• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.29-33
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• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.334-337
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• 2005

The speech intelligibility is one of the most important factors in communication system. The speech intelligibility is related with speech to noise ratio. To enhance the speech to noise ratio, background noise reduction techniques are being developed. As a part of solution to noise reduction, this paper introduces directional microphone using beamforming method and speech filtering method. The directional microphone narrows the spatial range of processing signal into the direction of the target speech signal. The noise signal located in the same direction with speech still remains in the processing signal. To sort this mixed signal into speech and noise, as a following step, a speech-filtering method is applied to pick up only the speech signal from the processed signal. The speech filtering method is based on the characteristics of speech signal itself. The combined directional microphone and speech filtering method gives enhanced performance to speech intelligibility in communication system.

• JSTS:Journal of Semiconductor Technology and Science
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• v.9 no.3
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• pp.125-135
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• 2009

An analytical 2-dimensional model to explain the small signal and noise properties of an AlGaN/GaN modulation doped field effect transistor has been developed. The model is based on the solution of two-dimensional Poisson's equation. The developed model explains the influence of Noise in ohmic region (Johnson noise or Thermal noise) as well as in saturated region (spontaneous generation of dipole layers in the saturated region). Small signal parameters are obtained and are used to calculate the different noise parameters. All the results have been compared with the experimental data and show an excellent agreement and the validity of our model.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.1-6
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• 2014

In many medical image devices, dc noise often prevents normal diagnosis. In wireless capsule endoscopy systems, multipath fading through indoor wireless links induces inter-symbol interference (ISI) and indoor electric devices generate impulsive noise in the received signal. Moreover, dc noise, ISI, and impulsive noise are also found in optical fiber communication that can be used in remote medical diagnosis. In this paper, a blind signal processing method based on the biased probability density functions of constant modulus error that is robust to those problems that can cause error propagation in decision feedback (DF) methods is presented. Based on this property of robustness to error propagation, a DF version of the method is proposed. In the simulation for the impulse response of optical fiber channels having slowly varying dc noise and impulsive noise, the proposed DF method yields a performance enhancement of approximately 10 dB in mean squared error over its linear counterpart.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.327-329
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• 2021

Underwater acoustic communication channel is influenced by environmental parameters such as multipath, background noise and scattering. Therefore, a transmitted signal is influenced by the sea surface and the sea bottom boundaries, and a received signal shows a delay spread. These factors create a noise in the image and degrade the quality of underwater acoustic communication. To solve these problems, in this paper, we evaluate the performance of an underwater acoustic communication model using a denoising auto-encoder used for unsupervised learning. Noise images generated by the underwater multipath channel were collected and used as training data. Experimental results were analyzed as a PSNR parameter that expressed the noise ratio of the two images.

• ETRI Journal
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• v.23 no.4
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• pp.199-201
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• 2001

The phase noise reduction in a configuration of the HEMT oscillator with a dielectric resonator coupled by a quarter-wavelength impedance inverter is investigated. Two HEMT oscillators for a satellite payload system are manufactured in the same configuration except for the coupling configuration of the dielectric resonator (DR) in order to empirically demonstrate the phase noise reduction. Experimental result shows that a phase noise reduction by 14 dB can be enhanced by increasing the characteristic impedance of a coupling microstrip impedance inverter.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.237-242
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• 2012

One of the major problems for accurate channel estimation in power line communication systems is impulsive noise. Traditional channel estimation algorithms are based on the assumption of Gaussian noise, or the need to locate the positions of impulsive noise. The algorithms may lose optimality when impulsive noise exists in the channel, or if the location estimation of impulsive noise is inaccurate. In the present paper, an effective channel estimation algorithm based on a robust cost function is proposed to mitigate impulsive noise. The proposed method can provide a closed-form solution, and the application of robust estimation theory enables the proposed method to be free from localization of impulsive noise and thus can guarantee that the proposed method has better performance. Simulations verified the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.345-346
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• 2006

In this paper, we propose the noise suppressor with the psychoacoustic model and wavelet packet transform. The objective of the scheme is to enhance speech corrupted by colored or non-stationary noise. If corrupted noise is colored, subband approach would be more efficient than whole band one. To avoid serious residual noise and speech distortion, we must adjust the Wavelet Coefficient threshold. In this paper, the subband is designed matching with the critical band. And WCT is adapted by noise masking threshold(NMT) and segmental signal to noise ratio(seg_SNR). Consequently this work improve the PESQ-MOS about 0.23 in the case of coded speech.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.12
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• pp.1159-1164
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• 2016

This study describes the design and implementation of SHF band Low Noise Amplifier of Digital Satellite Communication. We have applied to HEMT part to minimize Noise Figure of system. and minimized the potential for the occurrence of such erroneous operation of equipment through the simulations of the space environment. We designed a reliable Low Noise Amplifier through simulation for a TID according to the vibration generated during the launch and space radiation environment, and compared pre-simulation of main performance results to test results about main performances of Low Noise Amplifier after production.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.41-45
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• 2019

In this paper, we propose the interference cancellation scheme of end-to-end method algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information of receiver by applying a deep learning model at the receiver. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.