• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.484-490
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• 2016

Full-duplex communication can enhance wireless capacity by enabling simultaneous transmission and reception of the signals on the same frequency spectrum. Such a benefit, however, is only achieved when strong self-interference is well canceled below a sufficient level. To achieve this goal, there have been several approaches for cancellation, each of which is combined with digital-domain cancellation for a higher gain. In this paper, we implement two self-interference cancellation techniques and integrate them with a software defined radio-based wireless communication testbed. Two cancellation techniques (antenna cancellation and noise subtraction) are implemented and the cancellation gain is measured via real experiments. The results show that the gain of the antenna placement technique highly depends on the placement of a receiving antenna and the highest gain is achieved at the expected point, and we show that combining the noise subtraction circuit with the antenna placement further improves the cancellation gain.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.12-20
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• 1999

In this paper, we compared speaker verification performance of the speech data collected in clean environment and in channel environment. For the improvement of the performance of speaker verification gathered in channel, we have studied on the efficient feature parameters in channel environment and on the preprocessing. Speech DB for experiment is consisted of Korean doublet of numbers, considering the text-prompted system. Speech features including LPCC(Linear Predictive Cepstral Coefficient), MFCC(Mel Frequency Cepstral Coefficient), PLP(Perceptually Linear Prediction), LSP(Line Spectrum Pair) are analyzed. Also, the preprocessing of filtering to remove channel noise is studied. To remove or compensate for the channel effect from the extracted features, cepstral weighting, CMS(Cepstral Mean Subtraction), RASTA(RelAtive SpecTrAl) are applied. Also by presenting the speech recognition performance on each features and the processing, we compared speech recognition performance and speaker verification performance. For the evaluation of the applied speech features and processing methods, HTK(HMM Tool Kit) 2.0 is used. Giving different threshold according to male or female speaker, we compare EER(Equal Error Rate) on the clean speech data and channel data. Our simulation results show that, removing low band and high band channel noise by applying band pass filter(150～3800Hz) in preprocessing procedure, and extracting MFCC from the filtered speech, the best speaker verification performance was achieved from the view point of EER measurement.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.10-19
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• 2021

In this paper, we tried to derive characteristic parameters that reflect mental fatigue through EEG measurement and analysis. For this purpose, mental fatigue was induced through a resting state with eyes closed and performing subtraction operations in mental arithmetic for 30 minutes. Five subjects participated in the experiment, and all subjects were right-handed male students in university, with an average age of 25.5 years. Spectral analysis was performed on the EEG collected at the beginning and the end of the experiment to derive feature parameters reflecting mental fatigue. As a result of the analysis, the absolute power of the alpha band in the occipital lobe and the temporal lobe increased as the mental fatigue increased, while the relative power decreased. Also, the difference in power between resting state and task state showed that the relative power was larger than the absolute power. These results indicate that alpha relative power in the occipital lobe and temporal lobe is a feature parameter reflecting mental fatigue. The results of this study can be utilized as feature parameters for the development of an automated system for mental fatigue determination such as fatigue and drowsiness while driving.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.327-329
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• 2017

We report a spatial fluctuation analysis of the sky brightness in the near-infrared from observations towards the north ecliptic pole (NEP) by AKARI at 2.4 and $3.2{\mu}m$. As a follow up study of our previous work on the Monitor field of AKARI, we used NEP deep survey data, which covered a circular area of about 0.4 square degrees, in order to extend fluctuation analysis at angular scales up to 1000". After pre-processing, additional correction procedures were done to correct time varying components and instrumental effects such as MUXbleed. To remove resolved objects, we applied $2{\sigma}$ clipping and point spread function (PSF) subtraction. We finally obtained mosaicked images which can be used for the study of various diffuse emissions in the near-infrared sky and found that there are spatial structures in the mosaicked images using a power spectrum analysis.

