Design of a Compact Microstrip Triple Independently Controlled Pass Bands Filter for GSM, GPS and WiFi Applications
Bibliographic record
Abstract
An ultra-compact triple-band bandpass filter based on dual-mode quarter-wave resonator for GSM, GPS, and WiFi applications is presented in this study. The filter consists of three quarter-wave resonators assisting in controlling each passband independently. The first operating frequency band is obtained by the direct-feed resonator which acts as a source to load coupling for the inner two resonators which are operating at higher frequency bands. The outermost resonator is coupled to the inner loaded resonators, thus a pair of transmission zeros between each passband can be excited and hence high-frequency selectivity can be obtained. To miniaturize the overall size of the filter, all resonators are folded and are jointly connected through a common metallic via with the ground. The design has a symmetric structure therefore, even-odd mode analysis method is applied to obtain the three controllable operating frequency bands. The first operating band is centered at 850 MHz which caters for GSM applications, while the second and third frequency bands centered at 1.57 GHz and 2.4 GHz fall in the GPS and WiFi wireless applications. The filter with an ultra-compact size of 0.10 λg× 0.09 λg(0.009λ2g) despite feed lines (λgis the waveguide length centered at 850 MHz) is designed, fabricated and measured for the purpose of validation. Both the simulated and measured results are in good agreement and endorse the design concept.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".