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Self-Decoupled Square Patch Antenna Arrays by Exciting and Using Mixed Electric/Magnetic Coupling Between Adjacent Radiators

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Chinese Journal of Electronics 2025年第2期34卷 483-494页

作者： Liu Qianwen Zhu Lei Lu Wenjun College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing 210003 China

This article presents and develops a simple decoupling method for the planar square patch antenna arrays by virtue of mixed electric and magnetic coupling property. Since the resonant modes of TM10 and TM01 are a pair of degenerate modes in the square patch radiator which are intrinsically orthogonal, a superposed mode of them can be generated to possess consistent field distributions along all the four sides of the patch by adjusting the feeding position. By employing such superposed mode, the mutual coupling between two horizontally adjacent patch elements will become identical to that between two vertical ones, indicating an expected possibility that the complex 2-D decoupling problem in a large-scale antenna patch array can be effectively facilitated and simplified to a 1-D one. Subsequently, metallic pins and connecting strip are properly loaded in each square patch resonator, such that appropriate electric and magnetic coupling strengths can be readily achieved and thus the mutual coupling can get highly decreased. A 1 × 2 antenna array with an edge-to-edge separation of 1 mm, which corresponding to 0.0117λ0, is firstly discussed, simulated, and fabricated. The measured results show that the isolation can be highly improved from 4 dB to 17 dB across the entire passband. In final, 1 × 3, 2 × 2, and 4 × 4 antenna array prototypes are constructed and studied for verification of the expansibility and feasibility of the proposed decoupling method to both linear and 2-D antenna arrays.

关键词： Large-scale antenna array Mutual coupling Mixed electric and magnetic coupling effect Self-decoupling Square patch antenna

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Machine Learning based Intelligent Diagnosis of Orthodontics: A comprehensive review 3

Machine Learning based Intelligent Diagnosis of Orthodontics...

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3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Iwin Thanakumar Joseph, S. Sarveshwaran, Velliangiri Vijayakumar, K. Raj, S Benson Edwin Elngar, Ahmed A Devadurai, Anand Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Guntur India SRM Institute of Science and Technology Kattankulathur Campus Department of Computer Science and Engineering Chennai India Higher Colleges of Technology Department of Computer Information Sciences Fujairah United Arab Emirates Beni-Suef University Faculty of Computers and Artificial Intelligence Beni-Suef City Egypt King Khalid University Department of Computer Science Gregar Abha Saudi Arabia

ISBN: (纸本)9798350338263

An effective prediction of orthodontics treatment outcome is highly essential for further scheme of clinical treatment for the patient with the simplified cost. An appropriate utilization of information technology simplifies the treatment for teeth irregularities and misaligned jaws in an effective manner. Lot of research works carried out in the area of dentistry to come out with an optimistic result. With the advent of artificial intelligence and machine learning methodologies in recent years, lot of clinical problems and difficulties can be efficiently managed and overcome by orthodontists. This research article provides the complete insight about the impact of intelligent technologies in the field of orthodontics. It provides a complete overview of futuristic applications in the field of orthodontic clinical diagnosis, planning of treatment for various cases and the correct prediction of outcome of clinical treatment which regularly faced by an orthodontist in the health care sector. © 2023 IEEE.

关键词： Dentistry

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Quaternion Tensor Completion Via Qr Decomposition and Nuclear Norm Minimization

SSRN

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SSRN 2024年

作者： Sun, Jian Liu, Xin Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Department of Mathematics University of Manitoba Winnipeg Canada

The task of tensor (matrix) completion has been widely used in the fields of computer vision and image processing, etc. To achieve the completion, the existing methods are mostly based on singular value decomposition of the real tensor and nuclear norm minimization. However, the real tensor completion methods cannot simultaneously maintain color channel correlation and evolution robustness of color video frames, they need high computational cost to handle the high-dimensional data. Hence they have some limitations in model generalization ability and computational efficiency. In this paper, a new completion method for the quaternion tensor (matrix) is explored via the QR decomposition and the definition of novel quaternion tensor norm, which can well balance the model generalization ability and efficiency, and the performance of the completion method has been substantially improved. Numerical experiments on color images and videos prove the effectiveness of our proposed method. © 2024, The Authors. All rights reserved.

关键词： Tensors

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Isolation Forest Anomaly Detection in Vital Sign Monitoring for Healthcare 1

Isolation Forest Anomaly Detection in Vital Sign Monitoring ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Yadav, Kanchan Aswal, Upendra Singh Saravanan, V. Dwivedi, Shashi Prakash Shalini, N. Kumar, Navneet Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The use of the isolate forest technique for recognizing anomalies in monitoring vital signs in healthcare is examined in this work. A deductive approach, based on interpretivism, uses secondary data along with a descriptive design. The procedure's strong metrics of performance are demonstrated by the results, wherein effective identification of anomalies is indicated by high precision, recollection, and AUC numbers. Its advantage over conventional methods is demonstrated by comparisons. The impact of parameter tuning is discussed, highlighting the careful balancing act between mathematical efficiency and accuracy. Opportunities healthcare issues can be qualitatively understood through the assessment of anomalies that have been detected. Improvements to interpretability, validation of results with medical professionals, and parameter refinement are among the suggestions put forward. Parameter improvement, understanding, real-world verification, and combined models should be the main areas of future research. © 2023 IEEE.

