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Pervasive Indoor User Identification Leveraging Mobile Single Station Localization

IEEE Internet of Things Journal 2025年第11期12卷 15224-15237页

作者： Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Gui, Guan Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China Institute of Industrial Internet Chongqing401120 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using Information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single station localization (MSL) and MAC de-randomization based on users' spatio-temporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%. © 2014 IEEE.

关键词： Medium access control

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The Use of Both Temperature Field and Heat Fluxes to Identify the Thermal Conductivity and Volumetric Heat Capacity 15th

The Use of Both Temperature Field and Heat Fluxes to Iden...

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Zubov, Vladimir Gorchakov, Andrei Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Institutskii per. 9 Dolgoprudny141701 Russia

ISBN: (纸本)9783031791185

The study of nonlinear problems associated with the process of heat transfer in material is very important for practice. Previously, the authors proposed an effective algorithm for determining the volumetric heat capacity and thermal conductivity of a substance based on the results of experimental observations of the dynamics of the temperature field in an object or the dynamics of heat flows at the boundary of the domain. In this work, a new cost functional is used to determine temperature-dependent thermodynamic parameters, which is a weighted sum of the deviation of temperature in the domain and heat fluxes at the boundary of the domain from the experimental values. The use of the new functional showed a noticeable increase in the efficiency of the proposed algorithm and the reliability of the results obtained. The consideration is carried out on the basis of the first boundary value problem for a one-dimensional non-stationary heat equation. The inverse coefficient problem under consideration is reduced to a variational problem, which is solved by gradient methods based on the application of the Fast Automatic Differentiation technique. The question of the uniqueness of the solution to the inverse problem is investigated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost functions

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Modeling and Optimization of controlled Conveyor Systems Using Intelligent controllers 15th

Modeling and Optimization of Controlled Conveyor Systems U...

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Petrov, Alexey Druzhinina, Olga Masina, Olga Bunin Yelets State University Yelets Russia Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031791185

The construction of generalized mathematical models is proposed that describe the dynamics of a controlled belt conveyor with a variable angle between the horizontal plane and the plane of the belt. The models under consideration are defined using nonlinear nonstationary systems of four differential equations with switching. Switching modes are characterized by a change in the loading and unloading sequence. Statements of optimal control problems are presented under restrictions on the magnitude of control and on the speed of control. Synthesis of neural network controllers for stabilization of the angular position of the conveyor is carried out. controllers are synthesized using such direct propagation neural networks for which reinforcement learning is implemented based on the optimization algorithm of differential evolution. A comparative analysis of the effectiveness of synthesized controllers is carried out. Algorithmic support for modeling controlled conveyor systems is developed using intelligent analysis and global parametric optimization methods. The results of computational experiments are presented. The obtained results can find application in solving problems of optimal stabilization of dynamic systems with switching. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Belt conveyors

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Interaction model estimation-based robotic force-position coordinated optimization for rigid-soft heterogeneous contact tasks

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Biomimetic Intelligence & Robotics 2025年第1期5卷 77-86页

作者： Haochen Zheng Xueqian Zhai Hongmin Wu Jia Pan Zhihao Xu Xuefeng Zhou Institute of Intelligent Manufacturing Guangdong Academy of SciencesGuangdong Key Laboratory of Modern Control TechnologyGuangzhou 510070China School of Rail Transportation Wuyi UniversityJiangmen 529020China Department of Computer Science The University of Hong KongHong Kong 999077China

Inspired by Model Predictive Interaction control(MPIC),this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid-soft heterogeneous contact *** the MPIC,robot dynamics are linearized,and Extended Kalman Filters are used for the online estimation of geometry-aware ***,a geometry-aware Hertz contact model is introduced for the online estimation of contact *** then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization *** validations were designed for two contact modes:“single-point contact”and“continuous contact”,involving materials with four different Young’s moduli and tested in human arm“relaxation-contraction”*** indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee *** method reduces the maximum impact force by 50%and decreases the average force error by 42%.The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid,soft,and deformable objects.

关键词： Heterogeneous contact Interaction model estimation Coordination optimization Model Predictive control

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Motion planning for robotics:A review for sampling-based planners

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Biomimetic Intelligence & Robotics 2025年第1期5卷 15-34页

作者： Liding Zhang Kuanqi Cai Zewei Sun Zhenshan Bing Chaoqun Wang Luis Figueredo Sami Haddadin Alois Knoll School of Computation Information and Technology(CIT)Technical University of MunichMunich 85748Germany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunich 80992Germany School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science University of NottinghamNottingham 315199United Kingdom

Recent advancements in robotics have transformed industries such as manufacturing,logistics,surgery,and planetary exploration.A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length,sweep area,execution time,and energy *** the available algorithms,sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments,explore free space,and offer probabilistic completeness along with other formal *** their widespread application,significant challenges still *** advance future planning algorithms,it is essential to review the current state-of-the-art solutions and their *** this context,this work aims to shed light on these challenges and assess the development and applicability of sampling-based ***,we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various *** findings highlight the strides made in sampling-based methods while underscoring persistent *** work offers an overview of the important ongoing research in robotic motion planning.

