• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.55-60
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• 2018

This paper shows the initial results of a study applying deep learning technology in power line communication. In this paper, we propose a system that effectively removes noise by applying a deep learning technique to eliminate noise, which is a cause of reduced power line communication performance, by adding a deep learning model at the receive part. To train the deep learning model, it is necessary to store the data. Therefore, it is assumed that the existing data is stored, and the proposed system is simulated. we compare the theoretical result of the additive white Gaussian noise channel with the bit error rate and confirm that the proposed system model improves the communication performance by removing the noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.357-359
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• 2010

The Viterbi Decoding is one of the most researched areas of the convolutional decoding methods. In this paper, we use various parameters for the substantial Viterbi decoding and discuss some viterbi decoding methods. And, the viterbi algorithms of the methods, we discuss 'Hard Decision' and 'Soft Decision'. So, we compare differences of two methods about decoding methods, performance. Because of having various parameters and decision methods, we discuss the values of various parameter and decision methods in the Gaussian channel about the viterbi decoding methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.411-417
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• 2007

The rate control is an essential component in video coding to provide better quality under given coding constraints, such as channel capacity, frame rates, etc. In general, source data cannot be described as a single distribution in a video coding, hence it can cause an exhaustive approximation problem. It drops a coding efficiency under weak channel environments, such as mobile communications. In this paper, we design a new quantization parameter decision model that is based on a rate-distortion function of generalized Gaussian distribution. In order to adaptively express various source data distribution, we decide a shape parameter by observing a ratio of samples, which have a small value. For experiment, the proposed algorithm is implemented into H.264/AVC video codec, and its performance is compared with that of MPEG-2 TM5, H.263 TMN8 rate control algorithm. As shown in simulation results, the proposed algorithm provides an improved quality rather than previous algorithms and generates the number of bits closed to the target bits.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.145-150
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• 2013

This paper has presented the analysis for threshold voltage and drain induced barrier lowering among short channel effects occurred in subthreshold region for double gate(DG) MOSFET as next-generation devices, based on scaling theory. To obtain the analytical solution of Poisson's equation, Gaussian function has been used as carrier distribution to analyze closely for experimental results, and the threshold characteristics have been analyzed for device parameters such as channel thickness and doping concentration and projected range and standard projected deviation of Gaussian function. Since this potential model has been verified in the previous papers, we have used this model to analyze the threshold characteristics. As a result to apply scaling theory, we know the threshold voltage and drain induced barrier lowering are changed, and the deviation rate is changed for device parameters for DGMOSFET.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.3
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• pp.9-18
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• 2001

In this paper, we present the joint symbol detection and channel estimation for an orthogonal frequency division multiplexing (OFDM) system in fading channels. The proposed methods are based on decision-directed channel estimation (DDCE) method and their symbol detection is achieved by using Viterbi algorithm. This Viterbi decision-directed channel estimation (VDDCE) method tracks time-varying channels and detects a maximum likelihood symbol sequence. Recursive Viterbi decision-directed channel estimation (RVDDCE) method based on VDDCE method is proposed to shorten the detecting depth. In this method, channel estimate and Viterbi processing are recursively performed every interval of training symbol. Also, average chann'el estimation (ACE) technique to reduce the effect of additive white Gaussian noise (AWGN) is applied VDDCE method and RVDDCE method. These proposed methods arc demonstrated by computer simulation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.8
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• pp.797-803
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• 2002

In this paper we derive the probability density function of the phase error of the received signal over Rician fading channel and verify its propriety as the probability density function using the zeroth moment. In general, for the error probability over fading channel we compute the error probability in the first place when it is only AWGN, and then we get the final result by averaging the first result and the probability density function of the corresponding fading channel. In this paper, however, we compute the error probability by double integration after the probability density function over fading channel is computed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.888-891
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• 2011

In this paper, drain induced barrier lowering(DIBL) has been analyzed as one of short channel effects occurred in double gate(DG) MOSFET. The DIBL is very important short channel effects as phenomenon that barrier height becomes lower since drain voltage influences on potential barrier of source in short channel. The analytical potential distribution of Poisson equation, validated in previous papers, has been used to analyze DIBL. Since Gaussian function been used as carrier distribution for solving Poisson's equation to obtain analytical solution of potential distribution, we expect our results using this model agree with experimental results. The change of DIBL has been investigated for device parameters such as channel thickness, oxide thickness and channel doping intensity.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.8
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• pp.7-18
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• 1999

We have designed and tuilt the quasioptical system for the dual channel receiver which is used for the simultaneous observation of the cosmic radio with 100GHz band and 150GHz band. The quasioptical system has been widely used to guide the beam for the millimeter and submillimeter waves. A Gaussian distribution of field and power transverse to their axis of propagation allow the simple and elegant theory of Gaussian quasioptics. Using the theory of Gaussian beam, we introduced the analysis of image beam which is applied for a wide range of frequency. In order to guide two beams from the Cassegrain antenna simultaneously, the quasioptical system and its components for the dual channel receiver were designed by using the image beam method. We have checked the characteristics of the quasioptical components and the system by using the heam measurement system, which is made by us. The quasioptical system has been installed in the dual channel receiver on the Cassegrain antenna. The performance of this system has been finally confimed through the successful simultaneous observation with two bands of the cosmic radio.

• Journal of Communications and Networks
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• v.5 no.2
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• pp.157-166
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• 2003

This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.