Search results for: noise estimation

Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Khenfer Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

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This paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a human-machine interface with a voice recognition system that allows the operator to teleoperate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands pronounced in two languages: French and Arabic. The obtained recognition rate is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equals 30 dB, in this case; the Arabic speech recognition rate is 69%, and the French speech recognition rate is 80%. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: Arabic speech recognition, Hidden Markov Model (HMM), HTK, noise, TIMIT, voice command

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: Amandeep Kaur

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Digital Image processing is a fundamental tool to perform various operations on the digital images for pattern recognition, noise removal and feature extraction. In this paper noise removal technique has been described for various types of noises. This paper comprises discussion about various noises available in the image due to different environmental, accidental factors. In this paper, various de-noising approaches have been discussed that utilize different wavelets and filters for de-noising. By analyzing various papers on image de-noising we extract that wavelet based de-noise approaches are much effective as compared to others.

Keywords: de-noising techniques, edges, image, image processing

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Authors: Olesya Bolkhovskaya, Alexander Maltsev

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Antenna arrays are widely used in modern radio systems in sonar and communications. The solving of the detection problems of a useful signal on the background of noise is based on the GLRT method. There is a large number of problem which depends on the known a priori information. In this work, in contrast to the majority of already solved problems, it is used only difference spatial properties of the signal and noise for detection. We are analyzing the influence of the degree of non-coherence of signal and noise unhomogeneity on the performance characteristics of different GLRT statistics. The description of the signal and noise is carried out by means of the spatial covariance matrices C in the cases of different number of known information. The partially coherent signal is simulated as a plane wave with a random angle of incidence of the wave concerning a normal. Background noise is simulated as random process with uniform distribution function in each element. The results of investigation of degradation of performance characteristics for different cases are represented in this work.

Keywords: GLRT, Neumann-Pearson’s criterion, Test-statistics, degradation, spatial processing, multielement antenna array

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Authors: Jianwei Ma, Diriba Gemechu

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In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

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Authors: Rajani S. Pujar, Pandurangarao N. Kulkarni

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Increased temporal masking affects the speech perception in persons with sensorineural hearing impairment especially under adverse listening conditions. This paper presents a cascaded scheme, which employs a noise reduction algorithm as well as temporal splitting of the speech signal. Earlier investigations have shown that by splitting the speech temporally and presenting alternate segments to the two ears help in reducing the effect of temporal masking. In this technique, the speech signal is processed by two fading functions, complementary to each other, and presented to left and right ears for binaural dichotic presentation. In the present study, half cosine signal is used as a fading function with crossover gain of 6 dB for the perceptual balance of loudness. Temporal splitting is combined with noise reduction algorithm to improve speech perception in the background noise. Two noise reduction schemes, namely spectral subtraction and Wiener filter are used. Listening tests were conducted on six normal-hearing subjects, with sensorineural loss simulated by adding broadband noise to the speech signal at different signal-to-noise ratios (∞, 3, 0, and -3 dB). Objective evaluation using PESQ was also carried out. The MOS score for VCV syllable /asha/ for SNR values of ∞, 3, 0, and -3 dB were 5, 4.46, 4.4 and 4.05 respectively, while the corresponding MOS scores for unprocessed speech were 5, 1.2, 0.9 and 0.65, indicating significant improvement in the perceived speech quality for the proposed scheme compared to the unprocessed speech.

Keywords: MOS, PESQ, spectral subtraction, temporal splitting, wiener filter

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Authors: M. Z. Kurian, M. V. Chidananda Murthy, H. S. Guruprasad

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An approach to super-resolve the low-resolution (LR) image is presented in this paper which is very useful in multimedia communication, medical image enhancement and satellite image enhancement to have a clear view of the information in the image. The proposed Advanced B-Spline method generates a high-resolution (HR) image from single LR image and tries to retain the higher frequency components such as edges in the image. This method uses B-Spline technique and Crispening. This work is evaluated qualitatively and quantitatively using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The method is also suitable for real-time applications. Different combinations of decimation and super-resolution algorithms in the presence of different noise and noise factors are tested.

