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共 89044 条记录，以下是221-230 订阅

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Design and Development of Chip Design for Digital Electronic System: Evolution and Trends 1

Design and Development of Chip Design for Digital Electronic...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Deep, Pragya Anand, John Rawat, Himani Kumar, Pankaj Kubba, Akshaya Karn, Ankriti IIMT College of Engineering Department of AI & DS Greater Noida India UP Greater Noida India Tula's Institute Department of Electronics and Communication Engineering Dehradun India Amity University Department of Statistics Kolkata India Chandigarh University Deparment of Computer Science Punjab Mohali India Tula's Institute Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331518578

System on-a-chip (SoC) achievement relies upon a very much planned, incorporated come nearer from a few disciplines, including application, plan, and gadget. From the viewpoint of the gadget, quick progressing VLSI innovation makes it conceivable to incorporate billions of semiconductors on a solitary chip, empowering the mix of various capabilities on a solitary chip. Since they rely upon these electrical interconnects at the actual level, on-chip correspondence models like transports and networks on-chip (NoC), which are utilized to empower between part correspondence in multi-processor systems on-chip (MPSoC) plans, are dependent upon the full scope of troubles and impediments that influence copper-based electrical interconnects. Various examination projects have begun looking at imaginative association choices, for example, on-chip optical interconnects, remote interconnects, and carbon nanotube-based interconnects, trying to beat the downsides of traditional copper-based electrical interconnects. An outline and flow research status for these three promising association advancements are introduced in this review. It additionally discusses the issues that should be fixed for every one of these advances before they can ultimately be utilized to supplant copper-based electrical interconnects. © 2025 ieee.

关键词： Network-on-chip

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Med-VQA: Performance Analysis of Question-Answering systems on Medical Images 29

Med-VQA: Performance Analysis of Question-Answering Systems ...

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29th international conference on Information Technology, IT 2025

作者： Karaca, Z. Aydin, I. Firat University Computer Engineering Department Elazig23119 Turkey

ISBN: (纸本)9798331517649

This study explores multimodal fusion methods and deep learning architectures for visual question answering (VQA) systems in the healthcare domain. ResNet50, DenseNet, LSTM, and BiLSTM models were evaluated for image feature extraction and text-based question processing, along with their integration into architectures enhanced by attention mechanisms. The ResNet50+DenseNet+LSTM model achieved the highest accuracy of 74.90% on the VQA-RAD dataset. Models such as ResNet50+DenseNet+BiLSTM and ResNet50+DenseNet+BiLSTM+Attention demonstrated strong performance with an accuracy of 72.80%, whereas the ResNet50+DenseNet+LSTM+Attention model showed comparatively lower performance. The results highlight the effectiveness of deep learning-based models, particularly in contextual information extraction and multimodal data fusion. This study underscores the significance of leveraging deep learning architectures in VQA systems to generate accurate and context-aware responses, especially in critical domains like healthcare. © 2025 ieee.

关键词： Question answering

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Nonplanar Wireless Differential Microsensor for Integration on Intravenous Catheters for Therapeutic Drug Monitoring and Other Applications 38

Nonplanar Wireless Differential Microsensor for Integration ...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Jiang, Jiaxin Chidambaran, Vidya Li, Tao University of Cincinnati Department of Electrical and Computer Engineering United States Cincinnati Children's Hospital Medical Center Department of Anesthesia United States

ISBN: (纸本)9798331508890

This paper reports a nonplanar passive wireless differential microsensor integrated with intravenous (IV) catheters of small diameters to facilitate in situ real-time monitoring of concentrations of drugs or other target molecules/biomarkers in the bloodstream. The sensor is based on magnetoelastic (ME) transduction with passive wireless interrogation to detect target molecules as mass loading using a functionalized coating. It uses a differential configuration to distinguish the mass loading effect from variations caused by changes in surrounding fluid medium. The sensor was curved to match the catheter form factor with maximized active sensor surface area, packaged in housing with embedded microfluidic features for sample filtration/pretreatment, and integrated on an 18-gauge catheter with 1.3 mm outer diameter for demonstration. Preliminary experimental results validated functions of curved ME sensors with degraded performance due to curving fully restored by an optimized annealing process, demonstrating 5.3 Hz/ng mass sensitivity and proper differential operation with the medium effect eliminated. © 2025 ieee.

