Noise is known to be one of the environmental and occupational hazards listed in the Factory and Machinery Act 1967. Quarries with loud deafening sounds from trucks and machineries pose the risk of noise-induced hearing loss to workers. This study was designed to assess the knowledge, attitude and practice towards noise-induced hearing loss and to determine the prevalence of noise-induced hearing loss and its associated factors among quarry workers in a north-eastern state of Malaysia.
A widely tunable low stimulated Brillouin scattering (SBS) photonic crystal fiber (PCF) based multi-wavelength Brillouin-erbium fiber laser is presented. The fiber laser structure utilizes a pre-amplified Brillouin pump (BP) technique with 100 m of PCF and a tunable band-pass filter within a Fabry-Perot cavity. A total of 14 Brillouin Stokes lines can be tuned over 29 nm from 1540 nm to 1569 nm. The wide tunability was only limited by the bandwidth of the tunable band-pass filter. A constant channel spacing of 0.079 nm and signal to noise ratio (SNR) of more than 20 dB for each Brillouin Stokes lines were also observed.
We demonstrate a multiple-wavelength Brillouin comb laser with cooperative Rayleigh scattering that uses Raman amplification in dispersion-compensating fiber. The laser resonator is a linear cavity formed by reflector at each end of the dispersion-compensating fiber to improve the reflectivity of the Brillouin Stokes comb. Multiple Brillouin Stokes generation has been improved in terms of optical signal-to-noise ratio and power-level fluctuation between neighboring channels. Furthermore, the linewidth of the Brillouin Stokes is uniform within the laser output bandwidth.
This paper analyses electromagnetic signal scattered from the target crossing the Forward Scattering
Radar (FSR) system baseline. The aim of the analysis was to extract the Doppler signal of a target under the influence of high ground clutter and noise interference. The extraction was used for the
automatic target detection (ATD) in the FSR system. Two extraction methods, namely Hilbert Transform and Wavelet Technique, were analyzed. The detection using the Hilbert Transform is only applicable for some conditions; however, the detection using the Wavelet Technique is more robust to any clutter and noise level. From 55 sets of signal, only 4% of false alarm was detected or occurred when the Wavelet Technique was applied as a detection scheme. Two sets of field experimentation were carried out and the target’s signal under the influence of high clutter had successfully been detected using the proposed method.
Combining Mel Frequency Cepstral Coefficient with wavelet transform for feature extraction is not new. This paper proposes a new architecture to help in increasing the accuracy of speaker recognition compared with conventional architecture. In conventional speaker model, the voice will undergo noise elimination first before feature extraction. The proposed architecture however, will extract the features and eliminate noise simultaneously. The MFCC is used to extract the voice features while wavelet de-noising technique is used to eliminate the noise contained in the speech signals. Thus, the new architecture achieves two outcomes in one single process: ex-tracting voice feature and elimination of noise.
This paper deals with the analysis of different Fuzzy membership type performance for Extended Kalman Filter (EKF) based mobile robot navigation. EKF is known to be incompetent in non-Gaussian noise condition and therefore the technique alone is not sufficient to provide solution. Motivated by this shortcoming, a Fuzzy based EKF is proposed in this paper. Three membership types are considered which includes the triangular, trapezoidal and Gaussian membership types to determine the best estimation results for mobile robot and landmarks locations. Minimal rule design and configuration are also other aspects being considered for analysis purposes. The simulation results suggest that the Gaussian memberships surpassed other membership type in providing the best solution in mobile robot navigation.
The purpose of this study is to quantify the quality of the available imaging modes for various iodine-based contrast agent concentration in paediatric cardiology. The figure of merit (FOM) was defined as the squared signal to noise ratio divided by a patient dose related parameter. An in house constructed phantom simulated a series of vessel segments with iodine concentrations from 10% or 30 mg/cc to 16% or 48 mg/cc of iodine in a blood plasma solution, all within the dimensional constraints of a paediatric patient. The phantom also used test inserts of tin (Sn). Measurements of Entrance Surface Air Kerma (ESAK) and exit dose rate were performed along with calculations of the signal-to-noise ratio (SNR) of all the objects. A first result showed that it was favourable to employ low dose fluoroscopy mode and lower frame rate modes in cine acquisition if dynamic information is not critical. Normal fluoroscopy dose mode provided a considerably higher dose level (in comparison to low dose mode) with only a slight improvement in SNR. Higher frame rate cine modes should be used however when the clinical situation dictates so. This work also found that tin should not be intended as iodine replacement material for research purposes due to the mismatching SNR, particularly on small vessel sizes.
