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Record W2371171758

Research on encode and decode of LDPC used to CMMB system and its performance analyses

2009· article· en· W2371171758 on OpenAlexaff
Huang Jun, Mii Key

Bibliographic record

VenueApplication of Electronic Technique · 2009
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsComputer scienceLow-density parity-check codeCoding (social sciences)Decoding methodsAlgorithmCode (set theory)MATLABENCODEOrthogonal frequency-division multiplexingEncoding (memory)Computer hardwareComputer engineeringTelecommunicationsMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

CMMB system introduces new technologies such as LDPC codes and OFDM,which have been the direction of the future of development of mobile multimedia TV standard.By introducing the basal rule of LDPC code check matrix briefly,CMMB system based on modified LU decomposition theory was presented,which can reduce the storage resource.In system simulation on Matlab, modified min sum-product decoding algorithm is proposed.The simulation result shows that the LDPC decoder has high efficiency to meet the demand of precision digital signals transmission.Analyzing and contrasting the improved algorithm to find the optimum modified value,this paper provides an effective coding and decoding algorithm scheme for hardware design in CMMB,which has some practical value.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.538
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.059
GPT teacher head0.404
Teacher spread0.344 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2009
Admission routes1
Has abstractyes

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