• Analytical Science and Technology
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• v.5 no.4
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• pp.455-464
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• 1992

Using FT-IR, we determined the depth of silylated layers produced from various gas-phase-silylation conditions was proposed by using Fourier Transform Infrared (FT-IR) spectroscopic analysis. The depth of silylated layer was determined from absorbance measurments of the significant peaks (Si-O-ph, Si-C, Si-H) of FT-IR spectra with background spectrum subtraction method. And the results were compared with thickness measurments of SEM. The results were well agree with SEM. It found to be well suited for determining silylation process window.

• Korean Journal of Optics and Photonics
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• v.11 no.2
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• pp.97-101
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• 2000

We proposed and demonstrated a new scheme for subtracting the source excess noise from the output of the fiber-optic gyroscope. Unlike the previous methods, this scheme has an advantage of not requiring a delaying fiber for source noise detection and can be simply applied to fiber gyroscope operating at the proper frequency. By usmg this scheme. the proper frequency component of the source excess noise was subtracted from the gyroscope output. As a result, we obtained the reduction of noise by 12 dB (electrical) which was measured from the noise floor spectrum in the fiber gyroscope modulated at the proper frequency. We also obtarned !be random walk coefficient of thc gyro output reduced by a factor of 3.5. f 3.5.

• ETRI Journal
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• v.41 no.3
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• pp.308-315
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• 2019

Due to its high spectrum efficiency, 64-amplitude phase-shift keying (64-APSK) is one of the primary technologies used in deep space communications and digital video broadcasting through satellite-second generation. However, 64-APSK suffers from considerable computational complexity because of the de-mapping method that it employs. In this study, a low-complexity de-mapping method for (4 + 12 + 20 + 28) 64-APSK is proposed in which we take full advantage of the symmetric characteristics of each symbol mapping. Moreover, we map the detected symbol to the first quadrant and then divide the region in this first quadrant into several partitions to simplify the formula. Theoretical analysis shows that the proposed method requires no operation of exponents and logarithms and involves only multiplication, addition, subtraction, and judgment. Simulation results validate that the time consumption is dramatically decreased with limited degradation of bit error rate performance.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.147-156
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• 2022

In this paper, we propose Time-Division-Multiplexing Tertiary Offset Carrier (TDMTOC), a novel GNSS modulation based on Tertiary Offset Carrier (TOC) modulation. The TDMTOC modulation multiplexes two three-level signals (i.e., -1, 0, and 1) while crossing over time, and is a type of TOC modulation designed specifically for signal multiplexing. The proposed modulation generates TDMTOC subcarriers of two different phases by simply combining two Binary Offset Carrier (BOC) subcarriers by addition or subtraction. TDMTOC has better correlation and spectral properties than conventional BPSK, BOC, and MBOC modulation techniques, and has good power and spectral efficiency since it can multiplex signals without power loss similar to time division multiplexing. To prove this, we introduce the multiplexing process of TDMTOC, and compare TDMTOC with Binary Phase Shift Keying (BPSK), BOC, Composite BOC (CBOC), and Time Multiplexed BOC (TMBOC) that are currently serviced in GNSS by simulations of various aspects. Through the simulation results, we prove that TDMTOC has better correlation property than modulations currently used in GNSS, less intersystem interference due to its wide spectrum property, and robustness in multipath and noise channel environments.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.342-350
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• 2022

In this paper, actual drone noise samples are collected for speech processing in disaster environments to build noise-corrupted speech database, and speech enhancement performance is evaluated by applying spectrum subtraction and mask-based speech enhancement techniques. To improve the performance of VoiceFilter (VF), an existing deep neural network-based speech enhancement model, we apply the Self-Attention operation and use the estimated noise information as input to the Attention model. Compared to existing VF model techniques, the experimental results show 3.77%, 1.66% and 0.32% improvements for Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligence (STOI), respectively. When trained with a 75% mix of speech data with drone sounds collected from the Internet, the relative performance drop rates for SDR, PESQ, and STOI are 3.18%, 2.79% and 0.96%, respectively, compared to using only actual drone noise. This confirms that data similar to real data can be collected and effectively used for model training for speech enhancement in environments where real data is difficult to obtain.