Keywords: advanced b-spline, image super-resolution, mean square error (MSE), peak signal to noise ratio (PSNR), resolution down converter

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Authors: Tallal Abdel Karim Bouzir, Nourdinne Zemmouri, Djihed Berkouk

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In this research, a quantitative assessment of the urban sound environment of the city of Biskra, Algeria, was conducted. To determine the quality of the soundscape based on in-situ measurement, using a Landtek SL5868P sound level meter in 47 points, which have been identified to represent the whole city. The result shows that the urban noise level varies from 55.3 dB to 75.8 dB during the weekdays and from 51.7 dB to 74.3 dB during the weekend. On the other hand, we can also note that 70.20% of the results of the weekday measurements and 55.30% of the results of the weekend measurements have levels of sound intensity that exceed the levels allowed by Algerian law and the recommendations of the World Health Organization. These very high urban noise levels affect the quality of life, the acoustic comfort and may even pose multiple risks to people's health.

Keywords: road traffic, noise pollution, sound intensity, public health

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Authors: Abdelouahab Bouttout, Mohamed Amara

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The present paper examines the impact noise transmission through some floor building assemblies. The Acoubat software numerical simulation has been used to simulate the impact noise transmission through different floor configurations used in Algerian construction mode. The results are compared with the available measurements. We have developed two experimental methods, i) field method, and ii) laboratory method using Brüel and Kjær equipments. The results show that the different cases of floor configurations need some improvement to ensure the acoustic comfort in the receiving apartment. The recommended value of the impact sound level in the receiving room should not exceed 58 dB. The important results obtained in this paper can be used as platform to improve the Algerian building acoustic regulation aimed at the construction of the multi-storey residential building.

Keywords: impact noise, building acoustic, floor insulation, resilient material

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Authors: Olusegun Oguntoke, Aramide Y. Tijani, Olayide R. Adetunji

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Environmental noise has become a major threat to the quality of human life, and it is generally more severe in cities. This study assessed the level of environmental noise, mapped the spatial pattern at different times of the day and examined the association with morbidity of auditory ailments in Abeokuta metropolis. The entire metropolis was divided into 80 cells (areas) of 1000 m by 1000 m; out of which 33 were randomly selected for noise levels assessment. Portable noise meter (AR824) was used to measure noise level, and Global Positioning System (Garmin GPS-72H) was employed to take the coordinates of the sample sites for mapping. Risk map of the noise levels was produced using Kriging interpolation techniques based on the spatial spread of measured noise values across the study area. Data on cases of hearing impairments were collected from four major hospitals in the city. Data collected from field measurements and medical records were subjected to descriptive (frequency and percentage) and inferential (mean, ANOVA and correlation) statistics using SPSS (version 20.0). ArcMap 10.1 was employed for spatial analysis and mapping. Results showed mean noise levels range at morning (42.4 ± 4.14 – 88.2 ± 15.1 dBA), afternoon (45.0 ± 6.72– 86.4 ± 12.5 dBA) and evening (51.0 ± 6.55–84.4 ± 5.19 dBA) across the study area. The interpolated maps identified Kuto, Okelowo, Isale-Igbein, and Sapon as high noise risk areas. These are the central business district and nucleus of Abeokuta metropolis where commercial activities, high traffic volume, and clustered buildings exist. The monitored noise levels varied significantly among the sampled areas in the morning, afternoon and evening (p < 0.05). A significant correlation was found between diagnosed cases of auditory ailments and noise levels measured in the morning (r=0.39 at p < 0.05). Common auditory ailments found across the metropolis included impaired hearing (25.8%), tinnitus (16.4%) and otitis (15.0%). The most affected age groups were between 11-30 years while the male gender had more cases of hearing impairments (51.2%) than the females. The study revealed that environmental noise levels exceeded the recommended standards in the morning, afternoon and evening in 60.6%, 61% and 72.7% of the sampled areas respectively. Summarily, environmental noise in the study area is high and contributes to the morbidity of auditory ailments. Areas identified as hot spots of noise pollution should be avoided in the location of noise sensitive activities while environmental noise monitoring should be included as part of the mandate of the regulatory agencies in Nigeria.

