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Record W2545125011 · doi:10.1109/icm.2014.7071793

Configurable hardware implementation of a pipelined DNLMS adaptive filter

2014· article· en· W2545125011 on OpenAlexaff
Raymond Lee, Mohammed Khalid, Esam Abdel‐Raheem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsStratixComputer scienceField-programmable gate arrayLatency (audio)Adaptive filterModular designComputer hardwareComputer architectureFloating pointLeast mean squares filterEmbedded systemFinite impulse responseRetimingParallel computingAlgorithm

Abstract

fetched live from OpenAlex

The delayed normalized least-mean-square (DNLMS) adaptive filtering algorithm is suitable for implementing pipelined architectures. Though previous literature has provided such architectures for DNLMS adaptive filters, none have given a detailed implementation. This paper presents the configurable hardware implementation of a pipelined, modular, low-latency, portable DNLMS adaptive filter which is tested for echo cancellation. The design is implemented onto the Altera Stratix FPGA and has a maximum operating frequency of 32.27 MHz. The design methodology consists of architectural derivation, fixed-point and RTL simulations, physical synthesis, and real-time hardware.

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: none
Teacher disagreement score0.891
Threshold uncertainty score0.598

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)0.0010.000

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 head0.253
Teacher spread0.239 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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