A blockchain is an underlying technology and basic infrastructure of the Bitcoin system. At present, blockchains and their applications are developing rapidly. However, the basic research of blockchain technology is still in the early stages. The efficiency and reliability of blockchain communication is one of the research problems that urgently need to be studied and addressed. Existing algorithms may be less feasible for blockchain-based communication because they only consider a single communication factor (node communication capability or node trust degree) and only focus on a single communication performance parameter(communication time or communication reliability). In this paper, to shorten the validation time of blockchain transactions and improve the reliability of blockchain-based communication, we first establish a multi-link concurrent communication model based on trust degree, and then we propose a novel integrated factor communication tree algorithm (IFT). This algorithm comprehensively considers the node communication link number and the node trust degree and selects several nodes with powerful communication capacity and high trust as the communication sources to improve the concurrency and communication efficiency. Simulation results indicate that the IFT algorithm outperforms existing algorithms. A blockchain communication routing scheme based on the IFT algorithm can increase communication efficiency by ensuring communication reliability.

This paper studies a multi-antenna wireless relay network in the presence of a jammer. In this network, the source node transmits signals to the destination node through a multi-antenna relay node which adopts the amplify-and-forward scheme, and the jammer attempts to inject additive signals on all antennas of the relay node. With the linear beamforming scheme at the relay node, this network can be modeled as an equivalent Gaussian arbitrarily varying channel (GAVC). Based on this observation, we deduce the mathematical closed-forms of the capacities for two special cases and the suboptimal achievable rate for the general case, respectively. To reduce complexity, we further propose an optimal structure of the beamforming matrix. In addition, we present a second order cone programming (SOCP)-based algorithm to efficiently compute the optimal beamforming matrix so as to maximize the transmission rate between the source and the destination when the perfect channel state information (CSI) is available. Our numerical simulations show significant improvements of our propose scheme over other baseline ones.

Wireless Sensor Networks hold the limelight because of significant potential for distributed sensing of large geographical areas. The radio duty cycling mechanism that turns off the radio periodically is necessary for the energy conservation, but it deteriorates the network congestion when the traffic load is high, which increases the packet loss and the delay too. Although many papers for WSNs have tried to mitigate network congestion, none of them has mentioned the congestion problem caused by the radio duty cycling of MAC protocols. In this paper, we present a simple and efficient congestion control technique that operates on the radio duty cycling MAC protocol. It detects the congestion by checking the current queue size. If it detects the congestion, it extends the network capacity by adding supplementary wakeup times. Simulation results show that our proposed scheme highly reduces the packet loss and the delay.

This paper analyzes the nodes deployment optimization problem in energy constrained wireless sensor networks, which multi-hop cooperative beamforming (CB) based cooperative-multi-input-single-output (CMISO) transmission is adopted to reduce the energy consumption. Firstly, we establish the energy consumption models for multi-hop SISO, multi-hop DSTBC based CMISO, multi-hop CB based CMISO transmissions under random nodes deployment. Then, we minimize the energy consumption by searching the optimal nodes deployment for the three transmissions. Furthermore, numerical results present the optimal nodes deployment parameters for the three transmissions. Energy consumption of the three transmissions are compared under optimal nodes deployment, which shows that CB based CMISO transmission consumes less energy than SISO and DSTBC based CMISO transmissions. Meanwhile, under optimal nodes deployment, the superiorities of CB based CMISO transmission over SISO and DSTBC based CMISO transmissions can be more obvious when path-loss-factor becomes low.

Virtualized small cell network is a promising architecture which can realize efficient utilization of the network resource. However, conventional full duplex self-backhauls lead to residual self-interference, which limits the network performance. To handle this issue, this paper proposes a virtual resource allocation, in which the residual self-interference is fully exploited by employing a physical-layer network coding (PNC) aided self-backhaul scheme. We formulate the features of PNC as time slot and information rate constraints, and based on that, the virtual resource allocation is formulated as a mixed combinatorial optimization problem. To solve the problem efficiently, it is decomposed into two sub problems, and a two-phase iteration algorithm is developed accordingly. In the algorithm, the first sub problem is approximated and transferred into a convex problem by utilizing the upper bound of the PNC rate constraint. On the basis of that, the convexity of the second sub problem is also proved. Simulation results show the advantages of the proposed scheme over conventional solution in both the profits of self-backhauls and utility of the network resource.