Keywords: noise pollution, associative analysis, auditory impairment, urban, human exposure

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Authors: Sun Zhe, Ruggero Micheletto

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As discussed extensively in many studies, noise in neural networks have an important role in the functioning and time evolution of the system. The mechanism by which noise induce stochastic resonance enhancing and influencing certain operations is not clarified nor is the mechanism of information storage and coding. With the present research we want to study the role of noise, especially focusing on the frequency peaks in a three variable Hindmarsh−Rose Small−World network. We investigated the behaviour of the network to external noises. We demonstrate that a variation of signal to noise ratio of about 10 dB induces an increase in membrane potential signal of about 15%, averaged over the whole network. We also considered the integral of the whole membrane potential as a paradigm of internal noise, the one generated by the brain network. We showed that this internal noise is attenuated with the size of the network or with the number of random connections. By means of Fourier analysis we found that it has distinct peaks of frequencies, moreover, we showed that increasing the size of the network introducing more neurons, reduced the maximum frequencies generated by the network, whereas the increase in the number of random connections (determined by the small-world probability p) led to a trend toward higher frequencies. This study may give clues on how networks utilize noise to alter the collective behaviour of the system in their operations.

Keywords: neural networks, stochastic processes, small-world networks, discrete Fourier analysis

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Authors: Qianhua He, Weili Zhou, Aiwu Chen

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A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.

Keywords: speech denoising, sparse representation, k-singular value decomposition, orthogonal matching pursuit

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Authors: Ayad A. Abdulkafi, Shahir F. Nawaf, Mohammed K. Hussein, Ibrahim K. Sileh, Fouad A. Abdulkafi

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Visible light communication (VLC) is a new approach of optical wireless communication proposed to support the congested radio frequency (RF) spectrum. VLC systems are combined with orthogonal frequency division multiplexing (OFDM) to achieve high rate transmission and high spectral efficiency. In this paper, we investigate the Pilot-Assisted Channel Estimation for DC biased Optical OFDM (PACE-DCO-OFDM) systems to reduce the effects of the distortion on the transmitted signal. Least-square (LS) and linear minimum mean-squared error (LMMSE) estimators are implemented in MATLAB/Simulink to enhance the bit-error-rate (BER) of PACE-DCO-OFDM. Results show that DCO-OFDM system based on PACE scheme has achieved better BER performance compared to conventional system without pilot assisted channel estimation. Simulation results show that the proposed PACE-DCO-OFDM based on LMMSE algorithm can more accurately estimate the channel and achieves better BER performance when compared to the LS based PACE-DCO-OFDM and the traditional system without PACE. For the same signal to noise ratio (SNR) of 25 dB, the achieved BER is about 5×10^-4 for LMMSE-PACE and 4.2×10^-3 with LS-PACE while it is about 2×10^-1 for system without PACE scheme.

Keywords: channel estimation, OFDM, pilot-assist, VLC

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Authors: Seda Yavuz, Anıl Çelebi, Aysun Taşyapı Çelebi, Oğuzhan Urhan

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Nowadays, demand for using real-time video transmission capable devices is ever-increasing. So, high resolution videos have made efficient video compression techniques an essential component for capturing and transmitting video data. Motion estimation has a critical role in encoding raw video. Hence, various motion estimation methods are introduced to efficiently compress the video. Low bit‑depth representation based motion estimation methods facilitate computation of matching criteria and thus, provide small hardware footprint. In this paper, a hardware implementation of a two-bit transformation based low-complexity motion estimation method using local binary pattern approach is proposed. Image frames are represented in two-bit depth instead of full-depth by making use of the local binary pattern as a binarization approach and the binarization part of the hardware architecture is explained in detail. Experimental results demonstrate the difference between the proposed hardware architecture and the architectures of well-known low-complexity motion estimation methods in terms of important aspects such as resource utilization, energy and power consumption.

