Signal-to-noise Ratio Data Research Articles

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.

A hybrid denoising method for low-field nuclear magnetic resonance data

Low-field nuclear magnetic resonance (NMR) has broad application prospects in the exploration and development of unconventional oil and gas reservoirs. However, NMR instruments tend to acquire echo signals with relatively low signal-to-noise ratio (SNR), resulting in poor accuracy of T2 spectrum inversion. It is crucial to preprocess the low SNR data with denoising methods before inversion. In this paper, a hybrid NMR data denoising method combining empirical mode decomposition-singular value decomposition (EMD-SVD) was proposed. Firstly, the echo data were decomposed with the EMD method to low- and high-frequency intrinsic mode function (IMF) components as well as a residual. Next, the SVD method was employed for the high-frequency IMF components denoising. Finally, the low-frequency IMF components, the denoised high-frequency IMF components, and the residual are summed to form the denoised signal. To validate the effectiveness and feasibility of the EMD-SVD method, numerical simulations, experimental data, and NMR log data processing were conducted. The results indicate that the inverted NMR spectra with the EMD-SVD denoising method exhibit higher quality compared to the EMD method and the SVD method.

Underwater Coherent Source Direction-of-Arrival Estimation Method Based on PGR-SubspaceNet

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet.

Deep learning-based image segmentation for instantaneous flame front extraction

This paper delves into the methodology employed in examining lean premixed turbulent flame fronts extracted from Planar Laser Induced Fluorescence (PLIF) images at elevated pressures. In such flow regimes, the PLIF signal suffers from significant collisional quenching, typically resulting in image data with low signal-to-noise ratio (SNR). This poses severe difficulties for conventional flame front extraction algorithms based on intensity gradients and requires intense user intervention to yield acceptable results. In this work, we propose Convolutional Neural Network (CNN)-based Deep Learning (DL) models as an alternative to problem specific conventional methods. The pretrained DL models were fine-tuned, employing data augmentation, on a small annotated dataset including a variety of conditions between SNR ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 1.6 to 2.6 and subsequently evaluated. All DL models significantly outperformed the best-scoring conventional implementation both quantitatively and visually, while having similar inference times. IoU-scores and Recall values were found to be up to a factor ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 1.2 and ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 2.5 higher, respectively, with ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 1.15 times improved Precision. Small-scale structures were captured much better with fewer erroneous predictions, becoming particularly pronounced for the lower SNR data investigated. Moreover, by applying artificially modeled noise, it was shown that the range of image conditions in terms of SNR that can be reliably processed extends well beyond the images included in the training data, and satisfactory segmentation performances were found for SNR as low as ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 1.1. The presented DL-based flame front detection algorithm marks a methodology with significantly increased detection performance, while a similar computational effort for inference is achieved and the need for user-based parameter tuning is eliminated. It enables a very accurate extraction of instantaneous flame fronts in large image datasets where supervised processing is infeasible, unlocking unprecedented possibilities for the study of flame dynamics and instability mechanisms at industry-relevant conditions.

Improved tide level prediction model combined GA-BP neural networks and GNSS SNR data

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction.

Can the phase of SNR oscillations in GNSS-IR be used to estimate sea-level height?

In existing global navigation satellite system-interference reflectometry (GNSS-IR) research, only the frequency of signal-to-noise ratio (SNR) oscillations has been used to estimate sea-level height. However, the characteristic parameters of SNR oscillations are not isolated from each other, and a single feature cannot accurately and comprehensively capture the environmental changes of reflecting surface. Our simulation results show that for the nonlinear least squares (NLS), when there is a certain difference between the fitting frequency and the actual frequency of SNR oscillations, the deviation of the phase solution obtained is approximately linear with the frequency difference. Consequently, a linear phase correction GNSS-IR sea-level estimation method is constructed in this study. This method integrates the Lomb–Scargle periodogram (LSP) and NLS to process SNR oscillations, using the phase obtained from NLS to correct the retrieval error of LSP. Through processing SNR data from four sites for nearly half a year, we verified the stability of the relationship between phase and frequency-based retrieval error at different sites in continuous monitoring, and established the relationship model between the two. Then, utilizing the relationship model acquired at different sites, we estimated the sea-level variations for the next 6 months at each site through joint frequency and phase versus reflector height relationships. Experimental results show that the phases acquired from NLS can effectively correct the retrieval error of LSP. Compared with the traditional method using only frequency, the root mean square error and mean absolute error of the retrieval results obtained from the linear phase correction GNSS-IR sea-level estimation method based on LSP-NLS are both reduced by about 60%. This multi-feature fusion technique introduces a new perspective and technical approach for GNSS-IR sea-level estimations.