关键词： anomaly detection healthcare monitoring interpretivism Isolation Forest parameter tuning

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Sequential Data Analysis in Healthcare: Predicting Disease Progression with Long Short-Term Memory Networks 1

Sequential Data Analysis in Healthcare: Predicting Disease P...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dixit, Krishna Kant Aswal, Upendra Singh Muthuvel, Suresh Kumar Chari, S Lakshmana Sararswat, Manish Srivastava, Amit Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

This study uses secondary data to forecast the course of disease using Long Short-Term Memory (LSTM) networks in an interpretive framework. By means of a descriptive design, temporal patterns are explored using a deductive approach. Metrics such as accuracy, precision, recall, alongside area under the {ROC} curve are used to illustrate the predictive accuracy of the LSTM model in the results. The temporal pattern analysis highlights the LSTM's ability to identify subtle trends by revealing the dynamic evolution of diseases over time. Analysis of feature importance sheds light on the temporal and clinical elements affecting predictions. The critical analysis identifies areas for improvement and highlights methodological strengths. Suggestions include sensitivity analyses, explained disclosure of hyper parameter tuning, and validation of data representativeness. For reliable, scalable applications in health care, future work must integrate various data modalities, provide real-time updating mechanisms, and take care of model explainability issues. © 2023 IEEE.

关键词： disease progression healthcare interpretivism Long Short-Term Memory temporal patterns

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Enabling Autonomous Deep Learning Strategies for Real-Time Data Analysis in Machine Learning 3

Enabling Autonomous Deep Learning Strategies for Real-Time D...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sahoo, Girija Shankar Sekaran, Ramesh Renuka, G. Dhabliya, Dharmesh Faujdar, Pramod Kumar Minhas, Deepak Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

This studies focuses on leveraging self-sustaining deep getting to know techniques for real-time records analysis in device getting to know. We propose a machine this is enabled by means of artificial intelligence to streamline the technique of extracting significant insights from sensor-based records. Our approach considers two middle components: interpreting data with recurrent neural networks and deploying a mastering agent to routinely whole the statistics analysis process. The getting to know agent's training is based on supervised mastering, enabling it to discover and traverse styles inside the information. The recurrent neural community (RNN) offers the functionality to seize temporal styles within the information, with its capacity to make predictions based on previous input. As a result, our system can produce fairly correct insights with minimal human intervention. This makes it suitable for time-sensitive information analysis situations in the industrial area in which decisions have to be taken in actual-time. © 2023 IEEE.

关键词： Recurrent neural networks

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Beyond Playlists: AI-Driven Emotion-Based Music Recommendation Systems 2

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Shiralaskar, Hrishikesh Mendhe, Sameer Datta Meghe Institute of Higher Education md Research Department of Computer Science & Engineering Faculty of Engineering & Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science & Design Faculty of Engineering & Technology Maharashtra India

ISBN: (纸本)9798331505745

Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems. © 2025 IEEE.

关键词： Deep learning

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An Optimized Algorithm for D2D-MIMO 5G Wireless Networks

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computers, Materials & Continua 2021年第9期68卷 3029-3044页

作者： Shahid Bashir Imran Khan Fahd NAl-Wesabi Nadhem Nemri Ammar Zahary Quang Ngoc Nguyen 无 Department of Business Studies Namal UniversityPakistan Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Communications and Computer Engineering Faculty of Science and Engineering Waseda University Shinjuku-kuTokyo169-0051Japan

The device-to-device(D2D)networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network,forming a heterogeneous 5G and beyond(B5G)communication network.D2D communication in a cellular cell will boost the efficiency of the spectrum,increase the ability of the device,and reduce the communication burden of base stations through the sharing of approved cell resources,causing serious interference as *** device-to-device(D2D)networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network,forming a heterogeneous 5G communication network.D2D communication in a cellular cell will boost the efficiency of the spectrum,increase the ability of the device,and reduce the communication burden of base stations through the sharing of approved cell resources,causing serious interference as *** paper proposes an efficient algorithm to minimize interference,based on the parity of the number of antennas,to resolve this *** primary concept is to generate the cellular connection precoding matrix by minimizing the power of interference from the base station to non-targeted *** through the criterion of maximum SINR,the interference suppression matrix of the cellular connection is ***,by removing intra-interference through linear interference alignment,the maximum degree of freedom is *** results of the simulation show that the proposed algorithm efficiently increases the performance of the spectrum,decreases interference,improves the degrees of freedom and energy efficiency compared to current algorithms.

关键词： 5G and beyond(B5G)networks device-to-device heterogeneous interference multiple-input and multiple-output

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Investigation of the Sensing Volume of Open-Ended Coaxial Probe for Heterogeneous Tissue Dielectric Properties Measurement at Microwave Frequencies 17

Investigation of the Sensing Volume of Open-Ended Coaxial Pr...

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17th International Congress on Image and Signal Processing, Biomedical engineering and Informatics, CISP-BMEI 2024

作者： Chen, Shuai Huang, Qiang Zhao, Gaowei Liao, Yupeng Ying, Yao School of Medical Information Engineering Gannan Medical University Ganzhou341000 China The First Affiliated Hospital of Guangzhou Medical University Equipment Department Guangzhou510000 China School of Computer Science Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331507398

Open-ended coaxial probe method is currently one of the most commonly used techniques for measuring the dielectric properties (DPs) of tissues. However, when measuring the surface of heterogeneous biological tissues, the effective sensing volume of the probe remains unclear, potentially leading to deviations in results if the target tissue volume is too small, causing non-target tissues to also be measured. In this study a measurement device was developed for the investigation of sensing volume (including sensing depth and sensing radius) of the coaxial probe method based on a layered DPs model. The influence of probe radius, DPs of the materials being measured, and frequency on the sensing volume was then discussed. Furthermore, the experimental results are compared and validated with electromagnetic simulation results. The results show that the effective sensing volume of the probe gradually increases with the radius of the coaxial probe. Within the frequency range of 300 MHz to 1.5 GHz, the effective sensing volume of the probe does not change significantly. Electromagnetic simulation results further validated these findings. © 2024 IEEE.

关键词： Probes

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