Humanoid robot has become popular research platforms in robotics and artificial intelligence. Humanoid robot can perform complex motions, including the balancing, walking, and kicking skills required in the RoboCup robot soccer competition. The humanoid robot from the Electronic Engineering Polytechnic Institute of Surabaya (EEPIS) research center named FLoW. This paper discusses about the FLoW head mechanism. Flow head mechanism is split into three parts there are eye mechanism, head mechanism and neck mechanism. The forward kinematic from FLoW head mechanism is obtained to generate workspace of motion. The FloW head workspace of motion is compared to human workspace of motion. From the experiment the motion of FLoW head already approached human easy head motion.

Multithreading implementation through real time operating system was held in several years to optimize resource management in embedded system. Several side effects, such as large binary size and memory usage were appeared while implementing multithreading mechanism and its supporting mechanism. We developed FLoW to reduce those side effects while performing multithread in embedded system. FLoW implemented event based microkernel to minimize the environment and simplified the services. Static array implementation with reduced priority number was chosen rather than dynamic allocated to reduce platform memory usage in maintained scheduling performance in FLoW. The results show average performance of native multithreading supported by microkernel relative to existing real time operating system with lower side effects.

In Indonesia, river water plays important roles in human life; for example, for transportation and economic activities of the inhabitants. However, industrial, agricultural and domestic water is discarded into rivers directly in many developing countries, since drainage systems have not been completely constructed. Water quality monitoring using radio-controlled submarine provide an opportunity to develop baseline monitoring system for river water ecosystem health indicator. We propose a design modification of radio-controlled submarine for river water quality monitoring. Our design consists of modification for navigation system by added external motor, IMU and GPS; modification of processing and control system by added micro controllers and mini pc, and modification of water sensors system.

Camera (webcam) as one of computer equipment is getting more widely used and developed using image processing sensor cameras. In this research, made an application to control cursor movement using object tracking methods based colors by sensor camera. Object tracking is process to follow object position that you want. Objects will be tracking in this research is various objects. Basis of object tracking is segmentation process, that process is used separate objects with background or background only. To ease detecting object, will labeling process. Intent of labeling process to give limitations on things, so that existing objects in the image can be separated one from another. Object movement was interfacing with cursor movement. Results obtained from this research is object pixels that most effective to move cursor when pixel are detected 3200 pixels with error average that happens 0.35 at light intensity ±300 - 400 Lux.

This paper describes a simple method for classifying the condition of morning and afternoon measurement of eye movement based on the Fuzzy rule, the first step to find the relation between eye movement and fatigue. The eye movement is taken by camera and processed by computer. The left eye pupil center coordinates are used as eye movement data. These coordinates are extracted to obtain their features or parameters, which are saccadic latency, velocity, saccadic duration, and deviation. Extracted parameters from eye movement data become an input of the Fuzzy Identification System to classify the measurement time category, conducted either in the morning or the afternoon. Twenty-six visually normal students participate as subjects in this research. Their eye movement data are measured in the morning and in the afternoon after 9 hours of class. We also investigate whether the parameters can be used to distinguish the two conditions. The results of our experiments are assumed to be the system performance, and an accuracy of 86.54% is achieved. However, only the velocity and duration parameters show significant difference (p<0.05) between the two measurement times. This result reflects the fatigue in the ocular muscle which the two parameters mentioned above are directly affected.

Currently, several streaming servers are available to provide a variety of multimedia applications such as VoD (Video-on-Demand), IP-phone, and IP-TV. As a result, the provision of multiple streaming servers on a single machine using the virtualization technology has become important in order to save the operational/management costs while enhancing the performance and the reliability of the system. In this paper, we show the performance evaluation of two representative open source softwares for the virtualization technology, Xen and OpenVZ, in various configurations of applications on three open source streaming servers, Red5, Darwin, and VLC. Our experimental results indicate that OpenVZ provides the better performance for streaming applications with Darwin and VLC whereas Red5 can run only on Xen.

This paper presents an automatic processing of human face from color images. The system works hierarchically from detecting the position of human face and its features (such as eyes, nose, mouth, etc.) to contours and feature points extraction. The position of human face and its parts are detected from the image by applying the integral projection method, which synthesize the color information (skin and hair color) and the edge information (intensity and sign). In order to extract the contour-line of face features we used a multiple active contour model with color information based energy terms. Facial feature points are decided based on the optimized contours. The proposed system is confirmed to be very effective and robust to deal with the image of faces in the complex background.

This paper presents a model-based face recognition approach that uses a hierarchical Gabor wavelet representation and neural network matching. Local features of grey level images are extracted by multiresolutions of Gabor wavelets, which are scaled and rotated versions of each other. The Gabor wavelet representation is use in a innovative neural network matching approach that can provide robust recognition. Neural network matching between a model and a input image is to find out the exact correspondence of local features and to map the model to the input image based on local similarity and neighborhood grouping of local features. The results on face recognition are presented, where the objects undergo rotation, translation, local distortions, and deformation caused by facial expression.

An automatic face and facial feature points (FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points (FFPs) labeled by their Gabor features and the edges describe their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works like a random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on a face identification system.

This work presents a model-based face recognition approach that uses a hierarchical Gabor wavelet representation and flexible neural network matching. The representation of local features is based on the Gabor wavelets transform of a number of scales and a number of orientations. The Gabor wavelet representation is used in a innovative self-organization flexible neural network matching approach that can provide robust recognition. The sparse centers of Gabor wavelets in the images and neurons placement are arranged according to the hexagonal grids. Neural network matching between the model and the input image is to find out the exact correspondence of local features and to map the model to the input image based on local similarity and neighborhood grouping of local features. Experimental results in recognizing faces that includes the variations of translation, rotation in plane, rotation in depth, and slightly changes of facial expressions are also presented.