GNSS-R snow depth retrieval algorithm based on PSO-LSTM

The global navigation satellite system (GNSS)-interferometric reflectometry technique has been applied to retrieve snow depth, which has a high potential for application. The GNSS reflectometry classical algorithm retrieves the snow depth by extracting the frequency of the multipath signal and substituting it into an empirical formula. However, the retrieval errors of high and low snow depths are large due to the influence of factors such as surface vegetation and terrain environment. In this paper, we propose a snow depth retrieval algorithm based on a particle swarm optimized long short-term memory (PSO-LSTM) neural network. The algorithm extracted three characteristic parameters (frequency, amplitude, and phase) from the signal-to-noise ratio (SNR) data as inputs, and optimized the LSTM hyperparameters by the PSO algorithm to improve the retrieval accuracy for low snow depths and snow depths close to the antenna. The snow depth retrieval results of global positioning system L1 band SNR data collected from the P351 station in 2022 and AB33 station in 2017 were evaluated in this paper. The snow depth retrieval results of the PSO-LSTM algorithm for P351 station were in high agreement with the snow depth data provided by the snowpack telemetry network; the coefficient of determination ( R2 ) reached 0.986, and the root mean square error (RMSE) and mean absolute error (MAE) were 7.30 cm and 4.94 cm, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 53.0% and 30.4% for the retrieval results of snow depths below 15 cm at the P351 station, and by 76.8% and 84.4% for the retrieval results of snow depths above 117 cm from the 1st day to the 137th day, respectively. Similarly, the RMSE, MAE, and R2 for the 2017 retrieval results at AB33 station were 5.90 cm, 4.25 cm, and 0.965, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 47.9% and 33.0% for the retrieval results of snow depths below 15 cm at the AB33 station, and by 75.4% and 82.3% for the retrieval results of snow depths above 56 cm from the day 46 to day 120. In addition, the snow depth retrieval algorithm was proposed in this paper does not require antenna height and empirical formulas to realize snow depth retrieval, and at the same time, the algorithm effectively improved the retrieval accuracy for both high and low snow depths with strong robustness.

Performance evaluation of different reflected signal extraction methods on GNSS-R derived sea level heights

In order to obtain reliable GNSS-R derived sea level heights (SLHs), it is crucial to extract accurate reflected signals from signal-to-noise ratio (SNR) data. In this study, Quadratic Fitting, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Singular Spectrum Analysis (SSA) are adopted for extracting the reflected signals from SNR data, which are then analyzed with Lomb Scargle Periodogram (LSP) assisted with tidal harmonic analysis for GNSS-R SLHs. Three continuous GNSS stations including Onsala in Sweden, Friday Harbor in the United States, and Brest in France with tidal ranges of approximately 1 m, 3 m, and 7 m, respectively, were used in the study. The derived SLHs were evaluated against the nearby or co-located tide gauge records. The results reveal that the three proposed algorithms effectively cope with the multi-peaks spectrum problems when using the conventional Quadratic Fitting without a priori reflected height constraint, and SSA can provide the solutions with fewer outliers among the proposed methods with the RMSEs of 6.7 cm, 13.4 cm and 39.6 cm at Onsala, Friday Harbor, and Brest stations, respectively. Without implementing tidal harmonic analysis, SSA is the method capable of acquiring reliable SLHs among the stations, as evidenced by comparing with co-located gauge records, particularly at the Brest station. In summary, SSA not only distinguishes signals across different frequencies, but also orders the signal components according to their eigenvalues, demonstrating the potential to extract the reflected signals from SNR and enhance the stability and accuracy of GNSS-R applications.

