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An Overview of LoRa Localization Technologies

computers, Materials & Continua 2025年第2期82卷 1645-1680页

作者： Huajiang Ruan Panjun Sun Yuanyuan Dong Hamid Tahaei Zhaoxi Fang Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Science and Engineering School of Mechanical and Electrical EngineeringShaoxing UniversityShaoxing312000China

Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.

关键词： LoRa localization IoT TDoA RSSI application hybrid models

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LoRA-MedSAM: Efficient Medical Image Segmentation 5th

LoRA-MedSAM: Efficient Medical Image Segmentation

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Hu, Jiamin Xu, Xuwei Zou, Zhenmin Department of Mechanical and Aerospace Engineering University of Manchester Manchester United Kingdom School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9789819638628

Medical image segmentation becomes increasingly important for identifying and delineating anatomical structures, diseases, and abnormalities within medical images. However, existing large pre-trained foundation models for medical image segmentation, such as MedSAM, are often limited by their substantial computing resource requirements when fine-tuned for specific tasks. This high computational burden hinders their deployment in real-world clinical scenarios where rapid model adaptation is crucial yet computing resources are typically constrained. To address the challenge, in this paper, we introduce LoRA-MedSAM, a highly efficient method for fine-tuning large pre-trained models on medical image segmentation tasks. In particular, we leverage the Low-Rank Adaptation technique to significantly expedite the fine-tuning process of MedSAM model while maintaining high performance. Extensive experiments on three widely adopted medical image segmentation datasets have demonstrated both the effectiveness and efficiency of our method. Specifically, LoRA-MedSAM drastically reduces the fine-tuning time by 22.7%, 14.8% and 29.4% while outperforming the vanilla MedSAM by 1.1%, 5.5% and 0.5% higher Dice Similarity Coefficient (DSC) on the AMOS, BTCV and MultiTalent datasets, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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A robust image encryption method based on block diagonal chaotic architecture with CNN performance evaluation metric

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Multimedia Tools and Applications 2025年 1-35页

作者： Mamandi, Vida Ardalani, Nahid Ghalamkari, Behbod Department of Electrical Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Mechanical Electrical and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In this paper, a new block diagonal chaotic model (BDC) is investigated due to higher necessity of advanced secure data transmission method in wireless medium and considerable limitation on computational storage space. In this model, the image sparse coding using BDC matrix in noisy and Rayleigh fading channels is studied. The structured BDC model is designed with two schemes, including chaotic pseudo inverse matrix (CPI-m) and block diagonal chaotic structured matrix (BDC-m) using two chaotic maps, in which the initial values for generating nonlinear chaotic sequences are used as private key at the receiver side. The simulation results show that the retrieved data measured based on CPI and BDC schemes outperforms the chaotic compressive sensing (CCS) method over AWGN and Rayleigh fading channels by 6% and 22.7%, respectively. Also, the sensitivity of recovered BDC-based image increases to 10(−32) compared to that of 10(−16) for conventional CCS based image. Moreover, the encryption key space increases from 10(37) for standard CCS-based measurements to β×10111 for measured data based on the BDC model, which guarantees that the competitors access no information. Considering datasets from iterative reconstruction process, the image features are extracted using convolutional neural network (CNN) model by ResNet-18 and GoogleNet architectures. The improved performance of data based on CPI and BDC model is validated by retrieved images accuracy over the classifier support vector machine (SVM). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fading channels

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

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computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks

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IEEE Transactions on Automatic Control 2025年第5期70卷 2843-2858页

作者： Wang, Xuan Cortés, Jorge George Mason University Department of Electrical and Computer Engineering FairfaxVA22030 United States University of California San Diego Department of Mechanical and Aerospace Engineering La Jolla CA92093 United States

