Being an open access on-line journal, PIER gives great prominence to special issues that draw together significant and emerging works to promote key advances on specific topics. The special issues are devoted to timely, relevant, and cutting-edge research and aim to provide a unique platform for researchers interested in selected topics.We are now calling for papers for the following PIER Spe
The 43rd PIERS in Hangzhou, CHINA
21 - 25, November 2021
(from Sunday to Thursday)
--- Where microwave and lightwave communities meet
Hybrid PIERS: Onsite + Web Access
To organize a Special Session, please fill out this
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Join Us in this Harvest Season - Onsite + Web Access
Night West Lake - PIERS 2021 Hangzhou, CHINA
PIERS 2021, Hangzhou, CHINA
Late autumn - PIERS 2021 Hangzhou, CHINA
West Lake - Hangzhou, CHINA
PIER Journals are a family of journals supported by the PhotonIcs and Electromagnetics Research Symposium (PIERS), which has become a major symposium in the area related to photonics and electromagnetics. The scope includes all aspects of electromagnetic theory plus its wide-ranging applications. Hence, it includes topics motivated by mathematics, sciences as well as topics inspired by advanced technologies. The spectrum ranges from very low frequencies to ultra-violet frequencies. The length scale spans from nanometer length scale to kilometer length scale. The physics covers the classical regime as well as the quantum regime.
Phase holographic metasurfaces encode the phase profiles of holograms in metasurfaces formed by the meta-atom arrays, and accurately modulate the field distribution in desired region. Iterative optimization methods or data-driven learning methods are used to retrieve the phase profile under the given physical setups, such as working wavelength λ, metasurfaces' period ∆x, and image distance Z. However, those methods usually repeat the optimization or training process to retrieve the phase profile for different physical setups. Here, we propose a generalized phase retrieval model (GPRM) based on physics-inspired network to retrieve the phase profile from the input λ, ∆x, Z, and desired image without retraining the neural network. The GPRM consists of deep neural network (DNN), parabolic phase, and Fresnel diffraction propagation, which is able to generate phase profile with high reconstruction quality in extraordinary broadband, such as visible, terahertz, and microwave region. By combining with corresponding meta-atom pool, the proposed method has great potential to design versatile meta-devices for image display, data encoding, and beam shaping. Furthermore, the proposed method accelerates the design of Fresnel phase hologram, which can cooperate with programmable metasurfaces to realize dynamic three-dimensional or full-color display.....