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Record W3006447537 · doi:10.1109/tcsi.2020.2971439

An Ultra-Low-Power Low-Voltage WuTx With Built-In Analog Sensing for Self-Powered WSN

2020· article· en· W3006447537 on OpenAlexaff
Mohammad Amin Karami, Kambiz Moez

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUltra low powerLow voltageElectrical engineeringPower (physics)Low-power electronicsVoltageComputer scienceEngineeringPower consumptionPhysics

Abstract

fetched live from OpenAlex

This paper presents an ultra-low-power, low-voltage wake-up transmitter (WuTx) capable of transmitting two sensors' analog output simultaneously using short pulses that their LOW and HIGH time are modulated with the analog inputs. A subthreshold relaxation oscillator along with an ultra-low-power comparator and voltage reference creates a baseband signal modulated based on two input analog signals, a ring oscillator upconverts the baseband signal to the desired transmission channel frequency (915 MHz or 2.4 GHz), and finally, an efficient Class E power amplifier with variable output power drives the antenna. The minimalist design of the proposed transmitter avoiding power-hungry data converters for sensor readout circuitry and modulation, short pulses at the output that enable the power amplifier for a short time during transmission along with the operation in the subthreshold region significantly reduces the overall power consumption. The proposed low-power and low-voltage transmitter is ideal for the long-term deployment of self-powered wireless sensors when the voltage gain and efficiency of harvesters are limited. Fabricated in a 65nm standard TSMC CMOS process, the proposed transmitter consumes a minimum of 5.41μW average power when it is on while delivering the output power of -1dBm and 7nW when it is completely off.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.547
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
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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