• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.623-630
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• 1995

The impulse response functions (force-strain relations) for Euler-Bernoulli and Timoshenko beams are considered. The response of a beam to a transverse impact force is numerically obtained with the convolution approach using the impulse response function obtained by Laplace transform. Using this relation, the impact force history is determined in the time domain and results are compared with those from Hertz's contact law. The parameters of timpact force model are identified using the recovered force and compared with the Hertz's contact model. In order to verify the proposed algorithm, measurements were done using an impact hammer and a steel ball drop test and these results are also compared with the simulated values.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.298-306
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• 2013

The scale invariant feature transform (SIFT) is an effective algorithm used in object recognition, panorama stitching, and image matching. However, due to its complexity, real-time processing is difficult to achieve with current software approaches. The increasing availability of parallel computers makes parallelizing these tasks an attractive approach. This paper proposes a novel parallel approach for SIFT algorithm implementation using a block filtering technique in a Gaussian convolution process on the SIMD Pixel Processor. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and input/output capabilities of the processor, which results in a system that can perform real-time image and video compression. We apply this implementation to images and measure the effectiveness of such an approach. Experimental simulation results indicate that the proposed method is capable of real-time applications, and the result of our parallel approach is outstanding in terms of the processing performance.

• Progress in Medical Physics
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• v.3 no.2
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• pp.67-81
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• 1992

Lots of physical phenomena which deteriorate the faithfulness of radiological images exist. The most common types of degradation are a loss of resolution and an increase in noise. This article deals with resolution. factors that influence it. and its characterisation. The problem of resolution failure due to a generic blurring phenomenum is described diagramatically and mathematically. for both one dimensional and two dimensional signals. The definition of the point spread function. the line spread function. the convolution integral and the modulation transfer function are made. The concept of frequency domain operations using the Fourier Transform is also discussed.

• Journal of the Korean Mathematical Society
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• v.42 no.3
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• pp.573-597
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• 2005

A continuous linear operator T, on the space of entire functions in d variables, is PDE-preserving for a given set $\mathbb{P}\;\subseteq\;\mathbb{C}|\xi_{1},\ldots,\xi_{d}|$ of polynomials if it maps every kernel-set ker P(D), $P\;{\in}\;\mathbb{P}$, invariantly. It is clear that the set $\mathbb{O}({\mathbb{P}})$ of PDE-preserving operators for $\mathbb{P}$ forms an algebra under composition. We study and link properties and structures on the operator side $\mathbb{O}({\mathbb{P}})$ versus the corresponding family $\mathbb{P}$ of polynomials. For our purposes, we introduce notions such as the PDE-preserving hull and basic sets for a given set $\mathbb{P}$ which, roughly, is the largest, respectively a minimal, collection of polynomials that generate all the PDE-preserving operators for $\mathbb{P}$. We also describe PDE-preserving operators via a kernel theorem. We apply Hilbert's Nullstellensatz.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.7
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• pp.29-38
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• 2009

In this paper, a high performance pipelined computing design of parallel multiplier-temporal buffer-parallel accumulator is present for the convolution-based non-cascaded architecture aiming at the real time Discrete Wavelet Transform(DWT) processing. The convolved multiplication of DWT would be reduced upto 1/4 by utilizing the filter coefficients symmetry and the up/down sampling; and it could be dealt with 3-5 times faster computation by LUT-based DA multiplication of multiple filter coefficients parallelized for product terms with an image data. Further, the reutilization of computed product terms could be achieved by storing in the temporal buffer, which yields the saving of computation as well as dynamic power by 50%. The convolved product terms of image data and filter coefficients are realigned and stored in the temporal buffer for the accumulated addition. Then, the buffer management of parallel aligned storage is carried out for the high speed sequential retrieval of parallel accumulations. The convolved computation is pipelined with parallel multiplier-temporal buffer-parallel accumulation in which the parallelization of temporal buffer and accumulator is optimize, with respect to the performance of parallel DA multiplier, to improve the pipelining performance. The proposed architecture is back-end designed with 0.18um library, which verifies the 30fps throughput of SVGA(800$\times$600) images at 90MHz.

• Geophysics and Geophysical Exploration
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• v.16 no.3
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• pp.139-144
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• 2013

A computer program has been developed to estimate time-domain electromagnetic (EM) responses for a onedimensional model with multiple source and receiver dipoles that are finite in length. The time-domain solution can be obtained by applying an inverse fast Fourier transform (FFT) to frequency-domain fields for efficiency. Frequency-domain responses are first obtained for 10 logarithmically equidistant frequencies per decade, and then cubic spline interpolated to get the FFT input. In the case of phases, the phase curve must be made to be continuous prior to the spline interpolation. The spline interpolated data are convolved with a source current waveform prior to FFT. In this paper, only a step-off waveform is considered. This time-domain code is verified with an analytic solution and EM responses for a marine hydrocarbon reservoir model. Through these comparisons, we can confirm that the accuracy of the developed program is fairly high.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1710-1718
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• 2015

Selected mapping (SLM) is an effective scheme to reduce the peak to average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) system. For data recovery, the receiver needs to know the side information (SI) on the scrambling sequence selected by the transmitter. In this paper, a new SLM scheme is proposed, which can reduce implementation complexity substantially by allowing the receiver to recover the data without SI. In the proposed SLM method, the concept of virtual channel corresponding to the convolution of the multipath channel and the inverse discrete fourier transform (IDFT) of the scrambling sequence is assumed. The receiver can recover the data without SI by using the virtual channel estimated with pilot signals. It is shown by simulation that the proposed SLM has PAPR reduction and BER performances similar to the previous SLM schemes while it can reduce implementation complexity substantially.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.81-87
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• 1998

This paper proposes an efficient face recognition system using phase difference between the face images. We use a Karhunen-Loeve transform for image compression and reconstruction, and obtain the phase difference by using normalized inner product of the two compressed images. The proposed system is rotation and light-invariant due to using the normalized phase difference, and somewhat shift-invariant due to applying the cosine function. The faster recognition than the conventional system and incremental training is possible in the proposed system. Simulations are conducted on the ORL images of 40 persons, in which each person has 10 facial images, and the result shows that the faster recognition than conventional recognizer using convolution network under the same recognition error rate of 8% does.

• Journal of IKEEE
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• v.21 no.4
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• pp.358-367
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• 2017

Advanced Driver Assistance Systems(ADAS), such as Front Collision Warning System (FCWS) are currently being developed. FCWS require high processing speed because it must operate in real time while driving. In addition, a low-power system is required to operate in an automobile embedded system. In this paper, FCWS is implemented in CPU-FPGA architecture in embedded system to enable real-time processing. The lane detection enabled the use of the Inverse Transform Perspective (IPM) and sliding window methods to operate at fast speed. To detect the vehicle, a Convolutional Neural Network (CNN) with high recognition rate and accelerated by parallel processing in FPGA is used. The proposed architecture was verified using Intel FPGA Cyclone V SoC(System on Chip) with ARM-Core A9 which operates in low power and on-board FPGA. The performance of FCWS in HD resolution is 44FPS, which is real time, and energy efficiency is about 3.33 times higher than that of high performance PC enviroment.