Search

A study of 20 factories belonging to textile, printing, publishing and paper products industries in Jeddah was conducted. Data on Leq, Maximum and Minimum SPL at different octave bands, as well as dBA, were collected. It has been found that textile, publishing and paper products industries are the most noisy industries. The paper is concluded with suitable recommendation for noise control and worker protection. [ABSTRACT FROM AUTHOR]

The recognition of telugu hand written text is been one of the problems in many applications. To overcome the problem a deep learning technique is proposed in this work i.e. a Dense convolutional neural network (DCNN) model. A telugu dataset which is taken form IIIT-HW-Telugu is utilized to perform the proposed model. In this paper a four stage telugu word retrieval is performed, initially thinning of image is performed using morphological operation, secondly Densenet-CNN is applied for thinning image, thirdly perform OCR based image segmentation, finally two models like HARRIS and BRISK features to extract the features and evaluate information from the given input HWT images. The parameters evaluated are hamming distance, PSNR, MSE, Noise Sensitivity and rate of thinning. The proposed model outperformed well compared to other methods. The PSNR obtained using proposed model is 54.74, hamming distance is 1.2. [ABSTRACT FROM AUTHOR]

As the eLoran signal propagates, its strength gradually diminishes with increasing distance, making subsequent signal capture and terminal development challenging. To address this phenomenon, this paper proposes an improved method based on wavelet hard thresholding. This method applies hierarchical processing to the coefficients obtained after wavelet decomposition, based on the signal's center frequency. It effectively addresses issues like the disappearance of trailing edges and the presence of the noise with large coefficients. Simulation results show that the improved method has the largest output signal-to-noise ratio and effectively improves the problem of tailing vanishing and eliminates the noise with large coefficients. In analog source signal testing, the results show that the method can extract signals of 30 dBμv/m and above well. In actual signal testing, the improved method can extract eLoran signals transmitted over a distance of approximately 1000 km. Based on the results, it can be deduced that the input signal-to-noise ratio is −28.8 dB. Therefore, this method is a suitable and effective solution for extracting weak eLoran signals, providing strong support for signal monitoring in areas at the coverage boundaries of eLoran signals. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for fault detection and isolation (FDI) in transient conditions using a multiway principal component analysis (MPCA) approach where practical data have been augmented with simulated data to conduct FDI when there are insufficient practical data. The motivation for using a heated two-tank system is due to the fact that it resembles a basic process in terms of controllable variables, noise, disturbances, and changes in operating points. Normal and faulty condition data of the practical heated two-tank system as well as a Simulink® model of the heated two-tank system were used. The MPCA technique has enhanced ability to detect and isolate faults in transient conditions compared to classic principal component analysis (PCA). MPCA, however, requires a vast amount of normal process transient conditions data to train the model to then enable meaningful fault detection and isolation. In this study, the practical normal transient conditions data are augmented with simulated normal transient conditions data to meet the requirement of a large amount of data. Utilising different datasets for the training of the MPCA model, the fault detection and isolation performance was evaluated with various metrics. This paper presents positive results towards the implementation of MPCA for fault detection in transient conditions. [ABSTRACT FROM AUTHOR]

Monopole time reversal is an advanced noise source localization technique, but its sidelobe level in localizing sound sources is greatly affected by noise, leading to a decrease in the accuracy of sound source localization under a low signal-to-noise ratio. To address this issue, this paper proposes a time-domain monopole time reversal technique that excludes autocorrelation to reduce the impact of noise on sound source localization results. Numerical simulation and experimental results show that, compared to conventional monopole time reversal techniques, the proposed method can effectively reduce the sidelobes in sound source localization results and achieve accurate sound source localization under −20 dB low signal-to-noise ratio conditions. [ABSTRACT FROM AUTHOR]

