Learning the Malay language has been a challenging task for foreign language learners. Learners have to learn Malay grammar structure rules in order to write simple sentences. The word choice is important in constructing a sentence. Therefore, the study focuses on the use of adjectives in advertisement. The objectives of the study were to identify and discuss adjectives incorporated into the advertisements. The students involved in the study were ten male and female subjects from a Malay language class. The subjects had to choose one television advertisement and view it several times. They were given three weeks to identify and discuss the adjectives in the advertisements. The subjects were interviewed on their views about the adjectives in the advertisements. The interviews were video recorded and analyzed for the purpose of the study. The results of the study revealed that each subject managed to identify five adjectives per advertisement. They also managed to offer their views on the adjectives, which were not directly uttered by the models in the advertisements. It is hoped that a future study will focus on the use of adjectives via other means of media technology.
In this work, an average framing linear prediction coding (AFLPC) technique for text-independent speaker identification systems is proposed. Conventionally, linear prediction coding (LPC) has been applied in speech recognition applications. Nevertheless, in this study the combination of modified LPC with wavelet transform (WT), termed AFLPC, is presented for speaker identification. The study procedure is based on feature extraction and voice classification. In the phase of feature extraction, the distinguished speaker’s vocal tract characteristics were extracted using the AFLPC technique. In the phase of classification, feed forward backprobagation neural network (FFBPN) is applied because of its rapid response and ease in implementation. In the practical investigation, performances of different wavelet transforms in conjunction with AFLPC were compared with one another. In addition, the capability analysis on the proposed system was examined by comparing it with other systems proposed in literature. Consequently, the FFBPN classifier achieves a better recognition rate (97.36%) with the wavelet packet (WP) and AFLPC termed WPLPCF feature extraction method. It is also suggested to analyze the proposed system in additive white Gaussian noise (AWGN) and real noise environments.
The purpose of this research was to identify the reasons for the rejection of companies’ books of accounts in tax offices which leads to many costs due to ex officio assessment of taxes. Thus two hypotheses are postulated that consider unawareness of managers of companies and auditors regarding tax laws and regulations (in particular, Paragraph 1, Clause 2 of Article 95 and Article 97 of the Direct Tax Law) as the reasons for rejection of the books of accounts. Two questionnaires have been used in the research one of which was filled out by the companies’ administrators and the other by auditors. These questionnaires contained both descriptive questions for acquiring a better understanding of the studied population and the main questions used for hypothesis testing. In the concluding section of the questionnaires several questions were addressed to both groups regarding Article 272 of Direct Tax Law in order to provide solutions and recommendations. The results indicate that the main reason for the rejection of the companies’ books of accounts was not the inexperience and unawareness of the auditors, but the lack of awareness of the companies’ administrators regarding the tax laws and regulations that specify their responsibilities.
This paper addresses the combination of GA and wavelet transform scheme for denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using Genetic Algorithm (GA) and wavelet transform. GA uses different combinations of wavelet transform and threshold selection rules and the combination that provides the best results for the existing method and proposed method in terms of signal to noise ratio and percent root mean square difference is compared. The noise reduction of a signal depends on the optimum value of the level of decomposition, the suitable forms of wavelet family and the thresholding techniques. Taken into consideration that GA is a powerful tool for parameter selection and optimization, Wavelet Transform-Genetic Algorithm combination makes this denoising technique more powerful than the existing systems.
Tourist arrivals to Langkawi accelerated once it was awarded the World Geopark status from UNESCO in 2007. Lessons from others indicate that drastic development in a fragile environment and unprepared society may lead to a number of problems due to insufficient physical infrastructure and tolerance among residents toward impacts from tourism development. This paper explores Kilim resident tolerance level in the context of social tourism carrying capacity for the development of the Langkawi Geopark. Kilim is chosen as a case in this study as it is located close to a nature park which is one of the main elements in this World Geopark. The intent is to highlight the social aspects of contemporary destination growth relating to the hosts’ well-being that need greater attention by policy makers and planners. Through a case study involving a series of in-depth interviews, the study finds that the negative impacts it produces are still tolerable by the residents. The study did not find the community to be protective of its environment as much as they are protective of their economy and cultural values.
