Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Peyman Mahouti"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic param
Externí odkaz:
https://doaj.org/article/08d3ca1e70f04b52b7017fda1a229bd2
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-18 (2023)
Abstract Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of
Externí odkaz:
https://doaj.org/article/f94dfea53f4048b0bdf7307d1d32e689
Autor:
Reyhan Yurt, Hamid Torpi, Ahmet Kizilay, Slawomir Koziel, Anna Pietrenko-Dabrowska, Peyman Mahouti
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-22 (2023)
Abstract This work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool,
Externí odkaz:
https://doaj.org/article/829e5a46da3549538cfeeade7b2b7660
Publikováno v:
IEEE Access, Vol 11, Pp 114415-114423 (2023)
Phased Array Antenna (PAA) technology plays an important role in fields such as radar, 5G and satellite or any application which requires wide bandwidth and high gain. However, achieving such design is a difficult and complex task that requires an ac
Externí odkaz:
https://doaj.org/article/b607c5d2d86e41b4beab63f5d3e8588c
Publikováno v:
IEEE Access, Vol 11, Pp 24175-24184 (2023)
Reflectarrays (RAs) exhibit important advantages over conventional antenna arrays, especially in terms of realizing pencil-beam patterns without the employment of the feeding networks. Unfortunately, microstrip RA implementations feature narrow bandw
Externí odkaz:
https://doaj.org/article/608ea1555d5c4f1cbf63ab4b59e0e039
Publikováno v:
IEEE Access, Vol 11, Pp 13309-13323 (2023)
This work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) ob
Externí odkaz:
https://doaj.org/article/90798f5e430a4edf87dce175e440b485
Autor:
Nurullah Calik, Filiz Güneş, Slawomir Koziel, Anna Pietrenko-Dabrowska, Mehmet A. Belen, Peyman Mahouti
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract Accurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to
Externí odkaz:
https://doaj.org/article/134b0c05bf7e4a51b1ca51fef4c67e4e
Publikováno v:
IEEE Access, Vol 9, Pp 38396-38410 (2021)
Surrogate modeling has become an important tool in the design of high-frequency structures. Although full-wave electromagnetic (EM) simulation tools provide an accurate account for the circuit characteristics and performance, they entail considerable
Externí odkaz:
https://doaj.org/article/564cdbc6de9047c49a62b1094c1055d8
Publikováno v:
IEEE Access, Vol 9, Pp 71470-71481 (2021)
Fast replacement models (or surrogates) have been widely applied in the recent years to accelerate simulation-driven design procedures in microwave engineering. The fundamental reason is a considerable—and often prohibitive—CPU cost of massive fu
Externí odkaz:
https://doaj.org/article/7b6f05cc8d704caab861a625e2d1c9da
Publikováno v:
IEEE Access, Vol 9, Pp 161318-161325 (2021)
Recent years have witnessed a growing interest in reconfigurable antenna systems. Travelling wave antennas (TWAs) and leaky wave antennas (LWAs) are representative examples of structures featuring a great level of flexibility (e.g., straightforward i
Externí odkaz:
https://doaj.org/article/2279a18c56f34d7f8c0eabb0618cb7e1