• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.309-316
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• 2019

In this paper, a low-jitter delay-locked loop that compensates for local clock skew is presented. The proposed DLL consists of a phase splitter, a phase detector(PD), a charge pump, a bias generator, a voltage-controlled delay line(VCDL), and a level converter. The VCDL uses self-biased delay cells using current mode logic(CML) to have insensitive characteristics to temperature and supply noises. The phase splitter generates two reference clocks which are used as the differential inputs of the VCDL. The PD uses the only single clock from the phase splitter because the PD in the proposed circuit uses CMOS logic that consumes less power compared to CML. Therefore, the output of the VCDL is also converted to the rail-to-rail signal by the level converter for the PD as well as the local clock distribution circuit. The proposed circuit has been designed with a $0.13-{\mu}m$ CMOS process. A global CLK with a frequency of 1-GHz is externally applied to the circuit. As a result, after about 19 cycles, the proposed DLL is locked at a point that the control voltage is 597.83mV with the jitter of 1.05ps.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.67-78
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• 2022

Interest in bio-signal monitoring of wearable devices is increasing significantly as the next generation needs to develop new devices to dominate the global market of the information and communication technology industry. Accordingly, this research developed a resistive textile strain sensor through a wetting process in a single-wall carbon nanotube dispersion solution using an E-Band with low hysteresis. To measure the resistance signal in the E-Band to which electrical conductivity is applied, a universal material tester, an Arduino, and LCR meters that are microcontroller units were used to measure the resistance change according to the tensile change. To effectively handle various noises generated due to the characteristics of the fabric textile strain sensor, the filter performance of the sensor was evaluated using the moving average filter, Savitsky-Golay filter, and intermediate filters of signal processing. As a result, the reliability of the filtering result of the moving average filter was at least 89.82% with a maximum of 97.87%, and moving average filtering was suitable as the noise filtering method of the textile strain sensor.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.25-33
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• 2023

Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.107-116
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• 2021

This study aims to investigate the effect of multisensory stimulation on relieving the stress experienced by drivers. The photoplethysmograms (PPGs) of 30 healthy subjects were measured, and their subjective response to stressful situations and normal driving were evaluated. The subjects underwent nonstimulation and multisensory stimulation in stressful driving situations. Heart rate estimation from the PPG was collected via an ear-type sensor to reduce movement noise. The signals acquired were sampled at 200 Hz using BIOPAC PPG100C. Heart rate variability (HRV) was analyzed to compare the effect of multisensory stimulation on stress situations. In the multisensory stimulation, blue, green, and yellow were used for the visual sensory system; white, pink, and brown noises were used for the auditory sensory system; and lavender, lemon, and rosemary were used for the olfactory sensory system. No difference was observed in the subjective evaluation; however, the HRV results showed an increased HF (%) and decreased LF (%) and LF/HF (%) in the multisensory stimulation (e.g., green, pink noise, and rosemary) when compared to the nonstimulation.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.14-25
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• 2022

The passive surface wave method measures seismic signals from ambient noises or vibrations of natural phenomena without using an artificial source. Since passive sources are usually in lower frequencies than artificial ones being able to ensure the information on deeper geological structures, the passive surface wave method can investigate deeper geological structures. In the passive method, frequency dispersion curves are obtained after data acquisition, and the dispersion curves are analyzed by assuming 1D-layered earth, which is like the method of active surface wave survey. However, when computing dispersion curves, the passive method first obtains and analyzes coherence curves of received signals from a set of receivers based on spatial autocorrelation. In this review, we explain how passive surface wave methods measure signals, and make data processing and interpretation, before analyzing field application cases.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.15-21
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• 2009

In this paper, we proposed an iterative digital image watermarking technique using encrypted binary phase computer generated hologram in the discrete cosine transform(OCT) domain. For the embedding process of watermark, using simulated annealing algorithm, we would generate a binary phase computer generated hologram(BPCGH) which can reconstruct hidden image perfectly instead of hidden image and repeat the hologram and encrypt it through the XOR operation with key image that is ramdomly generated binary phase components. We multiply the encrypted watermark by the weight function and embed it into the DC coefficients in the DCT domain of host image and an inverse DCT is performed. For the extracting process of watermark, we compare the DC coefficients of watermarked image and original host image in the DCT domain and dividing it by the weight function and decrypt it using XOR operation with key image. And we recover the hidden image by inverse Fourier transforming the decrypted watermark. Finally, we compute the correlation between the original hidden image and recovered hidden image to determine if a watermark exits in the host image. The proposed watermarking technique use the hologram information of hidden image which consist of binary values and encryption technique so it is very secure and robust to the external attacks such as compression, noises and cropping. We confirmed the advantages of the proposed watermarking technique through the computer simulations.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.95-104
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• 2009

This paper proposes the noise robust algorithm to detect the starting point of music. Detection of starting point of music is necessary to solve computational-waste problem and retrieval-comparison problem with inconsistent input data in music content based retrieval system. In particular, such detection is even more necessary in time sequential retrieval method that compares data in the sequential order of time in contents based music retrieval system. Whereas it has the long point that the retrieval is fast since it executes simple comparison in the order of time, time sequential retrieval method has the short point that data starting time to be compared should be the same. However, digitalized music cannot guarantee the equity of starting time by bit rate conversion. Therefore, this paper ensured that recognition rate shall not decrease even while executing high speed retrieval by applying time sequential retrieval method through detection of music starting point in the pre-processing stage of retrieval. Starting point detection used minimum wave model that can detect effective sound, and for strength against noise, the noises existing in mute sound were swapped. The proposed algorithm was confirmed to produce about 38% more excellent performance than the results to which starting point detection was not applied, and was verified for the strength against noise.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1C
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• pp.45-51
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• 2010

In urban area, design and construction of civil engineering structures such as subway tunnel, underground space and deep excavation is impeded by unreliable site investigation. Variety of embedded objects, electric noises and traffic vibrations degrades the quality of site investigation, whatever the site-investigation technique would be. In this research, a preliminary research was performed to develop a dedicated site investigation technique for urban geotechnical sites, which can overcome the limitations of urban sites. HiRAS (Hybrid Integration of Surface Waves and Resistivity) technique which is the first outcome of the preliminary research was proposed in this paper. The technique combines surface wave as well as electrical resistivity. CapSASW method for surface-wave technique and PDC-R technique for electrical resistivity survey were incorporated to develop HiRAS technique. CapSASW method is a good method for evaluating material stiffness and PDC-R technique is a reliable method for determination of underground stratification even in a site with electrical noise. For the inversion analysis of HiRAS techniuqe, a site-specific relationship between stress-wave velocity and resistivity was employed. As for outgrowth of this research, the 2-D distribution of Poisson's ratio could be also determined.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.507-515
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• 2008

In this study, a method predicting the displacement response of structures from the measured dynamic strain signal is proposed by using mode decomposition technique. Evaluation of bridge stability is normally focused on the bridge completed. However, dynamic loadings including wind and seismic loadings could be exerted to the bridge under construction. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. As previous studies on the prediction of displacement response by using the FBG sensors, the static displacement has been mainly predicted. For predicting the dynamic displacement, it has been known that the measured strain signal includes higher modes and then the predicted dynamic displacement can be inherently contaminated by broad-band noises. To overcome such problem, a mode decomposition technique was used. Mode decomposition technique estimates the displacement response of each mode with mode shape estimated to use POD from strain signal and with the measured strain signal decomposed into mode by EMD. This is a method estimating the total displacement response combined with the each displacement response about the major mode of the structure. In order to examine the mode decomposition technique suggested in this study model experiment was performed.