Yong-Ik Yoon

Smart learning services based on smart cloud computing.

Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)—smart pull, smart prospect, smart content, and smart push—concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users’ needs by collecting and analyzing users’ behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users’ behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users.

Energy efficiency for cloud computing system based on predictive optimization

In recent years, power consumption has become one of the hottest research trends in computer science and industry. Most of the reasons are related to the operational budget and the environmental issues. In this paper, we would like to propose an energy-efficient solution for orchestrating the resource in cloud computing. In nature, the proposed approach firstly predicts the resource utilization of the upcoming period based on the Gaussian process regression method. Subsequently, the convex optimization technique is engaged to compute an appropriate quantity of physical servers for each monitoring window. This quantity of interest is calculated to ensure that a minimum number of servers can still provide an acceptable quality of service. Finally, a corresponding migrating instruction is issued to stack the virtual machines and turn off the idle physical servers to achieve the objective of energy savings. In order to evaluate the proposed method, we conduct the experiments using synthetic data from 29-day period of Google traces and real workload from the Montage open-source toolkit. Through the evaluation, we show that the proposed approach can achieve a significant result in reducing the energy consumption as well as maintaining the system performance. Balance between energy efficiency and quality of service in the cloud computing.Apply prediction technique to enhance the usefulness of monitoring statistics.Design optimal energy efficiency architecture to orchestrate the cloud system.After VM consolidation, adaptively turn-off idle physical machines to save energy.

Improving digital image watermarking by means of optimal channel selection

Novel digital image watermarking method using a wavelet-based quantization approach.Optimal color channel selection scheme for the embedding.Otsus classification-based adaptive threshold for the extraction process.Outperformance of imperceptibility and robustness to state-of-the-art techniques. Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owners information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsus algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4źdB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches.

A Meta Data Model of Context Information for Dynamic Service Adaptation on User Centric Environment

Human can increase the conversational bandwidth using implicit situational and environmental information. But it is difficult to apply implicit information to current data model in order to enhance the communication ability between human and computer. In this paper, we are interested in data modeling of context information required to support dynamic service adaptation for increasing communication ability in user-centric environments. By improving the access capacity to context information for service adaptation, we can increase the richness of communication in human computer interactions and make it possible to produce more useful computational services. We are interested in the structure of meta-data model of context information required to support dynamic service adaptation in user-centric environments. Users want to get services anytime, anywhere and on any devices in various dynamic environments. The meta-data of context information is important for offering dynamic services and the reconfiguration context information for dynamic service adaptation. This paper designs a structure of meta-data model that supports the reconfiguration of the context for dynamic service adaptation. We classify the context information, as well as define its relationship with other context information on the basis of the application services, changes in the external environments.

Using weighted dynamic range for histogram equalization to improve the image contrast

In this paper, an effective method, named the brightness preserving weighted dynamic range histogram equalization (BPWDRHE), is proposed for contrast enhancement. Although histogram equalization (HE) is a universal method, it is not suitable for consumer electronic products because this method cannot preserve the overall brightness. Therefore, the output images have an unnatural looking and more visual artifacts. An extension of the approach based on the brightness preserving bi-histogram equalization method, the BPWDRHE used the weighted within-class variance as the novel algorithm in separating an original histogram. Unlike others using the average or the median of gray levels, the proposed method determined gray-scale values as break points based on the within-class variance to minimize the total squared error of each sub-histogram corresponding to the brightness shift when equalizing them independently. As a result, the contrast of both overall image and local details was enhanced adequately. The experimental results are presented and compared to other brightness preserving methods.

Video Customization System Using Mpeg Standards

To generate dynamically a personalized video summary, a video customization system is designed and implemented incorporating usage environment. The customization system uses the three tier architecture to adapt and deliver some media contents for the user. The multimedia contents delivery includes today many challenges. There are increasing the diversity of user devices and network interface devices, and the pressure to satisfy the user preferences. These situations encourage the need of the customized contents for the user. In this paper, we describe the customization of multimedia contents, like video. For the customization of content, we propose the Hybrid Multimedia Access (HMA) model that uses the multimedia content descriptions such as MPEG-7 standard and MPEG-21 multimedia framework.

