• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.227-234
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• 2007

This paper describes a transmission method based on a single carrier with frequency domain equalization (SC-FDE) scheme with cyclic prefix(CP). The SC-FDE has similar features with orthogonal frequency division multiplexing(OFDM). Similar to OFDM, a SC-FDE system is computationally efficient since equalization is reformed on a block of data in the frequency domain. Especially, it has the advantage of low sensitivity to nonlinear distortion compared to OFDM. In this paper, we design a SC-FDE receiver using decision-directed method, and present simulation results.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.472-484
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• 2019

This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.

• Smart Structures and Systems
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• v.16 no.4
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• pp.683-699
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• 2015

Time Domain Reflectometry (TDR) has been extensively applied for various laboratory and field studies. Numerous different TDR probes are currently available for measuring soil moisture content and detecting interfaces (i.e., due to landslides or structural failure). This paper describes the development of an innovative spiral-shaped TDR probe that features much higher sensitivity and resolution in detecting interfaces than existing ones. Finite element method (FEM) simulations were conducted to assist the optimization of sensor design. The influence of factors such as wire interval spacing and wire diameter on the sensitivity of the spiral TDR probe were analyzed. A spiral TDR probe was fabricated based on the results of computer-assisted design. A laboratory experimental program was implemented to evaluate its performance. The results show that the spiral TDR sensor featured excellent performance in accurately detecting thin water level variations with high resolution, to the thickness as small as 0.06 cm. Compared with conventional straight TDR probe, the spiral TDR probe has 8 times the resolution in detecting the water level changes. It also achieved 3 times the sensitivity of straight TDR probe.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.5
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• pp.37-42
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• 1983

The elliptic functions have transmission zeros on the imaginary axis and exhibit equal ripples in the stopband as well as in the passband. As a consequence they can be made optimal in the sense that the transition band is minimal. However the time domain behaviors turned out to be inferior to those of Chebyshev and Butterworth responses. This paper investigates the unit step responses and impulse responses in order to analyze the effects of various parameters such as passband attenuation, stopband frequencies M. etc., The following are the prominent features. Step responses of elliptic filters rise faster and produce larger overshoots and undershoots with higher natural frequencies. In the case of even functions, the initial values are non-zero which decreases as $\omega$s increases. Unlike Butter-worth or Chebyshev cases the impulse responses start with nonzero valses which also decrease as $\omega$s or order of the function increases. Eight figures are included to illustrate above analysis.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.302-305
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and denpends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.5
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• pp.181-186
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• 2003

This paper presents a numerical dispersion relation for the two-dimensional finite-difference time-domain method based on the alternating-direction implicit time-marching scheme(2-D ADI-FDTD), which method has the potential to considerably reduce tile number of time iterations especially in case where the fine spatial lattice relative to the wavelength is used to resolve fine geometrical features. The proposed analytical relation for 2-D ADI-FDTD is compared with those relations in the Previous works. Through numerical tests, the dispersion equation of this work was shown as correct one for 2-D ADI-FDTD.

• ETRI Journal
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• v.30 no.4
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• pp.535-545
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• 2008

This paper introduces a systematic top-down and bottom-up design methodology to assist the designer in the implementation of continuous-time (CT) cascade sigma-delta (${\Sigma}{\Delta}$) modulators. The salient features of this methodology are (a) flexible behavioral modeling for optimum accuracy-efficiency trade-offs at different stages of the top-down synthesis process, (b) direct synthesis in the continuous-time domain for minimum circuit complexity and sensitivity, (c) mixed knowledge-based and optimization-based architectural exploration and specification transmission for enhanced circuit performance, and (d) use of Pareto-optimal fronts of building blocks to reduce re-design iterations. The applicability of this methodology will be illustrated via the design of a 12-bit 20 MHz CT ${\Sigma}{\Delta}$ modulator in a 1.2 V 130 nm CMOS technology.

• Journal of Korean Medical Ki-Gong Academy
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• v.10 no.1
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• pp.130-157
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• 2007

The purpose of this research is to seek for efficient health maintenance device and to suggest desirable daily habit, based on the inquiry on interrelationship between workers' daily lives and their heartbeat change level. The paper survey about general features, case history and daily habits was conducted on workers during medical examination in Jeollabukdo, and examined their change of heartbeat as well. The results of research deducted from data analysis are as follows; 1. There found very positive interrelations between time-domain analysis and frequency-domain analysis, and MHR and LF/HF ratio had negative connection with other analyses. 2. The recipient showed high time-domain analysis when they are younger, have worked shorter or have spouse, and it contributes to stable sympathetic nerve and parasympathetic nerve as it stimulates autonomic nervous system. 3. According to the result of frequency-domain analysis, recipients showed higher TP and LF when they are younger, and the highest HF when they are under 34.The level of VLF was higher for university graduates than the ones who finished high school. The recipients showed higher TP and HF when they don't have spouse, and lower TP, LF and HF when they have worked longer. 4. The level of RMSSD and TSRD was high for the people who don't have case history, and HF was high when they don't have any disease in progress. 5. According to the result concerning correlation of daily habits with time-domain analysis and frequency-domain analysis, cigarette, alcohol and sleeping hours don't affect heartbeat change, but the ones who regularly workout showed higher result in every analysis. It shows that the autonomic nervous system of recipients who regularly exercise response more actively. The result mentioned above suggests that the change of heartbeat is a direct index which shows the change of autonomic nervous system, and it depends on the exercise the most. Thus, workout is proved to be the best method in order for workers to take care of their health.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.255-258
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• 2011

An application of Gammatone auditory filterbank for emotion recognition from speech is described in this paper. Gammatone filterbank is a bank of Gammatone filters which are used as a preprocessing stage before applying feature extraction methods to get the most relevant features for emotion recognition from speech. In the feature extraction step, the energy value of output signal of each filter is computed and combined with other of all filters to produce a feature vector for the learning step. A feature vector is estimated in a short time period of input speech signal to take the advantage of dependence on time domain. Finally, in the learning step, Hidden Markov Model (HMM) is used to create a model for each emotion class and recognize a particular input emotional speech. In the experiment, feature extraction based on Gammatone filterbank (GTF) shows the better outcomes in comparison with features based on Mel-Frequency Cepstral Coefficient (MFCC) which is a well-known feature extraction for speech recognition as well as emotion recognition from speech.