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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Harsh Pratap Singh, Nagendra Mishra, Anuprita Sen, Santosh Kumar Swarnkar, Mamta Pandey, Deepak Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Computer Science and Engineering Indore India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Corporate institute of Science and Technology Department of Computer Science and Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India TIT Department of EEE Bhopal India

ISBN: (纸本)9798350374100

The detection of various reactions using computer vision, machine learning, and artificial intelligence is a rapidly growing field of research. In this paper, we present a sentiment analysis model based on the Python, NLTK (natural language toolkit) libraries, and machine learning algorithms that can detect multiple reactions from the public on a single platform. The proposed Sentiment Analysis System deals with the very usual problem faced by different companies, manufacturers, and sellers about knowing the customer review for their product. The model classifies the public reviews and the normal text as positive, negative, or neutral. This proposed system is categorised into two parts, one being a field-specific sentiment analysis and the other being a generalised system that can judge any particular word or sentence to be either positive, negative, or neutral. The proposed model is also capable of representing a huge dataset (input as a csv file) in the form of a graph, which can be easily understood by the desired person, and the graphical representation is possible with the Matplotlib library of Python. The model's performance is evaluated using several metrics, such as precision, recall, and others. The model's accuracy and efficiency make it a promising tool for sentiment analysis, which can be used by anyone in any field. In conclusion, the system can work for any specific field provided the data set, and the generalized way can help out with random sentences, and the model's potential can be boosted with some further research. © 2024 IEEE.

关键词： Python

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Gaps in value-driven connectivity: A case study on connectivity challenges in Northern Europe 35

Gaps in value-driven connectivity: A case study on connectiv...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Karppinen, Pasi Bandaranayake, Sameera Haukipuro, Lotta Elmistikawy, Yomn Saarnisaari, Harri University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland University of Oulu Faculty of Science Oulu Finland Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden

ISBN: (纸本)9798350362244

Well-working Internet connectivity is a fundamental citizen right and a necessity in modern digital societies. Nevertheless, the digital divide persists and often affects rural or remote areas. To bridge the digital divide, we need to understand the specific rural use cases and challenges. This would increase understanding about problems weak Internet connectivity causes, what is required from the connectivity technology and what is needed from policymakers and regulation. The goal of this paper is to identify rural use cases and analyze consequences to future connectivity technology through a mixed-methods approach: we investigated the divide and its consequences by a questionnaire and interviews conducted in northern Europe. The study found that the main issues faced by rural internet users are related to capacity, availability, and vulnerability of internet services. These issues hinder their daily life, work activities, and expose them to system-level risks. The article discusses how these findings can inform the development of more inclusive and resilient internet connectivity solutions. © 2024 IEEE.

关键词： digital divide mobile communication rural connectivity social sustainability

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NUMERICAL INVESTIGATION OF NONLINEAR INTERACTION OF GUIDED-WAVE MIXING AND MICRO-CRACKS IN PIPES 18

NUMERICAL INVESTIGATION OF NONLINEAR INTERACTION OF GUIDED-W...

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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Tan, Miaohong Sun, Maoxun Liu, Hongye Chen, Xiaorong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9798331523732

Pipes are widely used in various industries but are subject to plastic deformation and corrosion, during which microcracks may appear. Ultrasonic guided wave mixing has gained attention due to its high sensitivity. This study employs phase inversion and two-dimensional Fourier transform to investigate the nonlinear interaction between counter-propagating guided waves and microcracks. The generated sum-frequency components and difference-frequency components contain multiple flexural modes and propagate toward both ends of the pipeline. The generated sum-frequency components and difference-frequency components are used to locate the microcracks. The circumferential location of the microcracks is determined by polar diagrams (i.e., the amplitudes of difference-frequency or sum-frequency components and angles), while the axial location is determined by the relationship between the amplitudes of difference-frequency components or sum-frequency components and distance. © 2024 IEEE.

关键词： Pipeline corrosion

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Enhancing Mobility with Next-Generation Partial Hand Prosthesis Using Arduino Uno and Flex Sensors 2

Enhancing Mobility with Next-Generation Partial Hand Prosthe...

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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Matury, Sai Sathwik Sam Stewart, X. Ra, Nawin Kumba, Krishna School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350388664

The proposed work delves into the comprehensive design, development, and evaluation process of a 3D printed prosthetic arm, employing servo motors controlled by an Arduino Uno microcontroller to enable gesture mimicry through flex sensors integrated into a wearable glove. The primary objective is to provide an affordable and accessible solution for individuals with upper limb disabilities. The paper outlines the intricacies of the design process, system architecture, programming nuances, and rigorous testing procedures, showcasing promising results in terms of gesture accuracy, adaptability, and user-friendliness. The prosthetic arm demonstrates high precision in replicating various hand gestures. Future work will focus on enhancing the durability and responsiveness of the system to further improve its practical application. User feedback has been overwhelmingly positive, indicating significant potential for real-world adoption. © 2024 IEEE.

