Browsing by Author "Lee, WP"

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A memory-efficient deblocking filter for H.264/AVC video coding
(2005)
Liu, TM
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Lee, WP
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Lin, TA
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Lee, CY
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電子工程學系及電子研究所
A memory-efficient architecture design for de-blocking filter in H.264/AVC is presented. We use the novel data arrangement of Column-of-Pixel to facilitate the memory access and reuse the pixel value. Further, we propose a hybrid filter scheduling to improve the system throughput. As compared with some existing approaches of realizing de-blocking filter [1] [2], the proposed design saves about one-half of processing cycles. With novel data arrangement and hybrid filter scheduling, an efficient architecture design is implemented. Further, it is evaluated on H.264 system and easily achieved real-time decoding with 1080 HD (1920x1088@30fps) when working frequency is 100MHz.
An area-efficient and high-throughput de-blocking filter for multi-standard video applications
(2005)
Liu, TM
;
Lee, WP
;
Lee, CY
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, we propose an area-efficient design approach to cover both in-loop and post-loop filtering processes for multiple video coding standards. In addition, we propose a hybrid filter scheduling to improve system throughput. Compared with available designs [1][2], the proposed approach saves about one-half of processing cycles, and hence reduces power dissipation. Compared to the original loop-filter, the proposed loop/post filter only incurs 20.7% of extra cost. Simulation results show that our proposal can easily achieve real-time decoding for 1080HD when the working frequency is 100MHz.
An evolutionary system for automatic robot design
(1998)
Lee, WP
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友訊交大聯合研發中心
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D Link NCTU Joint Res Ctr
Designing a robot is generally considered to be demanding, because the designer not only has to predict the interaction between the robot and its environment, but also has to deal with the ensuing problems. This paper examines the way to use evolutionary techniques to synthesize robots automatically. The explorations range from evolving simple behavior controllers for robots, to complex behavior controllers, and finally to accomplishing a complete robot system including its controller and physical structure. Our evolutionary framework includes two subsystems: the Genetic Programming part that evolves the controller; and the Genetic Algorithm part for the robot body. We conducted a series of experiments to evaluate our evolutionary framework and the results show that our system can be used to reduce the load of a robot programmer.
Evolving complex robot behaviors
(1999-12-01)
Lee, WP
;
友訊交大聯合研發中心
;
D Link NCTU Joint Res Ctr
Building robots is a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to such difficulties in designing robots is to adopt learning methods. The evolution-based approach is a special method of machine learning and it has been advocated to automate the design of robots. Yet, the tasks achieved so far are fairly simple. In this work, we first analyze the difficulties of applying evolutionary approaches to synthesize robot controllers for complicated tasks, and then suggest an approach to resolve them. Instead of directly evolving a monolithic control system, we propose to decompose the overall task to fit in the behavior-based control architecture, and then to evolve the separate behavior modules and arbitrators using an evolutionary approach. Consequently, the job of defining fitness functions becomes more straightforward and the tasks easier to achieve. To assess the performance of the developed approach, we evolve a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach. (C) 1999 Elsevier Science Inc. All rights reserved.