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共 13045 条记录，以下是441-450 订阅

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Current Spread Spectrum Clock Oscillator with Adjustable Output Mode 4

Current Spread Spectrum Clock Oscillator with Adjustable Out...

引用

4th International Conference on Electronics, Circuits and information engineering, ECIE 2024

作者： Wu, Xudong Yang, Bing Wei, Qi Li, Peipei North China University of Technology dept. School of Information Beijing China Tsinghua University dept. School of precision instrument Beijing China Beijing Jiao tong University dept. School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350388312

This paper presents a Spread spectrum clock oscillator for the DC-DC converter. In this design, digital logic control is added based on the RC relaxation oscillator, which makes the charging and discharging current of the oscillator change randomly in the set range. This random change of current can achieve the corresponding spread spectrum to achieve the purpose of reducing EMI. In addition, this oscillator design can achieve four different operational modes through the external control signal: 7 -bit output mode and 8 -bit output mode without spread spectrum, 7-bit output mode and 8-bit output mode with spread spectrum. The simulation results show that the oscillating frequency of the oscillator is 16.7MHz without the spread spectrum under 7-bit output mode, after spreading spectrum, the dept. of spread spectrum is 54.4%, which the peak energy is reduced by 24dB compared with that before spreading spectrum. Under 8-bit output mode, the oscillator oscillates at 19.6MHz without spreading spectrum, after spreading spectrum, the dept. of spread spectrum is 58% with the 22dB peak energy reduction. © 2024 IEEE.

关键词： Relaxation oscillators

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling 3

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paul, Soumyadip Chakraborty, Sarit Government College of Engg. & Ceramic Technology Dept. Of Information Technology WB Kolkata700010 India Government College of Engineering and Leather Technology Dept. of Computer Science and Engineering WB Kolkata700106 India

ISBN: (纸本)9798331523893

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems. © 2025 IEEE.

关键词： Qubits

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Elevating Perception: Unified Recognition Framework and Vision-Language Pre-Training Using Three-Dimensional Image Reconstruction 2

Elevating Perception: Unified Recognition Framework and Visi...

引用

2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Wang, ZhiQiang Joshi, Aryan Zhang, GuiYing Ren, WenJia Jia, Feng Sun, XiaoHang Tianjin University of Technology and Education Dept. of Information Tech & Engineering Tianjin China Tianjin Ai Shen de Si Technology Co. Ltd Dept. of Information Tech & Engineering Tianjin China

ISBN: (纸本)9798350360363

This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework and Vision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy of advanced algorithms from computer vision & language processing, the project tries to enhance the precision and dept. of perception in reconstructed images. This innovative approach holds the potential to revolutionize fields such as medical imaging, virtual reality, and computer-aided design, providing a comprehensive perspective on the intersection of multimodal data processing and perceptual advancement. The anticipated research outcomes are expected to significantly contribute to the evolution of technologies that rely on accurate and contextually rich three-dimensional reconstructions. Moreover, the research aims to reduce the constant need for new datasets by improving pattern recognition through 3D image patterning on backpropagation. This continuous improvement of vectors is envisioned to enhance the efficiency and accuracy of pattern recognition, contributing to the optimization of perceptual systems over time. © 2023 IEEE.

关键词： Image enhancement

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Adapting Pedagogical Frameworks to Online Learning: Simulating the Tutor Role with LLMs and the QUILT Model 6

Adapting Pedagogical Frameworks to Online Learning: Simulati...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Madhubhashana, Sandaru Ranhinda, Vibhashana Jayakody, Dineth Shavinda, Lakmuthu Nawarathne, Malithi Haddela, Prasanna Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents SoloScholar, a novel asynchronous online learning platform designed to deliver personalized education to large undergraduate student groups by integrating established pedagogical frameworks with Large Language Models (LLMs). Leveraging Bloom's Revised Taxonomy, learners are categorized into beginner, intermediate, and advanced levels to personalize the educational content. The platform employs the Questioning and Understanding to Improve Learning and Thinking (QUILT) model to structure tutorial sessions and simulate the teaching role of a tutor at a Higher Education Institute (HEI). To generate contextually relevant and cognitively appropriate questions, carefully crafted prompts are fed into LLMs, synthesizing information from reliable knowledge sources. The efficacy of SoloScholar was evaluated through a dual assessment approach: a technical evaluation using the Retrieval Augmented Generation Assessment (RAGAS) framework and a human evaluation involving academic experts. The technical evaluation demonstrated high scores in answer relevancy and context utilization across all learning levels. Similarly, quantitative responses from academic experts indicated strong satisfaction with the quality and appropriateness of the generated content. The findings underscore the potential of integrating LLMs with pedagogical models to enhance personalization in online education. SoloScholar effectively addresses challenges associated with large-group teaching by providing adaptive learning experiences that cater to diverse cognitive needs. © 2024 IEEE.

