Motion Estimation which is used to reduce the redundant data plays an important role in video compressions. However, it requires huge computational complexity of the encoder part. And therefore many fast motion estimation methods has been developed to reduce complexity. Multi-view video is obtained by using many cameras at different positions and its complexity increases in proportion to the number of cameras. In this paper, we proposed a fast motion estimation method for multi-view video. The proposed method predicts a search start point by using correlated candidate vectors of the current block. According to the motion size of the start search point, a search start pattern of the current block is decided adaptively. The proposed method proves to be about 2 ~ 5 times faster than existing methods while maintaining similar image quality and bitrates.

Recently, computer vision application is increasing by using CNN which is one of the deep learning algorithms. However, CNN does not provide perfect classification performance due to gradient vanishing problem. Most of CNN algorithms use an activation function called ReLU to mitigate the gradient vanishing problem. In this study, four activation functions that can replace ReLU were applied to four different structural networks. Experimental results show that ReLU has the lowest performance in accuracy, loss rate, and speed of initial learning convergence from 20 experiments. It is concluded that the optimal activation function varied from network to network but the four activation functions were higher than ReLU.

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

This paper presents an augmented reality system for multiple users. The proposed system performs ar image-based pose estimation of users and pose of each user is shared with other uses via a network server. For camera-based pose estimation, we install multiple markers in a pre-determined space and select the marker with the best appearance. The marker is detected by corner point detection and for robust pose estimation. the marker's corner points are tracked by optical flow tracking algorithm. Experimental results show that the proposed system successfully provides an augmented reality application to multiple users even when users are rapidly moving and some of markers are occluded by users.

Most simulated shooting systems are TDM based method and dot type laser has been used. The proposed laser pattern based simulated shooting system is a new approach. It can distinguish shooters by calculating the angle of bar shaped laser pattern by each shooter. Unlike the existing TDM method, it is possible to distinguish shooters and lanes by patterns so that there is no time division restriction like TDM method. It is also possible to recognize overlapped impact points of laser patterns launched by multiple shooters. After the laser pattern based simulated shooting system was implemented, general shot and overlapped shot were tested for each lane. Through experiments, we confirmed the possibility of continuous shooting. In addition, it is possible to separate the pattern by each lane, and 100% recognition result was obtained even if impact points overlapped.

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.

As the use of public transportation is increasing, a service considering the convenience of passengers is being launched utilizing the Internet (IoT) data collected through the bus information system. However, the bus information system only provides limited information such as the current location of the bus (GPS) and the expected arrival time at the stop. Additional IoT sensor information is needed to provide various services for the safety and convenience of passengers. In this paper, to improve the safety and convenience of passengers, we have added the boarding doors imaging sensor, temperature and humidity sensors, and sensor for measuring the number of passengers. The proposed IoT bus system can attach various sensors to buses to provide high-quality safety services to passengers.

Maya Deren is well known as the 'mother' of the American avant-garde films by her first short, Meshes of the Afternoon (1943). One of the major contributions of Maya Deren's theoretical body of work to the visibility was the invention of a new vocabulary for independent film-making such as 'film-poems', and 'choreo-cinema'. To create experimental film forms, she chose poetry, dance, architecture and music as a metaphor to describe her images. On the top of these arts, Maya uses camera works and editing system to achieve an 'artificial reality' whose character is miraculous in that living whole, in order to help the audience to experience a protagonist's psychological journey.

This study aims to analyze the multicultural content of the Korean Elementary School Textbook revised in 2015. This study abstracted the framework of multicultural factors for analysis of multicultural contents from the research on "Revision Guidelines by Analysis of Multicultural Factors in Secondary Curriculum and Textbook". The analyzed textbook is the integrated winter textbooks for both the first and second grades revised curriculum in 2015. Several findings were represented as follows: first, the frequency of multicultural content displayed five times in first grade and nineteen times in second grade respectively. Second, the analyzed textbooks did not include contents directly associated with multiculturalism. Activities and experience is important in the acceptance of unfamiliar cultures Thus, multicultural contents' frequency of within textbooks is very crucial. Finally, the revised curriculum focused on activities and experiences about the theme and content in the textbook and was developed around block chassis. For teaching the textbook content, teachers must perfectly understand textbooks through teacher training. The textbooks of reflecting right values on multiculturalismIn. this respect, elementary school curriculum and textbook content should be improved by supplementary revision fitting for the growth of humanity in the multicultural era.