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A 1.65 to 1.84 GHz Filtering SPDT Switch With Fully Tunable Insertion Phase Using NRN and Mixed Electromagnetic Coupling Techniques

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IEEE Transactions on Circuits and systems II: Express Briefs 2023年第9期70卷 3353-3357页

作者： Wei, Zhihua Chi, Pei-Ling Xu, Ruimin Yang, Tao School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Research Center of Advanced RF Chips and Systems Nanhu Laboratory Jiaxing Zhejiang314000 China

This brief presents a novel filtering single-pole-double-throw (SPDT) switch with continuously tunable center frequency and insertion phase. It is comprised of six varactor-loaded microstrip resonators and three tunable non-resonating nodes (NRNs) at source and loads. The controllable mixed electromagnetic couplings are introduced on the two main paths of each channel of the switch. At ON-state, the designed filtering switch can generate a 4th-order bandpass filtering performance with high selectivity, and the insertion phase can be continuously tuned with a full 360° tuning range by changing the reactances of NRNs and switching the dominated modes of the mixed couplings. At OFF-state, the mixed electromagnetic couplings are set to zero, and hence high isolation can be realized. To validate the proposed technique, a circuit prototype is designed and fabricated. The measured results suggest that the designed filtering SPDT switch can operate with a tunable center frequency ranging from 1.65 GHz to 1.84 GHz, and its insertion phase at ON-state channel can be continuously tuned from -180° to 180°. During the whole tuning process, the measured in-band isolation for OFF-state channel keeps better than 40 dB. © 2023 IEEE.

关键词： Bandpass filters

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Integrated Piezo-Triboelectric Nanogenerators-Based Self-Powered Flexible Temperature and Pressure Sensor

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第2期2卷 84-91页

作者： Min, Guanbo Khandelwal, Gaurav Dahiya, Abhishek Singh Mulvihill, Daniel M. Dahiya, Ravinder University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom University of Glasgow Materials and Manufacturing Research Group GlasgowG12 8QQ United Kingdom Group GlasgowG12 8QQ United Kingdom Group Electrical and Computer Engineering Department BostonMA02115 United States

Efficient harvesting of ubiquitous ambient mechanical energy using various types of nanogenerators (NGs) has attracted considerable interest in recent years. Herein, we present a textile triboelectric NG (T-TENG) using polytetrafluoroethylene (PTFE) and nylon-based counter-surfaces in fabric form (both fixed to conductive fabric electrodes). T-TENG performance is enhanced by exposing a PTFE film with argon (Ar) plasma. The output voltage of the plasma-treated devices increased by a factor of 10, and their short-circuit current density increased by a factor of 9 (compared to pristine nonplasma-treated devices). Additionally, we demonstrate that the fabricated T-TENG can be used as a self-powered temperature sensor, tested in the 25 °C-90 °C range. The T-TENG output voltage decreased linearly with increasing temperature exhibiting a sensitivity of ∼1°C. Furthermore, we integrated a flexible piezoelectric NG (PENG) on top of the T-TENG to detect the contact forces. The PENG output is also used to compensate for the pressure-dependent output of TENG underneath and allows distinct temperature and pressure measurements. The excellent results observed here show the potential of T-TENGs for use as self-powered sensors in applications such as health monitoring, wearables, and interactive systems. © 2022 IEEE.

关键词： Textiles

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Event-driven bi-fidelity control for efficient and reliable robotic manipulation

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Results in engineering 2025年 26卷

作者： Alsaade, Fawaz W. Al-zahrani, Mohammed S. Alsaadi, Fuad E. Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper proposes a novel bi-fidelity control framework for robotic manipulators that integrates a high-fidelity model predictive control (MPC) scheme with a low-fidelity Long Short-Term Memory (LSTM) neural network surrogate. Unlike conventional fixed-schedule approaches, our method employs an event-triggered mechanism that dynamically selects the appropriate controller based on a real-time error metric. This mechanism ensures that the computationally intensive MPC is invoked only when the LSTM approximation deviates beyond acceptable bounds. We rigorously established the near-optimal performance and closed-loop stability of our approach via Lyapunov analysis under mild assumptions, instilling confidence in its reliability. The simulation results on a three-arm manipulator, subject to low-frequency sinusoidal and high-frequency chirp trajectories, demonstrate that the proposed approach achieves up to 80–90% reduction in MPC calls, significantly improving the accuracy of the follow-up and computational efficiency. These findings highlight the potential of integrating learning-based approximations with conventional optimization-based control to achieve reliable and time-efficient performance in complex robotic manipulation tasks. © 2025 The Authors

