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IEEE Networking Letters 2024年第2期6卷 97-100页

作者： Mondal, Washim Uddin Goyal, Veni Ukkusuri, Satish V. Das, Goutam Wang, Di Alouini, Mohamed-Slim Aggarwal, Vaneet Purdue University School of Industrial Engineering The School of Civil Engineering West LafayetteIN47907 United States Purdue University School of Civil Engineering West LafayetteIN47907 United States Indian Institute of Technology Kharagpur G. S. Sanyal School of Telecommunications Kharagpur721302 India Purdue University School of Industrial Engineering West LafayetteIN47907 United States King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955-6900 Saudi Arabia

This letter presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error (L1 difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth. © 2019 IEEE.

关键词： Stochastic systems

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Nasim, Ammara Velayudhan, Divya Ahmed, Abdelfatah Hassan Hassan, Taimur Akcay, Samet Akram, Muhammad Usman Werghi, Naoufel National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Khalifa University KUCARS and C2PS Department of Electrical Engineering and Computer Science United Arab Emirates Abu Dhabi University Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates Intel Corporation RandD Division United Kingdom

ISBN: (纸本)9781665434454

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-Aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-The-Art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time. © 2023 IEEE.

关键词： Semantic Segmentation

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A Stable Discontinuous Galerkin Time-Domain Method for Simulation of Thin Conductive Layers

A Stable Discontinuous Galerkin Time-Domain Method for Simul...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Özakin, M. Burak Chen, Liang Ahmed, Shehab Bagci, Hakan Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia Ali I. Al-Naimi Petroleum Engineering Research Center Thuwal23955 Saudi Arabia

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A conductive layer that is much thinner than the skin depth is often incorporated into electromagnetic solvers as a resistive boundary condition (RBC) to avoid fine meshes (and consequently small time steps for time-domain methods). When the discontinuous Galerkin time-domain (DGTD) method, which relies on an explicit Runge-Kutta (RK) scheme for time integration, is used in a simulation involving an RBC surface with high conductivity, the solution becomes unstable. In this work, to circumvent this bottleneck, a locally-implicit time marching scheme is developed. The proposed method uses implicit Euler backward and explicit Euler forward schemes to discretize the time derivatives in local DG equations associated with the elements that "touch"and do not "touch"the RBC surface, respectively. This yields a locally-implicit Euler (LIME) time marching scheme that maintains its stability in the presence of a highly conductive layer. Indeed, numerical results demonstrate that LIME-DGTD is more stable than RK-DGTD for a transmission problem involving a thin metal sheet. © 2022 IEEE.

关键词： Lime

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Biochip-PUF: physically Unclonable Function for Microfluidic Biochips

Biochip-PUF: Physically Unclonable Function for Microfluidic...

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2023 IEEE International Test Conference, ITC 2023

作者： Singh Baban, Navajit Orozaliev, Ajymurat Song, Yong-Ak Chatterjee, Urbi Bose, Sankalp Bhattacharjee, Sukanta Karri, Ramesh Chakrabarty, Krishnendu New York University Abu Dhabi Division of Engineering Abu Dhabi United Arab Emirates Indian Institute of Technology Kanpur Department of Computer Science and Engineering India Indian Institute of Technology Department of Chemical Engineering Kharagpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India New York University Department of Electrical and Computer Engineering New York United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9798350343250

Flow-based microfluidic biochips (FMBs) have microvalves as key components. The physical characteristics of the microvalves vary instance-to-instance due to the inherent variability of numerous fabrication parameters. In this work, we leverage this unclonable, unpredictable instance-specific behavior and propose physically unclonable functions (PUFs) for FMBs, namely Biochip-PUFs (Bio-PUFs in short). We utilize variability in the microvalve membrane deflection response associated with the actuation pressure challenge to be our Bio-PUF parameter. Based on the distributions of the parameters measured on actual FMBs, we complement our Bio-PUF measurements via simulations of the FMB's microvalves in Comsol Multiphysics. Furthermore, we present a scheme based on the transient response of the microvalve actuation to augment the Bio-PUF authentication. The major advantage of this scheme is that we do not need any additional hardware to generate/implement the PUF module. The biochip itself can act as PUF instances while continuing to operate in normal functioning mode. © 2023 IEEE.

