• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.911-913
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• 2013

The user's context awareness is important to the reliability of sensors data. The sensor data is constantly change to external temp, internal& external environment and vibration. This noise environment is affecting that the data collected information from sensors. Of course this method of digital filter and inference algorithm specifically request for the use of ripple noise and action inference. In this paper, experiment was a comparison of the KF(Kalman Filter) and WMAF(Weight Moving Average Filter) for noise decrease and distortion prevention according to user behavior. And, we compared the EWDF(Extended Weight Dual Filter) with several filer. In an experiment, in contrast to other filter, the proposed filter is robust in a noise-environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.268-270
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• 2013

According to the development and supply of Wibro technology digital technology is applied in several fields. Digital images are damaged by various noises in the process of transfer and storage; the image restoration is to reduce the influence of the noises on images by removing the noises. To make good image restoration several methods have been proposed but the noise removal property is not satisfactory. Therefore, to effectively remove noises noise decision is made and if it is decided as a noise, the size of mask is enlarged; this is adaptive median filter algorithm that is proposed in this paper. And through simulation the superiority of this algorithm to existing methods has been verified.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2300-2307
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• 2008

This study receives the noise transmitted in a constant audio frequency range through a microphone array in which the noise(like grease in a pan) occurs on the power supply line due to the troublesome partial discharge(arc). Then by going through a series of signal processing of removing noise, this study measures the distance and direction up to the noise caused by the troublesome partial discharge(arc) and monitors the result by displaying in the analog and digital method. After these, it determines the state of each size and judges the distance and direction of problematic part. When the signal sound transmitted by the signal source of bad insulator is received on each microphone, the signal comes only in the frequency range of 20 kHz by passing through the circuit of amplification and 6th low pass filter. Then, this signal is entered in a digital value of digital signal processing(TMS320F2812) through the 16-bit A/D conversion. By doing so, the sound distance, direction and coordinate of bad insulator can be detected by realizing the correlation method of detecting the arriving time difference occurring on each microphone and the algorithm of detecting maximum time difference.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.186-191
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• 2018

As influence of the 4th industry is growing with development of information society more electronic devices and sensor are used in the field. As this is the case, importance of signal processing during data transfer is rising Furthermore, the need for technology to remove noise caused by various reasons and to stabilize sensor output is growing as well. This research suggests digital filter algorithm that efficiently remove noise by stabilizing output of accelerating sensor. The standard value of this algorithm is calculated by applying Gaussian coefficient. To maintain its feature, final output is obtained by subtracting weight depending on variance from standard value For its evaluation, it is compared with other protocols and its function is checked through output features.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.90-97
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• 2023

Digital holographic microscopy (DHM) is a three-dimensional (3D) imaging technique that uses the phase information of coherent light. In the reconstruction process of DHM, a narrow region around the positive or negative sideband from the Fourier domain is windowed to avoid noise due to the DC spectrum of the hologram spectrum. However, the limited size of the window also degrades the high-frequency information of the 3D object profile. Although a large window can have more detailed information of the 3D object shape, the noise is increased. To solve this trade-off, we propose phase difference averaging (PDA). The proposed method yields high-frequency information of the specimen while reducing the DC noise. In this paper, we explain the reconstruction algorithm for this method and compare it to various conventional filtering methods including Gaussian, Wiener, average, median, and bilateral filtering methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.282-288
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• 2014

In this paper, we have analyzed and designed a digital RF receiver based on the optimization of the dynamic range parameter to secure the wideband characteristics and linearity of digital radar receivers. To improve the wideband characteristics and dynamic range, a low noise amplifier is matching design with a noise source to minimize the noise figure in 1 GHz bandwidth and we improved the linearity of RF-receiver by securing the conversion gain characteristics of receiver through the design of active mixer. RF receiver is designed to give gain 63 dB, noise figure 1.2 dB and dynamic range of RF receiver has 75.8 dB in a wide band of 8.8~9.8 GHz. It is shown to be applicable to X-band digital radar receiver.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• ETRI Journal
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• v.35 no.5
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• pp.819-826
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• 2013

A class-D audio amplifier for a digital hearing aid is described. The class-D amplifier operates with a pulse-code modulated (PCM) digital input and consists of an interpolation filter, a digital sigma-delta modulator (SDM), and an analog SDM, along with an H-bridge power switch. The noise of the power switch is suppressed by feeding it back to the input of the analog SDM. The interpolation filter removes the unwanted image tones of the PCM input, improving the linearity and power efficiency. The class-D amplifier is implemented in a 0.13-${\mu}m$ CMOS process. The maximum output power delivered to the receiver (speaker) is 1.19 mW. The measured total harmonic distortion plus noise is 0.015%, and the dynamic range is 86.0 dB. The class-D amplifier consumes 304 ${\mu}W$ from a 1.2-V power supply.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.199-204
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• 2007

In digital radiography, to improve the contrast of digital radiography image, the multi-scale nonlinear amplification algorithm based on unsharp masking is one of the major image enhancement algorithms. In this paper, we used the Laplacian pyramid to decompose a digital radiography(DR) image. In our simulation, the DR image was decomposed into seven layers and the coefficients of the each layer was amplified with nonlinear function. We also imported a noise containment algorithm to limit noise amplification. To enhance the contrast of image, we proposed a new adaptive non-linear gain amplification coefficients. As a result of having applied to some clinical data, a detail visibility was improved significantly without unacceptable noise boosting. Images that acquired with the proposed adaptive non-linear gain coefficients have shown superior quality to those that applied similar gain control method and expected to be accepted in the clinical applications.

• Korean Journal of Optics and Photonics
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• v.21 no.4
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• pp.146-150
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• 2010

In this paper we have applied phase conjugated holographyto DHM (digital holography microscopy) to remove phase aberration and noise. Generally,digital holographyincludes the phase information of the object, phase aberration terms introduced by the measurement system and noise terms (DC term and twin images). These aberrations and noise terms decrease the quality of the reconstructed phase image. We could obtain a conjugated hologram which includes only phase information of object. Experimentally we show that distortion of image and aberration of phase in a measurement system are removed using the conjugation hologram.