• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.9-12
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• 2001

In this paper, the natural space-time coding is applied for a DS-CDMA system with multiple transmit/receive antennas in a Rayleigh fading channel. With difference of arrival times from transmit antennas a modified maximum likelihood (ML) decoding algorithm is proposed for the space-time coded DS-CDMA system. The proposed decoding algorithm performs ML decoding over the transition of two consecutive branches by using a modified branch metric with the partial autocorrelation. By simulation, it is shown that the proposed decoding algorithm achieves significant performance improvement over the ML decoding algorithm without the modified branch metric.

• Journal of information and communication convergence engineering
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• v.22 no.3
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• pp.207-214
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• 2024

In wireless sensor networks, the implementation of routing protocols is crucial owing to their limited computational capacities. Tree routing is a suitable method in wireless sensors owing to its minimal routing overhead. However, single-hop metric schemes, such as hop count, cause congestion at specific nodes, whereas multiple metric schemes cannot control dynamically changing network environments. To address these issues, we propose a scheme to implement enhanced tree routing with adaptive metrics based on hop count. This approach assigns different weights to metrics to select suitable parent nodes based on hop count. The parent-selection algorithm utilizes hop count, buffer occupancy, and received signal strength indicator (RSSI) as metrics. This study evaluates the performance through simulation scenarios to analyze whether improvements can be made to address problems encountered in traditional tree routing. The performance metrics include packet delivery speed, throughput, and end-to-end delay, which vary depending on the volume of network traffic.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.595-602
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• 2022

This study tried to suggest the most efficient learning rate for accurate and efficient automatic diagnosis of medical images for chest X-ray pneumonia images using deep learning. After setting the learning rates to 0.1, 0.01, 0.001, and 0.0001 in the Inception V3 deep learning model, respectively, deep learning modeling was performed three times. And the average accuracy and loss function value of verification modeling, and the metric of test modeling were set as performance evaluation indicators, and the performance was compared and evaluated with the average value of three times of the results obtained as a result of performing deep learning modeling. As a result of performance evaluation for deep learning verification modeling performance evaluation and test modeling metric, modeling with a learning rate of 0.001 showed the highest accuracy and excellent performance. For this reason, in this paper, it is recommended to apply a learning rate of 0.001 when classifying the presence or absence of pneumonia on chest X-ray images using a deep learning model. In addition, it was judged that when deep learning modeling through the application of the learning rate presented in this paper could play an auxiliary role in the classification of the presence or absence of pneumonia on chest X-ray images. In the future, if the study of classification for diagnosis and classification of pneumonia using deep learning continues, the contents of this thesis research can be used as basic data, and furthermore, it is expected that it will be helpful in selecting an efficient learning rate in classifying medical images using artificial intelligence.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.201-208
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• 2010

Mobile Ad Hoc Network(MANET) has highly dynamic topology, hence presents a great challenge on the network performance evaluation and network protocol design. We proposed total path break up time, $\sum_{i}T_i$, as a metric to measure the performance of the total system as well as an individual connection. In this paper, we evaluate and analyze the performance of three mobility models(Random Waypoint, Manhattan, Blocked Manhattan) by applying the total path break up metric, investigate why network parameters such as packet delivery ratio, end-to-end delay, etc. vary by mobility models. We also present analysis result how much AODV Buffer improve packet delivery ratio and increase the end-to-end delay in spite of the path break up.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.327-332
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• 2016