The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at varying stages. This approach facilitates the assimilation of complementary features across scales, significantly bolstering the model's capacity for feature extraction. Furthermore, we constructed a dataset named Roushi60, which consists of 60 different categories of common meat dishes. Empirical evaluation of the ETH Food-101, ChineseFoodNet, and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%, 82.86%, and 92.50%, respectively. These figures not only mark an improvement of 1.04%, 3.42%, and 1.36% over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods. Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition, setting a new benchmark for the field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Atomic & Molecular Physics (1000-0364) is the property of Journal of Atomic & Molecular Physics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Signal‐to‐noise ratio and spatial resolution are quantitatively analysed in the context of in‐line (propagation based) X‐ray phase‐contrast imaging. It is known that free‐space propagation of a coherent X‐ray beam from the imaged object to the detector plane, followed by phase retrieval in accordance with Paganin's method, can increase the signal‐to‐noise in the resultant images without deteriorating the spatial resolution. This results in violation of the noise‐resolution uncertainty principle and demonstrates 'unreasonable' effectiveness of the method. On the other hand, when the process of free‐space propagation is performed in software, using the detected intensity distribution in the object plane, it cannot reproduce the same effectiveness, due to the amplification of photon shot noise. Here, it is shown that the performance of Paganin's method is determined by just two dimensionless parameters: the Fresnel number and the ratio of the real decrement to the imaginary part of the refractive index of the imaged object. The relevant theoretical analysis is performed first, followed by computer simulations and then by a brief test using experimental images collected at a synchrotron beamline. More extensive experimental tests will be presented in the second part of this paper. [ABSTRACT FROM AUTHOR]

The phenomenon of multiple tones is a typical feature of high-lift configuration noise, and the underlying nonlinear and nonstationary features need to be revealed through time-frequency analysis. This paper introduces the Hilbert–Huang transform method to detect the inherent amplitude modulation and frequency modulation phenomena of multiple tones from 30P30N three-element high-lift configuration with both slat and flap completely stowed. The acoustic modes are extracted from the multiple tones firstly and then the variation features of amplitude and instantaneous frequency are analyzed. The results show that the acoustic energy concentrates on the primary mode with much larger amplitude. Moreover, both the amplitude and instantaneous frequency are periodically varied in time and the predicted modulation frequencies are equal to the frequency intervals between nearby tones, confirming that the inherent temporal features of multiple tones are amplitude modulation and frequency modulation. [ABSTRACT FROM AUTHOR]

The paper presents the noise analysis of a MEMS and ASIC readout integrated circuit (ROIC) constituting the accelerometer developed in the frame of the InnoReh project, aiming at the development of methods for monitoring patients with imbalance disorders. Several experiments were performed at different temperatures and in different configurations: ROIC alone, ROIC with emulated parasitic capacitances, MEMS and ROIC in separate packages, and MEMS and ROIC in a single package. Many noise/interference sources were considered. The results obtained experimentally were compared to the results of theoretical investigations and were within the same order of magnitude, although in practice, the observed noise was always greater than the theoretical estimation. The paper also includes an in-depth analysis to explain these differences. Moreover, it is argued that, in terms of noise, the MEMS sensing element, and not the ROIC, is the quality-limiting factor. [ABSTRACT FROM AUTHOR]

Multifactor uncertain differential equations are powerful tools for studying dynamic systems under multi-source noise. A key challenge in this study is how to accurately estimate unknown parameters based on the framework of uncertainty theory in multi-source noise environments. To address this core problem, this paper innovatively proposes a least-squares estimation method. The essence of this method lies in constructing statistical invariants with a symmetric uncertainty distribution based on observational data and determining specific parameters by minimizing the distance between the population distribution and the empirical distribution of the statistical invariant. Additionally, two numerical examples are provided to help readers better understand the practical operation and effectiveness of this method. In addition, we also provide a case study of JD.com's stock prices to illustrate the advantages of the method proposed in this paper, which not only provides a new idea and method for addressing the problem of dynamic system parameter estimation but also provides a new perspective and tool for research and application in related fields. [ABSTRACT FROM AUTHOR]

