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2021 International Conference on Microelectronics, ICM 2021

作者： Sarg, Muhammad Khalil, Ahmed H. Mostafa, Hassan Cairo University Electronics and Communications Engineering Department Giza12613 Egypt University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza12578 Egypt

ISBN: (纸本)9781665408394

Convolutional Neural Networks (CNNs) have achieved high accuracy in many applications such as image recognition and classification. However, due to their large amount of parameters and intensive required operations, general purpose processors cannot achieve the desired inference performance levels. Recently, various hardware accelerators for deep CNNs have been carried out to enhance the throughput of CNNs. Among these accelerators, field programmable gate array (FPGA)-based ones have gained a lot of interest due to their high performance, low power consumption, high reconfigurability, and fast development cycle. Furthermore, the availability of high-level synthesis (HLS) tools lowers the programming burden and increases the productivity of the FPGA-based accelerator designers. In this paper, a C++ HLS implementation for FPGA-based accelerator for the convolutional layers of CNNs is proposed. As a case study, we evaluate the proposed accelerator using Resnet50 CNN on Xilinx Zynq UltraScale+ MPSoC ZCU104 evaluation board using SDSoC development environment, achieving up to 339x inference speedup. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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The Effect of Vehicles Speed on the Performance of VANET Protocol

The Effect of Vehicles Speed on the Performance of VANET Pro...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Osama A. Qasim Dania Mohammed Mahdi Mohammed S. Noori Rabei Raad Ali Christy Atika Sari Eko Hari Rachmawanto Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Electrical and Electronics Engineering Uniersiti Tenaga Nasional Selangor Malaysaia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

At present, ad hoc networks are getting a lot of attention, because they have many features that differ from the rest of the networks and because they are technically advanced. In routing protocols, the Vehicular Ad-hoc network (VANET) differ in performance because each protocol has different variables, such as variable density, speed, and traffic scenarios. In this work, we proposed topology-based routing namely Ad hoc On Demand Distance Vector (AODV) protocol. The AODV protocol has been simulated in case of the movement of vehicles at a fixed velocity at 60km/h, and in case of randomly changing the vehicle's locations in the network. There are three parameters used to investigate the performance of the proposed AODV protocol which are packet delivery ratio, and overheads. The Simulation results show a significant decrease from 0.55 to 0.82 in the packet delivery ratio. On the other hand, the performance of the AODV protocol demonstrates effective communication for the VANETs.

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A 35 V-5 V monolithic integrated GaN-based DC-DC floating buck converter

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Semiconductor Science and technology 2023年第9期38卷 095001-095001页

作者： Zhu, Yuhao Su, Haodong Li, Fan Li, Ang Lei, Xiaohaoyang Cui, Miao Lai, Wen-Cheng Wen, Huiqing Liu, Wen School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom Bachelor Program in Industrial Projects National Yunlin University of Science and Technology Yunlin Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology Yunlin Taiwan

The paper presents the development of a GaN DC-DC power converter with a pre-driver module and power device based on a monolithic integrated GaN E/D-mode metal-insulator-semiconductor high electron mobility transistors (MIS-HEMTs) platform. The pre-driver module with direct coupled field effect transistor logic circuit structure has been monolithically integrated with a GaN power transistor on a Si-based AlGaN/GaN commercial epitaxial wafer. The MIS-HEMTs structure adopts an insulated gate dielectric layer to suppress the leakage current and increase gate voltage robustness. The GaN-based floating buck converter employs a 1 mH inductor and a 9 μF capacitor to achieve 35 V-5 V power conversion. As a result, the pre-driver module is capable of delivering a 10 V driving voltage. GaN monolithic integration of the pre-driver module and power device can reduce the system area in the DC-DC application, which allows for an integrated chip size of 1.8 mm2 © 2023 IOP Publishing Ltd

关键词： III-V semiconductors

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way #08-03 Innovis Singapore 138634 Republic of Singapore；Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States；Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden；Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser Science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing ADHD Diagnosis Through Visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Fan, Zuo-Cian Lin, Ro-Wei Tsai, Ching-Shu Chou, Wen-Jiun Li, Chia-Jung Huang, Zih-Jun Shih, Bin-Yu Wang, Liang-Jen Ko, Li-Wei Department of Biological Science & Technology Hsinchu300 Taiwan National Yang Ming Chiao Tung University International Ph.D. Program in Interdisciplinary Neuroscience Hsinchu300 Taiwan Kaohsiung Chang Gung Memorial Hospit Department of Child and Adolescent Psychiatry National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Hsinchu300 Taiwan

ISBN: (纸本)9798350305791

We sought to revolutionize the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with ADHD and those without, aiming to reshape the landscape of ADHD diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral Density (PSD) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of ADHD classification. The landscape of ADHD diagnosis and intervention is perpetually evolving, most notably with the FDA's approval of game therapy for ADHD. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of ADHD intervention, offering objective, data-driven tools for the early management of ADHD. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with ADHD. In summary, our study represents a significant advancement in our comprehension and classification of ADHD. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with ADHD. Our findings, aligned with the FDA-approved game therapy, offer a glimpse into a

关键词： Electroencephalography

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Improving embedded programming skills through physical computing activities in engineering education: A course experience

Improving embedded programming skills through physical compu...

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International Symposium on engineering Accreditation (ICACIT)

作者： Jonathan Álvarez Ariza Program of Technology in Electronics Engineering Faculty Corporación Universitaria Minuto de Dios-UNIMINUTO Bogotá Colombia

ISBN: (纸本)9781665492799

Embedded programming is one of the most requested abilities by companies in industry sectors such as automation, the Internet of Things (IoT), robotics, and power electronics, among others. While these abilities are required to be developed in the curricula of the engineering programs, the dropout rate of the courses of programming in which these abilities are developed continues to increase. Several factors such as socioeconomic, the lack of support by the academic staff, or the teaching methodology are claimed of the most common dropout factors. In this context, this article proposes a methodology to learn to program embedded devices employing Arduino and Raspberry Pi in a course known as open hardware. Some identified factors in the students who took the course such as the lack of motivation and confidence in programming abilities were the catalysts to create and deploy the educational methodology. In addition, physical computing was incorporated as a conceptual pillar in the methodology. N=20 engineering students of a technological program in electronics participated in the methodology for 16 weeks. The methodology had a special emphasis on problem-solving, construction of algorithms, their interaction with sensors, motors, displays, etc., and teamwork. The results of the methodology show that it served to improve the abilities in programming, the understanding of the algorithms in embedded systems, the motivation, and it promoted habits such as the comments in the codes and the creation of flow diagrams that helped the students to comprehend the algorithms and their interaction with the hardware devices.

关键词： Industries Service robots Hardware Power electronics Teamwork Sensors Internet of Things

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