Signal-to-noise Research Articles

CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR

Radio spectrum resources are very limited and have become increasingly tight in recent years, and the exponential growth of various frequency-using devices has led to an increasingly complex and changeable electromagnetic environment. Wireless channel complexity and uncertainty have increased dramatically, and automated modulation recognition (AMR) performs poorly at low signal-to-noise ratios. It is proposed to use convolutional bidirectional long short-term memory deep neural networks (CNN-BiLSTM-DNNs) as a deep learning framework to extract features from single and mixed in-phase/orthogonal (I/Q) symbols in modulated data. The framework combines the capabilities of one- and two-dimensional convolution, a bidirectional long short-term memory network, and a deep neural network more efficiently, extracting characteristics from the perspective of time and space to enhance the accuracy of automatic modulation recognition. Modulation recognition experiments on the benchmark datasets RML2016.10b and RML2016.10a show that the average recognition accuracies of the proposed model from −20 dB to 18 dB are 64.76% and 62.73%, respectively, and the improvement ranges of modulation recognition accuracy are 0.29−5.56% and 0.32−4.23% when the signal-to-noise ratio (SNR) is −10 dB to 4 dB, respectively. The CNN-BiLSTM-DNN model outperforms classical models such as MCLDNN, MCNet, CGDNet, ResNet, and IC-AMCNet in terms of modulation type recognition accuracy.

Design and Dynamic In Vivo Validation of a Multi-Channel Stretchable Liquid Metal Coil Array.

Recent developments in the field of radiofrequency (RF) coils for magnetic resonance imaging (MRI) offer flexible and patient-friendly solutions. Previously, we demonstrated a proof-of-concept single-element stretchable coil design based on liquid metal and a self-tuning smart geometry. In this work, we numerically analyze and experimentally study a multi-channel stretchable coil array and demonstrate its application in dynamic knee imaging. We also compare our flexible coil array to a commonly used commercial rigid coil array. Our numerical analysis shows that the proposed coil array maintains its resonance frequency (<1% variation) and sensitivity (<6%) at various stretching configurations from 0% to 30%. We experimentally demonstrate that the signal-to-noise ratio (SNR) of the acquired MRI images is improved by up to four times with the stretchable coil array due to its conformal and therefore tight-fitting nature. This stretchable array allows for dynamic knee imaging at different flexion angles, infeasible with traditional, rigid coil arrays. These findings are significant as they address the limitations of current rigid coil technology, offering a solution that enhances patient comfort and image quality, particularly in applications requiring dynamic imaging.

GRU Based MCS Selection in Tactical Vehicle Communication

In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

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.

A clinically-recommended MR whole lung imaging protocol using free-breathing 3D isotropic zero echo time sequence

Rationale and objectivesThis study aimed to assess the feasibility and image quality of free-breathing 3D isotropic zero echo time (ZTE) whole-lung imaging and explore a clinically appropriate protocol for MR lung imaging. Materials and methodsThe study was approved by the local ethics committee. A total of thirty healthy volunteers were enrolled in this study from October 2022 to May 2023. Free-breathing pulmonary 3D isotropic ZTE scans were implemented with various acquisition planes and the number of excitations (NEX). ZTE images were evaluated by two radiologists for the overall Image quality and visibility of intrapulmonary structures as well as the signal-to-noise ratio (SNR) of the lung parenchyma. ZTE images with different acquisition parameters were compared. For preliminary clinical visual assessment, three patients with interstitial lung disease underwent both ZTE imaging and computed tomography (CT). ResultsThe overall image quality of the lung in healthy subjects was good to excellent. The visibilities of pulmonary arteries and bronchus were up to the 7th and 5th generation, respectively. The display of lung fissures was poor. The overall image quality, the visibility of the pulmonary artery, and lung fissures in the axial acquisition were better than in the coronal acquisition (P = 0.011, 0.008, 0.010, respectively) but not statistically different from those in the sagittal acquisition (all P > 0.05). ConclusionThe free-breathing pulmonary ZTE is feasible and may serve as an alternative method in chest imaging. Either axial or sagittal ZTE image acquisition would be preferred in clinical practice.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio (SNR) of noisy networks. The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce. Supplementary data are available at Bioinformatics online.

