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Tunable VO_(2) cavity enables multispectral manipulation from visible to microwave frequencies

Light(Science & Applications) 2024年第3期13卷 510-518页

作者： Hang Wei Jinxin Gu Tao Zhao Zhiyuan Yan He-Xiu Xu Shuliang Dou Cheng-Wei Qiu Yao Li Center for Composite Materials and Structure Harbin Institute of TechnologyHarbin 150001China National University of Singapore Department of Electrical&Computer EngineeringSingapore 117583Singapore School of Chemical Engineering and Technology Harbin Institute of TechnologyHarbin 150001China Suzhou Laboratory Suzhou 215123China

Optical materials capable of dynamically manipulating electromagnetic waves are an emerging field in memories,optical modulators,and thermal ***,their multispectral design preliminarily attracts much attention,aiming to enhance their efficiency and integration of ***,the multispectral manipulation based on these materials is challenging due to their ubiquitous wavelength dependence restricting their capacity to narrow *** this article,we cascade multiple tunable optical cavities with selective-transparent layers,enabling a universal approach to overcoming wavelength dependence and establishing a multispectral platform with highly integrated *** on it,we demonstrate the multispectral(ranging from 400 nm to 3 cm),fast response speed(0.9 s),and reversible manipulation based on a typical phase change material,vanadium *** platform involves tandem VO_(2)-based Fabry–Pérot(F-P)cavities enabling the customization of optical responses at target bands *** can achieve broadband color-changing capacity in the visible region(a shift of~60 nm in resonant wavelength)and is capable of freely switching between three typical optical models(transmittance,reflectance,and absorptance)in the infrared to microwave regions with drastic amplitude tunability exceeding *** work represents a state-of-art advance in multispectral optics and material science,providing a critical approach for expanding the multispectral manipulation ability of optical systems.

关键词： spectral microwave visible

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Capacity Bounds for Identification With Effective Secrecy

Capacity Bounds for Identification With Effective Secrecy

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Rosenberger, Johannes Ibrahim, Abdalla Bash, Boulat A. Deppe, Christian Ferrara, Roberto Pereg, Uzi Technical University of Munich Tum School of Computation Information and Technology Germany University of Arizona Electrical and Computer Engineering Department United States Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Helen Diller Quantum Center Israel

ISBN: (纸本)9781665475549

An upper bound to the identification capacity of discrete memoryless wiretap channels is derived under the requirement of semantic effective secrecy, combining semantic secrecy and stealth constraints. A previously established lower bound is improved by applying it to a prefix channel, formed by concatenating an auxiliary channel and the actual channel. The bounds are tight if the legitimate channel is more capable than the eavesdropper's channel. An illustrative example is provided for a wiretap channel that is composed of a point-to-point channel, and a parallel, reversely degraded wiretap channel. A comparison with results for message transmission and for identification with only secrecy constraint is provided. © 2023 IEEE.

关键词： Semantics

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Pattern Synthesis of a Resonant Slot on a Broad Wall of the Rectangular Waveguide Using Amplitude and Phase Control

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Progress In Electromagnetics Research C 2023年 129卷 51-61页

作者： Masouleh, Mahmoud Sharafi Behbahani, Amin K. Sharafi, Masoud Sajedi, Maryam Adjouadi, Malek Center for Advanced Technology and Education Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States

A design technique to develop the desired pattern with uniform spacing between elements for a resonant linear slot array on the broad wall of a rectangular waveguide is discussed in this study. First, linear array pattern synthesis is used to achieve the amplitude and phase of the array element. Then both radiation pattern synthesis and array input impedance matching are achieved using the least-squares method. In addition, the error function is created by combining the three terms of impedance matching, array pattern synthesis, and slot design equations. Genetic algorithm (GA) and the conjugate gradient (CG) technique are used to minimize the acquired error function. The utilized approach results in precise pattern synthesis, good impedance matching, development of appropriate design equations, and power loss minimization. The computing needs were also reduced using the suggested antenna design. The approach is particularly beneficial since it integrates slot parameter dimensions and impedance matching with array pattern synthesis, resulting in a faster and more accurate design. Full-wave simulation Software HFSS was utilized to validate the suggested design method. Moreover, the measurements were conducted on a prototype designed to validate the simulation’s accuracy and the designed antenna practicality, and excellent agreements between theoretical predictions and simulation results were achieved. © 2023, Electromagnetics Academy. All rights reserved.

关键词： Rectangular waveguides

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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computer Modeling in engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical Engineering Seoul National University IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Department of Chemical Engineering Stanford University Department of Mechanical Engineering Ajou University George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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Joint View Selection, Multigroup Multicast Beamforming, and DIBR for RIS-Aided Multi-View Videos

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6376-6393页

作者： Lee, Chi-Han Yang, De-Nian Lee, Guang-Siang Wang, Chih-Hang Liao, Wanjiun National Taiwan University Department of Electrical Engineering Taiwan Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan National Chung Cheng University Department of Computer Science and Information Engineering Taiwan

The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, Multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication. © 2002-2012 IEEE.

关键词： Beamforming

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Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

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computer Modeling in engineering & Sciences 2024年第2期138卷 1301-1322页

作者： Nure Alam Chowdhury Lulu Wang Md Shazzadul Islam Linxia Gu Mehmet Kaya Department of Biomedical Engineering and Science Florida Institute of TechnologyMelbourneFL 32901USA Biomedical Device Innovation Center Shenzhen Technology UniversityShenzhen518118China Department of Electrical and Computer Engineering International Islamic University MalaysiaJalan GombakSelangor53100Malaysia

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human *** size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast *** operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of *** the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor *** simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.

关键词： Antenna microwave wideband cancer breast phantom tumor detection

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Advanced Helical Antenna Design for X-Band Applications Using AI

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Progress in Electromagnetics Research C 2025年 153卷 201-211页

作者： Zeain, Mohammed Yousif Abu, Maisarah Toding, Apriana Zakaria, Zahriladha Alsariera, Hussein Ullah, Ihsan Abdulbari, Ali Abdulateef Yon, Hamizan Taha, Bilal Salman Inam Abbasi, Muhammad Hang Tuah Jaya Melaka Durian Tunggal76100 Malaysia Makassar Indonesia Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Information Technology Center University of Technology Baghdad Iraq Antenna Research Center College of Engineering School of Electrical Engineering Universiti Teknologi MARA Malaysia Department of Electrical and Electronics University Tenaga National Malaysia Malaysia

This paper presents the design, fabrication, and characterization of a novel 3D-printed helical antenna operating within the 9.4–10.8 GHz frequency band. The antenna, employing a lightweight paper substrate and a strip-based helical structure, exhibits robust circular polarization characteristics and wideband operation. Rigorous simulations predict a peak CP gain of 11.7 dBi at 9.8 GHz and a high simulated radiation efficiency of 95%. Experimental measurements validate these predictions, achieving a peak CP gain of 11.6 dBi at 9.8 GHz. This research demonstrates the potential of 3D-printed helical antennas for diverse applications in modern wireless communication systems, including 5G, satellite communication, and radar. Furthermore, this study leverages the power of Artificial Intelligence (AI) by employing the Grey Wolf Optimizer (GWO), a sophisticated metaheuristic algorithm, to optimize the antenna’s design. The GWO algorithm is utilized to efficiently search the design space and identify optimal values for key parameters, such as the number of turns, helix pitch, and helix diameter, with the objective of maximizing antenna gain to achieve a target of 15 dBi. This research highlights the potential of AI-driven optimization techniques in advancing the design of high-performance antennas for emerging wireless communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Helical antennas

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