Reflection reduction metasurface is capable of suppressing the radar cross section of a target, which is of great importance in stealth technology. However, it is still a challenge to realize broadband and low-profile simultaneously within a simple design. Here, we experimentally demonstrate an ultra-thin wideband reflection reduction metasurface, which is achieved by utilizing polarization conversion instead of resonant absorption. The simple cut-wire unit cell is adopted to perform efficient cross polarization conversion, which leads to a polarization conversion ratio above 90% ranging from 8.4 to 14.7 GHz. By arranging the 0/1 units in chessboard layout, the reflection reduction reaches 10\,dB from 8.1 GHz to 14.6 GHz. Measured results agree well with simulated ones, which validates the effectiveness of the proposed structure. The ratio of thickness to maximum wavelength reaches 0.56 while the relative bandwidth reaches 57.3%, demonstrating an excellent comprehensive performance. Since our structure consists of refractory ceramic materials, it is promising for radar cross section reduction in high temperature environment.

The multilevel fast physical optics (MLFPO) is proposed to accelerate the computation of the fields scattered from electrically large coated scatterers. This method is based on the quadratic patch subdivision and the multilevel technology. First, the quadratic patches are employed rather than the planar patches to discretize the considered scatterer. Hence, the number of the contributing patches is cut dramatically, thus making the workload of the MLFPO method much lower than that of the traditional Gordon's method. Next, the multilevel technology is introduced in this work to avoid calculating the physical optics scattered fields from the considered scatterer directly, so that the proposed algorithm can significantly reduce the computational complexity. Finally, numerical results have demonstrated the accuracy and efficiency of the MLFPO method based on the quadratic patches.

In recent years, topological states in photonic artificial structures have attracted great attention due to their robustness against certain disorders and perturbations. To readily understand the underlying principles, topological edge modes in one-dimensional (1D) system have been widely investigated, which bring aboutthe discovery of novel optical phenomena and devices. In this article, we review our recent advances in topological edge modes. We introduce the connection between topological orders and effective electromagnetic parameters of photonic artificial structures in band gaps, discuss experimental demonstration of robust topological modes and their potential applications in wireless power transfer, sensing and field of optics, and give a brief introduction of future opportunities in 1D topological photonics.

The zeroing of second order correlation functions between output fields after interferences in a 50/50 beam splitter has been accepted decades-long in the quantum optics community as an indicator of the quantum nature of lights. But, a recent work [1] presented some notable discussions and experiments that classical electromagnetic fields can still exhibit the zero correlation under specific conditions. Here, we examine analytically classical and quantum electromagnetic field interferences in a 50/50 beam splitter in the context of the second order correlation function for various input conditions. Adopting the Heisenberg picture in quantum electromagnetics, we examine components of four-term interference terms in the numerator of second order correlation functions and elucidate their physical significance. As such, we reveal the fundamental difference between the classical and quantum interference as illustrated by the Hong-Ou-Mandel (HOM) effect. The quantum HOM effect is strongly associated with: (1) the commutator relation that does not have a classical analogue; (2) the property of Fock states needed to stipulate the one-photon quantum state of the system; and (3) a destructive wave interference effect. Here, (1) and (2) imply the indivisibility of a photon. On the contrary, the classical HOM effect requires the presence of two destructive wave interferences without the need to stipulate a quantum state.

This article contains a digest of the theory of electromagnetism and a review of the transformation between inertial frames, especially under low speed limits. The covariant nature of the Maxwell's equations is explained using the conventional language. We show that even under low speed limits, the relativistic effects should not be neglected to get a self-consistent theory of the electromagnetic fields, unless the intrinsic dynamics of these fields has been omitted completely. The quasi-static limits, where the relativistic effects can be partly neglected are also reviewed, to clarify some common misunderstandings and imprecise use of the theory in presence of moving media and other related situations. The discussions presented in this paper provide a clear view of why classical electromagnetic theory is relativistic in its essence.

We demonstrate the recovery of simple geometric and permittivity information of breast models in an experimental microwave breast imaging system using a synthetically trained machine learning workflow. The recovered information consists of simple models of adipose and fibroglandular regions. The machine learning model is trained on a labelled synthetic dataset constructed over a range of possible adipose and fibroglandular regions and the trained neural network predicts the geometry and average permittivty of the adipose and fibroglandular regions from calibrated experimental data. The proposed workflow is tested on two different experimental models of the human breast. The first model is comprised of two simple, symmetric phantoms representing the adipose and fibroglandular regions of the breast that match the model used to train the neural network. The second, more realistic model replaces the symmetric fibroglandular phantom with an irregularly shaped, MRI-derived fibroglandular phantom. We demonstrate the ability of the machine learning workflow to accurately recover geometry and complex valued average permittivity of the fibroglandular region for the simple case, and to predict a symmetric convex hull that is a reasonable approximation to the proportions of the MRI-derived fibroglandular phantom.