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Humanoid robots are employed in a wide range of fields to replicate human actions. This paper presents the mechanism, configuration, mathematical modeling, and workspace of a 3D printed humanoid robot – Amaranthine. It also discusses the potential scope of humanoid robots in the present day and future. Robots can be programmed for automation as per the demand of the task or operations to be performed. Humanoid robots, while being one of the small groups of service robots in the current market, have the greatest potential to become the industrial tool of the future. Introducing a Humanoid Robot-like Amaranthine holds huge scope majorly in the fields of medical assistance, teaching aid, large industries where heavy-duty operations require application-specific software, etc. Amaranthine was 3D printed and assembled at the RISC Lab of University of Bridgeport.

Due to the increasing number of accidents happening when flying target landing in the weapon testing field, a smart video surveillance system based on moving target recognition was designed. The system adopts the capturing front-server-decision model. In our paper, a method for detecting moving targets images using background difference method and frame difference method is first introduced. Secondly, target recognition is studied by the technology of contour extraction and edge detection. Finally, characteristic parameters of target are extracted by feature algorithm. Based on it, key technologies involved in the system are described in detail. Additionally, corresponding algorithm is designed using OpenCV in Visual C++ 6.0, and part of the key codes are given. Simulation results shows that the system designed can meet the needs of monitor and control of flying target, and also verify the effectiveness of the algorithm.

In this paper, an algorithm based on monocular vision is applied into length measurement on plane and the process of extracting corner using OpenCV are introduced in detail. This algorithm is realized using OpenCV 2.4.9 and Visual Studio 2013. Firstly, extract all the target corners on chessboard using OpenCV functions. Then calculate the model parameters consisting of extrinsic and intrinsic parameters of camera with the use of the least square method and the corners extracted. Coordinates of all the points on chessboard can be obtained by its image coordinates and the model parameters correspondently. Thereby, positions of target points and length can be measured. In our experiment, the relative error of the positioning is less than 2%. The result proves the feasibility of the algorithm.