This paper presents an image quality enhancement algorithm for dark image acquired under poor lighting condition. Various retinex algorithms which are human perception-based image processing methods were proposed to solve this problem. Although MSR(Multi-Scale Retinex) among these algorithm works well under most lighting condition, it shows color degradation because their separate nonlinear processing of RGB color channels. To compensate for the loss of the color, MSRCR(Multi-Scale Retinex with Color Restoration) was proposed. However, it requires high computational load and has additional parameters that need to be adjusted according to input image. In order to overcome this problem, a new retinex algorithm based on MSR is proposed in this paper. The proposed method consists of V channel MSR, saturation correction, and separate contrast enhancement process. Experimental results show that the subjective and objective image quality of the proposed method better than those of the conventional methods.

In this paper, we propose a method to measure respiration rate by dividing the respiration related region in depth image using level set method. In the conventional method, the respiration related region was separated using the pre-defined region designated by the user. We separate the respiration related region using level set method combining shape prior knowledge. Median filter and clipping are performed as a preprocessing method for noise reduction in the depth image. As a feasibility test, respiration activity was recorded using depth camera in various environments with arm movements or body movements during breathing. Respiration activity was also measured simultaneously using a chest belt to verify the accuracy of calculated respiration rate. Experimental results show that our proposed method shows good performance for respiration rate estimation in various situation compared with the conventional method.

A musical key is a fundamental concept in Western music theory. It is a collective characterization of pitches and chords that together create a musical perception of the entire piece. It is based on a group of pitches in a scale with which a music is constructed. Each key specifies the set of seven primary chromatic notes that are used out of the twelve possible notes. This paper presents a method that identifies the key of a song using Hidden Markov Models given a sequence of chroma features. Given an input song, a sequence of chroma features are computed. It is then classified into one of the 24 keys using a discrete Hidden Markov Models. The proposed method can help musicians and disc-jockeys in mixing a segment of tracks to create a medley. When tested on 120 songs, the success rate of the music key identification reached around 87.5%.

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

The existing DSRC(Dedicated Short Range Communication) scheme has a limitation in providing various services through short-range wireless communication. Therefore, it is necessary to develop an intelligent WAVE/DSRC hybrid wireless platform that can improve data transmission speed and extend the effective communication range. This paper introduces WAVE/DSRC integrated platform technology using WAVE(Wireless Access in Vehicle Environments) to overcome the disadvantages of DSRC. First, the desinged software configuration of the WAVE/DSRC integrated platform is described. Then each task operation scheme and WAVE software design are described. By applying the WAVE/DSRC integrated platform, we can compensate the shortcomings of DSRC. It can provide more accurate and reliable services.

Recently, due to the rapid development of the internet, the production and demand of high capacity contents are rapidly increasing. In order to accommodate the changing trend of internet usage, researches on CCN, the future internet architecture, are actively being conducted. Mobility support is an important challenge in CCN real-time services, given that today's internet environment is changing to mobile and real-time services are increasing. In CCN real-time services, the mobility problem can be largely divided into consumer mobility, provider mobility, and intermediate-node mobility. Among them, when the intermediate-node moves in the CCN real-time services, the service disconnection occurs and the QoS degradation is caused. In this paper. we propose Intermediate-Node Mobility Support(INMS) to support the intermediate-node mobility in CCN real-time services. Experimental results show that the proposed scheme shows better performance than CCN in terms of service disconnection time and packet loss.

The increasing number of multimedia content offered to the user demands content recommendation. In this paper, we propose a system for recommending content related to the content that user is watching. In the proposed system, relationship information between content is generated using relationship information between representative keywords of content. Relationship information between keywords is generated by analyzing keyword collocation frequencies in Internet news corpus. In order to handle big corpus data, we design an architecture that consists of a distributed search engine and a distributed data processing engine. Furthermore, we store relationship information between keywords and relationship information between keywords and content in NoSQL to handle big relationship data. Because the query optimizer of NoSQL is not as well developed as RDBMS, we propose query optimization techniques to efficiently process complex queries for recommendation. Experimental results show that the performance is improved by up to 69 times by using the proposed techniques, especially when the number of requested related keywords is small.

Audio power amplifiers have been designed based on the premise that the impedance of loudspeakers is fixed at nominal 4 ohms or 8 ohms. However, it is known that the impedance varies with frequency and takes on the nominal value at some limited frequencies. The principle of the loudspeaker operation reveals that the sound pressure produced by the loudspeaker is proportional to the current flowing in the voice coil, not the voltage between the two terminals. We take the characteristics of the loudspeaker into account and compare the frequency responses of the loudspeaker in voltage-drive mode and current-drive mode via computer simulations, to conclude that the audio amplifier drive mode should be re-considered in an effort to improve the sound quality.

