Signal-to-noise Ratio Of Signal Research Articles

Wall thinning quantification with a lift-off distance for ferromagnetic structures using pulsed ECT equipped with ICA-Gauss filter and Hough Transform

Determining the thickness of ferromagnetic materials with a lift-off distance poses a significant challenge for current non-destructive testing (NDT) techniques. Pulsed eddy current (PEC) testing is deemed as a powerful candidate to evaluate this type of defect. However, the signal-to-noise ratio (SNR) of the PEC response signal obtained with large lift-off distance is very poor, so that the signal feature can hardly be extracted. To improve the SNR of PEC response signals and capture the signal feature adaptively, this paper proposed a novel PEC signal processing algorithm based on ICA-Gauss filter and Hough Transform (HT). Firstly, the principle of the proposed method was introduced. Then, two case studies, a comparison experiment and an application experiment were conducted to verify the effectiveness and accuracy of this method. Results from these experiments show that (a) the ICA-Gauss filter can effectively suppress the power-line noises and random noises in PEC signals, (b) the ICA-Gauss filter outperforms traditional filters in feature robustness and computing efficiency, including double-logarithmic median filter and Savitzky-Golay filter, and (c) HT is an adaptive and accurate method to extract the PEC signal feature, thus achieving a small detection error.

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations-including SNR measurements paired with disdrometer and rain-gauge data-we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth-satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

Research on optimizing the optical local-oscillator power of Coherent Doppler LiDAR to enhance wind velocity measurements

Due to weak echo signals that become progressively overwhelmed by noise, measurement accuracy and effective detection range of the Coherent Doppler wind LiDAR (CDL) are often compromised. While increasing the optical local-oscillator power (OLP) can amplify the echo signal, it is constrained by the nonlinear effects of the detector. This paper introduces a method for optimizing the OLP in CDL systems. Theoretical analysis has been proposed to explore the amplification effect of OLP on echo signals, and the nonlinear effects of detectors have been studied. Simulations are performed to explore the influence of varying OLP on the signal-to-noise ratio (SNR) across different detector α (quadratic nonlinear coefficient) values. The spectral analysis method is used to directly compute the SNR of actual atmospheric wind field signals under various OLP settings. Results demonstrate consistency between calculated and simulated values, enabling determination of optimal OLP and the detector α values from fitted curves. Comparative experiments confirm significant enhancement in effective detection range (> 1.5 km) with ± 0.5 m/s accuracy. The innovation of this study lies in combining the OLP optimization method with real atmospheric wind field echo signal experiments. It addresses the challenges of directly measuring the nonlinear parameters of the detector and determining the optimal OLP. This study offers valuable theoretical and experimental insights for enhancing the wind measurement performance of CDL.

Non-invasive ultrasonic sensing of internal conditions on a partial full-scale spent nuclear fuel canister mock-up

The safe storage of spent nuclear fuel (SNF) in dry cask storage systems (DCSSs) is critical to the nuclear fuel cycle and the future of nuclear energy. A critical component of DCSSs is the SNF canister. The canister is a sealed stainless-steel structure, which is first vacuum dried and then backfilled with helium. The structural deterioration within a canister can be monitored through its internal gas properties. This monitoring serves as the driving force behind the non-invasive ultrasonic sensing approach in this paper. A major challenge in collecting gas-borne signals using ultrasonic sensing is the impedance mismatch between the stainless-steel canister and the helium gas inside. Only a small fraction of the ultrasonic signal makes its way from the transmitter to the receiver through the gas medium. In this paper, experimental studies on a partial full-scale canister mock-up were carried out to capture the gas-borne signals. Damping materials were applied on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The research results showed that the excitation frequency played an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF) at 225 kHz. A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, acoustic impedance matching (AIM) layers were added, and it was found that the gas signal energy was improved by 160.4% compared with that of no AIM layers. Subsequently, the relative humidity (RH) level and temperature of the gas were varied to simulate abnormal internal conditions of the canister. The non-invasive testing system demonstrated reliability and sensitivity in detecting gas temperature and RH variations. Theoretical calculations demonstrated the potential for detecting low-level xenon and air within an actual SNF canister filled with helium. Last, an active noise cancellation (ANC) method, previously developed by the authors, was verified on the canister mock-up for the first time. The results showed that the SNR of the gas signal was improved by 213.6% compared with that of no ANC.