Many studies have been carried out to produce magnetic resonance imaging (MRI) phantoms as alternative to water phantom. Among the important properties of a phantom are the T1 and T2 relaxation times. The objective of this study is to investigate the T1 and T2 characteristics of the agarose gel phantoms with different relaxation modifier (gadolinium (III) oxide, Gd2O3) concentrations or [Gd2O3]. Six agarose gel phantoms were prepared with different [Gd2O3]. The T1 (fixed echo time (TE) and different repetition time (TR)) and T2 (fixed TR and different TE) measurements on all phantoms were conducted using the 3-T MRI system via spin echo (SE) and turbo spin echo (TSE) sequences, respectively. The signal-to-noise ratio (SNR) of all phantoms was calculated using Image-J software by implementing the region of interest (ROI) analysis. The SNR against TR and SNR against TE curves were fitted to the exponential equations for saturation, T1 and T2 determination. For every phantom, T1 curve demonstrated that the SNR increased exponentially with increasing TR, while T2 curves showed that the SNR decreased exponentially with increasing TE. Gd2O3 was found to successfully act as the relaxation modifier for the T1 but not the T2 curves. The T1 curve started to show saturated SNR (SNRo) and increasing SNRo for TR > 1000 ms and [Gd2O3] = 0.005 g/ml or higher. These behaviours are explained based on the dipole-dipole interaction that increases in phantoms with higher [Gd2O3], thus shortening the T1 relaxation. However, a systematic change in the T2 parameters with increasing [Gd2O3] was not observed. While Gd2O3 has significant effects on T1 relaxation parameters, the T2 relaxation parameters were minimally affected. With a shorter T1, the Gd2O3 added agarose gel can potentially be used as test phantom in fast imaging sequence, e.g. gradient echo pulse sequences.
Many recent studies focused on the patient’s safety from the administration of gadolinium-based contrast agents (GBCAs), their concentration, the dose of administration and their effects on the image quality. The present study was aimed at evaluating the effects of reduced GBCAs (gadobutrol and gadoterate meglumine) volume on the image quality by using phantoms. Eight (8) human brain mimicking phantom made of nickel chloride (NiCl2) doped agarose gel were added with 0.00500 ml (100% volume), 0.00350 ml (75% volume), 0.00250 ml (50% volume) and 0.00125 ml (25% volume) of gadobutrol, 0.0100 ml (100% volume), 0.0075 ml (75% volume), 0.0050 ml (50% volume) and 0.0025 ml (25% volume) of gadoterate meglumine. The phantoms were scanned using a 1.5-T and a 3 T-MRI system. Signal-to-noise ratio (SNR) and the contrast agents enhancement were evaluated quantitatively and qualitatively. The 50% volume of gadobutrol and gadoterate meglumine at 3 T showed greater enhancement when compared with 50% and 100% volumes of gadobutrol and gadoterate meglumine at 1.5 T. It can be concluded that the volume of gadobutrol and gadoterate meglumine contrast agents can be reduced when using a higher field system
The effects of background noise on speech-evoked cortical auditory evoked potentials (CAEPs) can provide insight into the physiology of the auditory system. The purpose of this study was to determine background noise effects on neural coding of different phonemes within a syllable. CAEPs were recorded from 15 young normal-hearing adults in response to speech signals /s/, /ɑ/, and /sɑ/. Signals were presented at varying signal-to-noise ratios (SNRs). The effects of SNR and context (in isolation or within syllable) were analyzed for both phonemes. For all three stimuli, latencies generally decreased and amplitudes generally increased as SNR improved, and context effects were not present; however, the amplitude of the /ɑ/ response was the exception, showing no SNR effect and a significant context effect. Differential coding of /s/ and /ɑ/ likely result from level and timing differences. Neural refractoriness may result in the lack of a robust SNR effect on amplitude in the syllable context. The stable amplitude across SNRs in response to the vowel in /sɑ/ suggests the combined effects of (1) acoustic characteristics of the syllable and noise at poor SNRs and (2) refractory effects resulting from phoneme timing at good SNRs. Results provide insights into the coding of multiple-onset speech syllables in varying levels of background noise and, together with behavioral measures, may help to improve our understanding of speech-perception-in-noise difficulties.
