• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.229-233
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• 2003

In this paper we theoretically analyze the probability of error for M-ary pulse position modulation (PPM) ultra-wideband (UWB) multiple access system using Gaussian monopulse. The optimum detection of UWB signals using M-ary orthogonal PPM in additive white Gaussian noise (AWGN) and multiple access interference (MAI) is considered, then receiver signal to noise power ratio (SNR) and upper bound fur the bit error rate (BER) are derived. Numerical results considering some practical parameters are presented.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.4
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• pp.499-506
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• 2012

In this paper, a dynamic modeling for the velocity and position information of a single frequency stand-alone GPS(Global Positioning System) receiver is described. In static condition, the position error dynamic model is identified as a first/second order transfer function, and the velocity error model is identified as a band-limited Gaussian white noise via non-parametric method of a PSD(Power Spectrum Density) estimation in continuous time domain. A Kalman filter is proposed considering both correlated/white measurements noise based on identified GPS error model. The performance of the proposed Kalman filtering method is verified via numerical simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.398-401
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• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1064-1070
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• 2012

Nowadays, image processing has been applied in a variety of fields. In order to preserve the high quality of visual the degradation phenomenon for images should be removed. Noise is one of the representative elements cause of the degradation phenomenon and AWGN(additive white Gaussian noise) always damages images. In this paper, an mixed filter algorithm, which is based on parallel denoising method, is proposed to suppress AWGN. This algorithm parallels the spatial domain wiener filter and the wavelet domain thresholding method which thresholding function is selected based on scale level. The proposed modified thresholding function which considers the dependency between parent and child coefficient performs well on suppressing noise.

• ETRI Journal
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• v.35 no.4
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• pp.644-654
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• 2013

In this paper, a signal detection scheme for cognitive radio (CR) based on the Bussgang theorem is proposed. The proposed scheme calculates the statistical difference between Gaussian noise and the primary user signal by applying the Bussgang theorem to the received signal. Therefore, the proposed scheme overcomes the noise uncertainty and gives scalable complexity according to the zero-memory nonlinear function for a mobile device. We also present the theoretical analysis on the detection threshold and the detection performance in the additive white Gaussian noise channel. The proposed detection scheme is evaluated by computer simulations based on the IEEE 802.22 standard for the wireless regional area network. Our results show that the proposed scheme is robust to the noise uncertainty and works well in a very low signal-to-noise ratio.

• Journal of IKEEE
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• v.25 no.3
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• pp.544-547
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• 2021

Recently, various display products are being applied to automobiles. In the process of acquiring an image from a display product, a large amount of additive white Gaussian noise(AWGN) is generated. Generally known denoising techniques focus on removing noise, so detailed components including image information are proportionally lost in the process of removing noise. The algorithm proposed in this paper proposes a method to effectively remove noise while preserving the detail of image information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2239-2246
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• 2011

In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.

• 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 IKEEE
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• v.23 no.1
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• pp.302-305
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• 2019

Non-orthogonal multiple access (NOMA) has been considered for the fifth generation (5G) mobile networks to provide high system capacity and low latency. We calculate the channel capacity for the weak channel user in NOMA and the channel capacity region for NOMA. In this paper, Gaussian mixture channel is compared to the additive white Gaussian noise (AWGN) channel. Gaussian mixture channel is modeled when we assume the practical signal modulation for the inter user interference, such as the binary phase shift keying (BPSK) modulation. It is shown that the channel capacity with BPSK inter user interference is better than that with Gaussian inter user interference. We also show that the channel capacity region with BPSK inter user interference is larger than that with Gaussian inter user interference. As a result, NOMA could perform better in the practical environments.