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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Lokesh Punwani Ratnalata Gupta Devansh Kadam Mamta Panjabi Deepak Nigam Computer science and Engineering Shivajirao Kadam Group of Institutions Indore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Parking has become a significant challenge in urban areas due to the increasing number of vehicles and limited spaces. Drivers often waste time and money searching for parking, which leads to traffic congestion. To address this, a prototype utilizing IoT, RFID, and IR sensors was developed. RFID captures vehicle details, IR sensors detect vehicle presence, and IoT enables remote access to parking data. The system provides real-time availability, supports advance booking, and includes theft detection. It enhances flexibility and security, making it ideal for large venues like airports and multiplexes, with minimal human intervention.

关键词： Space vehicles Urban areas Prototypes Real-time systems Sensors Internet of Things Security Resource management Traffic congestion Radiofrequency identification

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A Developer-Focused Genetic Algorithm for IoT Application Placement in the Computing Continuum

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IEEE Transactions on Services Computing 2025年

作者： Herrera, Juan Luis Moya, Alejandro Berrocal, Javier Murillo, Juan Manuel Navarro, Elena University of Bologna Department of Computer Science Engineering Italy University of Castilla-La Mancha LoUISE Research Group Spain University of Extremadura Department of Computer Science and Communications Engineering Spain

The rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this paper, we present the Genetic Algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability. © 2008-2012 IEEE.

关键词： Computing Continuum Deployment Genetic Algorithm Internet of Things Quality of Service Service Placement

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Interactive Agent Foundation Model: A Meta-Learning Strategy

Interactive Agent Foundation Model: A Meta-Learning Strategy

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Mani Dwivedi Anuj Kr. Dwivedi Department of Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Engineering Galgotias College of Engineering and Technology India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Artificial intelligence (AI) systems are evolving from static, task-specific models to dynamic, agent-based systems that perform well in a variety of scenarios. We present an Interactive Agent Foundation Model using a novel multi-task agent training methodology that includes many pretraining techniques such as language modelling, visual masked autoencoders, and next-action prediction. This framework's adaptability and versatility have been shown in the robotics, gaming AI, and healthcare sectors, yielding results that are pertinent to the situation. Effective multimodal and multitask learning is made possible by utilizing a variety of data sources, such as textual information, gaming data, robotics sequences, and large-scale video datasets. Our method offers a viable way forward for creating adaptable, proactive, multimodal systems.

关键词： Training Adaptation models Visualization Foundation models Soft sensors Medical services Predictive models Multitasking Data models Robots

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Optimizing Heart-Kidney Interaction for Cancer Detection through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwanj Othnman, Zalinda Yahya, Yazrina Nazri, Mohd Zakree Ahmad Goh, Say Leng Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran Iran Faculty of Information Science and Technology Center for Artificial Intelligence Technology Selangor Bangi43600 Malaysia Center for Software Technology and Management Faculty of Information Science and Technology UniversitiKebangsaan Malaysia Selangor Bangi43600 Malaysia Universiti Malaysia Sabah KampusAntarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical & Computer Engineering College ParkMD20742 United States Tehran University Science and Technology Park RayanMiniFab Tehran Iran

Decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization, using analysis of the available *** heart-kidney (HK) model proposed in this paper as a DSS simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA's local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm's effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4926-4939页

作者： Jin, Ming Shi, Guangsi Li, Yuan-Fang Xiong, Bo Zhou, Tian Salim, Flora D. Zhao, Liang Wu, Lingfei Wen, Qingsong Pan, Shirui Griffith University School of Information and Communication Technology Gold Coast Australia Monash University Department of Data Science and AI Melbourne Australia University of Stuttgart International Max Plank Research School for Intelligent Systems Stuttgart Germany Alibaba Group Hangzhou China University of New South Wales School of Computer Science and Engineering Sydney Australia Emory University Department of Computer Science Atlanta United States Anytime.AI New York United States Squirrel Ai Learning Bellevue United States

Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural network is a promising abstraction underlying most time series forecasting models that are based on graph neural networks (GNNs). However, more is needed to know about the underpinnings of this branch of methods. In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs. Our results show that linear spectral-temporal GNNs are universal under mild assumptions, and their expressive power is bounded by our extended first-order Weisfeiler-Leman algorithm on discrete-time dynamic graphs. To make our findings useful in practice on valid instantiations, we discuss related constraints in detail and outline a theoretical blueprint for designing spatial and temporal modules in spectral domains. Building on these insights and to demonstrate how powerful spectral-temporal GNNs are based on our framework, we propose a simple instantiation named Temporal Graph Gegenbauer Convolution (TGGC), which significantly outperforms most existing models with only linear components and shows better model efficiency. Our findings pave the way for devising a broader array of provably expressive GNN-based models for time series, and the code is at https://***/r/TGGC. © 1979-2012 IEEE.

关键词： Graph neural networks

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Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot

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IEEE Robotics and Automation Letters 2025年第6期10卷 6207-6214页

作者： Horyna, Jiří Jung, Roland Weiss, Stephan Ferrante, Eliseo Saska, Martin Czech Technical University in Prague Multi-Robot Systems Group Faculty of Electrical Engineering Technicka 2 Prague Czech Republic University of Klagenfurt Control of Networked Systems Institute of Smart Systems Technologies Klagenfurt Lakeside Park Austria Vrije Universiteit Amsterdam Department of Computer Science Amsterdam Netherlands New York University Abu Dhabi Department of Computer Science United Arab Emirates Dubai Future Labs Dubai United Arab Emirates

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization. © 2016 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Progressive Self-supervised Spatio-temporal Feature Learning Based on Video Sequence Saliency 8

Progressive Self-supervised Spatio-temporal Feature Learning...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Kang, Jinlong Xu, Tao Qu, Boting Wang, Xiang Lian, Xiaoli Guo, Jing Gao, Yuan School of Economics and Management Northwest University Xi’an China Key Laboratory of Changjiang Regulation and Protection of Ministry of Water Resources Changjiang Engineering Group Wuhan China School of Computer Science and Engineering Beihang University Beijing China School of Information Science and Technology Northwest University Xi’an China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

We observe that videos have different levels of frame/clip sequence saliency, and the effective utilization of frame/clip sequence saliency is beneficial to spatio-temporal feature learning. Therefore, we propose a new concept called video sequence saliency (VSS) to measure the degree of difficulty of models in identifying the correct frames/clip orders of videos. Accordingly, we developed a novel method named progressive self-supervised spatio-temporal feature learning based on VSS (PSSFL-VSS). For the pretext task of clip order prediction, the videos are input into networks in descending order by VSS values rather than randomly, as in traditional methods. In addition, we update the VSS value of each video based on clip order prediction results. The effectiveness of our pre-trained models is verified by carrying out the downstream tasks of clip/video retrieval and action recognition, and experimental results show that our method achieves apparent improvements over the state-of-the-art methods. © 2025 SPIE.

关键词： Self-supervised learning

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A high performance passive wireless SAW torque sensor

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IEEE Sensors Journal 2025年第10期25卷 18192-18200页

作者： Gao, Zihang Wen, Yumei Xiao, Qiang Lei, Huaming Wang, Weibiao Fan, Yanping Shanghai Jiao Tong University Shanghai200240 China 26th Institute of China Electronics Technology Group Corporation Chongqing400060 China Shoulder Electronics Ltd Wuxi214124 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

