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Accurate identification of the shape and position of organs and abnormal objects (e.g., tumors) in medical images plays an important role in surgical planning as well as in the diagnosis and prognosis of diseases. However, this is difficult to achieve from two-dimensional medical images as these images present inaccurate and ambiguous organ boundaries. Further, traditional image processing-based boundary detection methods such as the Canny edge detector and Sobel operator exhibit poor boundary detection performance for images with substantial noise. Recently, the use of deep learning has resulted in improvements in semantic segmentation in medical images. In this paper, we propose a generic boundary-aware loss function to facilitate the effective discernment of the boundaries of organs and abnormal objects in medical images. Specifically, the proposed loss function introduces a boundary area and assigns higher weights to the loss of pixels located in the boundary area than to those in the non-boundary areas, thereby promoting effective learning in the boundary area. The results of experiments conducted using public medical datasets comprising colon polyp, skin lesion, and chest X-ray data indicate that the standard loss functions, such as cross-entropy loss and Dice loss, combined with the proposed boundary-aware loss function, achieve comparable or better performance than those without the boundary-aware loss function.

We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

To develop and evaluate a deep learning (DL)–based system for measuring leg length on full leg radiographs of diverse patients, including those with orthopedic hardware implanted for surgical treatment. This study retrospectively assessed 2767 X-ray scanograms of 2767 patients who did or did not have orthopedic hardware implanted between January 2016 and December 2019. A cascaded DL model was developed to localize the relevant landmarks on the pelvis, knees, and ankles required for measuring leg length. Statistical analysis was performed using the correlation coefficient analysis and Bland–Altman plots to assess the agreement between the reference standard and DL-calculated lengths. Testing data comprised 400 radiographs from 400 patients. Of these radiographs, 100 were from patients with orthopedic hardware implanted in their pelvis, knees, or ankles. For all testing data, leg lengths derived from the DL-based measurement system, with or without internal fixation devices, showed excellent agreement with the reference standard (femoral length, r = 0.99 (P

왼손 X-ray 영상을 이용한 골연령 측정에서 주로 사용하는 TW3(Tanner-Whitehouse 3)기법은 왼손에서 13개의 ROI(Region Of Interests)를 추출하고 각 ROI의 등급을 판정한다. 그런데 판독 시간이 상대적으로 길고 판단하는 사람에 따라 정확도가 달라지는 문제점이 있다. 이를 해결하기 위해 딥러닝을 이용한 자동화 시스템이 개발되고 있는데 이 시스템의 판독 성능은 ROI 추출 정확도에 의해 크게 좌우된다. 그러나 왼손 X-ray 영상은 영상의 밝기(brightness)와 대비(contrast)에 따라 정확한 ROI 추출 성공률이 달라진다. 따라서 본 논문에서는 기계 학습을 통해 X-ray 영상의 밝기와 대비를 자동으로 조절하여 ROI를 정확히 추출할 수 있는 기법을 제안한다. 제안하는 기법을 사용하면 골연령 측정 자동화 시스템에 부담이 없으면서도 ROI 추출 성공률이 29.5% 정도 더 높아짐을 실험을 통해 알 수 있었다.

Deep learning technology has rapidly evolved in recent years. Bone age assessment (BAA) is a typical object detection and classification problem that would benefit from deep learning. Convolutional neural networks (CNNs) and their variants are hence increasingly used for automating BAA, and they have shown promising results. In this paper, we propose a complete end-to-end BAA system to automate the entire process of the Tanner-Whitehouse 3 method, starting from localization of the epiphysis-metaphysis growth regions within 13 different bones and ending with estimation of the corresponding BA. Specific modifications to the CNNs and other stages are proposed to improve results. In addition, an annotated database of 3300 X-ray images is built to train and evaluate the system. The experimental results show that the average top-1 and top-2 prediction accuracies for skeletal bone maturity levels for 13 regions of interest are 79.6% and 97.2%, respectively. The mean absolute error and root mean squared error in age prediction are 0.46 years and 0.62 years, respectively, and accuracy within one year of the ground truth of 97.6% is achieved. The proposed system is shown to outperform a commercially available Greulich-Pyle-based system, demonstrating the potential for practical clinical use.

Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segment lung areas in chest X-rays. The novel aspect of the proposed method is the self-attention module, where the outputs of the channel and spatial attention modules are combined to generate attention maps, with each highlighting those regions of feature maps that correspond to &ldquo, what&rdquo, and &ldquo, where&rdquo, to attend in the learning process, respectively. Thereafter, the attention maps are multiplied element-wise with the input feature map, and the intermediate results are added to the input feature map again for residual learning. Using X-ray images collected from public datasets for training and evaluation, we applied the proposed attention modules to U-Net for segmentation of lung areas and conducted experiments while changing the locations of the attention modules in the baseline network. The experimental results showed that our method achieved comparable or better performance than the existing medical image segmentation networks in terms of Dice score when the proposed attention modules were placed in lower layers of both the contracting and expanding paths of U-Net.

With the recent advancements in cloud computing technology, the number of cloud-based services has been gradually increasing. Symmetrically, users are asking for quality of experience (QoE) to be maintained or improved. To do this, it has become necessary to manage network resources more efficiently inside the cloud. Many theoretical studies for improving the users’ QoE have been proposed. However, there are few practical solutions due to the lack of symmetry between implementation and theoretical researches. Hence, in this study, we propose a ranking table-based network resource allocation method that dynamically allocates network resources per service flow based on flow information periodically collected from a software defined network (SDN). It dynamically identifies the size of the data transmission for each service flow on the SDN and differentially allocates network resources to each service flow based on this size. As a result, it maintains the maximum QoE for the user by increasing the network utilization. The experimental results show that the proposed method achieves 29.4% higher network efficiency than the general Open Shortest Path First (OSPF) method on average.

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, enhancing patient survival possibilities. A number of nodule segmentation techniques, which either rely on a radiologist-provided 3-D volume of interest (VOI) or use the constant region of interests (ROIs) for all the slices, are proposed; however, these techniques can only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to freely investigate the nodule presence outside the given VOI and also include the redundant structures (non-nodule) into VOI, which limits the segmentation accuracy. In this work, a novel semi-automated approach for 3-D segmentation of lung nodule in computerized tomography scans, has been proposed. The technique is segregated into two stages. In the first stage, a 2-D ROI containing the nodule is provided as an input to perform a patch-wise exploration along the axial axis using a novel adaptive ROI algorithm. This strategy enables the dynamic selection of the ROI in the surrounding slices to investigate the presence of nodules using a Deep Residual U-Net architecture. This stage provides the initial estimation of the nodule utilized to extract the VOI. In the second stage, the extracted VOI is further explored along the coronal and sagittal axes, in patchwise fashion, with Residual U-Nets. All the estimated masks are then fed into a consensus module to produce a final volumetric segmentation of the nodule. The algorithm is rigorously evaluated on LIDC–IDRI dataset, which is the largest publicly available dataset. The proposed approach achieved the average dice score of 87.5%, which is significantly higher than the existing state-of-the-art techniques.

The Software Defined Network (SDN) decouples control and data planes of the existing network architecture and a controller carries out centralized management and control of the network. This provides flexibility for the networks and enables efficient operation, depending on their condition. However, when the performance of switches comprising the SDN varies, any switches with low performance can cause flow table overflow and subsequent bottleneck with overhead concentrating in the controller. Besides, packet processing can be delayed, declining user QoS. The paper proposes a flow entry replacement scheme that takes into account the flow features to address the issue. The proposed Short Flow First (SFF) scheme discriminates the flow features based on the matching period of flow entry and determines a subject of the flow entry replacement with the features. For evaluation, the paper built the SDN environment using the Mininet emulator and compared the performance of the proposed SFF scheme with the representative flow entry replacement schemes. According to the results, the proposed scheme showed a good performance in the flow entry miss rate and packet delay due to failure of matching the flow entry.

Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high-resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN)-based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. First CG-SENSE reconstruction is employed to reconstruct an image from the motion-corrupted k-space data and then the GAN-based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several-fold the computational time reported by the current state-of-the-art technique.

