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Indonesia is a tropical country that experiences forest fires every year. Forest fires occur due to a prolonged summer season. A major effect of forest fires that commonly occurred in Indonesia is the respiratory disorder and the visual impairment caused by the thick smoke that can experienced by the people who lives around the forest. The effects of forest fire fumes when the forest fire spread across the bigger land or area are also experienced by neighboring countries such as Malaysia, Singapore and Brunei Darussalam. Forest is a habitat for many animals, one of which is a bird. Birds are able to communicate with their colonies through sound. The bird sound during communication to the group can be in the form of calls, marriage invitations, and warnings of danger or threat of forest fires. This paper presents bird sounds classification study using one of Deep Learning (DL) algorithms i.e. Convolutional Neural Network (CNN) method. The CNN method is used to classify bird sounds in the two conditions: (1) under normal circumstances or conditions, and (2) under threated or panic condition. The bird sound data used in this study were collected from the local birds in Indonesia. The classification result of two bird sounds based on CNN method achieved up to 96.45%. This paper is a preliminary study for bio inspired early warning of forest fires based on the sound of birds. [ABSTRACT FROM AUTHOR]

Marker in augmented reality plays a major part to initiate a virtual object in an augmented environment. Choosing a correct and reliable marker would increase the chance of creating a more stable augmented object especially for visualizing a building structure. Unfortunately, most research on visualizing building structure uses a marker-based tracking approach where users must always bring a pre-printed paper as a tracking media. This creates a problem for every time users demonstrate a specific augmented structure object, they still need to bring a printed marker to show the augmented object. The purpose of this research is to investigate the applicability of markerless-based tracking as a solution whether it can substitute the marker-based tracking on augmented reality problems. Simultaneous Localization and Mapping (SLAM) is used as a markerless tracking method where it tracked and mapped the surface environment using feature extraction, then set an anchor at the specified location where the augmented object visualization appears. The results of the research found that from a total of 30 object detection tests, 21 objects are successfully detected and 9 undetected objects, this shows that markerless-based tracking is applicable and can substitute marker-based tracking for a structural campus visualization. [ABSTRACT FROM AUTHOR]