In high diversity node situation, single-channel MAC protocols suffer from many collisions. To solve this problem, the research of multichannel MAC protocol has become a hotspot. And the cyclic quorum-based multichannel (CQM) MAC protocol outperformed others owing to its high frequency utilization. In addition, it can avoid the bottleneck that others suffered from and can be easily realized with only one transceiver. To obtain the accurate performance of CQM MAC protocol, a Markov chain model, which combines the channel hopping strategy of CQM protocol and IEEE 802.11 distributed coordination function (DCF), is proposed. The metrics (throughput and average packet transmission delay) are calculated in performance analysis, with respect to node number, packet rate, channel slot length and channel number. The results of numerical analysis show that the optimal performance of CQM protocol can be obtained in saturation bound situation. And then we obtain the saturation bound of CQM system by bird swarm algorithm (BSA). Finally, the Markov chain model and saturation bound are verified by Qualnet platform. And the simulation results show that the analytic and simulation results match very well.

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

IP multimedia subsystem (IMS) is an open standardized architecture for delivering multimedia service over IP network in a route-agnostic manner. With the increasing popularity of conversational class service, the delivery of a traffic flow with a certain bandwidth demand over a single network path is either not possible or not cost-effective. Multipath transmission is considered to be a promising solution to provide high-quality delivery service. This paper proposes a software defined service overlay network (SDSON) based multipath transmission framework for IMS, which is complementary to existing network architecture. The framework transforms original two-party session negotiation into three-party session negotiation that supports participants to negotiate multipath transmission capacity and path information by signaling message. Based on existing IETF standards, SIP and SDP are scalable to support these functions. Finally, the proposed framework is fully implemented on open source platform and examined by experiments. Experimental results show that multipath-enabled IMS is an effective way to improve the delivery performance of conversational class service.

This paper presents a viewport resolution scaling technique to reduce power consumption in mobile graphic processing units (GPUs). This technique controls the rendering resolution of applications in proportion to the resolution factor. In the mobile environment, it is essential to find an effective resolution factor to achieve low power consumption because both the resolution and power consumption of a GPU are in mutual trade-off. This paper presents a resolution factor that can minimize image quality degradation and gain power reduction. For this purpose, software and hardware viewport resolution scaling techniques are applied in the Android environment. Then, the correlation between image quality and power consumption is analyzed according to the resolution factor by conducting a benchmark analysis in the real commercial environment. Experimental results show that the power consumption decreased by 36.96% on average by the hardware viewport resolution scaling technique.

In this paper, a novel multiple description image coding (MDC) scheme is proposed, which is based on the characteristics of the human visual model. Due to the inherent characteristics of human vision, the human eye can only perceive the change of the specific thresholds, that is, the just noticeable difference (JND) thresholds. Therefore, JND model is applied to improve MDC syetem. This paper calculates the DCT coefficients firstly, and then they are compared with the JND thresholds. The data that is less than the JND thresholds can be neglected, which will improve the coding efficiency. Compared with other existing methods, the experimental results of the proposed method are superior.

With the increasing popularity of 3D technology such as 3D printing, 3D modeling, etc., there is a growing need to search for similar models on the internet. Matching non-rigid shapes has become an active research field in computer graphics. In this paper, we present an efficient and effective non-rigid model retrieval method based on topological structure and local volume. The integral geodesic distances are first calculated for each vertex on a mesh to construct the topological structure. Next, each node on the topological structure is assigned a local volume that is calculated using the shape diameter function (SDF). Finally, we utilize the Hungarian algorithm to measure similarity between two non-rigid models. Experimental results on the latest benchmark (SHREC' 15 Non-rigid 3D Shape Retrieval) demonstrate that our method works well compared to the state-of-the-art.

We propose a novel eye tracking method that can estimate 3D world coordinates using an infrared (IR) stereo camera for indoor and outdoor environments. This method first detects dark evidences such as eyes, eyebrows and mouths by fast multi-level thresholding. Among these evidences, eye pair evidences are detected by evidential reasoning and geometrical rules. For robust accuracy, two classifiers based on multiple layer perceptron (MLP) using gradient local binary patterns (GLBPs) verify whether the detected evidences are real eye pairs or not. Finally, the 3D world coordinates of detected eyes are calculated by region-based stereo matching. Compared with other eye detection methods, the proposed method can detect the eyes of people wearing sunglasses due to the use of the IR spectrum. Especially, when people are in dark environments such as driving at nighttime, driving in an indoor carpark, or passing through a tunnel, human eyes can be robustly detected because we use active IR illuminators. In the experimental results, it is shown that the proposed method can detect eye pairs with high performance in real-time under variable illumination conditions. Therefore, the proposed method can contribute to human-computer interactions (HCIs) and intelligent transportation systems (ITSs) applications such as gaze tracking, windshield head-up display and drowsiness detection.

