• Journal of KIISE:Computer Systems and Theory
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• v.27 no.7
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• pp.684-694
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• 2000

For designing circuits for low power systems, the capacitance is an important factor for the power dissipation. Since the capacitance of a gate is proportional to the area of the gate, we can reduce the total power consumption of a circuit by reducing the total area of gates, where total area is a simple sum of all gate areas in the circuit. To reduce the total area, transistor resizing can be used. While resizing transistors, inserting buffer in the proper position can help reduce the total area. In this paper we propose two methods for concurrent transistor sizing and buffer insertion. One method uses template window simulation and the other uses extrapolation. Experimental results show that concurrent transistor sizing with buffer insertion achieved 10-20% more reduction of the total area than when it was done without buffer insertion and template window simulation is more efficient than extrapolation.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.707-720
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• 2014

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.349-361
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• 2013

This paper proposes a fast face tracking method with only depth information. It is basically a template matching method, but it uses a early termination scheme and a sparse search scheme to reduce the execution time to solve the problem of a template matching method, large execution time. Also a refinement process with the neighboring pixels is incorporated to alleviate the tracking error. The depth change of the face being tracked is compensated by predicting the depth of the face and resizing the template. Also the search area is adjusted on the basis of the resized template. With home-made test sequences, the parameters to be used in face tracking are determined empirically. Then the proposed algorithm and the extracted parameters are applied to the other home-made test sequences and a MPEG multi-view test sequence. The experimental results showed that the average tracking error and the execution time for the home-made sequences by Kinect ($640{\times}480$) were about 3% and 2.45ms, while the MPEG test sequence ($1024{\times}768$) showed about 1% of tracking error and 7.46ms of execution time.