关键词： Catheters

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Occlusion-aware Data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments 6

Occlusion-aware Data Augmentation and Reconstruction for Imp...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Pham, Vincent Singh, Sonit University of New South Wales School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798331506520

The demand for adaptive learning in education is an important application for computer Vision (CV)-based detection models which extract learners' faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners' faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection. © 2025 ieee.

关键词： Contrastive Learning

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One-for-All: An Unified Learning-based Framework for Efficient Cross-Corner Timing Signoff 24

One-for-All: An Unified Learning-based Framework for Efficie...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Zhu, Linyu Cai, Yichen Guo, Xinfei Shanghai Jiao Tong University Shanghai China Shanghai China

ISBN: (纸本)9798400710773

In advanced technology nodes, the proliferation of process corners poses significant challenges in timing signoff, particularly in estimating wire-induced interconnect delay across process corners. This paper proposes a learning-based framework to perform cross-corner timing prediction efficiently and accurately. It seamlessly integrates learning-based reference corner selection and topology-aware interconnect timing prediction into the broader timing signoff steps and engineering Change Order (ECO) processes. Unlike previous methods, it only requires information about one single known corner while accurately predicting all unknown corners. Evaluated on two mainstream industry processes, the proposed framework surpasses alternative machine learning models and existing strategies, with an impressive average accuracy enhancement of 62.6% and 95.3% respectively, and maintains a low mean absolute error (MAE) under 0.37ps and 0.01ps. Additionally, a faster version of the framework is developed to predict interconnect delay directly from a single extracted SPEF, yielding over 2× speedup. Moreover, the single-corner approach featured by the framework significantly accelerates ECO processes by over 10× compared to standard timing signoff flows. The proposed framework is also set to be open-sourced at a later date. © 2024 Copyright is held by the owner/author(s).

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Ultra-High-Sensitivity Hydrogen Nanogap Microsensor for Environmental Applications based on Chemically Actuated Palladium Cantilever Beams 38

Ultra-High-Sensitivity Hydrogen Nanogap Microsensor for Envi...

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38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Nikeghbal, Amirali Hasan, Rabiul Sium, Farhan S. Momeni, Fatemeh Deshpande, Adwait Noh, Seungbeom Kim, Hanseup Mastrangelo, Carlos H. University of Utah Electrical and Computer Engineering Dept Salt Lake City United States

ISBN: (纸本)9798331508890

This paper presents a novel low-cost capacitive nanogap microsensor for detection of hydrogen gas with a sub-50 ppm minimum detectable signal (MDS) and sub-second response time. The sensor operation relies on the closing of a nanogap present between two Pd cantilever beams structures of linear and U geometries when the beams are exposed to very low levels of H2. The sensor achieves a high sensitivity using amplified chemically actuated nanogap closing caused by the dissimilar expansion of the two cantilever structures of different lengths, one much longer than the other. The larger the length difference, the higher is the amplification. The sensor display a low temperature dependence (~7 ppb/C°). The chemically actuated design significantly advances hydrogen-sensing technology by combining high sensitivity with low cost;thus, this sensor is suitable for large-scale environmental monitoring applications. © 2025 ieee.

关键词： Nanocantilevers

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Towards a Network Expansion Approach for Reliable Brain-computer Interface 13

Towards a Network Expansion Approach for Reliable Brain-Comp...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Lee, Byeong-Hoo Yin, Kang Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Dept. of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Robotic arms are increasingly being used in collaborative environments, requiring an accurate understanding of human intentions to ensure both effectiveness and safety. Electroencephalogram (EEG) signals, which measure brain activity, provide a direct means of communication between humans and robotic systems. However, the inherent variability and instability of EEG signals, along with their diverse distribution, pose significant challenges in data collection and ultimately affect the reliability of EEG-based applications. This study presents an extensible network designed to improve its ability to extract essential features from EEG signals. This strategy focuses on improving performance by increasing network capacity through expansion when learning performance is insufficient. Evaluations were conducted in a pseudo-online format. Results showed that the proposed method outperformed control groups over three sessions and yielded competitive performance, confirming the ability of the network to be calibrated and personalized with data from new sessions. © 2025 ieee.

