• 한국통신학회논문지
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• 제28권3C호
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• pp.286-295
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• 2003

본 논문에서는 최소의 메모리 사용량으로 원하는 비트율로 영상 부호화가 가능한 웨이브렛 기반의 영상 부호화기를 제안하였다. 제안된 방법은 부호화 과정시 요구되는 메모리 사용량을 줄이기 위해 웨이블릿 계수들의 비트 레벨 정보를 가지는 2D 중요 계수 배열을 사용하였다. 2D SCA는 웨이블릿 계수의 비트 레벨 정보를 저장하는 이차원 자료 구조로서, 제안된 알고리즘은 이것을 이용하여 중요한 계수에 대한 부호화 과정과 계수들의 비트 레벨 정보의 부호화 과정을 한 번에 수행할 수 있다. 실험 결과 기존의 부호화 방법보다 화질 면에서 비슷하거나 우수한 성능을 보였다. 특히 2D SCA를 이용한 최소의 메모리 사용으로 다양한 비트율에서 영상의 일그러짐 없이 안정적으로 동작함을 확인하였다.

• 한국임상보건과학회지
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• 제4권4호
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• pp.756-761
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• 2016

Purpose. Noise occurs at measuring Electoretinogram(ERG) signals as the other bio-signal measurement. It is compared the denoising performance according to the mother function of wavelet transforms. Methods. The ERG signal that generated power supply noise and white noise was used as a sampling signal. The noise of ERG signal was filtered by using haar, db7, bior mother function. The filtering performance of each mother functions was compared using Fourier transform spectrum and SNR(signal to noise ratio). Results. In the haar functioin, the result of the Fourier transform spectrum was that the power supply noise is removed and the white noise performance is not good. The SNR was 27.0404. In the db7 function, the results of Fourier transform spectrum was that the power supply noise is removed and the white noise performance is good. The SNR was 35.1729. In the db7 function, the results of Fourier transform spectrum was that the power supply noise is removed and the white noise performance is the bset. The SNR was 35.4445. Conclusions. The db7, bior function was good results in power supply noise and white noise filtered. The bior function is suitable for filtering noise of the ERG signal.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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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.

• 한국통신학회논문지
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• 제25권9B호
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• pp.1570-1578
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• 2000

본 논문은 채널간의 간섭을 줄이는 관점에서 코사인 변조 필터 뱅크와 사인 변조 필터 뱅크로 이루어진 필터뱅크의 쌍을 다중 반송파 부호 분할 다원 접속(multi-carrier code division multiple access: MC-CDMA) 시스템의 다중화 전송에 적용하였다. 웨이브렛 특성을 필터 뱅크의 구현에 활용하는 제안된 기법은 이산 퓨리에 변환(discrete Fourier transform: DFT)에 기반을 둔 기존의 MC-CDMA 시스템과 비교하였을 때, 전송 채널의 부채널화의 우수성으로 인해 채널간의 간섭을 감소시킬 수 있다. 제안된 직교 쌍 필터 뱅크를 기반으로 하는 MC-CDMA 시스템의 성능을 평가하기 위하여, 레이라이 페이딩 채널과 가우시안 잡음 채널에서 역 방향 링크의 신호 대 잡음비에 대한 비트 오율을 계산한다. 성능평가 결과는 제안한 시스템이 간섭의 영향을 최소화하는 측면에서 기존의 MC-CDMA 시스템 보다 우수한 성능을 보이고 있다.

• 한국구조물진단유지관리공학회 논문집
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• 제5권4호
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• pp.215-225
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• 2001

Stress waves monitored on the surface of structures under various loading conditions can provide useful information on the structural health status. In this paper, stress waves are measured by several sensors when a steel beam is impacted by a ball drop. The sensors used include the piezo-electric film Sensor, the electrical strain gage, and the ultrasonic transducer, and special attention is given to the pieza film sensor. The wavelet transform is used for the time-frequency analysis of dispersive waves propagating in the beam. The velocities of the wave produced in the team due to the lateral impact is found to be frequency-dependent and identified as the flexural wave velocity based on the comparisons with the Timoshenko beam theory. A linear impact site identification method is developed using the flexural wave, and the impact sites of the beam can be accurately estimated by the piezo film sensors. It is found that the piezo film sensor is appropriate for sensing stress waves due to impact and for locating impact sites in the beam.