Recently in the IoT(Internet of Things) environment, a data collection in real-time through device's sensor has increased with an emergence of various devices. Collected data from IoT environment shows a large scale, non-uniform generation cycle and atypical. For this reason, the distributed processing technique is required to analyze the IoT sensor data. However if you do not consider the optimal scheduling for data and the processor of IoT in a distributed processing environment complexity increase the amount in assigning a task, the user is difficult to guarantee the QoS(Quality of Service) for the sensor data. In this paper, we propose APQTA(Adaptive Priority Queue-driven Task Allocation method for sensor data processing) to efficiently process the sensor data generated by the IoT environment. APQTA is to separate the data into job and by applying the priority allocation scheduling based on the deadline to ensure that guarantee the QoS at the same time increasing the efficiency of the data processing.

The k-nearest neighbor (k-NN) algorithm is one of the most widely used benchmark algorithm in classification. Nowadays it has been further applied to predict time series. However, one of the main concerns of the algorithm applied on short-term electricity load forecasting is high computational burden. In the paper, we propose an approach of dimension reduction that follows the principles of highlighting the temperature effect on electricity load data series. The results show the proposed approach is able to reduce the dimension of the data around 30%. Moreover, with temperature effect highlighting, the approach will contribute to finding similar days accurately, and then raise forecasting accuracy slightly.

This paper proposes how to develop a multi-sensory virtual reality system based on realistic media that can improve the sense of immersion and reality experienced by the user. We suggest four types of multi-sensory virtual reality system; a realistic media experience system which provides sensory experiences to user by interlocking the media file with the sensory informations and reproducing the sensory information suitable for the scene, a real image-based panorama experience system which maximizes the sense of reality, an experience ball system in which users engage themselves into the system environment to lead the story and immersion of the content through interaction with the system, and a cultural heritage experience system based on hand movement recognition. The suggested systems can be applied in a various area such as education, advertisement, culture and arts, performance, exhibition, sports, game, 4D Experience Center, and so on. We supposed that it can contribute to create a variety of sensible contents services in the realistic media industry through the convergence of media, contents, and devices.

Each cultural community has developed a variety of fonts to express their own language and characters. Hangul has also diversified its font shapes through changing the composition ratio and look of the consonants and vowels. Rather, thanks to the variety of these fonts, a considerable amount of time and effort must be devoted to the selection of a specific font shape. This is related to the fact that the current Hangul service and classification system process the font only with its name or the name of the manufacturer. It means that there is no consensus about the font shape classification system for Hangul. In this study, we propose a shape property set that can be a basis for classifying Hangul fonts. The font shape property set was generated by performing statistical analysis with features which have been studied by the font design experts and was verified through questionnaire using representative fonts based on the classification scheme defined by the Hangul font design classification system standard. This study is meaningful in that it is a study on shape classification properties of K-means and PCA statistical techniques based on font data rather than design field study.

This study recognizes the narrative of the movie as a semiotic system and proposes a structured storytelling analysis model through theoretical basis and empirical analysis. It classifies as 'Narrative 17 Process' which considers the narrative of successful 11 animations as a continuous process of formal structure. It extract the paradigmatic sub-narrative units(NU) centered on the act of the character in each process. The structural pattern of the story types are extracted by comparing and analyzing with 5 NU analysis elements presented in this study. As a result, the 4 story types were consistently classified by the SSD distance value. Therefore, this study propose a storytelling analysis model that can be effectively applied to scenarios and narrative composition stages of movie production.

The explosion effect of computer graphics Visual Effects(VFX) used in films and animations is an important element that determines the completeness of the film, and its usage is getting extended. The realistic explosion effect of VFX should be made according to observations and analysis of various factors of actual explosion in real world. This experimental research would suggest the efficient production guideline for the technical characteristics of underwater explosion of VFX. For this research process, first, the comparison of actual explosion and VFX explosion effect, classification of actual explosion, and characteristics of underwater explosion effect will be addressed. Second, based on the literature reviews, the four steps of experimental production analysis tool will be derived. Third, the experimental research will be processed in along with technical factors four steps of the underwater explosion effect, (1)realistic creation and emission of fluid, (2)fluid expansion control by water pressure, (3)bubble effect, and (4)motion of bubble & dissipation of fluid. The effective method of fluid simulation production will be verified through experimental studies based on the characteristics of the actual explosion process. This experimental study suggested the VFX production technique is expected to be used as the basic data for related research field.