• Journal of Semiconductor Engineering
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• 제1권2호
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• pp.64-73
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• 2020

As CMOS technology scales down, the design of analog signal processing circuit becomes far more difficult because of steadily decreasing supply voltage and smaller intrinsic gain of transistors. With sub-1V supply voltage, the conventional analog signal processing relying on high-gain amplifiers is not an effective solution and different approach has to be sought. One of the promising approaches is "time-domain analog signal processing" which exploits the improving switching speed of transistors in a scaled CMOS technology. In this paper, various time-domain analog signal processing techniques are explained with some experimental results.

• 전기학회논문지
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• 제63권12호
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• pp.1683-1689
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• 2014

In this paper, we propose an image processing based time-frequency domain reflectometry(TFDR) in order to estimate the fault location of a cable. The Wigner-Ville distribution is used for analysis in both the time domain and the frequency domain when the conventional TFDR estimates the fault location in a cable. However, the Winger-Ville distribution is a bi-linear function, and hence the cross-term is occurred. The conventional TFDR cannot estimate the accurate fault location due to the cross-term in case the fault location is close to the position where the reference signal is applied to the cable. The proposed method can reduce the cross-term effectively using binarization and morphological image processing, and can estimate the fault location more accurately using the template matching based cross correlation compared to the conventional TFDR. To prove the performance of the proposed method, the actual experiments are carried out in some cases.

• Journal of the Optical Society of Korea
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• 제12권4호
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• pp.249-254
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• 2008

In this paper we present an electronic domain solution for chromatic dispersion (CD) monitoring algorithm based on the estimated time domain channel in electronic domain using channel estimation methods. The proposed scheme utilizes kurtosis as a CD measurement, directly computed from the estimated inter-symbol-interference (ISI) channel due to the CD distortion. Hence, the proposed scheme exhibits robust performance under OSNR variation, in contrast to the existing electronic domain approach based on minimum mean squared error (MMSE) fractionally-spaced equalizer taps [1]. The simulation results verify the CD monitoring ability of the proposed scheme.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1347-1351
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• 1987

This thesis aims at studying laplacian pyramid vector quantization which keeps a simple compression algorithm and stability against various kinds of image data. To this end, images are devied into two groups according to their statistical characteristics. At 0.860 bits/pixel and 0.360 bits/pixel respectively, laplacian pyramid vector quantization is compared to the existing spatial domain vector quantization and transform coding under the same condition in both objective and subjective value. The laplacian pyramid vector quantization is much more stable against the statistical characteristics of images than the existing vector quantization and transform coding.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.55-58
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• 1997

In this paper, we study a signal processing method which extracts each MUAP(motor unit action potential) from EMG(Electromyogram) interference pattern or clinical diagnostic purposes. First of all, differential digital filtering is selected or eliminating the spike components of the MUAP's from the background noise. And, the algorithm identifies the spikes over the certanin threshold by template matching in frequency domain. After missing or false firing actor is cut off at the IPI(inter pulse interval) histogram, we averages the MUAP waveforms from the raw signal using the identified spikes as triggers, and Finally, measures their amplitudes, durations, and numbers of phases. Specially, We introduce algorithm performed by template matching in the frequency domain. A typical 3-s signal recorded from the biceps brachii muscle using a conventional needle electrode during a isometric contraction is used. Finally, the method decomposed five simultaneous active MUAP's from original EMG signal.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• ETRI Journal
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• 제44권5호
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• pp.826-836
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• 2022

Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and empirical mode decomposition, on preterm birth classification was investigated. Ten time-domain features were extracted from the constituent components of electrohysterogram (EHG) signals, including EHG subbands and EHG intrinsic mode functions, and employed for preterm birth classification. Preterm birth classification and anticipation are crucial tasks that can help reduce preterm birth complications. The computational results show that the preterm birth classification obtained using wavelet-based decomposition is superior. This, therefore, implies that EHG subbands decomposed through wavelet-based decomposition provide more applicable information for preterm birth classification. Furthermore, an accuracy of 0.9776 and a specificity of 0.9978, the best performance on preterm birth classification among state-of-the-art signal processing techniques, were obtained using the time-domain features of EHG subbands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1999년도 춘계학술대회 논문집
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• pp.169-172
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• 1999

Recently, the wavelet transform has been a new and powerful tool for signal processing. It is more suitable specially for the feature extraction and detection of non-stationary signals than traditional methods such as, the Fourier Transform(FT), the Fast Fourier Transform(FFT) and the Least Square Method etc. because of the characteristic of the multi-scale analysis and time-frequency domain localization. The wavelet transform has been developed for the analysis of PD pulse signal to raise in the progress of insulation degradation. In this paper, the wavelet transform was applied to one foundational method for feature extraction. For the obtain experimental data, a computer-aided partial discharge measurement system with a single acoustic sensor was used. If we are applying to the neural network method the accumulated data through the extracted feature, it is expected that we can detect the PD pulse signal in the insulation materials on the on-line.

• 한국전자통신학회논문지
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• 제19권2호
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• pp.335-343
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• 2024

본 논문은 FMCW LiDAR의 실시간 표적 신호처리 기법에 관해 기술하고 있다. FMCW LiDAR는 높은 검출민감도를 가져 낮은 출력만으로 장거리 측정이 가능하면서도 눈, 비, 안개 등 열악한 환경에서 강건한 검출성능을 가져 자율주행자동차용 차세대 LiDAR로 주목받고 있다. 본 논문은 주파수 영역의 신호처리를 위해 필요한 고속 데이터 획득, 전송 및 병렬 신호처리를 위한 하드웨어 구조에 대해 기술하였다. 획득된 시계열 신호로부터 주파수 특성을 분석하기 위하여, 푸리에 변환 연산을 FPGA로 구현하였다. 변환된 주파수영역 데이터로부터 강건한 표적검출 성능을 확보하기 위한 C-FAR 알고리즘에 대해 기술하였다. 표적의 스펙트럼 신호로부터 주파수 측정값의 해상도를 향상하고, 측정된 주파수 값을 표적의 거리 및 속도 정보로 변환하는 과정에 대해 상세히 기술하였다. 스캐너 2D 위치 및 표적의 거리 정보를 활용하여 3차원 영상으로 변환하고 이를 전시하였다. 제안된 FPGA 구조의 병렬 신호처리 알고리즘 적용을 통하여 FMCW LiDAR의 실시간 표적 신호처리 및 고해상도 영상획득 성능을 확인하였다.