• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.230-234
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• 2010

심전도는 가장 일반화되어 있는 생체신호의 하나이다. 심전도를 측정하여 심장병의 유무와 여러 질환들을 예측하고 예방할 수 있다. 심전도 신호를 추출 하는 방법에는 여러 방법이 있는데, 본 논문에서 활용한 두 논문은 계층적인 분류로 HOS, HBF, HMH 세 방법으로 실험을 하였고, 적응가변형 윈도우를 이용한 R파 추출을 실행하였다. 두 논문은 공통적으로 MIT-BIH Arrhythmias Database(MIT-BIT 부정맥 데이터베이스)를 데이터로 실험 하였으며, 알고리즘으로는 SVM, Cross-Validation등을 사용하였다. 마지막으로 두 논문의 실험결과를 바탕으로 정확도를 높일 수 있는 효과적인 알고리즘 연구를 제안하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.417-418
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• 2023

최근 사용되는 스마트 홈 시스템은 다양한 환경의 효율성, 편안함, 기능성을 추구한다. 하지만 기존 스마트 홈 시스템에는 실내 채광 조절이 블라인드나 커튼과 같이 사람의 관리가 필요로 한 부분이 적용된다. 본 논문은 이를 보완하고자 현재 자동차 및 항공기에 사용되는 스마트 윈도우처럼 주변 조명 조건에 따라 투명도를 조절할 수 있는 스마트 글라스나 필름 사용을 제안한다. 기존 별도의 관리가 필요한 블라인드, 커튼 등과 달리 창문 자체적으로 외부 채광을 조절하고 실내 조명과 연동하여 자동적으로 실내의 환경을 변화시킨다면 사용자의 경제성과 편의성을 증가시키는 효과를 얻을 것이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1029-1039
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• 2009

In this paper, the existing disparity aquisition algorithms were analyzed, on the bases of which a disparity generation technique that is superior in accuracy to the generation time was proposed. Basically it uses a pixel-by-pixel motion estimation technique. It has a merit of possibility of a high-speed operation. But the motion estimation technique has a disadvantage of lower accuracy because it depends on the similarity of the matching window regardless of the distribution characteristics of the texture in an image. Therefore, an enhanced technique to increase the accuracy of the disparity is required. This paper introduced a variable-sized window matching technique for this requirement. By the proposed technique, high accuracies could be obtained at the homogeneous regions and the object edges. A hardware to generate disparity image was designed, which was optimized to the processing speed so that a high throughput is possible. The hardware was designed by Verilog-HDL and synthesized using Hynix $0.35{\mu}m$ CMOS cell library. The designed hardware was operated stably at 120MHz using Cadence NC-VerilogTM and could process 15 frames per second at this clock frequency.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.83-88
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• 2005

In this paper, detection based - adaptive windowed nonlinear filters(DB-AWNF) are proposed for removing one-side impulse noise in infrared image. They are composed of impulse detector and window-size-variable median filters. Impulse detector checks whether current pixel is impulse or not using range function and nonlinear location estimator. If impulse is detected, current pixel is filtered according to four kinds of local masks by use of median filter. If not. current pixel is delivered to output like identity filter. In qualitative view, the proposed could have removed heavy corrupted noise up to $20\%$ and reserved the details of image. In quantitative view, PSNR was measured. The proposed could have 13 - 31[dB] more improved performance than those of median($3{\times}3$) filter and 18 - 25[dB] more improved performance than those of median($5{\times}5$) filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8A
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• pp.613-619
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• 2003

Bluetooth is a communication technology enabling short-range devices to be wirelessly connected. A master and one or more slave devices are connected to form a piconet, and piconets are joined to form a scatternet. The units participating in two or more piconets in a scatternet, is called bridge or gateway nodes. In order to operate the scatternet efficiently, both piconet scheduling for the master and slaves of a piconet, and scatternet scheduling for the bridge nodes are playing important roles. In this paper, we propose a traffic-adaptive dynamic scatternet scheduling algorithm based on rendezvous points and rendezvous windows. The performance of the proposed algorithm is compared and analyzed with that of a static scheduling algorithm via simulations. Simulation results show that our algorithm can distribute wireless resources efficiently to bridge nodes depending on the traffic characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1333-1340
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• 2015

IEEE 802.11 MAC(Media Access Control) defines DCF(Distributed Coordination Function) for data transmission control. BEB(Binary Exponential Backoff) algorithm of DCF has a problem that if the number of stations connected are over a certain threshold, it degrades network performance because of packet collisions caused from the minimum contention window size. To cope with this problem, we proposed a novel algorithm, named as VBS(Variable Backoff Stage) algorithm, which adjusts the rate of backoff stage increment depending on the number of stations associated with an AP(Access Point). Analytic model of proposed algorithm was derived and simulations on the BEB and the VBS algorithms have been conducted on the OFDM (Orthogonal Frequency Division Multiplexing) method. Simulation results showed that when the rate of backoff state increment was 5 and 10, the number of retransmission were reduced to 1/5 and 1/10 comparing to that of BEB, respectively. Our algorithm showed improvement of 19% and 18% in network utilization, respectively. Packet delay was reduced into 1/12.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.268-273
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• 2017

Recently, deep learning encoder-based approach has been actively applied in the field of sentiment classification. However, Long Short-Term Memory network deep learning encoder, the commonly used architecture, lacks the quality of vector representation when the length of the documents is prolonged. In this study, for effective classification of the sentiment documents, we suggest the use of attention method-based deep learning encoder that generates document vector representation by weighted sum of the outputs of Long Short-Term Memory network based on importance. In addition, we propose methods to modify the attention method-based deep learning encoder to suit the sentiment classification field, which consist of a part that is to applied to window attention method and an attention weight adjustment part. In the window attention method part, the weights are obtained in the window units to effectively recognize feeling features that consist of more than one word. In the attention weight adjustment part, the learned weights are smoothened. Experimental results revealed that the performance of the proposed method outperformed Long Short-Term Memory network encoder, showing 89.67% in accuracy criteria.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.185-188
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• 2005

This paper proposes a new peak-windowing algorithm with window-length adaptation for peak-to-average power reduction (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. Conventional peak windowing algorithm has advantages, such as moderate system complexity with good spectral shape. However, adjacent peak signals within the length of window functions produce the distortion of signal amplitude since window functions might overap with each other. These undesired characteristics of conventional peak windowing algorithm result in the degradation of BER performance. The proposed algorithm outperforms the conventional one with the aid of window-length adaptation. Simulation results show the efficiency of the proposed algorithm under the environments of WiBro downlink systems.