Signal-to-noise Research Articles

Segmentation-Free Velocity Field Super-Resolution on 4D Flow MRI.

Blood flow observation is of high interest in cardiovascular disease diagnosis and assessment. For this purpose, 2D Phase-Contrast MRI is widely used in the clinical routine. 4D flow MRI sequences, which dynamically image the anatomic shape and velocity vectors within a region of interest, are promising but rarely used due to their low resolution and signal-to-noise ratio (SNR). Computational fluid dynamics (CFD) simulation is considered as a reference solution for resolution enhancement. However, its precision relies on image segmentation and a clinical expertise for the definition of the vessel borders. The main contribution of this paper is a Segmentation-Free Super-Resolution (SFSR) algorithm. Based on inverse problem methodology, SFSR relies on minimizing a compound criterion involving: a data fidelity term, a fluid mechanics term, and a spatial velocity smoothing term. The proposed algorithm is evaluated with respect to state-of-the-art solutions, in terms of quantification error and computation time, on a synthetic 3D dataset with several noise levels, resulting in a 59% RMSE improvement and factor 2 super-resolution with a noise standard deviation of 5% of the Venc. Finally, its performance is demonstrated, with a scale factor of 2 and 3, on a pulsed flow phantom dataset with more complex patterns. The application on in-vivo were achievable within the 10 min. computation time.

Reactive oxygen species-mediated organic long-persistent luminophores light up biomedicine: from two-component separated nano-systems to integrated uni-luminophores.

Organic luminophores have been widely utilized in cells and in vivo fluorescence imaging but face extreme challenges, including a low signal-to-noise ratio (SNR) and even false signals, due to non-negligible background signals derived from real-time excitation lasers. To overcome these challenges, in the last decade, functionalized organic long-persistent luminophores have gained much attention. Such luminophores could not only overcome the biological toxicity of inorganic long-persistent luminescent materials (metabolic toxicity and leakage risk of inorganic heavy metals), but also continue to emit long-persistent luminescence after removing the excitation source, thus effectively improving imaging quality. More importantly, organic long-persistent luminophores have good structure tailorability for the construction of activable probes, which is favorable for biosensing. Recently, the development of reactive oxygen species (ROS)-mediated long-persistent (ROSLP) luminophores (especially organic small-molecule ROSLP luminophores) is still in the rising stage. Notably, ROSLP luminophores for in vivo imaging have experienced from two-component separated nano-systems to integrated uni-luminophores, which obtained gradually better designability and biocompatibility. In this review, we summarize the progress and challenges of organic long-persistent luminophores, focusing on their development history, long-persistent luminescence working mechanisms, and biomedical applications. We hope that these insights will help scientists further develop functionalized organic long-persistent luminophores for the biomedical field.

Investigating the Influence of Wavelength-Specific Textured Backgrounds in Background Oriented Schlieren (BOS) Imaging

This study investigates the influence of wavelength-specific textured backgrounds on the effectiveness of Background-Oriented Schlieren (BOS) imaging, focusing on wavelengths from 400 nm to 670 nm at intervals of 30 nm intervals and multiple captured recordings for each background wavelength interval. By analyzing the signal-to-noise ratio (SNR) computationally, and the image gradient magnitude, we aimed to determine the optimal wavelengths for capturing turbulence and determine the effectiveness of colored backgrounds in natural external environments for schlieren. The SNR, calculating the ratio of mean signal intensity to noise standard deviation, revealed the highest value at 550 nm (SNR = 22.8), indicating maximized clarity. Similarly, image gradient magnitude, computed using the Sobel operator to assess spatial intensity changes, peaked at 550 nm (G=52.3), confirming effective turbulence visualization. Our findings align with the Bayer color filter trend, suggesting that the green spectrum is particularly advantageous for BOS imaging. Deviations at 490 nm and 580 nm, characterized by lower SNR and gradient magnitude, could be attributed to atmospheric scattering, refractive index overlap, or slight digital video capture differences., highlighting environmental factors that can influence imaging performance and value variation. These insights emphasize the importance of wavelength selection and background design in real-world BOS applications, suggesting that while 550 nm provides optimal results, further refinement may enhance the effectiveness of other wavelengths.

