• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.93-101
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• 1996

In this paper, we implement the system for speech processing to provide the subscribers who are using the telephone network with better speech services. We develop the specified board which is processing speech signal and devise the system which carries out storing and replaying the speech signal under the condition that one master board controls multiple DSP(Digital Signal Processing) boards using VME bus. We use CPU30 board as a maste board and develop SPM(Signal Processing Module) board as a DSP board and then evaluate performance of the system.

• Journal of Semiconductor Engineering
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• v.1 no.2
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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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.2
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• pp.251-254
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• 2014

In this paper, we studied a technique of extracting a Frequency Hopping(FH) signal for analysis using high-speed parallel processing structure. Unlike fixed frequency signal, FH signal is difficult to detect and analyze because FH systems use many random frequencies instead of a single carrier frequency. To solve this problem we designed a method that analyze FH signal using high-speed parallel processing. In order to apply parallel processing, we use CUDA using GPU and compare single processing with prarallel processing. As a result, using CUDA on a GPU is about 8.53 times faster than single processing.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1999-2005
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• 2005

Collaboration in wireless sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. This paper focuses on finding signal processing algorithms for collaborative target detection based on the generalized approach to signal processing in the presence of noise that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor network. Two algorithms, namely, value fusion and decision fusion constructed according to the generalized approach to signal processing in the presence of noise, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. The use of the generalized approach to signal processing in the presence of noise under designing value and decision fusion algorithms in wireless sensor networks allows us to obtain the same performance, but at low values of signal energy, as under the employment of universally adopted signal processing algorithms widely used in practice.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.97-97
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• 2000

In this paper, a signal processing algorithm for software GPS receiver is proposed. The signal processor takes snapshot of the sampled If signal from the RF section of the GPS receiver. All the processing for code and carrier tracking and correlation are implemented using the digital signal processing techniques. In order to achieve fast code acquisition, correlation of the incoming GPS signal is performed using the FFT method, After code acquisition, to reduce the Doppler shift effect and increase the accuracy, the interpolation or the tracking are performed. The performance of the proposed processing algorithm is first evaluated using matlab/simulink. A signal acquisition board for sampling and logging GPS IF signal form the Mitel GPS RF chip set is constructed. In order to analyze the performance of the designed algorithm the experiments are performed and the results are analyzed.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3120-3122
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• 1999

In this paper we have designed the signal processing system for driving the Poly-Si TFT LCD of EWS. The signal processing system consist of timing controller, ramp signal generator and video signal processing system. Timing controller includes the top-down inversion. left right inversion, left-right shifting and control signal generator according to multi-source signal. The video signal processing system generates sawtooth-shaped waveform by using PROM and DAC for multi-gray scales and implements gamma correction function for compensating the TFT-LCD nonlinear charcteristic of the TFT-LCD. Finally we have discussed the experiment results and its application according to the designed TFT-LCD driving system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.331-337
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• 2013

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.107-113
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• 2011

LSPR(Localized Surface Plasmon Resonance) sensor measures the refractive index change on the sensor surface. The detection of biological reaction with the unknown refractive index needs to be converted into the signal sensitivity for the refractive index change for comparison with other measurements. To find the signal sensitivity, the three steps of signal processing are proposed, which are signal modeling, signal calibration and signal normalization of LSPR sensor. The detected signal of biotin-streptavidin interaction has been converted into unit of [RU](Resonance Unit) using the proposed method. The converted signal directly can be compared with the other sensors including commercialized one.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.5
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• pp.28-35
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• 2015

This paper introduces a resistance scanning-type flexible tactile sensor for intelligent robots and presents the output characteristics of the sensor via signal processing. The sensor was produced via the lamination method using multi-walled carbon nanotubes (a conductive material), an insulator, and Tango-plus (an elastic material). Analog and digital signal processing boards were produced to analyze the output signal of the sensor. The analog signal processing board was made up of an integrator and an amplifier for signal stability, and the digital signal processing board was made up of an IIR filter for noise removal. Finally, the sensor output for the contact force was confirmed through experiments.