Bibliographic record
Abstract
In this work, a linearized ultra-low-power (ULP) low noise amplifier (LNA) designed in GF 130 nm CMOS technology is presented for wireless sensor network (WSN) applications. The LNA targets the 915MHz ISM band to meet the stringent power requirements of the wireless sensor node. Furthermore, to enhance the linearity, the design employs complementary derivative superposition (DS) to a common-source (CS) topology. The NFET is optimally biased for voltage gain, power, and bandwidth efficiency. The PFET load is sized accordingly for nonlinear compensation. The simulated voltage gain, IIP3, input compression point, and NF are 15.7 dB, -6.5 dBm, -17.5 dBm and 5.7 dB respectively. The total power consumption is 35 μW from a 0.7 V supply voltage. The evaluated performance of the design using a classical figure of merit (FOM), achieves the highest known value by a significant margin in comparison with state-of-the-art work.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".