• 지능정보연구
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• 제4권2호
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• 디지털산업정보학회논문지
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• 제14권2호
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• pp.49-59
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• 2018

Encryption of digital images has been requested various fields. In the meantime, many algorithms based on a text - based encryption algorithm have been proposed. In this paper, we propose a method of encryption in wavelet transform domain to utilize the characteristics of digital image. In particular, wavelet transform is used to reduce the association between the encrypted image and the original image. Wavelet packet transformations can be decomposed into more subband images than wavelet transform, and various position permutation, numerical transformation, and visual transformation are performed on the coefficients of this subband image. As a result, this paper proposes a method that satisfies the characteristics of high encryption strength than the conventional wavelet transform and reversibility. This method also satisfies the lossless symmetric key encryption and decryption algorithm. The performance of the proposed method is confirmed by visual and quantitative. Experimental results show that the visually encrypted image is seen as a completely different signal from the original image. We also confirmed that the proposed method shows lower values of cross correlation than conventional wavelet transform. And PSNR has a sufficiently high value in terms of decoding performance of the proposed method. In this paper, we also proposed that the degree of correlation of the encrypted image can be controlled by adjusting the number of wavelet transform steps according to the characteristics of the image.

• 디지털산업정보학회논문지
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• 제8권3호
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• pp.133-145
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• 2012

This paper describes the high density discrete wavelet transformation which is one that expands an N point signal to M transform coefficients with M > N. The double-density discrete wavelet transform is one of the high density discrete wavelet transformation. This transformation employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. And it is nearly shift-invariant. Similarly, triple-density discrete wavelet transformation is a new set of dyadic wavelet transformation with two generators. The construction provides a higher sampling in both time and frequency. Specifically, the spectrum of the first wavelet is concentrated halfway between the spectrum of the second wavelet and the spectrum of its dilated version. In addition, the second wavelet is translated by half-integers rather than whole-integers in the frame construction. This arrangement leads to high density wavelet transformation. But this new transform is approximately shift-invariant and has intermediate scales. In two dimensions, this transform outperforms the standard and double-density discrete wavelet transformation in terms of multiple directions. Resultingly, the proposed wavelet transformation services good performance in image and video processing fields.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.148-151
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• 2010

심장관련 질환은 현대사회에서 업무 과중과 스트레스에 의해 발병 가능성이 높아지고 있으며, 일상생활 중 건강상태를 지속적으로 모니터링하여 심장질환 관련 응급상황에 대처하기위한 많은 연구들이 수행되고 있다. 본 연구에서는 가정 또는 사무실에서 무구속적인 방법으로 지속적인 심장 활동상태의 모니터링이 가능한 무구속 의자형 심탄도 계측 시스템을 구현하였다. 구현된 시스템에서 계측된 심탄도 신호로부터 건강모니터링을 위한 특징성분을 검출하기위해 웨이브렛 변환과 템플릿 매칭을 혼합한 신호처리방법을 제안하였다. 또한 적응 문턱치를 통해 심탄도 신호에서 심박동을 검출하였으며 심박동의 간격으로부터 HRV(heart rate variabillity)를 계산하였다. 구현된 시스템의 성능평가를 위하여 심전도와 동시에 심탄도를 측정하였으며, 두 신호로부터 심박동 검출 성능을 비교하여 구현된 무구속 의자형 심탄도 계측 시스템의 유용성 및 무자각 건강모니터링의 가능성을 확인하였다. 또한 스트레스에 따른 HRV의 변화를 관찰하기 위하여 피실험자로부터 인위적으로 숨을 참고 강제호기를 통해 흉강내압을 증가시켜 인위적인 육체적 스트레스를 가하는 발살바를 유도하였으며, HRV의 시간 및 주파수 영역에서 도출되는 파라미터들을 평가하여 심탄도 모니터링을 통해 안정 상태와 스트레스 상태의 판별 및 무구속 건강모니터링의 가능성을 평가하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2184-2186
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the fast wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of fast wavelet transform to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.