The concept of coding at school has enabled educators and parents around the globe to become interested in coding as, these days, coding is regarded as a gateway to computational thinking for children. However, coding education in Thailand appears to lag behind that of many other countries due to the lack of accessible coding learning resources suitable for students as well as the limited number of teachers with coding experience. Regarding these issues, we have developed an open-source hardware-based coding platform named KidBright, based on these requirements: 1. making coding simple for novice learners through the use of graphical blocks with Thai and English support; 2. inspiring young students to develop creative embedded applications with minimal effort; and 3. providing sustainable support for coding education. KidBright is proposed as a coding learning tool that can motivate children to learn to code and develop embedded system projects using its block-based coding environment, the KidBright IDE, in conjunction with its embedded device, the KidBright board. KidBright is distinguished from other embedded programming platforms due to the deployment of a dedicated software framework as the backend of the KidBright IDE. In this article, we introduce KidBright and present the design, architecture, and demonstrations of the software framework, the key roles of which are to conceal low-level hardware issues from learners and to enable makers to develop new command blocks and hardware peripherals in support of KidBright. We claim that, with this particular design, KidBright can help support coding education sustainably. In particular, we present how a small research team introduces coding to a large number of learners who have little or no coding experience, resulting in an impact on coding education in the country.

This paper proposes the methods for identifying time period of sleep apnea events with several statistic values. Firstly, we calculate the RR-interval values from electrocardiography (ECG) signal. Then we compute several statistic values, e.g. mean, standard deviation and coefficient of variation from the calculated RR-interval values. In this research, we consider both the large number of sleep apnea case and the small number of sleep apnea case and then propose the several conditions for identifying time period of sleep apnea events correspond to both case. These conditions are derived from our experiment with data set from standard apnea database. Finally, the detection results shown that the accuracy of our proposed method with the large number of sleep apnea case is between 60% - 88% and average of the accuracy is 71.03% for the proposed method based on condition 3 that can be comparable with one of reference paper, but in the small number of sleep apnea case our method has the accuracy of our proposed method close to 100% which show that our method is very efficient in detecting time period of sleep apnea compared with result from reference paper and annotation file from database.

This paper presents the classification of cardiac arrhythmia based on the signal variation characteristic of each beat type. Considered beat types including the normal beat, fusion beat and premature ventricular contraction beats are differentiated to obtain feature sets. Using the principal component analysis estimation, the detection selects the class by searching the minimal norm of the error vector obtained by basis of each type. Without the help of the timing interval information, the proposed classifier outperforms the algorithm presented in the literature. The classification accuracy of the proposed algorithm achieves perfect detection.

For original paper see S. Ray, ibid., vol 53, no. 6, pp. 1983-2009, 2007. The multiple-input multiple-output (MIMO) wideband Rayleigh block-fading channel where the channel state is unknown to both the transmitter and the receiver and there is only an average power constraint on the input. This mutual information can be upper bounded by the capacity of AWGN channel with the same power constraint.