Moving pictures experts group (MPEG) media transport (MMT) and MPEG-dynamic adaptive streaming over hypertext transfer protocol (MPEG-DASH) are emerging as the content-delivery technologies for the next-generation broadcasting systems, as they seamlessly utilize various underlying delivery networks to provide hybrid multimedia services in any location. In this paper, we present an empirical analysis of the channel-zapping time, which is an important metric in the measurement of the quality of experience (QoE) of broadcast services, regarding the MMT and MPEG-DASH technologies. During this process, we also clarify the important factors that determine the channel-zapping time of the individual technologies. In addition, we propose a simple yet effective method that enables fast channel zapping for the MMT technology.

A Software Defined Network (SDN) separates an existing network structure into a control plane and a data plane and centrally manages the network at the control plane. SDN is able to control the network in detail and operate efficiently by managing the traffic by flow unit. However, if the performance of the switches comprising the SDN varies greatly from one to another, it has a problem of delayed handling of traffic due to the bottleneck caused by an overhead concentration at the controller when the message exchanges between the low performance switch and the controller for flow table replacement increase in number. This research paper suggests a flow table management scheme, called Proactive-LRU, to alleviate this problem and compares its performance to that of the existing replacement algorithms such as LRU, LFU, and FIFO. Proactive-LRU scheme is a flow table management scheme that deletes the flow from all switches available within the forwarding path when a flow is deleted and reduces the number of message exchanges and the controller overhead. In order to compare the performance of the suggested scheme against that of the existing algorithms (FIFO, LRU, LFU), video traffic and web browsing traffic have been mixed in certain ratios in an SDN environment for the experiment. The analysis of the results showed that the number of message exchanges as well as the overhead and table overflow of switches in the suggested scheme were significantly reduced.

The objective of this paper is to improve the overall performance of distributed encoding, with a particular focus on encoding speed. The proposed scheme consists of content-aware video segmentation and scheduling that consists of two major parts. In the first part, segmentation is carried out with greater efficiency by considering changes in the video content, and in the second part, the segment assignment process is carried out using an efficient scheduling scheme that changes the encoding order of the segments. We measured the content similarity by using the sum of absolute difference algorithm and then applied a threshold to define the degree of change in similarity. The video was segmented based on the extent to which the similarity had changed, and the encoding order of the segments was rearranged to perform distributed encoding. Finally, this paper introduces the MapReduce-based distributed video encoding, using the content-aware video segmentation and scheduling described above, and presents the results of the performance using this scheme, which indicate that the proposed scheme increases the bitrate by a maximum of 2.9% over existing segmentation schemes, and also increases the speed by a maximum of 15.3%.

Purpose The aim of this study was to evaluate the clinical efficacy of a Tanner-Whitehouse 3 (TW3)-based fully automated bone age assessment system on hand-wrist radiographs of Korean children and adolescents. Materials and methods Hand-wrist radiographs of 80 subjects (40 boys and 40 girls, 7-15 years of age) were collected. The clinical efficacy was evaluated by comparing the bone ages that were determined using the system with those from the reference standard produced by 2 oral and maxillofacial radiologists. Comparisons were conducted using the paired t-test and simple regression analysis. Results The bone ages estimated with this bone age assessment system were not significantly different from those obtained with the reference standard (P>0.05) and satisfied the equivalence criterion of 0.6 years within the 95% confidence interval (- 0.07 to 0.22), demonstrating excellent performance of the system. Similarly, in the comparisons of gender subgroups, no significant difference in bone age between the values produced by the system and the reference standard was observed (P>0.05 for both boys and girls). The determination coefficients obtained via regression analysis were 0.962, 0.945, and 0.952 for boys, girls, and overall, respectively (P=0.000); hence, the radiologist-determined bone ages and the system-determined bone ages were strongly correlated. Conclusion This TW3-based system can be effectively used for bone age assessment based on hand-wrist radiographs of Korean children and adolescents.

