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.
In the last two decades, a variety of techniques for signal-to-noise ratio (SNR) estimation in scanning electron microscope (SEM) images have been proposed. However, these techniques can be divided into two groups: first, SNR estimators of good accuracy, but based on impractical assumptions; second, estimators based on realistic assumptions but of poor accuracy. In this paper we propose the implementation of autoregressive (AR)-model interpolation as a solution to the problem. Unlike others, the proposed technique is based on a single SEM image and offers the required accuracy and robustness in estimating SNR values.
During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.
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.
The study examined the use of linguistic contextual cues among native, Malay-speaking normal hearing young adults. Ten undergraduate students of Universiti Kebangsaan Malaysia participated in the study. All subjects had normal hearing with the average hearing threshold levels for the overall left and the right ears of 7.8 dB (SD 4.1). The Malay Hearing in Noise Test (MyHINT) materials were employed and presented to the subjects at an approximately 65 dBA presentation level. Testing was conducted in a sound field in three different listening conditions: in quiet, in noise with +5 dB signal-to-noise ratio (SNR) and 0 dB SNR. In every test condition, three lists of MyHINT were administered to each subject. The magnitude of context effects was measured using the j factor, which was derived from measurements of recognition probabilities for whole sentences (13,) and the constituent words in the sentences (PP) in which j = log P./ log P P. Results showed that all subjects scored 100% identification of words in sentences and whole sentences in quiet listening condition, while subjects' performances in 0 dB SNR were significantly poorer than that in quiet and in +5 dB SNR (p < 0.001). The j-values were significantly correlated with the probability of recognizing words in the sentences (r = 0.515, p = 0.029) in which lower j values were associated with lower P ps. Subjects were not significantly different from each other in their use of contextual cues in adverse listening conditions [F(9, 7) = 1.34, p = 0.359]. Using the linear regression function for j on word recognition probabilities, the predicted P. were calculated. It was found that the predicted and measured probabilities of recognizing whole sentences were highly correlated: r = 0.973, p < 0.001. The results suggested that linguistic contextual information become increasingly important for recognition of sentences by normal hearing young adult listeners as SNR deteriorates.
We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.
An analysis of the power spectral density of ultra-wideband (UWB) signals is presented in order to evaluate the effects of cumulative interference from multiple UWB devices on victim narrowband systems in their overlay bands like WiFi (i.e. IEEE802.11a) and 3rdG systems (Universal mobile telecommunications system/wideband code division multiple access). In this paper, the performances are studied through the bit-error-rate as a function of signal-to-noise ratio as well as signal-to-interference power ratio using computer simulation and exploiting the realistic channel model (i.e. modified Saleh-Valenzuela model). Several modifications of a generic Gaussian pulse waveform with lengths in the order of nanoseconds were used to generate UWB spectra. Different kinds of pulse modulation (i.e. antipodal and orthogonal) schemes were also taken into account.
This paper proposes a signal-to-noise-ratio (SNR) improvement by using an external phase modulator that allowed flexible control of the spectrum amplitude by varying the modulation index for linewidth measurements. Compared with the conventional self-heterodyne detection technique, the results obtained in this study showed an SNR improvement as high as 10 dB. This 10 dB improvement in SNR could help to reduce the usage of a particular length of a single mode fibre (normally about 50 Km) when measuring a linewidth in the region of 10 kHz.
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 multi-wavelength light source using a semiconductor optical amplifier (SOA) in conjunction with an array waveguide grating (AWG). The experimental results showed more than 20 channels with a wavelength separation of 0.8 nm and an optical signal-to-noise ratio of more than 10 dB under room temperature. The channels operated at the wavelength region from 1530.4 nm to 1548.6 nm, which corresponded to AWG filtering wavelengths with SOA drive current of 350 mA. The proposed light source had the advantages of a simple and compact structure, multi-wavelength operation and the system could be upgraded to generate more wavelengths.
A method for the determination of aflatoxins B1 and B2 in peanuts and corn based products is described. The samples were extracted with a mixture of acetonitrile-water (84:16), followed by multifunctional clean-up and liquid chromatography with fluorescence detection. Both calibration curves showed good correlation from 4.0 to 32.0 ppb for aflatoxin B1 (r=0.9999) and 1.2 to 9.6 ppb for aflatoxin B2 (r=0.9997). The detection limit of aflatoxins B1 and B2 were established at 0.1 and 0.03 ppb, respectively, based on signal-to-noise ratio of 3:1. Average recoveries for the determination of aflatoxins B1 and B2 at 10 and 3 ppb spiking levels, respectively ranged from 94.2 to 107.6%. A total of 20 peanut samples and corn based products were obtained from retail shop and local market around Kuala Terengganu and analyzed for aflatoxins B1 and B2 contents, using the proposed method. Aflatoxins B1 and B2 were detected in 5 out of the 9 peanuts samples and 5 out of the 11 corn based products, at levels ranging from 0.2 to 101.8 ppb.
