Block-based connected components labeling is by far the fastest algorithm to label the connected components in 2D binary images, especially when the image size is quite large. This algorithm produces a decision tree that contains 211 leaf nodes with 14 levels for the depth of a tree and an average depth of 1.5923. This article attempts to provide a faster method for connected components labeling. We propose two new scan masks for connected components labeling, namely, the pixel-based scan mask and the block-based scan mask. In the final stage, the block-based scan mask is transformed to a near-optimal decision tree. We conducted comparative experiments using different sources of images for examining the performance of the proposed method against the existing methods. We also performed an average tree depth analysis and tree balance analysis to consolidate the performance improvement over the existing methods. Most significantly, the proposed method produces a decision tree containing 86 leaf nodes with 12 levels for the depth of a tree and an average depth of 1.4593, resulting in faster execution time, especially when the foreground density is equal to or greater than the background density of the images.

Quick Response (QR) codes seem to appear everywhere these days. We can see them on posters, magazine ads, websites, product packaging and so on. Using the QR codes is one of the most intriguing ways of digitally connecting consumers to the internet via mobile phones since the mobile phones have become a basic necessity thing of everyone. In this paper, we present a methodology for creating QR codes by which the users enter text into a web browser and get the QR code generated. Drupal module was used in conjunction with the popular libqrencode C library to develop user interface on the web browser and encode data in a QR Code symbol. The experiment was conducted using single and multiple lines of text in both English and Thai languages. The result shows that all QR encoding outputs were successfully and correctly generated.

In this paper, we investigate predicting the Stock Exchange of Thailand Index movement. Currently, there are two stock markets in Thailand; the Stock Exchange of Thailand (SET) and the Market for Alternative Investment (MAI). This paper focuses on the movement of the Stock Exchange of Thailand Index (SET Index). The back propagation neural network (BPNN) technology was employed in forecasting the SET index. An experiment was conducted by using data of 124 trading days from 2 July 2004 to 30 December 2004. The data were divided into two groups: 53 days for BPNN training and 71 days for testing. The experimental results show that the BPNN successfully predicts the SET Index with less than 2% error. The BPNN also achieves a lower prediction error when compared with the Adaptive Evolution Strategy, but a higher prediction error when compared with the (1+1) Evolution Strategy.

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