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Record W1792738620 · doi:10.1109/pacrim.1999.799573

End-system architecture for distributed networked multimedia applications: issues, trends and future directions

2003· article· en· W1792738620 on OpenAlexaff
Ashraf Hossain, Kuiyan Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceMultimediaInterfacingQuality of serviceComputer architectureComputer networkEmbedded systemComputer hardware

Abstract

fetched live from OpenAlex

The emerging broadband computer networks (wired and wireless) are likely to have numerous multimedia applications, such as videoconferencing, interactive games, and collaborative computing standard features in desktop PCs and portables. In recent years there have been significant developments in the field of multimedia coding algorithms and their VLSI implementations along with the development of high speed networking technology (e.g., ATM, FDDI, fast Ethernet). But the end-system architecture (software and hardware) as a whole is lagging behind these advancements. Optimized hardware and software architectures are needed for end systems that will guarantee predictable performance of real-time multimedia applications according to user provided QoS. In this paper, we identify the major end-system hardware and software requirements for networked multimedia applications. Focusing mainly on hardware and operating system levels, we advocate an integrated end-system architecture rather than some ad-hoc solutions for real-time multimedia computing, in addition to general purpose computing in a distributed environment. A core-based system architecture is proposed. We observe that interfacing with high speed networks, inter-device level data transfer, power efficiency and in addition, system resource utilization are some of the issues that need serious consideration in an end-system architecture.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.240
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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