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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Saeed, Zubair Torfeh, Tarraf Aouadi, Souha Ji, Jim Xiuquan Bouhali, Othmane Texas A&m University Department of Electrical and Computer Engineering College StationTX47080 United States Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar Hamad Medical Corporation National Center for Cancer Care and Research Department of Radiation Oncology Doha3050 Qatar Hamad Bin Khalifa University Qatar Center for Quantum Computing College of Science and Engineering Doha34110 Qatar

ISBN: (纸本)9798350387384

The rising incidents of brain tumors pose significant challenges in diagnosis and treatment. Tumorous brain cancers present unique difficulties due to their critical locations and functional implications. Magnetic Resonance Imaging (MRI) offers safe and precise imaging, free from the risks associated with radiation-based methods. Deep learning, particularly Convolutional Neural Networks (CNN), has emerged as a valuable tool in identifying brain tumors in MRI scans, enabling more accurate diagnoses by fusing two well-known DL models. This study explores a fusion strategy combining AlexNet and VGG16 models, outperforming individual models trained with different learning rates. While VGG16 and AlexNet achieved accuracies of 79% and 77% respectively. Our fusion approach potentially achieved an accuracy of 81%, underscoring its efficacy in enhancing brain tumor classification accuracy. This study helps medical professionals to accurately and timely classify tumors for better outcomes for patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Squid-inspired and wirelessly controllable display for active camouflage in aquatic-environment

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npj Flexible Electronics 2024年第1期8卷 900-909页

作者： Doyoung Kim Seung Won Seon Minkyung Shin Jihwan Kim Bogeun Kim Janghoon Joo Sang Uk Park Wooseok Kim Hee Kyu Lee Byeong Woon Lee Se Gi Lee Su Eon Lee Ji-Hun Seo Seung Ho Han Bong Hoon Kim Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon 16419Republic of Korea Department of Smart Wearable Engineering Soongsil UniversitySeoul 06978Republic of Korea Electronic Convergence Materials and Devices Research Center Korea Electronics Technology Institute(KETI)Seongnam 13509Republic of Korea Department of Materials Science and Engineering Korea UniversitySeoul 02841Republic of Korea Department of Robotics and Mechatronics Engineering DGISTDaegu 42988Republic of Korea

Achieving optimal camouflage in an aquatic environment necessitates the ability to modulate transmittance in response to the surrounding obscurity and potential *** adaptation involves a dynamic transition from transparency to a deep-blue color,especially in low-light or dark *** a strategy promotes a seamless assimilation with the surroundings,enabling the absorption of searchlights and,subsequently,diminishing the risk of detection by ***,the presence of sophisticated mechanisms that facilitates stable and efficient control of transmittance is imperative,enabling smooth transition between transparent and deep-blue hues within the aquatic *** study presents nature-inspired programmable camouflage system that integrates an electrochromic display as the primary transmittance change element and a wireless base module for power and data *** technology offers a robust and flexible construction,ensuring stable operation as demonstrated through mechanical-fatigue experiments and quantitative simulation.A custom circuit and a power-control software package enable active control of multiple electrochromic displays while submerged in water.

关键词： transparency submerged lights

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Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems

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computer Modeling in engineering & Sciences 2024年第12期141卷 2165-2183页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon College of Business Administration Management DepartmentPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.

关键词： Phishing BERT convolutional neural networks email security deep learning

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Inverse Design of Perfectly-Matched Metamaterials

Inverse Design of Perfectly-Matched Metamaterials

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Thakkar, Shrey Szymanski, Luke Ruiz-Garcia, Jorge Gok, Gurkan Grbic, Anthony University of Michigan Department of Electrical Engineering and Computer Science Ann Arbor United States Raytheon Technologies Research Center East Hartford United States

ISBN: (纸本)9798350347647

In this work, inverse design is used to develop inhomogeneous, anisotropic 2D metamaterials (electrical networks) that perform specified electromagnetic field transformations. The unique feature of the proposed metamaterials is that all their unit cells are impedance matched to each other, as well as to the surrounding space. These perfectly-matched metamaterials, unlike absorptive perfectly matched layers (PMLs), are lossless and passive. As an example, a 2D metamaterial that collimates the field of a point source excitation is reported. © 2023 IEEE.

关键词： Metamaterials

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Impact of dissolution and precipitation on pore structure in CO_(2)sequestration within tight sandstone reservoirs

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Petroleum Science 2025年第2期22卷 868-883页

作者： Hui Gao Kai-Qing Luo Chen Wang Teng Li Zhi-Lin Cheng Liang-Bin Dou Kai Zhao Nan Zhang Yue-Liang Liu School of Petroleum Engineering Xi'an Shiyou UniversityXi'an710065ShaanxiChina Engineering Research Center of Development and Management for Low to Ultra-Low Permeability Oil&Gas Reservoirs in West China Ministry of EducationXi'an710065ShaanxiChina Xi'an Key Laboratory of Tight Oil(shale Oil)Development Xi'an710065ShaanxiChina Department of Electrical Engineering and Computer Science University of StavangerStavanger4036Norway School of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China

Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration *** investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after ***,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is *** quantifies CO_(2)needed for mineralization of soluble ***,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage ***,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller *** pressure levels rise,the magnitude of pore alterations progressively *** a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small *** observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.

