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Holographic Microwave Image Classification Using a Convolutional Neural Network
(2022-12) Wang, Lulu; Department of Engineering
Holographic microwave imaging (HMI) has been proposed for early breast cancer diagnosis. Automatically classifying benign and malignant tumors in microwave images is challenging. Convolutional neural networks (CNN) have demonstrated excellent image classification and tumor detection performance. This study investigates the feasibility of using the CNN architecture to identify and classify HMI images. A modified AlexNet with transfer learning was investigated to automatically identify, classify, and quantify four and five different HMI breast images. Various pre-trained networks, including ResNet18, GoogLeNet, ResNet101, VGG19, ResNet50, DenseNet201, SqueezeNet, Inception v3, AlexNet, and Inception-ResNet-v2, were investigated to evaluate the proposed network. The proposed network achieved high classification accuracy using small training datasets (966 images) and fast training times.
Verk
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
(2022-09-12) Apruzzese, Giovanni; Andreolini, Mauro; Ferretti, Luca; Marchetti, Mirco; Colajanni, Michele; Department of Computer Science
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios, because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning, we identify and model the real capabilities and circumstances required by attackers to carry out feasible and successful adversarial attacks. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this article can help hardening defensive systems by letting cyber defenders address the most critical and real issues and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.
Verk
Tunable oriented mid-infrared wave based on metasurface with phase change material of GST
(2022-03) Guo, Kai; Li, Xiaoyu; Ai, Huifang; Ding, Xiya; Wang, Lulu; Wang, Wei; Guo, Zhongyi; Department of Engineering
Dynamically controlling the mid-infrared electromagnetic waves with tunable metasurfaces remains a challenging task. Phase change material of Ge2Sb2Te5 (GST) has shown tremendous advantages in constructing reconfigurable metasurface. In this work, we design a metasurface with GST to dynamically focus and deflect the beam at wavelength of 8.5 μm with a fixed structure. The designed metasurface is composed of GST nanocylinders on a Calcium Fluoride (CaF2) substrate. The focusing and deflection characteristics are manipulated by artificially controlling the value of crystalline fraction of each GST cylinder. In addition, we studied the diffraction pattern at a distance of 2 m for oriented deflection and its broadband performance. This method improves the degree of freedom of active modulation and provides a new route for tunable optical communication devices.
Verk
Quad-port multiservice diversity antenna for automotive applications
(2021-12-01) Kannappan, Lekha; Palaniswamy, Sandeep Kumar; Wang, Lulu; Kanagasabai, Malathi; Kumar, Sachin; Alsath, Mohammed Gulam Nabi; Rao, Thipparaju Rama; Department of Engineering
A quad-element multiple-input-multiple-output (MIMO) antenna with ultra-wideband (UWB) performance is presented in this paper. The MIMO antenna consists of four orthogonally arranged microstrip line-fed hexagonal monopole radiators and a modified ground plane. In addition, E-shaped and G-shaped stubs are added to the radiator to achieve additional resonances at 1.5 GHz and 2.45 GHz. The reliability of the antenna in the automotive environment is investigated, with housing effects taken into account. The housing effects show that the antenna performs consistently even in the presence of a large metal object. The proposed MIMO antenna has potential for various automotive applications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X), intelligent transport system (ITS), automatic vehicle identifier, and RFID-based electronic toll collection.
Verk
Multiplexed multi-focal and multi-dimensional SHE (spin Hall effect) metalens
(2021-12-20) Wang, Wei; Yang, Qingyuan; He, Shan; Shi, Yan; Liu, Xiangmin; Sun, Jinghua; Guo, Kai; Wang, Lulu; Guo, Zhongyi; Department of Engineering
Metalenses are two-dimensional ultrathin metalenses composed of subwavelength artificial microstructures. In this paper, various multi-focal spin Hall effect (SHE)-based metalenses are designed to provide spin-dependent splitting in transverse and longitudinal directions, which possess spin-dependent two focal points under left-circularly polarized (LCP) or right-circularly polarized (RCP) incidence, and all four focal points can be observed under the linearly polarized (LP) incidence. A spin-independent bifocal metalens was investigated, which possesses the same bifocal focusing phenomena for LCP and RCP incidences. Our method is significant for designing high-efficiency multifunctional optics devices based on optical SHE.

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