Keywords: binarization, hardware architecture, local binary pattern, motion estimation, two-bit transform

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Authors: Emily A. Gilbert, Jephte Sompud, Andy R. Mojiol, Cynthia B. Sompud, Alim Biun

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Gaya Island of Sabah is known for its wildlife and marine biodiversity. It has marks itself as one of the hot destinations of tourists from all around the world. Gaya Island tourism activities have contributed to Sabah’s economy revenue with the high number of tourists visiting the island. However, it has led to the increased anthropogenic noise derived from tourism activities. This may greatly interfere with the animals such as understory birds that rely on acoustic signals as a tool for communication. Many studies in other parts of the regions reveal that anthropogenic noise does decrease species richness of avian community. However, in Malaysia, published research regarding the impact of anthropogenic noise on the understory birds is still very lacking. This study was conducted in order to fill up this gap. This study aims to investigate the anthropogenic noise’s impact towards understory bird population. There were three sites within the Primary forest of Gaya Island that were chosen to sample the level of anthropogenic noise in relation to the understory bird population. Noise mapping method was used to measure the anthropogenic noise level and identify the zone with high anthropogenic noise level (> 60dB) and zone with low anthropogenic noise level (< 60dB) based on the standard threshold of noise level. The methods that were used for this study was solely mist netting and ring banding. This method was chosen as it can determine the diversity of the understory bird population in Gaya Island. The preliminary study was conducted from 15th to 26th April and 5th to 10th May 2015 whereby there were 2 mist nets that were set up at each of the zones within the selected site. The data was analyzed by using the descriptive analysis, presence and absence analysis, diversity indices and diversity t-test. Meanwhile, PAST software was used to analyze the obtain data. The results from this study present a total of 60 individuals that consisted of 12 species from 7 families of understory birds were recorded in three of the sites in Gaya Island. The Shannon-Wiener index shows that diversity of species in high anthropogenic noise zone and low anthropogenic noise zone were 1.573 and 2.009, respectively. However, the statistical analysis shows that there was no significant difference between these zones. Nevertheless, based on the presence and absence analysis, it shows that the species at the low anthropogenic noise zone was higher as compared to the high anthropogenic noise zone. Thus, this result indicates that there is an impact of anthropogenic noise on the population diversity of understory birds. There is still an urgent need to conduct an in-depth study by increasing the sample size in the selected sites in order to fully understand the impact of anthropogenic noise towards the understory birds population so that it can then be in cooperated into the wildlife management for a sustainable environment in Gaya Island.

Keywords: anthropogenic noise, biodiversity, Gaya Island, understory bird

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Authors: Weihao Wang, Zhulin Zong

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Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

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Authors: Neha Pranav Kolhe, Harithapriya Vijaye, Arushi Kamle

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The economic growth engines are urban regions. As the economy expands, so does the need for peace and quiet, and noise pollution is one of the important social and environmental issue. Health and wellbeing are at risk from environmental noise pollution. Because of urbanisation, population growth, and the consequent rise in the usage of increasingly potent, diverse, and highly mobile sources of noise, it is now more severe and pervasive than ever before, and it will only become worse. Additionally, it will expand as long as there is an increase in air, train, and highway traffic, which continue to be the main contributors of noise pollution. The current study will be conducted in two zones of class I city of central India (population range: 1 million–4 million). Total 56 measuring points were chosen to assess noise pollution. The first objective evaluates the noise pollution in various urban habitats determined as formal and informal settlement. It identifies the comparison of noise pollution within the settlements using T- Test analysis. The second objective assess the noise pollution in silent zones (as stated in Central Pollution Control Board) in a hierarchical way. It also assesses the noise pollution in the settlements and compares with prescribed permissible limits using class I sound level equipment. As appropriate indices, equivalent noise level on the (A) frequency weighting network, minimum sound pressure level and maximum sound pressure level were computed. The survey is conducted for a period of 1 week. Arc GIS is used to plot and map the temporal and spatial variability in urban settings. It is discovered that noise levels at most stations, particularly at heavily trafficked crossroads and subway stations, were significantly different and higher than acceptable limits and squares. The study highlights the vulnerable areas that should be considered while city planning. The study demands area level planning while preparing a development plan. It also demands attention to noise pollution from the perspective of residential and silent zones. The city planning in urban areas neglects the noise pollution assessment at city level. This contributes to that, irrespective of noise pollution guidelines, the ground reality is far away from its applicability. The result produces incompatible land use on a neighbourhood scale with respect to noise pollution. The study's final results will be useful to policymakers, architects and administrators in developing countries. This will be useful for noise pollution in urban habitat governance by efficient decision making and policy formulation to increase the profitability of these systems.

