The discussion in this paper aims to introduce an approach to detect drowsiness with Android based smartphones using the OpenCV platform tools. OpenCV for Android actually provides powerful tools for real-time body's parts tracking. We discuss here about the maximization of the accuracy in real-time eye tracking. Then we try to develop an approach for detecting eye blink by analyzing the structure and color variations of human eyes. Finally, we introduce a time variable to capture drowsiness.

Recently, the wearable computing technology with bio-sensors has been rapidly developed and utilized in various areas such as personal health, care-giving for senior citizens who live alone, and sports activities. In particular, the wearable computing equipment to measure vital signs by means of digital yarns and bio sensors is noticeable. The wearable computing devices help users monitor and manage their health in their daily lives through the customized healthcare service. In this paper, we suggest a system for monitoring and analyzing vital signs utilizing smart healthcare clothing with bio-sensors. Vital signs that can be continuously acquired from the clothing is well-known as unstructured data. The amount of data is huge, and they are perceived as the big data. Vital sings are stored by Hadoop Distributed File System(HDFS), and one can build data warehouse for analyzing them in HDFS. We provide health monitoring system based on vital sings that are acquired by biosensors in smart healthcare clothing. We implemented a big data platform which provides health monitoring service to visualize and monitor clinical information and physical activities performed by the users.

The existing R-peak detection research suggests improving the distortion of the signal such as baseline variations in ECG signals by using preprocessing techniques such as a bandpass filtering. However, preprocessing can introduce another distortion, as it can generate a false detection in the R-wave detection. In this paper, we propose an R-peak detection algorithm in ECG signal, based on primitive signal in order to detect reliably an R-peak in baseline variation. First, the proposed algorithm decides the primitive signal to represent the QRS complex in ECG signal, and by scaling the time axis and voltage axis, extracts multiple primitive signals. Second, the algorithm detects the candidates of the R-peak using the value of the voltage. Third, the algorithm measures the similarity between multiple primitive signals and the R-peak candidates. Finally, the algorithm detects the R-peak using the mean and the standard deviation of similarity. Throughout the experiment, we confirmed that the algorithm detected reliably a QRS group similar to multiple primitive signals. Specifically, the algorithm can achieve an R-peak detection rate greater than an average rate of 99.9%, based on eight records of MIT-BIH ADB used in this experiment.

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

Tone mapping for High Dynamic Range(HDR) image provides matching human visual perception between real world scene and displayable devices. Recently, a tone mapping algorithm based on localized gamma correction is proposed. This algorithm is using human visual properties of contrast and colorfulness with background intensity, generating a weight map for gamma correction. However, this method have limitations of controlling enhancement region as well as generating halo artifacts caused by the weight map construction. To overcome aforementioned limitations, proposed algorithm in this paper modifies previous weight map, considering base layer intensity of input luminance channel. By determining enhancement region locally and globally based on base layer intensity, gamma values are corrected accordingly. Therefore, proposed algorithm selectively enhances local brightness and controls strength of edges. Subjective evaluation using z-score shows that our proposed algorithm outperforms the conventional methods.

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

Recently, HD transmission was commonplace in the case of the terrestrial broadcasting, furthermore, 4k UHD broadcasts are also being pilot and such as HD contents are common even in smart-phones. Like this, through both the content and audience are in the situation to take advantage of the HD mobile TV, only QVGA video contents are being transmitted in the terrestrial DMB broadcasting. In this paper, introduction plan of HD DMB service for activation of DMB industry will be described in terms of technical, institutional, and policy to overcome the limitations of such as DMB broadcasting. Due to the introduction of HD DMB, high quality contents will be provided to audience and the advertising revenue DMB broadcasters increased so that the market share of DMB in mobile broadcasting be recovered.

