Signal Quality Research Articles

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Claw coloration in the fiddler crab Leptuca uruguayensis has no correlation with male quality

Abstract Sexual selection is thought to play a major role in the evolution of color due to the correlation between a signaler’s physiological state and the displayed color. As such, researchers often investigate how color correlates to the quality of the signaler. However, research on the relationship between color and individual quality is often taxonomically limited and researchers typically investigate how color phenotypes relate to one index of quality, such as a linear measure of body size. Here, we investigated the relationship among body size, claw size, claw muscle mass, lipid content, and the color of the claw in male fiddler crabs (Leptuca uruguayensis) which wield an exaggerated claw that varies in color from brown to red. We hypothesized that if the color was correlated to one or more indices of male quality, the color displayed on the claws of male L. uruguayensis could be under sexual selection. We found L. uruguayensis claw color varies substantially among the individuals we photographed. However, we did not find a correlation between claw color and indices of quality; neither brightness nor hue correlated to the indices of quality we measured. Our findings suggest that claw color in L. uruguayensis is unlikely to have evolved to signal quality, but may instead function as a species identity or as a non-indicator sexual signal.

Investigating the Influence of Temperature on UAV Signal Quality

Advancements in drone technology make them important in many areas. military, industry, and disaster The efficacy of a drone's communication systems can be greatly impacted by temperature fluctuations, either from environmental conditions or mechanical problems in the drone's construction. This study gives an analysis and computational model of the impact of temperature on the performance of drone communication. Utilizing a one-dimensional convolutional neural network, we aim to forecast the signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Following the initial stage of dataset creation in the drone laboratory, proceed to reprocess the dataset and divide it into a 70% training set and a 30% testing set. Subsequently, a graphical user interface (GUI) was developed using MATLAB App Designer to enhance user friendliness. The outcome suggests that the efficiency of the drone communication system declines with rising temperatures. Using 1DCNN is our contribution to this work; other studies depend only on simulation to assess performance. One benefit of 1DCNN is that the impact may be evaluated by automatically extracting important features from the input dataset. Using 1DCNN is our special addition to this project; other research evaluate the UAV communication system's effectiveness only through simulation. We propose in this work to optimize system characteristics for improved performance, including power transfer, by adding a feedback loop between the CNN result and the communication system. Furthermore, we investigate how different weather conditions, such wind and rain, affect UAV communication systems.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

Evaluating FPGA-based denoising techniques for improved signal quality in electrocardiograms

Evaluating FPGA-based denoising techniques for improved signal quality in electrocardiograms

Microwave-Based State Diagnosis of Three-Way Catalysts: Impact Factors and Application Recommendations.

This study reassesses an overview of the potential of the radio frequency (RF)-based state diagnostics of three-way catalysts (TWC) based on a previous study with an emphasis on the defect chemistry of the catalyst material during reoxidation and reduction. Some data are based on the previous works but are newly processed, and the signal parameters resonant frequency and inverse quality factor are evaluated with respect to applicability. The RF-based method uses electromagnetic resonances in a cavity resonator to provide information on the storage level of the oxygen storage component. The analysis focuses on a holistic investigation and evaluation of the major effects influencing the RF signal during operation. On the one hand, the response to the oxygen storage behavior and the resolution of the measurement method are considered. Therefore, this study merges original data from multiple former publications to provide a comprehensive insight into important measurement effects and their defect chemistry background. On the other hand, the most important cross-sensitivities are discussed and their impact during operation is evaluated. Additionally, the effect of catalyst aging is analyzed. The effects are presented separately for the two resonant parameters: resonant frequency and (unloaded) quality factor. Overall, the data suggest that the quality factor has a way higher signal quality at low temperatures (<400 °C) and the resonant frequency is primarily suitable for high operating temperatures. At most operating points, the quality factor is even more robust against interferences such as exhaust gas stoichiometry and water content. Correctly estimating the catalyst temperature is the most important factor for reliable results, which can be achieved by combining the information of both resonant signals. In the end, the data indicate that microwave-based state diagnosis is a powerful system for evaluating the oxygen storage level over the entire operating range of a TWC. As a research tool and in its application, the system can therefore contribute to the improvement of the emission control of future gasoline vehicles.