Keywords: noise pollution, formal settlements, informal settlements, built environment, silent zone, residential area

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Authors: Ana Staneva, Vessela Stoimenova

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A parametric estimation of the MBP with Power Series offspring distribution family is considered in this paper. The MLE for the parameters is obtained in the case when the observable data are incomplete and consist only with the generation sizes of the family tree of MBP. The parameter estimation is calculated by using the Monte Carlo EM algorithm. The estimation for the posterior distribution and for the offspring distribution parameters are calculated by using the Bayesian approach and the Gibbs sampler. The article proposes various examples with bivariate branching processes together with computational results, simulation and an implementation using R.

Keywords: Bayesian, branching processes, EM algorithm, Gibbs sampler, Monte Carlo methods, statistical estimation

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Authors: I. Schiller, D. Morsomme, A. Remacle

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Children’s ability to process spoken language develops until the late teenage years. At school, where efficient spoken language processing is key to academic achievement, listening conditions are often unfavorable. High background noise and poor teacher’s voice represent typical sources of interference. It can be assumed that these factors particularly affect primary school children, because their language and literacy skills are still low. While it is generally accepted that background noise and impaired voice impede spoken language processing, there is an increasing need for analyzing impacts within specific linguistic areas. Against this background, the aim of the study was to investigate the effect of speech-shaped noise and imitated dysphonic voice on first-grade primary school children’s speech perception and sentence comprehension. Via headphones, 5 to 6-year-old children, recruited within the French-speaking community of Belgium, listened to and performed a minimal-pair discrimination task and a sentence-picture matching task. Stimuli were randomly presented according to four experimental conditions: (1) normal voice / no noise, (2) normal voice / noise, (3) impaired voice / no noise, and (4) impaired voice / noise. The primary outcome measure was task score. How did performance vary with respect to listening condition? Preliminary results will be presented with respect to speech perception and sentence comprehension and carefully interpreted in the light of past findings. This study helps to support our understanding of children’s language processing skills under adverse conditions. Results shall serve as a starting point for probing new measures to optimize children’s learning environment.

Keywords: impaired voice, sentence comprehension, speech perception, speech-shaped noise, spoken language processing

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Phuc D. Nguyen, Kristy L. Hansen, Branko Zajamsek

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The existence of special characteristics of wind farm noise such as amplitude modulation (AM) contributes significantly to annoyance, which could ultimately result in sleep disturbance and other adverse health effects for residents living near wind farms. In order to detect and quantify this phenomenon, several methods have been developed which can be separated into three types: time-domain, frequency-domain and hybrid methods. However, due to a lack of systematic validation of these methods, it is still difficult to select the best method for identifying AM. Furthermore, previous comparisons between AM methods have been predominantly qualitative or based on synthesised signals, which are not representative of the actual noise. In this study, a comparison between methods for detecting and quantifying AM has been carried out. The results are based on analysis of real noise data which were measured at a wind farm in South Australia. In order to evaluate the performance of these methods in terms of detecting AM, an approach has been developed to select the most successful method of AM detection. This approach uses a receiver operating characteristic (ROC) curve which is based on detection of AM in audio files by experts.