This paper presents a selective harmonics elimination in a cascaded H-bridge multi-level inverter. The basic concept of this reduction is to eliminate specific harmonics, which are generally the lowest orders, with an appropriate choice of switching angles. This paper employs Homotopy algorithm to solve the transcendental equations for finding the switching angles. This method solves the nonlinear transcendental equations with a much simpler formulation and without complex analytical calculations for any number of voltage levels. Also, several informative simulation results verify the validity and effectiveness of the proposed algorithm.
This paper proposes a new method for speaker feature extraction based on Wavelet Entropy and Neural Networks denoted as WENN. In the first stage, five formants and seven Shannon entropy wavelet packets are extracted from the speakers’ signals as the speaker feature vector. In the second stage, these 12 identified parameters are used as inputs to feed-forward neural networks. Probabilistic neural network is also proposed for comparison. In contrast to conventional speaker identification methods that extract features from sentences (or words), the proposed method extracts the features from vowels. Advantages of using vowels include the ability to identify speakers when only partially-recorded words are available. This may be useful for deaf-mute persons. Experimental results show that the proposed method succeeds in the speaker identification task with high classification rate. This is done with minimum amount of information using only 12 coefficient features (i.e. vector length) and only one vowel signal which is the major contribution of this work. The results are further compared to classical benchmark algorithms for speaker identification and are found to be superior.
Modeling of a crack propagating through a finite element mesh under mixed mode conditions is of prime importance in fracture mechanics. Two different crack growth criteria and the respective crack paths prediction for several test cases are compared between the maximal circumferential stress criterion (MCSC) and the minimum strain energy density criterion (MSEDC). In this paper, the displacement extrapolation technique was used to calculate the stress intensity factors to predict then the angle direction at each step of the crack propagation. Several examples are presented to check for the robustness of the numerical techniques applied to these criteria.
Sulfate Reducing Bacteria (SRB) are nonpathogenic and anaerobic bacteria. These bacteria can produce enzyme to accelerate the reduction of sulfate compounds to hydrogen sulfide (H2S) that corrodes metal. There are a few methods of detecting SRB such as laboratory analysis and fields test kit but the procedures are costly and take longer time, whereby the detection period can reach more than 12 hours. This research will study the possibility of using electronic sensors and artificial neural network (ANN) to detect SRB. A few experiments are carried out using nutrient agar medium to determine the presence of SRB. The sensors that are used in this research are hydrogen sulfide (H2S) sensor, temperature sensor and humidity sensor. Microcontroller is used to collect data from these sensors and data are saved in Microsoft Excel for statistical analysis. Based on ANOVA analysis, it is found that H2S sensor, temperature sensor and humidity sensor contribute 94.0%, 89.6% and 54.6% respectively to determine the presence of SRB in three hours time. In ANN, Multilayer Perceptron architecture (MLP) with Levenberg-Marquardt (LM) training algorithm can be used to detect SRB. By using log-sigmoid transfer function in the ANN, the system can achieve 100% of accuracy.
abstract\n\n \nThis Research Was Conducted To Evaluate The Validity And Reliability Of Social Phobia Inventory (SPIN) In Nonclinical Iranian Sample. Research\'s Data Were gathered from 415(180 females and 235males) Shahed University Students who were selected by Clustering Sampling Method completed this scale. The Reliability Was Determined Using Internal Consistency, concurrent validity, Divergent validity, Test-Retest Stability and cronbach α in Spin Factors for Nonclinical Iranian Sample computed. In Order To Investigate The Validity Of SPIN, relationship between SPIN And Four Other Instrument: Anxiety From SCL-90-R, Cognition Error Questionnaire (CEQ), Self-Esteem Rating Scale (SERS), Multidimensional Body-Self Relations Questionnaire (MBRSQ), Were Compared, And The Pearson Correlation Coefficient, For Spin Scores And The Scores Of Each Of The Four Above Mention Scales Were Determined. The Result Indicated Good test - retest reliability, internal consistency, convergent and divergent validity. The version of the SPIN studied is quite adequate for use in the context of Iranian university students, favoring the screening of social phobia. However, further studies using more diverse samples are needed.\nKey Word: Social Phobia Inventory, Validity, Reliability
