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2021 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2021

作者： Pardomuan, Remon Suryanegara, Muhammad Universitas Indonesia Graduate Program in Telecommunications Management Department of Electrical Engineering Jakarta Indonesia

ISBN: (纸本)9781665438650

This paper aims to analyze passenger needs for network connectivity on the ship during traveling at the shipping line of Indonesian XYZ Company. The current connectivity infrastructure on board is supported by VSAT technology, but limited only for the ship's crew. Meanwhile, the passengers have difficulty to connect mobile cellular services while sailing. Therefore, providing the service over such a connectivity to the passengers can be an added values for the XYZ Company. This study uses Kano Model of the 11 attributes, and collect a primary data from 138 passengers sailing at PQR ship. It results recommendations portraying the passenger's need for shipping telecommunications during the sailing trip. © 2021 IEEE.

关键词： Ships

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Suppression of Receiver Harmonic Currents in Wireless Power Transfer Systems

Suppression of Receiver Harmonic Currents in Wireless Power ...

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Wireless Power Week (WPW)

作者： Daisuke Kobuchi Gregory E. Moore Yoshiaki Narusue Joshua R. Smith Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Electrical and Computer Engineering University of Washington Seattle USA Department of Electrical and Computer Engineering Paul G. Allen School of Computer Science and Engineering University of Washington Seattle USA

Harmonic currents in a receiver in wireless power transfer (WPT) systems can degrade performance of the receiver device by corrupting the DC power plane. The DC plane at the receiver serves as a constant reference that allows for dependent, sensitive electronics to operate with acceptable precision. However, if that reference is corrupted by noise, then the functions of the system, such as sensing and wireless communication, can degrade significantly. To reduce the harmonic current content in the receiver, and thereby improve the DC supply constancy, the optimal arbitrary voltage transmitter excitation is derived. To physically generate the derived arbitrary voltage, the signal is converted to a multilevel voltage using a phase-shifted carrier modulation. This multilevel voltage is then realized via the design of a full-bridge flying capacitor multilevel inverter and circuit simulations demonstrated the reduction of the harmonic currents in the receiver. By comparison to using a square-wave input voltage, circuit simulations demonstrate that a 5-level multilevel inverter reduces the total harmonic distortion (THD) of the received current from 6.79 % to 0.69 %, effectively reducing the harmonic currents in the receiver by 90 %.

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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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Super-Resolution of Satellite Images Based on Two-Dimensional RRDB and Edge-Enhanced Generative Adversarial Network

Super-Resolution of Satellite Images Based on Two-Dimensiona...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Chen, Yu-Zhang Liu, Tsung-Jung Liu, Kuan-Hsien Graduate Institute of Communication Engineering National Chung Hsing University Department of Electrical Engineering Taichung40227 Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung40401 Taiwan

ISBN: (纸本)9781665441544

With the increasing demand for high-resolution images, image super-resolution (SR) technology has become one of the focuses in related research fields. Generally speaking, high resolution is usually achieved by increasing the density and accuracy of the sensor. However, such an approach is quite expensive for equipment and design. In particular, increasing the density of satellite sensors must be undertaken great risks. Inspired by EEGAN and based on it, the Ultra-Dense Subnet (UDSN) and Edge Enhanced Network (EEN) were modified. Among them, the UDSN is used for feature extraction and obtains high-resolution results that look clear in the intermediate but are deteriorated by artifacts, and the Edge-Enhanced Subnet (EESN) is used to purify, extract and enhance the image contour and use mask processing to eliminate images contaminated by noise. Finally, the restored intermediate image and the enhanced edge are combined to produce a high-resolution image with high credibility and clear content. We use Kaggle open experimental dataset to test and compare the results among different methods. It proves the performance of the proposed model is better than other SR methods. © 2022 IEEE.

关键词： Generative adversarial networks

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Non-Hermitian metasurface for asymmetric thermal emission

Non-Hermitian metasurface for asymmetric thermal emission

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Prasad, Ciril S. Everitt, Henry O. Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States U.S. Army DEVCOM Army Research Laboratory-South HoustonTX United States

ISBN: (纸本)9781957171258

Non-Hermitian optics provides a unique platform to take advantage of absorption losses in materials and control radiative properties. We demonstrate a non-Hermitian metasurface that exhibit directional suppression of thermal radiation while maintaining transmission in mid-infrared. © Optica Publishing Group 2023 © 2023 The Author(s)

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A Transparent Ultrasound Array for Real-Time Optical,Ultrasound,and Photoacoustic Imaging