Analysis of electrode performance on amplitude integrated electroencephalography in neonates: evaluation of a new electrode aCUP-E vs. liquid gel electrodes

BackgroundNeonatologists and clinical neurophysiologists face challenges with the current electrodes used for long-duration amplitude-integrated electroencephalography (aEEG) in neonatal intensive care units (NICU), limiting the capacity to diagnose brain damage.ObjectivesThe objectives of this study were to develop methods for comparing the performance of different electrodes to be used in aEEG. The comparison was done between a newly designed neonate-specific electrode, aCUP-E, with commercial liquid gel electrodes used in amplitude-integrated electroencephalography (aEEG). The comparison included impedance stability, electrode survival, recording quality, usability, and satisfaction of NICU staff.MethodsaEEG recordings with bipolar montage was used, with one hemisphere fitted with commercial electrodes and the other with aCUP-E electrodes, alternated among subjects. Continuous impedance and raw EEG data were collected over a minimum of 24 h, and signal processing was performed using Python and MATLAB.Main resultsaCUP-E electrodes demonstrated superior performance, including: Increased impedance stability and electrode survival, enhanced recording quality with fewer artifacts, high correlation in signal capture between electrodes during optimal brain activity segments, higher signal-to-noise ratio (SNR) across varying impedance levels, greater staff satisfaction and ease of use. Moreover, Kaplan-Meier curves indicated a higher survival rate for aCUP-E electrodes over 24 h compared to commercial electrodes. Impedance variability analysis showed statistically significant stability improvements for aCUP-E.ConclusionaCUP-E electrodes outperform commercial liquid gel electrodes in impedance stability, electrode survival, and recording quality. These results suggest that aCUP-E electrodes could significantly enhance aEEG utilization in diagnosing and treating neonatal brain conditions in NICUs. Future improvements to the aCUP-E electrode may further reduce artifacts and increase electrode longevity, potentially leading to a significant improvement in neonatal brain monitoring by means of aEEG.

Log-Cholesky filtering of diffusion tensor fields: Impact on noise reduction

Diffusion tensor imaging (DTI) is a powerful neuroimaging technique that provides valuable insights into the microstructure and connectivity of the brain. By measuring the diffusion of water molecules along neuronal fibers, DTI allows the visualization and study of intricate networks of neural pathways.DTI is a noise-sensitive method, where a low signal-to-noise ratio (SNR) results in significant errors in the estimated tensor field. Tensor field regularization is an effective solution for noise reduction.Diffusion tensors are represented by symmetric positive-definite (SPD) matrices. The space of SPD matrices may be viewed as a Riemannian manifold after defining a suitable metric on its tangent bundle. The Log-Cholesky metric is a recently developed concept with advantages over previously defined Riemannian metrics, such as the affine-invariant and Log-Euclidean metrics. The utility of the Log-Cholesky metric for tensor field regularization and noise reduction has not been investigated in detail.This manuscript provides a quantitative investigation of the impact of Log-Cholesky filtering on noise reduction in DTI. It also provides sufficient details of the linear algebra and abstract differential geometry concepts necessary to implement this technique as a simple and effective solution to filtering diffusion tensor fields.

Use of ultra-low contrast dose CT aortography for the management of aortic aneurysmal disease

PurposeComputed tomography aortography (CTA) is used in the assessment of aortic pathologies and planning of surgical intervention. However, its dependence on iodinated contrast can result in development of contrast-induced acute kidney injury (CI-AKI). The potential concern of CI-AKI has spurred research into the potential of administration of low contrast volumes in CTA investigations while maintaining overall diagnostic appeal. Several studies have shown that CTA using contrast volumes as low as 30 mL (equivalent to 10.5 g of iodine) can still yield scans of diagnostic quality. We present a retrospective pilot study to evaluate the feasibility of utilising an ultra-low volume of iodinated intravenous contrast in a population of patients with severe renal insufficiency with referral from our vascular surgery unit for CTA evaluation of the thoracic and abdominal aorta.MethodsThis retrospective pilot study examined 12 CTA scans performed with 20 mLs of iodinated contrast and assessed image quality with both quantitative and qualitative markers. All scans were performed on a Siemens SOMATOM Force dual-source CT scanner. Quantitative assessment values were measured via attenuation values at eight aortoiliac locations and used to calculate a signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at each location. Qualitative analysis of image quality and viability for surgical intervention was obtained from subjective clinical assessment by an interventional radiologist and vascular surgeon.ResultsObtained quantitative assessment values included mean attenuation 189.9 HU, mean SNR 9.6 and mean CNR 8.0. All 12 scans demonstrated individual mean SNR values above predetermined quality thresholds while only five scans produced individual mean CNR values above threshold. Eleven of 12 scans were determined to be of sufficient quality for diagnosis and planning of surgical intervention.ConclusionsOur results suggest that CTA utilising an ultra-low contrast dose of 20 mLs (6 g iodine) yields scans of diagnostic quality for therapeutic decision-making in vascular surgical intervention.

