Background The main obstacle for local and daily or weekly time-series mapping using very high-resolution satellite imagery is the high price and availability of data. These constraints are currently obtaining solutions in line with the development of improved UAV drone technology with a wider range and imaging sensors that can be used. Findings Research conducted using Inspire 2 quadcopter drones with RGB cameras, developing 3D models using photogrammetric and situation mapping uses geographic information systems. The drone used has advantages in a wider range of areas with adequate power support. The drone is also supported by a high-quality camera with dreadlocks for image stability, so it is suitable for use in mapping activities. Conclusions Using Google earth data at two separate locations as a benchmark for the accuracy of measurement of the area at three variations of flying height in taking pictures, the results obtained were 98.53% (98.68%), 95.2% (96.1%), and 94.4% (94.7%) for each altitude of 40, 80, and 100 m. The next research is to assess the results of the area for more objects from the land cover as well as for the more varied polygon area so that the reliability of the method can be used in general

In this paper, we would like to present the development of intelligent humanoid robot system. Our previous research have designed humanoid robot which could receive command and do speech recognition based on Bioloid GP robot and Raspberry Pi 2 as controller. In here, the robot system is extended to be able to detect and recognize human face. Based on OpenCV and Python, Viola-Jones method is used for face detection and PCA (Principal Component Analysis) for face recognition. Viola-Jones method provides a fast and robust framework for extracting and detecting face features. While, PCA or frequently called as eigenfaces is one of the algorithms to recognize face by reducing the image dimension using its eigen values. Generally, face recognition is influenced by many variables like light intensity, poses, and expression. By controlling these variables, the result of face recognition accuracy is about 93% (28 correct recognition of 30 experiments). Based on the recognized face, our humanoid robot gives the response action.

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The main objective of our research is to develop an intelligent humanoid robot for teaching children by listening and answering the questions. In our previous research, we have designed a humanoid robot that can detect human face and receive commands by using speech recognition. Our robot is based on Bioloid GP robot and Raspberry Pi2 as control system. In this study, we would like to expand the capability of the robot system in order to isolate the speech of one speaker from all the other sounds. The problem for separating multi speeches from stereo audio record is called as Blind Speech Separation (BSS). We propose FastICA algorithm to solve the BSS problem. FastICA is an efficient algorithm to separate several signals based on Independent Component Analysis (ICA) algorithm. Some assumption must be met to use FastICA, that is the number of mixtures are equal to the number of sources and the sources are linearly independent from each other. To evaluate the algorithm, we use several simulations based on two speech sources and its mixing matrix. Our simulation shows FastICA algorithm can solve BSS problem by separating two sound signals, but its linearly independent assumption makes it difficult to implement in our humanoid robot.

Intelligent Humanoid Robots for education and entertainment in uncontrolled environments need to be based on vision and voice recognition. This paper propose a high speed embedded system based on Raspberry Pi 2 and voice recognition method for Humanoid robot, because the ability to accurately recognize commands is important feature for education and entertainment. An audio module PiAudio from Python is used in this system. The proposed method is able recognize commands and face recognition. We evaluate and present the performance of the system.