• Journal of Communications and Networks
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• v.12 no.4
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• pp.334-345
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• 2010

In this paper, we study optimal tradeoffs of achievable throughput versus consumed power in wireless ad-hoc networks formed by a collection of multiple antenna nodes. Relying on adaptive modulation and/or dynamic channel coding rate allocation techniques for multiple antenna systems, we examine the maximization of throughput under power constraints as well as the minimization of transmission power under throughput constraints. In our examination, we also consider the impacts of enforcing quality of service requirements expressed in the form of channel coding block loss constraints. In order to properly model temporally correlated loss observed in fading wireless channels, we propose the use of finite-state Markov chains. Details of fading statistics of signal-to-interference-noise ratio, an important indicator of transmission quality, are presented. Further, we objectively inspect complexity versus accuracy tradeoff of solving our proposed optimization problems at a global as oppose to a local topology level. Our numerical simulations profile and compare the performance of a variety of scenarios for a number of sample network topologies.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.130-139
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• 2010

Environmental issues and the need to reduce energy consumption for lowering operating costs have pushed power efficiency to become one of the major issues of current research in the field of wireless networks. This paper addresses a number of multiple input multiple output (MIMO) precoding and scheduling techniques across the PHY and MAC layers that can operate under a reduced link budget and collectively improve the transmit power efficiency of a base station, while maintaining the same levels of service. Different MIMO transmission and precoding schemes proposed for LTE, achieving varying degrees of multiuser diversity in both the time, frequency as well as the space domain, are examined. Several fairness-aware resource allocation algorithms are applied to the considered MIMO schemes and a detailed analysis of the tradeoffs between power efficiency and quality of service is presented. This paper explicitly examines the performance of a system serving real-time, VoIP traffic under different traffic loading conditions and transmit power levels. It is demonstrated that by use of efficient scheduling and resource allocation techniques significant savings in terms of consumed energy can be achieved, without compromising QoS.

• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.48-54
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• 2008

This paper analyzes cost tradeoffs between memory usage and computation for window-based operators in data stream environments. It identifies generic operator network constructs, and sets up a cost model for the estimation of the expected memory reduction and the computation overheads when window memory relocations are applied to each operator network construct. This cost model helps to identify the utility of window memory relocations. It also helps to apply window memory relocation to improve a query execution plan to save memory usage. The proposed approach contributes to expand the scope of query processing and optimization in data stream environments. It also provides a basis to develop a cost estimation model for the query optimization using window memory relocations.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.338-357
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• 2016

The recent advent of increasingly affordable and powerful 3D scanning devices capable of capturing high resolution range data about real-world objects and environments has fueled research into effective 3D surface reconstruction techniques for rendering the raw point cloud data produced by many of these devices into a form that would make it usable in a variety of application domains. This paper, therefore, provides an overview of the existing literature on surface reconstruction from 3D point clouds. It explains some of the basic surface reconstruction concepts, describes the various factors used to evaluate surface reconstruction methods, highlights some commonly encountered issues in dealing with the raw 3D point cloud data and delineates the tradeoffs between data resolution/accuracy and processing speed. It also categorizes the various techniques for this task and briefly analyzes their empirical evaluation results demarcating their advantages and disadvantages. The paper concludes with a cross-comparison of methods which have been evaluated on the same benchmark data sets along with a discussion of the overall trends reported in the literature. The objective is to provide an overview of the state of the art on surface reconstruction from point cloud data in order to facilitate and inspire further research in this area.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.12-25
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• 2012

For real-time systems it is important to obtain the accurate worst-case execution time (WCET). Furthermore, how to improve the WCET of applications that run on multicore processors is both significant and challenging as the WCET can be largely affected by the possible inter-core interferences in shared resources such as the shared L2 cache. In order to solve this problem, we propose an innovative approach that adopts a code positioning method to reduce the inter-core L2 cache interferences between the different real-time threads that adaptively run in a multi-core processor by using different strategies. The worst-case-oriented strategy is designed to decrease the worst-case WCET among these threads to as low as possible. The other two strategies aim at reducing the WCET of each thread to almost equal percentage or amount. Our experiments indicate that the proposed multicore-aware code positioning approaches, not only improve the worst-case performance of the real-time threads but also make good tradeoffs between efficiency and fairness for threads that run on multicore platforms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3090-3102
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• 2015

The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

• Convergence Security Journal
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• v.20 no.3
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• pp.71-78
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• 2020

Bitcoin-based blockchain technology provides an infrastructure that enables anonymous smart contracts, low-cost remittances, and online payments. However, the block-chain technology that implements the bitcoin has scalability constraints in tradeoffs between throughput and latency. To solve these problems, the Byzantine fault tolerant block-chain technique has been proposed. This technique improves throughput without increasing latency by selecting a leader and constructing many microblocks that do not contain proofs of work within the existing block by the leader. However, this technique may be less secure than existing techniques in selecting the reader.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2173-2183
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• 1998

We consider a frequency-hopped multiple-access communication system that employs reed-solomon code over GF(Q) and M-ary FSK signaling ($M{\leq}Q$) in rayleigh fading channel. We investigate the tradeoff among the modulation symbol size (M), the number of frequency slots, and the code rate in maximizing the average number of successfully transmitted information bits per unit time and unit bandwidth (called normalized throughput). We find that it is desirabel to use a large M in noise-limited environment. In interference-limited environment, it is more improtant to prevent errors (hits) by increasing the number of frequency slots than to correct them with formward error correction techniques or to reduce the error rate by increasing M.

• Informatization Policy
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• v.27 no.3
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• pp.3-18
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• 2020

In the growing digitalized world, the European Union implemented the General Data Protection Regulation(GDPR) to establish a comprehensive data protection framework across member states. Given the constitutional roots of GDPR, the EU's regulatory approach is different than other data protection regimes. The new regulation has strengthened individual rights to data protection, but it also introduced several obligations for businesses that collect and process personal data. We review the existing literature on privacy, particularly GDPR, from a policy perspective. The evidence outlines data regulation's effects on competition, innovation, marketing activities, and cross-border data flows. The discussion highlights the tradeoffs between increased regulation of data protection and its effects on the market.