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SHIS: secure healthcare intelligent scheme in internet of multimedia vehicular environment

Multimedia Tools and Applications 2024年第40期83卷 87781-87800页

作者： Mangla, Cherry Rani, Shalli Dhiman, Gaurav Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Bikram Government College Punjab Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Division of Research and Development Lovely Professional University Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India MEU research Unit Middle East University Amman Jordan

For providing a safe and secure healthcare environment in vehicular multimedia networks has been emerged as a promising area because of data exchange in multimedia format. Because of properties of Long Term Evolution (LTE) with low delay, high data rate, high mobility, an efficient safe environment can be provided to the in healthcare environment for multimedia content (images, audio, text, video). Despite these benefits, due to the high mobility and data transmission using an open channel, i.e., Internet, multimedia vehicular networks are facing many security breaches in recent years. The Internet of Multimedia Vehicular Environment calls for the establishment of a safe and intelligent healthcare system, and the proposed work problem lays the groundwork for those requirements. One of the security breaches is the Denial of Service (DoS) attack in healthcare. To mitigate this issue, in this paper, we propose, SHIS, a model for detection of Imposter location using two parameters (i) packet counter and (ii) average ratio of packet delivery. It helps to detect attacks faster in comparison to the existing approach (MEC-DoS) for healthcare data. We proposed SHIS to detect denial of service attack to find the location of malicious nodes in the multimedia network. Simulation results showed that SHIS outperformed the existing techniques as it has 20% improvement in detection of attacks as compared to the existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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Experimental validation of multiferroic antennas in GHz frequency range

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Applied Physics Letters 2023年第16期123卷 1-6页

作者： Xu, Rui-Fu Ippet-Letembet, Louis-Charles Tiwari, Sidhant Yao, Zhi [Jackie] Huang, Shih-Ming Candler, Rob N. Chen, Shih-Yuan Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States California NanoSystems Institute Los AngelesCA90095 United States Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan

Various mechanical antennas have emerged to overcome the inherently narrower bandwidth and degraded efficiency in electrically small antennas. Among them, multiferroic antennas are expected to realize high-frequency applications and maintain their performance, even with significantly reduced sizes. However, experimental proof of such radiation from multiferroic or magnetoelectric coupling in the GHz range deserves further examination. This paper designs and fabricates a series of multiferroic antenna samples with mechanical resonances at around 3.5 and 6 GHz, and their radiation transmissions are tested at these resonances. Nickel, a magnetoelastic material, consistently exhibits magnetically induced radiation at both resonances. However, magnetic material consisting of Permalloy (Ni78Fe22), known for its much weaker magnetoelastic effects, still shows similar transmission behaviors at 3.67 GHz and enhanced power absorption at 6.42 GHz. Our results indicate that the dynamic response of magnetoelastic materials in the GHz band should differ from their response at the MHz and below bands. This evidence calls for further investigations of the source of magnetoelectric radiation. © 2023 Author(s).

关键词： Iron alloys

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Multi-Pose Fusion for Autonomous Polycrystalline Material Decomposition in Hyperspectral Neutron Tomography 58

Multi-Pose Fusion for Autonomous Polycrystalline Material De...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Yang, Diyu Nur Chowdhury, Mohammad Samin Tang, Shimin Venkatakrishnan, Singanallur V. Bilheux, Hassina Z. Buzzard, Gregery T. Bouman, Charles A. School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Ornl Electrical and Engineering Infrastructure Division Oak RidgeTN37830 United States Purdue University Department of Mathematics West LafayetteIN47907 United States

ISBN: (纸本)9798350354058

Hyperspectral neutron computed tomography (HSnCT) is an effective technique for characterizing poly crystalline material samples. A typical scan involves making multiple HS projection measurements by rotating the sample about a single axis and using a standard algorithm for tomographic reconstruction. Recently, an autonomous polycrystalline material decomposition (APMD) algorithm was proposed to obtain accurate 3D reconstruction of the different materials or crystallographic phases in the object. However, for objects with complex compositions and shapes, using data from a single rotation axis may result in reconstructions with significant noise and inaccuracies in the material decomposition. In this paper, we present a multi-pose reconstruction algorithm to produce a single reconstruction from HSnCT data corresponding to multiple poses of the object. Our algorithm extends previous APMD work to incorporate hyperspectral neutron measurements from multiple poses, utilizing the Multi-Agent Consensus Equilibrium (MACE) framework to integrate projections from different rotation axes into a single reconstruction. We apply our method to simulated data and demonstrate that multi-pose APMD achieves significantly improved material decomposition accuracy compared to single-pose APMD. © 2024 IEEE.

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A Super-Resolution Electromagnetic Inversion Scheme for Brain Stroke Detection

A Super-Resolution Electromagnetic Inversion Scheme for Brai...

