• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.6
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• pp.247-252
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• 2003

In this paper, we have designed and fabricated a broadband 2-stage MMIC amplifier. Broadband characteristics could be obtained by compensated matching networks in a 2-stage amplifier design. This method is compensating low gains at lower frequencies in the 1st-stage with higher gains at lower frequencies in the 2nd- stage and then finally flat gains are obtained in the wide frequency ranges. Also, we have obtained not only broadband characteristics but also high gain using compensation matching network. The fabricated amplifier is measured by attaching on the test PCB(Printed Circuits Board). The measurement results are bandwidth of 1.1~2.8 GHz, S$_{21}$ gain of 11.1$\pm$0.3 ㏈ and P1㏈ of 12.6 ㏈m at 2.4 GHz.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.85-96
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• 1999

In a number of predictive video compression standards, the motion is compensated by the block-based motion compensation (BMC). The effective motion field used for the prediction by the BMC is obviously discontinuous since one motion vector is used for the entire macro-block. The usage of discontinuous motion field for the prediction causes the blocky artifacts and one obvious approach for eliminating such artifacts is to use a smoothed motion field. The optimal procedure will depend on the type of motion within the video. In this paper, several procedures for the motion vectors are considered. For any interpolation or approaches, however, the motion vectors as provided by the block matching algorithm(BMA) are no longer optimal. The optimum motion vectors(still one per macro-block) must minimize the of the displaced frame difference (DFD). We propose a unified algorithm that computes the optimum motion vectors to minimize the of the DFD using a conjugate gradient search. The proposed algorithm has been implemented and tested for the affine transformation based motion compensation (ATMC), the bilinear transformation based motion compensation (BTMC) and our own filtered motion compensation(FMC). The performance of these different approaches will be compared against the BMC.