Classification of speech dysfluencies with MFCC and LPCC features

The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature extraction. Two simple classifiers are used for testing the proposed features. Conventional validation method is used for testing the reliability of the classifier. The experimental investigation elucidates MFCC and LPCC features which can be used for identifying the stuttered events and LPCC features were slightly outperformed than MFCC features.     

Screening of factor influencing wax deposition using full factorial experimental design

The purpose of this work was to study the rate of wax deposition of Malaysian crude oil using full factorial design. Important parameters affecting wax deposition such as experimental duration, speed of rotation, cold finger temperature, and inhibitor concentration were investigated. The individual effects of variables and its interaction effects towards the dependent variables were studied. Wax deposit is the dependent variable. The results of the study showed that less wax deposit was obtained (0.75 g) with optimal conditions of initial poly(ethylene-co-vinyl acetate) concentration of 5000 ppm, 2 h duration without rotation speed, and 15°C cold finger temperature. These results demonstrate that cold finger temperature and experimental duration could be the main factors affecting wax deposit formation. The minimum amount of wax deposit obtained could reduce the chances of blocked pipelines; however, crude oil production increased. Nevertheless, Design Expert can be a valuable tool to quantify and detect the special relationships of two of more factors known as interactions regarding how they factors could affect a process, especially for screening purpose.     

Review of Energy Conservation Using Duty Cycling Schemes for IEEE 802.15.4 Wireless Sensor Network (WSN)

Energy conservation is one of the crucial issues in wireless sensor network (WSN). A significant solution to conserve energy is done by deploying duty cycle management mechanisms in the WSN applications. This paper reviews several duty cycle mechanisms in WSN such as Duty Cycle Learning Algorithm, adaptive media access control (MAC) protocol for efficient IEEE 802.15.4 (AMPE), distributed duty cycle management (DDCM), distributed duty cycle management low power broadcast (DDCM + LPB) and distributed beacon only period. These mechanisms change their parameters such as idle listening, packet accumulation and delay in the end device transmitting queue to improve the energy conservation in WSN. The performances of these different energy conservation mechanisms have been compared at the MAC layer of IEEE 802.15.4 standard. It is found that the DDCM + LPB has made approximately 100 % enhancement in terms of average energy efficiency as compared to the other mechanisms. DDCM + LPB has significant enhancements by adapting the duty cycle according to the network traffic load condition. Using this mechanism, the duty cycle is increased when the traffic load increases and vice versa. Its energy efficiency also outperforms the conventional DDCM by the average of 10 %.     

Fabrication of nickel aluminide intermetallic-alumina nanocomposite via reaction synthesis

Nickel aluminide–alumina nanocomposite was prepared by a conventional powder metallurgy technique. Nanosize alumina as dispersed phase was mixed with Ni and Al powder in a planetary ball mill for 4 h at 175 rpm. The mixture was then compacted using a hydraulic press at 400 MPa for 15 min. Sintering was done under inert condition in a tube furnace at 660 °C with 5 h soaking time. The occurrence of reaction synthesis during sintering was detected by the presence of a ‘large’ exothermic peak of the differential thermal analysis curve, which occurred below 600 °C. The hardness value for the composite containing 5% alumina was about two times higher than Ni₃Al intermetallic. Its saturation magnetization (M_s) was very low indicating the presence of a small amount of elemental Ni. X-ray diffraction (XRD) measurements showed peaks corresponding to Ni–Al and Ni₃Al indicating the formation of the desired intermetallics.     

Topographically Distinguished Microbiome Taxonomy and Stress-Response Genes of Royal Belum Rainforest and Raja Muda Musa Peat Swamp Revealed through Metagenomic Inquisition