Torque measurement presents challenges particularly in rotating structures, where traditional measurement methods are often limited. Surface Acoustic Wave (SAW) sensors, with their inherent wireless and passive characteristics, possess unique advantages in mechanical measurements of rotating structures. Currently, most of the existing resonant SAW mechanical sensors suffer from issues of short reading distances or poor signal quality, primarily due to the difficulty in achieving high Q factor of SAW resonators. Additionally, the problem of high temperature coefficient generally exists in the field of SAW sensors, which will seriously interfere with the measurement accuracy of the sensors. In this paper, a SAW torque sensor with excellent wireless performance was proposed based on the X-112°Y LiTaO3 substrate due to its large electromechanical coupling coefficient (K2) and favourable temperature characteristics. We investigated and simulated the characteristics of the sensing element including strain sensitivity, temperature coefficient, Q factor and electromechanical coupling coefficient, and designed SAW resonators on X-112°Y LiTaO3 substrates. The experimental results show that the proposed SAW sensor has higher signal-to-noise ratio echo signal quality at the same wireless measurement distance compared with the case traditional high Q factor SAW sensor based on quartz substrate. Moreover, from temperature tests, the proposed SAW sensor has a linear temperature effect, which is beneficial to the compensation of temperature drift. Based on the manufactured SAW sensors, a SAW torque measurement system with wonderful linearity, repeatability and hysteresis was developed. Our work provides a valuable and promising solution for wireless and passive measurement of mechanical quantities. (Figure presented) © 2001-2012 IEEE.

关键词： Q factor measurement

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A Broadband Multipole Method for Accelerated Mutual Coupling Analysis of Large Irregular Arrays Including Rotated Antennas

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IEEE Transactions on Antennas and Propagation 2025年第5期73卷 3133-3145页

作者： Gueuning, Quentin Acedo, Eloy de Lera Brown, Anthony Keith Craeye, Christophe O'Hara, Oscar University of Cambridge Astrophysics Group Cavendish Laboratory United Kingdom University of Cambridge Kavli Institute for Cosmology Cavendish Laboratory United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom University of Manchester United Kingdom Louvain-la-Neuve Belgium

We present a numerical method for the analysis of mutual coupling effects in large, dense and irregular arrays with identical antennas. Building on the Method of Moments (MoM), our technique employs a Macro Basis Function (MBF) approach for rapid direct inversion of the MoM impedance matrix. To expedite the reduced matrix filling, we propose an extension of the Steepest-Descent Multipole expansion which remains numerically stable and efficient across a wide bandwidth. This broadband multipole-based approach is well suited to quasi-planar problems and requires only the pre-computation of each MBF's complex patterns, resulting in low antenna-dependent pre-processing costs. The method also supports arrays with arbitrarily rotated antennas at low additional cost. A simulation of all embedded element patterns of irregular arrays of 256 complex log-periodic antennas completes in just 10 minutes per frequency point on a current laptop, with an additional minute per new layout. © 2025 IEEE.

关键词： Steepest descent method

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Big Data Analytics for Healthcare Resource Allocation

Big Data Analytics for Healthcare Resource Allocation

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sharma, Brijesh Kumar Jayant, Khel Prakash Pujari, Purvi Vikram, Devendra Saraswat, Manish Kathuria, Kajol Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engieering UP Ghaziabad India Dy Patil University Vijay Patil School of Management Department of Management Navi Mumbai India Abes Institute of Technology Computer Science & Engieering UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Dronacharya Group of Institutions Dept Computer Science and Information Technology UP Greater Noida India

ISBN: (纸本)9798331508685

Big Data Analytics for Healthcare Resource Allocation is the new mantra as it integrates vast amounts of complementary data to allocate healthcare resources efficiently. There is an increasing demand for healthcare services, and resources are in limited supply, so doing something as simple as allocating resources can ensure that care quality remains high. The process includes gathering and organizing large datasets from several resources, including electronic health records, patient demographics, service utilization and detailed analysis to generate meaningful findings. Utilizing advanced statistical and analytical techniques, by definition, amongst others, Big Data Analytics can help you decipher the data to show you patterns in resource utilization (how resources are being used), areas where inefficiencies occur which have gone unnoticed long enough that cause them become ripe for incremental year over year cost savings targets or staff reactions if not corrected as then gain media attention likely) some even assembled through different techniques to predict future needs based on past use cases. The advantages of this are manifold. It will help identify high-risk patients, enhance clinical decision-making, and facilitate evidence-based resource allocation. It can also help quantify how well-existing resource distribution strategies work and influence resource planning. Big Data Analytics for Healthcare Resource Allocation: Better Outcome, Cutting Cost and Effective Utilization of Resources This have the potential to revolutionize healthcare resource allocation and improve national healthcare systems' efficiency and effectiveness. © 2025 IEEE.

关键词： Electronic health record

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