• 한국정보통신학회논문지
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• 제12권12호
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• pp.2323-2329
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• 2008

저가형 이미지 센서 기술의 발전과 무선 센서 네트워크 기술의 발전으로 인해 WMSN(Wireless Multimedia Sensor Networks) 기술이 활발히 연구되고 있다. WMSN은 기존의 무선 센서 네트워크 기술에 멀티미디어 컨텐츠를 센싱하고 전송 및 처리하는 기반기술을 포함한다. 멀티미디어 컨텐츠는 많은 데이터량을 가지므로 이를 처리하기 위해서는 많은 컴퓨팅 자원과 높은 네트워크 대역폭을 필요로 한다. 저사양의 센서 노드에서 멀티미디어 컨텐츠를 수용하기 위해서는 컴퓨팅 자원을 고려한 압축 기법 및 효율적인 전송에 대한 연구가 필요하다. 본 논문에서는 무선 센서 네트워크에서 이미지를 효율적으로 압축하고 전송하기 위한 압축기법인 YWCE기법을 제안한다. YWCE는 웨이블릿의 Resolution Scalability 특성을 이용한 4가지 움직임 보상/예측 기법을 도입한다. 실험을 통하여 4가지 각 압축 기법들의 조합에 따라 매우 높은 성능을 나타냄을 보였다.

• 한국멀티미디어학회논문지
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• 제18권9호
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• pp.1083-1090
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• 2015

A generalized recursive circulant network(GR) is widely used in the design and implementation of local area networks and parallel processing architectures. In this paper, we investigate the routing of a message on this network, that is a key to the performance of this network. We would like to transmit maximum number of packets from a source node to a destination node simultaneously along paths on this network, where the i^th packet traverses along the i^th path. In order for all packets to arrive at the destination node securely, the i^th path must be node-disjoint from all other paths. For construction of these paths, employing the Hamiltonian Circuit Latin Square(HCLS), a special class of (n x n) matrices, we present O(n²) parallel routing algorithm on generalized recursive circulant networks.

• 한국멀티미디어학회논문지
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• 제13권6호
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1466-1480
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• 2019

This paper addresses the efficient processing of all-farthest-neighbors (AFN) queries in spatial network databases. Given a set of data points P={p₁,p₂,…,p_|p|} in a spatial network, where the distance between two data points p and s, denoted by dist (p,s), is the length of the shortest path between them, an AFN query is defined as follows: find the farthest neighbor ω(p)∈P of each data point p such that dist(p,ω(p)) ≥ dist(p,s) for all s∈P. In this paper, we propose a shared execution algorithm called FAST (for All-Farthest-neighbors Search in spatial neTworks). Extensive experiments on real-world roadmaps confirm the efficiency and scalability of the FAST algorithm, while demonstrating a speedup of up to two orders of magnitude over a conventional solution.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• Journal of Communications and Networks
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• 제18권5호
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• pp.826-836
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• 2016

Increase of hypertext transfer protocol (HTTP)-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated, among others, by systems which employ adaptive bit-rate streaming. In our work, we investigate traffic originating from 120 client-server pairs, situated in an emulated laboratory environment, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative correlations, anti-persistence, and its distribution function is skewed to the right. Furthermore, we show that the traffic generated by clients employing the same or similar play-out strategies is positively correlated and synchronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations.

• 한국멀티미디어학회논문지
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• 제16권11호
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• pp.1338-1347
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• 2013

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

• Journal of Multimedia Information System
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• 제3권4호
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• pp.129-134
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• 2016

A routing protocol in mobile ad hoc networks is important, and a location based routing has attracted attention. We have proposed a method, in which plural mobile agents (MA) manage location information and construct a route and the number of MA dynamically changes depending on the network situation. However, the area where each MA manages is not always appropriate. This would increase the network load due to unnecessary split and mergence of MAs. To solve this problem, in this paper, we propose an enhanced method. In this method, the MA management area is properly determined based on distribution of nodes. From the performance evaluation, we show that the proposed method outperforms conventional methods in terms of packet delivery rate when network load is high.

• Journal of Multimedia Information System
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• 제3권4호
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2003년도 춘계종합학술대회논문집
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• pp.361-367
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• 2003

Ad-hoc 네트워크는 유선 인프라의 구성이나 도움 없이 이동 단말기들로만 손쉽게 통신망을 구성하여 긴급구조나 전쟁터 등에서 무선 데이터 서비스를 제공할 수 있는 무선 네트워크를 말한다. 최근에는 Ad-hoc 릴레이 시스템과 같은 확장된 개념의 Ad-hoc 망으로 범위가 넓혀지고 있어 품질에 대한 요구 수준이 증대되고 있다. 본 논문에서는 이상과 같은 추세에 맞춰 보다 안정적이고, 품질이 보장될 수 있는 분산형 005 모니터링 기법을 제안함으로써 Ad-hoc 네트워크에서 멀티미디어 정보 등 실시간 데이터 전송이 가능한 005 보장형 라우팅 프로토콜 설계가 가능토록 한다.