An analytical method using HPLC with UV detection was developed to investigate the quassinoid content of Eurycoma longifolia Jack (Simaroubaceae) collected from various sources. Eurycomanone (1), longilactone (2), 14,15beta-dihydroxyklaineanone (3), 15beta-acetyl-14-hydroxyklaineanone (4), 6alpha-hydroxyeurycomalactone (5), and eurycomalactone (7) were isolated as reference standards and together with the synthesized 1beta,12alpha,15beta-triacetyleurycomanone (6, internal standard), were identified by NMR, MS, UV and IR spectroscopies. Their coefficient of variation values for 0.50-35 microg ml(-1) concentrations of quassinoids and their retention times measured within- and between-day were small. The recoveries of the spiked quassinoids in E. longifolia samples and their detection limits at 8.5 times signal to noise ratio were 99.75-109.13% and 0.01 microg ml(-1), respectively. From the root samples analysed, 1 had the highest concentration, being about 16.8-39.6 fold higher than the other quassinoids 2, 3, 5, 7 but 145.3 fold higher than 4 which showed the lowest concentration.
Interference resulting from Cognitive Radios (CRs) is the most important aspect of cognitive radio networks that leads to degradation in Quality of Service (QoS) in both primary and CR systems. Power control is one of the efficient techniques that can be used to reduce interference and satisfy the Signal-to-Interference Ratio (SIR) constraint among CRs. This paper proposes a new distributed power control algorithm based on game theory approach in cognitive radio networks. The proposal focuses on the channel status of cognitive radio users to improve system performance. A new cost function for SIR-based power control via a sigmoid weighting factor is introduced. The existence of Nash Equilibrium and convergence of the algorithm are also proved. The advantage of the proposed algorithm is the possibility to utilize and implement it in a distributed manner. Simulation results show considerable savings on Nash Equilibrium power compared to relevant algorithms while reduction in achieved SIR is insignificant.
This paper presents a two-level scheduling scheme for video transmission over downlink orthogonal frequency-division multiple access (OFDMA) networks. It aims to maximize the aggregate quality of the video users subject to the playback delay and resource constraints, by exploiting the multiuser diversity and the video characteristics. The upper level schedules the transmission of video packets among multiple users based on an overall target bit-error-rate (BER), the importance level of packet and resource consumption efficiency factor. Instead, the lower level renders unequal error protection (UEP) in terms of target BER among the scheduled packets by solving a weighted sum distortion minimization problem, where each user weight reflects the total importance level of the packets that has been scheduled for that user. Frequency-selective power is then water-filled over all the assigned subcarriers in order to leverage the potential channel coding gain. Realistic simulation results demonstrate that the proposed scheme significantly outperforms the state-of-the-art scheduling scheme by up to 6.8 dB in terms of peak-signal-to-noise-ratio (PSNR). Further test evaluates the suitability of equal power allocation which is the common assumption in the literature.
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.
Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.
Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces.
Speaker identification under noisy conditions is one of the challenging topics in the field of speech processing applications. Motivated by the fact that the neural responses are robust against noise, this paper proposes a new speaker identification system using 2-D neurograms constructed from the responses of a physiologically-based computational model of the auditory periphery. The responses of auditory-nerve fibers for a wide range of characteristic frequency were simulated to speech signals to construct neurograms. The neurogram coefficients were trained using the well-known Gaussian mixture model-universal background model classification technique to generate an identity model for each speaker. In this study, three text-independent and one text-dependent speaker databases were employed to test the identification performance of the proposed method. Also, the robustness of the proposed method was investigated using speech signals distorted by three types of noise such as the white Gaussian, pink, and street noises with different signal-to-noise ratios. The identification results of the proposed neural-response-based method were compared to the performances of the traditional speaker identification methods using features such as the Mel-frequency cepstral coefficients, Gamma-tone frequency cepstral coefficients and frequency domain linear prediction. Although the classification accuracy achieved by the proposed method was comparable to the performance of those traditional techniques in quiet, the new feature was found to provide lower error rates of classification under noisy environments.
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.
Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.
In this paper, the influence of processing input parameters on the heat-affected zone (HAZ) of three different material thicknesses of sugar palm fiber reinforced unsaturated polyester (SPF-UPE) composites cut with a CO2 laser was investigated. Laser power, traverse speed, and gas pressure were selected as the most influential input parameters on the HAZ to optimize the HAZ response with fixing all of the other input parameters. Taguchi's method was used to determine the levels of parameters that give the best response to the HAZ. The significance of input parameters was also determined by calculating the max-min variance of the average of the signal-to-noise ratio (S/N) ratio for each parameter. Analysis of variation (ANOVA) was used to determine each input parameter's contribution to the influence on HAZ depth. The general results show that the minimum levels of laser power and the highest levels of traverse speed and gas pressure gave the optimum response to the HAZ. Gas pressure had the most significant effect on the HAZ, with contribution decreases as the material thickness increased, followed by the traverse speed with contribution increases with the increase in material thickness. Laser power came third, with a minimal contribution to the effect on the HAZ, and it did not show a clear relationship with the change in material thickness. By applying the optimum parameters, the desired HAZ depth could be obtained at relatively low values.