Multi-constellation GNSS interferometric reflectometry for tidal analysis: mitigations for K1 and K2 biases due to GPS geometrical errors

It has been observed that when using sea levels derived from GPS (Global Positioning System) signal-to-noise ratio (SNR) data to perform tidal analysis, the luni-solar semidiurnal (K2) and the luni-solar diurnal (K1) constituents are biased due to geometrical errors in the reflection data, which result from their periods coinciding with the GPS orbital period and revisit period. In this work, we use 18 months of GNSS SNR data from multiple frequencies and multiple constellations at three sites to further investigate the biases and how to mitigate them. We first estimate sea levels using SNR data from the GPS, GLONASS, and Galileo signals, both individually and by combination. Secondly, we conduct tidal harmonic analysis using these sea-level estimates. By comparing the eight major tidal constituents estimated from SNR data with those estimated from the co-located tide-gauge records, we find that the biases in the K1 and K2 amplitudes from GPS S1C, S2X and S5X SNR data can reach 5 cm, and they can be mitigated by supplementing GLONASS- and Galileo-based sea-level estimates. With a proper combination of sea-level estimates from GPS, GLONASS, and Galileo, SNR-based tidal constituents can reach agreement at the millimeter level with those from tide gauges.

Optimal Wavelet Selection for Signal Denoising.

Wavelet denoising plays a key role in removing noise from signals and is widely used in many applications. In denoising, selection of the mother wavelet is desirable for maximizing the separation of noise and signal coefficients in the wavelet domain for effective noise thresholding. At present, wavelet selection is carried out in a heuristic manner or using a trial-and-error that is time consuming and prone to error, including human bias. This paper introduces a universal method to select optimal wavelets based on the sparsity of Detail components in the wavelet domain, an empirical approach. A mean of sparsity change ( ) parameter is defined that captures the mean variation of noisy Detail components. The efficacy of the presented method is tested on simulated and experimental signals from Electron Spin Resonance spectroscopy at various SNRs. The results reveal that the values of signal vary abruptly between wavelets, whereas for noise it displays similar values for all wavelets. For low Signal-to-Noise Ratio (SNR) data, the change in between highest and second highest value is ≈ 8 - 10% and for high SNR data it is around 5%. The mean of sparsity change increases with the SNR of the signal, which implies that multiple wavelets can be used for denoising a signal, whereas, the signal with low SNR can only be efficiently denoised with a few wavelets. Either a single wavelet or a collection of optimal wavelets (i.e., top five wavelets) should be selected from the highest values. The code is available on GitHub and the signalsciencelab.com website.

A regression approach for seismic first-break picking

The picking efficiency of seismic first breaks (FBs) has been greatly accelerated by deep learning (DL) technology. However, the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio (SNR) situations. To address this issue, we propose a regression approach to pick FBs based on bidirectional long short-term memory (BiLSTM) neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region. We employ a regressive model that represents the relationships among the elevation of shots, offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs, from common-shot gathers with sparsely distributed traces. Different from image segmentation methods which automatically extract image features and classify FBs from seismic data, the proposed method can learn the inner relationship between field geometry and FBs. In addition, the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution. The picking results of synthetic data shows that the proposed method has low dependence on label data, and can obtain reliable and similar predicted results using two types of label data with large differences. The picking results of 9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data. The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method. The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS.

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA=1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. With the SAE model, the SNR of low NSA data (NSA=1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

Evaluation of GNSS reflectometry method for sea level estimation in Indonesia

Over the last decade, GNSS-R (Global Navigation Satellite System reflectometry) has emerged as a technique for observing sea level height using data from GNSS observations. GNSS-R estimates sea level height from the phase center antenna using the reflection of the extracted GNSS signal at sea level. With a large number of tidal stations equipped with GNSS antennas, GNSS-R has the potential to be implemented in Indonesia. GNSS-R observations can also cover sea surface areas tens to hundreds of kilometres away from where the antenna is located over the coast. Furthermore, the installation of a GNSS antenna at a safe height on land allows for the observation of sea levels under extreme conditions such as during storms and cyclones. To employ GNSS-R effectively, several factors must be considered, including signal-to-noise ratio (SNR) data analysis, data processing, filtering control variables, and increased frequency extraction. This study will focus on trying to use GNSS-R method by identifying the best control variables for each study area and evaluating the results. This study used three months of GPS and GLONASS satellite SNR data from two stations, Barus and Morotai. The separated multipath data was then analyzed using the Lomb-Scargle Periodogram (LSP) based on its frequency. The results of GNSS-R sea surface height observations were validated using tide gauge data at both stations. Based on the test results, the RMSE values were 8.7 cm and 8.4 cm at Barus and Morotai stations, respectively. GNSS-R results strongly correlate with tide gauge data, with correlation coefficients of 95% and 98% at Barus and Morotai stations, respectively. Based on these findings, the GNSS-R method can be used to complement tidal sensor data by applying proper quality control.

