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

作者： Ibrahim, Mohannad Bronn, Nicholas T. Byrd, Gregory T. Nc State University Dept. of Electrical and Computer Engineering RaleighNC United States Ibm Quantum Ibm T.J. Watson Research Center Yorktown HeightsNY United States

ISBN: (纸本)9798350343236

In this paper, we propose an ansatz approximation approach for variational quantum algorithms (VQAs) that uses one of the hardware's main attributes, its crosstalk behavior, as its main approximation driver. By utilizing crosstalk-adaptive scheduling, we are able to apply a circuit-level approximation/optimization to our ansatz. Our design procedure involves first characterizing the hardware's crosstalk and then approximating the circuit by a desired level of crosstalk mitigation, all while effectively reducing its duration and gate counts. We demonstrate the effect of crosstalk mitigation on expressibility, trainability, and entanglement: key components that drive the utility of parameterized circuits. We tested our approach on real quantum hardware against a base configuration, and our results showed superior performance for the circuit-level optimized ansatz over a base ansatz for two quantum chemistry benchmarks. We take into consideration that applications vary in their response to crosstalk, and we believe that this approximation strategy can be used to create ansatze that are expressive, trainable, and with crosstalk mitigation levels tailored for specific workloads. © 2023 IEEE.

关键词： Crosstalk

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Variational Density Propagation Continual Learning 13

Variational Density Propagation Continual Learning

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13th International Symposium on Image and Signal Processing and Analysis, ISPA 2023

作者： Angelini, Christopher F. Bouaynaya, Nidhal C. Rasool, Ghulam Rowan University Dept. of Electrical and Computer Engineering GlassboroNJ United States Moffitt Cancer Center and Research Institute Depts. of Machine Learning and Neuro-Oncology TampaFL United States

ISBN: (纸本)9798350315363

Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data, various types of noise, and shifting conceptual objectives. This paper proposes a framework for adapting to data distribution drift modeled by benchmark Continual Learning datasets. We develop and evaluate a method of Continual Learning that leverages uncertainty quantification from Bayesian Inference to mitigate catastrophic forgetting. We expand on previous approaches by removing the need for Monte Carlo sampling of the model weights to sample the predictive distribution. We optimize a closed-form Evidence Lower Bound (ELBO) objective approximating the predictive distribution by propagating the first two moments of a distribution, i.e. mean and covariance, through all network layers. Catastrophic forgetting is mitigated by using the closed-form ELBO to approximate the Minimum Description Length (MDL) Principle, inherently penalizing changes in the model likelihood by minimizing the KL Divergence between the variational posterior for the current task and the previous task's variational posterior acting as the prior. Leveraging the approximation of the MDL principle, we aim to initially learn a sparse variational posterior and then minimize additional model complexity learned for subsequent tasks. Our approach is evaluated for the task incremental learning scenario using density propagated versions of fully-connected and convolutional neural networks across multiple sequential benchmark datasets with varying task sequence lengths. Ultimately, this procedure produces a minimally complex network over a series of tasks mitigating catastrophic forgetting. © 2023 IEEE.

关键词： Complex networks

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SAR onboard Broadband C-band Circularly Polarized Antenna for In situ Volcanic Lava Observation 8

SAR onboard Broadband C-band Circularly Polarized Antenna fo...

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8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023

作者： Yoshimoto, Yuki Miyazaki, Takahiro Gao, Steven Pramono, Subuh Kashihara, Hisato Ito, Koichi Sumantyo, Josaphat Tetuko Sri Takahashi, Ayaka Santosa, Cahya Edi Naito, Motoyuki Sumitomo Metal Mining Co. Ltd. Computer Aided Engineering and Development Dept. Tokyo Japan Chiba University Dept. of Environmental Remote Sensing Chiba Japan The Chinese University of Hong Kong Dept. of Electronic Engineering Hong Kong Hong Kong Sebelas Maret University Dept. of Electrical Engineering Surakarta Indonesia Chiba University Center for Environmental Remote Sensing Chiba Japan Teikyo University Dept. of Aerospace Engineering Utsunomiya Japan National Research and Innovation Agency Research Center for Aeronautics Technology Bogor Indonesia

ISBN: (纸本)9798350393590

Estimating the volcanic eruption scale is important for mitigating eruption hazards. While the eruption scale can be explained by the eruption mode and its temporal variation, the dominant factor is expected to be the formation reaction of volcanic products, but there are still many unknowns, including the relationship with the thermal history. To solve this problem, we proposed a broadband circularly polarized microwave antenna for a synthetic aperture radar (SAR) sensor that is suitable for in situ observation of volcanic materials such as lava. As a result of fabricating the proposed antenna, the antenna characteristics were almost the same as the simulation one, with a center frequency of 5.394 GHz, IBW=32.6%, ARBW=23.7%, and Gain=12.0dBic. The measurement results indicate that the proposed antenna has loss and radiation characteristics suitable for the purpose. © 2023 IEEE.

关键词： Synthetic aperture radar

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Cybersecurity Based on Converged Form of Blockchain, Internet-of-Things and Machine Learning in Smart Micro-Grid 1

Cybersecurity Based on Converged Form of Blockchain, Interne...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Said, Dhaou Rehmani, Mubashir Husain Mellal, Idir Oukaira, Aziz Lakhssass, Ahmed Uuniversity of Sherbrooke Dept. Electrical and Computer Engineering SherbrookeQC Canada Munster Technological University Bishopstown Cork Ireland Temerty Center for AI Research and Education in Medicine University of Toronto TorontoON Canada University of Quebec in Outaouais GatineauQC Canada

ISBN: (纸本)9798350351736

This paper explains the main cybersecurity problems facing SMG systems and the role of IoT, ML and Blockchain technologies in addressing them. It highlights how IoT, ML, and Blockchain can converge to meet SMG data privacy preservation and cyber-attack protection. Finally, Blockchain, standards and regulations are discussed as open issues and challenges of the perfect fitting of technologies in the SMG cybersecurity. © 2024 IEEE.