To reduce undesirable charging effects in scanning electron microscope images, Rayleigh contrast stretching is developed and employed. First, re-scaling is performed on the input image histograms with Rayleigh algorithm. Then, contrast stretching or contrast adjustment is implemented to improve the images while reducing the contrast charging artifacts. This technique has been compared to some existing histogram equalization (HE) extension techniques: recursive sub-image HE, contrast stretching dynamic HE, multipeak HE and recursive mean separate HE. Other post processing methods, such as wavelet approach, spatial filtering, and exponential contrast stretching, are compared as well. Overall, the proposed method produces better image compensation in reducing charging artifacts.
A new and robust parameter estimation technique, named image noise cross-correlation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with nearest neighborhood and first-order interpolation. Overall, the proposed method is best as its estimations for the noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree, compared with the others.
In this paper, a variable threshold voice activity detector (VAD) is developed to control the operation of a two-sensor adaptive noise canceller (ANC). The VAD prohibits the reference input of the ANC from containing some strength of actual speech signal during adaptation periods. The novelty of this approach resides in using the residual output from the noise canceller to control the decisions made by the VAD. Thresholds of full-band energy and zero-crossing features are adjusted according to the residual output of the adaptive filter. Performance evaluation of the proposed approach is quoted in terms of signal to noise ratio improvements as well mean square error (MSE) convergence of the ANC. The new approach showed an improved noise cancellation performance when tested under several types of environmental noise. Furthermore, the computational power of the adaptive process is reduced since the output of the adaptive filter is efficiently calculated only during non-speech periods.
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.
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.
The recognition of microcalcifications and masses from digital mammographic images are important to aid the detection of breast cancer. In this paper, we applied morphological techniques to extract the embedded structures from the images for subsequent analysis. A mammographic phantom was created with embedded structures such as micronodules, nodules and fibrils. For the preprocessing techniques, intensity transformation of gray scale was applied to the image. The structures of the image were enhanced and segmented using dilation for a morphological operation with morphological closing. Next, low pass Gaussian filter was applied to the image to smooth and reduce noises. It was found that our method improved the detection of microcalcifications and masses with high Peak Signal To Noise Ratio (PSNR).
The ability to produce performances at highest level under physically and emotionally demanding conditions underline the worth of a sportsperson. These stressful conditions places demands on the cognitive resources of the sportsperson; especially in anticipatory actions that require the allocation of cognitive resources. This study investigated the effects of cognitive stress on the temporal anticipation of a timing motor task. A repeated measures design was applied with two independent variables; cognitive stress and levels of difficulty, which included easy, intermediate and difficult. Study participants were 18 male and 18 female undergraduates of the Physical Education programme of Universiti Putra Malaysia. The experimental task involved performing a timing motor task across the three levels of difficulty, under two conditions as follows: (i) without cognitive stress, and (ii) under cognitive stress. Cognitive stress was induced via the continuous subtraction of two from a two-digit number. Participants performed the task individually and the sequence of the experimental task was counter-balanced. A two-way within subject ANOVA was
performed to ascertain the effects of cognitive stress on the temporal anticipation of the timing motor task. Data yielded significant difference in means for the stress main effect [Λ = .64, F (1.35) = 19.89, p < 0.05]; and the task main effect [Λ = .84, F (2, 34) = 3.35, p < 0.05]. Post hoc comparisons produced a significant difference in the means of the performance of the timing motor task at all three levels of difficulty. Data showed that cognitive stress had an effect on the temporal anticipation of the timing motor task. These results are explained from attentional and the neuromotor noise perspectives. It was concluded that the significant difference in the performance of the experimental task was due to the competition for intentional resources and the decrease of the signal to noise ratio due to cognitive stress.
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.
Because blocking agent occupies most binding surface of a solid phase, its ability to prevent nonspecific binding determines the signal-to-noise ratio (SNR) and reliability of an enzyme-linked immunosorbent assay (ELISA).