A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals

A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

Background:The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. Method:The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. Result:The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108–142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. Conclusion:The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

Evaluation of vibration behavior at different sensing positions on gearboxes

Monitoring systems for rolling contacts in machinery are widespread. There are multiple solutions in various applications like Health Usage Monitoring Systems (HUMS) for Helicopters and smart bearings from different manufacturers. The number of solutions and possibilities is rising. This paper outlines how the vibrational behavior depends on the measuring position outside and inside the gearbox. The effect of the sensor location is evaluated by examining different locations close to the main excitation of gearboxes—the loaded tooth contact—as well as positions on the gearbox housing. Based on the results, recommendations for an optimal sensor position for gear vibration monitoring are given by considering the efforts of sensor integration. The results can be used to select sensors with the lowest cost to support the widespread gear vibration monitoring.Important locations for health monitoring are the gear meshes inside the gearbox. Due to the requirement of high power density, a compact design is mandatory. This does not allow for sensor application close to the gear meshes but only on the outside of the gearbox. An impediment of signal quality and less chance of early damage detection is the consequence. Hence, efforts have been made to directly integrate multiple sensors inside the gear body without negatively impacting the package.For the investigation of the vibration transmission behavior, an FZG gear test rig is selected. These standardized test rigs are approved and established all around the world for the creation and evaluation of various gear failure data. The presented test rig has been modified to be able to sense vibration in the gear wheel, at the gear wheel, and on the gearbox housing to evaluate different sensor positions and vibration sensors for their suitability on vibration measurement.

Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.

Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.

Unraveling cEMG-wet sEMG Correlation Dynamics: Investigating Influential Factors

The electromyography (EMG) signal provides insight into neuromuscular activity which is used in medical and technological fields. Traditional needle electrodes and surface electrodes have several drawbacks making them less suitable for portable and long-term use. In contrast, emerging capacitive electrodes offer promising features over the existing electrodes. Yet, the full potential of capacitive electrodes remains untapped due to the lack of comprehensive design optimization for consistently reliable signal quality. This study highlights the complex interplay of factors influencing correlation in capacitive EMG (cEMG) and wet surface EMG (wet sEMG) signals. The study emphasizes the importance of the surface area of capacitive electrodes, muscle force, preprocessing, and sampling frequency in understanding and improving the correlation between cEMG and wet sEMG signals, providing valuable insights for future research and applications in the field. The study reveals that the electrode area has no significant effect on the correlation. However, the correlation significantly depends on the muscle force. In addition, removing artifacts from the cEMG signal increases the correlation, especially for lower force where artifacts are significant. Again, oversampling the EMG signal above 800 Hz does not have any impact on increasing the correlation but the correlation decreases with higher inter-electrode distance (IED). In this research, the highest correlation of 82.89% (normalized-91.62%) between cEMG and sEMG has been achieved for high muscle force with a plate area of 4 cm2. Therefore, the capacitive electrode can be an alternative for EMG signal acquisition.

From Audience to Evaluator: When Visibility into Prior Evaluations Leads to Convergence or Divergence in Subsequent Evaluations Among Professionals

Collective evaluation processes, which offer individuals an opportunity to assess quality, have transcended mainstream sectors (e.g., books, restaurants) to permeate professional contexts from within and across organizations to the gig economy. This paper introduces a theoretical framework to understand how evaluators’ visibility into prior evaluations influences the subsequent evaluation process: the likelihood of evaluating at all and the value of the evaluations that end up being submitted. Central to this discussion are the conditions under which evaluations converge—are more similar to prior evaluations—or diverge—are less similar—as well as the mechanisms driving observed outcomes. Using a quasinatural experiment on a platform where investment professionals submit and evaluate investment recommendations, I compare evaluations that are made with and without the possibility of prior ratings influencing the subsequent evaluation process. I find that when prior ratings are visible, convergence occurs. The visibility of prior evaluations decreases the likelihood that a subsequent evaluation occurs by about 50%, and subsequent evaluations become 54%–63% closer to the visible rating. Further analysis suggests that peer deference is a dominant mechanism driving convergence, and only professionals with specialized expertise resist peer deference. Notably, there is no evidence that initial ratings are related to long-term performance. Thus, in this context, convergence distorts the available quality signal for a recommendation. These findings underscore how the structure of evaluation processes can perpetuate initial stratification, even among professionals with baseline levels of expertise. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2017.11285 .

Heavy metal pollution exposure affects egg coloration but not male provisioning effort in the pied flycatcher Ficedula hypoleuca

Heavy metal pollution is known to negatively affect numerous traits in birds, including foraging, metabolism, immunity, and reproductive success. In this study, our primary aim was to assess the impact of metal pollution exposure on the visual appearance of the pied flycatcher Ficedula hypoleuca eggs. Specifically, we focused on blue‐green biliverdin‐based coloration, a trait expected to function as a signal of female quality to males. In line with the sexually selected egg coloration (SSEC) hypothesis, which posits that males respond to more intensely colored eggs by increasing their provisioning effort, our second objective was to investigate whether metal pollution exposure affects this specific signaling mechanism and subsequent male behavior. Our results showed that although coloration did not correlate with female quality or male provisioning effort, egg blue‐green coloration decreased in polluted areas compared to non‐polluted control areas. Our analysis of reflectance data revealed that this difference was due to an increased ultraviolet reflectance of eggs from polluted areas, likely caused by changes in eggshell microstructure (e.g. porosity). We therefore propose that metal pollution exposure may compromise crucial color signals of bird eggs. Avian visual modeling indicated that eggs laid by different flycatcher females are generally very similar, making discrimination by males challenging and perhaps impossible especially in dark cavities. Overall, our results suggest that the SSEC hypothesis may lack adaptive relevance for the pied flycatcher in northern Europe, even in environments influenced by anthropogenic activities.

