• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.80-84
• /
• 2010

Data multicasting from a source to multiple sinks in wireless sensor networks is used to achieve both reducing communication costs and energy efficiency. Almost all existing schemes for the multicasting might be effectively performed by optimal construction of a multicasting tree between a source and multiple stationary sinks. However, in practical sensor applications, sinks could move around on the wireless sensor networks for own missions, such as scouting of soldiers and saving lives of victims by firefighters. Unfortunately, the sink mobility causes frequent entire reconstruction of the multicasting tree and thus it leads to exhaustion of battery power of sensors. Hence, we propose an energy-efficient multicast protocol to support multiple mobile sinks by the local multicast tree reconstruction, called Local Update-based geographic Multicasting for Mobile sinks (LUMM) for wireless sensor networks. Our simulation results show that our scheme for mobile sinks is more efficient in terms of energy resource management than other exist works.

• Journal of Digital Contents Society
• /
• v.9 no.4
• /
• pp.607-616
• /
• 2008

By the advancement of computer & network technology, the paridigm of 'Network Computer' has been realizing`. In what is called network computer, computer system and computing resource is incomparably seem to be expanded compared with conventional network technology[1]. Network connected computer system consitute a massive virtual computer, it is possible for people to use an enourmous amout of computing resource distributed widely through the network. It is also possible that we make client lightweight by the use of computer system & all shared computing resources on the network in our computer processing and we call this type of client system as thin-client. Thin-client and network computer are on and the same network paradigm in that both paradigm featuring the active use of computer system and resource on the network[2]. In network computer paragem, network itself is regarded as a basic platform for the transfer of application, so it is possible that client access remote serve system to run remote applications through the network[3]. In this paper, we propose the system architecture for the implementation of network computer by the use of Web browser, X window system and Pyjamas. By the use of network computer proposed in this paper, it is possible for people to run application on the server system as if he run local application, and it is expected to improve the security and maintenance efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.5
• /
• pp.1009-1019
• /
• 2017

Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.

• IEIE Transactions on Smart Processing and Computing
• /
• v.1 no.3
• /
• pp.143-151
• /
• 2012

A new data-driven method for the design of a blind modulation frequency filter that suppresses the slow-varying noise components is proposed. The proposed method is based on the temporal local decorrelation of the feature vector sequence, and is done on an utterance-by-utterance basis. Although the conventional modulation frequency filtering approaches the same form regardless of the task and environment conditions, the proposed method can provide an adaptive modulation frequency filter that outperforms conventional methods for each utterance. In addition, the method ultimately performs channel normalization in a feature domain with applications to log-spectral parameters. The performance was evaluated by speaker-independent isolated-word recognition experiments under additive noise environments. The proposed method achieved outstanding improvement for speech recognition in environments with significant noise and was also effective in a range of feature representations.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.5
• /
• pp.313-318
• /
• 2014

Recently, Ethernet-based Diagnostic Over Internet Protocol (DoIP) was applied to automotive systems, and in-vehicle gateways have been introduced to integrate Ethernet with traditional in-vehicle networks, such as the local interconnect network (LIN), controller area network (CAN) and FlexRay. The introduction of in-vehicle gateways and of Ethernet based diagnostic protocols not only decreases the complexity of the networks, but also reduces the update time for ECU software reprogramming while enabling the use of a range of services, including remote diagnostics. In this paper, a diagnostic gateway was implement for an automotive system, and the performance measurements are presented. In addition, a range of applications provided by the diagnostic gateway are proposed.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1995.04a
• /
• pp.187-194
• /
• 1995

Traditional genetic algorithms(GA) have mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its targe computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of read code GA are developed to use continuous design variables directly. The results of real code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As results of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the rent code GA developed here can be used for the general optimization problem.

• Proceedings of the IEEK Conference
• /
• 2001.06d
• /
• pp.265-268
• /
• 2001

This paper proposes the strategy of noise injection into inputs in the Kohonen learning algorithm (KKA) to improve the local convergence problem of the KLA. Noise strengths are high in the begin of the learning and gradually lowered as the teaming proceeds. This strategy is a kind of stochastic relaxation (SR) method which is broadly used in the general optimization problems. It is convenient to implement and improves the convergence properties of the KLA with moderately increased computing time compared to the KLA. Experimental results for Gauss-Markov sources and real speech demonstrate that the proposed method can consistently provide better codebooks than the KLA.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.3
• /
• pp.153-163
• /
• 2016

Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

• Journal of Broadcast Engineering
• /
• v.7 no.2
• /
• pp.136-144
• /
• 2002

In this paper, a novel approach that makes use of both shape and color information to retrieve character images in terms of similarity distance from a large-capacity image database or from a streaming image database, in particular, character image logo or trademark. In order to combine both features of completely different characteristics bipartite matching has been employed in computing similarity distance, The proposed method turned out to bealso very effective in matching natural object or human-drawn images whose shape varies substantially.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.5
• /
• pp.366-370
• /
• 2015

This paper proposes a method for improving a branch predictor for the extendable instruction set computer (EISC) processor. The original EISC branch predictor has several shortcomings: a small branch target buffer, absence of a global history, a one-bit local branch history, and unsupported prediction of branches following LERI, which is a special instruction to extend an immediate value. We adopt a G-share branch predictor and eliminate the existing shortcomings. We verified the new branch predictor on a field-programmable gate array with the Dhrystone benchmark. The newly proposed EISC branch predictor also accomplishes higher branch prediction accuracy than a conventional branch predictor.