• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.542-545
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• 2006

In recent years, RFID is widely used in Industrial applications including factory, material flow, logistics and defense areas. In this paper, we developed a RFID baseband system with ASK modulation and convolutional channel code. A commercial ASK RF module is used and its frequency range in $350\sim351MHz$ and power is 10mW and the convolution code is constraint length k=3 and rate R=1/2. The performance is measured by frame error rate and the convolutional code is very useful in performance improvement and, also we implemented the binary search algorithm as anti-collision method and we show the wave shapes whit collision occurrence. This RFID system is designed by FPGA therefore it produces result of speed-up and improvement of reliability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.253-261
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• 2005

In this paper, we propose an efficient block-based disparity estimation algorithm fur multiple view image coding in EE2 and EE3 in 3DAV. The proposed method emphasizes on visual quality improvement to satisfy the requirements for multiple view generation. Therefore, we perform an adaptive disparity estimation that constructs variable blocks by considering given image features. Examining neighboring features around desired block search range is set up to decrease complexity and additional information than only using quad-tree coding through applying binary-tree and quad-tree coding by taking into account stereo image feature having big disparity. The experimental results show that the proposed method improves PSNR about 1 to 2dB compared to existing other methods and decreases computational complexity up to maximum 68 percentages than FBMA.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.39-47
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• 2020

This paper introduces a novel system-level framework that derives energy efficient electric vehicle (EV) driving speed profile to extend EV driving range without additional cost. This paper first implements an EV power train model considering forces acting on a driving vehicle and motor efficiency. Then, it derivate the minimum-energy driving speed profile for a given driving mission defined by the route. This framework first formulates an optimization problem and uses the dynamic programming algorithm with a weighting factor to derive a speed profile minimizing both of energy consumption and driving time. This paper introduces various weighting factor tracking methods to satisfy the driving time constraint. Simulation results show that runtime of the proposed scaling algorithm is 34% and 50% smaller than those of the binary search algorithm and greedy algorithm, respectively.