SUPAR: Smartphone as a ubiquitous physical activity recognizer for u-healthcare services.

Current generation smartphone can be seen as one of the most ubiquitous device for physical activity recognition. In this paper we proposed a physical activity recognizer to provide u-healthcare services in a cost effective manner by utilizing cloud computing infrastructure. Our model is comprised on embedded triaxial accelerometer of the smartphone to sense the body movements and a cloud server to store and process the sensory data for numerous kind of services. We compute the time and frequency domain features over the raw signals and evaluate different machine learning algorithms to identify an accurate activity recognition model for four kinds of physical activities (i.e., walking, running, cycling and hopping). During our experiments we found Support Vector Machine (SVM) algorithm outperforms for the aforementioned physical activities as compared to its counterparts. Furthermore, we also explain how smartphone application and cloud server communicate with each other.

Intelligent Smart Cloud Computing for Smart Service

The cloud computing technology causes much attention in IT field. The developments using this technology have done actively. The cloud computing is more evolved than the existing offer. So, the current cloud computing only has a process that responds user requirements when users demand their needs. For intelligently adapting the needs, this paper suggests a intelligent smart cloud model that is based on 4S/3R. This model can handle intelligently to meet users needs through collecting user’s behaviors, prospecting, building, delivering, and rendering steps. It is because users have always mobile devices including smart phones so that is collecting user’s behavior by sensors mounted on the devices. The proposed service model using intelligent smart cloud computing will show the personalized and customized services to be possible in various fields.

Fast recognition of asian characters based on database methodologies

Character recognition has been an active research area in the field of pattern recognition. The existing character recognition algorithms are focused mainly on increasing the recognition rate. However, as in the recent Google Library Project, the requirement for speeding up recognition of enormous amount of documents is growing. Moreover, the existing algorithms do not pay enough attention to Asian characters. In this paper, we propose an algorithm for fast recognition of Asian characters based on the database methodologies. Since the number of Asian characters is very large and their shapes are complicated, Asian characters require much more recognition time than numeric and Roman characters. The proposed algorithm extracts the feature from each of Asian characters through the Discrete Fourier Transform (DFT) and optimizes the recognition speed by storing and retrieving the features using a multidimensional index. We improve the recognition speed of the proposed algorithm using the association rule technique, which is a widely adopted data mining technique. The proposed algorithm has the advantage that it can be applied regardless of the language, size, and font of the characters to be recognized.

Interactive activity recognition using pose-based spatio-temporal relation features and four-level Pachinko Allocation Model

Novel interactive activity recognition method using a topic modeling technique.Pose-based spatio-temporal relation features for intra- and inter-person.Two types of codeword corresponding to joint distance and angle.Four-level Pachinko Topic Model (PAM) for flexibly modeling interactions.Outperformance of recognition accuracy to state-of-the-art methods. In this paper, we go beyond the problem of recognizing video-based human interactive activities. We propose a novel approach that permits to deeply understand complex person-person activities based on the knowledge coming from human pose analysis. The joint coordinates of interactive objects are first located by an efficient human pose estimation algorithm. The relation features consisting of the intra and inter-person features of joint distance and angle, are suggested to use for describing the relationships between body components of the individual persons and the interacting two participants in the spatio-temporal dimension. These features are then provided to the codebook construction process, in which two types of codeword are generated corresponding to distance and angle features. In order to explain the relationships between poses, a flexible hierarchical topic model constructed by four layers is proposed using the Pachinko Allocation Model. The model is able to represent the full correlation between the relation features of body components as codewords, the interactive poselets as subtopics, and the interactive activities as super topics. Discrimination of complex activities presenting similar postures is further obtained by the proposed model. We validate our interaction recognition method on two practical data sets, the BIT-Interaction data set and the UT-Interaction data set. The experimental results demonstrate that the proposed approach outperforms recent interaction recognition approaches in terms of recognition accuracy.