• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.709-712
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• 1988

Ultrasonic Doppler Diagnostic System utilizes the Doppler effect for measurement of blood velocity. The sign of the Doppler frequency shift represents blood flow direction. CW(Continuous-Wave) Doppler System uses quadrature detection and phase rotation method to produce simultaneous independent audio and velocity signals for forward and reverse blood flow direction in the time-domain, had been fabricated. But time-domain analyzing such as audio evaluation and zero- crossing detection for instantaneous and mean frequnecy measurement do not provide both an accurate and quantitative result. Therefore, it is necessary to adopt frequency-domain technique to improve system performance. In this paper, we describe a unit which is composed of CW Doppler System and real-time spectrum analyzer (installed TMS 32010 DSP Chip). This unit shows time-dependent spectrum variation and mean velocity of Blood signal.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.91-94
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• 2003

In this paper, we achieve implementation of a Time-Frequency Domain Reflectometry(TFDR) system through comparatively low performance(100MS/s) PCI extensions for Instrumentation(PXI). The TFDR is the general methodology of Time Domain Reflectometry(TDR) and Frequency Domain Reflectometry(FDR). This methodology is robust in Gaussian noises, because the fixed frequency bandwidth is used. Moreover, the methodology can get more information of the fault by using the normalized time-frequency cross correlation function. The Arbitrary Waveform Generator(AWG) module generates the input signal, and the digital oscilloscope module acquires the input and reflected signals, while PXI controller module performs the control of the total PXI modules and execution of the main algorithm. The maximum range of measurement and the blind spot are calculated according ta variations of time duration and frequency bandwidth. On the basis of above calculations, the algorithm and the design of input signals used in the TFDR system are verified by real experiments. The correlation function is added to the TDR methodology for reduction of the blind spot in the TFDR system.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.543-548
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• 2019

The tonal signal caused by the machinery component of a vessel such as an engine, gearbox, and support elements, can be modeled as a sparse signal in the frequency domain. Recently, compressive sensing based techniques that recover an original signal using a small number of measurements in a short period of time, have been applied for the tonal frequency detection. These techniques, however, cannot avoid a basis mismatch error caused by the discretization of the frequency domain. In this paper, we propose a method to detect the tonal frequency with a small number of measurements in the continuous domain by using the atomic norm minimization technique. From the simulation results, we demonstrate that the proposed technique outperforms conventional methods in terms of the exact recovery ratio and mean square error.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.37-39
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. Since the background noise presenting to record will introduce disturbance while we just want to get the stationary parameters to represent the corresponding speech section, in particular, a major source of error in automatic recognition system of isolated words is the inaccurate detection of beginning and ending boundaries of test and reference templates, thus we must find potent method to remove the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two simple time-domain measurements - short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Comparing with the conventional methods based on short-time energy and short-time zero-crossing rate, the novel approach based on time-domain Lyapunov Exponents(LEs) is low complexity and suitable for Digital Isolated Word Recognition System.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.63-64
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• 2013

Induction motors' faults detection is almost a popular topic among researchers. Monitoring the output of motors is a key factor in detecting these faults. (Short-time) Fourier, (continuous, discrete) wavelet, and extended Park vector transformations are among the methods for fault detection. One major deficiency of these methods is not being able to detect the severity of faults that carry low energy information, e.g. in ball bearing system failure, there is absolutely no way to detect the severity of fault using Fourier or wavelet transformations. In this paper, the authors have applied the Discrete Wavelet Transform (DWT) frequency-domain analysis to detect bearing faults in an induction motor. In other words, in discrete transform which the output signal is decomposed in several steps and frequency resolution increases considerably, the frequency-band analysis is performed and it will be verified that first of all, fault sidebands become more recognizable for detection in higher levels of decomposition, and secondly, the inner race bearing faults turn out easier in these levels; and all these matter because of eliminating the not-required high energy components in lower levels of decomposing.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.285-289
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• 2009

In this paper, we propose a novel double-talk detection (DTD) technique based on soft decision in the frequency domain. In the proposed method, global near-end speech presence probability (GNSPP) considering the statistical model assumption and voice activity detection (VAD) decision of the near-end and far-end signal are applied to the DTD algorithm in the frequency domain instead of the traditional hard decision scheme using cross-correlation coefficients. The performance of the proposed algorithm is evaluated by the objective test under various environments, and yields better results compared with the conventional scheme.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.263-270
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• 2008

We studied partial discharge detection by sensing electromagnetic waves emitted from the partial discharge source in an HV Rotating Machine using a UWB sensor. In this study, we design a new type of compact low frequency UWB sensor based on micro-strip technology. We also perform many experiments of offline and dismantled testing compared with the existing HFCT on stator winding of the HV generator. We mention the detailed design of a new compact UWB sensor along with the time domain PRPD pattern and frequency domain results of partial discharge in the stator winding of a 6.6kV rotating machine by offline testing performed in a laboratory.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.303-307
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• 2011

This paper describes an audio watermarking using Quantization Index Modulation (QIM) on significant peaks in frequency domain. The audio signal is broken up into L samples length frames with non-overlapping and rectangular window. The zero-crossing rate of each frame is calculated for decision whether it is proper to be watermarked or not. If the frame is legitimate, frequency magnitude response is computed by discrete Fourier transform. For the QIM, we set the quantization step size based on maximum value of frequency magnitude response and select n significant peaks with w samples around them in frequency domain, totally $n{\times}(w+1)$ samples. Finally, watermark embedding is performed. Decoder extract watermarks based on Euclidean distance, that is a blind detection. The proposed method is robust against many attacks of watermark benchmark.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.271-280
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• 2011

Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.