According to results of a survey by Cisco, the largest networking company in the world, a high proportion of all network traffic is multimedia, and its proportion has increased daily. As a result, the importance of multimedia traffic processing has increased. There are two existing traffic processing techniques: unicast and multicast modes. The multicast mode is more efficient in terms of multimedia traffic processing. To process network traffic using the multicast mode, a multicast tree should be generated first, which in many cases, can be dealt with by the Steiner tree generation problem. The Steiner tree generation involves nondeterministic polynomial time problems, called NP-hard. There are a number of approximation algorithm studies to generate a Steiner tree. However, existing studies have not yet found the optimal Steiner tree, and the advancement of performance has also been minimal. Thus, this study proposes a multicast tree generation technique that applies reinforcement learning, which has not been discussed in previous studies. For reinforcement learning, the context of a problem has to be represented by the Markov decision process (MDP), which can be achieved by a software-defined networking (SDN) environment. This paper proposes a technique to represent a network context with MDP and shows how the learned policies are operated for multicast routing.

As cloud computing has emerged as a promising technique in mainstream application domains, significant attention has been paid to distributed video encoding, in which resource-intensive encoding tasks are distributed across unlimited computational resources available in the cloud environment. For distributed video encoding, the input video must be partitioned into several segments. This approach decreases the total encoding time but may suffer from quality degradation associated with a lack of information, such as the coding complexity of the previous video segment. In this paper, two well-known video partitioning methods are explored from different performance perspectives, including encoding time, bitrates, and peak signal-to-noise ratio (PSNR).

Smart devices interconnected through Internet became one of everyday items. In particular, we are now able to access Internet anywhere and anytime with our smartphones. To support the ad-hoc access to Internet by using smartphones, the computer network structure has become more complex. Also, a certain network node is highly connected to support the diverse Internet services. In this paper, we note that when a node is infected by malicious programs, their propagation speeds from the node with a high level of centrality will be faster than those from the node with a low level of centrality, which identifies the most important nodes within a network. From experiments under diverse worm propagation parameters and the well-known network topologies, we evaluate the influence of Centrality-based random scanning strategy on early worm propagation rate. Therefore, we show that centrality-based random scanning strategy, where an initial infected node selects the victim based on the level of centrality, can make random scanning worms propagate rapidly compared to Anonymity-based random scanning strategy, where an initial infected node selects the victim uniformly.

Smart connected devices such as smartphones and tablets are battery-operated to facilitate their mobility. Therefore, low power consumption is a critical requirement for mobile hardware and for the software designed for such devices. In addition to efficient power management techniques and new battery technologies based on nanomaterials, cloud computing has emerged as a promising technique for reducing energy consumption as well as augmenting the computational and memory capabilities of mobile devices. In this study, we designed and implemented a framework that allows for the energy-efficient execution of mobile applications by partially offloading the workload of a mobile device onto a resourceful cloud. This framework comprises a development toolkit, which facilitates the development of mobile applications capable of supporting computation offloading, and a runtime infrastructure for deployment in the cloud. Using this framework, we implemented three different mobile applications and demonstrated that considerable energy savings can be achieved compared with local processing for both resource-intensive and lightweight applications, especially when using high-speed networks such as Wi-Fi and Long-Term Evolution.

Since its introduction in the late 90’s, Augmented Reality (AR) has emerged as a killer service in various domains. In contrast to conventional monolithic AR services that do not consider individual user contexts, AR services are currently evolving into their next phase. Two important requirements for next generation AR services include a personalized contextaware presentation of AR information and the provisioning of an enhanced user experience in a more realistic manner. Therefore, in this paper, we present methods for supporting such functionalities using the Binary Format for Scenes (BIFS) technology standardized by the Moving Picture Experts Group (MPEG).

