• Proceedings of the KSRS Conference
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• v.1
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• pp.60-63
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• 2006

Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of oceancolor around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. In this paper radiometric calibration concept of the GOCI is introduced. The GOCI radiometric response is modeled as a nonlinear system in order to reflect a nonlinear characteristic of detector. In this paper estimation approaches for radiometric parameters of GOCI model are discussed. For the GOCI, the offset signal depends on each spectral channel because dark current offset signal is a function of integration time which is different from channel to channel. The offset parameter can be estimated by using offset signal measurements for two integration time setting is described.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2109-2118
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• 1996

The neural convolutional decoder(NCD) was proposed as a method of decoding convolutional codes. In this paper, simulation results are presented for coherent BPSK in memoryless AWGN channels and coherent QPSK in the satellite channels. The NCD can learn the nonlinear distortion caused by the charactersitics of the satellite channel including the filtering effects and the nonlinear effects of the travling wave tube amplifier(TWTA). Thus, as compared with the AWGN channel, the performance difference in the satellite channel between the NCD for the systematic code and the Viterbi decoder for the nonsystematic code is reduced.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.169-180
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• 2013

The modification of conditional Fuzzy C-Means (CFCM) with Gaussian weights (CFCM_GW) is accomplished for blind equalization of channels in this paper. The proposed CFCM_GW can deal with both of linear and nonlinear channels, because it searches for the optimal desired states of an unknown channel in a direct manner, which is not dependent on the type of channel structure. In the search procedure of CFCM_GW, the Bayesian likelihood fitness function, the Gaussian weighted partition matrix and the conditional constraint are exploited. Especially, in contrast to the common Euclidean distance in conventional Fuzzy C-Means(FCM), the Gaussian weighted partition matrix and the conditional constraint in the proposed CFCM_GW make it more robust to the heavy noise communication environment. The selected channel states by CFCM_GW are always close to the optimal set of a channel even when the additive white Gaussian noise (AWGN) is heavily corrupted. These given channel states are utilized as the input of the Bayesian equalizer to reconstruct transmitted symbols. The simulation studies demonstrate that the performance of the proposed method is relatively superior to those of the existing conventional FCM based approaches in terms of accuracy and speed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.103-108
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• 1998

The increase in the capacity of the digital magnetic recording systems inevitably causes severe intersymbol interference (ISI) and nonlinear distortions in the digital magnetic recording channel. In this paper, to cope with severe ISI and nonlinear distortions a neural decision feedback equalizer (NDFE) is applied to the digital magnetic recording channel - partial erasure channel model. In the performance comparison of bit error probability (or bit error ratio : BER) between the NDFE and the conventional decision feedback equalizer (DFE) via computer simulations. It has been found that as nonlinear distortions increase the NDFE has more SNR (SIgnal-to-Noise Ratio) advantage over the conventional DFE. In addition, in spite of the same recording density, as nonlinear distortions are increased, NDFE has the better performance of BER and the greater stability over conventional DFE.

• 전자공학회논문지 IE
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• v.45 no.1
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• pp.44-49
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• 2008

In this paper, the performance for the non-linear satellite channel including the characteristics of group delay and gain ripple of transponder is analyzed with multi-level QAM-OFDM schemes. Comparing the BER performances between general OFDM and CI(Carrier Interferometry)-OFDM for various QAM schemes, the degree of performance improvement is presented in AWGN environments for specified nonlinear characteristics. The simulations are performed with the 36MHz bandwidth of transponder channel and 120Mbps transmission rate for QPSK, 8QAM, 16QAM, 32QAM, 64QAM schemes between normal and worst case condition. It is shown that the improvement measure by the CI-OFDM for the group delay of channel and nonlinear characteristic of HPA outperforms that for the gain ripple in the case of higher level QAM scheme in normal condition. And the simulation results show that the additional techniques like the channel coding and compensation scheme against the nonlinear characteristic are required for 32QAM and higher level QAM in worst case condition.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.42-54
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• 1998

For decreasing intersymbol interference (ISI) due to band-limited channels in digitalcommunication, the uses of equalization techniques are necessary. Among the useful adaptive equalization techniques, because of their ease of implementation and nonlinear capabilites, the neural networks have been used as an alternative for effectively dealing with the channel distortion. In this paepr, a complex-valued multilayer percepron is proposed as a nonlinear adaptive equalizer. After the important properties that a suitable complex-valued activation function must possess are discussed, a new complex-valued activation function is developed for the proposed schemes to deal with M-ary QAM signals of any constellation sizes. It has been further proven that by the nonlinear transformation of the proposed function, the correlation coefficient between the real and imaginary parts of input data decreases when they are jointly Gaussian random variables. Lastly, the effectiveness of the proposed scheme is demonstrated by simulations. The proposed scheme provides, compared with the linear equalizer using the least mean squares (LMS) algorith, an interesting improvement concerning Bit Error Rate (BER) when channel distortions are nonlinear.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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• pp.116-127
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• 2007

Background and Purpose: Acording to chaos theory, irregular signals of electroencephalogram can interpretated by nonlinear method. Chaotic nonlinear dynamics in EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze EEG by correlation dimension and do Correlation Analysis of correlation dimension and cold-heat score Method: EEG raw data were measured during 15 minutes and choosed 40 seconds. We calculated correlation dimension and used surrogate data method for checking nonlinear data. After then do correlation analysis Result and Conclusion: Correlation dimension of channel 7 and channel 8 are showed significant correlation with cold score.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.143-150
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• 2004

In this paper, a recursive digital filter realization for an ionospheric channel model is proposed. This realization is in the form of a cascade of identical second-order all-pass filters, and is determined by only three parameters; two coefficients of an all-pass section, and the number of sections. The values of these parameters are optimized by a nonlinear optimization algorithm called the "downhill simplex method", so that the resulting time delay function closely approximates that of the ionospheric channel model. Comparing with the nonrecursive digital filter realization, it can be shown that the proposed recursive-digital-filter-realization is advantageous in points of view for the numbers of filter coefficients and the realization.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.11-18
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• 1995

This paper proposes a nonlinear adaptive equalizer which is based on fuzzy rules and fuzzy inference of several affine mapping for the received channel data. The proposed nolonlinear adaptive equalizers with the significantly lower computational complexity. Also it can be applied to the on-line adaptation environments owing to its fast convergence characteristics and the lower computational load. When using the decision feedback vectors, this equaalizer can be easily realized in the form of the DFE structure with out the requirement for the perfect channel knowledge as in the case of the fuzzy adaptive filter.