Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Wen-Chih Tsai

dblp:29/4444 · DBLP profile ↗
← Back
4ranked-venue papers
2as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 2 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video coding · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Integrated circuit design · 50% Hardware accelerators and domain-specific architectures · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding › complexity control for video encoding
fast mode decision
0.112008
A Low Complexity Detection of Discrete Cross Differences for Fast H.264/AVC Intra Prediction · IEEE Trans. Multim. 2008
Image and video coding › video compression
intra prediction
0.112008
A Low Complexity Detection of Discrete Cross Differences for Fast H.264/AVC Intra Prediction · IEEE Trans. Multim. 2008
Hardware accelerators and domain-specific architectures
video coding accelerator
0.112008
A Low Complexity Detection of Discrete Cross Differences for Fast H.264/AVC Intra Prediction · IEEE Trans. Multim. 2008
Integrated circuit design
VLSI design
0.112008
A Low Complexity Detection of Discrete Cross Differences for Fast H.264/AVC Intra Prediction · IEEE Trans. Multim. 2008
Image and video coding › video coding standards
H.264/AVC
0.012008
A Low Complexity Detection of Discrete Cross Differences for Fast H.264/AVC Intra Prediction · IEEE Trans. Multim. 2008

Methods — techniques the papers use, named apart from their topics

discrete cross differences · 0.2RDO · 0.2
YearPublicationVenuePosition
2011 Cloud Computing Architecture for Social Computing - A Comparison Study of Facebook and Google
abstract
As far as we know, cloud computing is a new business model and the cloud computing architecture is the famous topic recently. Today, everyone enjoy the innovative search engine or social network application for new Internet services no longer require the large capital outlays in hardware to access those service or the human expense to operate it. Currently, Google is the largest search engine and Face book is the largest social network in the Software as a Service (SaaS). But how them can support the huge requests from world thought each personal computer, mobile device, and smart phone. In this paper, we will try to analysis their backend cloud computing architecture to support future SaaS especially in large social network.
Bo-Wen Yang, Wen-Chih Tsai, An-Pin Chen, Singh Ramandeep
ASONAM2
2011 Using the XCS classifier system for portfolio allocation of MSCI index component stocks
Wen-Chih Tsai, An-Pin Chen
Expert Syst. Appl.1
2010 Strategy of global asset allocation using extended classifier system
Wen-Chih Tsai, An-Pin Chen
Expert Syst. Appl.1
2008 A Low Complexity Detection of Discrete Cross Differences for Fast H.264/AVC Intra Prediction
abstract
A low complexity fast mode decision algorithm for H.264/AVC intra prediction that uses discrete cross differences (DCD) to reduce the unlikely candidate modes in the RDO calculation is proposed. By using horizontal and vertical differences in different locations, the directions of the edges can be precisely detected. Experimental results show that the proposed fast mode decision algorithm reduces the encoding time by about 56%, with negligible loss of video quality. To realize the proposed algorithm, a VLSI design, which is comprised of a cross difference unit and direction detection unit, is implemented for the mode pre-selection stage of intra prediction. The design is synthesized using UMC 0.18 mum CMOS technology and simulated with Verilog-XL. The operating frequency of the synthesized core can exceed 50 MHz.
K. Bharanitharan, Bin-Da Liu, Jar-Ferr Yang, Wen-Chih Tsai
IEEE Trans. Multim.4