White Noise Research Articles

Multisensory stimulation to reduce procedural pain in retinopathy of prematurity: A randomized controlled trial

AbstractBackgroundRetinopathy is frequently seen in the neonatal intensive care unit (NICU), and its examination is a painful procedure for infants.AimThis randomized active‐controlled trial aimed to investigate the impact of multisensory stimulation (MSS) on neonatal pain during retinopathy of prematurity (ROP) examinations, in comparison with a white noise (WN) and control group receiving standard care.Study DesignConducted as a three‐arm, randomized controlled trial, the study was implemented in the NICU of a local university hospital. Recruitment spanned from July 2023 to November 2023, with preterm infants (gestational age < 37 weeks) randomly assigned to either a MSS, WN or a control group. MSS components included visual, auditory, tactile, olfactory and gustatory stimuli, all designed to create a synergistic, comforting environment for the infant during the procedure. Procedural pain, heart rate and oxygen saturation were assessed at various stages before and after ROP examinations.ResultsAnalysis of 90 participants revealed that the MSS group exhibited lower Premature Infant Pain Profile (PIPP) scores than the WN and control groups (mean difference: −2.12, 95% confidence interval [CI]: −2.62 to −1.62; odds ratio [OR]: 0.004, 95% CI [0.001, 0.012], p < 0.001). Additionally, heart rates were significantly lower in the MSS group (mean difference: −15.3 beats/min, 95% CI: −20.5 to −10.1; OR: 0.025, 95% CI [0.008, 0.073], p < .001) and oxygen saturation levels were higher (mean difference: 3.2%, 95% CI: 1.8% to 4.6%; OR: 1.12, 95% CI [1.05, 1.20], p < .001) than in the other groups.ConclusionsMSS emerges as a favourable, safe and non‐pharmacological intervention for pain management in ROP and similar procedures.Relevance to Clinical PracticeMultisensory stimulation can be effectively integrated into the routine care provided by critical care nurses during retinopathy of prematurity examinations in preterm infants. This non‐pharmacological intervention offers a practical approach for critical care nurses to reduce procedural pain and improve physiological stability in this vulnerable population.

A new spectral measure of complexity and its capabilities for detecting signals in noise

This article is devoted to the improvement of signal recognition methods based on the information characteristics of the spectrum. A discrete function of the normalized ordered spectrum is established for a single window function included in the DFT. Lemmas on estimates of entropy, imbalance and statistical complexity in processing a time series of independent Gaussian quantities are proved. New concepts of one-dimensional and two-dimensional spectral complexities are proposed. The theoretical results obtained were verified by numerical experiments, which confirmed the effectiveness of the new information characteristic when detecting a signal mixed with white noise at low signal-to-noise ratios.

Novel sounds, native responses: exploring the acoustic consequences of Eleutherodactylus johnstonei’s invasion in urban areas

BackgroundBiological invasions pose a critical threat to biodiversity, affecting ecological balance and native species’ communication. Eleutherodactylus johnstonei, an exotic anuran in São Paulo, vocalizes at intensities that could interfere with native anuran species, potentially causing acoustic masking.MethodsWe evaluated the effects of E. johnstonei's calls on the vocalizations of two native species, Scinax imbegue and Physalaemus cuvieri, both with and without spectral overlap with the invasive species. Field playbacks were conducted using six versions of stimuli, including E. johnstonei's calls, the native Boana bischoffi (as a control), and white noise. We recorded response calls and behavioral changes of S. imbegue and P. cuvieri males.ResultsThe calls of E. johnstonei did not affect the spectral or temporal parameters of the native species’ announcement calls. However, S. imbegue males displayed behavioral responses such as cessation of vocalization or movement away from the noise source. Additionally, B. bischoffi's calls and white noise influenced native species’ call parameters.DiscussionOur findings reveal that exotic species’ vocalizations may disrupt native anurans’ acoustic behavior. This impact varies with species and context, underlining the need for further research on anuran acoustic interactions across different frequencies and acoustic environments to fully understand the effects of exotic acoustic interference.

