• Journal of the Korean Institute of Telematics and Electronics
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• v.15 no.6
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• pp.8-22
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• 1978

Recently developed Computed Topography (CT) reconstruction algorithms are reviewed in a more generalized sense and a few reconstruction examples are given for illustration. The construction of an image function from the physically measured projections of some object is Discussed with reference to the least squares optimum filters, originally derived to enhance the signal-to-noise ratio in communications theory. The computerifed image processing associated with topography is generalized so as to include 3 distinct parts: the construction of an image from the projection, the restoration of a blurred, noisy image, degraded by a known space-invariant impulse response, and the further enhancement of the image, e.g. by edge sharpening. In conjunction with given versions of the popular convolution algorithm, n6t 19 be confused with filtering by a 2-diminsional convolution, we consider the conditions under which a concurrent construction, restoration, and enhancement are possible. Extensive bibliographical limits are given in the references.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.121-126
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• 2009

Plane-based computational integral imaging (CIIR) provides the reconstruction of depth-dependent 3D plane images. However, it has problem degrading the resolution of reconstructed images due to the artifact noise according to the depth. In this paper, to overcome this problem, a signal model for plane-based CIIR is explain. Also the compensation process is introduced to remove the noise caused from CIIR. Computational experiments show that we analyze the characteristics of noise in the reconstructed image of 2D Gaussian image and the high-resolution images can be obtained by using the compensation process.

• ETRI Journal
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• v.42 no.3
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• pp.376-387
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• 2020

Massive computation of the reconstruction algorithm for compressive sensing (CS) has been a major concern for its real-time application. In this paper, we propose a novel high-speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Least-squares problem solving, which requires a huge amount of computations in the OMP, is implemented by using systolic arrays with four new processing elements. In addition, a distributed-arithmetic-based circuit for matrix multiplication is proposed to counterbalance the area overhead caused by the multi-stage pipelining. The results of logic synthesis show that the proposed design reconstructs signals nearly 19 times faster while occupying an only 1.06 times larger area than the existing designs for N = 256, M = 64, and m = 16, where N is the number of the original samples, M is the length of the measurement vector, and m is the sparsity level of the signal.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.398-407
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• 2013

Optical tomography was used to measure the pattern of spray cross-section. The maximum-likelihood estimation (MLE) algorithm was used to reconstruct the spray cross-section from the measured transmission rate of the spray. A swirl-type injector was used to form an optically dense spray, and the test was carried out in a high-pressure chamber, to control the pressure condition of the test site. Before the experiment, the reliability of the MLE-based reconstruction algorithm was verified, by comparing it with a conventional filtered back projection reconstruction (FBP) method. The MLE algorithm showed superior reconstruction of the image. In the spray patternation experiment, the results of the optical tomography and optical line patternator, which uses Mie scattering signal information, were compared. While measuring the cross-section of optically dense spray, the intensity of the scattering signal had attenuated to an uncorrectable level, which led to incorrect spray pattern measurement by the optical line patternator. However, reliable results were obtained by optical tomography, under the same condition. Finally, the pattern of the optically dense spray was measured at various chamber pressures, of up to 3 MPa. As the chamber pressure increased, the hollow cone-shaped swirl spray shrank, and the attenuation coefficient value of the inner region increased.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.145-150
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• 2019

We develop the system realization of whale sound reconstruction by inverse MFCC algorithm with the weighted L2-norm minimization techniques. The output products from this research will contribute to the whale tourism and multimedia content industry by combining whale sound contents with the prototype of 3D printing. First of all, we develop the softwares for generating whale sounds and install them into Raspberry Pi hardware and fasten them inside a 3D printed whale. The languages used in the development of this system are the C++ for whale-sounding classification, MATLAB and Python for whale-sounding playback algorithm, and Rhino 6 for 3D printing.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.153-160
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• 2022

A greedy algorithm is proposed to select a subset of nodes of a graph for bandlimited graph signals in which each signal value is generated with its weight. Since graph signals are weighted, we seek to minimize the weighted reconstruction error which is formulated by using the QR factorization and derive an analytic result to find iteratively the node minimizing the weighted reconstruction error, leading to a simplified iterative selection process. Experiments show that the proposed method achieves a significant performance gain for graph signals with weights on various graphs as compared with the previous novel selection techniques.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.153-156
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• 1990

High voltage impulse measuring system gives an output signal that is a deformed responce of the input signal. This paper describes a method of reconstructing the input signal from the deformed output. The method is applicable to any wave shape, full or chopped impulse voltages.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.11
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• pp.616-621
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• 2000

This paper deals with the multiresolution analysis of wavelet transform for partial discharge(PD). Test arrangement is based on the needle-plane electrode system and applied AC high voltage. The measured PD signal was decomposed into "approximations" and "details". The approximation are the high scale, low-frequency components of the PD signal. The details are the low-scale, high frequency components. The decomposition process are iterated to 3 level, with successive approximation being decomposed in turn, so that PD signal is broken down into many lower-resolution components. Through the procedure of signal wavelet transform, signal noise extraction and signal reconstruction, the signal is analyzed to determine the magnitude of PD.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.49-52
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• 2000

The Estimation method for spectral reflectance of the object using five-band and nine-band have been developed. The five-band acquisition are required of five or three times same work for color image acquisition process. To solve the above problems, we proposed a new method that can be reconstructed spectral reflectance of object. The proposed method was to classify same hues corresponding a color stimulus, by using hue angle and chroma vector of a color stimulus. The reconstruction of spectral reflectance was examined by computer simulation, and evaluated by MSE(Mean Square Error) and color difference between the original and reconstructed spectral reflectance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4160-4176
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• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.