anlin Li

Electromagnetic reprogrammable coding-metasurface holograms

Metasurfaces have enabled a plethora of emerging functions within an ultrathin dimension, paving way towards flat and highly integrated photonic devices. Despite the rapid progress in this area, simultaneous realization of reconfigurability, high efficiency, and full control over the phase and amplitude of scattered light is posing a great challenge. Here, we try to tackle this challenge by introducing the concept of a reprogrammable hologram based on 1-bit coding metasurfaces. The state of each unit cell of the coding metasurface can be switched between ‘1’ and ‘0’ by electrically controlling the loaded diodes. Our proof-of-concept experiments show that multiple desired holographic images can be realized in real time with only a single coding metasurface. The proposed reprogrammable hologram may be a key in enabling future intelligent devices with reconfigurable and programmable functionalities that may lead to advances in a variety of applications such as microscopy, display, security, data storage, and information processing.Realizing metasurfaces with reconfigurability, high efficiency, and control over phase and amplitude is a challenge. Here, Li et al. introduce a reprogrammable hologram based on a 1-bit coding metasurface, where the state of each unit cell of the coding metasurface can be switched electrically.

Controlling Energy Radiations of Electromagnetic Waves via Frequency Coding Metamaterials

Metamaterials are artificial structures composed of subwavelength unit cells to control electromagnetic (EM) waves. The spatial coding representation of metamaterial has the ability to describe the material in a digital way. The spatial coding metamaterials are typically constructed by unit cells that have similar shapes with fixed functionality. Here, the concept of frequency coding metamaterial is proposed, which achieves different controls of EM energy radiations with a fixed spatial coding pattern when the frequency changes. In this case, not only different phase responses of the unit cells are considered, but also different phase sensitivities are also required. Due to different frequency sensitivities of unit cells, two units with the same phase response at the initial frequency may have different phase responses at higher frequency. To describe the frequency coding property of unit cell, digitalized frequency sensitivity is proposed, in which the units are encoded with digits “0” and “1” to represent the low and high phase sensitivities, respectively. By this merit, two degrees of freedom, spatial coding and frequency coding, are obtained to control the EM energy radiations by a new class of frequency‐spatial coding metamaterials. The above concepts and physical phenomena are confirmed by numerical simulations and experiments.

Supervised Automatic Detection of UWB Ground-Penetrating Radar Targets Using the Regression SSIM Measure

This letter introduces the concept of regression structural similarity index measure (regression SSIM) and builds a supervised graph framework of automatic detection of small-size ultrawideband (UWB) radar targets. The twofold contribution made in this letter includes the following: 1) The regression SSIM is proposed to measure the similarity of the local pattern between a test image and a reference image; and 2) the framework of a supervised graph, together with the regression SSIM, has been developed to address the automatic detection of UWB radar objects. As opposed to other detection techniques reported in the literature, our methodology does not rely on statistical modeling or imposing typical shape parameters. Selected results of processing simulated and real UWB ground-penetrating radar data are provided, which verifies the state-of-the-art performance of the proposed methodology and provides important potential for a wide class of target detection.

A Probabilistic Model for the Nonlinear Electromagnetic Inverse Scattering: TM Case

Electromagnetic inverse scattering (EMIS) is a noninvasive examination tool, which holds the promising potential in science, engineering, and military applications. In contrast to conventional tomography techniques, the inverse scattering is a quantitative superresolution imaging method since it is capable of accommodating more realistic interactions between the wavefield and the probed scene. In this paper, a full probabilistic formulation of the EMIS is presented for the first time, which is then solved by applying the well-known expectation maximization method. Afterward, the concept of the complex-valued alternating direction method of multipliers has been proposed as an alternative approach to solve the resulting nonlinear optimization problem. Finally, exemplary numerical and experimental results are provided to validate the proposed method.

