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Record W3090939031 · doi:10.1109/tap.2020.3026871

Arbitrary Near-Field Spatial Signal Processing

2020· article· en· W3090939031 on OpenAlexaff
Ruizhi Liu, Ke Wu

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2020
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSignal processingComputer scienceNear and far fieldElectromagnetic radiationAntenna (radio)Electromagnetic fieldSIGNAL (programming language)ReconfigurabilityElectronic engineeringAcousticsPhysicsTelecommunicationsOpticsEngineeringRadar

Abstract

fetched live from OpenAlex

Electromagnetic waves have been widely exploited as signal, carrier, and power for wireless and wireline systems over the electromagnetic spectrum. As the classical approach, parametric interactions among localized or discrete signal processing devices are usually accomplished by electromagnetic fields or waves guided in conductor and/or dielectric bounded structures. These structures feature specific circuit functions in an electronic or photonic manner. Such electromagnetic circuits now face a number of application issues, such as geometrical complexity, power handling capability, and flexible reconfigurability, when they are used in certain large-scale electromagnetic systems, like in a massive multiple-input and multiple-output (MIMO). In this work, we propose and present an unconventional approach to realize a desired interaction or operation among devices. Such an interaction relies on wave or field interferences induced by a set of coherent antenna array over their near-field region where signal processing can be accomplished spatially. Fundamental functions and operation mechanisms of the proposed spatial signal processing approach are discussed and demonstrated in this work. It is worth mentioning that the proposed approach is universally applicable to all physical wave devices and systems, such as acoustic and electromagnetic waves.

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 score0.982
Threshold uncertainty score0.481

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 integrity0.0000.000
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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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