关键词： Predictive control systems

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Item Verification on the Smart Trolley System using Object Recognition based on the Structural Similarity Index 8

Item Verification on the Smart Trolley System using Object R...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Alexander Agung Santoso Andreal, Ernest Julio Budiharto, Widodo Ngarianto, Heri Attamimi, Muhammad Tolle, Herman Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Media Game and Mobile Technologies Research Group Brawijaya University Malang Indonesia

Supermarket customers often encounter significant delays during checkout due to manual verification processes, wherein items are removed from the cart, handed over to the cashier, and individually scanned. To address this inefficiency, this paper introduces the design and development of an automatic payment system as part of a smart trolley solution aimed at expediting the verification process. Our goal is to develop an automatic moving trolley with smart payment devices to solve the problem. This system features a web-based payment application, which allows customers to scan their items using a barcode reader while shopping. After shopping, customers can review and confirm their items in the trolley and proceed to an exit room. Here, each item is individually verified using a camera and purchase finalization occurs. Our item verification method leverages object recognition using deep learning and similarity measurement with the structural similarity index (SSIM), which compares detected items to images stored in the supermarket's database. Our findings suggest the successful implementation of the proposed method and demonstrate that verification using the SSIM is a better alternative to traditional procedures. © 2023 The Authors. Published by Elsevier B.V.

关键词： item verification object recognition payment system smart trolley structural similarity index supermarket

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Feasibility Study of Noise-Cancelling Low Noise Amplifiers for Millimeter-Wave Wi-Fi 8 Applications 7

Feasibility Study of Noise-Cancelling Low Noise Amplifiers f...

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7th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2025

作者： Alhilo, Hassan Albasha, Lutfi Budimir, Djuradj American University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates School of Computer Science and Eng University of Westminster Wireless Communications Research Group London United Kingdom

ISBN: (纸本)9798331531836

A literature review on low noise amplifier (LNA) designs was conducted to validate the feasibility of an LNA design. The LNA is meant to operate in a receiver system for a Wi-Fi 8 communication system, which requires ultra-high reliability in its communication links. Additionally, due to its requirement for high data throughput, the system will operate in the millimeter wave (mm-wave) frequency range as this would allow for large bandwidths. Due to this frequency range as well as its potential operation in industrial applications, techniques to help mitigate any drawbacks were explored. The use of the noisecancelling technique was identified as a suitable one, as its improvement in the noise performance of the receiver system can ensure that a communication link can be reliably maintained. The literature review first identified designed LNAs for other related wireless communication technologies, such as previous generations of Wi-Fi and WiGig to understand the requirements needed for such a system. Next, a review of LNAs designed to operate in the mm-wave were conducted to help understand the effects encountered in this range as well as any useful techniques. Finally, a review of noise-cancelling LNAs designed in the mm wave and for wireless communication purposes was performed for similar reasons. With this review performed, a comprehensive understanding was obtained to aid in the design of an LNA for Wi-Fi 8 purposes. © 2025 IEEE.

关键词： Telecommunication links

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Optimized Design of Sub-Harmonic Synchronous Machines

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IEEE Transactions on Magnetics 2025年

作者： Sajjad Hossain Rafin, M. Ali, Qasim Hussein, Hossam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States Sukkur IBA University Department of Electrical Engineering Sukkur Pakistan