Improving signal-to-noise ratios of ambient noise cross-correlation functions using local attributes

Summary For seismographic stations with short acquisition duration, the signal-to-noise ratios (SNRs) of ambient noise cross-correlation functions (CCFs) are typically low, preventing us from accurately measuring surface wave dispersion curves or waveform characteristics. In addition, with noisy CCFs, it is difficult to extract relatively weak signals such as body waves. In this study, we propose to use local attributes to improve the SNRs of ambient noise CCFs, which allows us to enhance the quality of CCFs for stations with limited acquisition duration. Two local attributes: local cross-correlation and local similarity, are used in this study. The local cross-correlation allows us to extend the dimensionality of daily CCFs with computational costs similar to global cross-correlation. Taking advantage of this extended dimensionality, the local similarity is then used to measure non-stationary similarity between the extended daily CCFs with a reference trace, which enables us to design better stacking weights to enhance coherent features and attenuate incoherent background noises. Ambient noise recorded by several broadband stations from the USArray in North Texas and Oklahoma, the Superior Province Rifting EarthScope Experiment in Minnesota and Wisconsin and a high-frequency nodal array deployed in the northern Los Angeles basin are used to demonstrate the performance of the proposed approach for improving the SNR of CCFs.

Electromagnetic informed data model considerations for near-field DOA and range estimates

Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

Deep Learning–Based Denoising Improves Receiver Function Imaging Using Dense Short-Period Teleseismic Data

Abstract Receiver function (RF) imaging using seismic data from dense short-period arrays has gained increasing importance in recent years in investigating fine-scale structures of the crust and uppermost mantle. A crucial step in such studies is to remove the instrument response (IR) to enhance teleseismic signals at ∼0.01 to 5 Hz, thereby simulating broadband records. However, this procedure also amplifies noise within the same frequency band. For weak signals, distinguishing them from noise is often challenging and in some cases is even impossible with traditional denoising methods such as filtering. To address this challenge, we develop a new convolutional neural network model, NodalWaden, using decades of high-quality global broadband teleseismic body waves for training. The broadband data exhibit the characteristics we target to achieve by removing the IR from the short-period records. The applicability of NodalWaden is justified by denoising the three-component short-period records of more than 18 months from 155 nodes deployed in northern Sumatra. We find that NodalWaden substantially improves the signal-to-noise ratio (SNR), upgrading ∼50% of the teleseismic data from the “very-low-SNR” (∼1) to “very-high-SNR” (&amp;gt;10) categories. RFs calculated from the denoised dataset show better separation of merged phases and noticeable enhancement of weak signals, resulting in improvement in the quality of structure imaging. In particular, a positive phase is consistently detected at ~2 s throughout the dataset and interpreted as the Conrad discontinuity, which is unresolvable in the original RFs. This denoising technique would be particularly useful for short-duration (e.g., one month) deployment with limited teleseismic data, both from the past and in the future.

Deep Learning-Based Hybrid Precoding Approach in the Massive Multiple-Input Multiple-Output System

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

Deep Learning-Based Hybrid Precoding Approach in the Massive Multiple-Input Multiple-Output System

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders.

Enhancing vascular network visualization in 3D photoacoustic imaging: in vivo experiments with a vasculature filter

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.

Mixed event separation and identification based on a convolutional neural network trained with the domain transfer method for a φ-OTDR sensing system

In phase-sensitive optical time domain reflectometer (φ-OTDR) based distributed acoustic sensing (DAS), correct identification of event types is challenging in complex environments where multiple events happen simultaneously. In this study, we have proposed a convolutional neural network (CNN) with a separation module and an identification module to simultaneously separate a mixed event into individual single-event components and identify each type of component contained in the mixed event. The domain transfer method is used in the training, fine-tuning, and testing of the proposed CNN, which saves 94% of the workload for massive DAS data collection and signal demodulation. A fine-tuning stage is added to minimize the impact of the dataset shift between the audio data and DAS data, hence enhancing the separation and identification performance. The model has good noise tolerance and achieves nearly 90% identification accuracy even at a relatively low signal-to-noise ratio (SNR). Compared with the conventional method using DAS data for training, domain transfer using a large amount of diverse audio data for training well generalizes the model to the target domain and hence provides more stable performance with only little degradation of identification accuracy.

