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Cell-Free MIMO Perceptive Mobile Networks: Cloud Vs. Edge Processing

IEEE Transactions on Vehicular Technology 2025年第6期74卷 9520-9532页

作者： Jeong, Seongah Kang, Jinkyu Simeone, Osvaldo Shamai, Shlomo University of Seoul School of Advanced Fusion Studies Department of Intelligent Semiconductor Engineering Seoul02504 Korea Republic of Myongji University Department of Information and Communication Engineering Gyeonggi-do Korea Republic of LondonWC2B 4BG United Kingdom Technion Department of Electrical and Computer Engineering Haifa3200003 Israel

Perceptive mobile networks (PMNs) implement sensing and communication by reusing existing cellular infrastructure. Thanks to the cooperation among distributed access points, cell-free multiple-input multiple-output (MIMO) systems support the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, in a cell-free MIMO system, distributed access points (APs) communicate over fronthaul links with a central processing unit (CPU) in the cloud. This work proposes four different types of PMN uplink solutions based on cell-free MIMO systems, in which the sensing and decoding functionalities are carried out at either the cloud or the edge. Accordingly, we develop and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation for fronthaul quantization to maximize the achievable rate under sensing and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications. © 2025 IEEE.

关键词： Multistatic radars

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Design and Development of a Mobile-Based Data Acquisition System for Aerial Bridge Inspection 15

Design and Development of a Mobile-Based Data Acquisition Sy...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Himongala, Ryan P. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Alagon, Francis Jann A. Salaan, Carl John O. Lavilles, Rabby Q. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Philippines College of Computer Studies Mindanao State University - Iligan Institute of Technology Department of Information Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Infrastructures such as bridges are ubiquitous in today's world, but over time, corrosion, continuous vibrations, heavy loads, natural disasters, and aging have actively weakened their structural integrity. Hence, it is crucial to periodically assess the bridge's condition to maintain its structural soundness. Nevertheless, conventional bridge inspection techniques involve inspectors to visually assess a bridge's condition and manually capture images of its defects. UAVs for bridge inspections typically improve the conventional approach. This method provides a visual system by mounting a camera on the UAV equipped with a software application dedicated to data acquisition. However, the application operates primarily on desktop computers or laptops while incompatible with smartphones and tablets, limiting its portability and making it inconvenient for inspectors. In this study, the researchers developed an application for mobile devices to further improve data acquisition. The application is designed to display a real-time video feed from the shelled UAV, capture visual data of bridge defects, and allow users to review the data after the inspection. The researchers tested and evaluated the application's performance regarding its functionality. Results show that the application has successfully improved the portability and efficiency of the inspection process, achieving its intended features and functionalities while adhering to the requirements. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Identify The Potential Instance of Copy Forgery Using Similar Algorithm 9

Identify The Potential Instance of Copy Forgery Using Simila...

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9th International Conference on advanced Computing and Communication Systems, ICACCS 2023

作者： Manikandan, A. Kumar, N. Thirumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350397376

In a similar fashion one of the most popular types of digital art, counterfeit detection necessitates knowledge of picture structures and the ability to generate visual diversions for many purposes, including entertainment. The use of color is very crucial for creating city features. Previous studies have examined the interrelationships of color within homogenous metropolitan zones, looking for connections between neighboring areas and culturally comparable peoples. Color, acclaim, the dissemination of one's color, and satisfaction. When communicating with a foreign audience, it is common practice to have the message reviewed and edited to conform to the local language and cultural norms. © 2023 IEEE.