Selecting a Time-Series Model to Predict Drinking Water Extraction in a Semi-Arid Region in Chihuahua, Mexico

As the effects of global climate change intensify, it is increasingly important to implement more effective water management practices, particularly in arid and semi-arid regions such as Meoqui, Chihuahua, situated in the arid northern center of Mexico. The objective of this study was to identify the optimal time-series model for analyzing the pattern of water extraction volumes and predicting a one-year forecast. It was hypothesized that the volume of water extracted over time could be explained by a statistical time-series model, with the objective of predicting future trends. To achieve this objective, three time-series models were evaluated. To assess the pattern of groundwater extraction, three time-series models were employed: the seasonal autoregressive integrated moving average (SARIMA), Prophet, and Prophet with extreme gradient boosting (XGBoost). The mean extraction volume for the entire period was 50,935 ± 47,540 m3, with a total of 67,233,578 m3 extracted from all wells. The greatest volume of water extracted has historically been from urban wells, with an average extraction of 55,720 ± 48,865 m3 and a total of 63,520,284 m3. The mean extraction volume for raw water wells was determined to be 20,629 ± 19,767 m3, with a total extraction volume of 3,713,294 m3. The SARIMA(1,1,1)(1,0,0)12 model was identified as the optimal time-series model for general extraction, while a “white noise” model, an ARIMA(0,1,0) for raw water, and an SARIMA(2,1,1)(2,0,0)12 model were identified as optimal for urban wells. These findings serve to reinforce the efficacy of the SARIMA model in forecasting and provide a basis for water resource managers in the region to develop policies that promote sustainable water management.

Empirical Modal Decomposition Combined with Deep Learning for Photoacoustic Spectroscopy Detection of Mixture Gas Concentrations.

In photoacoustic spectroscopy based multicomponent gas analysis, the overlap of the absorption spectra among different gases can affect the measurement accuracy of gas concentrations. We report a multicomponent gas analysis method based on empirical modal decomposition (EMD), convolutional neural networks (CNN), and long short-term memory (LSTM) networks that can extract the exact concentrations of mixed gases from the overlapping wavelength-modulated spectroscopy with second harmonic (WMS-2f) detection. The WMS-2f signals of 25 different concentration combinations of acetylene-ammonia mixtures are detected using a single distributed feedback laser (DFB) at 1531.5 nm. The acetylene concentrations range from 2.5 to 7.5 ppm and the ammonia concentrations from 12.5 to 37.5 ppm. The data set is enhanced by cyclic shifting and adding Gaussian noise. The classification accuracy of the test set reaches 99.89% after tuning. The mean absolute errors of the five additional sets of data measured under different conditions are 0.092 ppm for acetylene and 1.902 ppm for ammonia, within the above concentration ranges.

Study on Cubic Stiffness Nonlinear Energy Sink Controlling Dynamic Responses of Multi-Degree-of-Freedom Structure by Shake Table Tests

A nonlinear energy sink (NES) has such advantages as controlling broader band responses and better robustness than conventional control devices like tuned mass dampers (TMDs). In this research, a cubic stiffness NES mitigating the dynamic responses of a multi-degree-of-freedom structure under white noise, harmonic and seismic excitations was tested using a shake table, and the influences of the parameters of the NES on vibration mitigation effects were investigated. The test results indicate that the NES has the same vibration mitigation effects on the acceleration responses under the white noise and harmonic excitations as TMDs, even though the mass ratio of the NES is less than that of a TMD. The average control effects of the NES on the acceleration responses of the structure under the effect of 100 seismic waves are better than those of a TMD, which indicates that an NES has better robustness than a TMD.

Noise assessment of cold atom interferometry gradiometry observations

Over the past few decades, the development of interferometric techniques utilizing cold atoms has significantly advanced, particularly in the realm of gravimetry. This paper explores the characterization of noise within atomic gradiometry systems, which is essential for enhancing measurement accuracy as new methodologies and tools are introduced. Specifically, we examine the noise components using advanced techniques such as least squares component estimation and Allan variance analysis. Applying these sophisticated methods to two distinct datasets, our results robustly indicate that white noise is the predominant and most resilient type of noise in gravity gradiometry measurements based on atomic interferometry, with variances of σP12=2.97×10−8 for dataset 1 and σP22=5.66×10−7 for dataset 2. The predominance of white noise simplifies the system dynamics and enhances the reliability of the estimation methods. Furthermore, our calculations yield an uncertainty in the gravity gradient resolution of σ Γ1 = 0.13 E (where 1 E = 10−9 s−2) and σ Γ2 = 3.55 E over time, underscoring the high precision achievable with current interferometric techniques.

