Notice bibliographique
Résumé
The next generation of guided radar (GUIDAR) is based on ultra wide band (UWB) radar signal processing. Just as spread spectrum technology has revolutionized the communications industry UWB is dramatically changing radar signal processing. These advanced signal processing techniques are adapted to leaky coaxial cable technology in the next generation GUIDAR to provide new features and enhanced performance. At the core of the new technology is an ultra high-speed digital correlator implemented in a field programmable gate array (FPGA). Complementary orthogonal codes based on Golay codes are used to produce thumbtack correlation functions simultaneously in multiple range bins. The net result is "near continuous wave (CW)" performance (97% duty cycle) in forty to eighty 11.6-meter long-range bins with targets located within one meter along the length of cable. This is a dramatic improvement over the 3% duty cycle of the original GUIDAR and the typical 100 to 200 meter long zones of current CW leaky cable sensors. Orthogonal complementary codes are transmitted on each of two leaky coaxial cables. The responses from the parallel receive cables are fed to a direct digital receiver. The orthogonal nature of the code allows the composite coded pulse response to be de-multiplexed into the independent response for each of the two cables. This ultra-high speed correlation process involves the addition and subtraction of the sampled in-phase and quadrature-phase responses to the multiple range bin accumulators at 10 million samples per second. Synchronous sampling at twice the chip rate ensures that each target is observed in three adjacent sample bins. The phase and amplitude response in the three adjacent samples are combined to precisely pinpoint (within 1 meter) the locations of targets along the length of each of the two cables. The ability to precisely locate and track multiple simultaneous targets on each of two cables leads to numerous new features and performance benefits relative to existing leaky cable sensors. With a separate calibrated threshold for every meter of cable the sensor sensitivity is much more uniform and installation restrictions on burial depth, cable spacing, and medium homogeneity can be relaxed. Potential sources of nuisance alarms can easily be located and overcome. The pinpoint location can be used to provide better CCTV assessment, target capture for video motion sensors and more effective response to intrusions. Through the use of parallel cables the sensor can be used to detect the direction of crossing and to classify targets such as small animals, people, and vehicles. This patented next generation of GUIDAR technology represents a dramatic step forward from that which was introduced at the 1976 Carnahan Conference in Lexington, Kentucky, and the numerous CW leaky coaxial cable sensors that evolved from that work. This technology effectively addresses residential, commercial, industrial, and governmental requirements including those relating to homeland security, military operations, and prisons.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,016 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».