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作者： Xiangyu, Guo Jinyong, Chen Guisheng, Zhang Guofeng, Zou Qilei, Li Mingliang, Gao School of Electrical and Electronic Engineering Shandong University of Technology Zibo China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

Accurate cell counting in biomedical images is a fundamental yet challenging task for disease diagnosis. The early manual cell counting methods are mainly based on detection and regression, which are time-consuming and prone to errors. Benefiting from the advent of deep learning, convolutional neural network (CNN)-based cell counting has become the mainstream method. Despite the outstanding performance of CNN-based cell counting methods, the complex tissue background in medical images still hinders the accuracy of cell counting. In this paper, to solve the problem of complex tissue background and improve the performance of cell counting, an attentive recognition network (ARNet) is built. Specifically, the ARNet is composed of five convolution blocks and a channel attention (CA) module. The convolution blocks are employed to extract the basic features, and the CA module is introduced to suppress the complex background by recalibrating the weight of each channel to pay more attention to cells. Subjective and objective experiments on synthetic bacterial cells (SBC) dataset and modified bone marrow (MBM) dataset prove that the proposed ARNet outperforms the mainstream methods in accuracy and stability. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Convolution

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High resolution and high sensitivity imaging magnetometer with magnetic nanoparticle and optical heterodyne detection

High resolution and high sensitivity imaging magnetometer wi...

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2024 Laser science, LS 2024

作者： Bartos, Jan Ba Tis, Taleb Park, Wounjhang Huang, Shu-Wei Department of Electrical Computer and Energy Engineering University of Colorado Boulder80309 United States Material Science and Engineering Program University of Colorado Boulder80309 United States

We present progress towards an imaging magnetometer using a magneto-optical polymer nanocomposite thin film, experimentally demonstrating 29nT/√Hz sensitivity with © 2024 The Author(s). © Optica Publishing ... 详细信息

关键词： Heterodyne detection

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Cracking Down on Digital Misogyny with MULTILATE a MULTImodaL hATE Detection System 25

Cracking Down on Digital Misogyny with MULTILATE a MULTImoda...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Vetagiri, Advaitha Mogha, Prateek Pakray, Partha Department of Computer Science and Engineering National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering National Institute of Technology Assam Silchar788010 India

Sexism in social networks manifests in various forms, from blatant misogyny to subtle, implicit biases, presenting a significant societal challenge that necessitates effective detection and mitigation strategies. Addressing this issue involves participation in the EXIST 2024 tasks, a competition designed to advance the identification of sexist content in social media. This year's contest includes both traditional text-based data from tweets and an innovative meme dataset, incorporating both images and text. The approach leverages sophisticated models to analyze these multimodal inputs. For textual modalities, a Convolutional Neural Network - Bidirectional Long Short-Term Memory model is employed to discern sexist language and tweet behaviours. For image modalities, a combination of Residual Network 50 and text-based analysis is utilized to detect and interpret sexist elements within memes. Both models undergo hyperparameter tuning and k-fold cross-validation to ensure robustness and accuracy. Preliminary results indicate that integrating these methods enhances the precision and effectiveness of sexism detection, providing a comprehensive tool for identifying and addressing sexist content in diverse social media formats. © 2024 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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Neural Network Based Fault Location in Power Distribution System

Neural Network Based Fault Location in Power Distribution Sy...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Patel, Viresh Ghosh, Soumyajit Chakrabarti, Saikat Sharma, Ankush Pannala, Sanjeev Indian Institute of Technology Dept. of Electrical Engineering Uttar Pradesh Kanpur208016 India University of New Brunswick Dept. of Electrical and Computer Engineering FrederictonNB Canada Washington State University School of Electrical Engineering and Computer Science PullmanWA United States

ISBN: (纸本)9798350327748

Locating the fault in the power distribution system is a tedious process that involves manually searching along the line. While some methods rely on complete system parameters or centralized data processing, it has become difficult due to the bidirectional power flow in the active distribution system. This paper proposes a framework consisting of three steps: detecting, classifying, and locating the fault in the active distribution system. The proposed method reduces the search area by using the sending-end sample value of the line current. It can be implemented in a distributed form without knowledge of system parameters using pole-mounted data recording meters and relaying the approximate fault location and type to the control center. In the first step, the fault is detected using an isolation forest. Then, the fault is classified by the support vector machine, and finally, the fault is located using an adaptive weight convolutional neural network (CNN). The CNN weights are modified according to fault type information that uses time-frequency information extracted by a continuous wavelet transform (CWT). The proposed framework is tested using an IEEE 13-node test feeder with a solar photovoltaic system. Different training parameters for the CNN are also tested to analyze the proposed framework © 2023 IEEE.

关键词： Support vector machines

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Optimization of EV Scheduling in a Microgrid Integrated with a Rooftop Solar System Using Forward Dynamic Programming 10

Optimization of EV Scheduling in a Microgrid Integrated with...

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10th International Conference on Power and Energy Systems engineering, CPESE 2023

作者： Cuong, Le The Nhat, Hoang Duc, Tuyen Nguyen Takano, Hirotaka Hanoi University of Science and Technology School of Electronic and Electrical Engineering Hanoi Viet Nam Gifu University Faculty of Engineering Dept. of Electrical Electronic and Computer Engineering Japan

ISBN: (纸本)9798350327625

Electric vehicles (EVs) have been a trend in terms of transportation for a decade because of the energy transition. However, as the number of EVs increases, various problems related to the power quality, such as voltage and load profile, arise. To reduce the negative impact of EV charging, several solutions are suggested, with the integration of renewable energy sources into the charging station combined with Vehicle-to-Grid technology. This paper presents an algorithm to minimize the charging cost of all EVs and flatten the load curve, considering the travel schedule of EV owners and voltage constraints in the power grid. © 2023 IEEE.

