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Record W2768887839 · doi:10.1109/tmtt.2017.2769079

Design of Low-Power Active Tags for Operation With 77–81-GHz FMCW Radar

2017· article· en· W2768887839 on OpenAlex
M. Sadegh Dadash, Jürgen Hasch, P. Chevalier, Andreia Cathelin, Ned Cahoon, Sorin P. Voinigescu

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

VenueIEEE Transactions on Microwave Theory and Techniques · 2017
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContinuous-wave radarRadarElectronic engineeringElectrical engineeringComputer scienceRemote sensingEngineeringTelecommunicationsRadar imagingGeology

Abstract

fetched live from OpenAlex

The system and transistor-level design of low-power millimeter wave (mm-wave) active tags in silicon is discussed in detail. Two active mm-wave tags with identical system architecture, padframe, and chip size were designed and fabricated in 55-nm SiGe BiCMOS and 45-nm SOI CMOS technologies, respectively. They feature a three-stage low-noise amplifier (LNA), a wake-up detector, a BPSK modulator, and two variable gain output stages, each driving a separate transmit antenna in antiphase. The wake-up detector can be used to switch OFF all the blocks except for the LNA and detector, thus further reducing power consumption. The measured performance of the SiGe and SOI chips is remarkably similar: 19- and 20-dB gain, 9- and 8-dB noise figure, and 25-/10.8-mW (active/idle) and 18-mW power consumption, respectively. The SiGe tag was flip-chip-mounted on a mini-PCB with one receive and two transmit antennas for system level functionality tests carried out over a distance of 5 m. The SiGe-tag wake-up sensitivity was verified to be -62 dBm, in excellent agreement with simulation results.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.825

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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