Signal-to-noise Research Articles

Performance evaluation of compansion-based clipped OFDM systems

The peak-to-average power ratio (PAPR), commonly used to describe the amplitude variations of an OFDM (orthogonal frequency-division multiplex) signal, does not accurately reflect its impact on the system performance. This paper applies the mutual information as a metric to assess the effects of nonlinear PAPR reduction schemes on the performance of OFDM systems. Evaluation of the achieved mutual information shows that a significant capacity loss from clipping occurs only at high SNR (signal-to-noise ratio) and a simple compression/expansion technique is proposed to achieve close to optimal performance in this regime. The effectiveness of this method is validated through WER (word error rate) simulations with several modulation and coding schemes.

Deep Learning-based Enhancement of Fluorescence Labeling for Accurate Cell Lineage Tracing During Embryogenesis.

Automated cell lineage tracing throughout embryogenesis plays a key role in the study of regulatory control of cell fate differentiation, morphogenesis and organogenesis in the development of animals, including nematode Caenorhabditis elegans. However, automated cell lineage tracing suffers from an exponential increase in errors at late embryo because of the dense distribution of cells, relatively low signal-to-noise ratio (SNR) and imbalanced intensity profiles of fluorescence images, which demands a huge amount of human effort to manually correct the errors. The existing image enhancement methods are not sensitive enough to deal with the challenges posed by the crowdedness and low signal-to-noise ratio. An alternative method is urgently needed to assist the existing detection methods in improving their detection and tracing accuracy, thereby reducing the huge burden for manual curation. We developed a new method, termed as DELICATE, that dramatically improves the accuracy of automated cell lineage tracing especially during the stage post 350 cells of C. elegans embryo. DELICATE works by increasing the local SNR and improving the evenness of nuclei fluorescence intensity across cells especially in the late embryos. The method both dramatically reduces the segmentation errors by StarryNite and the time required for manually correcting tracing errors up to 550-cell stage, allowing the generation of accurate cell lineage at large-scale with a user-friendly software/interface. All images and data are available at https://doi.org/10.6084/m9.figshare.26778475.v1. The code and user-friendly software are available at https://github.com/plcx/NucApp-develop. Supplementary data are available at Bioinformatics online.

Optimization of Interference Suppression Algorithm for Polarization Sensitive Conformal Array in Complex Electromagnetic Environment

With the development of modern electronic technology, the electromagnetic environment is becoming increasingly complex, which poses a severe challenge to the normal work of radar, communication and other systems. Polarization sensitive array can more effectively distinguish and suppress interference signals by using polarization information of electromagnetic waves, and improve the signal-to-noise ratio (SNR) and anti-interference ability of the system. Conformal array can be closely attached to the carrier surface, reducing space occupation and improving signal receiving efficiency and accuracy. However, at present, polarization-sensitive conformal arrays still face technical problems in interference suppression, especially in complex electromagnetic environment, how to optimize interference suppression algorithms to improve system performance has become the research focus. In this paper, an adaptive interference suppression algorithm combining spatial-polarization domain processing is proposed. The algorithm digitally processes the received signal, extracts spatial and polarization domain information, constructs the joint feature vector of the signal in spatial and polarization domain, and designs a joint filter in spatial and polarization domain. The minimum mean square error (MMSE) criterion and the least mean square (LMS) algorithm are used to update the weight vector of the filter in real time, enabling the filter to automatically adapt to changes in the electromagnetic environment and achieve optimal interference suppression. Simulation experiments show that the algorithm exhibits good stability and adaptability under different electromagnetic environments, effectively suppressing interference and extracting the desired signal. Compared with traditional interference suppression algorithms based solely on spatial domain, the joint spatial-polarization domain adaptive interference suppression algorithm significantly improves the interference suppression ratio (ISR) and signal-to-interference-plus-noise ratio (SINR) performance indicators, verifying that the introduction of polarization domain information can enhance the effect of interference suppression.

Heteronuclear Polarization Transfer under Steady-State Conditions: The INEPT-SSFP Experiment.

