Separating Biological Variance from Noise by Applying Expectation-Maximization Algorithm to Modified General Linear Model.

In this study, a novel multiple frame based image and texture independent Convolutional Neural Network (CNN) noise estimator is introduced. Noise estimation is a crucial step for denoising algorithms, especially for ones that are called “non-blind”. The estimator works for additive Gaussian noise for varying noise levels. The noise levels studied in this work have a standard deviation equal to 5 to 25 increasing 5 by 5. Since there is no database for noisy multiple images to train and validate the network, two frames of synthetic noisy images with a variety of noise levels are created by adding Additive White Gaussian Noise (AWGN) to each clean image. The proposed method is applied on the most popular gray level images besides the color image databases such as Kodak, McMaster, BSDS500 in order to compare the results with the other works. Image databases comprise indoor and outdoor scenes that have fine details and richer texture. The estimator has an accuracy rate of 99 % for the classification and favourable results for the regression. The proposed method outperforms traditional methods in most cases. And the regression output can be used with any non-blind denoising method.

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Piecewise linear regression is a fundamental challenge in science and engineering. For typical applications where noise level varies in observations, the problem becomes much more challenging. In this paper, we propose a convex optimization based piecewise linear regression method which incorporates variation of the noise level. More precisely, we newly design a convex data-fidelity function as a weighted sum of approximation errors to mitigate effect of the noise level variation. The weights are automatically adjusted to the varying noise level within the framework of convex optimization. Numerical examples show performance improvements by the proposed method.

Approximations to noisy data are constructed to obtain unimodality of successively updated data. Precisely, a method is developed that calculates least squares monotonic increasing - decreasing approximations to n data, for successive values of n. Having fixed n, the statement of the increasing-–decreasing constraints in terms of first differences of the data subject to one sign change gives rise to a discrete search calculation, because the position of the sign change is also an unknown of the optimization problem. Although this problem may have many local minima for each value of n, the method is made quite efficient by taking advantage of two properties of the optimization calculation. The first property, for fixed n, provides necessary and sufficient conditions that reduce the optimal selection of the position to solving two sequences of monotonic approximation problems to subranges of data in the least complexity O ( n ) . The sufficiency conditions is a new result. The second property shows that the optimal position increases as n increases. Hence, if this position of a fit to the first n−1 data is available, then in order to calculate the required position of a fit to the first n data there is no need to consider data that are before the current position. A Fortran program has been written and some timings of numerical results are given for up to n = 30,000 data and various levels of noise. They seem to be proportional to n. The method is applied to data of daily Covid-19 deaths of the United Kingdom for a period that exhibits a major outbreak, and obtains insights of the evolution of the process as new data enter the calculation. The results reveal certain features of the calculation that may be helpful to supporting policy making, when used in modelling epidemic processes. Further, a procedure is proposed where our method initializes the nonlinear least squares calculation of Richards epidemiological model, and the numerical results confirm some useful advantages over the plain use of this model. One benefit of our method for unimodal smoothing of successive data and estimation of the associated turning points is that it provides a property that appears in a wide range of processes from biology, economics, finance and social sciences, for instance.

Noise levels in an Australian emergency department

Increasing pollution poses serious health risks, necessitating urgent control measures for a healthier future for all. Among various pollution types, sound pollution significantly impacts quality of life and well-being, especially in densely populated urban areas. This proposal describes the creation and deployment of an Internet of Things (IoT)-based noise pollution monitoring system intended to track sound levels in certain regions in real time. The core of this system comprises advanced sensors strategically placed to measure sound intensity accurately. These sensors can detect even minor variations in noise levels, providing precise and reliable data. After being gathered, the data is sent to a microcontroller, which effectively processes it. This microcontroller ensures the data is accurately captured and sent over the internet to an online server. Once the data reaches the online server, it becomes accessible for analysis and monitoring by authorities. The system's real-time data capability is crucial for timely intervention. Authorities can continuously monitor noise levels and identify trends or spikes in sound pollution, allowing them to address issues promptly. This feature is particularly beneficial for sensitive areas such as schools, hospitals, and residential zones, areas with heavy traffic, library, Where excessive noise can have severe health effects, including stress, sleep disturbances, and hearing loss. The system includes an alert mechanism that notifies relevant authorities when noise levels exceed predefined thresholds. These alerts enable quick responses to mitigate noise pollution. For instance, in a school, reducing noise levels can enhance the learning atmosphere and improve students' concentration. In hospitals, maintaining a quiet environment is crucial for patient recovery. The integration of IoT technology in this noise pollution monitoring system not only enhances efficiency but also provides a scalable solution adaptable to various urban and rural settings. This scalability ensures the system can be expanded to cover larger areas or multiple locations as needed.

A new network of 16 broadband seismic stations is operational in the northern Chhattisgarh region to monitor the seismicity arising in the otherwise seismically quiescent zone. Ambient noise is comprehensively analyzed to evaluate station performance and validate the data quality. Power spectral densities are computed for all stations, comparing ambient noise levels against global limits for data from November 2022 to December 2023. Results indicate that noise levels consistently fall within global noise limits. The study emphasizes the pronounced effect of instrumental tilt on the horizontal components of broadband seismometers. Spatial variation of ambient noise levels across various period bands of the noise spectrum highlights the contribution of diverse noise sources such as anthropogenic activities, surface waves, body waves, and barometric effects. Seasonal variations in short-period, microseism, and long-period bands reveal temporal variation of noise levels. Diurnal variation in the short-period band indicates a prominent effect of cultural noise. Furthermore, the proximity of seismic stations to major mining areas induces high noise levels in the very short-period band during blasting activities compared to non-blasting periods. Our study thus explores the variability of the ambient noise environment while validating the robustness and stability of the seismic installation across the study region.