Feasibility and performance evaluation of low-cost GNSS devices for sea level measurement based on GNSS-IR

The low-cost GNSS devices are more convenient, flexible, and can be used as the low-cost Global Navigation Satellite System interferometric reflectometry (GNSS-IR) sensors. In this study, the feasibility and performance of low-cost GNSS devices for the temporary sea level measurement are assessed, by jointly utilizing signal-to-noise ratio (SNR) data from an Android smartphone Redmi Note 9 Pro and a low-cost antenna BT560 connected to a CHCNAV P5 GNSS receiver, while another identical receiver equipped with a standard geodetic-quality antenna and a pressure tide gauge (TG) were placed nearby for comparison. 80-hours of SNR data from multiple GNSS constellations were collected and processed. First, the SNR data from the three devices are compared and analyzed. In most cases, the SNR data of low-cost antennas have longer and clearer oscillations. But they are maybe more vulnerable due to the manufacturing of antennas or positioning modules. Second, quality control and height rate correction are crucial to stable sea level measurement of low-cost GNSS devices. Finally, the performance of sea level measurement of multiple GNSS constellations, dual-frequency, and dual-antenna was also assessed. It is found that the low-cost GNSS devices could yield stable sea level measurements, and have a root-mean-square error (RMSE) of about 16 cm, of which the performance is equivalent to or even slightly better than standard geodetic-quality devices, making it a promising solution for the temporary TGs.

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When rm=0.600 and rn=0.700, the correlation coefficient (r) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology.

SNR Enhancement for BOTDR With Spatial-Adaptive Image Denoising Method

A spatial-adaptive image denoising (SAID) method is proposed to enhance the signal-to-noise ratio (SNR) of Brillouin optical time domain reflectometer (BOTDR) without deterioration of spatial resolution. Through analyzing the principle of denoising, the spatial-adaptive denoising network structure is constructed, which can effectively reduce noise and avoid over-smoothing. Moreover, a high-similarity dataset generation method is designed to further enhance the denoising effect. The SAID method is experimentally implemented on two-dimension Brillouin gain spectrum matrix over a 25.1 km-long optical fiber with the spatial resolution of 2 m. The experimental results show that the SNR enhancement of SAID method is 21.92 dB, and the data processing time is only 2.48 s. Whereas the SNR enhancement of conventional 10000 times trace averaging method is merely 9.98 dB, but the data processing time is increased to 174.52 s. Therefore, it is demonstrated that the SAID method has obvious advantages in SNR enhancement and data processing speed without deteriorating the spatial resolution of BOTDR.

Predicting the performance of radio over free space optics system using machine learning techniques

The efficiency and reliability of radio over free space optical communication may be significantly degraded by different weather conditions, thus leading to decrease and increase of signal to noise ratio (SNR) and bit error rate (BER) of the recovered signal respectively. Therefore, real time optical performance estimation is of utmost importance to enable the awareness of channel conditions and for achieving high quality of service. In this work, a terrestrial radio over free space optics (RoFSO) system has been investigated by employing 32-QAM and 64-QAM OFDM modulation schemes under rain, haze and clear weather conditions. Performance of the system is further estimated by employing artificial neural networks (ANN), k-nearest neighbours (KNN) and decision tree (DT) machine learning (ML) techniques with root mean square error (RMSE) and coefficient of determination (R2) as performance metrices. Atmospheric attenuation and different internal system parameters act as input features with BER and SNR of the received signal as the modelling targets. The best performing model has been found to be by applying an ANN approach with lower RMSE values for SNR and BER as 1.87 and 1.26 respectively. The value of R2 predicted by the model are 0.97139 and 0.97802 for SNR and BER data sets respectively.