Approaching Maximum Resolution in Structured Illumination Microscopy via Accurate Noise Modeling.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

Deep learning model for fiber optic distributed acoustic sensing seismic data compression based on autoencoder and recurrent neural networks

Because of the extremely high sampling rate of fiber optic distributed acoustic sensing (DAS) equipment, the amount of DAS seismic data collected is enormous, which poses great challenges to the transmission and storage of DAS seismic data. Therefore, it is essential to study the compression methods of DAS seismic data. Existing data compression methods such as wavelet transform, cosine transform, and convolutional autoencoder (CAE) still have room for improvement in the compression performance and compression ratio (CR). Thus, we have proposed what we believe to be is a novel deep learning model called the recurrent autoencoder (RAE) for high-performance compression of DAS seismic data. Under different CRs, we have designed performance evaluation experiments for RAE models based on different RNN modules with different loss functions. When the CR of the RAE model is 8, the signal-to-noise ratio (SNR) of the reconstructed DAS seismic data reaches 40.60 dB, which is better than that of the CAE’s 11.58 dB. The ultimate CR was increased to 512 without reducing the compression quality, which is 4.12 times higher than the CAE model. The LSTM with a weighted loss function improves the SNR to 43.66 dB at a CR of 8, which is 3.06 dB higher than the LSTM with additive loss function. The results show that the RAE model proposed with a weighted loss function in this paper has excellent DAS seismic data compression performance and provides a high CR, which can be widely applied in large-scale DAS seismic data compression.

Passively harmonic dissipative soliton generation in normal dispersion erbium-doped fiber laser using SMS fiber as artificial saturable absorber

Passively harmonic mode-locking has been experimentally demonstrated in an erbium-doped fiber laser with large normal dispersion using single-multi-single mode structure as artificial saturable absorber. By increasing the pump power under the same polarization setting, the mode-locking operation can switch from fundamental mode-locked to 5th order harmonic mode-locked. Highest repetition rate of 4.26 MHz (5th order harmonic) is observed, with pulse width and pulse energy ascertained at 290 fs and 3.0 nJ, respectively. Excellent signal-to-noise ratio (SNR) of above 50 dB is observed for all harmonic orders. The findings validated that SMS structure can be used to generate stable and switchable high order of harmonic mode-locked. The low-cost SMS fiber for harmonic mode-locked generation technique could lay the groundwork for future sustainable industrial growth.

Magnetic Resonance Imaging-Compatible Electromagnetic Actuator: Design and Tests

This paper presents the detailed design, construction and tests of a protype iron-free MRI-compatible electromagnetic actuator. The originality of this proposal lies in the use of the homogeneous static magnetic field B0, present in the MRI bore, to ensure the electromechanical energy conversion. The armature is composed of three rectangular coils in a three-phase arrangement, which makes the actuator very light-weight and compact. The operating principle is that of an AC synchronous motor with a rotating armature. In order to design the actuator, a 3D analytical electromagnetic model is developed to predict the magnetic field produced by the armature winding. Then, a 3D finite element (FE) computation is performed to validate the analytically calculated magnetic field. The developed analytical model is then inserted into an optimization routine based on Genetic Algorithms (GAs) to obtain the prototype dimensions to be realized. Finally, the prototype is constructed and tested inside an MRI research scanner. The results indicate that the reduction in the Signal-to-Noise Ratio (SNR) and the geometrical distortion are less than 5% when the actuator is powered with a current of 10 times the rated one and when it is located very close to the subject to be imaged.

Separating Common Signal From Proxy Noise in Tree Rings

AbstractTree rings are the most widely‐used proxy records for reconstructing Common Era temperatures. Tree‐ring records correlate strongly with temperature on an interannual basis, but studies have found discrepancies between tree rings and climate models on longer timescales, indicating that low‐frequency noise could be prevalent in these archives. Using a large network of temperature‐sensitive tree‐ring records, we partition timeseries variance into a common (i.e., “signal”) and non‐climatic (i.e., “noise”) component using a frequency‐resolved signal‐to‐noise ratio (SNR) analysis. We find that the availability of stored resources from prior years (i.e., biological “memory”) dampens the climate signal at high‐frequencies, and that independent noise reduces the SNR on long timescales. We also find that well‐replicated, millennial‐length records had the strongest common signal across centuries. Our work suggests that low‐frequency noise models are appropriate for use in pseudoproxy experiments, and speaks to the continued value of high‐quality data development as a top priority in dendroclimatology.