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Scientific argumentation has been greatly emphasized in the National Science Standard due to its ability to enhance students' understanding of scientific concepts. This study investigated the mastery level of scientific argumentation, based on Toulmin's Argumentation Model (TAP), when students engage in individual and group argumentations. A total of 120 students were selected and were first randomly divided into two groups to answer the Scientific Argumentation Test (ScAT). One group of students answered individually, while the other group was allowed to collaborate among group members. The Student Semi Structured Interview (SSSI) and Teacher Semi Structured Interview (TSSI) were also conducted on a selected group of students and their teachers to gather additional information to support the ScAT data. The findings showed that there is a significant difference in the mastery level of scientific argumentation between groups and individuals. Students who participated in group argumentation tend to perform better than those who participated in individual argumentation. However, the mastery level of scientific argumentation for both groups of students was generally unsatisfactory. Therefore, the teaching and learning of science in Malaysian schools need to emphasize more on group argumentative activities to enhance students' mastery of scientific argumentation, which will also their reasoning capabilities and scientific knowledge.

Fuzzy logic-based image fusion for multi-view through-the-wall radar AbstractIn this paper, we propose a new technique for image fusion in multi-view through-the-wall radar imaging system. As most existing image fusion methods for through-the-wall radar imaging only consider a global fusion operator, it is desirable to consider the differences between each pixel using a local operator. Here, we present a fuzzy logic-based method for pixel-wise image fusion. The performance of the proposed method is evaluated on both simulated and real data from through-the-wall radar imaging system. Experimental results show that the proposed method yields improved performance, compared to existing methods.

This paper addresses the problem of combining multiple radar images of the same scene to produce a more informative composite image. The proposed approach for probabilistic fuzzy logic-based image fusion automatically forms fuzzy membership functions using the Gaussian-Rayleigh mixture distribution. It fuses the input pixel values directly without requiring fuzzification and defuzzification, thereby removing the subjective nature of the existing fuzzy logic methods. In this paper, the proposed approach is applied to through-the-wall radar imaging in urban sensing and evaluated on real multi-view and polarimetric data. Experimental results show that the proposed approach yields improved image contrast and enhances target detection.

A linear Support Vector Machine (SVM) classifier is designed to detect and classify seizures in EEG signals based on a few simple features such as mean, variance, dominant frequency, and the mean power spectrum. The SVM classifier is tested on a benchmark EEG database. Using a combination of these features, classification rates up to 98% were achieved. The proposed classifier that utilizes a few simple features is computationally efficient to be deployed in a real-time seizure monitoring system.

In this paper, we propose a Gausssian-Rayleigh mixture modeling approach to segment indoor radar images in urban sensing applications. The performance of the proposed method is evaluated on real 2D polarimetric data. Experimental results show that the proposed method enhances image quality by distinguishing between target and clutter regions. The proposed method is also compared to an existing NeymanPearson (NP) target detector that has been recently devised for through-the-wall radar imaging. Performance evaluation of both methods shows that the proposed method outperforms the NP detector in enhancing the input images.

A two-stage fuzzy image fusion approach, which combines multiple radar images of the same scene, is proposed to produce a more informative image. In this approach, two different image fusion methods are first applied. Then, a fuzzy logic fusion method is applied to the outputs of the first fusion stage. The performance of the proposed approach is evaluated on through-the-wall radar images obtained using different polarizations. Experimental results show that the proposed approach enhances image quality by producing outputs with high target intensity values and low clutter. Index Terms-Fuzzy logic, image fusion, through-the-wall radar imaging (TWRI).

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