Quality-Aware Signal Processing Mechanism of PPG Signal for Long-Term Heart Rate Monitoring.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

SmartEM: machine-learning guided electron microscopy.

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.

Localization for surface microseismic monitoring based on arrival time correction and VFOM

Abstract Unconventional resources have emerged as the primary source to meet the escalating demand for energy consumption, with hydraulic fracturing standing out as an effective means of boosting production. The utilization of microseismic monitoring is crucial for acquiring real-time or semi-real-time extension information of the fracture network to guide the fracturing process. The precise positioning of microseismic events is a fundamental aspect of microseismic monitoring. Traditional methods relying on (relative) arrival time significantly impact positioning accuracy due to picking errors. While waveform-based methods offer high accuracy, they require precise velocity models and are time-consuming. To overcome challenges associated with arrival time pickup and velocity accuracy, we introduce a virtual field optimization method (VFOM) based on arrival time correction. Initially, an equivalent velocity model is established, and the arrival time difference resulting from the model transformation of the master event is calculated to correct the observed arrival time of the target event. Subsequently, we match detector pairs, establish hyperboloids based on the corrected arrival time difference, and employ the intersection point of all hyperboloids as the positioning result. After that, we use the location results of the master event to enhance the accuracy of the target event. Finally, we apply the proposed method to both synthetic test and field datasets, demonstrating a significant improvement in the positioning accuracy and stability provided by the novel method. The robustness against arrival time error renders it a suitable choice for surface monitoring applications where signal quality is compromised. Furthermore, the simplified velocity model significantly diminishes the computational requirements in the positioning process, enhancing its efficiency, and consequently holds vast potential for application in real-time monitoring.

Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers' performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.

A scalable platform for acquisition of high-fidelity human intracranial EEG with minimal clinical burden.

Human neuroscience research has been significantly advanced by neuroelectrophysiological studies from people with refractory epilepsy-the only routine clinical intervention that acquires multi-day, multi-electrode human intracranial electroencephalography (iEEG). While a sampling rate below 2 kHz is sufficient for manual iEEG review by epileptologists, computational methods and research studies may benefit from higher resolution, which requires significant technical development. At adult and pediatric Stanford hospitals, research ports of commercial clinical acquisition systems were configured to collect 10 kHz iEEG of up to 256 electrodes simultaneously with the clinical data. The research digital stream was designed to be acquired post-digitization, resulting in no loss in clinical signal quality. This novel framework implements a near-invisible research platform to facilitate the secure, routine collection of high-resolution iEEG that minimizes research hardware footprint and clinical workflow interference. The addition of a pocket-sized router in the patient room enabled an encrypted tunnel to securely transmit research-quality iEEG across hospital networks to a research computer within the hospital server room, where data was coded, de-identified, and uploaded to cloud storage. Every eligible patient undergoing iEEG clinical evaluation at both hospitals since September 2017 has been recruited; participant recruitment is ongoing. Over 350+ terabytes (representing 1000+ days) of neuroelectrophysiology were recorded across 200+ participants of diverse demographics. To our knowledge, this is the first report of such a research integration within a hospital setting. It is a promising approach to promoting equitable participant enrollment and building comprehensive data repositories with consistent, high-fidelity specifications towards new discoveries in human neuroscience.

Hilbert signal envelope-based multi-features methods for GNSS spoofing detection

The Global Navigation Satellite System (GNSS) provides precise Positioning, Navigation, and Timing (PNT) service for various industries such as military and national economy. However, civil GNSS is fragile and vulnerable to spoofing for the weak signal strength and the open architecture. To address the problem of limited performance of existing detection methods due to the strong concealment of spoofing, this paper proposes a novel scheme using Hilbert Signal Envelope-based multi-features. Firstly, multi-features are picked and calculated from the receiving signal, especially we propose a Tweaked Signal Quality Monitoring (TSQM) metric in this step. Secondly, an algorithm for extracting Hilbert Signal Envelope (HSE) is proposed for enhancing the features, which reduces features redundancy and pushes clean and spoofing samples away from each other. Thirdly, the enhanced features are sent to train and test the Machine Learning (ML) models. Finally, experimental validation is conducted on the Texas Spoofing Test Battery (TEXBAT) dataset and measurement data collected by low-cost Software-Defined Radio (SDR). The Area Under Curve (AUC) of the proposed method improves by 5.34 % and 3.07 % (reaching 97.34 % and 98.44 %) compared to the latest multi-parameters method on TEXBAT and measurement data, respectively, which provides an effective scheme for spoofing detection.