A Quantitative Comparison of 31P Magnetic Resonance Spectroscopy RF Coil Sensitivity and SNR between 7T and 10.5T Human MRI Scanners Using a Loop-Dipole 31P-1H Probe.

In vivo phosphorus-31 (31P) magnetic resonance spectroscopy (MRS) imaging (MRSI) is an important non-invasive imaging tool for studying cerebral energy metabolism, intracellular nicotinamide adenine dinucleotide (NAD) and redox ratio, and mitochondrial function. However, it is challenging to achieve high signal-to-noise ratio (SNR) 31P MRS/MRSI results owing to low phosphorus metabolites concentration and low phosphorous gyromagnetic ratio (γ). Many works have demonstrated that ultrahigh field (UHF) could significantly improve the 31P-MRS SNR. However, there is a lack of studies of the 31P MRSI SNR in the 10.5 Tesla (T) human scanner. In this study, we designed and constructed a novel 31P-1H dual-frequency loop-dipole probe that can operate at both 7T and 10.5T for a quantitative comparison of 31P MRSI SNR between the two magnetic fields, taking into account the RF coil B1 fields (RF coil receive and transmit fields) and relaxation times. We found that the SNR of the 31P MRS signal is 1.5 times higher at 10.5T as compared to 7T, and the power dependence of SNR on magnetic field strength (B0) is 1.9.

An Adaptive Noise Reduction Method for High Temperature and Low Voltage Electromagnetic Detection Signals Based on SVMD Combined with ICEEMDAN.

In view of the low signal-to-noise ratio (SNR) of shear wave electromagnetic acoustic transducers (EMAT) in the detection of high-temperature equipment, the use of low excitation voltage (LEV) further deteriorates the detection results, resulting in the echo signal containing defects being drowned in noise. For the extraction of the EMAT signal, an adaptive noise reduction method is proposed. Firstly, the minimum envelope entropy is taken as the fitness function for the Harris Hawks Optimizer (HHO), and the optimal successive variational mode decomposition (SVMD) balance parameter is searched by HHO adaptive iteration to decompose LEV EMAT signals at high temperatures. Then the filter is carried out according to the excitation center frequency and correlation coefficient threshold function. Then, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the filtered signal and combine the kurtosis factor to select the appropriate intrinsic mode functions. Finally, the signal is extracted by the Hilbert transform. In order to verify the effectiveness of the method, it is applied to the low-voltage detection of 40Cr from 25 °C to 700 °C. The results show that the method not only suppresses the background noise and clutter noise but also significantly improves the SNR of EMAT signals, and most importantly, it is able to detect and extract the 2 mm small defects from the echo signals. It has great application prospects and value in the LEV detection of high-temperature equipment.

An SNR Enhancement Method for Φ-OTDR Vibration Signals Based on the PCA-VSS-NLMS Algorithm.

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is -1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19-10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system's position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

Spontaneous increasing of sensitivity and resolution in parahydrogen-induced hyperpolarization by RASER

Enhancing the sensitivity of nuclear magnetic resonance (NMR) technology has been the focus of NMR research for decades, which offers the potential to significantly expand its applications in chemistry, biology, and medical imaging. Parahydrogen-induced polarization (PHIP) emerges as a cost-effective approach to substantially enhance the sensitivity of NMR. Nevertheless, the amplification of the 1H signal in PHIP is susceptible to interference from the thermal polarization state 1H NMR signal. Employing RASER (radiofrequency amplification by stimulated emission of radiation) proves effective in mitigating such interference, which can reduce the linewidth and increase the sensitivity at the same time. In this work, we utilized PHIP and RASER to enhance the signal-to-noise ratio (SNR) of a series of biocompatible alkynyl organic acid molecules. The alkynyl acid with the highest enhancement factor was first identified through PASADENA (parahydrogen and synthesis allow dramatically enhanced nuclear alignment) experiments. Subsequently, RASER experiments were carried out through hyperpolarization of 5-hexynoic acid, exploring its signal characteristics under varying flow rates and pressures. The SNR of proton signals of 5-hexynoic acid surpassed 150,000, an 18.62-fold improvement compared with traditional hyperpolarized signals in PASADENA, and a markedly narrowed linewidth of 0.06 Hz.

