Measurement Noise Research Articles

Excess noise and thermoelectric effect in magnetron-sputtered VO2 thin films

This work presents the excess noise and thermoelectric (Seebeck) measurements on polycrystalline vanadium dioxide (VO2) thin films. Noise spectral power density (SPD) of current fluctuations in the semiconducting (SC) phase had a typical flicker noise (f−γ) characteristic with an average slope parameter γ of 1.13. Normalized SPD (Sn) values obtained in the SC-phase indicate that the noise originates in the bulk of the film. On the contrary, in the metallic (M)-phase, γ values were greater than unity, and the observed Sn values indicated that the origin of the noise is most likely from the contacts or surface rather than the bulk. A general decrease was observed in Sn by a factor of 4–5 from the SC- to M-phase. Moreover, Sn in the SC-phase showed no temperature dependence. An interpretation based on the number of charge carrier fluctuations in Hooge's model led to an unrealistically high Hooge parameter and had to be ruled out. We propose that the fluctuations are related to the mobility fluctuations of carriers arising primarily from grain-boundary scattering which explains the observed characteristics well. The Seebeck coefficients (S) obtained under both heating and cooling schedules showed the n-type nature of magnetron-sputtered VO2 films in the SC-phase. Differently, in the M-phase, the S value was positive. The S values obtained from the cooling schedule signified the low percolation threshold of the metal-to-insulator transition already demonstrated for VO2 thin films grown on r-cut sapphire using the Efros–Shklovskii percolation model.

Physics-guided deep learning based on modal sensitivity for structural damage identification with unseen damage patterns

Recently, wide attention has been paid to develop deep learning-based models for structural damage identification. However, in most of the studies, the pure deep learning-based structural damage identification methods lack physical interpretability and scientific consistency for generalization. Therefore, although such methods can achieve good damage identification results when data with similar damage patterns used for training and testing the network model, they usually give poor predictions in the unseen domain, i.e., the network’s extrapolation ability is limited. In this research, a physics-guided deep learning neural network (PGDLNN) is proposed by incorporating the physical loss constructed by structural modal parameters sensitivity analysis into the original Convolutional Neural Network (CNN) for structural damage identification. The model’s physical interpretability is improved, which can lead to more accurate damage identification results, especially in the domain with unseen damage patterns in training the network model. Numerical and experimental studies on simply supported beams are carried out to demonstrate the feasibility and effectiveness of the proposed method. The influences of measurement noise and incomplete measurements are also investigated. The results show that the incorporation of physical knowledge to deep learning model can enhance the generalization of the network, and hence improve the accuracy of damage severity quantification. This study not only performs damage localization and quantification simultaneously using the physics-guided deep learning neural network, but also demonstrates the superiority of incorporating physical knowledge into data-driven deep learning models for structural health monitoring.

Optimizing kernel width for new risk-sensitive loss: A generalized algorithmic approach

This paper outlines a novel approach to the development of adaptive filtering algorithms, which minimizes susceptibility to the effects of non-Gaussian measurement noise. Using the generalized Gaussian density (GGD) function as the kernel function, we developed a generalized kernel risk-sensitive loss (GKRSL) criterion by which to derive the generalized minimum kernel risk-sensitive loss (GMKRSL) algorithm. We also developed a variable kernel width algorithm for GMKRSL (VKW-GMKRSL) to address the trade-off between convergence rate and steady-state misalignment resulting from a constant kernel width. In simulations, GMKRSL and VKW-GMKRSL outperformed existing works in system identification performance.