Adaptive Stochastic Resonance Enhanced Weak Linear Frequency Modulated Signal Perception in Low Signal-to-Noise Ratio Environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-frequency product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system's implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner-Ville distribution (WVD) transform's time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Design and inversion study of a 1.6 µm high resolution non-modulated laser heterodyne radiometer (NM-LHR)

Laser heterodyne detection boasts exceptional advantages such as high spectral resolution and high signal-to-noise ratio (SNR). It excels at capturing spectral line broadening information of upper atmospheric molecules, which presents substantial research value in the realms of greenhouse gas profile measurement and the assessment of laser propagation effects in the atmosphere. This paper delves into the investigation of the processing method for heterodyne signals, adopting a non-modulated signal processing method to construct a near-infrared non-modulated laser heterodyne radiometer. This innovative design significantly enhanced the response speed and SNR. The radiometer achieved a spectral resolution of 0.006 cm-1 and an SNR of 300. This facilitated the acquisition of vertical profile distribution and column concentration of CH4 by measuring the absorption spectrum. Comparative tests revealed compelling advantages of the non-modulated device. The modulated device collected data 6 times in 6 minutes, yielding an SNR of 58. In contrast, the non-modulated device demonstrated superior efficiency by collecting data 6000 times in 2 minutes, resulting in a remarkable SNR of 103. In the process of inversion, the influence of the solar spectrum was coupled to improve the accuracy of inversion results. The inversion results of the CH4 column concentration from the laser heterodyne radiometer were compared with those from the Fourier transform spectrometer (EM27/SUN), with average concentrations of 1.946 ppmv and 1.930 ppmv, and exhibited an overall deviation of approximately 0.8%. The non-modulated laser heterodyne radiometer provides a new reference for the rapid, accurate and high spectral resolution measurements of greenhouse gas concentration.

A-327 Statistical Technique-based Image Analysis for Digital Reading of Immunochemistry Cassette

Abstract Background Covid-19 transformed healthcare and how we think about clinical diagnostics. People prefer at home testing options that enable them to focus on staying healthy at their own comfort and privacy. Fast and accurate lateral flow assays or rapid diagnostic test (RDT) meets that need, however ensuring high test specificity and accuracy are of utmost importance. Visual readout of RDTs is subjective and time consuming. With the current technological advancement, medical devices are smarter and can be used for automated readout. The core of such devices are the algorithms that process the information despite several limitations and unknown variations affecting input signals. Many such algorithms are developed and are used for similar applications. Here, a statistical model for any optical device that is designed to automatically read an RDT with one or more test lines is described. Methods The described LFA algorithm is a innovative way to analyze images acquired with Complementary Metal-Oxide-Semiconductor (CMOS) based optical system with low Signal-to-noise ratio (SNR), giving a semi quantitative result. This also takes care of variations in illumination sources with relatively reduced computation, by converting the images to a 1 Dimensional (1D) signal. Algorithm workflow: Image pre-processing: Affine transformations such as rotation, translation, etc. (depends on the inherent setup) are applied on the acquired images as a pre-processing step. Pattern matching techniques using Gaussian templates are applied to determine the window region of the LFA and precisely identify the test and control line regions. Image formatting: The sample region of the cassette is converted from RGB color space to the weighted gray (Gw) for digital interpretation of the primary biomarker present. Characterization of the Gw channel enables test peak detection with enhanced resolution. This helps in early call out of results and improved accuracy, thereby reducing chances of erroneous results. A second order Butterworth low pass filter is applied to reduce system noise and enhance SNR. The images are converted to 1D signal by averaging for mathematical read out. Mathematical interpretation: Maximum peak height (max) and minimum peak depth (min) of Gw channel values in the test line region (t-peak) are extracted from the 1D signal. The mathematical representation of the test line is calculated as: Test Peak Signal = {max(t-peak) - min(t-peak)} / Alpha; Normalization Factor, Alpha = (sum of all Gw channel values for any clear non-reactive part of the test device) / (number of Gw channel values) Results TPS values for various biomarkers from statistically significant replicates were computed at Low, Medium, and High levels. Two sample T-tests were performed, and respective p values are distinctly different as p-value<0.05. For a concentration level of Low to medium, p-value is 0.044 and for Medium - High p-value is 0.004. Conclusions This algorithm can run on any off-the-shelf available RDT coupled with a CMOS based optical system. The proposed algorithm can differentiate between low, medium, and high concentrations with high confidence. High throughput semiquantitative digital readouts are robust, accurate and are preferred for diagnostic test result interpretation.

Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.

A proportional fusion adaptation algorithm to reduce noise in sEMG signals of the lower limb

Abstract To address issues related to unknown noise pollution and the inadequate performance of single adaptive reduction noise algorithms in measuring lower limb surface electromyography (sEMG) signals, this paper proposes an algorithm that combines the fast convergence speed and strong reduction noise capability of recursive least-squares (RLS) with the low computational complexity of the normalized least-mean-square (NLMS) and improved proportionate NLMS (IPNLMS). Through the proportional fusion of RLS with NLMS and RLS with IPNLMS, the proposed algorithm greatly improves convergence speed, stability, and reduction noise performance while effectively reducing computational complexity. Moreover, considering the influence of the initial value of weights on the reduction noise performance during the updating process of the adaptive reduction noise algorithm, a weight initial value setting (WIS) module is proposed to optimize the initial value of the weights by the known amount of data. Based on 50 independent experiments, an adaptive reduction noise algorithm and WIS module were used to reduce the unknown noise in the lower limb sEMG signals, which was generated by white Gaussian noise, power line interference (PLI), or hybrid noise interfered by an unknown environment, and the reduction noise performance was evaluated by using the average value of the signal-to-noise ratio (SNR), the root-mean-square error (RMSE), and R-square. The simulation results verify that the proportional fusion adaptive reduction noise algorithm and the WIS module effectively accelerate the convergence speed, enhance the reduction noise capability, and reduce the computational complexity.

B-117 Rapid Classification of Cleanliness Grades of Vaginal Discharge through Computational Analysis of SERS Spectra via Variational Auto-Encoder Model And Long Short-Term Memory Networks

Abstract Background Classification of cleanliness grades of vaginal discharge plays a pivotal role in clinical laboratories, serving as indicators for the presence of various vaginal infections such as bacterial vaginosis and candidal vulvovaginitis, etc. The conventional method for assessing vaginal discharge cleanliness involves manual microscopy, a technique susceptible to variations based on the examiner's expertise and subjective judgment, hindering the attainment of swift and accurate diagnoses. Methods This study introduces a novel approach by integrating Surface-Enhanced Raman Spectroscopy (SERS) with a deep learning algorithm for the rapid classification of vaginal discharge cleanliness grades. The proposed classification method combines the Variational Auto-Encoder (VAE) and Long Short-Term Memory (LSTM) to process SERS spectra. The quality of the training data is assessed using the signal-to-noise ratio (SNR) as a standard indicator. Results The application of the VAE-LSTM algorithm significantly enhances classification performance, providing swift and accurate results while reducing the required time. Comparative analyses with various classical machine learning algorithms and evaluation metrics reveal the superior predictive capability and time efficiency of the VAE-LSTM algorithm, demonstrating 100% accuracy and a fitting time of 2 seconds. Blind testing of SERS spectra collected from unknown patients further validates the reliability of the proposed method. Conclusions This study presents a promising and practical approach for the rapid classification of vaginal discharge cleanliness grades. The integration of the SERS technique with the VAE-LSTM algorithm not only ensures accuracy and efficiency in clinical diagnosis but also lays the foundation for advancements in non-invasive diagnostic technologies in gynecological healthcare.

Local stimulation of pyramidal neurons in deep cortical layers of anesthetized rats enhances cortical visual information processing

In the primary visual cortex area V1 activation of inhibitory interneurons, which provide negative feedback for excitatory pyramidal neurons, can improve visual response reliability and orientation selectivity. Moreover, optogenetic activation of one class of interneurons, parvalbumin (PV) positive cells, reduces the receptive field (RF) width. These data suggest that in V1 the negative feedback improves visual information processing. However, according to information theory, noise can limit information content in a signal, and to the best of our knowledge, in V1 signal-to-noise ratio (SNR) has never been estimated following either pyramidal or inhibitory neuron activation. Therefore, we optogenetically activated pyramidal or PV neurons in the deep layers of cortical area V1 and measured the SNR and RF area in nearby pyramidal neurons. Activation of pyramidal or PV neurons increased the SNR by 267% and 318%, respectively, and reduced the RF area to 60.1% and 77.5%, respectively, of that of the control. A simple integrate-and-fire neuron model demonstrated that an improved SNR and a reduced RF area can increase the amount of information encoded by neurons. We conclude that in V1 activation of pyramidal neurons improves visual information processing since the location of the visual stimulus can be pinpointed more accurately (via a reduced RF area), and more information is encoded by neurons (due to increased SNR).

