Displaying publications 1 - 20 of 76 in total

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  1. Ahmad H, Albaqawi HS, Yusoff N, Yi CW
    Sci Rep, 2020 Jun 17;10(1):9860.
    PMID: 32555280 DOI: 10.1038/s41598-020-66664-9
    A wide-band and tunable Q-switched erbium-doped fiber (EDF) laser operating at 1560.5 nm with a tungsten ditelluride (WTe2) saturable absorber (SA) is demonstrated. The semi-metallic nature of WTe2 as well as its small band gap and excellent nonlinear optical properties make it an excellent SA material. The laser cavity uses an 89.5 cm long EDF, pumped by a 980 nm laser diode as the linear gain while the WTe2 based SA generates the pulsed output. The WTe2 based SA has a modulation depth, non-saturable loss and saturation intensity of about 21.4%, 78.6%, and 0.35 kW/cm2 respectively. Stable pulses with a maximum repetition rate of 55.56 kHz, narrowest pulse width of 1.77 µs and highest pulse energy of 18.09 nJ are obtained at the maximum pump power of 244.5 mW. A 56 nm tuning range is obtained in the laser cavity, and the output is observed having a signal to noise ratio (SNR) of 48.5 dB. The demonstrated laser has potential for use in a large number of photonics applications.
    Matched MeSH terms: Signal-To-Noise Ratio
  2. Masroor K, Jeoti V, Drieberg M, Cheab S, Rajbhandari S
    Sensors (Basel), 2021 Apr 22;21(9).
    PMID: 33922288 DOI: 10.3390/s21092943
    The bi-directional information transfer in optical body area networks (OBANs) is crucial at all the three tiers of communication, i.e., intra-, inter-, and beyond-BAN communication, which correspond to tier-I, tier-II, and tier-III, respectively. However, the provision of uninterrupted uplink (UL) and downlink (DL) connections at tier II (inter-BAN) are extremely critical, since these links serve as a bridge between tier-I (intra-BAN) and tier-III (beyond-BAN) communication. Any negligence at this level could be life-threatening; therefore, enabling quality-of-service (QoS) remains a fundamental design issue at tier-II. Consequently, to provide QoS, a key parameter is to ensure link reliability and communication quality by maintaining a nearly uniform signal-to-noise ratio (SNR) within the coverage area. Several studies have reported the effects of transceiver related parameters on OBAN link performance, nevertheless the implications of changing transmitter locations on the SNR uniformity and communication quality have not been addressed. In this work, we undertake a DL scenario and analyze how the placement of light-emitting diode (LED) lamps can improve the SNR uniformity, regardless of the receiver position. Subsequently, we show that using the principle of reciprocity (POR) and with transmitter-receiver positions switched, the analysis is also applicable to UL, provided that the optical channel remains linear. Moreover, we propose a generalized optimal placement scheme along with a heuristic design formula to achieve uniform SNR and illuminance for DL using a fixed number of transmitters and compare it with an existing technique. The study reveals that the proposed placement technique reduces the fluctuations in SNR by 54% and improves the illuminance uniformity up to 102% as compared to the traditional approach. Finally, we show that, for very low luminous intensity, the SNR values remain sufficient to maintain a minimum bit error rate (BER) of 10-9 with on-off keying non-return-to-zero (OOK-NRZ) modulation format.
    Matched MeSH terms: Signal-To-Noise Ratio
  3. Islam MA, Jassim WA, Cheok NS, Zilany MS
    PLoS One, 2016;11(7):e0158520.
    PMID: 27392046 DOI: 10.1371/journal.pone.0158520
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  4. Chin SC, Chow CO, Kanesan J, Chuah JH
    Sensors (Basel), 2022 Jan 14;22(2).
    PMID: 35062601 DOI: 10.3390/s22020639
    Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.
    Matched MeSH terms: Signal-To-Noise Ratio
  5. Al-Gumaei YA, Noordin KA, Reza AW, Dimyati K
    PLoS One, 2014;9(10):e109077.
    PMID: 25286044 DOI: 10.1371/journal.pone.0109077
    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.
    Matched MeSH terms: Signal-To-Noise Ratio*
  6. Maman Hermana, Hammad Hazim Mohd Azhar, Zuhar Zahir Tuan Harith
    Sains Malaysiana, 2012;41:953-959.
    This paper presents the improvement of quality factor (Q) estimation using shift frequency method. A new method was developed based on two previous methods; peak frequency shift (PFS) method and centroid frequency shift (CFS) method. The proposed algorithm has been tested to gauge its performance using three different scenarios; Q variation, travel
    time variation, and signal to noise ratio (SNR) variation. The test was performed using the Ricker wavelet with random noise included. Based on the results obtained, it can be concluded that the new proposed method was able to improve Q estimation using shift frequency method. This method can also be implemented in the low and high Q condition, shallow and deep wavelet targets and in the low and high SNR conditions of seismic data. The limitations in the PFS and CFS methods can be reduced by this method.
    Matched MeSH terms: Signal-To-Noise Ratio
  7. Mohamed Moubark A, Ali SH
    ScientificWorldJournal, 2014;2014:107831.