Recent advances in wireless communications technology and microelectromechanical systems have enabled the development of low cost, low power, network-enabled, and multifunctional microsensors. Due to their ease of deployment, reliability, scalability, flexibility, and self-organization, the existing and potential applications of wireless sensor networks (WSNs) span a wide spectrum in various domains, the environmental and technical requirements of which may differ significantly. The purpose of deploying a WSN is to collect relevant data for processing and reporting. In particular, based on data reporting, WSNs can be classified as time-driven or event-driven. In this paper, we propose a hybrid data-gathering protocol that dynamically switches between the event-driven data-reporting and time-driven data-reporting schemes. The novel aspect of our approach is that sensor nodes that seem to detect an event of interest in the near future, as well as those nodes detecting the event, become engaged in the time-driven data-reporting process. This capability enables data from neighboring areas to be gathered proactively without requiring observer intervention. As such, the proposed protocol accurately analyzes the environment being monitored using only moderate resource consumption. We have implemented the proposed protocol on a network simulator and analyzed its behaviors using synthetic environments that model the occurrence of a fire.

GINGA-Nested Context Language (GINGANCL) is a subsystem of GINGA, the standard for the Brazilian mobile broadcasting service, and it specifies the mechanisms and services used to enable various interactive data services. The integral part of GINGA-NCL is the NCL declarative language that provides non-invasive control of a multimedia presentation in time and space, as well as user interaction. In particular, one of its core competitive advantages over other existing technologies, such as Broadcast Markup Language (BML) and Open Mobile Alliance-Rich Media Environment (OMA-RME), is that it is substantially light-weight. Therefore, in this paper, we introduce an implementation of a GINGANCL receiver for mobile handsets. The proposed solution supports full feature GINGA-NCL sets, including XHTML and ECMAScript1.

Internet protocol TV (IPTV) has emerged as a new platform in converged environments. In particular, due to its support for the convergence of devices, IPTV allows almost every IP-enabled terminal to simultaneously consume the same rich media service delivered over an IP-based network. However, this convergence potentially leads to a significant decrease in both quality of service (QoS) and quality of experience (QoE) in IPTV services because different terminals present the same service differently based on their capabilities, thus making the service inconsistent. Recently, the LASeR standard, a technology competing for rich media representation, was revised to deal with this problem. This paper presents the relevant amendment of the standard and, using a prototype implementation, shows the effectiveness of the approach taken by the LASeR.

IP Datacast over DVB-H has been adopted as a core technology to build complete end-to-end mobile broadcast TV systems. In order for this technology to be successful in the market, provisioning of acceptable QoE (Quality of Experience) to the users, as well as a wide range of business models to the service providers, is essential. In this paper, we analyze the channel zapping time, which is an important metric to measure QoE for mobile broadcast TV services. In particular, we clarify primary components that determine the channel zapping time for protected services in IP Datacast over DVB-H. Our analysis is based on the data gathered during the trial service of the OMA-BCAST Smartcard profile in Singapore, Asia. Based on the analysis, we show that a significant reduction in channel zapping time can be achieved by optimizing the transmission parameters related to the key derivation time and the synchronization time between the content stream and the key stream.

Demand for rich media services is rapidly increasing and, as a result, several technologies are competing to deliver rich media services to mobile, resource-constrained devices. In this paper, we explore the existing rich media technologies and analyze their key features. In addition, in order to accommodate the new requirements introduced by emerging service models, such as service convergence, these technologies must evolve from their current status. We examine how the community intends to achieve this.

ATSC-MH is an emerging standard for mobile digital TV services in the U.S. From broadcaster's point of view, core competitive advantages that broadcasters have relative to the other technologies for enabling mobile broadcast TV services include substantially lower capital requirements, as well as low cost and routine access to broadcast content. From customer's point of view, on the other hand, they can enjoy information rich services in mobile environment because ATSC-MH incorporates various technologies that have proven successful in other mobile broadcast TV standards. In this paper, therefore, we introduce an implementation of ATSC-MH receiver, especially for mobile handsets. The proposed solution is in full support of the ATSC-MH signaling system that uses OMA-BCAST SG in combination with the signaling information embedded in the ATSC-MH transport streams.

Rich media and convergence are two terms that represent the recent trends in the mobile broadcast services domain. In particular, rich media enables a wide variety of interactive, information-rich services. The existing standards for mobile broadcast services, however, have focused primarily on service delivery and consumption, and, therefore, significant efforts are required to integrate rich media capability, which relates to service presentation, into mobile broadcast services. In this paper, we present a solution for mobile handsets in support of rich media enabled broadcast services as well as hybrid services delivered over converged networks.

