A new SAW band reject filter and its applications in wireless systems
Bibliographic record
Abstract
SAW notch or band reject filters can achieve very low insertion losses and withstand very high powers in their pass bands. Despite these very promising attributes relatively little work has been invested to develop this technology, and of the little work which has been conducted, most of it focused on developing narrow band notch filters to suppress single tone carriers. The present paper introduces band reject filters constructed of single pole resonators in the well known ladder topology. The ability to reject a specific band while providing significantly less than 1 dB of insertion loss in the pass band make this technology very attractive for the front end of base station receivers or handset transmit or receive chains. The paper discusses the design methodology, highlight the performance of several prototype filters and discuss the numerous applications which we are currently considering for these components. It is our belief that such components will find many applications in RF design and open new markets for SAW technology in wireless systems.
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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 | 0.000 |
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.
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".