• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.853-856
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• 2021

In this paper, a low noise amplifier having high gain and low noise by using input and inter stage matching circuits has been designed. A current-reused two-stage common-source topology is adopted, which can obtain high gain and low power consumption. Deterioration of noise characteristics according to the source inductive degeneration matching is compensated by adopting additional matching circuits. Moreover trade-offs among noise, gain, linearity, impedance matching, and power dissipation have been considered. In this design, 0.18-mm CMOS process is employed for the simulation. The simulated results show that the designed low noise amplifier can provide high power gain and low noise characteristics.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10B
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• pp.1210-1215
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• 2011

In this paper, a supervised adaptive equalization algorithm based on probability density function (PDF) matching method is introduced and its decision-feedback version is proposed for underwater communication channels with strong impulsive noise and severe multipath characteristics. The conventional least mean square (LMS) algorithm based on mean squared error (MSE) criterion has shown to be incapable of coping with impulsive noise and multipath effects commonly shown in underwater communications. The linear PDF matching algorithm, which shows immunity to impulsive noise, however, has revealed to yield unsatisfying performance under severe multipath environments with impulsive noise. On the other hand, the proposed nonlinear PDF matching algorithm with decision feedback proves in the simulation to possess superior robustness against impulsive noise and multipath characteristics of underwater communication channels.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.354-357
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• 2003

The low noise and balanced amlifier has been designed by using PBG. Usually balanced LNAis used to matching the input and output mismatching that caused by matching the low noise matching point. And the PBG supresses the harmoincs. This paper proposed balanced LNA by using PBG. And this configuration improve the performance - noise figure, VSWR.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.263-270
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• 2009

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirected ness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.327-340
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• 2009

To achieve the noise reduction effect in boundary image matching, we use the moving average transform of time-series matching. Our motivation is based on an intuition that using the moving average transform we may exploit the noise reduction effect in boundary image matching as in time-series matching. To confirm this simple intuition, we first propose $\kappa$-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our $\kappa$-order image matching identifies similar images in this time-series domain by comparing the $\kappa$-moving average transformed sequences. Next, we propose an index-based matching method that efficiently performs $\kappa$-order image matching on a large volume of image databases, and formally prove the correctness of the index-based method. Moreover, we formally analyze the relationship between an order $\kappa$ and its matching result, and present a systematic way of controlling the noise reduction effect by changing the order $\kappa$. Experimental results show that our $\kappa$-order image matching exploits the noise reduction effect, and our index-based matching method outperforms the sequential scan by one or two orders of magnitude.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.329-334
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• 2016

In this paper, we demonstrate a Non-Foster matching method for an electrically small antenna to improve the signal-to-noise ratio (SNR) of communication link. For the experiment, we used a general FM antenna whose resonance frequency is about 52-57 MHz and a floating type Linvill negative impedance converter(NIC)-based circuit as a Non-Foster matching element. By implementing the Non-Foster circuit to cover FM band, we can achieve a wide bandwidth matching covers 40-200 MHz. Our measurement shows 3-7 dB improvement of SNR for the same bandwidth though there are several spikes which means no improvement of SNR in the band.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.111-114
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• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.273-285
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• 2007

Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

• Journal of electromagnetic engineering and science
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• v.16 no.4
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• pp.241-247
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• 2016

This paper proposes the design and measurement of a 6-18 GHz front-end receiver module that has been combined into a one- channel output from a two-channel input for electronic warfare support measures (ESM) applications. This module includes a limiter, high-pass filter (HPF), power combiner, equalizer and amplifier. This paper focuses on the design aspects of reducing the noise figure (NF) and matching the phase and amplitude. The NF, linear equalizer, power divider, and HPF were considered in the design. A broadband receiver based on a combined configuration used to obtain low NF. We verify that our receiver module improves the noise figure by as much as 0.78 dB over measured data with a maximum of 5.54 dB over a 6-18 GHz bandwidth; the difference value of phase matching is within $7^{\circ}$ between ports.