NMR finds a wide range of applications, ranging from fundamental chemistry to medical imaging. The technique, however, has an inherently low signal-to-noise ratio (SNR)─particularly when dealing with nuclei having low natural abundances and/or low γs. In these cases, sensitivity is often enhanced by methods that, similar to INEPT, transfer polarization from neighboring 1Hs via J-couplings. In 1958, Carr proposed an alternative approach to increase NMR sensitivity, which involves generating and continuously detecting a steady-state transverse magnetization, by applying a train of pulses on an ensemble of noninteracting spins. This study broadens Carr's steady-state free precession (SSFP) framework to encompass the possibility of adding onto it coherent polarization transfers, allowing one to combine the SNR-enhancing benefits of both INEPT and SSFP into a single experiment. Herein, the derivation of the ensuing INEPT-SSFP (ISSFP) sequences is reported. Their use in 13C NMR and MRI experiments leads to ca. 300% improvements in SNR/ over conventional J-driven polarization transfer experiments, and sensitivity gains of over 50% over 13C SSFP performed in combination with 1H decoupling and NOE. These enhancements match well with numerical simulations and analytical evaluations. The conditions needed to optimize these new methods in both spectroscopic and imaging studies are discussed; we also examine their limitations, and the valuable vistas that, in both analytical and molecular imaging NMR, could be opened by this development.

Time-Delay Interferometry: The Key Technique in Data Pre-Processing Analysis of Space-Based Gravitational Waves

Space gravitational wave detection primarily focuses on the rich wave sources corresponding to the millihertz frequency band, which provide key information for studying the fundamental physics of cosmology and astrophysics. However, gravitational wave signals are extremely weak, and any noise during the detection process could potentially overwhelm the gravitational wave signals. Therefore, data pre-processing is necessary to suppress the main noise sources. Among the various noise sources, laser phase noise is dominant, approximately seven orders of magnitude larger in strength than typical gravitational wave signals, and requires suppression using time-delay interferometry (TDI) techniques, which involve combining raw data with time delays. This paper will be based on the basic principles of TDI to present methods for obtaining multi-type TDI combinations, including algebraic methods for solving indeterminate equations and geometric methods for symbolic search. Furthermore, the applicability of TDI under actual operating conditions will be considered, such as the arm locking in conjunction with the TDI algorithm. Finally, the sensitivity functions for different types of TDI combinations will be provided, which can be used to evaluate the signal-to-noise ratio (SNRs) of different TDI combinations.

Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.

Microwave Coincidence Imaging with Phase-Coded Stochastic Radiation Field

Microwave coincidence imaging (MCI) represents a novel forward-looking radar imaging method with high-resolution capabilities. Most MCI methods rely on random frequency modulation to generate stochastic radiation fields, which introduces the complexity of radar systems and imposes limitations on imaging quality under noisy conditions. In this paper, microwave coincidence imaging with phase-coded stochastic radiation fields is proposed, which generates spatio-temporally uncorrelated stochastic radiation fields with phase coding. Firstly, the radiation field characteristics are analyzed, and the coding sequences are designed. Then, pulse compression is applied to achieve a one-dimensional range image. Furthermore, an azimuthal imaging model is built, and a reference matrix is derived from the frequency domain. Finally, sparse Bayesian learning (SBL) and alternating direction method of multipliers (ADMM)-based total variation are implemented to reconstruct targets. The methods have better imaging performance at low signal-to-noise ratios (SNRs), as shown by the imaging results and mean square error (MSE) curves. In addition, compared with the SBL and ADMM-based total variation methods based on the direct frequency-domain solution, the proposed method’s computational complexity is reduced, giving it great potential in forward-looking high-resolution scenarios, such as autonomous obstacle avoidance with vehicle-mounted radar and terminal guidance.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Sliding window SA-CNN-based CFAR detector for extended target in shipborne HFSWR

ABSTRACT In shipborne high-frequency surface wave radar (HFSWR), the change of platform speed or heading will cause variations in the extension of the Doppler spectrum for vessel targets located in different directions, subsequently resulting in alterations to the signal-to-noise ratio (SNR). These alterations in vessel target echo present challenges for manual parameter adjustments in traditional constant false alarm rate (CFAR) methods for shipborne HFSWR, thereby hindering the maintenance of stable target detection capabilities. In this paper, a self-attention-convolutional neural network (SA-CNN)-based CFAR detector is proposed, which transforms the detection problem into signal structure classification. First, the extension characteristics of vessel target echoes resulting from changes in speed or heading of the shipborne platform are quantitatively analysed, thereby guiding the selection of an optimal sliding window and constructing input vectors for the neural network. Subsequently, the SA-CNN is designed to efficiently extract the structural features of the signal and accurately predict the probability of target presence. Finally, the Monte Carlo method is used to control the false alarm rate effectively. Simulation and real dataset verification demonstrate that the proposed method exhibits superior detection performance compared to traditional methods in shipborne HFSWR, especially for detecting extended targets.