Evaluation of the relationship between occupational noise exposure and stress levels in a healthcare facility

Objectives. This study aimed to assess noise levels in various units in hospitals and explore the correlation between healthcare employees’ daily noise exposure level (L ex,8h) and their stress and symptoms. Methods. A healthcare facility was selected to study noise pollution. Personal noise dosimeters were used during 8-h working shifts. Data were collected from 126 employees using the 36-item ‘A stress scale’ and a socio-demographic information form. Results. The results of noise measurements exceeded World Health Organization and US Environmental Protection Agency recommended limits at all sites. The lowest L ex,8h (52 dB) was detected in the endocrinology clinic, while the highest (91 dB) was recorded in the podology unit. According to the stress scale scores, the lowest mean psychological symptom score (PsSS), physiological symptom score (PhSS) and total scale score (TSS) were observed in diagnostic units with 35 ± 14, 41 ± 14 and 76 ± 27, respectively, while the highest mean PsSS, PhSS and TSS were observed in the polyclinics with 44 ± 6, 64 ± 10 and 107 ± 10, respectively. The PsSS of employees exposed to noise (L ex,8h) above 75 dB was found to be higher than for other employees. Conclusion. L ex,8h above 75 dB can increase psychological symptoms in healthcare employees.

Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

The angle/range-based integrated navigation system is a favorable navigation solution for deep space explorers. However, the statistical characteristics of the measurement noise are time-varying, leading to inaccuracies in the derived measurement covariance even causing filter divergence. To reduce the gap between theoretical and actual covariances, some adaptive methods use empirically determined and unchanged forgetting factors to scale innovations within the sliding window. However, the constant weighting sequence cannot accurately adapt to the time-varying measurement noise in dynamic processes. Therefore, this paper proposes an Adaptive Robust Unscented Kalman Filter with Time-varying forgetting factors (TFF-ARUKF) for the angle/range integrated navigation system. Firstly, based on a statistically linear regression model approximating the nonlinear measurement model, the M−estimator is adopted to suppress the interference of outliers. Secondly, the covariance matching method is combined with the Huber linear regression problem to adaptively adjust the measurement noise covariance used in the M−estimation. Thirdly, to capture the time-varying characteristics of the measurement noise in each estimation, a new time-varying forgetting factors selection strategy is designed to dynamically adjust the adaptive matrix used in the covariance matching method. Simulations and experimental analysis compared with EKF, AMUKF, ARUKF, and Student’s t-based methods have validated the effectiveness and robustness of the proposed algorithm.

Making a racket in America's fastest growing sport: Evaluation of noise exposure in pickleball

ObjectiveTo measure noise exposure present on pickleball courts and assess the risk of noise-induced hearing loss (NIHL) per guidelines put forward by the National Institute of Occupational Safety and Health (NIOSH). MethodsObservational study measuring noise levels at multiple recreational pickleball courts in the Richmond, VA area, documenting LAeq, LASmax, and LCpeak at courtside and waiting areas of pickleball courts. Measurements were completed using the NIOSH SLM application on an iPhone 13 with iMM-6 Calibrated Measurement Microphone (equivalent to IEC 61672-1 Class II) that was calibrated using ND-9 Sound Level Calibrator (IEC942 Class I). ResultsAverage sound levels recorded at waiting areas adjacent to the courts, measured in LAeq, LASmax, and LCpeak, were 69.1 dBA, 92.0 dBA, and 112.1 dBC, respectively, while courtside measurements were 69.7 dBA, 92.2 dBA, and 115.6 dBC, respectively. These measurements were within NIOSH and OSHA recommendations. ConclusionThe data demonstrates that randomly sampled pickleball courts have noise levels that do not increase risk for NIHL for participants or bystanders alike based on NIOSH guidelines. However, prolonged noise exposure and ambient noise pollution may have other health implications and warrant further investigations. Level of evidenceLevel 2.

Submillisievert Abdominal Photon-Counting CT versus Energy-integrating Detector CT for Urinary Calculi Detection: Impact on Diagnostic Confidence.