In recent years, Tracking-Learning-Detection(TLD) system has been widely used as a detection and tracking algorithm for vision sensors. While conventional algorithms are vulnerable to occlusion, and changes in illumination and appearances, TLD system is capable of robust tracking by conducting tracking, detection, and learning in real time. However, the detection and tracking algorithms of TLD system utilize rotation-variant features, and the margin of tracking error becomes greater when an object makes a full out-of-plane rotation. Thus, we propose a rotation-invariant TLD system(RI-TLD). we propose a simplified average orientation histogram and rotation matrix for a rotation inference algorithm. Experimental results with various tracking tests demonstrate the robustness and efficiency of the proposed system.

The gesture classification has been applied to many fields. But it is not efficient in the environment for game interface with low specification devices such as mobile and tablet, In this paper, we propose a effective way for realistic game interface using k-nearest neighbors algorithm for gesture classification. It is time consuming by realtime rendering process in game interface. To reduce the process time while preserving the accuracy, a reconstruction method to minimize error between training and test data sets is also proposed. The experimental results show that the proposed method is better than the conventional methods in both accuracy and time.

Ocular region recently emerged as a new biometric trait for overcoming the limitations of iris recognition performance at the situation that cannot expect high user cooperation, because the acquisition of an ocular image does not require high user cooperation and close capture unlike an iris image. This study proposes a new method for ocular image recognition based on BRISK (binary robust invariant scalable keypoints). It uses the distance ratio of the two nearest neighbors to improve the accuracy of the detection of corresponding keypoint pairs, and it also uses geometric constraint for eliminating incorrect keypoint pairs. Experiments for evaluating the validity the proposed method were performed on MMU public database. The person recognition rate on left and right ocular image datasets showed 91.1% and 90.6% respectively. The performance represents about 5% higher accuracy than the SIFT-based method which has been widely used in a biometric field.

This paper presents a multiple target tracking system using target feature information. In the proposed system, the state of target is defined as its kinematic as well as feature : the kinematic includes a location and a velocity; the feature contains the image correlation between a prior target and a current measurement. The feature information is used for generating the validation matrix and association probability of joint probabilistic data association (JPDA) algorithm. Through the Kalman filter, the target kinematic is updated. Then the tracking information is cycled by the track management algorithm. The system has been evaluated using the images obtained from Electro-Optics/ InfraRed (EO/IR) sensor. It is verified that the proposed system can reduce the complexity burden of JPDA process and can enhance the track maintenance rate.

Unprecedented attention is being given to Smart Grid, Micro Grid and Internet of Things (IoT) in the Republic of Korea recently but such systems' effect is not well experienced by the market since they require additional and costly reforms for the existing household electrical system where adaptive communication platforms are needed. As such platforms, both wireless and wire communication technologies are being considered at the moment. Usually, they include WiFi, Zigbee technologies and the latter, LAN technology. However, communication speed decline due to signal attenuation and interference during wireless communications are considered to be the major problem and the extra works involving time and costs for the LAN system construction can be another demerit. Therefore, in this paper, we have introduced a Power Line Communication-based Smart Outlet System Expandable at Home to complement these disadvantages. Proposed IoT system involves Power Line Communication (PLC) technology which is essential to constructing a Smart Grid.

Sensors have limited resources in sensor networks, so efficient use of energy is important. Representative clustering methods, LEACH, LEACHC, TEEN generally use direct transmission methods from cluster headers to the sink node to pass collected data. However, the communication distance of the sensor nodes at low cost and at low power is not long, it requires a data transfer through the multi-hop to transmit data to the sink node. In the existing cluster-based sensor network studies, cluster process and route selection process are performed separately in order to configure the routing path to the sink node. In this paper, in order to use the energy of the sensor nodes that have limited resources efficiently, a cluster-based multi-hop routing protocol which merges the clustering process and routing process is proposed. And the proposed method complements the problem of uneven cluster creation that may occur in probabilistic cluster methods and increases the energy efficiency of whole sensor nodes.

IoT should be considered security risk environments such as various platforms and services including smart devices that can be mounted on household electric appliances, healthcare, car, and heterogeneous networks that are connected to the Internet, cloud services and mobile Apps.. In this paper, we provide analysis of new security threats, caused by open-platform of IoT and sensors via the Internet. Also, we present the key recovery mechanism that is applied to IoT. It results to have compatibility with given research, reduces network overhead, and performs key recovery without depending on key escrow agencies or authorized party.