Feedback Stabilization of Chain-Like MDOF Nonlinear Structural Systems Under Purely Parametric Gaussian White Noises

A feedback control method is proposed to asymptotically stabilize the chain-like multi-degree-of-freedom (MDOF) nonlinear structural system under purely parametric Gaussian white noises. First, the motion equation of a chain-like nonlinear structural system with [Formula: see text] coupled elements and an undetermined control law is derived. It is reduced to a one-dimensional partially averaged Itô stochastic differential equation of the controlled Hamiltonian by applying the stochastic averaging method, which bypasses the challenge of calculating multiple Lyapunov exponents. Next, based on the dynamical programming principle, the dynamical programming equation of the averaged system with an undetermined cost function is established and addressed to obtain the optimal control law. Then, the Lyapunov exponent of the completely averaged Itô equation is derived. For the given requirement of stabilizing the system, the Lyapunov exponent is entirely fixed and used to analyze the stability of the originally chain-like MDOF nonlinear structural system. A controlled chain-like 8-DOF nonlinear structural system is taken as an example to illustrate the application and effectiveness of the proposed procedure and the effect of control forces on stabilizing the system.

Enhanced auditory responses in visual cortex of blind rats using intrinsic optical signal imaging

Functional maturation of the visual cortex is induced by visual experiences during critical periods. Blind animals and humans exhibit improved auditory abilities after losing their vision. Here we investigated the response of the visual cortex to white noise stimuli during the progression of photoreceptor degeneration in a rat model of blindness (Royal College of Surgeons [RCS] (rdy/rdy) rats). Optical coherence tomography of RCS (+/+) rats with normal visual function revealed normal photoreceptor cells, whereas 3-month-old RCS (rdy/rdy) rats demonstrated photoreceptor cell degeneration. Visual cortex responses (VCRs) to a single flash stimulus were negligible in 3-month-old photoreceptor-degenerated rats. However, VCRs with white noise stimuli were significantly increased in blind versus RCS rats (+/+). Slight changes in the intrinsic optical signals of the control rats were observed on the ventral side of the visual cortex. In contrast, responses were markedly increased throughout the visual cortex of RCS (rdy/rdy) rats. These results indicate that the visual cortex rapidly acquires auditory system function over the first 3 months of life and that the entire visual cortex, rather than just the portion close to the auditory cortex, responds to white noise.

Utilization of Stochastic P-bifurcation for Simultaneous Energy Harvesting and Vibration Suppression: An Experimental Investigation

Abstract This research focuses on the prediction and experimental verification of P-bifurcation as well as the effectiveness in reducing vibrations and harvesting energy with the use of an inertially nonlinear energy harvesting device attached to a single-degree-of-freedom structure subjected to Gaussian broadband base excitation, modeled as white noise. Four experimental scenarios were tested, including three with different resistive loads and one with an open circuit. Frequency domain optimization involved an optimization routine that was designed to minimize the mean squared error in the pendulum velocity's frequency content below two cycles per second while constraining the RMS velocity discrepancy between the simulations and actual experiments to be below 3%. This facilitated accurate predictions of power, vibration suppression, and P-bifurcation. Using the fitted model, an analytically derived P-bifurcation boundary in the noise intensity versus electrical damping plane was presented and experimentally verified. Additionally, power spectral densities for electric power and relative suspended mass velocity were determined for the IPVA system and compared with a top-performing linear system. Results indicated that the monomodal system was the least effective in energy harvesting, while the bimodal and rotational systems significantly enhanced mean and resonant peak power by up to four and two times, respectively. Near the resonant frequency, the peak relative velocity power spectral density decreased by a factor of four, and the mean square relative velocity improved by a factor of two.

High-precision IFM receiver based on random forest algorithm

Abstract The six-port network-based Instantaneous Frequency Measurement (IFM) receiver is widely utilized in industrial applications for signal frequency demodulation. However, current IFM receivers face the issue of low algorithmic demodulation accuracy (ACC). According to the four-channel output characteristics of the IFM receiver, we use the Random Forest algorithm to extract features, train and test the output voltage data set, and recognize frequency labels. The experimental results show that the ACC of the proposed algorithm reaches 0.9583, which is 13.99% higher than that of the traditional polynomial fitting algorithm. To evaluate the noise reduction performance of the algorithm, we add Gaussian white noise to the original data and demodulate the signal with noise. Comparative tests prove the advantages of the algorithm in high stability and flexibility. Finally, multiple model evaluation indicators demonstrate that the algorithm features strong applicability and robustness to the experimental data with IFM receiver.

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

A novel carrier index M‐ary differential chaos shift keying modulation scheme

AbstractTo obtain better spectral efficiency and higher bit rate, a novel carrier index M‐ary differential chaos shift keying modulation scheme is proposed. In this system, part of subcarriers is assigned to the chaotic reference so that it can carry extra data bits through carrier index modulation, while one of the remaining subcarriers is activated to transmit the data‐bearing signal. In addition, M‐ary DCSK modulation is also applied to data‐bearing signals based on the constellation theory and Walsh codes. Theoretical bit error rate expressions are derived over the multipath Rayleigh fading and additive white Gaussian noise channels. Simulations and comparisons are performed with various combinations of chaotic sequence lengths, subcarrier numbers and constellation sizes. Results show that the proposed scheme can outperform other counterparts in both spectral efficiency and bit error rate performances.