关键词： Servomotors

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection 6

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Qiu, Zhiyu Chen, Tianqi Hua, Yuxiao Todo, Yuki Graduate School of Natural Science & Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798331527426

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Dependence Induced Representations 60

Dependence Induced Representations

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Xu, Xiangxiang Zheng, Lizhong Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

ISBN: (纸本)9798331541033

We study the problem of learning feature repre-sentations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for such dependence induced representations, and illustrate their connections to Hirschfeld-Cebelein-Renyi (HGR) maximal correlation functions and minimal sufficient statistics. We characterize a large family of loss functions that can learn dependence induced representations, including cross entropy, hinge loss, and their regularized variants. In particular, we show that the features learned from this family can be expressed as the composition of a loss-dependent function and the maximal correlation function, which reveals a key connection between representations learned from different losses. Our development also gives a statistical interpretation of the neural collapse phenomenon observed in deep classifiers. Finally, we present the learning design based on the feature separation, which allows hyperparameter tuning during inference. © 2024 IEEE.

关键词： Contrastive Learning

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning 21st

Enhancing Multimodal Rumor Detection with Statistical Image...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhou, Chenyu Li, Xiuhong Li, Zhe Chen, Fan Sheng, Jiabao Chen, Bin Wang, Haoyu School of Computer Science and Technology Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering Stanford University CA United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819601219

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, including mean and variance, to capture spatial attributes effectively and improve the detection of image-tampered tweets. To tackle complex intermodal correlations, we introduce a contrastive learning approach that aligns features across modalities efficiently. Additionally, we introduce a cross-attention fusion module (CAFM) that enhances the integration of image and text modalities, thereby improving multimodal rumor detection performance. In conclusion, we propose the cross-attention fusion network (ConCAFN), leveraging contrastive learning for robust multimodal rumor detection. Extensive experiments on two real-world datasets confirm the model’s enhanced capability to detect multimodal rumors accurately, demonstrating our methods’ effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Large Signal Stability Assessment of MPC-based Control for HVDC electrical Grid

Large Signal Stability Assessment of MPC-based Control for H...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Tarcar, Rohan Kamat Shetgaonkar, Ajay Popov, Marjan Lekić, Aleksandra Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science DelftCD 2628 Netherlands

ISBN: (纸本)9789531842976

The stability of an HVDC transmission network is very important for the reliable transfer of power from renewable energy resources. Therefore, this paper proposes a region of attraction stability analysis method for grid-forming-based modular multilevel converters. The grid forming control uses a model predictive control-based controller for the inner current loop. The stability analysis is carried out using the direct Lyapunov method. For the model predictive control, the modified version of the cost function is used;the same function is also used as the cost function for the direct Lyapunov method. Furthermore, the boundaries formed by the limiters and Lyapunov function entirely explain transient event behavior. © 2024 IEEE.

关键词： Renewable energy

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Efficient Framework with Sparse Acquisition in CMOS Image Sensors for Low-Power Edge Devices 10

Efficient Framework with Sparse Acquisition in CMOS Image Se...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Kisku, Wilfred Kaur, Amandeep Mishra, Deepak Indian Institute of Technology Department of Electrical Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331533229

Memory bandwidth and power consumption is of utmost importance in the design of low power edge devices. This makes it essential to conserve power both at the sensor node and the computational unit. Our paper proposes a system design methodology to save power by acquiring compressed data at the CMOS Image Sensor (CIS) and making the Convolutional Neural Network (CNN) models lightweight for edge applications. A detailed study highlights the effect of sensor directed sparsity at the CIS, and its effect on CNN models such as VGG-16, MobileNet, ResNet50, EfficientNet, and ResNext models. It provides empirical evidence that the performance is mostly maintained even after pruning and quantization of considered models. The imagenette dataset is used to curate the data with the proposed acquisition pipeline that introduces data sparsity, and the experimental results provide evidence that the proposed pipeline performs at par with conventional CIS acquisition mode. For example, the analysis of VGG-16 model for a sparsity threshold level of 0.2 shows compression of × 7.6, × 16, and × 61 using only pruning (P), only quantization (Q), and with both pruning+quantization (P+Q), respectively. It is also observed that the proposed acquisition pipeline can achieve significant parameter reduction through pruning for model compression, comparable to convectional pipeline. © 2024 IEEE.

关键词： CMOS integrated circuits

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