Acoustics issues such as noise in the workplace remains one of the most prevalence occupational hazard especially in
the manufacturing industry with heavy machineries. Increasing mechanization in all industries and most trades has
since proliferated the noise problem. In Malaysia, much has been studied and is known about the auditory effects of
noise. However less attention has been given to the non-auditory effects of noise such as annoyance, stress, and work
performance, and concern about such effects is a relatively recent phenomenon. In view of this, this study aims to
determine the level of noise from different type of machines and tools in a manufacturing plant and also the effects
of noise to the employees. A structured questionnaire was used to assess the effects of noise on the workers and
sound level meter was used to measure the noise level at selected work areas. The results of this study showed that
nearly all the identified work areas exceeded the action level of 85 dB(A) and four of these areas noise levels’ are
more than 90 dB(A) which is the permissible exposure limit according to the Factories and Machinery (Noise
Exposure) Regulations 1989. For the questionnaire, it was found that annoyance topped the noise effects list with
51.4%, followed by stress with 40.0%, hearing deterioration (14.3%) and job performance deterioration (2.9%). As a
conclusion, noise control or preventive measures are suggested in order to minimize the health risks from noise
exposure.
In this paper, an improved method of reducing ambient noise in speech signals is introduced. The proposed noise canceller was developed using a computationally efficient (DFT) filter bank to decompose input signals into sub-bands. The filter bank was based on a prototype filter optimized for minimum output distortion. A variable step-size version of the (LMS) filter was used to reduce the noise in individual branches. The subband noise canceller was aimed to overcome problems associated with the use of the conventional least mean square (LMS) adaptive algorithm in noise cancellation setups. Mean square error convergence was used as a measure of performance under white and ambient interferences. Compared to conventional as well as recently developed techniques, fast initial convergence and better noise cancellation performances were obtained under actual speech and ambient noise.
Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7-1.9db) while (35.83-47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.
This paper demonstrates an erbium/ytterbium co-doped fi bre amplifi er (EYDFA) which used a pumping wavelength of 1058 nm, whereby the amplifi cation was assisted by the energy transfer between Yb and Er ions. The energy transfer increased the erbium doping concentration limit that was imposed by concentration quenching in erbium-doped fi bre. The optimum length was obtained at 4m~6m for erbium/ytterbium co-doped fi bre with Er ion concentration of 1000 p.p.m. This enabled the development of a compact amplifi er with a shorter gain medium compared to erbium-doped fi bre amplifi ers which use a gain medium of up to 15 m. A 1058 nm pumping wave-length was used for the EYDFA, as 1480 nm pumping resulted in severely degraded gain and noise fi gures because the energy transfer could not be achieved. The use of the optical isolator improved the small signal gain and noise fi gure by about 4.8 dB and 1.6 dB, respectively. Without the isolator, gain saturation and a noise fi gure penalty were observed due to the oscillating laser which was created at around 1534 nm by spurious refl ection. This showed that the usage of optical isolators was an important aspect to consider when designing an EYDFA.
The study aimed to conduct an ergonomic intervention on a conventional line (CL) in a semiconductor factory in Malaysia, an industrially developing country (IDC), to improve workers' occupational health and safety (OHS). Low-cost and simple (LCS) ergonomics methods were used (suitable for IDCs), e.g., subjective assessment, direct observation, use of archival data and assessment of noise. It was found that workers were facing noise irritation, neck and back pains and headache in the various processes in the CL. LCS ergonomic interventions to rectify the problems included installing noise insulating covers, providing earplugs, installing elevated platforms, slanting visual display terminals and installing extra exhaust fans. The interventions cost less than 3 000 USD but they significantly improved workers' OHS, which directly correlated with an improvement in working conditions and job satisfaction. The findings are useful in solving OHS problems in electronics industries in IDCs as they share similar manufacturing processes, problems and limitations.
Matched MeSH terms: Noise, Occupational/prevention & control
This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.