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

Internet of Things (IoT) will transform our daily life by making different aspects of life smart like smart home, smart workplace, smart health and smart city etc. IoT is based on network of physical objects equipped with sensors and actuators that can gather and share data with other objects or humans. Secure communication is required for successful working of IoT. In this paper, a total of 13 lightweight cryptographic algorithms are evaluated based on their implementation results on 8-bit, 16-bit, and 32-bit microcontrollers and their appropriateness is examined for resource-constrained scenarios like IoT. These algorithms are analysed by dissecting them into their logical and structural elements. This paper tries to investigate the relationships between the structural elements of an algorithm and its performance. Association rule mining is used to find association patterns among the constituent elements of the selected ciphers and their performance. Interesting results are found on the type of element used to improve the cipher in terms of code size, RAM requirement and execution time. This paper will serve as a guideline for cryptographic designers to design improved ciphers for resource constrained environments like IoT.

Session initiation protocol (SIP) is a kind of powerful and common protocols applied for the voice over internet protocol. The security and efficiency are two urgent requirements and admired properties of SIP. Recently, Hamed et al. proposed an efficient authentication and key agreement scheme for SIP. However, we demonstrate that Hamed et al.'s scheme is vulnerable to de-synchronization attack and cannot provide anonymity for users. Furthermore, we propose an improved and efficient authentication and key agreement scheme by using elliptic curve cryptosystem. Besides, we prove that the proposed scheme is provably secure by using secure formal proof based on Burrows-Abadi-Needham logic. The comparison with the relevant schemes shows that our proposed scheme has lower computation costs and can provide stronger security.

Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVM algorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.

With the development of high-speed railway technologies, train velocities can now reach speeds up to 350 km/h, and higher in the future. In high-speed railway systems (HSRs), loss of communication can result in serious accidents, especially when the train is controlled through wireless communications. For to this reason, operators of Long Term Evolution for Railway (LTE-R) communication systems install eNodeBs (eNBs) with high density to achieve highly reliable communications. However, densely located eNBs can result in unnecessary frequent handovers (HOs) resulting in instability because, during every HO process, there is a period of time in which the communication link is disconnected. To solve this problem, in this paper, an HO scheme based on the maximum cell residence time (CRT) and adaptive time to trigger (aTTT), which are collectively called CaT, is proposed to reduce unnecessary HOs (using CRT estimations) and decrease HO failures by improving the handover command transmission point (HCTP) in LTE-R HSR communications.

This paper presents the design of a broadcasting scenario and system for an 8K-resolution content. Due to an 8K content is four times larger than the 4K content in terms of size, many technologies such as content acquisition, video coding, and transmission are required to deal with it. Therefore, high-quality video and audio for 8K (ultra-high definition television) service is not possible to be transmitted only using the current terrestrial broadcasting system. The proposed broadcasting system divides the 8K content into four 4K contents by area, and each area is hierarchically encoded by Scalable High-efficiency Video Coding (SHVC) into three layers: L0, L1, and L2. Every part of the 8K video content divided into areas and hierarchy is independently treated. These parts are transmitted over heterogeneous networks such as digital broadcasting and broadband networks after going through several processes of generating signal messages, encapsulation, and packetization based on MPEG media transport. We propose three methods of generating streams at the sending entity to merge the divided streams into the original content at the receiving entity. First, we design the composition information, which defines the presentation structure for displays. Second, a descriptor for content synchronization is included in the signal message. Finally, we define the rules for generating "packet_id" among the packet header fields and design the transmission scheduler to acquire the divided streams quickly. We implement the 8K broadcasting system by adapting the proposed methods and show that the 8K-resolution contents are stably received and serviced with a low delay.