Convergence between broadcasting services and telecommunication services and provisioning of rich media services are two major requirements that recent mobile broadcast TV services try to satisfy. Although there exist independent technologies and international standards for each of the requirements, few efforts have been paid for providing solutions supporting these requirements together. In this paper, we present a standard-based implementation for mobile handsets in support of rich media enabled mobile broadcast TV services.

In wearable healthcare systems (WHCSs), sensor devices, which can be deployed around parts of the human body and have limited lifetimes, control its transmission power level (TPL) based on transmission power control (TPC) algorithms for saving the energy consumption. A transmission and a reception part of WHCSs bring more energy consumption than other modules such as a sensing and a logging. Therefore, if sensor devices can reduce a number of transmission and reception packets, they can increase their lifetimes as much as reduced packets. Because many times sensed data packets are negligible, in this case sensor devices cannot send data packets. In this paper, we first analyze the characteristics of the human body conditions using the real-collected body condition data set to show the existence of the negligible period during communication times. Then, we implement the TPC algorithm to compare the performance of with or without the consideration of the human body conditions.

The increase in services that utilize ultra-high-speed networks have caused an increase in network traffic problems. Existing networks require new technologies because the management of these networks is difficult and the traffic problems cannot be easily solved. These challenges have prompted the development of software-defined networks (SDNs). SDNs divide existing network structures into data planes, which manage data transmission, and control planes, which manage network controls. These planes enable easy, flexible network management or expansion through centralized structures. Specifically, SDNs efficiently control networks by monitoring network traffic or utilizing the state information of the connected switches. However, due to the characteristics of SDNs that centralize network management, specifically switch performance, controller overhead may increase, which may lead to problems throughout the entire network. This paper discusses these problems and proposes an algorithm that promotes efficient network management when the flows are replaced at the flow tables.

As users demand increasingly for high-quality, high-resolution digital video content such as Ultra High-Definition (UHD), the complexity of video coding technologies supporting such requirements also increases. Recently, for performance and cost reasons, distributed video coding via cloud computing has gained increasing attention. In this paper, we explore different performance aspects of distributed HEVC encoding employing two basic video partitioning methods.

This paper presents the design of a new system architecture, Virtual Service Grid (VSG), for delivering high-performance network services. The VSG is based on the concept of the virtual service which provides location, replication, and fault transparency to clients accessing remotely deployed high-end services. One of the novel features of the virtual service is the ability to self-scale in response to client demand. The VSG exploits network and service information to make adaptive dynamic replica selection, creation, and deletion decisions. We describe the VSG architecture, middleware, and replica management policies. We have deployed the VSG on a wide-area Internet testbed to evaluate its performance. The results indicate that the VSG can deliver efficient performance for a wide range of client workloads, both in terms of reduced response time and in the utilization of system resources. Copyright © 2002 John Wiley & Sons, Ltd.

As smart device hardware and software technologies have advanced, performance of devices has improved and their types and categories have diversified. In addition, as smart devices have become popular, the number of multi-devices held by a single person has increased. Following this trend, data production and management through multi-devices of a single user have increased, and various data sharing services have emerged to meet the need for data sharing across multi-devices. Much attention has been given to data sharing services utilizing a cloud computing environment. Cloud computing is a computing technology to provide IT resources (e.g., software, storage, and server) with internet technology. Data sharing services are provided by virtualizing unlimited storage space and computing resources utilizable via the cloud computing environment. Users have access to unlimited storage space for their devices and can share the same data regardless of which device is used. There are a number of pay or free cloud storage services available, but cloud service providers have their own proprietary interfaces so that users may have to install a specific application program for the use of a cloud or data management service in order to access multiple cloud storage services. In the present paper we define and propose the Meta-Cloud, which is an integrated platform for the use of multiple cloud storage services.