Weak signal detection technique based on Durbin-Watson test and one-bit sampling.

Correlation-based detection techniques are widely used in the weak periodic signal detection field. Traditionally, they are based on extracting the correlation of a weak signal from noise. Considering the impact of a weak signal on the randomness of background noise, this article takes the opposite approach and proposes a weak signal detection technique based on the Durbin-Watson (DW) test and one-bit sampling, detecting the weak signal due to the extent to which the randomness of noise is affected. The randomness of noise is analyzed through the DW test, which is a method for detecting the randomness of data sequences through first-order autocorrelation. One-bit sampling is adopted to reduce the complexity of the sampling circuit and data processing algorithm. The effectiveness of the DW test in the situation of one-bit sampling is demonstrated through simulation and analysis. Simulation results show that the proposed technique is capable of detecting weak sinusoidal and square-wave signals with a signal-to-noise ratio (SNR) above -30 dB, and the frequency or SNR of a weak signal can be further estimated based on mutual constraints. The measured results confirm the capability. In addition, the factors of coherent sampling, noise bandwidth, and comparator threshold that influence the performance of the proposed technique are simulated and discussed in detail.

Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients’ active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Two-stage benefits of internal and external noise to enhance early fault detection of machinery by exciting fractional SR

For early mechanical fault diagnosis, stochastic resonance (SR) breaks the previous perception that “noise is useless” and uses internal noise or external noise to enhance weak fault characteristics. Moreover, the fractional-order derivative can reinforce noise-enhanced weak fault detection. However, in the face of extremely low signal-to-noise ratio conditions, the enhancement effect of fractional SR induced by a single excitation of internal noise or external noise is unsatisfactory. To solve the above drawbacks, this paper investigates two-stage benefits of both internal and external noise to enhance early fault detection of machinery by exciting parallel array of fractional SR, and using the signal-to-noise ratio (SNR) to adjust these parameters of fractional SR. Then, two experiments including rolling element bearings and gearboxes were performed to validate it. Experimental results show that the proposed method takes on obvious detection ability for low SNR signals, where the amplitude at the fault characteristic frequency is amplified by beyond 300 times in bearing and gearbox fault experiments than one-stage those. In addition, it is also found that as the noise intensity and the number of iterations increase, the amplitude at the fault characteristic frequency tends to the peak value and then falls into a saturation value. Finally, compared with empirical mode decomposition (EMD), the proposed method can amplify the amplitude at the fault characteristic frequency beyond 1000 times in bearing and gearbox fault experiments. It was concluded that the proposed method has obvious advantages in extracting weak fault characteristics of machinery submerged by strong background noise.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals.

Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

High-performance deep-sea long-range underwater acoustic communication: Deconvolved conventional beamforming based approach

To address the challenges posed by large propagation loss and severe multipath delay spread, while aiming to improve communication rates in long-range underwater acoustic (UWA) communication, this paper introduces a novel approach: multi-beam (MB) Orthogonal Frequency Division Multiplexing (OFDM) based on deconvolved conventional beamforming (dCv). The proposed method establishes a signal processing framework for UWA channel receivers, wherein received signals corresponding to each angle beam are isolated and concentrated using the dCv technique. Subsequently, the separated signals from each angle undergo maximal-ratio combining (MRC) channel equalization followed by demodulation. Compared to conventional single-beam (SB) processing methods, the proposed approach capitalizes on multipath diversity gain achieved by combining MB outputs originating from various arrival angles. Moreover, employing dCv processing demonstrates superior performance compared to popularly employed beamforming techniques, especially when dealing with paths arriving from closely aligned angles. Additionally, the array beamforming utilized significantly enhances the signal-to-noise ratio (SNR) of each beam output, mitigating the elevated error rates in channel estimation associated with combining low SNR signals received from individual elements. Simulation results utilizing BELLHOP and experimental data from the South China Sea showcase notably improved bit error rate (BER) performance for the proposed method compared to SB equalization, MB-MRC equalization based on conventional beamforming (CBF), minimum variance distortionless response (MVDR), and worst-case performance optimization (WCPO), as well as standard MRC equalization techniques. The proposed receiver achieves error-free decoding results at a communication distance of 80 km with a data rate of 247 bps.

CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing.

Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises. The objective of this research is to conduct a noise reduction experiment for a multi-channel speech enhancement (MCSE) system in stationary and non-stationary environmental noisy situations with varying speech signal SNR levels. The experiments examined the performance of the existing and the proposed MCSE systems for environmental noises in filtering low to high SNRs environmental noises (-10 dB to 20 dB). The experiments were conducted using the AURORA and LibriSpeech datasets, which consist of different types of environmental noises. The existing MCSE (BAV-MCSE) makes use of beamforming, adaptive noise reduction and voice activity detection algorithms (BAV) to filter the noises from speech signals. The proposed MCSE (DWT-CNN-MCSE) system was developed based on discrete wavelet transform (DWT) preprocessing and convolution neural network (CNN) for denoising the input noisy speech signals to improve the performance accuracy. The performance of the existing BAV-MCSE and the proposed DWT-CNN-MCSE were measured using spectrogram analysis and word recognition rate (WRR). It was identified that the existing BAV-MCSE reported the highest WRR at 93.77% for a high SNR (at 20 dB) and 5.64% on average for a low SNR (at -10 dB) for different noises. The proposed DWT-CNN-MCSE system has proven to perform well at a low SNR with WRR of 70.55% and the highest improvement (64.91% WRR) at -10 dB SNR.

An FSCEEMD method for downhole weak SNR signal extraction of near-bit attitude parameters

In practice, the near-bit drilling tool confronts with strong vibrations and high-speed rotation. Therein the original signal amplitude of the tool attitude measurements is relatively feeble, and the signal-to-noise ratio (SNR) is exceptionally low. To handle this issue, this paper proposes a weak SNR signal extraction method, frequency selecting complementary ensemble empirical mode decomposition, which is based on ensemble empirical mode decomposition combining with complementary noise and frequency selecting. This method firstly adds different positive and negative pairs of auxiliary white noise to the original near-bit weak SNR signal, secondly adopts empirical mode decomposition on each pair of noise-added signals, then performs ensemble averaging on the obtained multiple sets of intrinsic mode function (IMF) to output more stable IMF of each order and set suitable weights according to designed frequency threshold, and finally reconstructs the original useful signal through weighted summing IMFs. Simulation results show that the extraction accuracy of well inclination angle ranges about ± 0.51°, and the extraction accuracy of tool face angle ranges about ± 1.35°, and meanwhile experimental results are provided compared with other advanced methods, which verifies the effectiveness of our method.

Vectorial‐Optics‐Enabled Multi‐View Non‐Line‐Of‐Sight Imaging with High Signal‐To‐Noise Ratio

AbstractNon‐line‐of‐sight (NLOS) imaging enables the reconstruction of targets beyond the direct line of sight, which has extensive applications across various fields. However, it remains unclear how and to what extent the vectorial nature of light, which plays a critical role in optical imaging and object recognition, can enhance the imaging performance of active NLOS imaging. Here, this work proposes a method that utilizes vector optical fields (VOFs) with arbitrarily designed polarization distributions to realize NLOS imaging in challenging conditions with low signal‐to‐noise ratio (SNR). A generalized VOF reflection model is established at the relay surface for active NLOS imaging to determine the optimal illumination angle and polarization as well as the received polarization. Based on this model, the feasibility of multi‐view NLOS imaging is demonstrated with enhanced SNR of echo signals under proper VOF modulation, surpassing the limitations of conventional single‐view NLOS imaging. Simulations and experiments validate the superior performance of the proposed approach to realize accurate reconstruction and recognition of hidden objects.

