Signal Clarity Research Articles

A Strong Noise Reduction Network for Seismic Records

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications.

Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.

Gender quotas influence the appointment of women to precarious leadership positions: A signalling approach

AbstractIn this research we address the ongoing debate about the existence of the glass‐cliff phenomenon by investigating boundary conditions and mechanisms influencing its persistence and decline. Drawing on signalling theory, we hypothesize that the glass‐cliff's presence fluctuates with the clarity of signals associated with appointing women board members at various stages of quota policy implementation. In Study 1, we analyzed a dataset of 258 board appointments in German‐listed companies from 2003 to 2020. We found that women executives were more likely to be appointed following periods of declining performance during Stage 1 (pre‐quota announcement) and Stage 3 (post‐quota enforcement), but not in Stage 2 (post‐announcement, pre‐enforcement). In Study 2, an experiment with 476 respondents, we tested for changes in signal clarity as an underlying mechanism. Results indicate that signal clarity associated with appointing women following performance decline is influenced by quota policies. Signal clarity was highest during stages 1 and 3, but diminished in Stage 2. These findings support the signalling explanation for the glass‐cliff, highlighting that its occurrence is influenced by country‐level policies and emphasizing that the symbolic value of appointing women to board positions as indicators of change depends on the clarity of these signals.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Localization of AE sources in rocks improved by enhanced arrival time localization

Accurate localization of Acoustic Emission (AE) events in rock structures is crucial for investigating rock failure mechanisms. This paper presents a comprehensive analysis of 3D AE localization in basalt-type rock samples, utilizing a novel combination of three powerful techniques: the Simulated Annealing Algorithm (SAA), an innovative AIC-EEMD picking method, and a velocity-free approach to wave propagation. We employed a localized Hsu–Nielsen source to generate elastic waves, which were captured by six AE sensors strategically placed on the rock surface. To address challenges in identifying P-wave arrivals, particularly in noisy or spurious data, we developed an innovative technique that integrates the Akaike Information Criterion (AIC) with Empirical Ensemble Mode Decomposition (EEMD) to enhance signal clarity. The performance of our proposal was validated achieving an average absolute distance error of 3.74 mm. Compared to traditional methods, our technique demonstrates superior accuracy and offers enhanced robustness in the presence of noise.

Arduino-Based Real-Time ECG Monitoring System for Heart Signal Visualization

Electrocardiogram (ECG) monitoring is an essential tool in healthcare for diagnosing and tracking heart conditions. However, existing ECG devices are often expensive, limiting accessibility for personal or educational use. This project aims to develop a low-cost ECG monitoring system using an Arduino UNO, AD8232 sensor, and TFT SPI display. The system collects ECG signals via electrodes and displays the real-time PQRST waveform on the TFT screen and the Arduino IDE serial monitor. Resistors are utilized to ensure signal clarity by reducing electrical noise. The project successfully demonstrated the ability to visualize clear ECG signals in real time, offering a practical and cost-effective solution for continuous cardiac monitoring

Study of Acoustic Emission Signal Noise Attenuation Based on Unsupervised Skip Neural Network.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Advancing annual global mean surface temperature prediction to 2 months lead using physics based strategy

Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023–2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere’s mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980–2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.

Application of ARIS and Adaptive Filtering Algorithm in Underwater Acoustic Communication

As the core of marine communication, underwater acoustic communication technology significantly improves the performance of underwater communication by using technologies such as modulation, multiple access and anti-interference. Among them, adaptive filtering technology plays a key role in eliminating noise and channel equalization. This paper explores the novel application of acoustic reconfigurable intelligence surface (ARIS) and adaptive filtering in underwater acoustic communication. It introduces the concept of ARIS-assisted underwater acoustic communication, integrating the least mean squares (LMS) algorithm to dynamically estimate and mitigate oceanic noise. This approach further enhances signal clarity and communication quality by leveraging orthogonal frequency division multiplexing (OFDM) technology. Simulation results show that the underwater acoustic channel with ARIS is superior to the traditional channel without ARIS in terms of bit error rate and anti-interference performance. This technology significantly enhances the reception quality of underwater communication signals, making it suitable for applications in marine monitoring, military communication, and other complex environments.

A Deep Learning Application for Dolph-Tschebyscheff Antenna Array Optimisation

This paper proposes a design for a Dolph-Tschebyscheff-weighted microstrip antenna array using a deep learning application. For this purpose, a multilayer perceptron and a deep learning model, both created using the same data set generated by a genetic algorithm, were compared. The antenna array population is initially generated randomly and then optimised with a genetic algorithm. The data produced by this model becomes a data set used for training in the deep learning application. The dimensions and specifications of the antenna array are obtained from this application, ensuring precision and optimisation in the design process. A new microstrip antenna array structure is employed for the proposed method, taking advantage of this design technique. The Dolph-Tschebyscheff weights are applied to achieve better characteristics for the microstrip antenna array, thus obtaining low side lobe levels, which are crucial for enhancing signal clarity and reducing interference. The results demonstrate that the proposed algorithm significantly improves the specifications of the structure. This improvement highlights the potential for integrating deep learning with traditional optimisation algorithms for advanced antenna design.

