• Journal of the Korean Magnetic Resonance Society
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• v.15 no.2
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• pp.157-174
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• 2011

Although the use of apodization functions in connection with postprocessing of a 2D NMR spectrum proves improved spectral quality, there is usually a trade-off between resolution enhancement and noise suppression due to a classical "uncertainty principle." In this study, therefore, a mathematical deconvolution technique called "Maximum Likelihood Deconvolution (MLD)" was adopted to achieve the spectral resolution and sensitivity enhancement simultaneously. The MLD technique greatly facilitates visualization and restoration of the genuine spectral information from complex 2D NMR spectra that would be problematic with the conventional apodization/FT processing. In particular, application of the MLD to the 2D-NOE spectrum would be very useful to derive the important proton connectivities, which are essential to achieve elucidating the 3D molecular structure.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.951-959
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• 2014

Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimator for the deconvolution problem. In the case of Gaussian kernels and measurement error, they argued that the weighted kernel density estimator is a competitive estimator over the classical deconvolution kernel estimator. In this paper we consider weighted kernel density estimators when sample observations are contaminated by double exponentially distributed errors. The performance of the weighted kernel density estimators is compared over the classical deconvolution kernel estimator and the kernel density estimator based on the support vector regression method by means of a simulation study. The weighted density estimator with the Gaussian kernel shows numerical instability in practical implementation of optimization function. However the weighted density estimates with the double exponential kernel has very similar patterns to the classical kernel density estimates in the simulations, but the shape is less satisfactory than the classical kernel density estimator with the Gaussian kernel.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.84-89
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• 2001

Ultrasonic pulse echo method comes to be difficult to apply to the multi-layered structure with very thin layer, because the echoes from the top and the bottom of the layer are overlapped. Conventionally method, deconvolution technique has been used for the decomposition of overlapped UT signals, however it has disabilities when the waveform of the transmitted signal is distorted according to the propagation. In this paper, the wavelet transform based deconvolution (WTBD) technique is proposed as a new signal processing method that can decompose the overlapped echo signals in A-Scan signal with superior performances compared to the conventional deconvolution technique. Performances of the proposed method are shown by through computer simulations using model signal with noise and are demonstrated by through experiments for the fabricated acryl rod with a thin steel plate bonded to it.

• Geophysics and Geophysical Exploration
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• v.13 no.4
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• pp.323-329
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• 2010

Three deconvolution methods were applied to stacked seismic data obtained to investigate gas-hydrates in the Ulleung Basin, East Sea: (1) minimum-phase spiking deconvolution, (2) minimum-phase spiking deconvolution using an averaged wavelet from all traces, and (3) deterministic deconvolution using a wavelet with phases computed from well-logs. We analyzed the resolving property of these methods for lithological boundaries. The first deconvolution method increases temporal resolution but decreases lateral continuity. The second method shows, in an overall sense, similar results to the spiking deconvolution using a minimum phase wavelet for each trace; however, it results in a more consistent and continuous bottom-simulating reflector (BSR) and better resolved sub-BSR reflectors. The results from the third method reveal more detailed internal structures of debris-flow deposits and increased continuity of reflectors; in addition, the seafloor reflection and the BSR appear to have changed to a zero-phase waveform. These properties help more precisely estimate the distribution and reserves of gas hydrates in the exploration area by improving analysis of facies and amplitude of the BSR.

• Journal of the Korean Geophysical Society
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• v.9 no.1
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• pp.1-6
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• 2006

To remove ringing and increase vertical resolution of GRP data, signature deconvolution was applied to GPR data obtained using a 100 MHz antenna in the Soyang Lake. The signature was extracted through stacking reflection signals from the lake bottom. Results of this deterministic deconvolution was compared with those from the conventional Wienner method. Due to increased vertical resolution, both deconvolution methods are able to resolve three or more layers in an apparent single layer on the input data. However, identification of reflection boundaries from ringing is not easy due to poor definition in the output data of the Wienner filter. On the contrary, the signature deconvolution greatly enhances both vertical resolution and definition of reflection boundaries, showing detailed internal stratigraphic features of the three sedimentary layers. Since extraction of signature at various depths is possible, this deconvolution method can be appled effectively to unstationary GPR data.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.495-498
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• 1998

수중폭발성음원인 SUS(signal underwater sound)는 수중에서의 폭발과 동시에 충격파와 기포파들이 연속적으로 발생한다. 이러한 신호는 광범위한 주파수대역을 가지고 전파거리가 길어지면서 굴절과 반사등의 다중경로 전파효과에 의한 신호의 변형까지 생긴다. (1,2,3). 본 연구에서는 deconvolution 방법을 이용하여 수신신호중에서 기포파효과를 최소화시켜 충격파의 수신기 도달시간을 파악하고, 각 충격파들의 전파경로를 추정하여 이 중 직접경로와 해저면 반사경로의 신호를 이용하여 해저면 반사계수를 계산하였다.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.265-276
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• 1996

The nonparametric deconvolution problems are studied to recover an unknown density when the data are contaminated with Gaussian error. We propose the estimator which is a linear combination of kernel type estimates of derivertives of the observed density function. We show that this estimator is consistent and also consider the properties of estimator at small sample by simulation.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.217-225
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• 2000

Scanning Acoustic Microscope (SAM) is used as an important nondestructive test tool in semiconductor reliability evaluation and failure analysis. However, inspections of chip attach adhesive interface fer thin chip has proven difficulty as the reflected signals from the chip top and bottom are superimposed. In this paper, in order to overcome this difficulty, a new signal processing method based on the deconvolution technique combined with the wavelet transform is proposed. The wavelet transform complements a disability of deconvolution technique of which performance largely decreases when the waveform of target signal is not identical to that of reference signal. Performances of the proposed method are demonstrated by through computer simulations using model signal and experiments for the fabricated semiconductor samples, and satisfactory results are obtained.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2594-2600
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• 2020

A gamma camera system using radionuclide has a functional imaging technique and is frequently used in the field of nuclear medicine. In the gamma camera, it is extremely important to improve the image quality to ensure accurate detection of diseases. In this study, we designed a blind-deconvolution framework after a noise-reduction algorithm based on a non-local mean, which has been shown to outperform conventional methodologies with regard to the gamma camera system. For this purpose, we performed a simulation using the Monte Carlo method and conducted an experiment. The image performance was evaluated by visual assessment and according to the intensity profile, and a quantitative evaluation using a normalized noise-power spectrum was performed on the acquired image and the blind-deconvolution image after noise reduction. The result indicates an improvement in image performance for gamma camera images when our proposed algorithm is used.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.277-286
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• 2022

Due to the uniquely slow convergence speed of the EM algorithm, it suffers form a lot of processing time until the desired deconvolution image is obtained when the image is large. To cope with the problem, in this paper, an immediate solution of the EM algorithm is provided under the Gaussian image model. It is derived by finding the recurrent formular of the EM algorithm and then substituting the results repeatedly. In this paper, two types of immediate soultion of image deconboution by EM algorithm are provided, and both methods have been shown to work well. It is expected that it free the processing time of image deconvolution because it no longer requires an iterative process. Based on this, we can find the statistical properties of the restored image at specific iterates. We demonstrate the effectiveness of the proposed method through a simple experiment, and discuss future concerns.