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Thermal Hall effect in insulating quantum materials

The Innovation 2022年第5期3卷 3-4页

作者： Shucheng Guo Youming Xu Ran Cheng Jianshi Zhou Xi Chen Department of Electrical and Computer Engineering University of CaliforniaRiversideRiversideCA 92521USA Department of Physics and Astronomy University of CaliforniaRiversideRiversideCA 92521USA Materials Science and Engineering Program Department of Mechanical EngineeringUniversity of Texas at AustinAustinTX 78712USA

The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials include,but are not limited to,superconductors,topological quantum matter,and systems with frustrated spins,which enable a wide range of potential applications in biomedicine,energy transport and conversion,quantum sensing,and quantum information processing。

关键词： properties. quantum topological

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A Review on Active Customers'Participation in Smart Grids

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 3-16页

作者： Konrad Schmitt Rabindra Bhatta Manohar Chamana Mahtab Murshed Ilham Osman Stephen Bayne Luciane Canha Electrical and Computer Engineering Department Texas Tech UniversityLubbockTX79409USA Wind Energy Program Texas Tech UniversityLubbockTX79409USA Electromechanical and Power Systems Department Federal University of Santa MariaSanta MariaRS97105-900Brazil Design Release Engineer at Electrification and Power conversion General MotorsGM Tech Center RoadWarrenMI48092USAl IEEE

Industrial,commercial,and residential facilities are progressively adopting automation and generation *** having flexible demand and renewable energy generation,traditional passive customers are becoming active participants in electric power system *** profound coordination among grid operators and active customers,the facilities’capability for demand response(DR)and distributed energy resource(DER)management will be valuable asset for ancillary services(ASs).To comply with the increasing demand and flexible energy,utilities urgently require standards,regulations,and programs to efficiently handle load-side resources without trading off stability and *** study reviews different types of customers’flexibilities for DR,highlighting their capabilities and limitations in performing local ancillary services(LASs),which should benefit the power grid by profiting from it through incentive *** financial incentives and techniques employed around the world are presented and *** potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

关键词： Active distribution network(ADN) ancillary services energy management flexible demand resiliency

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Using Random Forest and Linear SVM for Dyslexia Classification on Hebb-Williams Mazes

Using Random Forest and Linear SVM for Dyslexia Classificati...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Padmanabh Kaushik Yih-Choung Yu Lisa Gabel Department of Electrical and Computer Engineering Lafayette College Easton PA USA Department of Psychology and Neuroscience Program Lafayette College Easton PA USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia; however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments.

关键词： Support vector machines Radio frequency Accuracy Dyslexia Prediction algorithms Boosting Classification algorithms Pattern recognition Random forests Synthetic data

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Multiband Patch Antenna Design Using Nature-Inspired Optimization Method

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 151-162页

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Pierezan, Juliano Mariani, Viviana C. Coelho, Leandro S. Sarigiannidis, Panagiotis Koulouridis, Stavros Goudos, Sotirios K. ELEDIA@AUTH Research Group School of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Electrical Engineering Federal University of Parana Curitiba80060-000 Brazil Industrial and Systems Engineering Graduate Program and Mechanical Engineering Graduate Program Pontifical Catholic University of Parana Curitiba80215-901 Brazil Department of Electrical and Computer Engineering University of Western Macedonia Kozani501 00 Greece Electrical and Computer Engineering Department University of Patras Patras26504 Greece

Radio frequency energy harvesting has attracted considerable interest as a technique of enabling self-powered wireless networks. This technique faces several challenges, such as the receiving and the rectifying modules of a rectenna system. Multiband antennas provide several comparative advantages against the goal of maximizing the amount of energy harvesting. In this work, we present a multiband microstrip patch antenna with three slits operating in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. A feasible solution of the antenna is obtained by the application of a recently introduced nature-inspired optimization technique, namely the Coyote Optimization Algorithm. The proposed antenna operates satisfactorily in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. Measured results of the proposed antenna exhibit an acceptable performance (multiband frequency operation, maximum gain of 3.94 dBi, broadside operation) and demonstrate features of operation, which make it a strong candidate for various RF energy harvesting applications. © 2020 IEEE.