This paper presents an optimized design approach for sub-harmonic synchronous machines (SHSMs) to minimize torque ripple and improve performance for high-power electric vehicle applications. The proposed design incorporates a novel rotor harmonic winding configuration and utilizes advanced skewing techniques to achieve optimal spatial distribution of magnetic forces. Using Ansys Maxwell software, finite element analysis (FEA) was conducted on 8-pole, 48-slot SHSMs with a rated output of 1.3 kW and 900 rpm. Compared to earlier designs, the optimized 2-layer and 3-layer SHSMs demonstrated significant reductions in torque ripple—83.94% and 86.84%, respectively. This improvement is attributed to the uniform distribution of harmonic windings and carefully calibrated rotor skewing, both of which enhance spatial efficiency and magnetic field uniformity across the rotor. These configurations achieve lower torque ripple and maintain high efficiency and competitive average torque output, making them suitable alternatives to conventional wound rotor synchronous machines (WRSMs) and permanent magnet synchronous motors (PMSMs). The findings validate the potential of the optimized SHSMs as high-performance, brushless solutions for electric vehicle propulsion, contributing to advancements in synchronous machine design and sustainable transportation technologies. © 1965-2012 IEEE.

关键词： Permanent magnets

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Enhancing Mobile Multimedia Trustworthiness through Federated AI-based Content Authentication: Enhancing Mobile Multimedia

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Journal of Mobile Multimedia 2023年第6期19卷 1415-1438页

作者： Rajesh, M. Vengatesan, K. Sitharthan, R. Dhanabalan, Shanmuga Sundar Gawali, Mahendra Bhatu Department of Computer Engineering Sanjivani College of Engineering India Centre for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Chennai India Functional Materials and Microsystems Research Group RMIT University Melbourne 3001 VIC Australia Department of Information Technology Sanjivani College of Engineering India

The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches. © 2023 River Publishers.

关键词： content authentication decentralized trust management Federated AI mobile multimedia trustworthiness

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NS3-Based Communication Network Modeling for Multi-Microgrid systems: Design and Evaluation

NS3-Based Communication Network Modeling for Multi-Microgrid...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Ola Ali Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

This paper presents a communication emulation model for enhancing the efficiency of multi-microgrid (MMG) networks, employing Network Simulator 3 (ns3). MMG systems require robust communication frameworks for coordination, particularly under varying traffic conditions. The introduced model accommodates multiple local area networks (LANs) that support efficient communication across multiple autonomous AC microgrids. The developed model facilitates seamless bidirectional information exchange between the microgrids' local controllers and the MMG control center (MMGCC), employing TCP/IP protocol to ensure high data transmission integrity and low latency. A detailed design in C++ is demonstrated, including the configuration of the communication nodes, topology, communication links, and quality of service (QoS) analysis. The network performance is rigorously assessed across low and high traffic conditions, with performance metrics such as throughput, delay, and percentage packet loss at each node. Results confirm the effectiveness of the introduced model in sustaining minimal packet loss, high throughput, and low delays under dynamic operating scenarios. These findings establish the developed model as a viable solution for meeting the real-time communication requirements of MMG energy management systems.

关键词： Emulation Packet loss TCPIP Quality of service Microgrids C++ languages Throughput Real-time systems Delays Load modeling

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Predicting Ice Hockey Results Using Machine Learning Techniques

Predicting Ice Hockey Results Using Machine Learning Techniq...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Chin, Jeremiah Samson Juwono, Filbert Hilman Chew, Ing Ming Sivakumar, Saaveethya Wong, W.K. Curtin University Malaysia Sensor Research Group Miri Malaysia University of Southampton Malaysia Computing Programme Nusajaya Malaysia Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

Sport activities such as ice hockey experiences significant growth, however it is followed by lacking sports analytical techniques that enhances the success rate of ice hockey team. This study aims to analyze and compare the accuracy of various machine learning techniques in predicting ice hockey results, ultimately applying Logistic Regression to achieve higher accuracy and enhance the predictive capabilities in the sport. This study explores the prediction of field hockey scores using a number of machine learning models (ML), which were trained on data from the National Hockey League (NHL) seasons 2015-2021. The findings are presented in the form of confusion matrices, which contain number values to summarise accurate and inaccurate predictions according to class. To train the model, all of its features were carefully looked at and the most useful ones were drawn. It is shown that match-specific data, such as the features of home and away matches, is helpful to the model. The analysis result shows that the improved accuracy in predicting the outcome of ice hockey games, whereby the logistic regression model has the highest accuracy at 77.82% and therefore recommended for use in future analysis. © 2023 IEEE.

关键词： Sports

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