MSAACNN for intense noise suppression in DAS-VSP records

Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Realizing submeter spatial resolution for Raman distributed fiber-optic sensing using a chaotic asymmetric paired-pulse correlation-enhanced scheme

The sensing spatial resolution and signal-to-noise ratio (SNR) of Raman distributed optical fiber sensors are limited by the pulse width and weak Raman scattering signals. Notably, the sensing spatial resolution cannot exceed the order of meters at several kilometers sensing distances. To break through this physical bottleneck, a novel, to our knowledge, Raman scattering model based on paired-pulse sensing is constructed. The fundamental origins of the observed limited spatial resolution of conventional schemes are analyzed, and a chaotic asymmetric paired-pulse correlation-enhanced scheme for Raman distributed fiber-optic sensing is proposed and experimentally demonstrated. The proposed scheme uses a chaotic asymmetric paired-pulse as the sensing signal and extracts the light intensity information of each data point of the sensing fiber, which carries the random undulation characteristics of chaotic time series, based on the time-domain differential reconstruction method. This scheme overcomes the pulse width limitation of spatial resolution via correlation and demodulation, enhances the correlation characteristics between the temperature-modulated Raman scattered light field and detection signal, and improves the SNR. Finally, a sensing performance of 10 km, a spatial resolution of 30 cm, and an SNR of 6.67 dB are realized in the experiment. This scheme provides a new research idea for a high-performance Raman distributed optical fiber sensing system.

Steady-State Free Precession (SSFP) NMR Spectroscopy for Sensitivity Enhancement in Complex Environmental and Biological Samples Using Both High-Field and Low-Field NMR.

Nuclear magnetic resonance (NMR) spectroscopy is a valuable and complementary tool in environmental research, but it is underutilized due to the cost, size, and maintenance requirements of standard "high-field" NMR spectrometers. "Low-field" NMR spectrometers are a financially and physically accessible alternative, but their lower sensitivity and increased spectral overlap limit the analysis of heterogeneous environmental/biological media, especially with fast-relaxing samples that produce broad, low-intensity spectra. This study therefore investigates the potential of the steady-state free precession (SSFP) experiment to enhance signal-to-noise ratios (SNRs) of fast-relaxing, complex samples at both high- and low-field. SSFP works by obtaining steady-state transverse signal using a train of equally spaced radiofrequency pulses with the same flip angle and a time between pulses less than the transverse relaxation time, allowing for thousands of scans to be summed in a short time period. Here, 13C-SSFP is applied to samples of varying complexity (egg white, dissolved organic matter, and crude oil) at low-field and at high-field for testing and comparison. The potential of in vivo SSFP NMR is additionally investigated by applying 31P-SSFP to live Eisenia fetida at high-field. In some samples, SSFP increased 13C SNR by over 2000% at both high-field and low-field compared to standard 13C NMR and enabled detection of peaks that were not observable by standard 13C NMR. Ultimately, SSFP holds great potential for improving analysis of fast-relaxing, complex samples, which could in turn make low-field NMR spectroscopy a more effective tool not only in environmental/biological research but also in numerous other disciplines.

Stress evaluation of ferromagnetic materials based on a new Barkhausen noise sensor composed by high entropy alloy magnetic core

