• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.123-132
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• 1999

With the rapid increases of the amount of data and computing, the parallelization of the computing algorithm becomes necessary more than ever. However the parallelization had been conducted mostly in a super-computer until the rod 1990s, it was not for the general users due to the high price, the complexity of usage, and etc. A new concept for the parallel processing has been emerged in the form of K-clustering form the late 1990s, it becomes an excellent alternative for the applications need high computer power with a relative low cost although the installation and the usage are still difficult to the general users. The mapping algorithms (cut, join, resizing, warping, conversion from raster to vector and vice versa, etc) in GIS are well suited for the parallelization due to the characteristics of the data structure. If those algorithms are manipulated using PC-clustering, the result will be satisfiable in terms of cost and performance since they are processed in real flu with a low cos4 In this paper the tools and the libraries for the parallel processing and PC-clustering we introduced and how those tools and libraries are applied to mapping algorithms in GIS are showed. Parallel programs are developed for the mapping algorithms and the result of the experiments shows that the performance in most algorithms increases almost linearly according to the number of node.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.128-138
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• 2010

In this paper, we propose a new bit assignment scheme for Wyner-Ziv video coding. Distributed video coding (DVC) is a new video coding paradigm which enables greatly low complexity encoding because it does not have any motion prediction module at encoder. Therefore, it is very well suited for many applications such as video communication, video surveillance, extremely low power consumption video coding, and other portable applications. Theoretically, the Wyner-Ziv video coding is proved to achieve the same rate-distortion (RD) performance comparable to that of the joint video coding. However, its RD performance has much gap compared to MC-DCT-based video coding such as H.264/AVC. Moreover, Transform Domain Wyner-Ziv (TDWZ) video coding which is a kind of DVC with transform module has difficulty of exact bit assignment because the entire image is treated as a same message. In this paper, we propose a feasible bit assignment algorithm using adaptive quantization matrix selection for the TDWZ video coding. The proposed method can calculate suitable bit amount for each region using the local characteristics of image. Simulation results show that the proposed method can enhance coding performance.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1603-1615
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• 2018

In order to achieve the low complexity and area, power in the design of Optical Image Stabilization (OIS) suitable for the smart phone, this paper presents the following design explorations, such as; optimization of gyroscope sampling rate, simple and accurate gyroscope filters, and reduced operating frequency of motion compensation, optimized bit width in ADC and DAC, evaluation of noise effects due to PWM driving. In experiments of gyroscope sampling frequencies, it is found that error values are unvaried in the frequency above 5KHz. The gyroscope filter is efficiently designed by combining the Fuzzy algorithm, to illustrate the reasonable compensation for the angle and phase errors. Further, in the PWM design, the power consumption of 2MHz driving is shown to decrease up to 50% with respect to the linear driving, and the imaging noises are reduced in the driving frequency above 2MHz driving frequency. The operating frequency could be reduced to 5KHz in controller and 10KHz in driver, respectively, in the motion compensation. For ADC and DAC, the optimized exploration experiments verify the minimum bit width of 11bits in ADC as well as 10bits in DAC without the performance degradation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.2
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• pp.106-114
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• 2010

We proposed an unified design methodology and verification platform for giga-scale System on Chip (SoC). According to the growth of VLSI integration, the existing RTL design methodology has a limitation of a production gap because a design complexity increases. A verification methodology need an evolution to overcome a verification gap. The proposed platform includes a high level synthesis, and we develop a power-aware verification platform for low power design and verification automation using it's results. We developed a verification automation and power-aware verification methodology based on control and data flow graph (CDFG) and an abstract level language and RTL. The verification platform includes self-checking and the coverage driven verification methodology. Especially, the number of the random vector decreases minimum 5.75 times with the constrained random vector algorithm which is developed for the power-aware verification. This platform can verify a low power design with a general logic simulator using a power and power cell modeling method. This unified design and verification platform allow automatically to verify, design and synthesis the giga-scale design from the system level to RTL level in the whole design flow.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.703-712
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• 2019

Single image Super-Resolution (SISR) aims to generate a visually pleasing high-resolution image from its degraded low-resolution measurement. In recent years, deep learning - based super - resolution methods have been actively researched and have shown more reliable and high performance. A typical method is WaveletSRNet, which restores high-resolution images through wavelet coefficient learning based on feature maps of images. However, there are two disadvantages in WaveletSRNet. One is a big processing time due to the complexity of the algorithm. The other is not to utilize feature maps efficiently when extracting input image's features. To improve this problems, we propose an efficient single image super resolution method, named RDB-WaveletSRNet. The proposed method uses the residual dense block to effectively extract low-resolution feature maps to improve single image super-resolution performance. We also adjust appropriated growth rates to solve complex computational problems. In addition, wavelet packet decomposition is used to obtain the wavelet coefficients according to the possibility of large scale ratio. In the experimental result on various images, we have proven that the proposed method has faster processing time and better image quality than the conventional methods. Experimental results have shown that the proposed method has better image quality by increasing 0.1813dB of PSNR and 1.17 times faster than the conventional method.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.630-637
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• 2018

Harmony search (HS) is a recently developed metaheuristic optimization algorithm. HS is inspired by the process of musical improvisation and repeatedly searches for the optimal solution using three operations: random selection, memory recall (or harmony memory consideration), and pitch adjustment. HS has been applied by many researchers in various fields. The increasing complexity of real-world optimization problems has created enormous challenges for the current technique, and improved techniques of optimization algorithms and HS are required. We propose an improved clustering harmony search (ICHS) that uses a clustering technique to group solutions in harmony memory based on their objective function values. The proposed ICHS performs modified harmony memory consideration in which decision variables of solutions in a high-ranked cluster have higher probability of being selected than those in a low-ranked cluster. The ICHS is demonstrated in various optimization problems, including mathematical benchmark functions and water distribution system pipe design problems. The results show that the proposed ICHS outperforms other improved versions of HS.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.12
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• pp.1250-1260
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• 2014

In this paper, a novel resource scheduling, which is used for hub network based long distance airborne tactical communication, is proposed. Recently, some countries of the world has concentrated on developing data rate and networking performance of CDL, striving to keep pace with modern warfare, which is changed into NCW. And our government has also developed the next generation high capacity CDL. In hub network, a typical communication structure of CDL, hybrid FDMA/TDMA can be considered to exchange high rate data among multiple UAVs simultaneously, within limited bandwidth. However, due to different RTT and traffic size of UAV, idle time resource and unnecessary packet transmission delay can occur. And these losses can reduce entire efficiency of hub network in long distance communication. Therefore, in this paper, we propose RTT and data traffic size based UAV scheduling, which selects time/frequency resource of UAVs by using iterative sorting algorithm. The simulation results verified that the proposed scheme improves data rate and packet delay performance in low complexity.