Earticle

Using acceleration sensor on smart phone, we derive data on x, y, and z axis on spatial coordinate. With the proposed method we signalize this sensor information. The result is inferred and analyzed to detect the exact movement information along with the size of the directional information to the right, to the left, to the up and to the bottom. It is then output on the graph to propose the sensor application algorithm to visualize movement information. To verify the data gotten from the proposed method, we make 'third-person, top-view game' and apply it to the purpose. By using orientation sensor (acceleration sensor and magnetic sensor) we move the object in cyber space, and with the proposed method, we get movement information and directional information and apply them in comparison. After application, the proposed method provided consistent and correct movement information whether the object was moving or not, enabling us to identify correct information on the movements of the object in cyber space. For correct detection of sensor data, we used Kalman filter. To get stable data, we collected the data at regular intervals. We could verify the performance and validity of the proposed algorithm through experiments.

Recently, the density of Flash memory has dramatically increased by moving to smaller geometries and storing more bits per cell with enhanced memory technologies. However, with the density increasing rapidly, the error rate of Flash memory is also increasing rapidly. To improve the reliability issue of upcoming flash memory, usually, redundancy techniques were adopted in both of architecture and operations for flash memory based systems. For outside the flash memory page, several reliability schemes were proposed which employ Redundant Array of Inexpensive Disks (RAID). In this paper, we propose in-block level redundancy scheme for providing reliability of flash memory while minimizing maintaining overhead of the redundancy. In the proposed scheme, the redundancy is generated in a flash memory block level. During the programming stage of a flash block, the redundancy is kept in Dram memory. If a block is exhausted with last-1 page to record incoming data, the in-keeping redundancy is flushed to last page of the block. By doing this, block level redundancy is maintained with minimal overhead.

The advantage of proxy mobility management protocol over IPv6 is that it efficiently manages the latency time caused by a handover as the router on the access network takes the role of the mobility management of a mobile device instead. However, the frequent occurrences of handovers on the nested access network causes the increase of load on the MAG, which then brings about the loss of packets and delay of the handover process. Although fast proxy mobility management protocol has been suggested to resolve this problem, various implementations of the beacon or router advertisement on the wireless network and continuous research are required to provide stable multimedia traffic services. The proposed advanced cross-layering algorithm (ACL) builds a bi- directional tunnel between MAGs, based on the mobility prediction between the access routers on the nested network, using the reverse binding mechanism to optimize the route of the packets and therefore increase the efficiency of packet forwarding.

Abnormal event detection is a challenging task in video analysis. In this paper, we propose a new abnormal event detection algorithm for surveillance videos. It is well accepted that human eyes are extremely sensitive to abnormal events and they can quickly pay attention to the locations of these abnormal events in visual scenes. Thus, the characteristics of the Human Visual System (HVS) can be used for abnormal event detection. By exploiting the characteristics of the HVS, we propose an abnormal event detection algorithm based on saliency information. Firstly, the saliency information is extracted from video frames based on the feature contrast. The motion information of video frames is calculated by the multi-scale histogram optical flow (MHOF). Based on the features of saliency information and MHOF, the Support Vector Machine (SVM) is used to train and predict the abnormal events in visual scenes. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones over the public video database.

Until recently, it was expected that the web users’ tagging would be used in classification, recommendation, and information search. However, the users’ subjective decision or infrequent tags with high interrelationship have caused inaccurate results. This research proposes an algorithm to disregard users’ subjective tags and utilize infrequent tags with high semantic similarity using WordNet. Furthermore, to enhance the efficiency of the proposed algorithm, a weighted matrix is proposed. To evaluate the proposed algorithm, the current co-appearing frequency between tag-pairs method, tag-pair semantic similarity extraction algorithm, and tag-pair weight matrix method were analyzed and compared.

This paper proposes a novel optical flow method using SVD(Singular Value Decomposition) for extracting flow velocity field included in an input video. SVD calculates principal components of the image sequence, and the intermediate frame between sequential images is approximated. We generate optical flow for fluid movements using a groundbreaking optical flow estimation method which is the combination of Lucas/Kanade and Horn/Schunck to achieve the best of the local and global differential methods. While fluid simulation plays central role of generating fluid animations, our video based flow field generation scheme shows the potential for novel paradigm on physics based animation.

At present, online teaching platform is applying and promoting gradually. Course management systems have matured rapidly and now are considered critical software for many universities, the Moodle is an open and perfect source course management system. By analyzing the characteristics of specialized courses and learning situation of students majoring in computer science and technology of Computer Department of Jining Normal University, this paper analyzes its feasibility to construct the project-case teaching platform and then builds the platform based on Moodle .finally we take the " theory and technology of CAI " course as an example to apply and test the teaching platform.