Keywords: amplitude modulation, wind farm noise, ROC curve

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Authors: Qing K. Zhu

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Very Large Scale Integration (VLSI) design becomes very complex due to the continuous integration of millions of gates in one chip based on Moore’s law. Designers have encountered numerous report files during design iterations using timing and noise analysis tools. This paper presented our work using data mining techniques combined with HTML tables to extract and represent critical timing/noise data. When we apply this data-mining tool in real applications, the running speed is important. The software employs table look-up techniques in the programming for the reasonable running speed based on performance testing results. We added several advanced features for the application in one industry chip design.

Keywords: VLSI design, data mining, big data, HTML forms, web, VLSI, EDA, timing, noise

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Authors: C. Temaneh-Nyah

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Electromagnetic compatibility (EMC) is the ability of a Radio Communication Equipment (RCE) to operate with a desired quality of service in a given Electromagnetic Environment (EME) and not to create harmful interference with other RCE. This paper presents an algorithm which improves the simulation speed of estimating EMC of RCE in a complex EME, based on a stage by stage frequency-energy criterion of filtering. This algorithm considers different interference types including: Blocking and intermodulation. It consist of the following steps: simplified energy criterion where filtration is based on comparing the free space interference level to the industrial noise, frequency criterion which checks whether the interfering emissions characteristic overlap with the receiver’s channels characteristic and lastly the detailed energy criterion where the real channel interference level is compared to the noise level. In each of these stages, some interference cases are filtered out by the relevant criteria. This reduces the total number of dual and different combinations of RCE involved in the tedious detailed energy analysis and thus provides an improved simulation speed.

Keywords: electromagnetic compatibility, electromagnetic environment, simulation of communication network

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Authors: Mohammad Javad Afroughi, Vahid Hosseini, Jason S. Olfert

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Both traffic-generated particles and noise have been associated with the development of cardiovascular diseases, especially in near-highway environments. Although noise and particulate matters (PM) have different mechanisms of dispersion, sharing the same emission source in urban areas (road traffics) can result in a similar degree of variability in their levels. This study investigated the temporal variation of and correlation between noise levels, PM concentrations and traffic intensities near a major highway in Tehran, Iran. Tehran particulate concentration is highly influenced by road traffic. Additionally, Tehran ultrafine particles (UFP, PM<0.1 µm) are mostly emitted from combustion processes of motor vehicles. This gives a high possibility of a strong association between traffic-related noise and UFP in near-highway environments of this megacity. Hourly average of equivalent continuous sound pressure level (Leq), total number concentration of UFPs, mass concentration of PM2.5 and PM10, as well as traffic count and speed were simultaneously measured over a period of three days in winter. Additionally, meteorological data including temperature, relative humidity, wind speed and direction were collected in a weather station, located 3 km from the monitoring site. Noise levels showed relatively low temporal variability in near-highway environments compared to PM concentrations. Hourly average of Leq ranged from 63.8 to 69.9 dB(A) (mean ~ 68 dB(A)), while hourly concentration of particles varied from 30,800 to 108,800 cm-3 for UFP (mean ~ 64,500 cm-3), 41 to 75 µg m-3 for PM2.5 (mean ~ 53 µg m-3), and 62 to 112 µg m-3 for PM10 (mean ~ 88 µg m-3). The Pearson correlation coefficient revealed strong relationship between noise and UFP (r ~ 0.61) overall. Under downwind conditions, UFP number concentration showed the strongest association with noise level (r ~ 0.63). The coefficient decreased to a lesser degree under upwind conditions (r ~ 0.24) due to the significant role of wind and humidity in UFP dynamics. Furthermore, PM2.5 and PM10 correlated moderately with noise (r ~ 0.52 and 0.44 respectively). In general, traffic counts were more strongly associated with noise and PM compared to traffic speeds. It was concluded that noise level combined with meteorological data can be used as a proxy to estimate PM concentrations (specifically UFP number concentration) in near-highway environments of Tehran. However, it is important to measure joint variability of noise and particles to study their health effects in epidemiological studies.

