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Computer science for high school faces many challenging issues. One of these is whether the students possess the appropriate cognitive ability for learning the fundamentals of computer science. Online tests were created based on known cognitive factors and fundamental algorithms and were implemented among the second grade students in the Information Systems course track at TokyoTech High School of Science and Technology. The online test results were evaluated together with the results of the students' midterm exam on algorithms. The findings of this study may be used in devising strategies for effective learning of computing fundamentals. The correlated cognitive factors may be the bases for designing a course outline and problem exercises for introductory computer science. The correlated algorithms may be incorporated in an assessment test for students who plan to pursue computing-related field.

More and more videos are now being used in e-learning context. For improving learning effect, to understand how students view the online video is important. In this research, we investigate how students deploy their attention when they learn through interactive slide video in the aim of better understanding observers' learning style. Felder and Silverman's learning style scale was applied to identify learner's learning preference. Participants' eye movement was recorded in an eye-tracking lab. "Eye-mind" assumption is supported by a mild correlation between posttest score and viewing ratio. Different viewing behavior of different learners with strong and intermediate visual learning preference is investigated. Some eye movement metrics like fixation time, speed and direction related to global and sequential learning style are investigated. The result showed a high consistence with corresponding learning style characteristics, which provides another way to verify the validity of learning style. Possible applications such as incorporating with adaptive learning management system are discussed. (Contains 3 figures and 8 tables.)

Flat lowpass FIR systems are examined.

New adaptive algorithms based on multi-band decomposition of the error signal and application of a different convergence factor for each band are derived. With this approach, tracking ability and performance in steady state can be traded off along the frequency domain giving rise to estimates of the adaptive filter coefficients closer to the ideal response as compared to those obtained with conventional least-mean-square (LMS) and recursive least-squares (RLS) algorithms, particularly when the statistical properties of the analyzed signal vary along the frequency spectrum. A new adaptation technique for the forgetting factor depending exclusively on the autocorrelation values of the input signal is also introduced and a multi-band RLS algorithm, with an independent variable forgetting factor for each band, suitable for the analysis of nonstationary signals is described. Computer experiments comparing the performance of multi-band and conventional LMS and RLS algorithms are shown. Index Terms - Adaptive algorithms, variable convergence factors, filter banks.

This paper proposes a design method of three-dimensional (3-D) Y-C separation filters for the NTSC signals. The 3-D spectral structure of the NTSC signals, together with some existing and previously proposed Y-C separation methods, are first reviewed to point out some drawbacks in these methods. Since the Y-C separation filter proposed in this paper consists of a horizontal one-dimensional (1-D) FIR filter and a temporal-vertical two-dimensional (2-D) nonseparable FIR filter, the design of the temporal-vertical 2-D nonseparable FIR filter is intensively discussed, which is more difficult to design than the 1-D one. The design procedure is simplified by applying some 2-D frequency transformations and then formulated as a simple linear programming problem, which can be solved easily by the standard method (e.g., the revised simplex method). Another advantage of our approach is the flexibility that enables us to design various shapes of the passband and the support of the impulse response. Two design examples are given to show the usefulness of our approach. A hardware implementation of the proposed filters is also considered., The design and realization of three-dimensional filters which fully separates interleaved luminance and chrominance signals fortelevision broadcasting is discussed. The proposed design is comprised of a one-dimensional finite impulse response (FIR) filter and a two-dimensional FIR filter and is derived through frequency transformations and a linear programming framework. Two examples are provided to demonstrate the proposed design's validity and efficacy.

Abstract Background A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Method Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. Result The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. Conclusion The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.