• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.1-9
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• 2006

Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.2
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• pp.18-25
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• 2008

The current display systems including TVs are going digital and large-sized, and high visual quality of those systems becomes a very important selling point in the current display system market. Thus, various researches have been carried out for enhancing the visual quality of digital display systems. One of the important digital image(or video) enhancement techniques is sharpness enhancement, and it is generally based on a transient improvement technique that reduces the edge transition time. However, this technique often generates overshoot and undershoot, which cause undesirable pixel-level changes around the transient improved edge. In this paper, we propose a new nonlinear filter-based adaptive shoot elimination method for effectively suppressing the overshoot and undershoot that occur in the transient improvement, so that we can obtain visually sharper and clearer digital images(or videos). The proposed method uses two orthogonal directional min/max nonlinear filters with an adaptive shoot elimination scheme in order to effectively suppress the visually sensitive overshoot and undershoot. Experimental results show that the proposed method suppresses the overshoot and undershoot almost perfectly while maintaining the effect of the transient improvement. The applications of the proposed method include digital TVs, digital monitors, digital cameras/camcoders, portable media players(PMP), etc.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.311-320
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• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.8 s.338
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• pp.17-24
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• 2005

This paper proposes a Decision Feedback Equalizer (DFT) with an error feedback filter for blind channel equalization. The proposed equalizer uses Least Mean Square(LMS) Algorithm and Multi-Modulus Algorithm (MMA), and has been designed for 64/256 QAM constellations. The existing MMA equalizer uses either two transversal filters or feedforward and feedback filers, while the proposed equalizer uses feedforward, feedback and error feedback filters to improve the channel adaptive performance and to reduce the number of taps. The proposed equalizer has been simulated using the $SPW^{TM}$ tool and it shows performance improvement. It has been modeled by VHDL and logic synthesis has been performed using the $0.25\;\mu m$ Faraday CMOS standard cell library. The total number of gates is about 190,000 gates. The proposed equalizer operates at 15 MHz. In addition, FPGA vertification has been performed using FPGA emulation board.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.102-110
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• 2009

In this paper, we propose an object detection method for real-time in infrared (IR) images and PowerPC (PPC) and H/W design based on field programmable gate array (FPGA). An open H/W architecture has the advantages, such as easy transplantation of HW and S/W, support of compatibility and scalability for specification of current and previous versions, common module design using standardized design, and convenience of management and maintenance. Proposed background modeling for an open H/W architecture design decreases size of search area to construct a sparse block template of search area in IR images. We also apply to compensate for motion compensation when image moves in previous and current frames of IR sensor. Separation method of background and objects apply to adaptive values through time analysis of pixel intensity. Method of clutter reduction to appear near separated objects applies to median filter. Methods of background modeling, object detection, median filter, labeling, merge in the design embedded system execute in PFC processor. Based on experimental results, proposed method showed real-time object detection through global motion compensation and background modeling in the proposed embedded system.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1289-1297
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• 2006

모든 곳에 존재하는 네트워크 환경을 의미하는 '유비쿼터스' 시대와 최신 기술로 구현되어 인간을 도와주는 '지능형 로봇'의 시대가 도래하고 있다. 기술의 흐름은, 이제 우리에게 공장과 공원 등의 공공 장소뿐 만이 아니라, 생활의 기본이 되는 가정 안에서의 로봇을 받아들일 준비를 요구하고 있다. 로봇과 사용자는 실제 생활 속에서 많은 상호 작용을 하게 되며, 필연적으로 여러 가지의 불확실성을 내포하게 되는데, 각각의 요청들과 상황들은, 미리 정해진 규칙에 의거해 처리하기에는 너무 다양하다. 그 어려움을 극복하는 방법으로, 어떤 상황에 적응하는 방법으로 기억을 사용 하는 인간과 마찬가지로, 로봇은 새로운 요청을 처리하기 위해 과거의 기록을 사용할 수 있다. 여러 가지 과거의 기록들을 잘 정리해서 분류하여 저장해둔 후, 현재의 요청에 대한 답으로, 가장 가능성 있는 과거의 기록을 찾아내는 것이다. 본 논문에서는 사용자와 로봇 사이에서 상호 작용에서 발생할 수 있는 불확실성을 과거기록의 탐색을 통해 해결하고자 하였다. 과거 기록은 시간, 장소, 대상 물건, 행동 유형으로 구분되어 저장하였으며, 각각의 유사 가능성(Possibility)들의 합을 기준으로, 전체 기록을 K-Means 알고리즘을 통하여 군집화하고 협력 필터링을 기반으로 현재의 요청이 담고 있는 불확실성에 대한 가능성 있는 값을 추천해 주었다. 제한된 공간과 제한된 자료의 수에 의한 실험 결과로서의 한계를 가지고 있지만, 실제 가정용 로봇에서의 적용 가능성을 보여주었다.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.452-459
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• 2021

Estimating the mutual time delay between two acoustic sensors is used in various fields such as tracking and estimating the location of a target in room acoustics and sonar. In the time delay estimation methods, there are a non-parametric method, such as Generalized Cross Correlation (GCC), and a parametric method based on system identification. In this paper, we propose a time delay estimation method based on the parametric method. In particular, we propose a method that considers the noise in each receiving acoustic sensor. Simulation confirms that the proposed algorithm is superior to the existing generalized cross-correlation and adaptive eigenvalue analysis methods in white noise and reverberation environments.

• Journal of IKEEE
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• v.13 no.1
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• pp.77-86
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• 2009

A wavelet Electrocardiogram(ECG) detector and its analog-to-digital converter(ADC) for low-power implantable cardiac pacemakers are presented in this paper. The proposed wavelet-based ECG detector consists of a wavelet decomposer with wavelet filter banks, a QRS complex detector of hypothesis testing with wavelet-demodulated ECG signals, and a noise detector with zero-crossing points. To achieve high-detection performance with low-power consumption, the multi-scaled product algorithm and soft-threshold algorithm are efficiently exploited. To further reduce the power dissipation, a low-power ADC, which is based on a Successive Approximation Register(SAR) architecture with an on/off-time controlled comparator and passive sample and hold, is also presented. Our algorithmic and architectural level approaches are implemented and fabricated in standard $0.35{\mu}m$ CMOS technology. The testchip shows a good detection accuracy of 99.32% and very low-power consumption of $19.02{\mu}W$ with 3-V supply voltage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.456-463
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• 2021

In this paper, Ultra Wide-Band(UWB) radar sensor is employed to detect flying drones and avoid collision in dense clutter environments. UWB signal is preferred when high resolution range measurement is required for moving targets. However, the time varying motion of flying drones may increase clutter noises in return signals and deteriorates the target detection performance, which lead to the performance degradation of anti-collision radars. We adopt a dynamic clutter suppression algorithm to estimate the time-varying distances to the moving drones with enhanced accuracy. A modified Constant False Alarm Rate(CFAR) is developed using an adaptive filter algorithm to suppress clutter while the false detection performance is well maintained. For this purpose, a velocity dependent CFAR algorithm is implemented to eliminate the clutter noise against dynamic target motions. Experiments are performed against flying drones having arbitrary trajectories to verify the performance improvement.