A Fast Patches-Based Imaging Algorithm for 3-D Multistatic Imaging

Three-dimensional multistatic imaging is a powerful noninvasive examination tool for many military and civilian applications. Recently, the sparsity-regularized optimization has been used as a popular imaging technique to enhance the image quality. However, it suffers from the expensive computational cost, since its solution is obtained by a time-consuming iterative scheme, which is typically computationally prohibitive for large-scale imaging problems. To overcome this difficulty, this challenging imaging problem is converted into an image processing problem in this letter, which can be performed over small-scale overlapping patches and be efficiently solved in a parallel or distributed manner. In this way, the proposed qualitative scheme could be utilized to solve large-scale imaging problems. Exemplary simulation results are provided to demonstrate the efficiency of the proposed methodology.

A novel physics-driven fast parallel three-dimension radar imaging method

Over the past few years, three-dimension fast imaging and identification with microwave has recently gained a lot of attention. Resulting from the over-simple imaging model and over-large computation cost, the existing three-dimension radar imaging methods are limited to few scenarios. To break this bottleneck, we introduce a novel physics-driven three-dimension fast radar imaging method based on general reflectivity model, far-field-approximation assumption and neighbor-cell approximation, in which the whole imaging region would be decomposed into a series of sub-regions to accelerate the imaging speed in parallel. The proposed method drastically decreases the cost of memory while maintaining fast imaging speed and high imaging quality. Therefore, the proposed fast imaging method based on general reflectivity model, far-field-approximation and neighbor-cell approximation is applicable to more large-scale radar imaging scenarios. Some selected simulation results are presented to demonstrate the state-of-the-art performance of the far-field-approximation methods.

A novel three-dimension fast radar imaging method based on far-field-approximation

Three-dimension imaging and identification with radar has recently gained a lot of attention. Resulting from the over-simple imaging model and over-large computational complexity, the existing three-dimension radar imaging methods are limited in few scenarios. To break above bottlenecks, we introduce a novel physics-driven three-dimension fast radar imaging method based on far-field-approximation assumption, in which the whole imaging region would be decomposed with a series of overlapping-patches to accelerate the imaging speed in parallel. The proposed method has four key steps: e.g., first, the whole imaging region would be divided into a series of overlapping-patches, which indicates that the large-scale imaging problem is efficiently decomposed into a set of small-scale imaging sub-problems and can be solved in the parallel or distributed manner. Second, the dyadic Greens function based on far-field-approximation will be applied to construct system response function of radar imaging problem. Third, a dual transform is further introduced to turn the imaging problem into the problem of physical-driven image processing problem. In this way, the image processing system response functions for all overlapping-patches will be identical for fixed transmitters and receivers. In other words, the system response functions of overlapping-patches are independent of specific imaging regions. So few system response functions matrices need be memorized. At last, the first-order method is introduced to implement the imaging process of all overlapping-patches in parallel. The far-field-approximation imaging method drastically decreases the memory requirement while maintaining fast imaging speed and high imaging quality. Therefore, the proposed fast imaging method based on far-field-approximation is applicable to more large-scale imaging scenarios. Some selected simulation results are presented to demonstrate the state-of-the-art performance of the far-field-approximation methods.

Knowledge-aided object-oriented three-dimensional microwave imaging

For the past few years, researchers hold a strong interests on knowledge-aided object-oriented high-resolution microwave imaging field. In a novel framework of object-oriented microwave imaging using sparse prior based on the generalized reflectivity model is presented. The proposed methodology has several distinct advantages over existing methods in terms of the imaging quality: e.g., the generalized reflectivity model, which is a function of frequencies, viewing angles and polarization, allows us to consider more realistic interaction between the scene and the wavefield, and thus establishes a more accurate imaging model. Second, this novel methodology is a natural closed-loop iterative operation, which admits us to apply more prior knowledge (object-oriented feature knowledge) into the algorithm procedure resulting in greatly improving the imaging quality. In this paper, target classification stage is further introduced into the closed-loop iteration process, which bring a feedback to image processing stage for reducing the size of sample library and further improving the imaging quality. In this way, the pattern or local structure of imaged scene could be enhanced significantly. Another benefit introducing prior knowledge and target classification (or target identification) is the remarkable reduction of the transceiver elements. Selected simulation results are provided to demonstrate the state-of-art performance of the proposed methodology.