关键词： Authentication

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Advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication 35

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Dutta, Joy Eldeeb, Hossien B. Ho, Tu Dac Khalifa University C2PS Research Center Department of Computer Science Abu Dhabi United Arab Emirates University of Cambridge Electrical Engineering Division CambridgeCB3 0FA United Kingdom Faculty of Information Technology and Electrical Engineering Trondheim Norway

ISBN: (纸本)9798350362244

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

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Reconstruction of Layered Rough Surfaces Using a Two-Step RTM Scheme

Reconstruction of Layered Rough Surfaces Using a Two-Step RT...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Ahmet Sefer Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

An imaging scheme, which uses the frequency- domain reverse time migration (RTM) method to reconstruct two rough surfaces between three dielectric media from the scattered field measurements, is developed. The proposed method requires two steps for the reconstructions. At the first step, the RTM function that is computed using the scattered field measurements reveals only the upper rough surface. At the second step, it is first assumed that there is only one surface, the upper one. The forward scattering problem is solved for this scenario. Then, to reconstruct the lower surface, the difference between the scattered fields obtained from this forward scattering problem and the scattered field measurements is used to compute the RTM function of the second step. This second RTM function reveals the lower surface. Numerical experiments demonstrate that the proposed two-step scheme is effective and promising.

关键词： Surface reconstruction Time-frequency analysis Integral equations Imaging Media Surface roughness Dielectric measurement

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Performance Analysis of a Novel Relay Selection Scheme for Wireless-Powered Cluster-Based Multi-Hop Cognitive Relay Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1551-1562页

作者： Sun, Hui Naraghi-Pour, Mort Qian, Yuwen Sheng, Weixing Han, Yubing Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering School of Electrical Engineering and Computer Science Baton RougeLA70803 United States

In this paper, we study the performance of wireless-powered cluster-based multi-hop cognitive relay networks (MCRNs), where secondary nodes harvest energy from multiple dedicated power beacons (PBs) and share the spectrum with multiple primary receivers (PRs) in the underlay paradigm. For this system, we propose a hop-by-hop relay selection scheme called the largest decoding set (LDS). In each stage, relay selection is based on the harvested energy from PBs and the channel state information (CSI) of both the interference links to PRs and the relaying links in the subsequent hop. Considering both harvested energy and maximum interference constraints, we derive the exact end-to-end outage probability and show that the results closely match those obtained from simulations. Moreover, the asymptotic end-to-end outage probabilities in two different scenarios are derived to provide more valuable insights. Numerical results show that the outage probability of the proposed LDS scheme is very close to that of the best-path scheduling (BPS) scheme, which provides a lower bound on outage probability but requires global CSI before the secondary source transmits. We also compare the LDS scheme with other relay selection schemes that have appeared recently in the literature and show that LDS has the best outage performance. © 2024 IEEE.

关键词： Cognitive radio

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Electromagnetic Inversion Regularized by a Plug-and-Play Deep Neural Network Denoiser

Electromagnetic Inversion Regularized by a Plug-and-Play Dee...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Lingqi Gao Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A plug-and-play scheme that relies on a deep neural network for image denoising is used to regularize the nonlinear electromagnetic (EM) inversion. It is shown that any state-of-the-art denoiser can be plugged into the conventional inversion framework as an implicit regularization step. Thus, a pretrained Swin-Conv-UNet (SCUNet) is employed in the EM inversion. SCUNet combines the advantages of residual convolutional layers and swin transformer blocks in accounting for different image priors and it is remarkably effective in image denoising. Nu-merical results obtained using this framework clearly shows its benefits over existing inversion algorithms.

关键词： Conferences Artificial neural networks Transformers Antennas and propagation Electromagnetics Image denoising

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A Ratio-Norm Regularization for Sparsity-Promoting Electromagnetic Inversion

A Ratio-Norm Regularization for Sparsity-Promoting Electroma...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

A nonlinear electromagnetic inversion method that promotes the sparsity in the model gradient is proposed for reconstruction of dielectric profiles. For regularization, the method uses the ratio of $l_{1}$ -norm to $l_{2}$ -norm ( $l_{1}/l_{2}$ -norm) which is a better approximation to $l_{0}$ -norm than $l_{1}$ -norm due to its scale-invariant property. To deal with the non-convexity of the resulting optimization problem, the alternating direction method of mul-tipliers is used to split the quotient structure of the $l_{1}/l_{2}$ -norm. Consequently, the optimization problem is separated into several sub-steps that are executed iteratively in an alternating fashion. Numerical results show that the proposed method produces more accurate reconstructions of spatially sparse dielectric profiles (by preserving edges and reducing artifacts) compared to the methods relying on Tikhonov and total variation regularization.

关键词： Accuracy Computational electromagnetics Dielectrics Electromagnetics Optimization

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