The routing protocol for low-power and lossy networks (RPL) is an internet protocol based routing protocol developed and standardized by IETF in 2012 to support a wide range of applications for low-power and lossy-networks (LLNs). In LLNs consisting of resource-constrained devices, the energy consumption of battery powered sensing devices during network operations can greatly impact network lifetime. In the case of inefficient route selection, the energy depletion from even a few nodes in the network can damage network integrity and reliability by creating holes in the network. In this paper, a composite energy-aware node metric ($RER_{BDI}$) is proposed for RPL; this metric uses both the residual energy ratio (RER) of the nodes and their battery discharge index. This composite metric helps avoid overburdening power depleted network nodes during packet routing from the source towards the destination oriented directed acyclic graph root node. Additionally, an objective function is defined for RPL, which combines the node metric $RER_{BDI}$ and the expected transmission count (ETX) link quality metric; this helps to improve the overall network packet delivery ratio. The COOJA simulator is used to evaluate the performance of the proposed scheme. The simulations show encouraging results for the proposed scheme in terms of network lifetime, packet delivery ratio and energy consumption, when compared to the most popular schemes for RPL like ETX, hop-count and RER.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.456-469
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• 2009

We develop a new wireless link quality metric, ECOT(Estimated Channel Occupancy Time) that enables a high throughput route setup in wireless mesh networks. The key feature of ECOT is to be applicable to diverse mesh network environments where IEEE 802.11 MAC (Medium Access Control) variants are used. We take into account the exact operational features of 802.11 MAC protocols, such as 802.11 DCF(Distributed Coordination Function), 802.11e EDCA(Enhanced Distributed Channel Access) with BACK (Block Acknowledgement), and 802.11n A-MPDU(Aggregate MAC Protocol Data Unit), and derive the integrated link metric based on which a high throughput end-to-end path is established. Through extensive simulation in random-topology settings, we evaluate the performance of proposed link metric and present that ECOT shows 8.5 to 354.4% throughput gain over existing link metrics.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.31-43
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• 2014

A depth map is an important component for stereoscopic image generation. Since the depth map acquired from a depth camera has a low resolution, upsamling a low-resolution depth map to a high-resolution one has been studied past decades. Upsampling methods are evaluated by objective evaluation tools such as PSNR, Sharpness Degree, Blur Metric. As well, the subjective quality is compared using virtual views generated by DIBR (depth image based rendering). However, works on the analysis of the relation between depth map upsampling and stereoscopic images are relatively few. In this paper, we investigate the relationship between subjective evaluation of stereoscopic images and objective performance of upsampling methods using cross correlation and linear regression. Experimental results demonstrate that the correlation of edge PSNR and visual fatigue is the highest and the blur metric has lowest correlation. Further, from the linear regression, we found relative weights of objective measurements. Further we introduce a formulae that can estimate 3D performance of conventional or new upsampling methods.

• Journal of the Korea Institute of Building Construction
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• v.12 no.1
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• pp.1-9
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• 2012

This study aims to propose a performance metric for the application of a horizontal air-exhausting system to be used for the reduction of vapor and/or moisture that exists in the waterproof layer, by evaluating the physical properties. For this reason, tests in accordance with current standards were carried out, and the results were examined. Finally, a proposal was established for a general performance metric that could be applied as fundamental data based on the user's judgment. This has some limitations, in that the object is existing merchandise, however it should be useful for application in the construction field. In the future, analysis of a wider area, including workability, should be added in the phase of field application.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.133-151
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• 2019

In order to achieve faster content distribution speed and stronger fault tolerance, a P2P peer can connect to multiple peers in parallel and receive chunks of the data simultaneously. A critical issue in this environment is selecting a set of nodes participating in swarming sessions. Previous related researches only focus on performance metrics, such as downloading time or the round-trip time, but in this paper, we consider a new performance metric which is closely related to the network and propose a peer selection algorithm that produces the set of peers generating optimal worst link stress. We prove that the optimal algorithm is practicable and has the advantages with the experiments on PlanetLab. The algorithm optimizes the congestion level of the bottleneck link. It means the algorithm can maximize the affordable throughput. Second, the network load is well balanced. A balanced network improves the utilization of resources and leads to the fast content distribution. We also notice that if every client follows our algorithm in selecting peers, the probability is high that all sessions could benefit. We expect that the algorithm in this paper can be used complementary to existing methods to derive new and valuable insights in peer-to-peer networking.