Recognition and classification of noise signals by dolphins under conditions of noise interference and spatial uncertainty of their simultaneous presentation

The ability of dolphin`s auditory system to recognize and classify noise signals according to certain invariant features under the influence of noise interference and spatial uncertainty of their simultaneous presentation was studied. The bottlenose dolphins trained to differentiate such signals had to solve this problem under conditions simulating real marine ones, when the perception of a useful signal occurs against the background of similar signals and noise interference. First, the noise signals were sequentially presented to the animal against the background of white masking noise. Further the dolphin had to identify a signal of a positive class from several simultaneously sounding sound sources. The efficiency of the animal was evaluated at several given levels of noise interference. In this case, the actual noise interference was both white noise and simultaneously sounding negative signals. It`s shown that the efficiency and noise immunity of dolphin`s auditory system depend on the degree of alternativeness of the spatial uncertainty of the simultaneous presentation of signals.

Figure-ground segmentation based medical image denoising using deep convolutional neural networks

Medical image noise can have a substantial impact on both diagnosis accuracy and image quality. Noise can cause problems with tasks related to diagnosis, like defining object boundaries, which are essential for precise diagnosis. By applying denoising techniques, medical imaging professionals can significantly improve image quality, reduce errors, and enhance the accuracy of diagnoses and treatments. This article presents a method for reducing noises like Gaussian noise, salt and pepper noise, speckle noise, and ring artefacts in medical images, such as magnetic resonance imaging (MRI), computed tomography (CT), and chest X-ray images. This study explores the integration of adaptive CNNs with guided image filtering for enhanced image quality and deep learning-driven Figure-Ground segmentation. The proposed method's performance is extensively evaluated on chest X-ray and MRI/CT images under diverse noise scenarios, with comparisons to existing techniques using established statistical metrics. The results validate that the proposed approach attains superior performance, yielding the highest values across these metrics.

Physics-informed ensemble learning with residual modeling for enhanced building energy prediction

Accurate modeling of building energy use is important to support a diverse spectrum of its downstream applications, such as building energy efficiency assessment, resilience analysis, smart control, etc. As mainstream approaches of building energy modeling, physics-based modeling builds on different fidelities of physics rules yet are usually compromised in modeling accuracy due to insufficiency of physics rules to capture real-world dynamics and incomplete input information. Data-driven approaches are computationally efficient, but black box (uninterpretable) in nature. For improved modeling of building energy use, this work proposes a physics-informed ensemble learning approach in building energy prediction through residual modeling. Specifically, we first analyze the components of building energy use data. Evidence suggests that the building energy use data can be decomposed into the physics-driven part, occupant-driven part, and white noise. Second, high-fidelity physics-based building models (EnergyPlus) and low-fidelity ones (RC models) are developed to capture the physics-driven part while time series methods are explored as the residual modeling approach to capture the occupant-driven discrepancies between physics-based simulation and measured building energy use (i.e., residuals). Finally, the physics-informed ensemble learning is proposed to integrate physics-based and data-driven models for enhanced accuracy of building energy modeling. Results demonstrate 40–90% decrease of discrepancies between modeling and field observations compared to traditional physics-based modeling methods. Moreover, when the training dataset size is small, the proposed ensemble model overperforms the pure data-driven models, demonstrating its higher robustness in extrapolation scenarios. This work makes fundamental contributions to the development of convergent modeling approaches in the building modeling field.

Deep evidential learning for radiotherapy dose prediction

Background:As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Method:Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. Results:We found that (i)epistemic uncertainty was highly correlated with prediction errors, with various association indices comparable or stronger than those for Monte-Carlo Dropout and Deep Ensemble methods, (ii)the median error varied with uncertainty threshold much more linearly for epistemic uncertainty in Deep Evidential Learning relative to these other two conventional frameworks, indicative of a more uniformly calibrated sensitivity to model errors, (iii)relative to epistemic uncertainty, aleatoric uncertainty demonstrated a more significant shift in its distribution in response to Gaussian noise added to CT intensity, compatible with its interpretation as reflecting data noise. Conclusion:Collectively, our results suggest that Deep Evidential Learning is a promising approach that can endow deep-learning models in radiotherapy dose prediction with statistical robustness. We have also demonstrated how this framework leads to uncertainty heatmaps that correlate strongly with model errors, and how it can be used to equip the predicted Dose-Volume-Histograms with confidence intervals.

Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The incidence of brain cancer-related mortality has risen from 140,000 to 250,000, accompanied by a doubling in new diagnoses from 175,000 to 350,000. In response, magnetic resonance imaging (MRI) has emerged as a pivotal diagnostic tool, facilitating early detection and treatment planning. However, the translation of deep learning approaches to brain cancer diagnosis faces a critical obstacle: the scarcity of public clinical datasets reflecting real-world complexities. This study aims to bridge this gap through a comprehensive exploration and augmentation of training data. Initially, a battery of pre-trained deep models undergoes evaluation on a main brain cancer MRI “BT-MRI” dataset, yielding remarkable performance metrics, including 100% accuracy, precision, recall, and F1-Score, substantiated by the Score-CAM methodology. This initial success underscores the potential of deep learning in brain cancer diagnosis. Subsequently, the model’s efficacy undergoes further scrutiny using a supplementary brain cancer MRI “BCD-MRI” dataset, affirming its robustness and applicability across diverse datasets. However, the ultimate litmus test lies in confronting the model with synthetic testing datasets crafted to emulate real-world scenarios. The synthetic testing datasets, a BCD-MRI testing sub-dataset enriched with noise, blur, and simulated patient motion, reveal a sobering reality: the model’s performance plummets, exposing inherent limitations in generalization. To address this issue, a diverse set of optimization strategies and augmentation techniques, ranging from diverse optimizers to sophisticated data augmentation methods, are exhaustively explored. Despite these efforts, the problem of generalization persists. The breakthrough emerges with the integration of noise and blur as augmentation techniques during the training process. Leveraging Gaussian noise and Gaussian blur kernels, the model undergoes a transformative evolution, exhibiting newfound robustness and resilience. Retesting the refined model against the challenging synthetic datasets reveals a remarkable transformation, with performance metrics witnessing a notable ascent. This achievement underscores the important role of correct selection of data augmentation in fortifying the generalization of deep learning models for brain cancer diagnosis. This study not only advances the frontiers of diagnostic precision in brain cancer but also underscores the paramount importance of methodological rigor and innovation in confronting the complexities of real-world clinical scenarios.

Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data-assimilation

Cardiac ventricular myocyte action potential dynamics are regulated by intricate and nonlinear interactions between the cell transmembrane potential and ionic currents and concentrations. Present technology limits the ability to measure transmembrane potential and multiple ionic currents simultaneously, which narrows the scope of experiments to provide a complete snapshot of the cardiac myocyte state. This limitation presents an obstacle for understanding how perturbations can trigger arrhythmias and more broadly how the myocyte responds to different conditions, such as changes in pacing rate or responses to drug treatment. In this study, we demonstrate that a data-assimilation approach can successfully reconstruct and predict the dynamics of a heterogeneous virtual cardiac ventricular myocyte population in the presence of parameter uncertainty. A population of heterogeneous cardiac ventricular myocytes is generated by varying ionic current conductance parameters, and additional observational uncertainty is mimicked by the addition of Gaussian noise to the transmembrane potential. We demonstrate that the data-assimilation approach accurately reconstructs transmembrane potential, with error less than the magnitude of the noise. Further, the data-assimilation approach successfully estimates the conductances of ionic currents generally with high accuracy and requiring low computational time. As a proof of concept, we apply the data-assimilation approach to reconstruct action potential dynamics from optical mapping experiments in an ex vivo isolated guinea pig heart. Critically, we demonstrate that the ionic conductance parameters estimated from a recording at one pacing frequency can accurately predict action potential dynamics at different rates.

An ultra-fast MMC-HVDC fault location algorithm based on transient voltage features and regression neural network

An ultra-fast fault location algorithm based on the single-ended transient voltage features and regression neural network (RNN) is proposed, which utilizes 2.5 ms postfualt data window and is suitable for modular multilevel converter-based high-voltage DC (MMC-HVDC) grids equipped with quick-action protections and hybrid DC circuit breakers (HDCCBs). Firstly, the analyses based on the lumped RLC equivalent circuit demonstrate that the delay time, the first negative peak time and its value all have exact relationships with the fault location. Nevertheless, considering the actual parameters and topology of the MMC-HVDC grid, three features can only be approximately extracted. Thus, RNN is utilized to estimate fault locations. 2.02 × 104 distinct fault cases validate the algorithm’s high accuracy across all fault locations and transition resistances up to 1005 Ω. It can well tolerate reasonable deviations of line parameter and current limiting reactor value, as well as 40-dB white noise. Besides, it also has remarkable adaptability, and can be suitable for different systems.