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Biomedical engineering Frontiers 2022年第1期3卷 284-297页

作者： Haoyang Chen Sumit Agrawal Mohamed Osman Josiah Minotto Shubham Mirg Jinyun Liu Ajay Dangi Quyen Tran Thomas Jackson Sri-Rajasekhar Kothapalli Department of Biomedical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA School of Electrical Engineering and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Penn State Cancer Institute The Pennsylvania State UniversityHersheyPA 17033USA Graduate Program in Acoustics The Pennsylvania State UniversityUniversity ParkPA 16802USA

Objective and Impact *** imaging of ultrasound and optical contrasts can help map structural,functional,and molecular biomarkers inside living subjects with high spatial *** is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and ***,combining ultrasound,photoacoustic,and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically *** a result,complex geometries are used to coalign both optical and ultrasound waves in the same field of *** elegant solution is to make the ultrasound transducer transparent to ***,we demonstrate a novel transparent ultrasound transducer(TUT)linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal *** TUT-array consists of 64 elements and centered at~6 MHz *** demonstrate a quad-mode ultrasound,Doppler ultrasound,photoacoustic,and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking *** TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer,neurological,and vascular diseases,including image-guided endoscopy and wearable imaging.

关键词： Ultrasound specificity transparent

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BAQET: BRAM-aware Quantization for Efficient Transformer Inference via Stream-based Architecture on an FPGA 25

BAQET: BRAM-aware Quantization for Efficient Transformer Inf...

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Proceedings of the 2025 ACM/SIGDA International Symposium on Field programmable Gate Arrays

作者： Ling-Chi Yang Chi-Jui Chen Trung Le Bo-Cheng Lai Scott Hauck Shih-Chieh Hsu Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Degree Program of College of Electrical and Computer Engineering in National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical and Computer Engineering in University of Washington Seattle USA Electrical and Computer Engineering University of Washington Seattle USA Physics University of Washington Seattle USA

ISBN: (纸本)9798400713965

FPGAs are a compelling substrate for supporting machine learning inference. Tools such as High-Level Synthesis and hls4ml can shorten the development cycle for deploying ML algorithms on FPGAs, but can struggle to handle the large on-chip storage needed for many of these models. In particular the high BRAM usage found in many of these flows can cause Place & Route failures during synthesis. In this paper we propose using a Simulated-Annealing based flow to perform BRAM-aware quantization. This approach trades off inference accuracy with BRAM usage, to provide a high-quality inference engine that still meets on-chip resource constraints. We demonstrate this flow for Transformer-based machine learning algorithms, which include Flash Attention in a Stream-based Dataflow architecture. Our system imposes minimal accuracy drops, yet can reduce BRAM usage by 20%-50%, and improve power efficiency by 264%-812% compared to existing Transformer-based accelerators on FPGAs

关键词： efficient transformer

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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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3D Stackable Vertical Ferroelectric Tunneling Junction (V-FTJ) with on/off Ratio 1500x, Applicable Cell Current, Self-Rectifying Ratio 1000x, Robust Endurance of 109Cycles, Multilevel and Demonstrated Macro Operation Toward High-Density BEOL NVMs

3D Stackable Vertical Ferroelectric Tunneling Junction (V-FT...

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2023 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2023

作者： Lee, J.-Y. Chang, F.-S. Hsiang, K.-Y. Chen, P.-H. Luo, Z.-F. Li, Z.-X. Tsai, J.-H. Liu, C.W. Lee, M.H. National Taiwan Normal University Institute and Undergraduate Program of Electro-Optical Engineering Taiwan National Taiwan University Graduate School of Advance Technology Taiwan National Yang Ming Chiao Tung University Institute of Electronics Department of Electronics Engineering Taiwan National Taiwan Normal University Department of Electrical Engineering Taiwan

ISBN: (纸本)9784863488069

A 1500x ILRS/IHRS with a high cell current of 100 nA/ cell (J = 83 A/cm2) is achieved by antiferroelectric (AFE) vertical ferroelectric tunnel junctions (V-FTJs) that demonstrates multilevel, a self-rectifying rate > 1000x, and macro operation. The stackable 3 D architecture integrating multiple layers of AFE V-FTJs with contact optimization further increases memory density. Robust endurance > 109 cycles at |4V| and stable nonvolatile data retention > 104 sec with extrapolation to 10 years are achieved. The proposed cell contact V-FTJ by AFE is a promising pathway toward BEOL NVMs by a NAND/NOR-based framework. © 2023 JSAP.

关键词： Ferroelectricity

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