• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.867-878
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• 2006

In the past few years, the research of sensor networks is forced dramatically. Specially, while the research for maintaining the power of a sensor is focused, we are also concerned nth query processing related with the optimization of multiple continuous queries for decreasing in unnecessary energy consumption of sensor networks. We present the fragmentation algorithm to solve the redundancy problem in multiple continuous queries that increases in the count or the amount of transmitting data in sensor networks. The fragmentation algorithm splits one query into more than two queries using the query index (QR-4ree) in order to reduce the redundant query region between a newly created query and the existing queries. The R*-tree should be reorganized to the QR-tree right to the structure suggested. In the result, we preserve 20 percentage of the total energy in the sensor networks.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.99-107
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• 2010

SNMP(Simple Network Management Protocol) is most commonly used as a standard protocol for effective network management by supporting the increasing size of the network and the variety of network elements such as router, switch, server and so on. However, SNMP has performance drawbacks of network overhead, processing latency, and the inefficiency in data retrieval. In this paper, we propose two schemes to improve the performance of SNMP; 1) the first scheme to reduce the amount of redundant OID information within a SNMP-GetBulk response message, 2) the second scheme of newly proposed SNMP-GetUpdate message combined with the cache in MNS. Through the analysis with real experiments, we show that our first scheme reduces the network overhead and the second scheme improves the processing latency and the retrieval of SNMP MB tables. And, therefore the scalability of network management can be improved.

• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.129-142
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• 2004

As the Internet gets faster, the demand for high-speed routers that are capable of forwarding more than giga bits of data per second keeps increasing. In the previous research, Bitmap Trie algorithm was developed to rapidly execute LPM(longest prefix matching) process which is Well known as the Severe performance bottleneck. In this paper, we introduce a novel algorithm that drastically enhanced the performance of Bitmap. Trie algorithm by applying three techniques. First, a new table called the Count Table was devised. Owing to this table, we successfully eliminated shift operations that was the main cause of performance degradation in Bitmap Trie algorithm. Second, memory utilization was improved by removing redundant forwarding information from the Transfer Table. Lastly. the range of prefix lookup was diversified to optimize data accesses. On the other hand, the processing delays were classified into three categories according to their causes. They were, then, measured through the execution-driven simulation that provides the higher quality of the results than any other simulation techniques. We tried to assure the reliability of the experimental results by comparing with those that collected from the real system. Finally the Enhanced Bitmap Trie algorithm reduced 82% of time spent in previous algorithm.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1086-1090
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• 2010

In recent, as many applications of sensor networks increase, various techniques have been studied to efficiently operate network systems. The query optimization scheme that is one of such techniques has been studied to reduce the data transmission cost. The data transmission is of great importance to the energy consumption of sensor networks. In this paper, we propose an energy-efficient multiple queries processing scheme by sharing sensor readings for multiple queries, when they are occurred in sensor networks. The proposed scheme reduces unnecessary data transmissions among the sensor nodes by intuitively identifying their locations using the grid structure. It also efficiently shares the data by recognizing the redundant regions of sensor nodes. In order to show the superiority of the proposed scheme, we compare it with the existing scheme in various experiments. As the result, the proposed scheme reduces about 65% energy consumption over the existing scheme.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.2
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• pp.85-92
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• 1989

The aim of this study is to find out the combination of effective transformed data, applying Remote Sensing techniques, as to the classification and particular objects by transforming the MSS data and TM data of the satellite LANDSAT into several linearly transformed data. Since one of the problems in the processing of the LANDSAT data is the vastness of the data, the Linear Transformation could be a method to perform analysis of those vast data, more efficiently and economically. This method is carried out as follows : (1) offering the simplicity over complex data, (2) selectional processing over redundant data and removing unnecessary data, (3) emphasizing on the object of the study ; by transforming multispectral data through linear calculation and statistical transformation. In this study, the analysis and transformation of the data have been performed by means of Band Ratioing and Principal Component Analysis. As the classificatory consequence, Infrared/RED Ratioing which expands the characterization of green vegetation, has been useful for a distinctive classification among other classes. For the Principal Component Analysis, band 1,2,7 are efficient in the classification of the green vegetation.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.60-70
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• 2018