Consider several source nodes communicating across a wireless network to a destination node with the help of several layers of relay nodes. Recent work by Avestimehr has approximated the capacity of this network up to an additive gap. The communication scheme achieving this capacity approximation is based on compress-and-forward, resulting in noise accumulation as the messages traverse the network. As a consequence, the approximation gap increases linearly with the network depth. This paper develops a computation alignment strategy that can approach the capacity of a class of layered, time-varying wireless relay networks up to an approximation gap that is independent of the network depth. This strategy is based on the compute-and-forward framework, which enables relays to decode deterministic functions of the transmitted messages. Alone, compute-and-forward is insufficient to approach the capacity as it incurs a penalty for approximating the wireless channel with complex-valued coefficients by a channel with integer coefficients. Here, this penalty is circumvented by carefully matching channel realizations across time slots to create integer-valued effective channels that are well suited to compute-and-forward. Unlike prior constant gap results, the approximation gap obtained in this paper also depends closely on the fading statistics, which are assumed to be i.i.d. Rayleigh. [ABSTRACT FROM PUBLISHER]

Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio. [ABSTRACT FROM AUTHOR]

Purpose: The mechanical fault diagnosis method based on deep learning mainly uses single-scale convolution kernels to extract fault features, which is difficult to extract fault feature information comprehensively. Under strong noise conditions, the performance of fault diagnosis using single-scale convolution kernels will decrease sharply. Methods: An intelligent fault diagnosis method based on multi-scale deep convolution neural network (MSD-CNN) model and data enhancement for strong noise is proposed in this paper. By the multi-scale cascade convolution kernels in the MSD-CNN, the multi-scale information of the original fault signal is extracted, and the ELU activation function is used to retain the negative information contained in the multi-scale information. By the data enhancement method for strong noise, the number and diversity of the MSD-CNN model training samples are improved, which enables the model to learn deeper features in the training stage. Results and Conclusion: Compared six common fault diagnosis models, the proposed model achieves the optimal diagnostic accuracy of 99.98% and 99.66% under normal conditions and strong noise conditions, respectively, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We establish a large deviation principle (LDP) for a class of stochastic porous media equations driven by Lévy-type noise on a σ-finite measure space (E,B(E),μ), with the Laplacian replaced by a negative definite self-adjoint operator. One of the main contributions of this paper is that we do not assume the compactness of embeddings in the corresponding Gelfand triple, and to compensate for this generalization, a new procedure is provided. This is the first paper to deal with LDPs for stochastic evolution equations with Lévy noise without compactness conditions. The coefficient Ψ is assumed to satisfy nondecreasing Lipschitz nonlinearity, so an important physical problem covered by this case is the Stefan problem. Numerous examples of negative definite self-adjoint operators are applicable to our results, for example, for open E⊂Rd, L= Laplacian or fractional Laplacians, i.e., L=-(-Δ)α, α∈(0,1], generalized Schrödinger operators, i.e., L=Δ+2∇ρ/ρ⋅∇, Laplacians on fractals is also included. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented which discusses topics within the issue including various aspects of small caliber firearm noise exposures, firearm noise that is not typically experienced by persons using firearms at a target range or when hunting, and noise exposure risks.

The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) presents new opportunities for the retrieval of trace gases in the upper troposphere and lower stratosphere. The radiometric calibration of the measured signal is achieved using in-flight measurements of reference blackbody and upward-pointing "deep space" scenes. In this paper, we present techniques developed specifically to calibrate GLORIA data exploiting the instrument's imaging capability. The algorithms discussed here make use of the spatial correlation of parameters across GLORIA's detector pixels in order to mitigate the noise levels and artefacts in the calibration measurements. This is achieved by combining a priori and empirical knowledge about the instrument background radiation with noise-mitigating compression methods, specifically low-pass filtering and principal component analysis. In addition, a new software package for the processing of GLORIA data is introduced which allows us to generate calibrated spectra from raw measurements in a semi-automated data processing chain. [ABSTRACT FROM AUTHOR]

This paper presents an implementation of an ~daptive beamformer in a hands-free unit. The proposec~ ~daptive beamformer suppresses stationary noise an~ nterference sound. The adaptive beamformer and acoustic `cho canceller were implemented in a compact hands-free mit with a low-cost DSP. Experimental results demonstrate he noise reduction performance of the hands-free unit'. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so-called peer group concept. Compared to the vector median filter and other commonly used multichannel filters, the proposed technique consistently yields very good results in suppressing both the random and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of color images in real-time applications. [Copyright &y& Elsevier]