This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) holds great potential to provide new metabolic information for clinical applications such as tumor, stroke and Parkinson's Disease diagnosis. Many active research and developments have been conducted to translate this emerging MRI technique for routine clinical applications. In general, there are two CEST quantification techniques: (i) model-free and (ii) model-based techniques. The reliability of these quantification techniques depends heavily on the experimental conditions and quality of the collected data. Errors such as noise may lead to misleading quantification results and thus inaccurate diagnosis when CEST imaging becomes a standard or routine imaging scan in the future. This paper investigates the accuracy and robustness of these quantification techniques under different signal-to-noise (SNR) levels and magnetic field strengths. The quantified CEST effect before and after adding random Gaussian White Noise using model-free and model-based quantification techniques were compared. It was found that the model-free technique consistently yielded larger average percentage error across all tested parameters compared to its model-based counterpart, and that the model-based technique could withstand SNR of about 3 times lower than the model-free technique. When applied on noisy brain tumor, ischemic stroke, and Parkinson's Disease clinical data, the model-free technique failed to produce significant differences between normal and abnormal tissue whereas the model-based technique consistently generated significant differences. Although the model-free technique was less accurate and robust, its simplicity and thus speed would still make it a good approximate when the SNR was high (>50) or when the CEST effect was large and well-defined. For more accurate CEST quantification, model-based techniques should be considered. When SNR was low (<50) and the CEST effect was small such as those acquired from clinical field strength scanners, which are generally 3T and below, model-based techniques should be considered over model-free counterpart to maintain an average percentage error of less than 44% even under very noisy condition as tested in this work.
The objective of study is to determine traffic noise level and non-auditory effect among shop lot workers at Kajang Selangor. This cross sectional study was carried to study traffic noise exposure with annoyance and work performance level among shop lot workers in Jalan Mendaling, JalanTukang and Jalan Sulaiman at Kajang town, Selangor. This study involves 120 shop lot workers that exposed to the traffic noise during their working hours where they are randomly selected. Noise exposure was estimated using the Sound Level Meter for environmental noise. The traffic volume was recorded using video recorder and calculated using tally counter. One set questionnaire consist standard questionnaire was used to assess the annoyance level and work performance level among the respondents. Respondents were predominantly by male which are 94 and female, 26 respondents. The mean age of the respondent were ranged between 41 to 60 years old. Only 12.5% of respondent are ranged 21 until 30 years old. In total of 120 respondent, 54.2% of them are Chinese while Malay and India only 30% and 15.8% respectively. The result showed that the traffic noise level at study areas are exceeded the permissible sound limit of commercial and business area during daylight which is 70 dB(A). Regarding work performance, 94 respondents are having low work performance level and 82% of respondent high annoyance level during the exposure of traffic noise from four different sources which are noise from the traffic, speeding vehicle, high traffic volume and exhaust system. There is a significant relationship between traffic noise level with work performance level (p=0.001) and annoyance level (p=0.026). The average traffic noise (Laeq) level at Jalan Mendaling, Jalan Tukang and Jalan Sulaiman is 71.19 dB(A) which were high and exceeds permissible sound level from road traffic, commercial and business place at day time, 70 dB(A). The exposure from the traffic noise effect the annoyance level and work performance level among the shop lot worker. In order to reduce traffic noise exposure towards the shop lot workers, some recommendation are needed to control the traffic noise such as build a noise barrier, plant trees and also enforcement of legal requirement in noise level.
Signals containing attended frequencies are facilitated while those with unexpected frequencies are suppressed by an auditory filtering process. The neurocognitive mechanism underlying the auditory attentional filter is, however, poorly understood. The olivocochlear bundle (OCB), a brainstem neural circuit that is part of the efferent system, has been suggested to be partly responsible for the filtering via its noise-dependent antimasking effect. The current study examined the role of the OCB in attentional filtering, particularly the validity of the antimasking hypothesis, by comparing attentional filters measured in quiet and in the presence of background noise in a group of normal-hearing listeners. Filters obtained in both conditions were comparable, suggesting that the presence of background noise is not crucial for attentional filter generation. In addition, comparison of frequency-specific changes of the cue-evoked enhancement component of filters in quiet and noise also did not reveal any major contribution of background noise to the cue effect. These findings argue against the involvement of an antimasking effect in the attentional process. Instead of the antimasking effect mediated via medial olivocochlear fibers, results from current and earlier studies can be explained by frequency-specific modulation of afferent spontaneous activity by lateral olivocochlear fibers. It is proposed that the activity of these lateral fibers could be driven by top-down cortical control via a noise-independent mechanism. SIGNIFICANCE: The neural basis for auditory attentional filter remains a fundamental but poorly understood area in auditory neuroscience. The efferent olivocochlear pathway that projects from the brainstem back to the cochlea has been suggested to mediate the attentional effect via its noise-dependent antimasking effect. The current study demonstrates that the filter generation is mostly independent of the background noise, and therefore is unlikely to be mediated by the olivocochlear brainstem reflex. It is proposed that the entire cortico-olivocochlear system might instead be used to alter the hearing sensitivity during focus attention via frequency-specific modulation of afferent spontaneous activity.
Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA-WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA-WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA-WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R ) (ANOVA, p ˂ 0.05). The AICA-WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA-WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.
