• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1846-1852
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• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.126-129
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel type s as states of molecules in a lattice-like physical system defined on a GRF. Because of the MRFGRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular mteractions. The proposed adaptive iterative method was evaluated using simulation data generated by the Monte Carlo method. In the extensive experiments of this study, the proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.688-691
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• 2004

We have studied the possibility of global noise reduction by the sound power control through selection of distribution and impedance of absorptive materials. It is necessary to investigate the relation between the global sound energy in the field and the total sound power radiated by sources. In the previous work (1,2), the authors presented a useful design method to change boundary condition that can be useful to reduce noise in acoustically small enclosures. The possibility of total acoustic potential energy reduction by acoustic source power control is examined in an acoustically small cavity. Using acoustic energy balance equation, the relation between global noise control performance and absorptive material's arrangement/impedance is deduced. Numerical simulation is performed to interpret its physical meaning in terms of absorbent's distribution and impedance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.255-260
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• 2006

Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

• Journal of the Korean Mathematical Society
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• v.38 no.2
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• pp.297-309
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• 2001

White noise distribution theory over the complex Gaussian space is established on the basis of the recently developed white noise operator theory. Unitarity condition for a white noise operator is discussed by means of the operator symbol and complex Gaussian integration. Concerning the overcompleteness of the exponential vectors, a coherent sate representation of a white noise function is uniquely specified from the diagonal coherent state representation of the associated multiplication operator.

• The Journal of the Acoustical Society of Korea
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• v.19 no.6
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• pp.53-61
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• 2000

In the speech recognition under the noisy environments, reducing the mismatch introduced between training and testing environments is an important issue. Spectral subtraction is widely used technique because of its simplicity and relatively good performance in noisy environments. In this paper, we introduce histogram method as a reliable noise estimation approach for spectral subtraction. This method has advantages over the conventional noise estimation methods in that it does not need to detect non-speech intervals and it can estimate the noise spectra even in time-varying noise environments. Even though spectral subtraction is performed using a reliable average noise spectrum by the histogram method, considerable amount of residual noise remains due to the variations of instantaneous noise spectrum about mean. To overcome this limitation, we propose a new over-estimation technique based on distribution characteristics of histogram used for noise estimation. Since the proposed technique decides the degree of over-estimation adaptively according to the measured noise distribution, it has advantages to be few the influence of the SNR variation on the noise levels. According to speaker-independent isolated word recognition experiments in car noise environment under various SNR conditions, the proposed histogram-based over-estimation technique outperforms the conventional over-estimation technique.

• Journal of the Microelectronics and Packaging Society
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• v.20 no.4
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• pp.41-45
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• 2013

Modern electronic devices such as tablets and smartphones are getting more powerful and efficient. The demand in feature sets, functionality and usability increase exponentially and this has posed a great challenge to the design of a power distribution network (PDN). Power rails merging is a popular option used today in a PDN design as numerous power rails are no longer feasible due to form factor limitation and cost constraint. In this paper, the criteria and limitations for power rails merging are discussed. Despite having all the advantages such as pin count reduction, decoupling capacitors sharing, lower impedance and cost saving, power rails merging can however, introduce coupling noise to the system. In view of this, a PDN design with power rails merging that fulfills design recommendations and specifications such as noise target, power well placement, voltage supply values as well as power supply quadrant assignment is extremely important.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2467-2470
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• 2003

This paper presents a new structure of MPEG-4 AAC encoder. The proposed encoder directly shapes quantization noise distribution according to the energy distribution curve and thereafter performs adjustment of the offset level of the noise distribution to meet the given bit rate. The direct noise shaping and the bit rate matching scheme of the proposed encoder algorithm significantly alleviate the problem of conventional encoder's processing burden related with the employment of the precise psychoacoustic model and iteration intensive quantizer. The encoder algorithm is implemented on ARM processor with fixed-feint arithmetic operations. The audio quality of the implemented system is observed comparable to those of commercially available encoders, white the complexity of the implementation is drastically reduced in comparison to the conventional encoder systems.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.740-745
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• 1994

In the actual situation of measuring the environmental noise, it is very often that only the resultant phenomenon fluctuation contaminated by the additional noise of arbitrary distribution type can be observed. Furthermore, the observed data is usually given in a sound level form the purpose of estimating only the undisturbed objective output response, some estimation method is necessary to reasonably remove the effect of the above additional noise. In this paper, first, a mathematical model of arbitrary sound insulation systems is introduced in the form of a linear system on intensity scale, by using the well-known additive property of energy quantities. Next, some estimation method of the output response under the existence of background noise is derived. Then, based on the expression of the above estimation method, a new prediction method of only the output response probability function form for arbitrary sound insulation systems without. a background noise is proposed by use of observed data contaminated by a background noise. Finally, the effectiveness of the proposed method is confirmed experimentally too by applying it to the actual various type sound wall systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.93-99
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• 2007

Robust nonlinear image denoising algorithms for the class of ${\alpha}$-stable distribution are introduced. The proposed amplitude-limited sample average filter(ALSAF) proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. The error norm for this estimator is equivalent to Huber#s minimax norm. It is optimal in the respect of maximizing the efficacy under the above noise environment. It is mired with the myriad filter to propose an amplitude-limited myriad filter(ALMF). The behavior and performance of the ALSAF and ALMF in ${\alpha}$-stable noise environment are illustrated and analyzed through simulation.