Abstract During the service of ferromagnetic structural steel materials, stress should be evaluated accurately. Although the magnetic Barkhausen noise (MBN) testing has the ability to sense the stress, it can be easily interfered by environment. In this paper, a new MBN sensor is fabricated by selecting FeCoNi(AlMn)0.25 high entropy alloy (HEA) as the case of magnetic core to improve the accuracy of stress evaluation. The process optimization results show that the stability of MBN signal characteristics is the largest when the excitation frequency is 4Hz and the voltage is 6V. The signal-to-noise ratio (SNR) of MBN indicates that the HEA and Ni-Zn ferrite probes have better anti-inference capability. The MBN signal characteristic values peak voltage (PV) and root-mean-square (RMS) measured by HEA probe can linearly quantify the stress level with higher efficiency, stability and accuracy. The underlying reason of high sensitivity of HEA probe to the variation of MBN signals is revealed based on the magnetic properties. The microstructure and the thermodynamic parameters are analyzed to clarify the additions of Al and Mn atoms can affect the short-ranged magnetic exchange interaction and lattice distortion, which affects the magnetization behavior of HEA. Finally, the availability of MBN sensor with HEA magnetic core to the stress evaluation on the retired slide rails of car seats is conducted, which demonstrates its great application value.

Post-Estimation Shrinkage in Full and Selected Linear Regression Models in Low-Dimensional Data Revisited.

The fit of a regression model to new data is often worse due to overfitting. Analysts use variable selection techniques to develop parsimonious regression models, which may introduce bias into regression estimates. Shrinkage methods have been proposed to mitigate overfitting and reduce bias in estimates. Post-estimation shrinkage is an alternative to penalized methods. This study evaluates effectiveness of post-estimation shrinkage in improving prediction performance of full and selected models. Through a simulation study, results were compared with ordinary least squares (OLS) and ridge in full models, and best subset selection (BSS) and lasso in selected models. We focused on prediction errors and the number of selected variables. Additionally, we proposed a modified version of the parameter-wise shrinkage (PWS) approach named non-negative PWS (NPWS) to address weaknesses of PWS. Results showed that no method was superior in all scenarios. In full models, NPWS outperformed global shrinkage, whereas PWS was inferior to OLS. In low correlation with moderate-to-high signal-to-noise ratio (SNR), NPWS outperformed ridge, but ridge performed best in small sample sizes, high correlation, and low SNR. In selected models, all post-estimation shrinkage performed similarly, with global shrinkage slightly inferior. Lasso outperformed BSS and post-estimation shrinkage in small sample sizes, low SNR, and high correlation but was inferior when the opposite was true. Our study suggests that, with sufficient information, NPWS is more effective than global shrinkage in improving prediction accuracy of models. However, in high correlation, small sample sizes, and low SNR, penalized methods generally outperform post-estimation shrinkage methods.

Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

Prospective Comparison of FOCUS MUSE and Single-Shot Echo-Planar Imaging for Diffusion-Weighted Imaging in Evaluating Thyroid-Associated Ophthalmopathy.

To prospectively compare single-shot (SS) echo-planar imaging (EPI) and field-of-view optimized and constrained undistorted single-shot multiplexed sensitivity-encoding (FOCUS MUSE) for diffusion-weighted imaging (DWI) in evaluating thyroid-associated ophthalmopathy (TAO). SS EPI and FOCUS MUSE DWIs were obtained from 39 patients with TAO (18 male; mean ± standard deviation: 48.3 ± 13.3 years) and 26 healthy controls (9 male; mean ± standard deviation: 43.0 ± 18.5 years). Two radiologists scored the visual image quality using a 4-point Likert scale. The image quality score, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) of extraocular muscles (EOMs) were compared between the two DWIs. Differences in the ADC of EOMs were also evaluated. The performance of discriminating active from inactive TAO was assessed using receiver operating characteristic curves. The correlation between ADC and clinical activity score (CAS) was analyzed using Spearman correlation. Compared with SS EPI DWI, FOCUS MUSE DWI demonstrated significantly higher image quality scores (P < 0.001), a higher SNR and CNR on the lateral rectus muscle (LRM) and medial rectus muscle (MRM) (P < 0.05), and a non-significant difference in the ADC of the LRM and MRM. Active TAO showed higher ADC than inactive TAO and healthy controls with both SS EPI and FOCUS MUSE DWIs (P < 0.001). Inactive TAO and healthy controls did not show a significant ADC difference with both DWIs. Compared with SS EPI DWI, FOCUS MUSE DWI demonstrated better discrimination of active from inactive TAO (AUC: 0.925 vs. 0.779; P = 0.007). The ADC was significantly correlated with CAS in SS EPI DWI (r = 0.391, P < 0.001) and FOCUS MUSE DWI (r = 0.645, P < 0.001). FOCUS MUSE DWI provides better images for evaluating EOMs and better performance in diagnosing active TAO than SS EPI DWI. The application of FOCUS MUSE will facilitate the DWI evaluation of TAO.