Open Access Publications

Permanent URI for this collection

Open access publications by faculty, postdocs, and graduate students in the Department of Electrical and Computer Engineering


Recent Submissions

Now showing 1 - 5 of 45
  • Item
    An Empirical Loss Model for an Additively Manufactured Luneburg Lens Antenna
    (The Applied Computational Electromagnetics Society Journal, 2022-11-14) LaRocca, Brian F.; Mirotznik, Mark S.
    This research applies Effective Medium Theory and 3D Finite Element Analysis to model the transmissive loss through a waveguide fed additively manufactured Luneburg lens. New results are presented that provide rational function approximations for accurately modeling the aperture, beam, and radiation loss factors of the antenna. It introduces a normalized loss tangent and shows that the loss factors are dependent on the product of this parameter and the lens radius. Applying the constraint that the main beam of the radiation pattern contains 50% of accepted power, a maximum useful radius is tabulated for common polymers used in additive manufacturing.
  • Item
    Recent advances in photonics of three-dimensional Dirac semimetal Cd3As2
    (Advanced Photonics Nexus, 2022-11-14) Zhou, Renlong; Ullah, Kaleem; Hussain, Naveed; Fadhali, Mohamed M.; Yang, Sa; Zubair, Muhammad; Iqbal, Muhammad Faisal
    Due to their unusual features in condensed matter physics and their applicability in optical and optoelectronic applications, three-dimensional Dirac semimetals (3D DSMs) have garnered substantial interest in recent years. In contrast to monolayer graphene, 3D DSM exhibits linear band dispersion despite its macroscopic thickness. Therefore, being a bulk material, it is easy to make nanostructures with 3D DSM, just as one normally does with metals such as gold and silver. Among 3D DSMs, cadmium arsenide (Cd3As2) is quite famous and considered an excellent 3D DSM due to its chemical stability in air and extraordinary optical response. In this review, advances in 3D DSM Cd3As2 fabrication techniques and recent progress in the photonics of 3D DSM Cd3As2 are given and briefly reviewed. Various photonic features, including linear and nonlinear plasmonics, optical absorption, optical harmonic generation, and ultrafast dynamics, have been explored in detail. It is expected that Cd3As2 would share an excellent tunable photonic response like graphene. We envision that this article may serve as a concise overview of the recent progress of photonics in 3D DSM Cd3As2 and provides a compact reference for young researchers.
  • Item
    A Coherent Integration Method for Moving Target Detection in a Parameter Jittering Radar System Based on Signum Coding
    (IEEE Signal Processing Letters, 2022-11-04) Huang, Penghui; Xia, Xiang-Gen; Wang, Lingyu; Liu, Xingzhao; Liao, Guisheng
    In this letter, we propose a novel long-time coherent integration detection method to detect an uncooperative moving target in a frequency and pulse repetition interval randomly jittering radar system based on signum coding (SC). In the proposed algorithm, an additional reference waveform is applied to eliminate the third-order harmonic influence induced by SC. Then, a generalized Keystone transform (GKT) is proposed to resolve the complex coupling among the range frequency, jittered carrier frequency, and nonuniformly sampled time. Simulation results are presented to validate the effectiveness and feasibility of the proposed method.
  • Item
    Channel Estimation for Massive MIMO: An Information Geometry Approach
    (IEEE Transactions on Signal Processing, 2022-10-04) Yang, Jiyuan; Lu, An-An; Chen, Yan; Gao, Xiqi; Xia, Xiang-Gen; Slock, Dirk T. M.
    In this paper, we investigate the channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Using the sampled steering vectors in the space and frequency domain, we first establish a space-frequency (SF) beam based statistical channel model. The accuracy of the channel model can be guaranteed with sufficient sampling steering vectors. With the channel model, the channel estimation is formulated as obtaining the a posteriori information of the beam domain channel. We solve this problem by calculating an approximation of the a posteriori distribution's marginals within the information geometry framework. Specifically, by viewing the set of Gaussian distributions and the set of the marginals as a manifold and its e -flat submanifold, we turn the calculation of the marginals into an iterative projection process between submanifolds with different constraints. We derive the information geometry approach (IGA) for channel estimation by calculating the solutions of projections. We prove that the mean of the approximate marginals at the equilibrium of IGA is equal to that of the a posteriori distribution. Simulations demonstrate that the proposed IGA can accurately estimate the beam domain channel within limited iterations.
  • Item
    Compressive Spectral X-Ray CT Reconstruction via Deep Learning
    (IEEE Transactions on Computational Imaging, 2022-10-20) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Restrepo, Carlos; Arce, Gonzalo R.
    Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.
Please look at individual material in order to see what the copyright and licensing terms are. Some material may be available for reuse under a Creative Commons license; other material may be the copyright of the individual author(s) or the publisher of the journal.