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MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation

Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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E-RL:Vehicle Driving Strategy Learning with Environmental Reinforcement Learning Framework 3

E-RL:Vehicle Driving Strategy Learning with Environmental Re...

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3rd International Conference on Electronics and information technology, EIT 2024

作者： Shuai, Yanshu Zheng, Jiaoling Wang, Haoquan Zhang, Sihui Liu, Yiming Guo, Peng Zeng, Yu Chengdu University of Information Technology School of Software Engineering Sichuan Chengdu China Sichuan Yifang Intelligent Technology Co. Sichuan Chengdu China

ISBN: (纸本)9798350369151

To address urban traffic congestion simulation after Traffic Management Initiatives (TMI) implementation, a method combining deep reinforcement learning with environmental optimization is proposed, optimizing the environmental strategy through a fixed agent policy. To tackle the challenges posed by road network topology changes post-TMI, the method designs an environmental Markov reinforcement learning framework and introduces graph neural networks to dynamically calculate road resistance in urban areas. Combined with the Dijkstra algorithm, the vehicle routing strategy is determined. This strategy is then applied to the road network after TMI to predict road resistance post-TMI. Experiments validate that this method achieves good congestion replication in the original road network environment and effectively predicts congestion after TMI implementation. © 2024 IEEE.

关键词： Traffic congestion

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Design of a Compact UWB BPF with an Embedded Folded-stub Loaded Square Loop Resonator

Design of a Compact UWB BPF with an Embedded Folded-stub Loa...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Yang, Lingling Cheng, Yongzhi Liu, Haihong Wuhan Vocational College of Software and Engineering Wuhan430074 China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

ISBN: (纸本)9798350375909

A new compact ultra-wideband bandpass (UWB) filter based on an Embedded Folded-Stub loaded Square Loop Resonator (EFS-SLR) is presented in this paper. The proposed unit cell is a Square Loop Resonator (SLR) embedded two quarter-wavelength folded-stubs which not only can achieve smaller size, but also make the filter have better pass-band characteristics because of the novel multi-mode resonator. Analysis and design procedure of the proposed resonator are presented using even-mode equivalent circuits since the symmetrical resonator structure. Two defected ground structures (DGS) with complementary split-ring resonator (CSRR) are introduced under the two feeding lines for getting sharp-rejection in stop-band. Design, simulation and optimization have been done in High-Frequency Structure Simulator (HFSS) software v.2020. Finally, the proposed BPF is fabricated using Roger 5880 substrate with a relative dielectric constant of 2.2 occupies a minimal size of 0.472λg × 0.472λg, where λg is the guided wavelength at the center frequency of 12 GHz. The measurement result and simulation result have a good agreement with each other. The test result shows that 2.5 dB fractional bandwidth is 50%. Because of four transmission zeros generated at each side of the passband, the proposed UWB filter has a sharp roll-off ratio of 41.2 and 28.8 dB/GHz, respectively, at the both sides of the pass-band. The novel filter band of 9-15 GHz in X and Ku-band frequency range can be used in satellite communication, 5G applications, radar technology and other ultra-wideband systems working in the bands. In addition, the topology adopts simple PCB manufacture process with lower cost, which has a high market competitiveness. © 2024 IEEE.

关键词： Defected ground structures

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Developing and Training a Mathematical Model for Optimizing a Given Interior Space of a Supermarket

Developing and Training a Mathematical Model for Optimizing ...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Alahakon, Shalitha Siriwardana, Tharindu Udupihilla, Deshan Wickramasinghe, Tharukshi Rajapaksha, Samantha Sri Lanka Institute of Information Technology Dept. Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers. © 2023 IEEE.

关键词： Python

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Personalized and Interactive Demented Care and Learning Mate with a Virtual System Using Emotion Recognition 5

Personalized and Interactive Demented Care and Learning Mate...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kaushan, Nipuna Fernando, Devshan Gayashan, Chamod Udayakantha, Sachitha De Silva, Hansi Perera, Sanduni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study investigates the development and application of a personalized, interactive virtual system to enhance dementia care and protect people with dementia. A significant challenge in providing care for individuals with dementia is the propensity to wander and become lost, which can lead to anxiety and distress for both the patient and the carers as well as safety concerns. The system combines cutting-edge emotion recognition technology and healthcare IOT technology to effectively identify and provide real-time solutions in response to users' emotional states. This initiative combines dementia care, educational support, and patient security to establish a comprehensive strategy for maintaining well-being and managing the disease in dementia patients. This project has experimentally designed a security system to enhance the safety of dementia patients, a Health Checker system aimed at maintaining their health stability, and a virtual assistant that offers guidance by identifying real-time emotions and providing activities to improve cognitive function and promote relaxation Given a vast amount of patient data, the AI model has been able to identify 98% of patients' illnesses early. security software successfully detected patients with 99% accuracy using GPS technology and machine learning algorithms. © 2023 IEEE.