Soil ecosystems are home to a diverse range of microorganisms, but they are only partially understood because no single-cell sequencing or whole-community sequencing provides a complete picture of these complex communities. Using one of such metagenomics approaches, we succeeded in monitoring the microbial diversity and stress-response gene in the soil samples. This study aims to test whether known differences in taxonomic diversity and composition are reflected in functional gene profiles by implementing whole gene sequencing (WGS) metagenomic analysis of geographically dispersed soils from two distinct pristine forests. The study was commenced by sequencing three rainforest soil samples and three peat swamp soil samples. Soil richness effects were assessed by exploring the changes in specific functional gene abundances to elucidate physiological constraints acting on different soil systems and identify variance in functional pathways relevant to soil biogeochemical cycling. Proteobacteria shows abundances of microbial diversity for 52.15% in Royal Belum Reserved Forest and 48.28% in Raja Musa; 177 out of 1,391,841 and 449 out of 3,586,577 protein coding represent acidic stress-response genes for Royal Belum and Raja Musa, respectively. Raja Musa indicates pH 2.5, which is extremely acidic. The analysis of the taxonomic community showed that Royal Belum soils are dominated by bacteria (98% in Sungai Kooi (SK), 98% in Sungai Papan (SP), and 98% in Sungai Ruok (SR), Archaea (0.9% in SK, 0.9% in SP, and 1% in SR), and the remaining were classed under Eukaryota and viruses. Likewise, the soils of Raja Muda Musa are also dominated by bacteria (95% in Raja Musa 1 (RM1), 98% in Raja Musa 2 (RM2), and 96% in Raja Musa 3 (RM3)), followed by Archaea (4% in RM1, 1% in RM2, and 3% in RM3), and the remaining were classed under Eukaryota and viruses. This study revealed that RBFR (Royal Belum Foresr Reserve) and RMFR (Raja Musa Forest Reserve) metagenomes contained abundant stress-related genes assigned to various stress-response pathways, many of which did not show any difference among samples from both sites. Our findings indicate that the structure and functional potential of the microbial community will be altered by future environmental potential as the first glimpse of both the taxonomic and functional composition of soil microbial communities.     

Evaluation of the inhibitor selection on wax deposition for Malaysian crude oil

Wax precipitates from crude oil when a fluid is cooled down below its wax appearance temperature (WAT). This particularly happens during the transportation of crude oil in the pipeline system. In this study, chemical inhibitors were chosen to prevent and reduce wax formation using seven different types of inhibitors, which are poly (ethylene-co-vinyl acetate) (EVA), poly (maleic anhydride-alt-1-octadecene) (MA), diethanolamine (DEA), cocamide diethanolamine (C–DEA), toluene, acetone, and cyclohexane. The total waxes deposited from the cold finger test are subjected to the type of inhibitor, rotation speed of impeller, and inhibitor concentration. EVA is suggested as the most effective inhibitor based on the amount of wax deposit weight and the value of viscosity. Therefore, this result might be useful for further research work related to wax deposition in the area of crude oil production.     

A hybrid expert system approach for telemonitoring of vocal fold pathology

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology.     

Observations on applications of importance sampling in structural reliability analysis

Monte Carlo with Importance Sampling (MCIS) has been used to both evaluate the failure probability and search for the dominant failure modes of the system. Information generated from other schemes such as the First- and Second-Order Reliability Methods, and knowledge of the dominant modes, has been used to construct effective sampling densities to estimate failure probabilities. The implications of this information are discussed. In a system problem, a stratified density was used. Such a density tends to bound the weights and hence not cause the variance of the estimator to diverge. Also, the informationsused implies the knowledge of the unimportant random variables. The lack of this knowledge can lead to bad estimates. The use of the estimated coefficient of variation (COV) of the estimator is discussed. The estimated COV arising from densities contructed with the previously mentioned information tends to be small, i.e., the estimator is accurate. However, the COV can be misleading. In the second application, it is shown easily that MCIS can give erroneous answers.     

Robust design of a spindle motor: a case study

Robust design is the process of minimising variability of products and processes in order to improve their quality and reliability. In recent years, it has seen wide spread application in many fields such as mechanical [J Quality Technol 24 (1992) 22], chemical [Quality Engng 9 (1997) 391] and civil [Quality Engng 9 (1997) 441] engineering. As an additional application, this paper investigates the use of robust design techniques during early design process, in order to predict, analyse and improve quality and reliability of spindle motors found within hard disks. The spindle motor is an integral component of the hard disk that rotates the recording media. Constant speed rotation is an absolute necessity for accurate reading and recording of information. However, it is difficult to achieve this due to the effects of cogging torque. In this paper, the robust design of a spindle motor is attempted. The motor was modelled using multiple regression analysis. A dual response approach [J Quality Technol 22 (1990) 38] was then used. This performed a constrained minimisation of the mean of the cogging response, the constraint being that the variance be within a specified limit. The results showed that the current operating point was near optimal. However, the redesign was still able to achieve a 4% and 25% reduction in the mean and the variance of the cogging torque, respectively, before actual prototypes were made.