A study on the quality of GNSS signals for extracting the sea level height and tidal frequencies utilizing the GNSS-IR approach

Coastal global navigation satellite system (GNSS) stations equipped with a standard geodetic receiver and antenna enable water level measurement using the GNSS interferometry reflectometry (GNSS-IR) technique. By using GNSS-IR, the vertical distance between the antenna and the reflector surface (e.g., water surface) can be obtained in the vertical (height) reference frame. In this study, the signal-to-noise ratio (SNR) data from four selected stations over three months are used for this purpose. We determined the predominant multipath frequency in SNR data that is obtained using Lomb–Scargle periodogram (LSP) method. The obtained sea surface heights (SSH) are assessed using tide gauge observations regarding accuracy and correlation coefficients. In this study, we investigated daily and hourly GNSS observations and used single frequencies of GPS (L1, L2 and L5), GLONASS (L1 and L2), Galileo (L1, L5, L6, L7 and L8), and BeiDou (L2 and L7) signals to estimate the SSH. The results show that the optimal signals for extracting the SSH are the L1 signal for the GPS, Galileo, and GLONASS systems and the L2 signal for the BeiDou system. The accuracy and correlation parameters for the optimal GPS signal in the daily mode are 2 cm and 0.87, respectively. The same parameters for the optimal GLONASS signal are 4 cm and 0.91. However, the obtained accuracy and correlation coefficients using the best Galileo and BeiDou signals are reduced, i.e., 4 cm and 0.88 using Galileo and 12 cm and 0.52 by employing the Galileo signals, respectively. Our results also show that the GPS L1 signal is more consistent with the tide gauge data. In the following, using the time series derived from the L1 signal and tide gauge readings, the tidal frequencies are extracted and compared using the Least Square Harmonic Estimation (LS-HE) approach. The findings demonstrate that 145 significant tidal frequencies can be extracted using the GNSS-IR time series. The existence of an acceptable correlation between the tidal frequencies of the GNSS-IR and the tide gauge time series indicates the usefulness of the GNSS-IR time series for tide studies. From our results, we can conclude that the GNSS-IR technique can be applied in coastal locations alongside tide gauge measurements for a variety of purposes.

Mapping the impact of nonlinear gradient fields with noise on diffusion MRI

In diffusion MRI, gradient nonlinearities cause spatial variations in the magnitude and direction of diffusion gradients. Studies have shown artifacts from these distortions can results in biased diffusion tensor information and tractography. Here, we investigate the impact of gradient nonlinearity correction in the presence of noise. We introduced empirically derived gradient nonlinear fields at different signal-to-noise ratio (SNR) levels in two experiments: tensor field simulation and simulation of the brain. For each experiment, this work compares two techniques empirically: voxel-wise gradient table correction and approximate correction by scaling the signal directly. The impact was assessed through diffusion metrics including mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and principal eigen vector (V1). The study shows (1) the correction of gradient nonlinearities will not lead to substantively incorrect estimation of diffusion metrics in a linear system, (2) gradient nonlinearity correction does not interact adversely with noise, (3) nonlinearity correction suppresses the impact of nonlinearities in typical SNR data, (4) for SNR below 30, the performance of both the gradient nonlinearity correction techniques were similar, and (5) larger impacts are seen in regions where the gradient nonlinearities are distinct. Thus, this study suggests that there were greater beneficial effects than adverse effects due to the correction of nonlinearities. Additionally, correction of nonlinearities is recommended when region of interests are in areas with pronounced nonlinearities.

Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR

Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years. Most existing methods focus on the data obtained by a single detector. However, the signal-to-noise ratio (SNR) of gravitational wave signals in a single detector is pretty low, making it hard for deep neural networks to learn effective features. Therefore, how to use the observation signals obtained by multiple detectors in deep learning methods is a serious issue. We simulate binary neutron star signals from multiple detectors, including the Advanced LIGO and Virgo detectors. We calculate coherent SNR of multiple detectors using a fully coherent all-sky search method and obtain the coherent SNR data required for our proposed deep learning method. Inspired by the principle of attention network Squeeze-and-Excitation Networks (SENet) and the soft thresholding shrinkage function, we propose a novel Squeeze-and-Excitation Shrinkage (SES) module to better extract effective features. Then we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network (GW-SESNet) detection model. We train and validate the performance of our model on the coherent SNR data set. Our model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.