关键词： Robotic arms

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MF-BERT: A Siamese Pre-training Framework for Motion Forecasting

MF-BERT: A Siamese Pre-training Framework for Motion Forecas...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shi, Jianxin Chen, Jinhao Chen, Xiaolong Ma, Jun Wo, Tianyu School of Computer Science and Engineering Beihang University Beijing China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China

ISBN: (纸本)9798350368741

Accurately predicting the future motions of traffic agents is essential for autonomous systems. Despite the significant success of existing motion forecasting methods based on supervised learning, they still exhibit two main limitations. First, when annotated data for a scene is limited, these methods often fail to achieve the expected accuracy. Second, they typically rely on complex architectures and extensive prior knowledge to improve performance. To overcome these challenges, we propose MF-BERT, a novel framework that adapts the concept of BERT to motion forecasting, inspired by advancements in the self-supervised pre-training paradigm. During pre-training, we design a siamese sequence modeling task with an asymmetric mask strategy to capture complex behavior patterns of agents. During fine-tuning, the pre-trained representation module initializes the feature encoder of the motion forecasting model, and a multimodal trajectory decoder generates all possible predictions. Experimental results demonstrate the superiority of MF-BERT over state-of-the-art methods. © 2025 ieee.

关键词： Autonomous Motion Forecasting Self-supervised Learning Time Series Analysis

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IoT based Agricultural Monitoring and Alert System 4

IoT Based Agricultural Monitoring and Alert System

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priya, V Vishnu Nisanth, G. Dhanushree, D. Sindhu, M. Varshini, T. Preteka, A.T. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Applications Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (纸本)9798331523923

The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such as soil moisture levels, soil temperature, water levels, irrigation pump operation status, and water quality, to improve precision farming practices. Enabling farmers to make well-informed decisions, maximize resources, and enhance crop output in real time is the primary objective of the AMAS system. The challenges of using IoT for agricultural monitoring, include issues with adoption among farmers, dependable technology, efficient data administration, and connectivity. Small-scale farmers' financial hardship and rural areas' restricted access to technology. Many obstacles stand in the way of the widespread adoption of IoT-based agricultural monitoring systems, such as smallscale farmers' limited financial resources, rural communities' restricted access to technology, and problems with connectivity, dependable data handling, and system scalability. Machine learning-based prediction models and IoT-integrated smart irrigation are two recent approaches that have showed promise in tackling these issues, but they still face obstacles such high implementation costs, technical complexity, and infrastructure dependence. By improving yields, increasing output, and facilitating remote monitoring, the AMAS system seeks to overcome these limits while taking into account the unique requirements and limitations of farmers. This study plan to enhance productivity, optimize yield, and implement remote monitoring. © 2025 ieee.

关键词： Soil moisture

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Detection of Brain Tumors from MRI scans using Deep Learning Model 14

Detection of Brain Tumors from MRI scans using Deep Learning...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Surendra, Yellanuri Ketan, S. Hupesh Naga Naaz, Mohebba Department of CSE G Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool India Department of ECE Ravindra College of Engineering for Women Andhra Pradesh Kurnool India

ISBN: (纸本)9798331531935

The diagnosis of brain tumors dominates the medical sector and great accuracy along with efficiency in their methods to detect the tumor is required. The ability to diagnose brain cancers from MRI data has been greatly aided by recent developments in computer-aided diagnosis (CAD) systems. Recent years have seen improvements in approaches used, for instance with CNN or tumor images being automated. In this work was proposed a model that is based on deep learning using Inception-V3 for the classification and localization of brain tumors. Our model was validated on a publicly available dataset and it was shown that the proposed model is accurate and robust in distinguishing among different kinds of brain tumors. The paper also addresses issues such as data imbalance and model interpretability and the feasibility of integrating the system to be used clinically for real time applications. © 2025 ieee.

关键词： Deep learning

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