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European Conference on Antennas and Propagation, EuCAP

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)9788831299107

ISBN: (纸本)9798350366327

A super-resolution electromagnetic inversion scheme is proposed for brain stroke detection. The scheme operates in two stages: (i) A quantitative Gauss-Newton method with Tikhonov regularization and frequency-hopping is employed to get a low-resolution inversion result. (ii) A U-Net-based super-resolution method is used to significantly enhance the spatial resolution of the inversion result from (i), The proposed scheme is demonstrated through several numerical examples, highlighting its effectiveness in accurately reconstructing the brain's permittivity and conductivity profiles, thereby contributing to advancements in stroke diagnosis.

关键词： Neuroimaging Location awareness Superresolution Europe Conductivity Classification algorithms Electromagnetics Permittivity Spatial resolution Image reconstruction

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Modified H-Plane Sectoral Horn for FSS Sensing Applications

Modified H-Plane Sectoral Horn for FSS Sensing Applications

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Wilcox, Logan M. Motes, Doyle T. Donnell, Kristen M. Laboratory Department of Electrical and Computer Engineering RollaMO United States Austin Inc. Nondestructive Evaluation Division AustinTX United States

ISBN: (纸本)9781665442282

The use of frequency selective surfaces (FSSs) as sensors has found recent success in the structural health monitoring realm. FSS theory assumes infinite FSS dimensions and planewave illumination. However, in practice, an FSS sensor is finite, and the illuminating field varies across the plane of the sensor. As such, it is of interest to reduce the nonuniformity of the incident electromagnetic energy incident on the sensor. To this end, this work explores the modification of a standard H-plane sectoral horn through the addition of a ridge to generate a uniform electric field over a specified region of interest. In addition to this modified antenna, standard gain (pyramidal) and H-plane sectoral horns were also considered for comparison. The results show that variation in the electric field pattern is reduced for the modified sectoral horn but at the cost of matching properties. © 2023 IEEE.

关键词： Frequency selective surfaces

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Embedded Aperture-Based FSS Sensor

Embedded Aperture-Based FSS Sensor

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Morris, Lauryn Ramesh, Swathi Muthyala Wilcox, Logan M. Motes, Doyle T. Donnell, Kristen M. Laboratory Department of Electrical and Computer Engineering RollaMO United States Austin Inc. Nondestructive Evaluation Division AustinTX United States

ISBN: (纸本)9781665442282

Frequency selective surfaces (FSSs) are two-dimensional periodic arrays of electrically conductive elements or apertures that have a specific electromagnetic reflection and/or transmission response. In this work, an aperture-based sensor design was proposed that is capable of sensing strain when embedded in a layered dielectric structure (i.e., an embedded sensor). An aperture approach was selected due to the potential for operation in reflection mode without a conductive backplane. The sensor was designed to operate within the K-band to improve the potential strain measurement resolution, as the unit cell dimensions are inversely related to the operating frequency and directly related to strain sensitivity. Simulation results for the small-scale strain range of 0-0.5% are presented, with a sensitivity of 20.4 MHz/O.1 % strain. © 2023 IEEE.

关键词： Strain measurement

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From Simulation to Reality with Random Noise

From Simulation to Reality with Random Noise

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Luo, Steven Lipkis, Rory López-Francos, Ignacio Vlastos, Pavlo Agogino, Adrian Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Intelligent Systems Division NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at relatively low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is not straightforward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult to perform. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network to classify camera position of an ego vehicle relative to a center line. Further models are trained with different types of noise progressively added to the data. In our experiments, we find that naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Chaotic-Cavity Surface-Emitting Lasers for Optical Wireless Communication

Chaotic-Cavity Surface-Emitting Lasers for Optical Wireless ...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Alkhazragi, Omar Dong, Ming Chen, Liang Liang, Dong Ng, Tien Khee Zhang, Junping Bagci, Hakan Ooi, Boon S. Division of Computer Electrical and Mathematical Science and Engineering Thuwal23955-6900 Saudi Arabia Huawei Technologies Co. Ltd Shenzhen518129 China

We demonstrated using chaotic cavities to lower the spatial coherence of vertical-cavity surface-emitting lasers. Our design achieved a 10-Gb/s data rate with a 60% increase in the optical power and double the number ... 详细信息

ISBN: (纸本)9781957171180

关键词： Surface emitting lasers

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Improving Quantum Circuit Synthesis with Machine Learning 4

Improving Quantum Circuit Synthesis with Machine Learning

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin University of California Department of Electrical Engineering and Computer Science Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9798350343236

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that mini-mize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7 x in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023 IEEE.

关键词： Machine learning

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Hydrodynamic parameter estimation using statistical machine learning for dynamic radiography

Hydrodynamic parameter estimation using statistical machine ...

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2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： De, Soumi Klasky, Marc Korobkin, Oleg Sidharth, G.S. Xu, Xiaojian Computer Computational and Statistical Sciences Division Los Alamos National Laboratory United States Theoretical Division Los Alamos National Laboratory United States Department of Aerospace Engineering Iowa State University United States Department of Electrical Engineering & Computer Science University of Michigan United States

Characterization of material properties of objects undergoing strong deformations is an important task in material science. Using neural networks with dynamic features extracted from radiographic projections we obtain... 详细信息

ISBN: (纸本)9781957171289

关键词： Parameter estimation

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