• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2465-2473
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• 2011

This paper describes a circuit-level optimization of DFU(decoding function unit) for LDPC decoder which is used in wireless communication systems including WiMAX and WLAN. A new design of DFU based on sign-magnitude arithmetic instead of two's complement arithmetic is proposed, resulting in 18% reduction of gate count for 96 DFUs array used in mobile WiMAX LDPC decoder. A multi-mode LDPC decoder for mobile WiMAX standard is designed using the proposed DFU. The LDPC decoder synthesized using a 0.18-${\mu}m$ CMOS cell library with 50 MHz clock has 268,870 gates and 71,424 bits RAM, and it is verified by FPGA implementation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.518-528
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• 2001

In this paper, we firstly define a new combined segmentation measure and propose a segmentation algorithm using this measure. The combined segmentation measure is a weighted sum of intensity, motion, and a change segmentation measure that is extracted from the resulting image of the proposed change detector. The change segmentation measure is defined as an absolute change value difference between an pixel and its neighboring region from the eroded image, which results from morphological erosion filtering to eliminate many inaccurate components included in the resulting image of a conventional change detector. The change segmentation measure can be used as an efficient segmentation measure for the accurate segmentation of neighboring moving objects and static background regions. Therefore, the proposed combined segmentation measure can determine exact boundaries in the segmentation process of region-based coding even though the estimated motion vectors around the boundaries of moving objects and static background regions are inaccurate and the intensities around the boundaries are similar.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.921-929
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• 2011

This paper describes an analysis of the decoding performance and decoding convergence speed of layered LDPC(low-density parity-check) decoder for mobile WiMAX system, and the optimal design conditions for hardware implementation are searched. A fixed-point model of LDPC decoder, which is based on the min-sum algorithm and layered decoding scheme, is implemented and simulated using Matlab model. Through fixed-point simulations for the block lengths of 576, 1440, 2304 bits and the code rates of 1/2, 2/3A, 2/3B, 3/4A, 3/4B, 5/6 specified in the IEEE 802.16e standard, the effect of internal bit-width, block length and code rate on the decoding performance are analyzed. Simulation results show that fixed-point bit-width larger than 8 bits with integer part of 5 bits should be used for acceptable decoding performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.363-371
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• 2013

This paper describes a multi-standard LDPC decoder which supports 19 block lengths(576~2304) and 6 code rates(1/2, 2/3A, 2/3B, 3/4A, 3/4B, 5/6) of IEEE 802.16e mobile WiMAX standard and 3 block lengths(648, 1296, 1944) and 4 code rates(1/2, 2/3, 3/4, 5/6) of IEEE 802.11n WLAN standard. To minimize hardware complexity, it adopts a block-serial (partially parallel) architecture based on the layered decoding scheme. A DFU(decoding function unit) based on sign-magnitude arithmetic is used for hardware reduction. The designed LDPC decoder is verified by FPGA implementation, and synthesized with a 0.13-${\mu}m$ CMOS cell library. It has 312,000 gates and 70,000 bits RAM. The estimated throughput is about 79~210 Mbps at 100 MHz@1.8v.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.