关键词： Robotics Motion planning Sampling-based algorithms

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Real-time reconstruction on micro-CT setup 5

Real-time reconstruction on micro-CT setup

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Gilmanov, Marat Bulatov, Konstantin Bugai, Oleg Ingacheva, Anastasia Chukalina, Marina Nikolaev, Dmitrii Arlazarov, Vladimir Smart Engines Service LLC Moscow117312 Russia Institute for Information Transmission Problems RAS Moscow Russia Federal Research Center Computer Science and Control RAS Moscow Russia

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Dose reduction in computed tomography is one of the most urgent questions with a deep research history. One of the rising potentially powerful tools for dose reduction is monitored tomographic reconstruction. This promising concept heavily relies on utilizing real-time reconstruction from currently available data, which is the main topic of the present paper. The main result of achieving a real-time reconstruction on a commercially available micro-CT setup utilizing SmartTomoEngine SDK is highlighted and presented in more detail. The new acquisition order is proposed, allowing for faster convergence of intermediate reconstruction results. An optimization of reconstruction from partial data is proposed, which allowed the production of 385 and 440 reconstructions for standard and proposed acquisition orders correspondingly during a single acquisition of 512 projections, achieving a real-time reconstruction. The results are produced with the use of only a single previous-generation consumer-grade graphical processing unit. The study results demonstrate that with proposed optimizations monitored tomographic reconstruction can be effectively utilized for practical applications using the current generation of existing setups in the micro CT application field. Copyright © 2025 SPIE.

关键词： computerized tomography

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Stability of artificial intelligence methods in computed tomography metal artifacts reduction task 5

Stability of artificial intelligence methods in computed tom...

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Gilmanov, Marat Kirkicha, Andrey Ingacheva, Anastasia Chukalina, Marina Nikolaev, Dmitrii Smart Engines Service LLC Moscow117312 Russia Institute for Information Transmission Problems RAS Moscow Russia Federal Research Center Computer Science and Control RAS Moscow Russia

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Reducing image artifacts in modern computed tomography is one of the most important directions, which also receives a research boost from the rapid development of artificial intelligence and machine learning. The neural network based methods often allow the achievement of the high artifact reduction quality unattainable by classical approaches. Simultaneously, these models tend to be less robust and generalize worse than algorithmic approaches. The output result may degrade severely under the use in cases not represented in learning datasets. In the present work, the robustness and generalization properties of neural network models are studied in the task of metal-like artifact reduction for computed tomography reconstruction. The pipeline for data augmentation is developed for the metal artifacts severity and geometry variations parameters. The pipeline is validated by augmentation of DICDNet model test dataset and evaluating of the corresponding pre-trained DICDNet model. The generated data consisted of 192000 samples for 12×8 combinations of experiment parameters. It was demonstrated that DICDNet is reasonably robust to geometry variations with a maximum deviation of PSNR≈1.5%. At the same time, even small artifact severity growth leads to rapid quality degradation and blurring of studied model output. The degradation reaches ≈22% PSNR deterioration from the baseline algorithmic approach, which is also one of the inputs for the studied model. Copyright © 2025 SPIE.

关键词： computerized tomography

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Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

Shifting Spotlight for Co-supervision: A Simple yet Efficien...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Yang Zhang, Jinxia Zhang, Kaihua Yuan, Yin Huang, Jiale Zhan, Zechao Wang, Xin Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Advanced Ocean Institute of Southeast University China Alibaba Group China

ISBN: (纸本)9798350368741

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS3Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS3Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS3Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness. © 2025 IEEE.

关键词： Camouflaged Object Detection Efficient Network Design Network Co-supervision

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Autonomous picking robot for elongated fruits and vegetables 3

Autonomous picking robot for elongated fruits and vegetables

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3rd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Gao, Yang Shuai, Wei Cui, Guowei Wu, Peichen Gan, Lin Chen, Xiaoping Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Foshan Industrial Technology Research Institute Guangdong Academy of Sciences China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510688179

This paper presents the design and implementation of an autonomous picking robot specifically for harvesting elongated fruits such as loofahs and bitter melons. The robot utilizes a tracked chassis to navigate field terrains, equipped with IMU and GNSS modules for navigation and positioning. Mounted on the chassis is a 6-degree-of-freedom robotic arm designed for picking actions, with a combination of a flexible gripper and electric scissors at its end to enhance picking efficiency and safety. Two RGBD cameras are used for global and local perception, while the YOLO model is employed for fruit recognition and localization. Experimental results demonstrate that the robot can complete a full picking cycle in an average of 20 seconds and temporarily store the harvested fruits in an onboard storage bin. Overall, this picking robot significantly reduces labor costs and improves the efficiency and quality of field operations and fruit harvesting. © The Authors.

关键词： Grippers

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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