关键词： Dynamic programming

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Angular Deflection and Frequency Translation Near the Critical Angle in Finite Time-Varying Media

Angular Deflection and Frequency Translation Near the Critic...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Fruhling, Colton Ozlu, Mustafa Goksu Boltasseva, Alexandra Shalaev, Vladimir M. Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States

We study time refraction and angular deflection in finite dynamic media. We find the effect is maximized at the critical angle. Its ultrafast nature suggests an attosecond streak camera as an application. © Optic... 详细信息

ISBN: (纸本)9781957171395

关键词： Streak cameras

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Optimal Deployment of Heterogeneous Visual Sensor Networks for Target Coverage and Feature Aggregation with Lifetime Guarantee 21

Optimal Deployment of Heterogeneous Visual Sensor Networks f...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Zhu, Xiaojian Zhou, MengChu School of Computer Science and Engineering Changshu Institute of Technology Changshu215500 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

Due to their need to handle a huge size of image data and limited battery power at each node, visual sensor networks (VSNs) suffer from short network lifetime and long network delay. Consequently, how to reduce network traffic is very important in their operation. The deployment of processing nodes for feature extraction is an effective way to reduce it. Deploying multiple types of camera nodes should help do so as well. Therefore, in this work we investigate how to deploy a heterogeneous VSN for target coverage and feature aggregation with lifetime guarantee at the minimum cost. Since this problem is NP-hard, we propose two heuristics, i.e., separate and joint node deployment algorithms, to solve it. A large number of experimental results expose that the latter has higher success rate and can obtain lower network deployment cost than the former at the expense of extra computation. © 2024 IEEE.

关键词： Sensor nodes

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PAPR Reduction of NOMA Using Vandermonde Matrix-Particle Transmission Sequence

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computer Systems science & engineering 2022年第10期43卷 193-201页

作者： Arun Kumar Sandeep Gupta Himanshu Sharma Mehedi Masud Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Department of Electrical Engineering JECRC UniversityJaipur303905India Department of Computer Science and Engineering JECRC UniversityJaipur303905India Department of Computer Science College of Computers and Information TechnologyTaif University11099Saudi Arabia

Non-Orthogonal Multiple Access(NOMA)is an ideal choice for 5G waveforms due to their characteristics such as high data rate,massive device connectivity,high spectral access,and effective frequency selective ***,it permits gigantic *** spectrum overlaps with NOMA,which consents several operators to segment the spectrum at the same *** features make NOMA more suitable for use beyond *** to Average Power(PAPR)is a major problem in Multi-Carrier Techniques(MCT)like NOMA and it also degrades the performance of the *** Partial Transmission Sequence(PTS)is a superior algorithm for moderating the ***,it also increases the complexity of the *** PAPR is reduced in NOMA by multiplying the NOMA signal with optimised phase vectors(P).The phase vectors are generated by using the Vandermonde Matrix(VM).In this work,we proposed a VM-PTS algorithm for the NOMA system,and the number of computations to search the optimal phase vectors for NOMA high PAPR signal is less as compared with existing *** outcomes demonstrate that the performance of the recommended PTS in terms of PAPR,Bit Error Rate(BER),and complexity is better than the conventional PTS(C-PTS).

关键词： VM-PTS NOMA PAPR 5G

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A 2.8-3.8-GHz Reconfigurable GaAs Low-Noise Amplifier With Improved Blocker Tolerance

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IEEE Microwave and Wireless Technology Letters 2024年第4期34卷 419-422页

作者： Wang, Jiashuai Wen, Jie Chi, Pei-Ling Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China Chengdu Spaceon Electronics Co. Ltd. Chengdu610036 China National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A reconfigurable low-noise amplifier (LNA) is proposed with integrated bandpass filter (BPF) responses covering 2.8 3.8 GHz in this letter. The proposed LNA consists of three cascaded transistor stages. By integrating the transformer-based second-order BPF into the interstage matching networks of the cascaded transistors, the blocker tolerance and linearity of the LNA can be significantly improved without sacrificing any additional chip area. To demonstrate the proposed technique, an LNA has been designed and fabricated in a 0.15-μ m GaAs process. The measured results show a maximum power gain of 35.5 dB and minimum noise figure (NF) of 1 dB with a stopband suppression of 43.5 dB. A blocker 1-dB compression point (B1dB) of -9 dBm is obtained at 4.25 GHz with LNA center frequency at 2.8 GHz and has been improved by at least 17 dB as compared to the in-band input 1-dB compression point (IP1dB). Good agreement between simulation and measurement is obtained © 2023 IEEE.

关键词： Low noise amplifiers

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Knowledge Graphs for Textbooks: Extraction and Completion Techniques 6

Knowledge Graphs for Textbooks: Extraction and Completion Te...

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6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Yao, Yutong Potikas, Petros Potika, Katerina Department of Computer Science San José State University San JoseCA United States School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798350331288

This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy Natural Language Processing (NLP), and Google Cloud Natural Language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space. © 2023 IEEE.

关键词： Knowledge graph

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