Multiscale dilated denoising convolution with channel attention mechanism for micro-seismic signal denoising

Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations. However, micro-seismic signals are commonly contaminated by various types of complex noise, which can hinder micro-seismic accurate P-wave pickup and analysis. In this study, we propose the Multiscale Dilated Convolutional Attention denoising method, referred to as MSDCAN, to eliminate complex noise interference. The MSDCAN denoising model consists of an encoder, an improved attention mechanism, and a decoder. To effectively capture the neighborhood features and multiscale features of the micro-seismic signal, we construct an initial dilated convolution block and a multiscale dilated convolution block in the encoder, and the encoder focuses on extracting the relevant feature information, thus eliminating the noise interference and improving the signal-to-noise ratio (SNR). In addition, the attention mechanism is improved and introduced between the encoder and decoder to emphasize the key features of the micro-seismic signal, thus removing the complex noise and further improving the denoising performance. The MSDCAN denoising model is trained and evaluated using micro-seismic data from Stanford University. Experimental results demonstrate an impressive increase in SNR by 11.237 dB and a reduction in root mean square error (RMSE) by 0.802. Compared to the denoising results of the DeepDenoiser, CNN-denoiser and Neighbor2Neighbor methods, the MSDCAN denoising model outperforms them by enhancing the SNR by 2.589 dB, 1.584 dB and 2dB, respectively, and reducing the RMSE by 0.219, 0.050 and 0.188, respectively. The MSDCAN denoising model presented in this study effectively improves the SNR of micro-seismic signals, offering fresh insights into micro-seismic signal denoising methodologies.

Research on U-Net seismic signal denoising combined with residual dense blocks

Aiming to address the limited noise reduction capabilities of conventional methods for reducing noise in seismic signals, the paper proposes a noise reduction model based on the RDBU-Net network. This model utilizes a residual dense block (RDB) instead of conventional convolutional layers in the U-Net network to enhance the feature extraction capacity for same-band noise, thereby elevating the signal-to-noise ratio (SNR) of seismic signals. The RDBU-Net model is trained, validated, and tested using the global seismic dataset from Stanford University. In comparison with the wavelet threshold method, the denoising RDB model, and the U-Net model, the RDBU-Net model demonstrates an improvement in SNRs by 7.82 dB, 6.13 dB, and 2.9 dB, respectively. Additionally, the root mean square errors are reduced by 0.4812, 0.3736, and 0.1938, and the correlation coefficients are enhanced by 0.3818, 0.2714, and 0.1205. The RDBU-Net model proposed in this study effectively enhances the SNR of seismic signals and offers fresh insights into eliminating noise within the same frequency band of seismic signals.

Eutectogels as a Semisolid Electrolyte for Organic Electrochemical Transistors.

Organic electrochemical transistors (OECTs) are signal transducers offering high amplification, which makes them particularly advantageous for detecting weak biological signals. While OECTs typically operate with aqueous electrolytes, those employing solid-like gels as the dielectric layer can be excellent candidates for constructing wearable electrophysiology probes. Despite their potential, the impact of the gel electrolyte type and composition on the operation of the OECT and the associated device design considerations for optimal performance with a chosen electrolyte have remained ambiguous. In this work, we investigate the influence of three types of gel electrolytes-hydrogels, eutectogels, and iongels, each with varying compositions on the performance of OECTs. Our findings highlight the superiority of the eutectogel electrolyte, which comprises poly(glycerol 1,3-diglycerolate diacrylate) as the polymer matrix and choline chloride in combination with 1,3-propanediol deep eutectic solvent as the ionic component. This eutectogel electrolyte outperforms hydrogel and iongel counterparts of equivalent dimensions, yielding the most favorable transient and steady-state performance for both p-type depletion and p-type/n-type enhancement mode transistors gated with silver/silver chloride (Ag/AgCl). Furthermore, the eutectogel-integrated enhancement mode OECTs exhibit exceptional operational stability, reflected in the absence of signal-to-noise ratio (SNR) variation in the simulated electrocardiogram (ECG) recordings conducted continuously over a period of 5 h, as well as daily measurements spanning 30 days. Eutectogel-based OECTs also exhibit higher ECG signal amplitudes and SNR than their counterparts, utilizing the commercially available hydrogel, which is the most common electrolyte for cutaneous electrodes. These findings underscore the potential of eutectogels as a semisolid electrolyte for OECTs, particularly in applications demanding robust and prolonged physiological signal monitoring.