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Mapping European high-digital intensive sectors—regional growth accelerator for the circular economy
(2023-01-06) Pirciog, Speranta Camelia; Grigorescu, Adriana; Lincaru, Cristina; Popa, Florin Marius; Lazarczyk Carlson, Ewa; Sigurdarson, Hallur Thor; Department of Business and Economics
Globalization and the Fourth Industrial Revolution or Industry 4.0 act as shocks on regional labor markets and regional economies. The presence of a digital economy has high spillover effects on regional development, job creation, economic resilience, and sustainability; furthermore, it valuates eco-innovation and the clean economy. We believe that the process of digital transformation has a robust impact on the green and clean aspects of the entire economy. The consistency of high digital-intensive (HDI) sectors can be evaluated through high digital-intensive employment, human resources, and technological infrastructure, as these are the main pillars of digital transformation. The shift-share analysis method (SSM) is used in this study on employment growth during 2008–2018 for the EU27, the United Kingdom, and Norway, combined with a second stage of exploratory spatial data analysis (ESDA). The findings on national growth, industrial mix, and competitiveness are presented in GIS mapping system considering the Local Indicators of Spatial Association (LISA) technique at the NUTS2 level. This approach allows us to determine the clustering level of high digital-intensive employment and sectors, resilience based on connectivity and eco-innovation, and the regional potential of digital transformation. Policymakers and political or governmental decision-makers could consider the results of the present study as the starting point for developing and implementing their policies for a sustainable green regional economy and determine the emerging area patches that need to be stimulated.
Verk
Early-Stage Lung Tumor Detection Based on Super-Wideband Microwave Reflectometry
(2023-01) Alamro, Wasan; Seet, Boon Chong; Wang, Lulu; Parthiban, Prabakar; Department of Engineering
This paper aims to detect early-stage lung tumors in deep-seated and superficial locations, and to precisely measure the size of the detected tumor using non-invasive microwave reflectometry over a super-wideband (SWB) frequency range. Human lung phantom and lung tumors are modeled using a multi-layer concentric cylinder structure and spherical-shaped inclusions, respectively. Firstly, a study on the dielectric properties of human torso tissues is carried out over an SWB frequency range of 1–25 GHz based on the Cole–Cole dispersion model. Intensive full-wave simulations of the modeled phantom under irradiation by a custom-designed SWB antenna array are then performed. Results show that small tumor sizes from 5 mm radius in both deep-seated and superficial locations of the lung tissue can be detected based on the contrast of reflection coefficients and reconstructed images produced from backscattered signals between normal and anomalous tissues. The potential of using SWB microwave reflectometry to successfully detect the lung tumors in their early stages and at different depths of the lung tissue has been demonstrated.
Verk
Practically Robust Fixed-Time Convergent Sliding Mode Control for Underactuated Aerial Flexible JointRobots Manipulators
(2022-12) Rsetam, Kamal; Cao, Zhenwei; Wang, Lulu; Al-Rawi, Mohammad; Man, Zhihong; Department of Engineering
The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) is proposed by using a fixed-time sliding mode observer (FxTSMO) for the trajectory tracking problem of the FJR attached to the drones system. First, the underactuated FJR is comprehensively modeled and converted to a canonical model by employing two state transformations for ease of the control design. Then, based on the availability of the measured states, a cascaded FxTSMO (CFxTSMO) is constructed to estimate the unmeasurable variables and lumped disturbances simultaneously in fixed-time, and to effectively reduce the estimation noise. Finally, the FxTSMC scheme for a high-order underactuated FJR system is designed to guarantee that the system tracking error approaches to zero within a fixed-time that is independent of the initial conditions. The fixed-time stability of the closed-loop system of the FJR dynamics is mathematically proven by the Lyapunov theorem. Simulation investigations and hardware tests are performed to demonstrate the efficiency of the proposed controller scheme. Furthermore, the control technique developed in this research could be implemented to the various underactuated mechanical systems (UMSs), like drones, in a promising way.
Verk
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.

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