• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.395-401
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• 2010

Image enhancement algorithm aims to improve the visual quality of low contrast image through eliminating the noise and blurring, increasing contrast, and raising detail. This paper proposes adaptive dynamic range linear stretching(ADRLS) algorithm based on advantages of existing methods. ADRLS method is focused on generating sub-histograms of the majority through partitioning the histogram of input image and applying adaptive scale factor. Generated sub-histograms are finally applied by linear stretching(LS) algorithm. In order to validate proposed method, it is compared with LS and histogram equalization(HE) algorithm generally used. As the result, the proposed method show to improve contrast of input image and to preserve distinct characteristics of histogram by controlling excessive change of brightness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.19-26
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• 2013

This paper deals with the performance comparison of blind adaptive equalization algorithm, the MMA and S-MMA, that is used for compensation of the amplitude and phase distortion simultaneously which occurs in the time dispersive channel. The present CMA algorithm is possible to compensates the amplitude only, but not in phase, so it needs to the another additional circuit for compensating the phase. In order to overcoming the abovemensioned shorthand, the improved cost function is applied to the MMA algorithm. In MMA algorithm, the error is consists of the dispersion constant only, but in S-MMA, the error is consists of the dispersion constant considering the output of decision device (sliced symbol) in order to updating the tap coefficients. By using the two kind error signal, the adaptive equalization algorithm has different performance. In this paper, we compare to the adaptive equalization algorithm performance by using the recovered constellation, residual isi, MD (Maximum Distortion) and SER as a index when the transmitting signal is 16 and 64-QAM and then passing through the same communication channel. As a result of simulation, the S-MMA can improving the Roburstness in SER performance compared to the MMA in the high order QAM signal.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.459-465
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• 2010

A new blind equalization algorithm that is based on maximizing the probability that the constant modulus errors concentrate near zero is proposed. The cost function of the proposed algorithm is to maximize the probability that the equalizer output power is equal to the constant modulus of the transmitted symbols. Two blind information-theoretic learning (ITL) algorithms based on constant modulus error signals are also introduced: One for minimizing the Euclidean probability density function distance and the other for minimizing the constant modulus error entropy. The relations between the algorithms and their characteristics are investigated, and their performance is compared and analyzed through simulations in multi-path channel environments. The proposed algorithm has a lower computational complexity and a faster convergence speed than the other ITL algorithms that are based on a constant modulus error. The error samples of the proposed blind algorithm exhibit more concentrated density functions and superior error rate performance in severe multi-path channel environments when compared with the other algorithms.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.23-30
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• 1997

In this paper, a decision feedback recurrent neural equalization (DFRNE) scheme is proposed for adaptive equalization problems. The proposed equalizer models a nonlinear infinite impulse response (IIR) filter. The modified Real-Time recurrent Learning Algorithm (RTRL) is used to train the DFRNE. The DFRNE is applied to both linear channels with only intersymbol interference and nonlinear channels for digital video cassette recording (DVCR) system. And the performance of the DFRNE is compared to those of the conventional equalizaion schemes, such as a linear equalizer, a decision feedback equalizer, and neural equalizers based on multi-layer perceptron (MLP), in view of both bit error rate performance and mean squared error (MSE) convergence. It is shown that the DFRNE with a reasonable size not only gives improvement of compensating for the channel introduced distortions, but also makes the MSE converge fast and stable.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.201-206
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• 2009

The third generation mobile communications system requiring the reliable multimedia data transmission has provided with the reliable voice, data and video services over the variable propagation environment. However the broadband wireless multiple access technologies cause Inter Symbol Interference (ISI) or Multiple Access Interference (MAI) to degrade the performance of W-CDMA (Code Division Multiple Access) system. Constant Modulus Algorithm (CMA) which is frequently used as the adaptive blind equalizers to remove the interfering signal has ill-convergence phenomenon without proper initialization. In this paper, new blind equalization method based on conventional CMA is proposed to improve the channel efficiency, and through computer simulation this is tested over the time varying fading environment of mobile communication system. Consequently, new blind equalization method into concatenated Kalman filter with CMA is verified better than conventional CMA through adopting minimum mean square errors and eye-pattern obtained from algorithm are compared.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.540-545
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• 2012

In this paper, the lagged cross-correlation of two probability density functions constructed by kernel density estimation is proposed, and by maximizing the proposed function, adaptive filtering algorithms for supervised and unsupervised training are also introduced. From the results of simulation for blind equalization applications in multipath channels with impulsive and slowly varying direct current (DC) bias noise, it is observed that Gaussian kernel of the proposed algorithm cuts out the large errors due to impulsive noise, and the output affected by the DC bias noise can be effectively controlled by the lag ${\tau}$ intrinsically embedded in the proposed function.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.159-165
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• 2012

This paper deals with the performance comparison of adaptive equalization algorithm, MMA and SCA, that is used for the minimization of the distortion and noise effect in the communication channel.. The transmitting signal will be distorted and received due to the nonlinearties of magnitude and phase transfer characteristics of communication channel, the compensation of it by using the self adaptive equalizer. The constant modulus has important metric in the self adaptive equalizer, the MMA uses the 2nd and 4th high order statistics of transmitting signal, the SCA uses the 2nd order statistics of transmitting signal only in order to the calculation of it. We compared to the compensation performance of the MMA and SCA by the computer simulation that are possible to the compensation of the two kinds of transfer characteristics at same times by the relatively simple arithmatic operation. We used to the recovered constellation, residual isi and MSE, SER that are the essential index for the comparison of the adaptive equalizer. The result of performance comparison of algorithms, the MMA which uses the high order statistics of transmitting signal has good performance in the MSE and SER compared to the SCA which is using the low order statistics. But in the recovered costellation and residual isi, the SCA has a good than the MMA.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.51-57
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• 2009

Blind equalization techniques have been widely used in wireless communication systems. In this paper, we investigate the information potentials in the criterion of minimizing Euclidian distance between two PDFs criterion for adaptive blind equalizers and evaluate BER performance of the method that has utilized an appropriate balance between the two information potentials, one from output samples and ramdomly generated desired samples at the receiver and another from the interactions among output samples. The balanced information potential method has shown in the BER performance results that it can produce significantly enhanced BER performance in blind equalization applications.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.3-8
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• 2015

In this paper, we propose a blind equalization algorithm based on the error signal linearly combined a constellation-matched error (CME) and a non-constellation-matched error (non-CME). The new error signal was designed to include the non-CME term for reaching initial convergence and the CME term for improving intersymbol interference (ISI) performance of output signals, and it controls the error terms through a combining factor. By controlling the error terms, it generates an appropriate error signal for equalization process and improves convergence speed and ISI cancellation performance compared to those of conventional algorithms. In the simulation for 64-QAM and 256-QAM signals under the multipath channel and additive noise conditions, the proposed method was superior to CMA and CMA+DD concurrent equalization.