MétaCan
Menu
Back to cohort
Record W2167870772 · doi:10.1109/iscas.2009.5117752

High-frequency systolic broadband beamforming using polyphase 3D IIR frequency-planar digital filters with interleaved A/D sampling

2009· article· en· W2167870772 on OpenAlexaff
Arjuna Madanayake, Thushara Gunaratne, L.T. Bruton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPolyphase systemBeamformingInfinite impulse responseComputer scienceClock rateFrame rateThroughputField-programmable gate arrayComputer hardwareDigital filterElectronic engineeringBandwidth (computing)TelecommunicationsEngineeringChipArtificial intelligence

Abstract

fetched live from OpenAlex

A massively-parallel polyphase systolic array processor is proposed for broadband beamforming using a 3D IIR space-time digital frequency-planar filter that is capable of operating at a throughput of M 2D spatial frames every clock cycle, where M is the number of (poly)phases. The method achieves an M-fold increase in throughput relative to previously known architectures, and has the potential to achieve highly-selective broadband radio-frequency (RF) digital beamforming at frame rates that are several times greater than the clock rate of the VLSI system. The practical real-time performance of the processor is demonstrated using a 3×3 section of a systolic array (that is part of a larger systolic N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> × N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ≈100 × 100 system), consisting of a locally-interconnected matrix of 9 identical fully-pipelined speed-optimized two phase (M=2) parallel processors on a Xilinx Sx35 FPGA device, having a corresponding measured spatial frame-rate of 100 million frames/second, when clocked at 50 MHz.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score1.000

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 communication0.0010.005
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 head0.032
GPT teacher head0.269
Teacher spread0.237 · 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.

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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicDigital Filter Design and ImplementationFrench-language works237,207