关键词： Cyber attacks

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Formal Analysis of an IoT-Based Healthcare Application 28

Formal Analysis of an IoT-Based Healthcare Application

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Elleuch, Maissa Tahar, Sofiene Digital Research Center of Sfax Technopark of Sfax Sfax Tunisia National School of Engineers of Sfax Sfax University CES Laboratory Tunisia Concordia University Dept. of Electrical and Computer Engineering MontrealQC Canada

ISBN: (纸本)9798350300482

In the healthcare context, remote monitoring based on the Internet of Things (IoT) technology is a widespread application. Underlying entities are interacting to bring up various services, so that their communication has to be ensured without defects such as deadlocks. The correct validation of these IoT applications is a major concern because of their distributed and concurrent features, as well as, the safety-critical nature of the health context. In this paper, we show how we use a model checking approach to accurately validate the behavior of an IoT-based healthcare application. We then focus on verifying three important classes of properties namely safety, liveness, and absence of deadlock. The verification is guaranteed by means of the UPPAAL model checker. © 2023 IEEE.

关键词： Internet of things

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Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Developing xApps for Rogue Base Station Detection in SDR-Ena...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Huang, Jun-Hong Cheng, Shin-Ming Kaliski, Rafael Hung, Cheng-Feng National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Sun Yat-sen University Dept. of Electrical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665494274

In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E- Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision 'recall' and F1 score. © 2023 IEEE.

关键词： Base stations

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Semantic Text Compression for Classification

Semantic Text Compression for Classification

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IEEE International Conference on Communications Workshops, ICC

作者： Emrecan Kutay Aylin Yener Dept. of Electrical and Computer Engineering INSPIRE@OhioState Research Center The Ohio State University

We study semantic compression for text where meanings contained in the text are conveyed to a source decoder, e.g., for classification. The main motivator to move to such an approach of recovering the meaning without requiring exact reconstruction is the potential resource savings, both in storage and in conveying the information to another node. Towards this end, we propose semantic quantization and compression approaches for text where we utilize sentence embeddings and the semantic distortion metric to preserve the meaning. Our results demonstrate that the proposed semantic approaches result in substantial (orders of magnitude) savings in the required number of bits for message representation at the expense of very modest accuracy loss compared to the semantic agnostic baseline. We compare the results of proposed approaches and observe that resource savings enabled by semantic quantization can be further amplified by semantic clustering. Importantly, we observe the generalizability of the proposed methodology which produces excellent results on many benchmark text classification datasets with a diverse array of contexts.

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Ponderomotive acceleration with high energy tilted ultrafast laser pulses

Ponderomotive acceleration with high energy tilted ultrafast...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Hunt, Patrick Wilhelm, Alex Wang, Shoujun Hollinger, Reed Shpilman, Ze'ev Anaraki, Sina Z. Davenport, Aaron Adams, Daniel E. Menoni, Carmen Rocca, Jorge Durfee, Charles G. Dept. of Physics Colorado School of Mines GoldenCO80401 United States Dept. of Electrical and Computer Engineering Colorado State University CO80523 United States Dept. of Physics Colorado State University CO80523 United States Sorec Nuclear Research Center Yavne81800 Israel

Using a novel pulse compressor for the CSU ALEPH facility, we demonstrate direct ponderomotive acceleration of electrons with 1.5J, tilted ultrafast pulses. The © Optica Publishing Group 2023, © 2023 The Aut... 详细信息

ISBN: (纸本)9781957171296

关键词： Deformable mirrors Fourier transforms Gaussian beams Laser beams Laser sources Ultrafast lasers

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Semantic Text Compression for Classification

arXiv

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arXiv 2023年

作者： Kutay, Emrecan Yener, Aylin Research Center Dept. of Electrical and Computer Engineering The Ohio State University United States

关键词： Semantics

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ROAR-Fed: RIS-Assisted Over-the-Air Adaptive Resource Allocation for Federated Learning

ROAR-Fed: RIS-Assisted Over-the-Air Adaptive Resource Alloca...

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IEEE International Conference on Communications (ICC)

作者： Jiayu Mao Aylin Yener Dept. of Electrical and Computer Engineering INSPIRE@OhioState Research Center The Ohio State University

Over-the-air federated learning (OTA-FL) integrates communication and model aggregation by exploiting the innate superposition property of wireless channels. The approach renders bandwidth efficient learning, but requires care in handling the wireless physical layer impairments. In this paper, federated edge learning is considered for a network that is heterogeneous with respect to client (edge node) data set distributions and individual client resources, under a general non-convex learning objective. We augment the wireless OTA-FL system with a Reconfigurable Intelligent Surface (RIS) to enable a propagation environment with improved learning performance in a realistic time varying physical layer. Our approach is a cross-layer perspective that jointly optimizes communication, computation and learning resources, in this general heterogeneous setting. We adapt the local computation steps and transmission power of the clients in conjunction with the RIS phase shifts. The resulting joint communication and learning algorithm, RIS-assisted Over-the-air Adaptive Resource Allocation for Federated learning (ROAR-Fed) is shown to be convergent in this general setting. Numerical results demonstrate the effectiveness of ROAR-Fed under heterogeneous (non i.i.d.) data and imperfect CSI, indicating the advantage of RIS assisted learning in this general set up.

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