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group (P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

Development of a diamagnetic loop in KAIMIR

We developed a diamagnetic loop for the estimation of plasma stored energy in the KAIST Magnetic Mirror magnetic mirror device [Oh et al., J. Plasma Phys. 90, 975900202 (2024)]. Diamagnetic loops are used to estimate the plasma stored energy from measurements of the diamagnetic flux in plasma with an applied external magnetic field. However, diamagnetic flux measurements are accompanied by the vacuum flux, which generally exceeds the diamagnetic flux by over 10 000 times. Therefore, it is critical to attain a high signal-to-noise ratio with minimized noise in diamagnetic flux measurements. In this study, we employed a novel method to reduce background noise and improve the signal-to-noise ratio. Using two identical loops with opposite polarities, we successfully removed parasitic capacitive noise from the external insulation while amplifying the inductive signal two times. To eliminate the vacuum flux, we utilized two coaxial loops with different radii positioned at the same axial location. Results obtained from six paired loops confirmed the successful removal of the vacuum flux. The plasma stored energy was also found to agree well with Langmuir probe measurements, which verifies the diamagnetic flux measurements using the developed loop.

Low-cost electrocardiogram monitoring system for elderly people using LabVIEW

Cardiovascular diseases increase due to factors such as obesity, an inadequate diet, and are a problem due to shortages of medical personnel and hospitals. In this case, the implementation of technological solutions is presented as a necessity to prevent heart diseases. Various approaches are used to design low-cost electrocardiogram (ECG) devices, from the use of Bluetooth technology to facilitate data transmission, to the development of wearable ECG devices that use artificial intelligence. The objective is to develop a monitoring system in LabVIEW to visualize the heart rhythms of older adults in the city of Lima (Peru), focusing on ease of use and adaptation to their needs, with the purpose of collaboration between health professionals. A development approach is used that encompasses design, implementation, and iterative testing, as well as practical evaluations and pilot testing. As a result, the correct functioning of the ECG device was validated. Electronic components and electrodes were integrated into the board to capture cardiac signals, energized with batteries and sending the information to an interface in LabVIEW. In conclusion, a portable ECG device has been developed that uses operational amplifiers (Op-amps) and analog filters to reduce noise in cardiac measurements and an intuitive interface in LabVIEW

Lomb-Scargle spectral analysis of plasma’s noise for space-based laser interferometric gravitational wave antennas

In space-based laser interferometric gravitational wave antennas, plasma from the solar wind is an important noise floor source for laser interferometric measurements between two distant spacecraft. In this study, we aim to analyze the impact of solar wind on the inter-satellite laser interferometric measurements of the Taiji mission, a space-based laser interferometric gravitational wave antenna proposed by the University of Chinese Academy of Sciences. Based on the 6.5-year electron number density data from the National Aeronautics and Space Administration Wind spacecraft and considering the uneven sampling characteristics of the data, we employed the Lomb–Scargle spectral analysis method to study the impact of solar wind plasma on intersatellite laser interferometric measurements within the frequency range of 1×10-6 Hz to 0.01 Hz. The study shows that the effect of the plasma on the intersatellite laser interferometric measurement is approximately 1 pm/Hz at 3.3 mHz, which is one order of magnitude smaller than the requirements on the displacement noise of the interferometric measurement of the Taiji mission, indicating that the noise due to plasma will not affect the aim of the Taiji mission to detect gravitational waves. The research can be applied to Taiji, Laser Interferometer Space Antenna, and future gravitational wave detection missions.

An investigation of acoustic cues to tonal registers and voicing in Donglei Kama).

The Kam language has experienced historical tonal splits, resulting in the development of a complex tonal system. However, there is still limited knowledge regarding the acoustic characteristics associated with aspiration-based tone splitting. This study aims to investigate the acoustic cues related to the tonal registers and laryngeal configurations in Donglei Kam, a dialect of Southern Kam. Sixteen native speakers of Donglei Kam participated, producing lexical tones. Statistical analyses were conducted to examine the acoustic distinctions between tonal registers, using measurements of voice onset time, spectral tilt, noise, and energy. The results indicated that Donglei Kam retained a two-way contrast of aspiration, albeit with a trend toward gradual loss. Additionally, a breathy voice was detected in the Ciyin tonal register, characterized by elevated spectral tilt values and spectral noise throughout the vowels. Moreover, machine learning classifiers effectively identified tonal registers using voice-quality data, suggesting that the phonation contrast between breathy and modal voice could contribute to the tonal split alongside pitch contrast. In summary, these findings enhance our understanding of the acoustic implementation of breathiness in Kam and offer valuable insights into the role of laryngeal contrast in tonal splits.

Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques

BackgroundThe image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR.MaterialsThis retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods.ResultsThe subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR.ConclusionsIn comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

Irregular extended target tracking with unknown measurement noise covariance

To address the challenge of tracking irregular extended target while adapting to unknown measurement noise, this paper presents a novel tracking algorithm for irregular extended target. Considering the flexibility of the Gaussian process (GP) model for representing shape, in the proposed algorithm, we utilize the GP model to delineate the radial function of the tracked extended target’s contour. Then, we incorporate the GP model into the variational Bayesian technique to formulate the posterior distribution for both the unknown measurement noise and the shape state of the tracked target. Subsequently, the square-root cubature Kalman filter is employed to tackle the nonlinearity filtering in the problem of irregular extended target tracking (ETT), enabling simultaneous estimation of intricate shape details and the unknown measurement noise covariance. Through extensive experiments using both simulated and real-time lidar data, the proposed algorithm demonstrates superior performance compared to the existing ETT algorithms for extended target tracking in both accuracy and robustness.

A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments

This study proposes and investigates an innovative two-dimensional Convolutional Neural Network (2D CNN) architecture for bridge damage classification. Employing strain time-history data transformed into grayscale images, the approach seamlessly combines feature extraction and classification, allowing for the precise identification and categorization of structural damage. The method’s effectiveness was validated through field experiments on a bridge mock-up subjected to several controlled damage states under nonstationary, commercial vehicle loads. The experiments included a range of scenarios, from minor impacts to significant structural damages, and the CNN model’s robustness was tested against fluctuating loads and introduced noise. Demonstrating remarkable accuracy, the 2D CNN successfully classified different damage states with over 95% accuracy, effectively identifying a damage state that was visually undetectable. Additionally, the proposed architecture was shown to be versatile when using varying number of sensors, wide range of frequency of measured sensor data, and elevated levels of measurement noise.

Enhanced grid integration through advanced predictive control of a permanent magnet synchronous generator - Superconducting magnetic energy storage wind energy system

In this study, the use of an Unscented Kalman Filter as an indicator in predictive current control (PCC) for a wind energy conversion system (WECS) that employs a permanent magnetic synchronous generator (PMSG) and a superconducting magnetic energy storage (SMES) system connected to the main power grid is presented. The suggested UKF indication in the hybrid WECS-SMES arrangement is in charge of estimating vital metrics such as stator currents, electromagnetic torque, rotor angle, and rotor angular speed. To optimize control strategies, PCCs use these projected properties rather than direct observations. To control the unpredictable wind energy's nature, SMES must be regulated to minimize fluctuations in the DC-link voltage and power output to the main grid. Fractional order-PI (FOPI) controllers are used in a novel control structure for the SMES system to regulate the output power and DC-link voltage. An artificial bee colony optimization approach is employed to optimize the FOPI controllers. Three commonly utilized indicators, including sliding-mode, EKF, and Luenberger, were evaluated using “MATLAB" to evaluate the performance of the UKF estimate. Assessment criteria such as mean absolute percentage error and root mean squared error were used to gauge the accuracy of the estimates. Simulation findings showed the efficiency of fractional order-PI controllers for SMES and the proposed UKF indication for predictive current control, especially in the presence of measurement noise and over a variety of wind speeds. An improvement in estimation accuracy of up to 99.9 % was demonstrated by the UKF indicator. Moreover, the stability of the suggested UKF-based PCC control for the hybrid WECS-SMES combination was confirmed using Lyapunov stability criteria."

Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.

