Bibliographic record
Abstract
ANALYZING PULSE POSITION MODULATION TIME HOPPING UWB IN IEEE UWB CHANNEL, multipath channelReferences 1. M. Z. Win and R. A. Scholtz, “Ultra-wide bandwidth time-hopping spread-spectrum impulse radio for wireless multiple-access communication”. IEEE Transactions on Publication Date: Volume: 52, Issue: 10, Oct. 2004, pp. 1786- 1796. 2. Federal Communications Commission, “Revision of Part 15 of the commission’s rules regarding ultra-wideband transmission systems, FIRST REPORT AND ORDER,” ET Docket 98-153, FCC 02-48, pp. 1–118, February 14, 2002. 3. Hao Zhang, “Performance and Capacity of PAM and PPM UWB Time-Hopping Multiple Access Communications with Receive Diversity” Department of Electrical & Computer Engineering, University of Victoria, BC, Canada V8W 3P 6,2005. 4. Goyal, Vikas, and B. S. Dhaliwal. "INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY ULTRA WIDEBAND PULSE GENERATION USING MULTIPLE ACCESS MODULATION SCHEMES." 5. Goyal, Vikas. "PULSE GENERATION AND ANALYSIS OF ULTRA WIDE BAND SYSTEM MODEL." Computer Science & Telecommunication 2, no. 34: 3-6. 6. Goyal, Vikas, and B. S. Dhaliwal. "Optimal Pulse Generation for the improvement of ultra wideband system performance." Engineering and Computational Sciences (RAECS), 2014 Recent Advances in. IEEE, 2014. 7. J. R. Foerster (2005), “The effects of multipath interference on the performance of UWB systems in an indoor wireless channel,”in Proc. IEEE 53rd Vehicular Technology Conference(VTC ’05), Rhodes, Greece ,vol. 2, pp. 1176–1180. 8. J. G. Proakis, Digital Communications. New York: McGraw-Hill,5th ed., 2001. 9. T. Rappaport, “Wireless Communications Principles and Practice”, Pearson Education, 2nd ed., 2004.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.515 | 0.354 |
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".