Metamaterials, Metasurfaces, and Nanotechnology, and their Applications to Antennas, Sensors, and Cognitive Radar
Bibliographic record
Abstract
Dr. Amir Zaghloul presents seminar on "Metamaterials, Metasurfaces, and Nanotechnology and Their Applications to Antennas, Sensors, and Cognitive Radar" On March 19th, Professor Amir I. Zaghloul, presented a seminar on "Metamaterials, Metasurfaces, and Nanotechnology and Their Applications to Antennas, Sensors, and Cognitive Radar" to Wireless@VT students and faculty. In addition, the seminar was streamed live allowing W@VT's IAP members to watch the event remotely in real time. Abstract: Metamaterials and metasurfaces are engineered materials and special surfaces that have parameters and features that do not exist in natural materials and simple surfaces. The prefix "meta" has been extensively used in recent years and has encompassed techniques that have been known under different names such as frequency selective surfaces (FSS), electromagnetic band-gap (EBG) structures, high permeability materials, and others. Among desirable features in metamaterials are: negative refractive index, coherent reflection, cloaking capability, and manipulation for transformation optics operations. The talk goes through some of the research work in this area, including the introduction of random metamaterials, which offer features that differ from those of conventional periodic materials. Examples include adaptive metasurfaces of special reflection phases that have applications in low-profile antennas, and introduce new tools in cognitive radar and anti-jamming operations. Nanotechnology is another area of intensive research in antennas and special surfaces for wide range of applications. This includes carbon nanotubes (CNT) and graphene-based structures and designs. Nanotechnology does not only offer light-weight and high tensile strength, it also introduces polarization selectivity in antennas and detection capability in gas sensors, among other features for communications and medical applications. Examples are given on antenna, sensor, and metamaterial designs. The talk will also address a variety of research topics of interest to the Army Research Laboratory, and will explain new initiatives for collaboration between ARL and academia. Opportunities for internship programs that benefit faculty and students will be discussed. Bio: Amir I. Zaghloul has been with the ECE Department at Virginia Tech since 2001, currently as a Research Professor and with the US Army Research Laboratory, Adelphi, MD. Prior to 2001, he was at COMSAT Laboratories for 24 years performing and directing R&D work on satellite communications and antennas. He is a Life Fellow of the IEEE, Fellow of the Applied Computational Electromagnetics Society (ACES), Associate Fellow of The American Institute of Aeronautics and Astronautics (AIAA), and Member of Commissions A, and B, and Chair of Commission C of the US National Committee (USNC) of the International Union of Radio Science (URSI). He was the General Chair of the 2005 "IEEE International Symposium on Antennas and Propagation and USNC/URSI Meeting," held in Washington, D.C., and is the Co-Chair of the 2014 ACES conference, to be held in Jacksonville, FL. He served as an Ad Com member of the IEEE AP Society, member of the IEEE Publication Services and Products Board (PSPB) and member of the Editorial Board of "The Institute." He was a Distinguished Lecturer for the IEEE Sensors Council. He received several research and patent awards, including the Exceptional Patent Award at COMSAT and the 1986 Wheeler Prize Award for Best Application Paper in the IEEE Transactions on Antennas and Propagation. He has over 300 publications, books, book chapters and patents in the areas of antennas, electromagnetics, and communications. Dr. Zaghloul received the Ph.D. and M.A.Sc. degrees from the University of Waterloo, Canada in 1973 and 1970, respectively, and the B.Sc. degree (Honors) from Cairo University, Egypt in 1965, all in electrical engineering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".