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Graphene Bridge Heterostructure Devices for Negative Differential Transconductance Circuit Applications

Nano-Micro Letters 2023年第2期15卷 161-171页

作者： Minjong Lee Tae Wook Kim Chang Yong Park Kimoon Lee Takashi Taniguchi Kenji Watanabe Min‑gu Kim Do Kyung Hwang Young Tack Lee Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea Department of Materials Science and Engineering The University of Texas at DallasRichardsonTX 75080USA Center for Opto‑Electronic Materials and Devices Post‑Silicon Semiconductor InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Physics Kunsan National UniversityGunsan 54150Republic of Korea Advanced Materials Laboratory National Institute for Materials ScienceTsukuba 305‑0044Japan Department of Information and Communication Engineering Inha UniversityIncheon 22212Republic of Korea Division of Nanoscience and Technology KIST SchoolUniversity of Science and Technology(UST)Seoul 02792Republic of Korea Department of Electronic Engineering Inha UniversityIncheon 22212Republic of Korea

Two-dimensional van der Waals(2D vdW)material-based heterostructure devices have been widely studied for high-end electronic applications owing to their heterojunction *** this study,we demonstrate graphene(Gr)-bridge heterostructure devices consisting of laterally series-connected ambipolar semiconductor/Gr-bridge/n-type molybdenum disulfide as a channel material for field-effect transistors(FET).Unlike conventional FET operation,our Gr-bridge devices exhibit nonclassical transfer characteristics(humped transfer curve),thus possessing a negative differential *** phenomena are interpreted as the operating behavior in two series-connected FETs,and they result from the gate-tunable contact capacity of the Gr-bridge ***-value logic inverters and frequency tripler circuits are successfully demonstrated using ambipolar semiconductors with narrow-and wide-bandgap materials as more advanced circuit applications based on non-classical transfer ***,we believe that our innovative and straightforward device structure engineering will be a promising technique for future multi-functional circuit applications of 2D nanoelectronics.

关键词： Graphene bridge Heterostructure device Non-classical transfer characteristics Multi-value logic inverter Frequency tripler

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Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search

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Journal of Bionic engineering 2024年第6期21卷 3098-3122页

作者： Sharaz Ali Mohammed Azmi Al-Betar Mohamed Nasor Mohammed A.Awadallah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman UniversityP.O.Box 346AjmanUnited Arab Emirates

The Unit Commitment Problem(UCP)corresponds to the planning of power generation *** objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown *** this paper,four different binary variants of the Bald Eagle Search(BES)algorithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer *** addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and *** variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and *** comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization *** other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit ***,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unit commitment *** Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the *** conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.

关键词： Swarm intelligence Bald eagle search Unit commitment problem Optimization

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How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at Scale 25

How Do Hackathons Foster Creativity? Towards Automated Evalu...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Computer Science Aalborg University Copenhagen Denmark Department of Computer Science Aalborg University Copenhagen Denmark Center for Hybrid Intelligence Department of Management Aarhus University Aarhus Denmark Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400713941

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025 Copyright held by the owner/author(s).

关键词： Hackathons

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kobayashi, Manabu Kumoi, Gendo Yagi, Hideki Hirasawa, Shigeichi Center for Data Science Waseda University Tokyo Japan Nagaoka University of Technology Department of Information and Management Systems Engineering Niigata Japan University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9781665410205

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for M class classification if the number of binary classifiers is O(M), then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and M and the difference between the maximum posterior probability and the second highest posterior probability for the categories. © 2024 IEEE.

关键词： Coding errors

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

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computer Systems science & engineering 2023年第11期47卷 2079-2095页

作者： C.S.S.Anupama Saud Yonbawi G.Jose Moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Skin cancer is one of the most dangerous *** of the high melanoma death rate,skin cancer is divided into non-melanoma and *** dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin ***,pathology and biopsy examinations are required for cancer *** studies have formulated computer-based systems for detecting skin cancer from skin lesion *** recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature ***,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)*** major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic ***,Dull razor approach-related hair removal and median filtering-based noise elimination are ***,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic ***,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for ***,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the *** simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion *** comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