In this paper, Various network adaptive MAC scheduling technique is proposed to effectively overcome limits of narrow bandwidth and low transmission speed in underwater. UPFC(Underwater Packet Flow Control) is a technique to reduce both the number of transmission and transmission time using three types (Normal, Blocked, Parallel) of data transmission. In this technique, the load information, in which a transmission node have, is transmitted to destination node using marginal bit in reserved header. Then the transmitted information is referred to determine weighting factor. According to the weighting factor, scheduling is dynamically changed adaptively. The performance of UPFC is analyzed and flow control technique which can be applied to Cluster Based Network and Ad Hoc network as well.

Recently, interactive media which maximizes audience engagement by making the audience appeal on a stage in digital media environment has been distributed more widely. In fact, there has been active movement to develop and promote a new participatory media genre with higher immersion by applying this kind of interactive media concept to advertisement, film, game and e-learning. In the conventional interactive media, digital media had to be enjoyed in particular environment where diverse sensors were installed or through a certain device to recognize a user's motion and voice. This study attempted to design and implement an image creation system which ensures interactions between a user and media in popular distribution-enabled web environment and through PC and smart devices to minimize the image producer-user constraints.

As the number of legacy database systems and the size of data to manipulate have been vastly increased, it has become more difficult and complex to analyze characteristics of data. To improve the efficiency of data analysis and help administrators to make decisions in business life, BI(Business Intelligence) system is used. To construct data warehouse and cube from legacy database systems makes it easy and fast to transform raw data into integrated and categorized meaningful information. In this paper, we built a BI system for an University administration. Several source system databases were integrated to data warehouse to build data cubes. The implemented BI system shows much faster data analysis and reporting ability than the manipulation in legacy systems. It is especially efficient in multi dimensional data analysis, nonetheless in single dimensional analysis.

Many franchise stores are in the surrounding. These stores offer many membership services. But, small traders do not provide much customer management service. For example, non-franchised cafes provide the services through paper coupons. However, most of paper coupons are available only in one store that issued the coupon. Besides, these coupons are in stamp format. Due to the absence of customers' management service, it is hard for small traders to attract customers compared to the franchise. Therefore, in this paper, We had implemented a mobile application, and applied to new customer management service in order to increase the price competitiveness of small traders. This service issues electronic coupon through a mobile app. customers can receive a relatively large amount of discount, and a deadline of coupons are short. When customers use coupons, new coupons are issued at the same time. This structure can be powerful means to lead customers' frequent visit. Small traders can gain a lot of regular customers by using this service.

The corporate image is one of the key elements improving competitiveness of the business, and is conveyed to the public through a design management strategy and its activities with design identity. Therefore, it is important to enhance the corporate image through systematic design management. Recently, in the field of communication services, as a technical gap between corporations has been narrowed, and the importance of design has been magnified, more and more corporations have adopted the design management. It is, however, not easy to build and convey the corporate image to the public systematically, because not only do the corporations run a communication service, the corporations face various business areas and targets. In this study, we analyzed the relationship between the intended image of the corporate and users' visual experiences based on the surveys on the case of KT, which pioneered design management and has made outstanding design results. According to the result, KT's strategic image concepts have positive effects on users' visual experience in each subarea of its design identity. In conclusion, it is necessary to narrow the gap between users' experiences and images that a corporate intends to convey through continuous research on users' experiences and improvement in a design management system.

This study suggests the visual contents of environment education using the Rainwater, Visual contents is more effective than writings in terms of delivering information. Environment education needs to be performed from the stage of early childhood as environment can be dealt throughout the overall daily life of learners. This study is to utilize animation contents for environment education to shift recognition of user on environment and deliver the information. For this purpose, virtual space, a theme park with motif of rain city operated by Rainwater was established. Then the process of utilizing Rainwater was shown using rides. Imaginary animal, a dragon and endangered animals were visualized as characters. So this paper is to suggest the possibility of visualization of environment education contents and stimulate 'interest' and provide educational information' on utilizing the Rainwater.