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The rising demand for energy growth worldwide is a major crisis in the present. Development of the renewable energy creates new efficient solutions to existing applications. Currently, solar power is the best renewable energy source. Air-conditioning provides comfort living in sub-tropical countries like Sri Lanka. Solar-powered air-conditioning will reduce the operating costs. Therefore, this paper focuses on design and construction of a solar-powered air-conditioner for automobiles. Implementation of the solar powered air-conditioner in an automobile, will increase the fuel efficiency while reducing the carbon emissions. Further, by isolating the compressor from the engine and powering through the solar energy, engine load decreases. Experimental calculations are presented considering a 35 seater bus.

A novel iris segmentation technique based on active contour is proposed in this paper. Our approach uses innovative algorithms, including two important ones, pupil segmentation and iris circle calculation. With our algorithms, we are able to find the center position and radius of pupil correctly and segment the iris precisely. The accuracy of our proposed method for ICE dataset is around 92% and also reached high accuracy level of 79% for UBIRIS. Our results demonstrate that the proposed iris segmentation method can perform well with high accuracy and better efficacy for Iris segmentation in images. Through a relatively high-performance algorithm to further cut up the round out the picture of the pupil conversion cutting growth square picture in order to make the judgment for biometric applications.

In this research, we propose a state-of-the-art 3D finger gesture tracking and recognition method. We use the depth sensors for both hands in real time music playing. In line with the development of 3D depth cameras, we implemented a set of 3D gesture-based instruments, such as Virtual Cello and Virtual Piano, which need precise finger tracking in 3D space. For hands tracking, model-based tracking for left hand and appearance-based tracking for right hand techniques are proposed. To detect finger gestures, our approaches consist number of systematic steps as reducing noise in depth map and geometrical processing for Virtual Cello. For Virtual Piano, we introduce the Neural Network (NN) method to detect special hand gestures. It has Multilayer Perceptron (MLP) structure with back propagation training. Literature has few examples using touch screen as medium, with fixed-coordinates, and 2D–gestures to control MIDI input. The end users should no longer carry anything on their hands. We use Senz3D and Leap Motion due to a few technical benefits. Senz3D and Leap Motion use a closer distance to hands, thus detailed finger gestures can be precisely identified. In the past years, we announced a set of virtual musical instruments and the MINE Virtual Band. Our research work is tested on lab environment and professional theatrical stage. More information and demonstrations of the proposed method can be accessed at: http://video.minelab.tw/DETS/VMIB/ .

3D model construction techniques using RGB-D information have been gaining a great attention of the researchers around the world in recent decades. The RGB-D sensor, Microsoft Kinect is widely used in many research fields, such as in computer vision, computer graphics, and human computer interaction, due to its capacity of providing color and depth information. This paper presents our research finding on calibrating information from several Kinects in order to construct a 3D model of a human object and to render texture captured from RGB camera. We used multiple Kinect sensors, which are interconnected in a network. High bit rate streams captured at each Kinect are first sent to a centralized PC for the processing. This even can be extended to a remote PC in the Internet. Main contributions of this work include calibration of the multiple Kinects, properly aligning point clouds generated from multiple Kinects, and generation of the 3D shape of the human objects. Experimental results demonstrate that the proposed method provides a better 3D model of the human object being captured.

An object surface extraction from a set of point clouds extracted using a Kinect V2 sensor is presented here. First several point clouds are extracted from different angles around the object. Second point clouds are registered with each other using the Iterative Closest Point algorithm. If the angle between two camera positions is large, point clouds are manually rough aligned. Then iterative closest point algorithm provides a finer alignment. Next the object boundary is found enclosing all the points in the aligned and merged point cloud. After that a triangular mesh is created with the boundary points. Finally colors acquired from the original point cloud are applied on the triangular surface generated. For a better visual effect colors of the points are interpolated on the triangular mesh generated.