Abstract: ‘Maker movement’ is the one of the hot keyword in Tech fields nowadays. Because of the evolution of manufacturing machinery such as 3D printer, 3D scanner, CNC machine, laser cutter, people can use these machinery with low cost. Also lots of sources are in web. People can find hardware design source, software source code, and its documents. People naturally have a motivation of making and creating something. This traditional motivation meets technology revolution so that maker movement emerged.
In this project I will make retro game machine which can be seen at video game room(Oraksil). Not X-box, not playstation, retro game machine has joysticks and colorful button. I will use Raspberry Pi 2 as main computer for this project. Raspberry pi 2 is relatively cheap and also sufficient to run retro games. Joystick and button can be found at open market like Gmarket, 11st. The most important hardware and software setting process could be searched at Maker forum. After making game machine, I will connect it to big screen at Business administration building. It will be free to play.
Furthermore, it will be fun if I use student union building wall as a screen with beam projector at night. I hope UNIST member to enjoy my game machine and remind their memories of childhood.

Machine-­learning technology powers many aspects of modern society. Conventional machine-­learning techniques were limited in their ability to process natural data in their raw form. However, deep neural network is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years.
In this project, we build an artificial poet with the recurrent neural network which can generate the series of Korean poetries by leveraging the knowledge of recent deep neural network which leads us to a great advance on Machine Learning research.

Recently, smartphone and any other smart devices are used by many people, Beyond the base function of telephone, smartphone does so many functions and tasks for humans. Fundamental technics are object detections by smartphone camera. In this project, I’ll use the face detection and finger detection in android devices for using human detection and comparison. First, independent face detection algorithm will be used in android device and then by using finger detection algorithm, smartphone device can catch the human face(pointing human face). Then analyze the human face by comparing the human face database(machine learning algorithm will be used). So, in this project I would find the optimal resource factor used of between three procedures(face detection, finger detection, and human face analysis) If these technologies are used properly, smartphone users could detect the suspicious or specific people individually. Also, if these technologies are developed more perfectly, I think many polices or soldiers do their works well economically by using their smart devices(including wearable devices)

Image inpainting is a method that reconstruct the missing area of an image by using the information in pixels of surrounding region. Its objective is to restore a degraded painting in such a way that the changes are not apparent to the observer. It is also an important step in many graphics algorithms. Image inpainting is an ill-posed inverse problem that has no well-defined unique solution.
To solve this problem, it is necessary to image priors. All methods are guided by the assumption that pixels in the known and unknown parts of the image share the same statistical properties or geometrical structures. This assumption translates into different local or global priors, with the goal of having an inpainted image as physically plausible and as visually pleasing as possible. The first category of methods, known as diffusion-based inpainting, introduces smoothness priors via parametric models or partial differential equations (PDEs) to propagate (or diffuse) local structures from the exterior to the interior of the hole. Many variants exist using different models (i.e. linear, nonlinear, isotropic, or anisotropic) to propagate in particular directions or to consider the curvature of the structure present in a local neighborhood. There also exists a method by using Gaussian process for image inpainting. But those methods are not suitable for recovering the texture of large areas. The second category of methods is based on the image statistical and self-similarity priors. It is more effective for the inpainting problem. The image gap is filled in recursively, inwards from the gap boundary. The statistics of image textures are assumed to be stationary or homogeneous. The synthesized texture is derived from similar regions in a texture sample or from the known part of the image. It is done by sampling, and by copying or stitching together patches taken from the known part of the image. These methods are called exemplar-based techniques.