In this paper, we propose a method of the zoom motion estimation for the depth video coding. The proposed method calculates the zoom ratio using the average of the depth values in the current block and in the reference block. It resizes the reference block by the zoom ratio and interpolates the reference block to size of the current block. It compares the current block with the reference block that is obtained by subtracting the average of pixels from the current block to the reference block in order to find the reference block that is the best closest one to the current block. The results of the simulation for the proposed method show that the motion estimation errors are significantly reduced.

Active shape model is widely used in the field of image processing especially on arbitrary meaningful shape extraction from single gray level image. Cootes et. al. showed efficient detection of variable shape from image by using covariance and mean shape from learning. There are two stages of learning and testing. Hahn applied enhanced shape alignment method rather than using Cootes's rotation and scale scheme. Hahn did not modified the profile itself. In this paper, the method using directional one dimensional profile is proposed to enhance Cootes's one dimensional profile and the shape alignment algorithm of Hahn is combined. The performance of the proposed method was superior to Cootes's and Hahn's. Average landmark estimation error for each image was 27.72 pixels and 39.46 for Cootes's and 33.73 for Hahn's each.

This paper describes a vision-based method that effectively recognize a traffic light. The method consists of two steps of traffic light detection and discrimination. Many related studies have used color information to detect traffic light, but color information is not robust to the varying illumination environment. This paper proposes a new method of traffic light detection based on intensity and saturation. When a traffic light is turned on, the light region usually shows values with high saturation and high intensity. However, when the light region is oversaturated, the region shows values of low saturation and high intensity. So this study proposes a method to be able to detect a traffic light under these conditions. After detecting a traffic light, it estimates the size of the body region including the traffic light and extracts the body region. The body region is compared with five models which represent specific traffic signals, then the region is discriminated as one of the five models or rejected as none of them. Experimental results show the performance of traffic light detection reporting the precision of 97.2%, the recall of 95.8%, and correct recognition rate of 94.3%. These results shows that the proposed method is effective.

In commercial digital-cameras, color-filter filters light according to wavelength range of color filter array (CFA) and the filtered intensities contain color information of light. Then, output data of CFA is transformed to final rendered image through demosaicing process. In image processing of digital-camera, the quality of the final rendered image is affected by optical cross talk of CFA, kind of CFA pattern etc. Basically, pattern of CFA plays important role in image quality of final image rendered by digital-camera. Therefore, an evaluation system capable of quantitatively evaluating CFA is needed. This paper proposes a novel evaluation system using existing and proposed image metrics for evaluating CFAs of digital-camera. Proposed CFA evaluation system consist of color difference in CIELAB and S-CIELAB, Structure SImilarity (SSIM), MTF50, moire starting point (MSP), and subjective preference (SP). MSP and SP are newly designed for the proposed evaluation system. Proposed evaluation system is expressed in polar coordinates to analyze the characteristics of CFA objectively and intuitively. Through simulations, we confirmed that proposed CFA evaluation system can objectively assess performance of developed CFAs.

In super-resolution, various methods with Convolutional Neural Network(CNN) have recently been proposed. CNN based methods provide much higher image quality than conventional methods. Especially, VDSR outperforms other CNN based methods in terms of image quality. However, it requires a high computational complexity which prevents real-time processing. In this paper, the method to apply a deconvolution layer to VDSR is proposed to reduce computational complexity. Compared to original VDSR, the proposed method achieves the 4.46 times speed-up and its degradation in image quality is less than -0.1 dB which is negligible.

Although Open API has been invigorated by advancements in the software industry, diverse types of malicious code have also increased. Thus, many studies have been carried out to discriminate the behaviors of malicious code based on API data, and to determine whether malicious code is included in a specific executable file. Existing methods detect malicious code by analyzing signature data, which requires a long time to detect mutated malicious code and has a high false detection rate. Accordingly, in this paper, we propose a method that analyzes and detects malicious code using association rule mining and an Naive Bayes classification. The proposed method reduces the false detection rate by mining the rules of malicious and normal code APIs in the PE file and grouping patterns using the DHP(Direct Hashing and Pruning) algorithm, and classifies malicious and normal files using the Naive Bayes.

The SIP(Session Initiation Protocol) is an application layer call signaling protocol which can create, modify and terminate the session of user, and provides various services in combination with numerous existing protocols. However, most of cryptosystems for SIP cannot prevent quantum computing attack because they have used ECC(Elliptic Curve Cryptosystem). In this paper, we propose a NTRU based authentication and key distribution protocol for SIP in order to protect quantum computing attacks. The proposed protocol can prevent various attacks such as quantum computing attack, server spoofing attack, man-in-the middle attack and impersonation attack anonymity, and our protocol can provide user's anonymity.

There are two types of WSN(wireless sensor networks) in terms of sensor node's capability, that is, homogeneous or heterogeneous WSN. Even though the latter has better performance than the former, it requires some overhead for deploying nodes or clustering the network. In this paper, we propose a new scheme, called VHS(Virtual Heterogenous Sensor-Network), which uses a homogeneous WSN regarding energy in a heterogeneous way. The proposed scheme's performance has been evaluated and compared with other homogeneous schemes by simulation. The results are shown to be better than the other existing homogeneous schemes used in a sample sensor network application.

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

A large amount of processing time is required if the process of detecting the touch position on the touch screen and displaying it on the display panel is performed only by software. In this paper, we propose a method to output information touched on the screen using H/W method in order to improve the response speed delay. In the FPGA module designed for the HDMI signal output to the display module, the touch information is input to the serial data signal including touch coordinate information, point size, and color information. Then the module render the image using HDMI signal input to the module and the touch information. This method has a pipeline structure so it has effect of reducing the delay time that occurs in outputting the touch information compared with the conventional software processing method.

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

With the development of 3D technology, image expression has been widely used in the field of 3D animation these days for the purpose of illustrating a certain image, feeling or status. In this paper, we examine how it affects to audiences' feelings and senses when we applied the handheld camera technique to other kinetic characteristics. In addition, we analyze the scenes which the handheld camera technique is applied into each characteristic from movie contents, present the kinetic characteristics of the handheld camera technique, and study the scenes that can apply this technique with 3D animation and its influence.

In the previous research, we proposed a Kinetic typography font engine that can easily add motion to text with function call only. However, since it is aimed at constructing movements for a sentence, there is still inconvenience in the production of various kinetic typography motions in word or letter unit. We propose Kinetic Typical Extended Motion API(Application Programming Interface) that extends Kinetic Motion API. The extended Kinetic Typographic Font Engine aims to simplify the process of making kinetic typography in words and letters, including the kinetic typographic motion library provided as a function. In addition, various applications that can apply Kinetic typography A kinetic typography authoring interface is provided for facilitating the construction of a motion library for the robot.

Recently, user motion estimation methods using various wearable devices have been actively studied. In this paper, we propose a motion estimation system using Myo, which is one of the wearable devices, using two Myo and their dependency relations. The estimated motion is used as a command for remotely controlling the robot. Myo's Orientation and EMG signals are used for motion estimation. These two type data sets are used complementarily to increase the accuracy of motion estimation. We design and implement the system according to the proposed method and analyze the results through experiments. As a result of comparison with previous studies, the accuracy of motion estimation can be improved by about 12.3%.