The advent of the Internet of Things (IoT) has contributed to an increase in the production volume of RF-featured equipment. According to statistics from the literature, the IoT industry will soon deploy billions of products. While the concept behind these applications seems exciting, this paper sought to assess the effects the radio emissions produced by IoT products would have on the ambient radio noise levels within the unlicensed frequency bands of 433 MHz, 868 MHz, and 2.4 GHz. The study extended to three environments: industrial, urban, and suburban. This study developed an IoT noise generator (ING) device to emulate RF noise signals in the desired IoT radio transmission band. The paper presents a simplified radio noise surveying system (RNSS) for data collection of ambient radio noise from five South African candidate sites. The statistical and empirical analysis agree that the level of ambient radio noise was directly proportional to the rate of IoT radio activities. The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Approximately 20% of the data show declining trends. [ABSTRACT FROM AUTHOR]

Copyright of Working Papers on Operations Management is the property of Editorial UPV and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper considers the determination of the frequency of a sinusoidal signal from its sampled values. A method is described by which the effect of the noise is reduced and the frequency is determined accurately. The method forms the sample set composed of four samples from the signal with unknown frequency, and the binary representation of the estimation is determined successively by varying the sampling frequency. However, the noise in the signal may produce an error. One means to eliminate the effect of the noise is to determine one bit of the binary number based on several sample sets. This paper proposes the following method to reduce the estimation error for the frequency due to the noise. The binary number is partitioned into the upper and lower bits. The upper bits are determined by the property of the rum, and the lower bits are determined by the property of the ramdom-walk. In either case, each bit is determined by several sample sets. By the evaluation based on the bit error rate, the accuracy is improved by combining the forementiomed methods, compared with the majority decision. A comprehensive evaluation was also made by simulation, indicating that the r.m.s. error can be reduced to 2 percent for the signal-to-noise ratio of 20 dB. [ABSTRACT FROM AUTHOR]

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear measurements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the measurement rate and per-sample signal-to-noise ratio (SNR) are finite constants independent of the length of the vector, then the optimal sparsity pattern estimate will have a constant fraction of errors. Lower bounds on the measurement rate needed to attain a desired fraction of errors are given in terms of the SNR and various key parameters of the unknown vector. The tightness of the bounds in a scaling sense, as a function of the SNR and the fraction of errors, is established by comparison with existing achievable bounds. Near optimality is shown for a wide variety of practically motivated signal models. [ABSTRACT FROM PUBLISHER]

Domain randomness is inherent in the fabrication of periodically poled lithium niobate (PPLN). The main cause of such domain randomness in fabrication of PPLN is due to electric poling process. This domain randomness not only impacts the effectiveness of frequency conversion, but also enhances the effectiveness of nonphase matched parasitic processes, which in many cases limits the performance of the device. In this paper, we experimentally demonstrate a nondestructive characterization of domain randomness in PPLN by diffraction-noise pattern with uniform illumination. We measure the domain randomness of PPLN and provide an effective method for poling quality assessment either as good or poor quality of PPLN through this paper. [ABSTRACT FROM AUTHOR]

In this paper, the flocking behavior of a Cucker-Smale model with a leader and noise is studied in a finite time. The authors present a Cucker-Smale system with two nonlinear controls for a complex network with stochastic synchronization in probability. Based on the finite-time stability theory of stochastic differential equations, the sufficient conditions for the flocking of stochastic systems in a finite time are obtained by using the Lyapunov function method. Finally, the numerical simulation of the particle system is carried out for the leader and noise, and the correctness of the results is verified. [ABSTRACT FROM AUTHOR]