Recently, an N-screen service has been drawing attention because of the widespread use of various smart devices. An N-screen service is a service that can use the same media content irrespective of the type of connected device. Therefore, to provide an optimal N-screen service, each connected device needs to be provided with optimized media contents. As the types and performances of smart devices have been constantly changing, various coding techniques have been developed to adapt to these changes. However, it is difficult for contents providers to flexibly cope with the changes in the contents-providing environment. Thus, in this paper, we propose a framework that makes it possible to flexibly create media contents by using a cloud-computing environment. In particular, one of the main characteristics of the proposed framework is to define the encoding process of media contents as a service concept and to provide a low-cost high-efficiency media contents encoding environment based on SaaS (Software as a Service), a service model of cloud computing.

Mobile cloud computing is one of the primary research areas that aims to overcome the problem of limited mobile device energy. For this purpose, computational tasks of mobile applications that consume a high amount of energy are offloaded to the cloud. In order to perform effective computation offloading, the energy consumed by application programs in mobile devices and the cloud needs to be estimated first. Previous studies have proven that energy consumption estimators showed optimal performance with targeted mobile application programs. However, optimal performance has yet to be achieved with other mobile application programs. Thus, this paper proposes a dynamic energy consumption estimator in which a mobile application program selects an appropriate energy consumption estimator for its own benefit. In addition, performance and effectiveness of the proposed dynamic energy consumption estimator are validated through experiments.

In wireless body sensor network systems (WB-SNSs), the sensor nodes have very limited battery power because they are tiny, lightweight, and wearable or implantable. As a result, WB-SNSs require a very efficient transmission power control (TPC) algorithm for effectively reducing energy consumption and extending the lifetime of sensor nodes. To achieve this goal, we propose a novel TPC algorithm referred to as hybrid TPC. The hybrid TPC algorithm adaptively selects a conservative or an aggressive control mechanism depending on current channel conditions. The conservative control mechanism, which slowly changes transmission power level (TPL), is suitable in a dynamic environment. On the other hand, the aggressive control mechanism, which rapidly changes TPL, is ideal in a static environment. In order to evaluate the effectiveness of the hybrid TPC algorithm, we implemented various TPC algorithms and compared their performances against the hybrid TPC algorithm in different channel environments. The experimental results showed that the hybrid TPC algorithm outperformed other TPC algorithms in all channel environments.

A tunable vibratory microgyroscope has been fabricated by surface-micromachining technology. The 7.5 μm thick polysilicon structural layer is deposited using LPCVD at 625 °C. The resonance frequency in the detection mode is designed to be higher than that in the driving mode so that the gyroscope could be matched to the resonance frequencies by applying an inter-plate d.c. bias. The gyroscope is driven in a resonant state by electrostatic force and the output detected is due to a capacitance change between the plate and the polysilicon electrode. The resonance frequencies of the gyroscope in the driving and detection directions are measured to be 10.48 and 10.93 kHz, respectively. The resonance frequency in the detection direction can be shifted to 10.52 kHz by applying a tuning voltage of 4.0 V. The quality factor and resonance frequency of the detection mode are significantly affected by ambient pressure. The electrical noise equivalent rate of this device is about 0.1 ° s −1 at 10 −2 torr.

For mobile cloud computing, one of the key issues is to minimize energy consumption in data communication. Although many studies have examined energy consumption of data transmissions, they are limited in that they have focused mainly on bitstream transmissions over existing 3G networks or Wi-Fi environments. Thus, the present paper explores energy efficiency of mobile devices when transferring data securely over various communication networks including high-speed 4G networks such as LTE and Wibro.

In the cloud computing environment, data replication strategy (DRS) is used to improve data access. Related studies have proposed data replication strategies. The performances of these strategies are closely related to the users' access patterns, and work optimally for a particular data access pattern. However, as the data access patterns become more flexible and unpredictable, it is difficult to manage them with traditional replication strategies. Given this circumstance, this paper proposes an algorithm that detects changes in a user's data access pattern and dynamically applies an optimal replication strategy. The proposed algorithm has the advantage of maintaining an optimal performance by responding to various data access patterns. We tested the proposed algorithm and validated its effectiveness.

