Design of Ultra-compact Graphene-based Superscatterers

The energy-momentum dispersion relation is a fundamental property of plasmonic systems. In this paper, we show that the method of dispersion engineering can be used for the design of ultra-compact graphene-based superscatterers. Based on the Bohr model, the dispersion relation of the equivalent planar waveguide is engineered to enhance the scattering cross section of a dielectric cylinder. Bohr conditions with different orders are fulfilled in multiple dispersion curves at the same resonant frequency. Thus the resonance peaks from the first and second order scattering terms are overlapped in the deepsubwavelength scale by delicately tuning the gap thickness between two graphene layers. Using this ultra-compact graphene-based superscatterer, the scattering cross section of the dielectric cylinder can be enhanced by five orders of magnitude.Comment: This paper has been accepted by IEEE Journal of Selected topics in Quantum Electronic

Chaos and bifurcations in chaotic maps with parameter q: Numerical and analytical studies

In this paper, a class of chaotic maps with parameter q are introduced and bifurcations and chaos in proposed maps are numerical and analytical studied. Euler method is employed to get the continuous systems corresponding to chaotic maps and the fractional styles in Caputo's definition. Based on that, we finally infer a class of chaotic maps with the Adams–Bashforth–Moulton predictor-corrector method. In the simulation and analysis, we discuss the Logistic map with q and Hénon map with q, observe the route from period to chaos and do tests to analyze properties of maps with parameter q

Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction

Sparse-view Computed Tomography (CT) image reconstruction is a promising approach to reduce radiation exposure, but it inevitably leads to image degradation. Although diffusion model-based approaches are computationally expensive and suffer from the training-sampling discrepancy, they provide a potential solution to the problem. This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation (CDDM) framework, including the low-quality image generation in latent space and the high-quality image generation in pixel space which contains data consistency and discrepancy mitigation in a one-step reconstruction process. The cascaded framework minimizes computational costs by moving some inference steps from pixel space to latent space. The discrepancy mitigation technique addresses the training-sampling gap induced by data consistency, ensuring the data distribution is close to the original manifold. A specialized Alternating Direction Method of Multipliers (ADMM) is employed to process image gradients in separate directions, offering a more targeted approach to regularization. Experimental results across two datasets demonstrate CDDM's superior performance in high-quality image generation with clearer boundaries compared to existing methods, highlighting the framework's computational efficiency