This article studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models. Identifying the system parameters directly leads to a large number of variables and a highly nonconvex objective function. Instead, our approach introduces a novel reformulation that incorporates biological organizational features and turns the identification problem into a scalar variable optimization of a discontinuous, nonconvex objective function. We prove that the minimizer of the objective function is unique and establish that the solution of the optimization problem leads to the identification of all the desired system parameters. These results are the basis to introduce an algorithm to find the optimizer by searching the different regions corresponding to the domain of definition of the objective function. To deal with measurement noise in sampled data, we propose a modification of the original algorithm whose identification error is linearly bounded by the magnitude of the measurement noise. We demonstrate the effectiveness of the proposed algorithms through simulations on synthetic and experimental data. © 1963-2012 IEEE.

关键词： Velocity measurement

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Classifying chest x-rays for COVID-19 through transfer learning: a systematic review

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Multimedia Tools and Applications 2025年第2期84卷 689-748页

作者： Mallick, Devanshi Singh, Arshdeep Ng, Eddie Yin-Kwee Arora, Vinay Thapar Institute of Engineering & Technology Punjab Patiala India Wipro Limited Gurugram India School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore Computer Science Engineering Department Thapar Institute of Engineering & Technology Punjab Patiala India

This study makes a comprehensive assessment of the predominant Transfer Learning (TL) techniques employed for the classification of COVID-19 cases in Chest X-rays (CXR) images. The methodologies have been selected on the basis of their merits and demerits, suitability, and possible impact on the development of the region being studied. The study examines the various methods of TL employed in the classification of COVID-19 cases with the objective to gain a deeper understanding about all the aspects of these methodologies. It can be of great significance for the researchers and medical professionals in making well-informed decisions about the implementation of these techniques to improve the precision and effectiveness of COVID-19 diagnosis. The practical consequences of these techniques help in early identification of such cases for having a suitable intervention. As many as 48 studies conducted during the period 2020–2023 have been included in the current research work for having an assessment about the problem under investigation. The study has specifically focused on transfer learning-based models utilized for the identification of COVID-19 through CXR pictures. It highlights the challenges posed by dataset dynamics, methodological variations, and performance metrics of different models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Space systems safety and mission assurance case study-based graduate education

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Journal of Space Safety engineering 2025年第1期12卷 12-16页

作者： Bosch, Liz Babiceanu, Radu Department of Aerospace Engineering Embry-Riddle Aeronautical University United States Department of Electrical Engineering and Computer Science Western Michigan University United States

There is a significant number of discussions lately, at both government agencies and private industry, about sending crewed missions further into space. Sending astronauts back to the Moon and, for the first time, to Mars seems to make space enthusiasts around the world excited given the noteworthy tests and preparations going on in the last few years. The decision to launch crewed missions into space depends primarily on three aspects: technology, budget, and mission risk. Even when the first two aspects are addressed, the third question still remains: is it safe enough? Generally, Safety and Mission Assurance (S&MA) for space systems is taught in space systems programs from an operations standpoint. There are very few to no courses across the USA that address space S&MA from the design engineering perspective. There is also limited teaching of ethics-based safety culture in the engineering programs offered across the country. The resultant gap between graduates’ knowledge of space S&MA and the needed skills to conduct design engineering of space systems is, at the moment, mostly filled by the space agencies and private space industry. The graduate course framework presented in this paper is built on a student-centered approach through customized case-study experiences that promotes understanding and motivation, which are significant aspects of making risk-based decisions with considerations to safeguarding human lives. By exploring the root causes of human space flight close calls, incidents, and mishaps, students can envision themselves in the space operational environment and can become aware of how design engineering, safety culture, and ethics act through a combined feedforward and feedback mechanism to ensure reliable and safe operations. Then, connecting back to the theoretical course material creates the student's understanding and motivation once they act as decision-makers after graduation. The proposed case study-based course framework also promotes crit

关键词： Case study-based instruction Design engineering of space systems Graduate engineering education Human spaceflight Safety and mission assurance

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic Systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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