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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Luo, Zhiming Fang, Lei Wang, Zengmao Liu, Juhua Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China Information Technology Co. Ltd. Ningbo China

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing stateof- the-art methods. The code is available at SA-CD. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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A new service composition method in the cloud-based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

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Concurrency and Computation: Practice and Experience 2024年第16期36卷 e8091-e8091页

作者： Vakili, Asrin Al-Khafaji, Hamza Mohammed Ridha Darbandi, Mehdi Heidari, Arash Jafari Navimipour, Nima Unal, Mehmet Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq Pôle Universitaire Léonard de Vinci Paris France Department of Computer Engineering Faculty of Engineering and Natural Science Istanbul Atlas University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Mathematics School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

Cloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP-hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy-saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost. © 2024 John Wiley & Sons Ltd.

关键词： MapReduce

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GRU-Based Fusion Models for Enhanced Non-Invasive Blood Pressure Estimation from PPG Signals

TechRxiv

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TechRxiv 2023年

作者： Rizal, Syamsul Yuniarti, Ana Rahma School of Electrical Engineering Telkom University Bandung40257 Indonesia Computer Engineering Universitas Pendidikan Indonesia Bandung Indonesia ICT Convergence Research Center Kumoh National Institute of Technology Gumi39177 Korea Republic of

The current study presents a novel, non-invasive method for estimating both systolic and diastolic blood pressure by combining photoplethysmogram (PPG) signals with physiological data, such as sex, age, weight, height, heart rate, and BMI, using two Gated Recurrent Units (GRUs) models. The first model processes dynamic patterns in PPG signals, while the second model incorporates physiological parameters. Both models are connected through a series of dense layers. To prepare the datasets for the GRU framework, rigorous preprocessing was conducted. This resulted in a robust architecture capable of accurately predicting systolic and diastolic blood pressure. The proposed method achieved a Mean Absolute Error (MAE) of 1.458 for systolic and 1.164 for diastolic blood pressure. These findings demonstrate the potential of this approach for continual and non-intrusive blood pressure monitoring in wearable health technology. The study’s results also make a significant contribution to the field of medical monitoring technology. The proposed solution addresses a major limitation in traditional blood pressure measurement practices and paves the way for advancements in personalized health monitoring, particularly for managing hypertension and cardiovascular conditions. © 2023, CC BY.

关键词： Blood pressure

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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

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computer Systems Science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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Revolutionizing Intrusive Neural Interfaces: Vascular Stent-Based Neural Electrodes

Revolutionizing Intrusive Neural Interfaces: Vascular Stent-...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Liang Shen Bo Wen Zihua Chen Minjie Wang Linlin Han Bo Shen Kai Cao Lihua Zhang Jianjun Wu Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders State Key Laboratory of Medical Neurobiology Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China

This review delves into the emerging field of endovascular stent electrodes in the realm of brain-computer interfaces, highlighting advancements in their fabrication methods, signal acquisition performance, electrical stimulation effects, and research progress in electrochemical characteristics, mechanical stability, and biocompatibility. In comparison to traditional invasive procedures, endovascular stent electrodes, implanted through minimally invasive means, mitigate the risks associated with extensive trauma and tissue inflammation, demonstrating commendable signal acquisition capabilities. While exhibiting a slight deficiency in visual evoked potentials, they showcase accuracy in the acquisition and decoding of motor perception signals, rivaling or surpassing traditional surgical methods. Additionally, the technology shows potential therapeutic efficacy in electrical stimulation applications. Nevertheless, challenges such as electrical stimulation thresholds and visibility issues warrant further research and refinement. In summary, endovascular stent electrodes, as an emerging avenue in brain-computer interfaces, hold promise for significant breakthroughs in future research, opening up new possibilities in the field of neuroscience.

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Dielectric Resonators-Based Large Frequency Ratio Antenna

Dielectric Resonators-Based Large Frequency Ratio Antenna

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Antennas and Propagation Society International Symposium

作者： Awab Muhammad Muhammad Umar Khan Reza Shamsaee Malfajani Mohammad S. Sharawi School of Electrical Engineering and Computer Science (SEECS) National University of Sciences & Technology (NUST) Islamabad Pakistan Electrical Engineering Department Poly-Grames Research Center Polytechnique Montréal Montréal QC Canada

This paper presents a DRA-based large frequency ratio antenna that covers two widely separated frequency bands. The overall antenna consists of an aperture-fed RDRA which radiates at 3.4 GHz, whereas a 1x4 CDRA array radiates at the center frequency of 28 GHz. To reduce the area occupied by the radiators of the different bands, the CDRA array is placed on the RDRA and excited through dielectric-loaded waveguides. Overall, the antenna has the dimensions of 50 mmx50 mmx7.5 mm, and the simulated return loss is better than -10 dB in the regions of 3.28 GHz to 3.47 GHz and 27.52 GHz to 28.42 GHz.