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

(Invited) Aiot-Integrated Digital Imaging Sensor for Molecular-Fingerprint Profiling of Extracellular Vesicles in Liquid Biopsy

The advancement of liquid biopsy techniques for non-invasive tumor analysis holds immense potential in continuously tracking tumor status—recurrence, progression, and treatment response—in real-time. To comprehensively assess tumor evolution, cellular diversity, and drug resistance mechanisms, a pivotal step involves scrutinizing proteins and nucleic acids within extracellular vesicles (EVs) present in biofluids. Our focus lies in crafting an advanced digital bead-based sensor system that seamlessly evaluates the molecular profile of EVs in glioblastoma cases. Our innovative approach enriches tumor-relevant genetic data within EVs using antibody-specific magnetic beads, subsequently amplifying it via reverse transcription recombinase polymerase amplification system across forty thousand droplets. These results are scrutinized using a compact imaging device. The core strengths of our methodology include: (a) Streamlined Sample Processing: Our system facilitates ultra-sensitive miRNA detection in EVs within a rapid ninety-minute window, owing to optimized sample preparation and a compact detection mechanism; (b) Enhanced Signal Clarity: Employing a straightforward isothermal assay significantly boosts the fluorescent signal-to-noise ratio within each 70-µm droplet, achieving over 104 turnovers of fluorescent reporters on a chip; (c) Real-Time Data Analysis: Our system seamlessly captures real-time imaging data for the edge computing, utilizing trained YOLOv5 models for classification, and presents genetic results on the smartphone. Its user-friendly design enables utilization by healthcare workers with varying skill levels. This pioneering approach has the potential to offer affordable, rapid, and informative diagnostics particularly beneficial for underserved regions and healthcare systems facing financial constraints. Furthermore, its adaptability for diverse cancers could revolutionize the pace of diagnosis and treatment decision-making through liquid biopsy, potentially establishing its widespread application in global healthcare.

FireSonic: Design and Implementation of an Ultrasound Sensing-Based Fire Type Identification System.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damage. However, traditional methods such as smoke detection, visual analysis, and wireless signals are not able to identify fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire type and sound propagation, FireSonic quantifies the heat release rate (HRR) of flames by analyzing the relationship between fire-heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. The experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Research on SSVEP‐EEG feature enhancement algorithm based on fractional differentiation

AbstractSteady‐state visual evoked potentials (SSVEP), significant in brain‐computer interfaces (BCI) and medical diagnostics, benefit from enhanced signal processing for improved analysis and interpretation. This study introduces a novel enhancement algorithm for SSVEP electroencephalogram (EEG) signals, employing fractional‐order differentiation operators combined with image processing techniques. Utilizing fractional‐order differentiation within a Laplace pyramid framework, the algorithm achieves hierarchical signal enhancement, facilitating detailed feature extraction and emphasizing SSVEP signal characteristics. This innovative approach merges the precision of fractional calculus with the structural benefits of the Laplace pyramid, leading to enhanced signal clarity and feature discrimination. The efficacy of this method was validated using canonical correlation analysis (CCA), filter bank CCA (FBCCA), and task‐related component analysis (TRCA) on a public dataset. Compared to conventional methods, our algorithm not only mitigates trend components in SSVEP signals but also significantly boosts the recognition accuracy of CCA, FBCCA, and TRCA algorithms. Experimental results indicate a marked improvement in recognition precision, underscoring the algorithm's potential to advance SSVEP‐based BCI research.

Application of Time Synchronous Averaging in Mitigating UAV Noise and Signal Loss for Continuous Scanning Laser Doppler Vibrometry

The laser Doppler vibrometer (LDV) has been shown to be effective for a wide application of vibration assessments that are well accepted. One of the new avenues for exploring alternative measurement scenarios, mounting LDVs on unmanned aerial vehicles (UAVs) is emerging as a potential avenue for remote and harsh environment measurements. Such configurations grapple with the challenge of the LDV sensor head being sensitive to UAV vibration during flight and signal loss due to tracking error. This study investigates the effectiveness of several Time Synchronous Averaging (TSA) techniques to circumvent these obstacles. Through comprehensive evaluations, all three TSA techniques under investigation demonstrated significant potential in suppressing UAV-induced noise and minimising the effects of signal dropout. Traditional TSA showcased a remarkable sixfold enhancement in signal quality when analysed via the mean square error. However, the study also highlighted that while TSA and Multi-Cycle Time Synchronous Average (MCTSA) elevated signal clarity, there is a trade-off between noise suppression and signal duration. Additionally, the findings emphasise the importance of synchronisation between scanning and target vibration. To achieve optimal results in Continuous Scanning Laser Doppler Vibrometer measurements, there is a need for advanced algorithms capable of estimating target vibration and synchronising scanning in real-time. As the study was rooted in steady-state vibrations, future research should explore transient vibration scenarios, thereby broadening the application scope of TSA techniques in UAV-mounted LDV systems.