关键词： Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning

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8-HQCdCl_(2)H_(2)O as an organic Q-switcher in erbium laser cavity

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Optoelectronics Letters 2023年第11期19卷 681-685页

作者： MUSTAFA MOHAMMED NAJM MOHAMMED NAJM ABDULLAH AYA A.ALMUKHTAR BELAL AHMED HAMIDA MOH YASIN SULAIMAN WADI HARUN Department of Electrical Engineering Universiti MalayaKuala Lumpur50603Malaysia Department of Computer Engineering University of TechnologyBaghdadIraq Medical Instrumentation Engineering Department Al-Esraa University CollegeBaghdadIraq Department of Electrical and Computer Engineering International Islamic University MalaysiaGombakMalaysia Department of Physics Faculty of Science and TechnologyAirlangga UniversitySurabayaIndonesia Research Center for Biophotonics(RCB) Airlangga UniversitySurabaya60115Indonesia

This paper demonstrated a Q-switched erbium-doped fiber laser(EDFL)using an organic saturable absorber(SA)based on 8-HQCdCl_(2)H_(2)O *** organic thin film was prepared using the casting *** proposed Q-switched EDFL has a maximum repetition rate of 143 kHz,minimum pulse duration of 1.85μs and the highest pulse energy of 167 *** Q-switched peak laser was at a central wavelength of 1531 nm with a 3 dB bandwidth of 3.52 nm and power intensity of 2.64 dBm.

关键词： switched laser material

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Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum Access in Sensor Ad Hoc Networks 15

Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Geetha, B. Kansal, Amit Sudhamsu, Gadug Gawali, Piyush P. Amudha, A. Samrat, Bhavuk Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University Uttar Pradesh India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Cognitive Radio-primarily based Spectrum Sensing (CRSS) is a crucial generation for permitting dynamic spectrum access in sensor ad hoc networks (SAHNs). This era allows customers to successfully make use of to be had spectrum whilst keeping off interference from existing customers. CRSS makes use of sensing and optimization strategies to identify free frequency bands to enable verbal exchange in SAHNs. Moreover, the CRSS additionally consists of protocols and techniques to locate and defend against malicious behavior, which include spectrum jamming and false sensing reports. Subsequently, CRSS may be blended with superior technology, such as multi-agent structures and system gaining knowledge of, to create an efficient and sturdy gadget for dynamic spectrum access. © 2024 IEEE.

关键词： Frequency Generation Malicious Permitting Primarily Spectrum Techniques technology

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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An Internet of Things Assisted Autism Spectrum Disorder Prediction Using Deep Learning Principles with Medical Image Processing Assistance

An Internet of Things Assisted Autism Spectrum Disorder Pred...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bindhu, A. Mol, A. Sruthi Ravi, T. Bodkhe, R.G. Safori, Amjad Omar Vijayalakshmi, S. Infant Jesus College of Arts and Science for Women Department of Computer Science Mulagumoodu India Sathyabama Institute of Science and Technology Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Zaraa University Faculty of Media Department of Journalism and Digital Media Zaraa 13110 Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Characteristics of the complex neuro-developmental condition Autism Spectrum condition (ASD) include impaired social communication and restricted, repetitive patterns of behavior. This study presents a novel approach for identifying ASD in young infants by integrating deep learning, Internet of Things (IoT) and medical image processing. The goal of this research is to create comprehensive and discriminative features by combining wearable sensor data with brain imaging and smart home environments. For the greatest results, early diagnosis is essential. The proposed method called IoT based Autism Predictive Framework (IoTAPF) has a high detection rate of 97.8% and is cross-validated against the current Learning based Autism Detection Strategy (LADS). The proposed approach aims at improving diagnostic specificity which helps in forecasting behaviors and thus pushing earlier interventions, providing better support for ASD individuals and their families. The paper discusses the design and implementation of the framework, including sensor integration, data management, and predictive modeling. Preliminary results from pilot studies are presented, demonstrating the framework's potential to improve diagnostic accuracy and support individualized care plans. The proposed solution represents a significant advancement in the application of IoT technology for early autism detection and management, paving the way for more proactive and data-driven approaches in the field of neuro-developmental disorders. © 2024 IEEE.

关键词： Diseases

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