Keywords: noise, particulate matter, PM10, PM2.5, ultrafine particle

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Authors: Chin-Yun Chen

Abstract:

Numerical integration is an essential tool for deriving different physical quantities in engineering and science. The effectiveness of a numerical integrator depends on different factors, where the crucial one is the error estimation. This work presents an error estimator that combines a derivative free method to improve the performance of verified numerical quadrature.

Keywords: numerical quadrature, error estimation, derivative free method, interval computation

Procedia PDF Downloads 431

Authors: Y. T. Lee, J. H. Na, S. H. Kim, S. U. Hong

Abstract:

In this study, an experimental research for estimation of slab depth, column size and location of rebar of concrete specimen is conducted using the Impact Echo Method (IE) based on stress wave among non-destructive test methods. Estimation of slab depth had total length of 1800×300 and 6 different depths including 150 mm, 180 mm, 210 mm, 240 mm, 270 mm and 300 mm. The concrete column specimen was manufactured by differentiating the size into 300×300×300 mm, 400×400×400 mm and 500×500×500 mm. In case of the specimen for estimation of rebar, rebar of ∅22 mm was used in a specimen of 300×370×200 and arranged at 130 mm and 150 mm from the top to the rebar top. As a result of error rate of slab depth was overall mean of 3.1%. Error rate of column size was overall mean of 1.7%. Mean error rate of rebar location was 1.72% for top, 1.19% for bottom and 1.5% for overall mean showing relative accuracy.

Keywords: impact echo method, estimation, slab depth, column size, rebar location, concrete

Procedia PDF Downloads 317

Authors: Marcel Duh, Jadranka Stricevic, David Halozan, Dusan Celan

Abstract:

Back pain is not only the result of structural or biomechanical abnormalities of the spine but is also associated with cognitive and behavioral aspects of pain and thus represents biopsychosocial problem. Stressors are not only interpersonal conflicts, negative life events, and dangerous situations but also noise. Effects of noise on human beings are psychological (excitement, stress), sensory, and physiological. The harmful effects of noise can be seen in the 40-65 dB range and are manifested as fatigue, irritability, poor sleep and psychological discomfort of the worker. Within 65-90 dB range, body metabolism increases, oxygen consumption is higher, tachycardia and hypertension appear, and the tone of skeletal muscles increases. The purpose of the study was to determine whether the stress caused by noise at the work place increases the incidence of back pain. Measurements of noise levels were carried out in three different wards of social care institution. The measurement on each ward was repeated 3 times (total of 9 measurements) for 8 hours during the morning shift. The device was set up in the room where clients spent most of the day. The staff on the ward replied to the questionnaire consisting of closed type questions about basic demographic information and information about back pain. We find that noise levels as measured in our study had no statistically significant effect on the incidence of back pain (p = 0.90). We also find that health care workers who perceive their work as stressful, have more back pain than those who perceive their job as unstressful, but correlation is statistically insignificant (p = 0.682). With our study, we have proven findings of other authors, that noise level below 65 dB does not have a significant influence on the incidence of back pain.

Keywords: health care workers, musculoskeletal disorder, noise, sick leave

Procedia PDF Downloads 97

Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee

Abstract:

In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.

Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences

Procedia PDF Downloads 703

Authors: Gorkem Algan, Ilkay Ulusoy, Saban Gonul, Banu Turgut, Berker Bakbak

Abstract:

Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of experts. Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning. As a result, if not handled properly, label noise significantly degrades the performance. Therefore, a label-noise-robust learning algorithm that makes use of the meta-learning paradigm is proposed in this article. The proposed solution is tested on retinopathy of prematurity (ROP) dataset with a very high label noise of 68%. Results show that the proposed algorithm significantly improves the classification algorithm's performance in the presence of noisy labels.

Keywords: deep learning, label noise, robust learning, meta-learning, retinopathy of prematurity

Procedia PDF Downloads 130