2013, Vol.18, No.6

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

In this paper, we design and implement a firmware for low-cost small PCR devices. To minimize machine code size, the proposed firmware controls real-time tasks simultaneously only with support of the hardware interrupt, but without support of the operating system program. The proposed firmware has the host-local structure in which the firmware receives operation commands from PC and sends operation results to PC through usb communication. We implement a low-cost small PCR device with the proposed firmware loaded on microchip PIC18F4550 chip, and verify that the implemented PCR device significantly reduces cost and volume size of existing commercial PCR devices with a similar performance.

This paper deals with fuzzy ∞ filtering problem of discrete-time nonlinear Markovian jump systems with state and output time delays. The purpose is to design fuzzy ∞ filter such that the corresponding estimation error system with time delays and initial state uncertainties is stochastically stable and satisfies an ∞ performance level. A sufficient condition for the existence of fuzzy ∞ filter is given in terms of matrix inequalities. In order to relax conservatism, a stochastic mode dependent fuzzy Lyapunov function is employed. The Lyapunov function not only is dependent on the operation modes of system, but also includes the fuzzy membership functions. An illustrative example is finally given to show the applicability and effectiveness of the proposed method.

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.