关键词： Color

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BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization

BAPO: Base-Anchored Preference Optimization for Overcoming F...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lee, Gihun Jeong, Minchan Kim, Yujin Jung, Hojung Oh, Jaehoon Kim, Sangmook Yun, Se-Young Graduate School of AI KAIST Korea Republic of Samsung Advanced Institute of Technology Korea Republic of Department of Electrical and Computer Engineering UBC Canada

ISBN: (纸本)9798891761681

While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents further challenges in preserving previous knowledge. This paper examines the impact of personalized preference optimization on LLMs, revealing that the extent of knowledge loss varies significantly with preference heterogeneity. Although previous approaches have utilized the KL constraint between the reference model and the policy model, we observe that they fail to maintain global knowledge and general alignment when facing personalized preferences. To this end, we introduce Base-Anchored Preference Optimization (BAPO), a simple yet effective approach that utilizes the initial responses of reference model to mitigate forgetting while accommodating personalized alignment. BAPO effectively adapts to diverse user preferences while minimally affecting global knowledge or general alignment. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Dual-logic-in-memory implementation with orthogonal polarization of van der Waals ferroelectric heterostructure

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InfoMat 2024年第2期6卷 107-117页

作者： Jingjie Niu Sumin Jeon Donggyu Kim Sungpyo Baek Hyun Ho Yoo Jie Li Ji-Sang Park Yoonmyung Lee Sungjoo Lee SKKU Advanced Institute of Nanotechnology(SAINT) Sungkyunkwan UniversitySuwonRepublic of Korea Department of Nano Science and Technology Sungkyunkwan UniversitySuwonRepublic of Korea Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea IMEC HeverleeBelgium Department of Nano Engineering Sungkyunkwan UniversitySuwonRepublic of Korea

The rapid advancement of AI-enabled applications has resulted in an increasing need for energy-efficient computing ***-in-memory is a promising approach for processing the data stored in memory,wherein fast and efficient computations are possible owing to the parallel execution of reconfigurable logic *** this study,a dual-logic-in-memory device,which can simultaneously perform two logic operations in four states,is demonstrated using van der Waals ferroelectric field-effect transistors(vdW FeFETs).The proposed dual-logic-in-memory device,which also acts as a twobit storage device,is a single bidirectional polarization-integrated ferroelectric field-effect transistor(BPI-FeFET).It is fabricated by integrating an in-plane vdW ferroelectric semiconductor SnS and an out-of-plane vdW ferroelectric gate dielectric material—CuInP_(2)S_(6).Four reliable resistance states with excellent endurance and retention characteristics were *** two-bit storage mechanism in a BPI-FeFET was analyzed from two perspectives:carrier density and carrier injection controls,which originated from the out-of-plane polarization of the gate dielectric and in-plane polarization of the semiconductor,*** conventional multilevel FeFETs,the proposed BPIFeFET does not require additional pre-examination or erasing steps to switch from/to an intermediate polarization,enabling direct switching between the four memory *** utilize the fabricated BPI-FeFET as a dual-logic-inmemory device,two logical operations were selected(XOR and AND),and their parallel execution was *** types of logic operations could be implemented by selecting different initial states,demonstrating various types of functions required for numerous neural network *** flexibility and efficiency of the proposed dual-logic-in-memory device appear promising in the realization of next-generation low-power computing systems.

关键词： ferroelectric field-effect transistor in-plane ferroelectricity logic-in-memory out-of-plane ferroelectricity

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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Mapping the Landscape of Generative AI in Network Monitoring and Management

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2441-2472页

作者： Bovenzi, Giampaolo Cerasuolo, Francesco Ciuonzo, Domenico Di Monda, Davide Guarino, Idio Montieri, Antonio Persico, Valerio Pescape, Antonio University of Naples Federico II Department of Electrical Engineering and Information Technologies Naples80125 Italy University of Naples Federico II DIETI Naples80125 Italy IMT School for Advanced Studies Lucca55100 Italy University of Verona Department of Computer Science Verona37129 Italy

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management. © 2004-2012 IEEE.

关键词： Generative adversarial networks

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost 3

Enhancing Stock Price Prediction Through Integration of Astr...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Vishal, S. Mahapatra, Sheila Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to Be University Nitte Institute of Professional Education Karnataka Mangalore India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R2, AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day's stock price would be higher or lower than the previous day's price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability. © 2025 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

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Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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Science China(Information Sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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