    PMID: 25197687 DOI: 10.1155/2014/107831
    This paper presents a new practical QPSK receiver that uses digitized samples of incoming QPSK analog signal to determine the phase of the QPSK symbol. The proposed technique is more robust to phase noise and consumes up to 89.6% less power for signal detection in demodulation operation. On the contrary, the conventional QPSK demodulation process where it uses coherent detection technique requires the exact incoming signal frequency; thus, any variation in the frequency of the local oscillator or incoming signal will cause phase noise. A software simulation of the proposed design was successfully carried out using MATLAB Simulink software platform. In the conventional system, at least 10 dB signal to noise ratio (SNR) is required to achieve the bit error rate (BER) of 10(-6), whereas, in the proposed technique, the same BER value can be achieved with only 5 dB SNR. Since some of the power consuming elements such as voltage control oscillator (VCO), mixer, and low pass filter (LPF) are no longer needed, the proposed QPSK demodulator will consume almost 68.8% to 99.6% less operational power compared to conventional QPSK demodulator.
    Matched MeSH terms: Signal-To-Noise Ratio
  8. Al-Gumaei YA, Noordin KA, Reza AW, Dimyati K
    PLoS One, 2015;10(8):e0135137.
    PMID: 26258522 DOI: 10.1371/journal.pone.0135137
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  9. Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, et al.
    Comput Biol Med, 2024 Feb;169:107835.
    PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835
    Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
    Matched MeSH terms: Signal-To-Noise Ratio
  10. Salleh SH, Hussain HS, Swee TT, Ting CM, Noor AM, Pipatsart S, et al.
    Int J Nanomedicine, 2012;7:2873-81.
    PMID: 22745550 DOI: 10.2147/IJN.S32315
    Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss-Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.
    Matched MeSH terms: Signal-To-Noise Ratio
  11. Billings CJ, Grush LD, Maamor N
    Physiol Rep, 2017 Nov;5(20).
    PMID: 29051305 DOI: 10.14814/phy2.13464
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  12. Gandam A, Sidhu JS, Verma S, Jhanjhi NZ, Nayyar A, Abouhawwash M, et al.
    PLoS One, 2021;16(5):e0250959.
    PMID: 33970949 DOI: 10.1371/journal.pone.0250959
    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.
    Matched MeSH terms: Signal-To-Noise Ratio*
  13. Ting CM, Samdin SB, Salleh ShH, Omar MH, Kamarulafizam I
    PMID: 23367426 DOI: 10.1109/EMBC.2012.6347491
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  14. Foo LS, Yap WS, Hum YC, Manan HA, Tee YK
    J Magn Reson, 2020 01;310:106648.
    PMID: 31760147 DOI: 10.1016/j.jmr.2019.106648
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  15. Al-Qazzaz NK, Hamid Bin Mohd Ali S, Ahmad SA, Islam MS, Escudero J
    Sensors (Basel), 2017 Jun 08;17(6).
    PMID: 28594352 DOI: 10.3390/s17061326
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  16. Khairur Rijal Jamaludin, Nolia Harudin, Faizir Ramlie, Mohd Nabil Muhtazaruddin, Che Munira Che Razali, Wan Zuki Azman Wan Muhamad
    MATEMATIKA, 2020;36(1):69-84.
    MyJurnal
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  17. Kamaruddin NH, Bakar AAA, Mobarak NN, Zan MSD, Arsad N
    Sensors (Basel), 2017 Oct 06;17(10).
    PMID: 28984826 DOI: 10.3390/s17102277
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  18. Priyadarshani N, Marsland S, Castro I, Punchihewa A
    PLoS One, 2016;11(1):e0146790.
    PMID: 26812391 DOI: 10.1371/journal.pone.0146790
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  19. Zamzuri AK, Md Ali MI, Ahmad A, Mohamad R, Mahdi MA
    Opt Lett, 2006 Apr 01;31(7):918-20.
    PMID: 16599211
    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.
    Matched MeSH terms: Signal-To-Noise Ratio
  20. Shaik Amir NS, Kang LZ, Mukari SA, Sahathevan R, Chellappan K
    Healthc Technol Lett, 2020 Feb;7(1):1-6.
    PMID: 32190334 DOI: 10.1049/htl.2018.5003
    A critical step in detection of primary intracerebral haemorrhage (ICH) is an accurate assessment of computed tomography (CT) brain images. The correct diagnosis relies on imaging modality and quality of acquired images. The authors present an enhancement algorithm which can improve the clarity of edges on CT images. About 40 samples of CT brain images with final diagnosis of primary ICH were obtained from the UKM Medical Centre in Digital Imaging and Communication in Medicine format. The images resized from 512 × 512 to 256 × 256 pixel resolution to reduce processing time. This Letter comprises of two main sections; the first is denoising using Wiener filter, non-local means and wavelet; the second section focuses on image enhancement using a modified unsharp masking (UM) algorithm to improve the visualisation of ICH. The combined approach of Wiener filter and modified UM algorithm outperforms other combinations with average values of mean square error, peak signal-to-noise ratio, variance and structural similarity index of 2.89, 31.72, 0.12 and 0.98, respectively. The reliability of proposed algorithm was evaluated by three blinded assessors which achieved a median score of 65%. This approach provides reliable validation for the proposed algorithm which has potential in improving image analysis.
    Matched MeSH terms: Signal-To-Noise Ratio
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