienced homing pigeons from single sites were conducted between 1972 and 1974 near Cornell University in upstate New York and between 1982 and 1983 near the University of Pittsburgh in western Pennsylvania, USA. No annual variation in homing performance was observed at these sites in eastern North America, in contrast to results from a number of similar experiments in Europe. Assuming pigeons home using low-frequency infrasonic signals (~0.1-0.3Hz), as has been previously proposed, the annual and geographic variability in homing performance within the northern hemisphere might be explained, to a first order, by seasonal changes in low-frequency atmospheric background noise levels related to storm activity in the North Atlantic Ocean, and by acoustic waveguides formed between the surface and seasonally reversing stratospheric winds. In addition, increased dispersion among departure bearings of test birds on some North American release days was possibly caused by infrasonic noise from severe weather events during tornado and Atlantic hurricane seasons.

Practical considerations for noise power spectra estimation for clinical CT scanners.

Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ∼0.15 mm−1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low‐frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task‐based image quality assessment.PACS number(s): 87.57.C‐, 87.57.Q‐

Low-frequency background noise may damage ears

Low-frequency background noise may damage ears

Immediate, independent adjustment of call pitch and amplitude in response to varying background noise by silvereyes (Zosterops lateralis)

Many birds raise the pitch of their vocalizations in urban environments, a shift generally attributed to avoidance of low frequencies vulnerable to masking by anthropogenic noise (acoustic adaptation hypothesis [AAH]). However, high frequencies could just be an incidental byproduct of singing more loudly (Lombard hypothesis). If birds cope with background noise by singing more loudly and increases in frequency are byproducts of increased amplitudes, they should respond with louder songs (and increased pitch) to either high- (HFN) or low-frequency noise (LFN) of similar amplitude. However, if birds adaptively adjust frequency to minimize interference, they should increase frequency in response to LFN but decrease it in response to HFN. We exposed silvereyes (Zosterops lateralis), which use higher songs and calls in urban areas, to high- and low-frequency background noise of standardized amplitude. Silvereyes from both rural and urban areas exposed to HFN responded by lowering the minimum frequencies of their calls, and this shift was independent of call amplitude, which increased in all noise treatments. These findings support the AAH. Calls during HFN treatments were also longer than those made during quiet treatments. Our results suggest that silvereyes are capable of flexible adjustments of call frequency, amplitude, and duration to maximize signal-to-noise ratio in noisy environments.

If a tree falls in a forest, can you hear it?

Anthropogenic noise, from slowly rotating/reciprocating machinery, contributes immensely to environmental low-frequency noise below 100 Hz. This poses an acoustic low-energy problem as our technology offers no effective passive method for remediation. While attention is given to negative impact on human health and ecosystems, we still know and publicly do too little about it. Technical standards in Europe are A-weighted measurements that disregard actual energetic contributions of low frequencies. Different aspects of low-frequency sound have been illuminated by complementing acoustic measurements with findings from technical acoustics, bioacoustics, and human medical science. Sound pressure levels (SPL) were measured from 2.5 Hz to 20 kHz. The measurement setup consisted of SINUS Soundbook quadro (2.3 Hz– 22 kHz; [email protected] dB; ±0.1 dB tolerance; 1 Hz high-pass enabled), 1/2-in. Preamplifier/Free-Field Microphone (3.15 Hz– 20 kHz: ±2.0 dB; 5Hz–10 kHz: ±1.0 dB). Signals were processed with SINUS Driver version 5.1.0.8 and Samurai version 2.0.134. SPL in quiet forest [during 60 s: LZ,eq = 59.3 dB; LA,eq = 29.5 dB(A)] were compared to a quiet university room [during 60 s: LZ,eq = 74.4 dB; LA,eq = 28.2 dB(A)]. Healing properties of felid purrs produced with strong frequencies at an SPL between 30 and 60 dB suggest a general correlation of low-frequency background noise levels and health. Perhaps nature knows better than we do.

Present Status of the Nd:YAG Thomson Scattering System Development for Time Evolution Measurement of Plasma Profile on Heliotron J

A new high repetition rate Nd:YAG Thomson scattering system is developed for the Heliotron J helical device. A main purpose of installing the new system is the temporal evolution measurement of a plasma profile for improved confinement physics such as the edge transport barrier (H-mode) or the internal transport barrier of the helical plasma. The system has 25 spatial points with ∼10 mm resolution. Two high repetition Nd:YAG lasers (> 550 mJ@50 Hz) realize the measurement of the time evolution of the plasma profile with ∼10 ms time intervals. Scattered light is collected by a large concave mirror (D = 800 mm, f/2.25) with a solid angle of ∼100 mstr and transferred to interference filter polychromators by optical fiber bundles in a staircase form. The signal is amplified by newly designed fast preamplifiers with DC and AC output, which reduces the low frequency background noise. The signals are digitized with a multi-event QDC, fast gated integrators. The data acquisition is performed by a VME-based system operated by the CINOS.

A study of resonance structures with different frequency scales in the spectrum of background extremely low-frequency noise at middle latitudes

Using the data of continuous monitoring of low-frequency background noise at the station “Novaya Zhizn” (Nizhny Novgorod Province, Russia) for 2006–2010, we studied a new spectral structure appearing in quasi-harmonic oscillations of the noise intensity with frequency scales a factor of 3 to 4 smaller than the scales of an ordinary spectral resonance structure. The features of the fine spectral structure were studied, i.e., frequency of occurrence, diurnal dynamics, and the frequency bandwidth. The features of an ordinary resonance structure were also studied in the “fine structure” appearance and the transition solar-activity periods (2010–2011). Using the calculations of the low-frequency magnetic fields propagating in a spherically horizontally inhomogeneous Earth–ionosphere waveguide, we analyzed the possible influence of the nonlocal ionospheric properties on the magnetic-component spectra in the range 0.1–20 Hz. It is shown that the influence of the nonlocal ionospheric properties on the extremely low-frequency spectra can be observed in the presence of an ionospheric horizontal inhomogeneity due to different shapes of the ionospheric-parameter profiles along the radio-wave propagation path.