关键词： Contrastive Learning

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Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism 14

Simplifying Structure and Pattern in Complex Social Networks...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Arunakumari, B.N. Kshama, S.B. Swasti, S. Sameeksha, B.H. Puri, Sonia Rubini, P.A. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

In recent years, networks have grown in size and complexity, traditional network testing methods have become time-consuming and inefficient. As a result, there is a need for more efficient and reliable network testing methods. One important topic in the field of network testing is graph pattern matching. Network analysis relies on graph pattern matching. The paper discusses the use of graph isomorphism for network testing. This paper also gives an overview of recent advances in isomorphism testing, which determines if two networks are structurally identical despite potential labeling discrepancies between them. The combination of isomorphism and pattern matching in network analysis is used to identify equivalent structures in different networks. This approach is used to identify and compare common patterns in complex network structures, such as those found in social networks. © 2023 IEEE.

关键词： Pattern matching

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Multiple Skin Lesions Diagnostics Using Cascaded Deep Convolutional Networks 26

Multiple Skin Lesions Diagnostics Using Cascaded Deep Convol...

引用

26th International Conference on Computer and information technology, ICCIT 2023

作者： Islam, Mainul Islam Khandaker, Md. Ariful Ahmad, Mohiuddin Khulna University of Engineering & Technology Dept. of Electrical and Electronics Engineering Bangladesh Technology Khulna University of Engineering & Technology Institute of Information and Communication Bangladesh

ISBN: (纸本)9798350359015

In recent years, skin cancer has emerged as one of the most prevalent types of cancer in the human population. Skin lesion classification plays a crucial role in dermatology, aiding in the early objective of this study to employ deep learning techniques, including ResNet152v2, InceptionV3, and VGG16, to classify skin lesions accurately. Additionally, the study includes the segmentation of skin lesions using Deep Res U-Net and Res U-Net. Experiments are conducted to achieve optimal evaluation metrics and fine-tuned model parameters using different regularization methods and hyperparameters. The results demonstrated that modifying Res U-Net led to higher accuracy and outperformed other approaches. This emphasizes the potential of using neural networks with fewer parameters and lower computational weight for improved skin lesion classification and segmentation. Furthermore, the deep learning models employed in this study showcased their capability to capture intricate features and patterns within skin lesion images, enabling a more precise and efficient diagnosis process. By implementing these deep learning techniques, a significant enhancement is anticipated in the current state-of-the-art for skin lesion classification and segmentation. This study underscores the importance of utilizing large and diverse datasets for training deep learning models and employing appropriate pre-processing techniques like data augmentation and normalization. Moreover, the study highlights the significance of fine-tuning pre-trained models to tailor them to skin lesion classification and segmentation. Overall, this research emphasizes the potential benefits of advanced deep-learning methods in advancing the field of dermatology. © 2023 IEEE.

关键词： Classification CNN Deep Learning Segmentation Skin Cancer Transfer learning

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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： Data integrity

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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Advanced CNN-RNN Model Based Automatic Modulation Classification on Resource-Constrained End Devices 8

Advanced CNN-RNN Model Based Automatic Modulation Classifica...

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8th IEEE International Conference on information and Communication technology, CICT 2024

作者： Tripathi, Parth Bhola, Bhavya Kumar, Rajeev Turlapaty, Anish Chand Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Andhra Pradesh Sri City517646 India

ISBN: (纸本)9798331531539

In this paper, we propose a hybrid convolutional recurrent neural network (CNN-RNN) model to operate on resource-constrained end (RCE) devices for automatic modulation classification (AMC). It extracts spatial features and temporal dependencies from data signals by exploiting CNN layer and RNN layer respectively in dynamic communication environments. Further, the dense layer is connected to cluster output of the RNN layer to robust the classification. Furthermore, the softmax layer is used to provide probability distribution to each modulation scheme. Our proposed hybrid model outperforms the available existing models in the literature. In addition, we provide promising future research directions. © 2024 IEEE.

关键词： Recurrent neural networks

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800V/48V/12V 6kW resonant dc-dc converter with dual transformers for Electric Vehicles 10

800V/48V/12V 6kW resonant dc-dc converter with dual transfor...

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10th IEEE International Power Electronics and Motion control Conference, IPEMC 2024 ECCE Asia

作者： Do, Ngoc-Quy Lee, Changseop Kim, Jinhak Kieu, Huu-Phuc Choi, Sewan Seoul National University of Science and Technology Dept. Electrical & Information Engineering Seoul139-743 Korea Republic of

ISBN: (纸本)9798350351330

This article introduces a new isolated, three-port, bidirectional DC-DC converter topology for EV APM. Through the manipulation of duty cycles and phase shifts between the LV and HV sides, the proposed topology achieves DCX at bridges 1 and 3, as well as near DCX at bridge 2, leading to a significant reduction in switching losses. Moreover, by controlling the phase shift between bridge 1 and bridge 2, the circulating current is halved, resulting in an overall 2% increase in efficiency compared to the existing configurations. A laboratory prototype of the three-port APM, featuring 140kHz operation and a wide voltage range of 400V to 840V has been implemented to validate the proposed structure. © 2024 IEEE.

关键词： DC-DC converters

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