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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT

arXiv

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arXiv 2025年

作者： Mei, Zihao Li, Jianhao Zhang, Bolin Wang, Chong Guo, Lijun Li, Guoqi Qian, Jiangbo Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing100045 China

With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high-frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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On Hyper-Stability Theory Based Multivariable Nonlinear Adaptive Control: Experimental Validation on Induction Motors

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IET Electric Power Applications 2025年第1期19卷

作者： Bekhiti, Belkacem Nail, Bachir Tibermacine, Imad Eddine Salim, Ramzi Blida Algeria Faculty of Sciences and Technology University of Ziane Achour Djelfa Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratoire d’Automatique et Analysis des Systems National Polytechnic School Oran Algeria

This paper presents a novel hyperstability-based adaptive control strategy for induction motors, distinguishing itself from conventional model reference adaptive control (MRAC) approaches by integrating enhanced robustness against parametric uncertainties and external disturbances. Unlike traditional adaptive controllers, the proposed Hyper-stable adaptive controller (H-MRAC) ensures improved transient performance and faster convergence rates, validated through both theoretical analysis and experimental verification. Key innovations include the integration of hyper-stability theory into adaptive control design and a comprehensive evaluation of parameter uncertainties, which significantly improves motor performance in variable conditions. Experimental results demonstrate a 42% reduction in integral squared error (ISE), a 37% improvement in integral absolute error (IAE), a 28.7% improvement in integral time absolute error (ITAE) and a 25% faster convergence compared to standard MRAC. A detailed comparison with passivity-based control strategies shows a 26.5% improvement in steady-state performance and 30% faster transient response. Despite these successes, the paper discusses limitations related to computational complexity, real-time implementation challenges, and the impact of sensor noise on control performance. The potential need for DSPs or FPGA-based solutions is also addressed. Finally, the generalisability of the proposed control method across different motor types and power ratings is considered with future directions for broader validation in diverse industrial scenarios. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Model reference adaptive control

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TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules

arXiv

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arXiv 2025年

作者： Zhang, Ruoyu Wang, Lulu He, Yi Pan, Tongling Yu, Zhengtao Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Key Laboratory of Computer Technologies Application Kunming650504 China Hongyun Honghe Group Honghe Cigarette Factory Kunming650202 China

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs;however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs’ inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs’ contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： Information filtering

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Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Jiale Yi, Longhao Gao, Xiaofeng Bhatti, Shahzad Sarwar Yuan, Ting Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering China Emerson University Faculty of Computing and Emerging Technologies Multan Pakistan Shanghai Jiao Tong University Department of Automation China

With the popularization of smart phones, mobile crowdsourcing emerged and gained growing attention in the recent years. Mobile users are now able to conduct complex tasks with the communication between each other. In this paper, we study the task scheduling problem in the mobile crowdsourcing systems based on the spontaneously formed mobile social networks (MSNs). We introduce two crowdsourcing task scheduling problems under this system model, with one problem aiming to minimize the total cost of some crowdsourcing tasks and the other focusing on minimizing the final completion time of the tasks belonging to the same project. We introduce a unified framework to solve the problems and propose two approximation algorithms for these two problems in the offline versions respectively and prove Their approximation ratios accordingly. Based on the two algorithms, we further design two online algorithms to deal with the workers' dynamism and also analyze their competitive ratios. Finally, we verify the effectiveness and efficiency of the proposed methods through numerical experiments on real and synthetic datasets. © 2002-2012 IEEE.

关键词： Approximation algorithms

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A Comprehensive Methodology of Field-Oriented Control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a Controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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LCS4: Linear Complexity State-Space Transformers With Multiscale Overlapping Scans for Efficient Semantic Segmentation

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Yueying Qing, Yuhao Fang, Zhijun Zhao, Xudong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai200051 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

With the advancement of intelligent systems, semantic segmentation - an essential aspect of intelligent perception - is garnering significant research interest. However, convolutional neural network (CNN)-based methods exhibit limitations in processing long-range features. Although Transformer-based models capably handle global information, their application in real-time semantic segmentation is computationally challenging due to their quadratic complexity. To address these issues, we introduce a state-space model-based Transformer architecture, LCS4, for semantic segmentation. LCS4 integrates the strengths of the Mamba system with the Transformer architecture, enabling the modeling of remote contextual information with linear complexity. Additionally, the newly proposed multiscale spatial overlap scanning module selectively processes spatial features from multiple directions, enhancing the construction of robust hierarchical features that improve both spatial and semantic continuity. Extensive experiments conducted across various datasets confirm the efficacy of LCS4 in semantic segmentation tasks, demonstrating lower parameter counts and higher accuracy. Specifically, on the Cityscapes dataset, LCS4 achieves a mean intersection over union (mIOU) of 79.1 with only 2.92 million parameters and 11.8 GFLOPS. In comparison with WaSR on the MODD dataset, LCS4 demonstrates competitive performance with merely 6% of the parameters and operates approximately six times faster. © 1963-2012 IEEE.

关键词： Semantic Segmentation

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Dynamic behaviors and digital circuit implementation of a Rulkov neuron with a non-polynomial memristor synaptic weight

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The European Physical Journal Special Topics 2025年 1-18页

作者： Tamba, Victor Kamdoum Tofou, Junior Tchiaze Pham, Viet-Thanh Grassi, Giuseppe Department of Telecommunication and Network Engineering IUT-Fotso Victor of Bandjoun University of Dschang Bandjoun Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam Department of Engineering for Innovation University of Salento Lecce Italy

Memristors have proven since their discovery to play a very important role as artificial synapses in the implementation of artificial neural networks. Just as biological synapses, memristors can significantly influence the behaviors of the neural network. A non-polynomial memristor is used in this work to investigate the memristive synaptic weight effect on the dynamics of a simplified Rulkov neuron. The model is first introduced from where theoretical analyses show the system’s ability to develop regular and irregular behaviors. Analysis methods such as two parameter charts, bifurcation diagrams and maximum Lyapunov exponents (MLE) are then used to study the dynamics of the proposed Rulkov model. The numerical results show that the memristive synaptic weight Rulkov neuron model can exhibit self-excited and hidden firings. Furthermore, the system can undergo interesting offset boosting dynamics and antimonotonicity features (i.e., bubble bifurcations) for some system parameters. To corroborate the computational findings, the proposed model is implemented on an Arduino Due microcontroller board, from where the experimental results are seen to be in agreement with the obtained numerical results.

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