关键词： Emotion Recognition

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Supervised Feature Selection Based on Sparse Representation and Mutual information 5

Supervised Feature Selection Based on Sparse Representation ...

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5th IEEE International Conference on Civil Aviation Safety and information technology, ICCASIT 2023

作者： Yao, Bingying Li, Chao Chen, Yuxia Software Engineering Institute of Guangzhou Software Engineering Department Guangdong Guangzhou510000 China Guangdong Industry Polytechnic College of Information Technology Foshan510300 China

ISBN: (纸本)9798350310603

Sparse representation can evaluate the importance of each feature from the overall structure of the data, and mutual information can evaluate the redundancy between features, utilizing the importance and the redundancy propose a supervised feature selection algorithm based on sparse representation and mutual information. Firstly, the algorithm weights 0-1 shows the relationship between the samples;then get low-dimensional embedding space which keeping the structure between sample classification by solving eigenvalue problem;thirdly, using sparse representation to evaluate the contribution degrees of each feature to keep the classification structure of inter-sample, and thus can select features which can maintain preferably the classification structure between the samples;finally, utilizing mutual information to reduce redundancy of the before feature selection results, in order to obtain the final results of the feature selection. In the results of clustering experiment in four different data sets, the method has a better feature selection effect, can achieve better results of features selection at a lower dimension ,meantime, it significantly improves the classification results. © 2023 IEEE.

关键词： Classification (of information)

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ML-Driven HR System: Candidate Assessment for Enhanced Recruitment Outcomes 3

ML-Driven HR System: Candidate Assessment for Enhanced Recru...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Dixon, G.C. Srithanujan, S. Ranaweera, H.D.T.N. Ariyarathne, R.M.P.M. Weerasinghe, K.L. Gamage, N. Department of Information Technology SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer System engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer Science and Software Engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka

ISBN: (纸本)9798331532420

This study developed and implemented a machine learning-driven HR system designed to enhance recruitment outcomes, particularly in online interviews. Challenges such as candidate impersonation, interview question memorization, and deepfake manipulations were addressed. The system incorporated several critical processes utilizing advanced algorithms: pre-interview shortlisting employed Support Vector Machines (SVM) achieving 92% accuracy;cognitive skills assessment utilized adaptive testing methods powered by fine-tuned Large Language Models (LLMs) with 88% accuracy;emotional intelligence evaluation was conducted using deep learning-based emotion detection algorithms attaining 85% accuracy;face verification leveraged Convolutional Neural Networks (CNNs) for identity and deepfake detection with 90% accuracy;online video interviews were analyzed through integrated audio-visual processing techniques;language proficiency testing employed Natural Language Processing (NLP) models reaching 87% accuracy;analysis of thinking patterns was performed using sentiment analysis algorithms with 83% accuracy;and candidate grading was facilitated by ensemble learning methods ensuring consistent and reliable evaluations. The results demonstrated the system’s effectiveness in verifying candidate authenticity and assessing competence, providing a robust solution for modern recruitment challenges. © 2024 IEEE.

关键词： Support vector machines

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Identification of Diabetic Related Eye Diseases Using Deep Learning 5

Identification of Diabetic Related Eye Diseases Using Deep L...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijesinghe, K.H. Dilshan, U.K.T. Dilshan, K.B.G.L. Tharupathi, M.A.U. Kasthuriarachchi, Sanvitha Rajapaksha, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Visual disability seems to be increasing and widespread worldwide. Current diagnostics require manual expertise for diagnosis. The advancement of Artificial Intelligence research for medical applications has been in focus in recent years. Fundus images and oct images are valuable sources of information to help ophthalmologists diagnose vision disorders or eye diseases. Early detection can improve the chances of recovery and prevent blindness. The application of an intelligent computer-based approach to classifying different eye disorders is extremely beneficial for both diagnosis and prevention. The proposed expert system for diagnosing diabetic macular edema, diabetic retinopathy, cataracts, and glaucoma diseases is discussed in this research study. It is based on deep learning image processing techniques. For the detection of eye diseases, software used retinal and OTC images. This study presents Yolo, UNET, customized CNN, and Auto ML model-based system for detecting eye diseases. For the tasks of disease identification and grading, the suggested methods each obtained more than 87% accuracy. The primary objective of this study is to achieve automated identification of eye disease images. This is accomplished through the implementation of a deep learning model alongside advanced image processing techniques, enabling the automatic classification of eyes into either a healthy or diseased category. © 2023 IEEE.

关键词： Diagnosis

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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