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Vertical GaN MOSFET Power Devices

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Micromachines 2023年第10期14卷 1937-1937页

作者： Langpoklakpam, Catherine Liu, An-Chen Hsiao, Yi-Kai Lin, Chun-Hsiung Kuo, Hao-Chung Department of Photonics and Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan International College of Semiconductor Technology National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan

Gallium nitride (GaN) possesses remarkable characteristics such as a wide bandgap, high critical electric field, robust antiradiation properties, and a high saturation velocity for high-power devices. These attributes position GaN as a pivotal material for the development of power devices. Among the various GaN-based devices, vertical GaN MOSFETs stand out for their numerous advantages over their silicon MOSFET counterparts. These advantages encompass high-power device applications. This review provides a concise overview of their significance and explores their distinctive architectures. Additionally, it delves into the advantages of vertical GaN MOSFETs and highlights their recent advancements. In conclusion, the review addresses methods to enhance the breakdown voltage of vertical GaN devices. This comprehensive perspective underscores the pivotal role of vertical GaN MOSFETs in the realm of power electronics and their continual progress. © 2023 by the authors.

关键词： Gallium nitride

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Knight Tour Patterns: Novel Handcrafted Feature Descriptors for Facial Expression Recognition 19th

Knight Tour Patterns: Novel Handcrafted Feature Descriptors ...

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19th International Conference on computer Analysis of Images and Patterns, CAIP 2021

作者： Kartheek, Mukku Nisanth Madhuri, Rapolu Prasad, Munaga V. N. K. Bhukya, Raju Center for Affordable Technologies Institute for Development and Research in Banking Technology Hyderabad Telangana India Department of Computer Science and Engineering National Institute of Technology Warangal Telangana India School of Computer and Information Sciences University of Hyderabad Hyderabad Telangana India

ISBN: (纸本)9783030891305

Facial expressions are used frequently in the social interaction and are considered important as they can reflect the inner emotional states of an individual. Automatic Facial Expression Recognition (FER) systems aim at classifying the facial images into various expressions. To do this task accurately, better feature descriptors are to be developed to effectively capture the facial information. The main contribution of this paper is our novel local texture based feature extraction techniques, inspired by Knight tour problem namely Knight Tour Patterns (kTP and KTP). kTP extracts two feature values in the 3 x 3 overlapping neighborhood, whereas, KTP extracts three feature values in the 5 x 5 overlapping neighborhood. To the proposed methods, apart from binary weights, different weights (fibonacci, prime, natural, squares and odd) have been applied to further reduce the feature vector length. The extensive experiments have been performed on JAFFE, MUG, TFEID, CK+ and KDEF datasets with respect to both six and seven expressions in person independent setup. The proposed methods are compared with the standard existing variants of binary patterns to demonstrate the efficiency of the proposed methods. © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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A Spatio-Temporal Switchable Data Prefetcher for Convolutional Neural Networks

A Spatio-Temporal Switchable Data Prefetcher for Convolution...

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International SoC Design Conference, ISOCC

作者： Jihoon Jang Hyun Kim Hyokeun Lee Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Secure and Advanced Computer Architecture (SACA) Research Group North Carolina State University (NCSU) Raleigh NC USA

In this paper, we propose a spatio-temporal switchable data prefetcher that can adapt to the locality characteristics of CNN models. The proposed prefetcher records the recent delta history by leveraging two tables. The first table predicts spatial address patterns by comparing the delta score with the last delta, while the second table predicts temporal address patterns by recording and reordering the delta sequence from the delta history. Consequently, the proposed prefetcher is capable of appropriately switching between these two prediction methodologies based on spatial and temporal localities. The experimental results on CNN inference workloads show that we achieved high average accuracy of 83.8% and coverage of 81.6%, and hence the proposed prefetcher improves system performance by 33.8% over a baseline with no data prefetcher and 21% over the best-performing prior spatio-temporal prefetcher.

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Multi-Task Driven User Association and Resource Allocation in In-vehicle Networks

Multi-Task Driven User Association and Resource Allocation i...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ziqi Chen Jun Du Guowei Yang Chunxiao Jiang Zhu Han Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

With the rapid development of intelligent vehicles, heterogeneous in-vehicle networks (HetIVNets) applying heterogeneous access technologies in terms of cellular and in-vehicle WiFi, are widely employed to provide stable and ubiquitous network environments for intelligent vehicles and their passengers. Most existing studies on the optimization of heterogeneous networks (HetNets) focus on user association, channel and power allocation. Additionally, these studies typically employ a single task metric to characterize the quality of service (QoS) requirements of the devices. In order to achieve green and energy-efficient intelligent vehicles, our work considers a HetIVNet composed of WiFi and cellular networks, and further optimizes the bandwidth allocation and energy consumption of WiFi access point (AP). Furthermore, to achieve more accurate resource allocation for different tasks in HetIVNet, we establish the QoS requirement model of various tasks for in-vehicle devices. Since the proposed optimization problem is non-convex and NP-hard, we formulate the objectives and constraints of user association and resource allocation (UARA) in HetIVNets as a markov decision process (MDP) and propose a proximal policy optimization (PPO) algorithm for in-vehicle intelligent resource allocation to uniformly schedule network resources. Simulation results validate that the proposed algorithm can achieve high task success rates under low-energy consumption conditions for WiFi AP. We also compare our algorithm with the state-of-art baselines to highlight its efficiency and stability.

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