关键词： Dissolution Precipitation Pore structure CO_(2)sequestration Unconventional reservoirs

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Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

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computers, Materials & Continua 2024年第12期81卷 4109-4124页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057MaharashtraIndia Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science Faculty of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)*** phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical *** paper proposes and optimizes a lightweight deep-learning model for phishing attack *** model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning ***,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)*** proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’***,the model’s recall and F1-score are 91%for both *** also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more *** study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

关键词： Deep learning phishing Cuckoo Search cable news network(CNN) IoT ANOVA F-test

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Improving Efficiency and Brightness in Colloidal Quantum Dot Light Emitting Diodes

Improving Efficiency and Brightness in Colloidal Quantum Dot...

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SID International Symposium Digest of Technical Papers, 2023

作者： Lee, Taesoo Kim, Jaeyoul Kwak, Jeonghun Department of Electrical and Computer Engineering Inter-university Semiconductor Research Center and Soft Foundry Institute Seoul National University Seoul08826 Korea Republic of

In this paper, we introduce multilateral strategies to improve the efficiency and brightness of quantum dot light-emitting diodes. First, electrically and thermally endurable device architecture is adopted for highly bright light-emission. Second, an exciton harvesting layer consisting of blue-emitting thermally-activated delayed fluorescent materials is introduced to improve device efficiency. Our approaches realize remarkable improvements in the device performance. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Luminance

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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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Enabling power measurement and control on Astra: The first petascale Arm supercomputer

Enabling power measurement and control on Astra: The first p...

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作者： Grant, Ryan E. Hammond, Simon D. Laros, James H. Levenhagen, Michael Olivier, Stephen L. Pedretti, Kevin Ward, Lee Younge, Andrew J. Electrical and Computer Engineering Department Queen's University KingstonON Canada Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States

Astra, deployed in 2018, was the first petascale supercomputer to utilize processors based on the ARM instruction set. The system was also the first under Sandia's Vanguard program which seeks to provide an evaluation vehicle for novel technologies that with refinement could be utilized in demanding, large-scale HPC environments. In addition to ARM, several other important first-of-a-kind developments were used in the machine, including new approaches to cooling the datacenter and machine. This article documents our experiences building a power measurement and control infrastructure for Astra. While this is often beyond the control of users today, the accurate measurement, cataloging, and evaluation of power, as our experiences show, is critical to the successful deployment of a large-scale platform. While such systems exist in part for other architectures, Astra required new development to support the novel Marvell ThunderX2 processor used in compute nodes. In addition to documenting the measurement of power during system bring up and for subsequent on-going routine use, we present results associated with controlling the power usage of the processor, an area which is becoming of progressively greater interest as data centers and supercomputing sites look to improve compute/energy efficiency and find additional sources for full system optimization. © 2022 John Wiley & Sons, Ltd.

关键词： Supercomputers

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Reputation-based User Vehicle Assignment in Intelligent and Connected Vehicle Platoons

Reputation-based User Vehicle Assignment in Intelligent and ...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Datta, Subham Nikolaou, Panagiota Michael, Maria K. University of Cyprus KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus KIOS Research and Innovation Center of Excellence University of Central Lancashire Cyprus Pyla Larnaca Cyprus

ISBN: (纸本)9798350346473

Vehicle platooning is a promising and emerging framework in intelligent transportation systems. Recent works consider reputation-based approaches for head selection in single platoons, in order to optimize safety and security. When a large number of user vehicles having the same destination want to benefit from platooning, several platoons need to be formed. To this end, user vehicles are allocated to different platoons, with each platoon being led by a platoon head. To ensure necessary network bandwidth and latency, the number of user vehicles between the newly formed platoons must be balanced. However, an arbitrary assignment of user vehicles in platoons can bias the future selection of the platoon heads in such reputation-based approaches. This work considers reputation-based platooning systems and proposes an optimal approach to balance the number of user vehicles in platoons while at the same time ensures fairness in the reputation score of platoon heads. A mixed-linear integer programming formulation is proposed, which provides an optimal allocation of the user vehicles in platoons based on the above objectives. The approach is validated using multiple synthetically generated datasets using 14 different input parameters. The obtained results demonstrate the optimality of the proposed method while achieving significant time performance speedups (\sim 140\mathrm{x} on average) when compared to a brute-force exhaustive method. © 2023 IEEE.

关键词： Integer programming

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