A new integrated front-end for a noninvasive brain imaging system based on near-infrared spectroreflectometry
Bibliographic record
Abstract
In this paper, we present a fully integrated front-end of a portable spectroreflectometry-based brain imaging system dedicated for acquisition of modulated optical signals at a frequency of 1 Hz to 25 kHz. The proposed front-end preamplifier is composed of a photodetector, a transimpedance preamplifier, a two-stage voltage amplifier and a mixer. Strict constraints regarding noise thus have to be considered. The preamplifier consists of a transimpedance block featuring a 95-dB/spl Omega/ gain and an average input current noise density at the frequency of interest of approximately 3 pA//spl radic/Hz. Each of the two subsequent voltage amplifiers allows the user to obtain an additional 25-dB gain. Considering the tuning capabilities and the losses due to the filters and the nonideal buffers, the proposed front-end allows us to obtain a total gain up to 145 dB. The back-end of the amplification chain is composed of a mixer which is used to produce a continuous voltage proportional to the amplitude of the input optical signals. All those features were integrated using CMOS 0.18-/spl mu/m technology and the experimental results are in agreement with the initial design requirements.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".