A software defined storage (SDS) is being deployed in cloud environment to allow multiple users to virtualize physical servers, but a solution for optimizing space efficiency with limited physical resources is needed. In the conventional data deduplication system, it is difficult to deduplicate redundant data uploaded to distributed storages. In this paper, we propose a distributed deduplication method using similarity-based clustering and multi-layer bloom filter. Rabin hash is applied to determine the degree of similarity between virtual machine servers and cluster similar virtual machines. Therefore, it improves the performance compared to deduplication efficiency for individual storage nodes. In addition, a multi-layer bloom filter incorporated into the deduplication process to shorten processing time by reducing the number of the false positives. Experimental results show that the proposed method improves the deduplication ratio by 9% compared to deduplication method using IP address based clusters without any difference in processing time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.465-469
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• 2000

This paper describes a single-chip full-custom implementation of pipelined adaptive decision-feedback equalizer (PADFE) using a 0.25-${\mu}{\textrm}{m}$ CMOS technology for wide-band wireless digital communication systems. To enhance the throughput rate of ADFE, two pipeline stage are inserted into the critical path of the ADFE by using delayed least-mean-square (DLMS) algorithm Redundant binary (RB) arithmetic is applied to all the data processing of the PADFE including filter taps and coefficient update blocks. When compared with conventional methods based on two's complement arithmetic, the proposed approach reduces arithmetic complexity, as well as results in a very simple complex-valued filter structure, thus suitable for VLSI implementation. The design parameters including pipeline stage, filter tap, coefficient and internal bit-width and equalization performance such as bit error rate (BER) and convergence speed are analyzed by algorithm-level simulation using COSSAP. The singl-chip PADFE contains about 205,000 transistors on an area of about 1.96$\times$1.35-$\textrm{mm}^2$. Simulation results show that it can safely operate with 200-MHz clock frequency at 2.5-V supply, and its estimated power dissipation is about 890-mW.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.2015-2024
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• 1999

Presented in this paper are a new complex-umber filter architecture, which is suitable for an efficient implementation of baseband signal processing of digital communication systems, and a chip-set design of adaptive decision-feedback equalizer (ADFE) employing the proposed structure. The basic concept behind the approach proposed in this paper is to apply redundant binary (RB) arithmetic instead of conventional 2’s complement arithmetic in order to achieve an efficient realization of complex-number multiplication and accumulation. With the proposed way, an N-tap complex-number filter can be realized using 2N RB multipliers and 2N-2 RB adders, and each filter tap has its critical delay of $T_{m.RB}+T_{a.RB}$ (where $T_{m.RB}, T_{a.RB}$are delays of a RB multiplier and a RB adder, respectively), making the filter structure simple, as well as resulting in enhanced speed by means of reduced arithmetic operations. To demonstrate the proposed idea, a prototype ADFE chip-set, FFEM (Feed-Forward Equalizer Module) and DFEM (Decision-Feedback Equalizer Module) that can be cascaded to implement longer filter taps, has been designed. Each module is composed of two complex-number filter taps with their LMS coefficient update circuits, and contains about 26,000 gates. The chip-set was modeled and verified using COSSAP and VHDL, and synthesized using 0.8- μm SOG (Sea-Of-Gate) cell library.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.591-602
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• 2003

Delay and discontinuance phenomenon of service are cause by sudden increase of the network communication amount and the quantity consumed of resources when Internet users are driven excessively to a conventional single large database sewer. To solve these problems, spatial database cluster consisted of several single nodes on high-speed network to offer high-performance is risen. But, research about spatial join operation that can reduce the performance of whole system in case process at single node is not achieved. So, in this paper, we propose efficient parallel spatial join processing method in a spatial database cluster system that uses data partitions and replications method that considers the characteristics of space data. Since proposed method does not need the creation step and the assignment step of tasks, and does not occur additional message transmission between cluster nodes that appear in existent parallel spatial join method, it shows performance improvement of 23% than the conventional parallel R-tree spatial join for a shared-nothing architecture about expensive spatial join queries. Also, It can minimize the response time to user because it removes redundant refinement operation at each cluster node.