• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1243-1257
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• 2019

As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.294-294
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• 2000

In this paper, we would like to discuss the signal processing and the algorithm for ECG analysis. The ECG gives us information about the condition of the heart muscle, because myocardial abnormality or infarction is inscribed on the ECG during myocardial depolarization and repolarization. Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. The wavelet transform decomposes the ECG signal into high and low frequency component using wavelet function. Recomposing high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the curve-fitting partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with some kinds of heart disease ECG pattern, we can detect and classify the kind of heart disease.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.353-356
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• 2000

This paper proposes a 16-bit $\times$ 16-bit multiplier for 2 twos-complement binary numbers with complex structure and implements it on a FPGA.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• Journal of Biomedical Engineering Research
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• v.6 no.2
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• pp.15-22
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• 1985

We have developed a microprogramir!able signal processor for real-time ultrasonic signal processing. Processing speed was increased by the parallelism in horizontal microprogram using 104bits microcode and the Pipelined architecture. Control unit of the signal processor was designed by microprogrammed architec- ture and writable control store (WCS) which was interfaced with host computer, APPLE- ll . This enables the processor to develop and simulate various digital signal processing algorithms. The performance of the processor was evaluated by the Fast Fourier Transform (FFT) program. The execution time to perform 16 bit 1024 points complex FF7, radix-2 DIT algorithm, was about 175 msec with IMHz master Clock. We can use this processor to Bevelop more efficient signal processing algorithms on the biological signal processing.

• Journal of Sensor Science and Technology
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• v.19 no.3
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• pp.230-237
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• 2010

In sensor signal conditioning and processing, offset and drift characteristics of an operational amplifier are an important factor when the amplifier is used for a precise sensor signal amplifier. In order to use it in high accuracy, an expensive trimming or a complex compensation circuit is required. This paper presents the improved sensor signal conditioning and processing device for ubiquitous sensor network(USN) application or general purpose by developing a hardware of the circuit for reducing the offset voltage and drift characteristics, and a software for its control and sensor signal processing. We realize better offset voltage and drift characteristics of the signal conditioning circuit using low cost operational amplifiers. The experimental results show that this technique is effective in improving the performance of the sensor signal processing device.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.3
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• pp.229-235
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• 1993

In this paper, a method of ghost cancelling for the television signals using complex adaptive filter is studied. The sin(x)/x signal is used as the reference signal a complex adaptive filter. The ghost cancelling characteristics considering the delay time, the attenuation, and the phase difference of multipath waves are investigated using horizontal sync pulse and color burst signal in composite video waveform. The influences of phase difference in ghost cancelling are investigated and the performances between the real processing and the complex processing are compared by the computer simulation. It was found that influences in ghost by phase difference are remarkably reduced by the complex adaptive filtering.

• Journal of Biomedical Engineering Research
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• v.6 no.2
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• pp.13-14
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• 1985

We have developed a microprogramir!able signal processor for real-time ultrasonic signal processing. Processing speed was increased by the parallelism in horizontal microprogram using 104bits microcode and the Pipelined architecture. Control unit of the signal processor was designed by microprogrammed architec- ture and writable control store (WCS) which was interfaced with host computer, APPLE- ll . This enables the processor to develop and simulate various digital signal processing algorithms. The performance of the processor was evaluated by the Fast Fourier Transform (FFT) program. The execution time to perform 16 bit 1024 points complex FF7, radix-2 DIT algorithm, was about 175 msec with IMHz master Clock. We can use this processor to Bevelop more efficient signal processing algorithms on the biological signal processing.

• Journal of KSNVE
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• v.8 no.5
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• pp.957-963
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• 1998

A new signal processing technique, the directional harmonic wavelet map(dHWM), is presented to characterize the instantaneous planar motion of a measurement point in a structure from its transient complex-valued vibration signal. It is proven that the directional auto-HWM essentially tracks the shape and directively of the instantaneous planar motion, whereas the phase of the directional cross-HWM indicates its inclination angle. Finally, the technique is suessfully applied to an automobile engine for characterization of its transient motion during crank-on/idling/engine-off.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.117-120
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• 2000

Pulse oximetry is a well established non-invasive optical technique for monitoring the SpO$_2$ during anaesthesia, recovery and intensive care. Pulse oximeters determine the oxygen saturation level of blood by measuring the light absorption of arterial blood. The sensors consists of red and infrared light sources and photodetectors. In the measurement of the hemoglobin oxygen saturation, conventional method has required the technique of filtering of remove the noise, and of complex signal processing algorithm. So much time have required to signal processing. In this research, we separate AC signal and DC signal in the stage of signal detection. We filter the noise from each signal and convert A/D. We obtain the SpO$_2$ using the DSP algorithm.