• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.69-74
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• 2004

Selectivity estimation for spatial query is very important process in finding the most efficient execution plan. Many works have been performed to estimate accurately selectivity. Although they deal with some problems such as false-count, multi-count, they require a large amount of memory to retain accurate selectivity, so they can not get good results in little memory environments such as mobile-based small database. In order to solve this problem, we propose a new technique called MW histogram which is able to compress summary data and get reasonable results. It also has a flexible structure to react dynamic update. The experimental results showed that the MW histogram has lower relative error than MinSkew histogram and gets a good selectivity in little memory.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.88-93
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• 2015

In modern computer systems, DRAM is commonly used as main memory due to its low read/write latency and high endurance. However, DRAM is volatile memory that requires periodic power supply (i.e., memory refresh) to sustain the data stored in it. On the other hand, PCM is a promising candidate for replacement of DRAM because it is non-volatile memory, which could sustain the stored data without memory refresh. PCM is also available for byte-addressable access and in-place update. However, PCM is unsuitable for using main memory of a computer system because it has two limitations: high read/write latency and low endurance. To take the advantage of both DRAM and PCM, a hybrid main memory, which consists of DRAM and PCM, has been suggested and actively studied. In this paper, we propose a novel page replacement algorithm for hybrid main memory. To cope with the weaknesses of PCM, our scheme focuses on reducing the number of PCM writes in the hybrid main memory. Experimental results shows that our proposed page replacement algorithm reduces the number of PCM writes by up to 80.5% compared with the other page replacement algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.663-671
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• 2016

According to rapidly increasing of network traffics, necessity of high-speed router also increased. For various purposes, like traffic statistic and security, traffic measurement function should performed by router. However, because of the nature of high-speed router, memory resource of router was limited. RCC proposed a way to measure traffics with high speed and accuracy. Additional quadratic probing hashing table used for accumulating elephant flows in RCC. However, in our experiment, quadratic probing performed many overheads when allocated small memory space or load factor was high. Especially, quadratic requested many calculations in update and lookup. To face this kind of problem, we use a cuckoo hashing which performed a good performance in update and loop for enhancing the RCC. As results, RCC with cuckoo hashing performed high accuracy and speed even when load factor of memory was high.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7B
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• pp.647-659
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• 2003

LPM(Longest Prefix Matching)searching in If address lookup is a major bottleneck of IP packet processing in the high speed router. In the conventional lookup table for the LPM searching in CAM(Content Addressable Memory) the complexity of fast update take 0(1). In this paper, we designed pipeline architecture for fast update of 0(1) cycle of lookup table and high throughput and low area complexity on LPM searching. Lookup-table architecture was designed by CAM(Content Addressable Memory)away that uses 1bit RAM(Random Access Memory)cell. It has three pipeline stages. Its LPM searching rate is affected by both the number of key field blocks in stage 1 and stage 2, and distribution of matching Point. The RTL(Register Transistor Level) design is carried out using Verilog-HDL. The functional verification is thoroughly done at the gate level using 0.35${\mu}{\textrm}{m}$ CMOS SEC standard cell library.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.601-612
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• 2006

This study proposes RAG (Region Adjancency Graph)-based image segmentation for large imagery in remote sensing. The proposed algorithm uses CN-chain linking for computational efficiency and multi-window operation of sliding structure for memory efficiency. Region-merging due to RAG is a process to find an edge of the best merge and update the graph according to the merge. The CN-chain linking constructs a chain of the closest neighbors and finds the edge for merging two adjacent regions. It makes the computation time increase as much as an exact multiple in the increasement of image size. An RNV (Regional Neighbor Vector) is used to update the RAG according to the change in image configuration due to merging at each step. The analysis of large images requires an enormous amount of computational memory. The proposed sliding multi-window operation with horizontal structure considerably the memory capacity required for the analysis and then make it possible to apply the RAG-based segmentation for very large images. In this study, the proposed algorithm has been extensively evaluated using simulated images and the results have shown its potentiality for the application of remotely-sensed imagery.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.292-299
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• 2010

Recently, as flash memory is used as digital storage devices, necessity for digital forensics is growing in a flash memory area for digital evidence analysis. For this purpose, it is important to recover crashed files stored on flash memory efficiently. However, it is inefficient to apply the hard disk based file recovery techniques to flash memory, since hard disk and flash memory have different characteristics, especially flash memory being unable to in-place update. In this paper, we propose a flash-aware file recovery technique for digital forensics. First, we propose an efficient search technique to find all crashed files. This uses meta-data maintained by FTL(Flash Translation Layer) which is responsible for write operation in flash memory. Second, we advise an efficient recovery technique to recover a crashed file which uses data location information of the mapping table in FTL. Through diverse experiments, we show that our file recovery technique outperforms the hard disk based technique.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.5
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• pp.225-234
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• 2018

Control flow errors are caused by the vulnerability of memory and result in system failure. Signature-based control flow monitoring is a representative method for alleviating the problem. The method commonly consists of two routines; one routine is signature update and the other is signature verification. However, in the existing signature-based control flow monitoring, monitoring target application is tightly combined with the monitoring code, and the operation of monitoring in a single thread is the basic model. This makes the signature-based monitoring method difficult to expect performance improvement that can be taken in multi-thread and multi-core environments. In this paper, we propose a new signature-based control flow monitoring model that separates signature update and signature verification in thread level. The signature update is combined with application thread and signature verification runs on a separate monitor thread. In the proposed model, the application thread and the monitor thread are separated from each other, so that we can expect a performance improvement that can be taken in a multi-core and multi-thread environment.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.95-104
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• 2022

A TPR-tree is a well-known indexing structure that is developed to answer queries about the current or future time locations of moving objects. For the purpose of space efficiency, the TPR-tree employs the notion of VBR (velocity bounding rectangle)so that a regionalrectangle presents varying positions of a group of moving objects. Since the rectangle computed from a VBR always encloses the possible maximum range of an indexed object group, a search process only has to follow VBR-based rectangles overlapped with a given query range, while searching toward candidate leaf nodes. Although the TPR-tree index shows up its space efficiency, it easily suffers from the problem of dead space that results from fast and constant expansions of VBR-based rectangles. Against this, the TPR-tree index is enforced to update leaf nodes for reducing dead spaces within them. Such an update-prone feature of the TPR-tree becomes more problematic when the tree is saved in flash storage. This is because flash storage has very expensive update costs. To solve this problem, we propose a new Bloom filter based caching scheme that is useful for reducing updates in a flash TPR-tree. Since the proposed scheme can efficiently control the frequency of updates on a leaf node, it can offer good performance for indexing moving objects in modern flash storage.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06b
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• pp.23-28
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• 1996

Model-based coding is one of several approaches to very low bit rate image coding and it can be used in many applications such as image creation and virtual reality. However, its analysis and synthesis processes remain difficult, especially in the sense that the resulting synthesized image reveals some degradation in detailed facial components such as furrows around eyes and mouth. To solve the problem, a large number of methods have been proposed and the texture update method is one of them. In this paper, we investigate texture synthesis for model-based coding. In the update process of the proposed texture synthesis algorithm, texture information is stored in a memory and the decoder reuses it. With this method, the transmission bit rate for texture data can be reduced compared with the conventional method updating texture periodically.