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Record W4404925828 · doi:10.1080/01468030.2024.2434810

Digital Image Transmission Over FSO System with OAM Beams: Quality Assessment

2024· article· en· W4404925828 on OpenAlexaff
Mehtab Singh, Ahmad Atieh, Ammar Armaghan, Moustafa H. Aly

Bibliographic record

VenueFiber & Integrated Optics · 2024
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsOptiwave Systems (Canada)
Fundersnot available
KeywordsTransmission (telecommunications)Image qualityQuality (philosophy)Computer scienceOpticsTelecommunicationsImage (mathematics)Computer visionPhysics

Abstract

fetched live from OpenAlex

This paper presents a new high-speed black and white image transmission across Free Space Optics (FSO) system utilizing Orbital Angular Momentum (OAM) multiplexing. The system is designed to simultaneously transmit four distinct black and white images (trees, cameraman, lighthouse, and peppers), each modulated onto a separate OAM beam (LG00, LG015, LG045, and LG075) at a rate of 10 Gbps, demonstrating the feasibility of parallel transmission of digital images. The system performance is evaluated over different weather conditions including Clear Air (CA), different fog levels, and real-world meteorological data from Riyadh city, Kingdom of Saudi Arabia (KSA). A median filtering technique is applied to improve image quality, leading to notable enhancements in Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The system successfully supports the transmission over a 16 km in CA weather and 10 km link under Riyadh weather conditions. At these ranges, SNR, PSNR, and SSIM are greater than 6.05 dB, 11.54 dB, and 0.56, respectively. This study underscores the potential of using OAM-based FSO systems for high-speed, real-time remote monitoring, particularly in adverse atmospheric environments.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.816

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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 designSimulation or modeling
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

Citations3
Published2024
Admission routes1
Has abstractyes

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