Fire hazard has destroyed humanity creations. Fire detectors have been developed by using different techniques. Thermoelectric generator (TEG) is a part of energy harvesting which is able to convert heat into electricity because of temperature difference between hot and cold side of thermoelectric device (TE). Different materials are used for thermoelectric generators which depend on the characteristics of the heat source, heat sink and the design of the thermoelectric generator. Many thermoelectric generator materials are currently undergoing research. This paper presented an investigation of seeking an alternative way of detecting fire hazard by developing architecture prototype of a fire detection technique using natural rubber. The thermoelectric prototype used self-powered device which improved the temperature difference gap and stabilized the cold side of TE alongside natural rubber as the cooling material. The technique is relatively simple system realization based on three viable components, i.e. a heat sensor, a low-power RF-transmitter and a RF-receiver. The heat sensor is designed and fabricated by thermoelectric and heat sink with natural rubber (NR) coating. The NR coating is heat absorption reduction. Therefore, the temperature difference is wildly resulting in the higher TE output voltage. The voltage is also supplied to the low-power RF transmitter module. In case of fire hazard, the temperature increases from 26 to 100 °C , the prototype can operate successfully. This technique will solve potentially the power supply issue in fluctuated situations. The rubber coating from rubber trees in Thailand would be a value chain added for bio-economy, supporting a sustainable development goal of the country

This article presents the segmentation of the brain tumors in the MRI images by using the optimal morphology thresholding methods. The number of patients with brain diseases is increased. Therefore, the needs for MRI are increased. Accordingly, the accurate and quick segmentation and identification methods for the brain tumors are really necessary. These also include planning and diagnosis tools for the automatic segmentation of the brain tumors in the MRI images. The optimal morphology thresholding methods are new methods that can automatically solve problems and diagnose the diseases. These consist of the RGB to Grayscale conversion process, the image quality improvement process and the optimum thresholding process, respectively. By testing the methods with the MRI images, it was found that the proposed methods could automatically segment the brain tumors in the MRI images with the shapes similar to that from the public databases. The highest absolute error was 3.96%, and the accuracy was 98.00% as compared to the other methods.

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