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Record W2055368218 · doi:10.1109/ccece.2012.6335011

Using an adaptive UPA scheme with a channel-aware OFDM technique for wireless transmission of JPEG2000 images

2012· article· en· W2055368218 on OpenAlexaff
Moein Shayegannia, Atousa Hajshirmohammadi, Sami Muhaidat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingComputer scienceTransmitterCoding (social sciences)FadingTransmitter power outputChannel state informationTransmission (telecommunications)Channel (broadcasting)MultiplexingElectronic engineeringAlgorithmWirelessComputer networkTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, an adaptive scheme is developed for an Unequal Power Allocation (UPA) algorithm in a way to allow variable size grouping of the coding passes to enhance the optimization process at different Signal to Noise Ratio (SNR) values. The optimization algorithm exploits the hierarchical structure of the JPEG2000 images and uses a distortion model along with the channel state information (CSI) for allocating optimal values of power on each coding pass to minimize the end-to-end distortion. Orthogonal Frequency Division Multiplexing (OFDM) technique is incorporated within the transmitter to enable transmission of the JPEG2000 images over block fading frequency selective channels. In this paper, we propose a channel assignment strategy, within our OFDM transmitter, which passes the more important coding passes over the subcarriers with higher channel gain. Simulation results indicate an improvement of up to 2 dB in the decoded image quality and potentiality of energy conservation when the adaptive UPA scheme along with the channel assignment strategy are used.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.355
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.055
GPT teacher head0.324
Teacher spread0.269 · 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
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

Citations1
Published2012
Admission routes1
Has abstractyes

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