In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.
A beamformer in seismology is a signal receptor with a series of geophones, in which a beam of elastic waves is formed like a light beam by adjusting signal delays at individual geophones. Recently, beamforming has extended its applications to surface-wave measurement. In surface-wave measurement, beamforming provides unique advantages over other surface-wave methods, such as full automation in data analysis as well as directional signal reception to minimize scattered noise and multiple reflections in signals. However, certain defects depreciate the value of beamforming in terms of its practicality and feasibility. These include the requirement of having many receivers and the loss of small wavelength data due to spatial aliasing. It leads to insensitivity in identification of lateral variability, which creates the problem of having to smooth out geologic features and complexities like folding, faults and fractures. In this paper, advances in the refinement of beamforming were described on two counts: improvement of sensitivity in identification of lateral variability and recovery of aliased wave numbers, which enables evaluation of shallow material. On the passage to refinement, synthetic waveforms for typical layering systems were generated to figure out characteristics of beamformer velocities in comparison with SASW velocities and theoretical normal-mode velocities.
Heschl’s gyrus (HG) is known to interact with other auditory related areas of the same hemisphere during the performance
of an auditory cognitive task. However, the information about how it interacts with the opposite HG is still lacking.
The aim of this study was to investigate the psychophysiologic interaction (PPI) between the bilateral HG during a
simple arithmetic addition task and to verify the role of noise as an experimental factor that would modulate the PPI.
Functional magnetic resonance imaging (fMRI) scans were performed on eighteen healthy participants, in which a
single-digit addition task were solved during in-quiet (AIQ) and in-noise (AIN) conditions. The fMRI data were analysed
using Statistical Parametric Mapping (SPM8). The interaction between the bilateral HG was investigated using PPI
analysis. The response in right HG was found to be linearly influenced by the activity in left HG, vice-versa, for both
in-quiet and in-noise conditions. The connectivity from right to left HG in noisy condition seemed to be modulated
by noise, while the modulation is relatively small oppositely, indicating a non-reciprocal behavior. A two-way PPI
model between right and left HG is suggested. The connectivity from right to left HG during a simple addition task in
noise is driven by a higher ability of right HG to perceive the stimuli in a noisy condition. Both the bilateral HGs took
part in the cognitive processes of arithmetic addition from which the interactions between the two were found to be
different in noise.
Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.
The study of binding affinity is essential in surface plasmon resonance (SPR) sensing because it allows researchers to quantify the affinity between the analyte and immobilised ligands of an SPR sensor. In this study, we demonstrate the derivation of the binding affinity constant, K, for Pb2+and Hg2+ions according to their SPR response using a gold/silver/gold/chitosan-graphene oxide (Au/Ag/Au/CS-GO) sensor for the concentration range of 0.1-5 ppm. The higher affinity of Pb2+to binding with the CS-GO sensor explains the outstanding sensitivity of 2.05 °ppm-1against 1.66 °ppm-1of Hg2+. The maximum signal-to-noise ratio (SNR) upon detection of Pb2+is 1.53, and exceeds the suggested logical criterion of an SNR. The Au/Ag/Au/CS-GO SPR sensor also exhibits excellent repeatability in Pb2+due to the strong bond between its functional groups and this cation. The adsorption data of Pb2+and Hg2+on the CS-GO sensor fits well with the Langmuir isotherm model where the affinity constant, K, of Pb2+and Hg2+ions is computed. The affinity of Pb2+ions to the Au/Ag/Au/CS-GO sensor is significantly higher than that of Hg2+based on the value of K, 7 × 10⁵ M-1and 4 × 10⁵ M-1, respectively. The higher shift in SPR angles due to Pb2+and Hg2+compared to Cr3+, Cu2+and Zn2+ions also reveals the greater affinity of the CS-GO SPR sensor to them, thus supporting the rationale for obtaining K for these two heavy metals. This study provides a better understanding on the sensing performance of such sensors in detecting heavy metal ions.
Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.
Clinical synopsis and investigations on fourteen patients with blast injuries of the ear successfully treated at the ENT Unit of the University Hospital, Kuala Lumpur, revealed few interesting observations in addition to usual findings. The most outstanding one was the absence of sensorineural deafness, although it has been claimed as one of the most constant features. Conservative management alone was found to be of significant value.
In spite of extensive research conducted to study how human brain works, little is known about a special function of the brain that stores and manipulates information-the working memory-and how noise influences this special ability. In this study, Functional magnetic resonance imaging (fMRI) was used to investigate brain responses to arithmetic problems solved in noisy and quiet backgrounds.