In this paper, an efficient 4K and 8K UHD(Ultra High Definition) transmission scheme is proposed on the convergence networks with broadcasting and LTE(Long Term Evolution) by using CoMP(Coordinated Multi-Point). A video data is compressed and divided into BL(Base Layer), E(Enhanced layer)1, E2 and E3 by scalable HEVC(High Efficiency Video Coding). The divided layers can be combined by the scalable HEVC such as mobile HD, full HD, 4K and 8K UHD(Ultra High Definition). The divided layers are transmitted through the convergence networks with DVB-T2(Digital Video Broadcasting-$2^{nd}$ Generation Terrestrial) broadcasting system and LTE CoMP. This scheme transmits mobile HD and full HD layers through DVB-T2 broadcasting system by using M-PLP(Multiple-physical Layer Pipes), and adaptively transmits 4K or 8K UHD layer through LTE CoMP with MMT(MPEG Media Transport) server. An adaptive transmitting and receiving scheme in the LTE CoMP system provides 4K or 8K UHD layer to a user according to the user status. The proposed scheme is verified by showing the system-level simulation results which is better BER(bit-error-rate) performance than the conventional scheme. The results show that the proposed scheme provides the stable video contents to the user especially at the cell edge.

Creativity is crucial in an ever-changing e-business environment. The creation of new value is essential for companies to gain the competitive edge and to pioneer new markets in e-business. Hence, many companies in e-business operate virtual teams as they are suitable to generate creativity. Even so, virtual teams possess the inherent weakness of a lack of cohesiveness. Hence, for a virtual team to be creative, team members should help each other and share information. This study emphasizes the importance of information sharing that is supposed to improve creativity and explains how to increase them for virtual teams. To explain these relationships, three dimensions are routinely examined in organizational behavior studies: leaders, teams, and members. As a consequence, and through empirical analysis, authentic leadership, sharing team climate, and psychological empowerment enhances information sharing and creativity through their respective roles, in addition to information sharing directly increasing creativity. To improve creativity and information sharing of virtual team members in e-business, this article has highlighted the importance of the three roles stated prior. Such factors can increase information sharing and creativity, and will help virtual teams and organizations to be more successful in e-business.

In this research, we implement a deformable object simulation system using OpenGL's shader language, GLSL4.3. Deformable object simulation is implemented by using volumetric mass-spring system suitable for real-time simulation among the methods of deformable object simulation. The compute shader in GLSL 4.3 which helps to access the GPU resources, is used to parallelize the operations of existing deformable object simulation systems. The proposed system is implemented using a compute shader for parallel processing and it includes a bounding box-based collision detection solution. In general, the collision detection is one of severe computing bottlenecks in simulation of multiple deformable objects. In order to validate an efficiency of the system, we performed the experiments using the 3D volumetric objects. We compared the performance of multiple deformable object simulations between CPU and GPU to analyze the effectiveness of parallel processing using GLSL. Moreover, we measured the computation time of bounding box-based collision detection to show that collision detection can be processed in real-time. The experiments using 3D volumetric models with 10K faces showed the GPU-based parallel simulation improves performance by 98% over the CPU-based simulation, and the overall steps including collision detection and rendering could be processed in real-time frame rate of 218.11 FPS.

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

This study is intended to examine the roles of the social capital and absorptive capacity in technology-intensive firms and verify these roles in an empirical way for the purpose of improving the technology commercialization performance in technology-intensive firms. To achieve the purpose, this study examined the concept and dimensions of social capital through a literature review, empirically verified the effect relationship between the social capital, and absorptive capacity, and technology commercialization performance in technology-intensive firms. This study is meaningful in that it has determined the importance in the formation of social capital and the enhancement of absorptive capacity and suggested strategic directions to improve technology commercialization performance.

The main purpose of this research is to investigate relationships between the significant control factors on acceptance intention to User Experience (UX) sports smart wearable devices by applying Technology Readiness (TR) and Unified Theory of Technology (UTAUT). Research survey targeted on users of golf smart devices in Seoul. A total 534 questionnaires were collected and used for testing hypotheses. Methods to analyze the data included frequency analysis, reliability analysis, confirmatory factor analysis, correlation analysis, and structural equation modeling in accordance with the purpose of the study by using SPSS and AMOS. The results are as follows; First, positive TR had a significantly positive effect on social influence, effort expectancy, facilitating conditions, perceived enjoyment, performance expectancy. Second, negative TR had a significant negative effect on performance expectancy, social influence, facilitating conditions, perceived enjoyment. Third, TR had a no significantly effect on behavioral intention. Fourth, performance expectancy, perceived enjoyment and facilitating conditions had a significantly positive effect on behavioral intention. Fifth, behavioral intention had a significantly positive effect on use behavior. Thus it became crucial to identify the difference in acceptance intention models per each products are as follows. Positive TR of golf-related mobile application users has a positive effect on both technology acceptance belief and acceptance intention, whereas negative TR has no statistically significant effect on technology acceptance belief nor acceptance intention.