Bibliographic record
Abstract
Signal processing deals with the analysis, interpretation, and manipulation of signals. Signals of interest include sound, images, biological signals, radar signals, and many others. Processing of such signals includes filtering, storage and reconstruction, separation of information from noise (e.g. aircraft identification by radar), compression (e.g. image compression), and feature extraction (e.g. speech-to-text conversion). In communications systems, signal processing is mostly performed at OSI layer 1, the physical layer (modulation, equalization, multiplexing, radio transmission, etc.), as well as at OSI layer 6, the presentation layer (source coding, including analog-to-digital conversion, and data compression). In cognitive radio networks, the major task of signal processing is spectrum sensing for detecting the unused spectrum and sharing it without harmful interference to other users. One important requirement in a cognitive radio network is sensing spectrum holes reliably and efficiently. Spectrum sensing techniques can be classified into three categories. First, cognitive radios must be capable of determining if a signal from a primary transmitter is locally present in a certain spectrum. Several approaches are used for transmitter detection, such as matched filter detection, energy detection, cyclostationary feature detection, and wavelet detection. Second, collaborative detection refers to spectrum sensing methods where information from multiple cognitive radio users is exploited for primary user detection. Third, the sensing devices can be separated from the secondary users and can be deployed into the cognitive network by the cognitive radio service provider. By doing this, the cost of the secondary user devices can be reduced and the hidden terminal problem/exposed terminal problem can be mitigated.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.042 |
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".