• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.323-328
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• 2008

This paper presents a Digital Signal Processor achieving high through-put for both decision intensive and computation intensive tasks. The proposed processor employees a multiplier, two ALU and load/store. Unit as operational units. Those four units are controlled and works parallel by superscalar control scheme, which is different from prior DSP architecture. The performance evaluation was done by implementing AC-3 decoding algorithm and 37.8% improvement was achieved. This study is valuable especially for the consumer electronics applications, which require very low cost.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.181-188
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• 2008

E-Science Grid is designed to cope with computation-intensive tasks and to manage a huge volume of science data efficiently. However, certain tasks may involve more than one grid can offer in computation capability or incur a long wait time on other tasks. Resource sharing among Grids can solve this problem with proper processing-integrity check via audit. Due to their computing-intensive nature, the processing time of e-Science tasks tends to be long. This potential long wait before an audit failure encourages earlier audit mechanism during execution in order both to prevent resource waste and to detect any problem fast. In this paper, we propose a Light-weight, Adaptive and Reliable Audit, LARA, of processing Integrity for e-Science applications. With the LARA scheme. researchers can verify their processing earlier and fast.

• Journal of Information Technology Services
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• v.3 no.2
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• pp.99-104
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• 2004

Conventional and public-key cryptography has been widely accepted as a base technology for the design of computer security systems. D-classes have the potential for application to conventional and public-key cryptography. However, there are very few results on D-classes because the computational complexity of D-class computation is NP-complete. This paper discusses the design of algorithms for the efficient computation of D-classes and the Java implementation of them. In addition, the paper implements the same D-class computation algorithms in C and shows the performance of C and Java programming languages for the computation-intensive applications by comparing their execution results.

• Journal of Broadcast Engineering
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• v.12 no.4
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• pp.350-359
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• 2007

In this paper we present a simple, efficient method for detection of sharp digital images. Recently many digital cameras are equipped with various autofocusing functions to help users take well-focused pictures as easily as possible. However, acquired digital pictures can be further degraded by motion, limited contrast, and inappropriate amount of exposure, to name a few. In order to decide whether to process the image or not, or whether to delete it or not, reliable measure of image quality to detect sharp images from blurry ones is needed. This paper presents a blurriness/sharpness measure, and demonstrates its feasibility using extensive experiments. This method is fast and easy to implement, and accurate. Regardless of the detection accuracy, existing measures are computation-intensive. However, the proposed measure in this paper is not demanding in computation time. Needless to say, this measure can be used for various imaging applications including autofocusing and astigmatism correction.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.139-155
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• 2000

An exclusive simulation study is conducted in computing means for order statistics in standard normal variate. Monte Carlo moments are used in Shapiro-Francia W' statistic computation. Finally, quantiles for Shapiro-Francia W' are generated. The study shows that in computing means for order statistics in standard normal variate, complicated distributions and intensive numerical integrations can be avoided by using Monte Carlo simulation. Lack of accuracy is minimal and computation simplicity is noteworthy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3254-3272
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• 2017

With the development of wireless access technologies and the popularity of mobile intelligent terminals, cloud computing is expected to expand to mobile environments. Attribute-based encryption, widely applied in cloud computing, incurs massive computational cost during the encryption and decryption phases. The computational cost grows with the complexity of the access policy. This disadvantage becomes more serious for mobile devices because they have limited resources. To address this problem, we present an efficient verifiable outsourced scheme based on the bilinear group of prime order. The scheme is called the verifiable outsourced computation ciphertext-policy attribute-based encryption scheme (VOC-CP-ABE), and it provides a way to outsource intensive computing tasks during encryption and decryption phases to CSP without revealing the private information and leaves only marginal computation to the user. At the same time, the outsourced computation can be verified by two hash functions. Then, the formal security proofs of its (selective) CPA security and verifiability are provided. Finally, we discuss the performance of the proposed scheme with comparisons to several related works.

• Smart Media Journal
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• v.12 no.3
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• pp.68-76
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• 2023

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2471-2474
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• 2003

The modified discrete cosine transform (MDCT) and its inverse transform (IMDCT) are employed in subband/transform coding schemes as the analysis/synthesis filter bank based on time domain aliasing cancellation (TDAC). And they are the most computational intensive operations in layer III of the MPEG audio coding standard. In this paper, we propose a new efficient algorithm for the MDCT/IMDCT computation. It is based on the MDCT/IMDCT computation algorithm using the discrete cosine transforms (DCTs), and it employs two discrete cosine transform of type II(DCT-II) to compute the MDCT/IMDCT. In addition to, it takes advantage of ability in calculating the MDCT/IMDCT computation, where the length of a data block is divisible by 4. The proposed algorithm in this paper requires less calculation complexity than the existing methods. Also, it can be implemented by the parallel structure,, and its structure is particularly suitable for VLSI realization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5465-5480
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• 2018

Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments' results show the cost of ICAS is much lower than the existing method.