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

Abstract To address issues related to unknown noise pollution and the inadequate performance of single adaptive noise reduction algorithms in measuring lower limb surface electromyography (sEMG) signals, this paper proposes an algorithm that combines the fast convergence speed and strong noise reduction capability of recursive least-squares (RLS) with the low computational complexity of the normalized least-mean-square (NLMS) and improved proportionate NLMS (IPNLMS). Through the proportional fusion of RLS with NLMS and RLS with IPNLMS, the proposed algorithm greatly improves convergence speed, stability, and noise reduction performance while effectively reducing computational complexity. Moreover, considering the influence of the initial value of weights on the noise reduction performance during the updating process of the adaptive noise reduction algorithm, a weight initial value setting (WIS) module is proposed to optimize the initial value of the weights by the known amount of data. Based on 50 independent experiments, an adaptive noise reduction algorithm and WIS module were used to reduce the unknown noise in the lower limb sEMG signals, which was generated by white Gaussian noise, power line interference, or hybrid noise interfered by an unknown environment. The noise reduction performance was evaluated by using the average value of the signal-to-noise ratio (SNR), the root-mean-square error, and R-square. Compared with the RLS, NLMS, and IPNLMS algorithms for noise reduction of vastus lateralis, rectus femoris, and biceps femoris signals containing unknown noise, the SNR of RLS-NLMS, RLS-IPNLMS, WIS-RLS-NLMS, and WIS-RLS-IPNLMS is improved by an average of [7.92%, 55.54%, 55.63%], [7.45%, 54.90%, 54.99%], [19.70%, 72.38%, 72.71%], [19.32%, 71.84%, 72.19%]. The simulation results verify that the proportional fusion adaptive noise reduction algorithm and the WIS module effectively accelerate the convergence speed, enhance the noise reduction capability, and reduce the computational complexity.

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

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

Multi-spectral radiation thermometry of space point targets based on spectral image pixel binning

The temperature characteristics of space point targets are essential indicators of their operational status and performance. To address the issue of significant temperature measurement errors in space point targets caused by low temperatures and a low imaging signal-to-noise ratio (SNR), we propose a mathematical model for multi-spectral radiation thermometry, derived from the principles of dual-band radiation thermometry. Furthermore, a multi-spectral image pixel binning method is introduced to enhance the SNR and minimize measurement errors. The experimental results indicate that the proposed multi-spectral radiation thermometry outperforms dual-band radiation thermometry. After merging 2 to 20 pixels, multi-spectral radiation thermometry in the 3.75–4.1 and 4.3–4.62 µm bands demonstrates an enhanced SNR and reduced temperature measurement errors. For a 378.15 K blackbody, the relative errors decrease from 1.52% and 2.19% to 0.26% and 0.74%, respectively, after merging six and eight pixels in the two different bands, compared to unmerged images. This method provides a valuable reference for developing techniques to enhance the SNR and improve temperature measurement accuracy for space point targets.

Reduction in Optical Feedback Noise of Atomic Force Microscopy by High-Frequency Current Modulation

Introduction: With the advancement of technology, nanotechnology has emerged as a prominent research field. The Atomic Force Microscope (AFM) serves as a vital tool in nanoscience and technology, playing an indispensable role across various domains. Method: However, in the AFM photoelectric sensing system, the optical feedback noise from the beam deflection method can lead to inaccuracies in signal identification and analysis, impacting the accuracy and reliability of AFM measurements. To mitigate this interference, this study proposes a highfrequency current superposition system aimed at reducing optical feedback noise. By superimposing high-frequency currents, the semiconductor laser transitions from a single-mode to a multi-mode operational state, thereby altering its mode of operation and consequently reducing optical feedback noise during sensing. Initially, mathematical modeling and simulation analysis were conducted on the high-frequency current superposition noise reduction system to examine the impact of high-frequency current on the intensity noise of the semiconductor laser. Subsequently, the design of the high-frequency current superposition noise reduction system was outlined, encompassing the development of a constant current drive circuit, a voltage-controlled oscillator circuit, and a biasing circuit. Finally, the high-frequency current superposition noise reduction system underwent testing. Results: During the high-frequency current superposition noise reduction test, the system's Signal-to-Noise Ratio (SNR) increased from 15.57 dB to 17.81 dB, and the system's noise peak-to-peak value decreased from 8 mV to 6 mV. Analysis of the superposition frequency and noise reduction effect determined the optimal superposition frequency of the AFM photoelectric sensor system to be 400 MHz. Characterization experiments of the high-frequency superposition noise reduction system compared the clarity of Escherichia coli images before and after noise reduction. Conclusion: The aforementioned experimental results demonstrated that high-frequency current superposition is an effective noise reduction method capable of mitigating optical noise in the AFM photoelectric sensing system.