A Dual-Stream-Modulated Learning Framework for Illuminating and Super-Resolving Ultra-Dark Images.

Enhancement of image resolution for scenes captured under extremely dim conditions represents a practical yet challenging problem that has received little attention. In such low-light scenarios, the limited lighting and minimal signal clarity tend to intensify issues such as diminished detail visibility and altered color accuracy, which are often more severe during the image enhancement process than in scenarios with adequate lighting. Consequently, standard methods for enhancing low-light images or improving their resolution, whether implemented independently or through a combined approach, generally face challenges in effectively restoring luminance, preserving color integrity, and detailing intricate features. To conquer these issues, this article introduces an innovative dual-stream (DS) modulated learning framework designed to tackle the real-world coupled degradation issues in super-resolution (SR) under low-light conditions. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint to specifically target uneven illumination. We develop illumination-semantic dual modulator (ISDM), a refinement middleware embedded in the decoding stage to bridge illumination and semantic features concurrently, aimed at safeguarding the integrity of lighting and color details at the feature level. Our approach replaces simple upsampling methods with the resolution-sensitive merging upsampler (RSMU) module, which integrates diverse sampling techniques to effectively reduce artifacts and halo effects. Comprehensive experiments on three benchmarks showcase the applicability and generalizability of our approach to diverse and challenging ultra-poorly lit settings, outperforming state-of-the-art methods with a notable improvement. The code and benchmark are publicly available at https://github.com/moriyaya/UltraIS.

Signal Processing for Spinal Cord Injury Monitoring with sEMG

Spinal cord injuries (SCIs) significantly impair motor and sensory functions, posing challenges to rehabilitation and patient management. Accurate monitoring of SCIs is crucial for evaluating the extent of injury and guiding therapeutic interventions. This study explores the application of surface electromyography (sEMG) as a non-invasive tool for monitoring muscle activity in individuals with SCIs. By capturing electrical signals generated by muscle fibers, sEMG provides real-time data essential for assessing neuromuscular function. To enhance the effectiveness of sEMG in spinal cord injury monitoring, advanced signal processing techniques are employed. These techniques include noise reduction, which improves signal clarity, feature extraction to identify key patterns in muscle activation, and pattern recognition algorithms to classify muscle responses. The integration of these methods enables more accurate interpretations of sEMG data, facilitating a better understanding of motor capabilities and rehabilitation progress. The findings highlight the potential of using signal processing in conjunction with sEMG to improve patient outcomes, optimize rehabilitation protocols, and provide insights into the underlying mechanisms of spinal cord injuries. This approach not only advances clinical practices but also supports ongoing research aimed at enhancing the quality of life for individuals affected by SCIs.

An efficient linear and elliptical antenna array design using sail fish optimization

Abstract This article introduces an innovative approach to antenna array design, focusing on synthesizing the optimal radiation pattern for fifth-generation (5G) communication. The authors have designed a reliable linear and elliptical antenna array (EEA) of dipole elements by employing sailfish optimization (SFO). 5G technology promises transformative improvements in wireless communication with high data rates, expanded capacity, minimal latency, and exceptional service quality. The crux of 5G lies in the precision of antenna array design, aiming for an emission pattern with minimal side lobe levels (SLLs) and a narrow half-power beam width (HPBW). A narrower HPBW is essential for efficient long-range communication, whereas reducing the SLLs enhances signal clarity. The SFO optimizes the current excitation of each antenna element for reducing the mutual coupling effects and lowering the SLL and HPBW values in linear and EEAs. This paper uses the exact excitation to each element to show the linear antenna arrays (LAA) (10-, 16-element) design examples and EAA (8-, 12-, 20-element) structures. The LAA and EAA design examples obtained with the SFO algorithm establish the advancement in SLL suppression over the uniform antenna array and the methods proclaimed in the recent article.

VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter

Filtering techniques have been elaborated in the HA field to improve signal clarity and enhance the hearing capacity of deaf people. However, public sounds are highly noisy, so filtering those signals is not an easy task. Hence, the present article has aimed to develop a novel Ant Lion based power Noise-Variable Bandwidth Filter (ALPN-VBF) for the HA applications. Here, the proposed optimized power efficient filter has incorporated several functions like de-noising and frequency tuning based on the word features. Here, the signal’s noise has been removed with the maximum possible range with the help of High-pass-Filter (HPF) and low-pass filter (LPF). Finally, the developed model is tested with a few audiograms, and the filter parameters have been analyzed and compared with other models. The testing results have proved that the designed filter is better in frequency tuning and signal transmission than the previous approaches by attaining less delay and reduced power consumption rate.

Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition.

As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and ε-Normalized Least Mean Squares (ε-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.