Uncertainty-aware output feedback model predictive combustion control of RCCI engines

Accurate model development is essential for effective model-based control of Reactivity Controlled Compression Ignition (RCCI) engines. However, due to the intricate nature of engine combustion process, achieving a precise model that can capture the complex dynamic behavior and ensure high control performance poses a significant challenge. In this paper, we propose an uncertainty-aware output feedback model predictive control approach for efficient combustion management in RCCI engines. In contrast to the previously developed approaches, this method adopts a data-driven approach within the linear parameter-varying (LPV) framework for model development. To address the model mismatch between the LPV model and the real system/data, Bayesian Neural Networks (BNNs) are employed which provide the probability distribution of the uncertainties. The BNNs enable the formation of a scenario tree, effectively characterizing the range of potential uncertainties in the system. Through the implementation of scenario-based model predictive control, our approach ensures high tracking performance for the RCCI engine in the presence of modeling uncertainties and measurement noise. Extensive simulations and experimental validations demonstrate the superiority of our uncertainty-aware model predictive control over traditional control strategies.

A monitoring method for wind loads exerted on offshore gravity wind turbine through optimised mathematical model

The actual load borne by a structure throughout its entire lifecycle, such as the wind load on fixed offshore wind turbines, is unknown. This leads to significant differences between the actual life of a structure and the fatigue analysis results during the design phase. Wind-load identification can effectively obtain the wind load borne by a fixed fan throughout its lifecycle. However, measurement noise and an ill-posed mathematical model affect the identification accuracy and stability. In this study, the optimal orientation of the sensor was determined to improve the signal-to-noise ratio according to the stress analysis of the tower structure. A successive addition method (SAM) was proposed to reduce the ill-posedness and dimensions of the mathematical model used for wind-load identification in the frequency domain by removing inefficient strain gauge locations and directions. A distributed successive addition method (DSAM) was developed to improve computational efficiency. The SAM and DSAM were described in detail using a specific case study of an offshore gravity wind turbine; a mathematical model was established through structural harmonic-response analysis. The spectrum superposition method and Dirlik distribution were used to analyse the fatigue damage of the wind-power tower. Several numerical examples were used to demonstrate the effectiveness of the SAM and DSAM. The simulation results demonstrated that the SAM and DSAM can completely eliminate the ill-posedness of mathematical models. In addition, the results of the structural fatigue analysis using the identified wind loads were extremely similar to the fatigue analysis results using the original wind loads, with an error of less than 3%.

Distributed Consensus Multi-Distribution Filter for Heavy-Tailed Noise

Distributed state estimation is one of the critical technologies in the field of target tracking, where the process noise and measurement noise may have a heavy-tailed distribution. Traditionally, heavy-tailed distributions like the student-t distribution are employed, but our observation reveals that Gaussian noise predominates in many instances, with occasional outliers. This sporadic reliance on heavy-tailed distributions can degrade performances or necessitate frequent parameter adjustments. To overcome this, we introduce a novel distributed consensus multi-distribution state estimation method that combines Gaussian and student-t filters. Our approach establishes a system model using both Gaussian and student-t distributions. We derive a multi-distribution filter for a single sensor, assigning probabilities to Gaussian and student-t noise models. Parallel estimation under both distributions, utilizing Gaussian and student-t filters, allows us to calculate the likelihood of each distribution. The fusion of these results yields a mixed-state estimation and corresponding error matrix. Recognizing the increasing degrees of freedom in the student-t distribution over time, we provide an effective approximation. An information consensus strategy for multi-distribution filters is introduced, achieving global estimation through consensus on fused local filter results via interaction with neighboring nodes. This methodology is extended to the distributed case, and the recursive process of the distributed multi-distribution consensus state estimation method is presented. Simulation results demonstrate that the estimation accuracy of the proposed algorithm improved by at least 20% compared to that of the traditional algorithm in scenarios involving both Gaussian and heavy-tailed distributions.

Robustness of higher-dimensional nonlocality against dual noise and sequential measurements

Robustness of higher-dimensional nonlocality against dual noise and sequential measurements