Space weather forecasting and its importance for the power and communication industry have inspired research related to TEC forecasting lately. Research has attempted to establish an empirical model approach for TEC prediction. In this paper, artificial neural networks (ANNs) have been applied in total electron content using GPS Ionospheric Scintillation and TEC Monitor (GISTM) data from UKM Station. The TEC prediction will be useful in improving the quality of current GNSS applications, such as in automobiles, road mapping, location-based advertising, personal navigation or logistics. Hence, a neural network model was designed with relevant features and customised parameters. Various types of input data and data representations from the ionospheric activity were used for the chosen network structure, which was a three-layer perceptron trained by feed forward back propagation method and tested on the chosen test data. We found that the optimum RMSE occurred with 10 nodes as the best NN for GISTM UKM station for the studied period with RMSE 1.3457 TECU. An analysis was made to compare the TEC from the measured TEC with neural network prediction and from IRI-corr model. The results showed that the NN model forecast the TEC values close to the measured TEC values with 9.96% of relative error. Thus, the forecasting of total electron content has the potential to be implemented successfully with larger data set from multi-centred environment.
Difficulty of understanding speech in noise among the elderly necessitates the need for Auditory Training which has made a renewal of interest in the last decade with the auditory training applications. This interest is perhaps spurred by advances in computer-based technology. In computer-based auditory training, speech signals are considered as auditory training stimuli where input speech signals need to be verified prior to training as the speech signals are mixed with noise signals. Computer-based Auditory Training System can be embedded with input speech verifying module. Input speech verifying module is employed with speech and noise separator simulator. This simulator needs to guarantee accurate separation of speech from noise signals. Therefore, in this research, Exploratory Projection Pursuit (EPP) technique under semi-Blind Source Separation (BSS) method is intended to separate the speech source signals which are mixed with competing speech (multitalker speech babble). This training uses Malay language based sentences which differ in word length and hence number of sample values. The experimental simulation considers two-channel random, linear mixing of speech sources and competing speech. The aim of this study is to evaluate the performance of source separation using the anticipated EPP technique for various sample values of speech signals which varies in time duration due to word length dissimilarity. Simulation results show that EPP technique is feasible for source separation. As a consequence, high correlation value of r ≥ 0.99 is obtained between extracted speech signal and original speech signal for all categories of speech signals. It is further verified by the maximum nongaussianity of extracted speech signal which has high kurtosis value of 32 approximately.
The use of photoplethysmography (PPG) as one of cardiovascular disease (CVD) marker has got more attention due to
its simplicity, noninvasive and portable characteristics. Two new markers had been developed from PPG namely PPG
fitness index (PPGF) and vascular risk prediction index (VPRI). The aim of the present study was to compare PPGF level
between young women with and without CVD risk factors, to investigate the relationship between PPGF with other CVD
markers and to assess the sensitivity of VRPI in classifying young women that have CVD risk factors. A total of 148 young
women aged 20-40 years old with and without CVD risk factors were involved in this study. CVD risk factors comprised of
abdominal obesity, hypertension, dyslipidemia, smoking and family history of premature CVD. Subjects were categorized
into healthy or having CVD risk factor. Measurements taken were anthropometric data, blood pressure, lipid profile,
pulse wave velocity (PWV), augmentation index (AIx), high sensitivity C-Reactive Protein (hs-CRP), PPGF and VRPI. SPSS
version 20 was used for data analysis with p<0.05 as significant value. The mean subjects’ age was 29.97±5.27 years
old. There was no difference in PPGF level between groups (p>0.05). PPGF was independently determined by PWV (β=-
0.31, p<0.001) and height (β=0.16, p=0.04). VRPI had 77.9% sensitivity in identifying subjects with CVD risk factor. In
conclusion, PPGF correlates with PWV and has potential to be an indicator of aortic stiffness while VRPI is sensitive to
classify those with CVD risk factor.