The term social network services (SNSs) refers to the formation of relationships among users through interaction via the Internet and the delivery of services through such social networks. SNSs have become globalized thanks to the development of Internet communications networks. As a result, users interact with friends in different places in the world and form relationships with them. However, such globalized SNSs have an inherent problem: The quality of services provided to users distant from the server is degraded due to the existence of a concentrated data server. Regional distribution and management of servers aimed at resolving this problem increases costs but improves performance. However, in this case, it is necessary to determine where and how to store the data. Accordingly, this paper proposes a method to enhance service performance for users located in different places when regionally distributed servers are used for SNS provision. To this end, this paper will present the major factors of SNSs and propose a method to store and duplicate regional data using friend relations, users' locations of contact, and the number of contacts.

Limited resources of battery-operated mobile devices are a major obstacle for mobile applications. An obvious solution to this limitation is to leverage cloud computing, which offers virtually infinite resources on demand through the virtualization of physically distributed computing resources. A mobile device could offload a resource-intensive application to the cloud and support thin client interaction with the application over the Internet. As such, cloud computing enhances the computing capability of mobile devices, as well as saving energy of mobile devices. In this paper, therefore, we propose a framework supporting the seamless execution of mobile applications on the cloud. In particular, the novel aspect of our approach is that a mobile cloud application, itself, is treated as data, so it can be replicated within the cloud, thus being able to reduce both latency and energy consumption of the communication. This paper is a work-in-progress report of our research.

Advancements in sensor technology have enabled the development of extremely small, low-power, low-cost sensing devices. Recently, wireless sensor networks (WSNs) with thousands of those tiny sensors have become increasingly common. The existing and potential WSN applications span a wide spectrum across various domains. In terms of data reporting schemes, WSNs can be categorized as time-driven and event-driven networks, both with their own pros and cons. In this paper, we propose a novel technique to combine these two data reporting schemes in such a way that the network identifies sensor nodes that will potentially detect an event in the near future and enables them to be proactively involved in the data reporting process. When selecting such nodes, our approach takes into consideration node contexts such as locality and past history of the sensed attributes. In so doing, both energy efficiency and proactive analysis of the environment, which is a major limitation of the event-driven data reporting scheme, can be achieved. To verify the effectiveness of our approach, we conducted several experiments with various synthetic scenarios, the results show that our approach is able to provide sufficient information for proactive actions from users with moderate consumption of energy.

In terms of data delivery required by the application, WSN can be categorized as continuous or event-driven. The former type provides an accurate snapshot of relevant attributes with high energy consumption, whereas the latter type is more energy efficient and yet less accurate because only sudden and drastic changes in the value of a sensed attribute trigger data delivery. In this paper, we propose a hybrid data delivery protocol that adaptively switches between these two data dissemination schemes. That is, during the period when the condition referring to an event is met, sensor nodes continuously send data to an observer. However, when the condition is no longer valid, the protocol behaves as event-driven, thus eliminating unnecessary data transmission. As such, the proposed protocol is able to enable an accurate analysis of the environment being monitored with moderate consumption of valuable resources.

In general, data communication among sensor nodes requires more energy than internal processing or sensing activities. In this paper, we propose a noble technique to reduce the number of packet transmissions necessary for query dissemination or query results relaying processes among neighboring nodes with the help of context-aware routing tables. The important information maintained in the context-aware routing table is which physical properties can be measured by descendent nodes reachable from the current node. Based on the information, the node is able to eliminate unnecessary packet transmission by filtering out the child nodes for query dissemination or query results relaying. The simulation results show that up to 80% of performance gains can be achieved with our technique.

Recently, cloud computing has emerged as a potential platform for distributed video encoding due to its advantages in terms of costs as well as performance. For distributed video encoding, the input video must be partitioned into several segments, each of which is processed over distributed resources. This paper describes the effect of different video partitioning schemes on overall encoding performance in the distributed encoding of High-Efficiency Video Coding (HEVC). In addition, we explored performances of video partitioning schemes on the basis of the types of the content to be encoded