Effective Acoustic Model-Based Beamforming Training for Static and Dynamic Hri Applications.

Human-robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human-robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods.

Enhancing Satellite Link Security Against Drone Eavesdropping Through Cooperative Communication

ABSTRACTIntegrated satellite terrestrial networks (ISTNs) are emerging as a promising next‐generation communication technology, for example, B5G and 6G, with low‐earth orbit (LEO) satellites playing a growing role. However, the complex and unique characteristics of ISTNs make them more susceptible to cyberattacks. Recently, the use of drones for public and private services has increased the risk of eavesdropping on LEO satellite links. Such scenario presents an extremely challenging environment due to dynamic nature of LEO satellite and drone along with atmospheric attenuation at sub‐THz frequencies. This study proposes a novel adaptive power‐bandwidth cooperative scheme designed to mitigate the likelihood of eavesdropping attacks on LEO satellite links communicating with a ground station when a drone is within the line of sight. The mathematical algorithm dynamically adapts the resources to maximize the normalized secrecy capacity in this challenging scenario while maintaining a reasonable signal‐to‐noise ratio (SNR) at the legitimate receiver. The adaptive scheme involves strategic cooperation with a nearby terrestrial third party to amplify and forward the satellite signal to the ground station receiver. The simulation results demonstrate the effectiveness of the proposed algorithm, showing significant improvements (> 70%) compared to the non‐adaptive scheme over a wide range of elevation angles.

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

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

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

Suppression of interference fringes in wavelength modulation spectroscopy based on convolutional neural networks

To mitigate interference fringes in wavelength modulated spectroscopy (WMS), we introduce a new filter based on convolutional neural networks (New CNNF). This filter effectively suppresses both high and low-frequency variations of interference fringes, including those with envelopes. In striving for comprehensive interference fringe suppression across the entire frequency spectrum, while accommodating diverse cavity length conditions, we confront the challenge of data sparsity. To address this, we introduce a pioneering methodology: finely segmenting the dataset through concentration column density normalization, thereby achieving notable noise reduction. Through the construction and training of the New CNNF model, it was found to exhibit superior performance compared to traditional filtering algorithms, particularly under low signal-to-noise ratio (SNR) conditions.Following processing with the novel convolutional neural network filters (New CNNF), the sample's signal-to-noise ratio (SNR) improved by 26.70 dB, increasing from the original −10.11 dB. When the etalon lengths were 2 cm and 100 cm, the goodness of fit between the predicted second harmonic signal amplitude by New CNNF and the corresponding label concentration reached 0.9995 and 0.9999, respectively. Experimental results demonstrate that New CNNF effectively suppresses high and low-frequency variations and enveloped interference fringes in the TDLAS-WMS system, thereby enhancing the accuracy and stability of methane concentration measurements. CH4 transitions at λ = 1.654 μm were selected to validate this approach. Our method shows promising application prospects and can be extended to the sensing of other gas molecules.

Pre-Detection and Pre-Registration Averaging of Full Wave Signals in Airborne LiDAR Bathymetry

A well-known technique to enhance the signal to noise ratio (SNR) of repetitive signals is to average them. The coherent parts of the signal add up constructively while the incoherent parts are averaged out. The prerequisite is that the signals are acquired under conditions of high repeatability, i.e., the signals must be sufficiently similar. In the present technical note, we describe an efficient method for maintaining signal similarity by ensuring spatial and temporal proximity of laser waveform signals obtained by a sensor operated from an airborne platform. The method makes use of a few auxiliary parameters such as laser pulse repetition rate, mirror rotation rate, platform altitude and flight speed. The method can be extended to be operated in real time.