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Record W1490106680 · doi:10.1109/iscas.1992.230002

A binary logic synthesis approach to the bit-level implementation of generalized rank-order filters

2003· article· en· W1490106680 on OpenAlex
Qing Gu, M.N.S. Swamy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsRank (graph theory)Binary numberComputer scienceRealization (probability)Very-large-scale integrationAlgorithmFilter (signal processing)Theoretical computer scienceModular designArithmeticMathematicsEmbedded systemProgramming language

Abstract

fetched live from OpenAlex

A binary logic synthesis approach is presented for the bit-level implementation of generalized rank-order filters. It is shown that rank-order filters can be realized directly from the binary weighted signal by using combinational logic circuits. A pipelined architecture based on the algorithm is also introduced. The realization is simple and modular in structure and suitable for VLSI implementation, and no threshold decomposition and reconstruction circuits are needed. Only one type of basic cell is employed. K basic cells connected together will perform K-bit rank-order filtering. The proposed filter has flexibility for various applications. Considering the proposed architecture as a building block, recursive rank-order filters, 2-D rank-order filters, and adaptive rank-order filters can easily be realized.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.927
Threshold uncertainty score0.277

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 head0.061
GPT teacher head0.312
Teacher spread0.251 · 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

Quick stats

Citations7
Published2003
Admission routes1
Has abstractyes

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