Background: While hearing aids for a contralateral routing of signals (CROS-HA) and bone conduction devices have been the traditional treatment for single-sided deafness (SSD) and asymmetric hearing loss (AHL), in recent years, cochlear implants (CIs) have increasingly become a viable treatment choice, particularly in countries where regulatory approval and reimbursement schemes are in place. Part of the reason for this shift is that the CI is the only device capable of restoring bilateral input to the auditory system and hence of possibly reinstating binaural hearing. Although several studies have independently shown that the CI is a safe and effective treatment for SSD and AHL, clinical outcome measures in those studies and across CI centers vary greatly. Only with a consistent use of defined and agreed-upon outcome measures across centers can high-level evidence be generated to assess the safety and efficacy of CIs and alternative treatments in recipients with SSD and AHL.Methods: This paper presents a comparative study design and minimum outcome measures for the assessment of current treatment options in patients with SSD/AHL. The protocol was developed, discussed, and eventually agreed upon by expert panels that convened at the 2015 APSCI conference in Beijing, China, and at the CI 2016 conference in Toronto, Canada.Results: A longitudinal study design comparing CROS-HA, BCD, and CI treatments is proposed. The recommended outcome measures include (1) speech in noise testing, using the same set of 3 spatial configurations to compare binaural benefits such as summation, squelch, and head shadow across devices; (2) localization testing, using stimuli that rove in both level and spectral content; (3) questionnaires to collect quality of life measures and the frequency of device use; and (4) questionnaires for assessing the impact of tinnitus before and after treatment, if applicable.Conclusion: A protocol for the assessment of treatment options and outcomes in recipients with SSD and AHL is presented. The proposed set of minimum outcome measures aims at harmonizing assessment methods across centers and thus at generating a growing body of high-level evidence for those treatment options. [ABSTRACT FROM AUTHOR]

Background noises are usually treated as redundant or even harmful to voice conversion. Therefore, when converting noisy speech, a pretrained module of speech separation is usually deployed to estimate clean speech prior to the conversion. However, this can lead to speech distortion due to the mismatch between the separation module and the conversion one. In this paper, a noise-robust voice conversion model is proposed, where a user can choose to retain or to remove the background sounds freely. Firstly, a speech separation module with a dual-decoder structure is proposed, where two decoders decode the denoised speech and the background sounds, respectively. A bridge module is used to capture the interactions between the denoised speech and the background sounds in parallel layers through information exchanging. Subsequently, a voice conversion module with multiple encoders to convert the estimated clean speech from the speech separation model. Finally, the speech separation and voice conversion module are jointly trained using a loss function combining cycle loss and mutual information loss, aiming to improve the decoupling efficacy among speech contents, pitch, and speaker identity. Experimental results show that the proposed model obtains significant improvements in both subjective and objective evaluation metrics compared with the existing baselines. The speech naturalness and speaker similarity of the converted speech are 3.47 and 3.43, respectively. [ABSTRACT FROM AUTHOR]

Current research on noise and vibration control of high-speed maglev trains pays more attention to far-field noise, while the level of interior noise has a direct impact on the ride comfort and should be placed equal weight on. In this paper, inter-coach space, one of the main pressure fluctuation sources of a specific type of high-speed maglev train with a design speed of 600 km·h−1, is taken as the research object. The turbulent and acoustic components of wall pressure fluctuations (WPF) are separated based on a wavenumber-frequency analysis approach, and then each component is applied as different forms of source input to the vibroacoustic model, namely, finite element method-boundary element method (FEM-BEM) and statistical energy analysis (SEA) for low- and high-frequency ranges respectively, to investigate the contribution of both components to interior acoustic cavity in all frequency range. It can be seen quantitatively from the results that the amplitude of turbulent component is generally much higher than that of the acoustic one, but it can be vice versa when it comes to the interior response. The conclusion drawn in this paper are able to provide guidance for future researches on more targeted interior noise control of high-speed maglev trains. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studied the numerical approximation of the stochastic differential equations driven by non-global Lipschitz drift coefficient and multiplicative noise. An efficient data-driven method, called extended continuous latent process flow, was proposed for the underlying problem. Compared with the piecewise construction of a variational posterior process used in the classical continuous latent process flow developed by Deng et al. [13], the principle idea of our method was to derive a variational lower bound by constructing a posterior latent process conditional on all information over the whole time interval to maximize the log-likelihood generated by the observations, which reduces the computational cost and, thus, provides a convenient way to approximate the considered equation. Particularly, our new method showed a better approximation to the underlying equation than the classical drift-θ discretization scheme through numerical error comparison. Numerical experiments were finally reported to demonstrate the effectiveness and generalization performance of the proposed method. [ABSTRACT FROM AUTHOR]