关键词： Microstrip antennas

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches 25

Preferences for Using Canva Apps as a Presentation Media Cre...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences

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Applied Sciences (Switzerland) 2024年第15期14卷 6573页

作者： Doungtap, Surasachai Wang, Jenq-Haur Phanichraksaphong, Varinya International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei10608 Taiwan

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. © 2024 by the authors.

关键词： Data mining

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Automated Image Captioning with Multi-layer Gated Recurrent Unit 30

Automated Image Captioning with Multi-layer Gated Recurrent ...

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30th European Signal Processing Conference, EUSIPCO 2022

作者： Moral, Özge Taylan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Turkey Department of Computer Engineering Izmir Katip Celebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Describing the semantic content of an image via natural language, known as image captioning, has recently attracted substantial interest in computer vision and language processing communities. Current image captioning approaches are mainly based on an encoder-decoder framework in which visual information is extracted by an image encoder and captions are generated by a text decoder, using convolution neural networks (CNN) and recurrent neural networks (RNN), respectively. Although this framework is promising for image captioning, it has limitations in utilizing the encoded visual information for generating grammatically and semantically correct captions in the RNN decoder. More specifically, the RNN decoder is ineffective in using the contextual information from the encoded data due to its limited ability in capturing long-term complex dependencies. Inspired by the advantage of gated recurrent unit (GRU), in this paper, we propose an extension of conventional RNN by introducing a multi-layer GRU that modulates the most relevant information inside the unit to enhance the semantic coherence of captions. Experimental results on the MSCOCO dataset show the superiority of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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Improving electrically Evoked Compound Action Potential Based on electrical Field Imaging and Electrode Interfaces

Improving Electrically Evoked Compound Action Potential Base...

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IEEE Conference on Electromagnetic Field Computation

作者： Charles T.M. Choi Chun Ting Ke Jelani Lawrence Alexander C. C. Wang Department of Electronics and Electrical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering & Computer Science International Graduate Program College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

The electrically evoked compound action potential (ECAP) has been used in various clinical studies and has become a key physiological signal for cochlear implants (CI). This study used four sensing electrodes to record ECAP signals based on the alternating polarity approach. An electrical field imaging (EFI) result based on the finite element method was used to obtain the interface impedance, then ECAP simulation results were computed and compared with a patient's clinical ECAP measurements. Preliminary modeling results show that the interface impedance obtained by this EFI-based technique can improve the simulation accuracy of the ECAP model. The ECAP modeling result will be compared with clinical ECAP measurements to validate the model in the full paper.

关键词： Electrodes Action potentials Impedance measurement Computational modeling Simulation Imaging Finite element analysis

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Information Rates Over DMCs with Many Independent Views

Information Rates Over DMCs with Many Independent Views

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IEEE International Symposium on Information Theory

作者： V. Arvind Rameshwar Nir Weinberger IUDX Program Unit Indian Institute of Science Bengaluru India Department of Electrical and Computer Engineering Technion Haifa Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we investigate the fundamental limits of reliable communication over a discrete memoryless channel (DMC) when there are a large number of noisy views of a transmitted symbol, i.e., when several copies of a single symbol are sent independently through the DMC. We argue that the channel capacity and dispersion of such a multi-view DMC converge exponentially quickly in the number of views to to the entropy and varentropy of the input distribution, respectively, and identify the exact rate of convergence. This rate equals the smallest Chernoff information between two conditional distributions of the output given unequal inputs. Our results hence help us characterize the largest finite-blocklength rates achievable for any fixed error probability. We also present a new channel model that we call the Poisson approximation channel-of possible independent interest-whose capacity closely approximates the capacity of the multi-view binary symmetric channel (BSC).

关键词： Monte Carlo methods Error probability Symbols Entropy Memoryless systems Reliability Noise measurement

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