To better understand the depth dependence of improvements in signal-to-noise ratio for borehole seismic data, we have collected and analyzed a data set recorded from a pair of high-fidelity, broadband (1 to 80 Hz) seismometers sited in two closely separated deep boreholes near Amarillo, Texas. The total decrease (surface to 1951 m) in minimum noise level is up to 30 dB; the total decrease in maximum noise level is up to 35 dB; and the range of noise values (maximum to minimum) at a given depth decreases from 30 to 10 dB. The majority of the noise reduction occurs within the first few hundred meters below the surface, but the decrease continues with depth and there is no indication that even lower noise levels do not exist below our deepest recording depth (1951 m). Cultural work-day noise (prominent during normal working hours) from 0 to 40 Hz is observed at all depths, suggesting a strong body-wave component. Wind-generated 15 to 60-Hz noise is strongest at the surface, and is observed as deep as 367 m without a properly shielded hole. With the addition of shielding to reduce the coupling between the borehole casing and/or cable with the wind, wind-generated noise above 40 Hz can be eliminated at 367 m. Events with near-vertical propagation paths and signal power above 1 Hz show signal strength decreasing uphole, with a steady winnowing of high frequencies that can be fit by a simple frequency-independent Q of 35.5 ± 8 for the material between 1951 and 367 m.

The variation of environmental noise outside six dwellings between three seasons

Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil

Purpose: Although many previous studies related to sentence recognition of the hearing-impaired elderly have found their poor performance under various distracting listening conditions, there is still a lack of information for their error type in the sentence. The purpose of present study was to analyze a prominent error type among nine parts of speech in various levels of noise and fast speech conditions for the hearing-impaired elderly. Methods: Seven old listeners with moderate sensorineural hearing loss participated. For estimating their sentence recognition, Korean Speech Perception in Noise was applied for four signal-to-noise ratios (no noise, +6, +3, 0 dB) and four time alteration conditions (±30%, ±15%), while being presented at the most comfortable level to each participant. Total percent error and substitution error patterns of the sentence recognition were analyzed as a function of conditions. Results: The percent error of sentence recognition was increased as either noise level increased or speech rate was faster. Among the parts of speech, noun substitution error pattern showed the highest number for all participants regardless levels of noise and time alteration. Interestingly, the noun substitution error pattern was outstood in quiet and 15% time compression. Conclusion: Results of the present study suggest that aural rehabilitation of the hearing-impaired elderly may focus on reducing and correcting the noun substitution error to improve their sentence recognition.

The expressivity of machine learning algorithms is considered to be critical in intelligent data analysis tasks for practical application. As an alternative set of classification rule learning algorithms to conventional decision tree, Prism family of algorithms induce modular rules concisely, thus exhibiting good expressiveness for human users. However, existing Prism rule induction techniques are limited by the assumption of Gaussian distribution for quantitative attributes, and may not be available for real life data analyzing, in which skewness is commonly observed. For this reason, we investigate a skew-adaptive mechanism for rule term boundary delimitation in Prism inductive learning. The propose algorithm, called P2-Prism, could learn expressive classification rules directly from quantitative data beyond Gaussian distribution. By employing statistical inference characteristics of Poisson process, our mechanism provides a significant contribution to classification rule inductive learning with adaption of skewed data distribution. The experimental evaluation of our algorithm demonstrates its skew-adaptive superiority on benchmark datasets, comparing with state-of-the-art algorithms. Furthermore, it is shown that P2-Prism is a robust classifier in the presence of various levels of noise, which further reveals its adaptability to the skewness of data distribution.

BackgroundTwo-dimensional gel electrophoresis (2DE) is a powerful technique to examine post-translational modifications of complexly modulated proteins. Currently, spot detection is a necessary step to assess relations between spots and biological variables. This often proves time consuming and difficult when working with non-perfect gels. We developed an analysis technique to measure correlation between 2DE images and biological variables on a pixel by pixel basis. After image alignment and normalization, the biological parameters and pixel values are replaced by their specific rank. These rank adjusted images and parameters are then put into a standard linear Pearson correlation and further tested for significance and variance.ResultsWe validated this technique on a set of simulated 2DE images, which revealed also correct working under the presence of normalization factors. This was followed by an analysis of p53 2DE immunoblots from cancer cells, known to have unique signaling networks. Since p53 is altered through these signaling networks, we expected to find correlations between the cancer type (acute lymphoblastic leukemia and acute myeloid leukemia) and the p53 profiles. A second correlation analysis revealed a more complex relation between the differentiation stage in acute myeloid leukemia and p53 protein isoforms.ConclusionThe presented analysis method measures relations between 2DE images and external variables without requiring spot detection, thereby enabling the exploration of biosignatures of complex signaling networks in biological systems.

A hypothesis-driven method based on machine learning for neuroimaging data analysis

<div class="section abstract"><div class="htmlview paragraph">Gear noise and vibration in automobile transmissions is a phenomenon of great concern. Noise generated at the gearbox, due to gear meshing, also known as gear whine, gets transferred from the engine cabin to the passenger cabin via various transfer paths and is perceived as air borne noise to the passengers in the vehicle. This noise due to its tonal nature can be very uncomfortable to the passengers. Optimizing micro-geometry of a gear pair can help in improving the stress distribution on tooth flank and reducing the sound level of the tonal noise generated during the running of the gearbox when that gear pair is engaged. This technical paper contains the study of variation in noise level in passenger cabin and contact on tooth flank with change in micro-geometry parameters (involute slope and lead slope) of a particular gear pair. Further scope of study has been discussed at the end of the paper.</div></div>