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Optimizing Cover Mapping in Coastal Areas Using Swin Transformer-Based Multi-Sensor Remote Sensing Satellite Data Fusion

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Peng, Min Huang, Shiqi Khan, Asad Barrios, Mauricio Barrios Madrakhimovich, Khudoynazarov Egambergan Djumaniyazova, Mukhayya Xusinovna Bhatti, Mughair Aslam Telba, Ahmad A. School of Information Technology & Engineering Guangzhou College of Commerce Guangzhou511363 China Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Mamun University non-state educational institution Uzbeskistan Uzbekistan Pedagogy and Psychology Department Urganch State University Uzbekistan Universidad de la Costa Department of computer science and electronics Colombia Department of Electrical Engineering College of Engineering King Saud University P.O. Box 800 Riyadh11421 Saudi Arabia SZABIST University Karachi Pakistan

The vital ecosystem services of coastal areas support biodiversity while storing carbon, protecting coasts, and conserving habitat for coastal species. Accurate mapping and monitoring of coastal ecosystems are essential for conservation and sustainable management, as these ecosystems face growing threats from human activities, sea-level rise, and climate change. A supervised Swin Transformer-based deep learning method using different hyperspectral datasets serves as the proposed algorithm for coastal cover mapping. The data requires pre-processing procedures that combine feature learning with normalization and dimensionality reduction to improve both spectral and spatial feature extraction. The Swin Transformer model extracts hierarchical features through its shifted window attention mechanisms, which combine local and global information. Through spectral-spatial fusion, the model utilizes the specific characteristics of each data source to enhance feature representation, enabling better discrimination of coastal area, ship detection, and large-scale coastal mapping. The integration of high-resolution spatial data with broader spectral information through multi-source data methods supports robust classification and object detection. The algorithm achieves 92.4% overall classification accuracy through cross-validation and hyperparameter optimization while minimizing overfitting. It specifically enhances coastal area identification (>91%) and ship object detection (>90%). The analysis demonstrates that combining deep learning methods with diverse remote sensing data sources enables effective and precise mapping of coastal ecosystems. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Dimensionality reduction

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Harmonic-Free AC/DC/AC Structure with Innovative Modulation Technique for Asymmetrical Cascade H-Bridge Multi-Level Inverter 4

Harmonic-Free AC/DC/AC Structure with Innovative Modulation ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Karania, Nabil Alali, Mohamad Alaaeddin Gennaro, Stefano Di Barbot, Jeannpierre University of l'Aquila Computer Science and Mathematics Department of Information Engineering L'Aquila Italy QUARTZ Laboratory EA7393 ENSEA 6 Avenue du Ponceau Cergy-Pontoise95014 France LS2N-UMR 6004-CNRS Centrale Nantes 1 Rue de la Noë Nantes44300 France

ISBN: (纸本)9798350306262

This paper presents an advanced solution for AC/DC/AC converter structure using a novel modulation approach for Asymmetrical Cascade H-Bridge Multi-Level Inverter (ACHB-MLI). This solution improves the structure performance and power quality of the output stepping voltage to fulfill the smart industrial grid requirements. Besides, a harmonic-free structure is employed to ensure integration with the grid, using conventional rectifiers combined with a shunt active *** novel approach presents a combination of two modulation techniques: Staircase Modulation Technique (SMT) and Selective Harmonics Elimination method (SHE). This combined approach (SMT-SHE) achieves an output stepping voltage closer to the sinusoidal waveform and free of predominant harmonics by determining the appropriate SHE switching ***-SHE approach will minimize the THD ratio, eliminate specific harmonic ranks, and allow an output voltage amplitude variation according to load specifications. Moreover, switching angles' limitations are proposed to prevent the appearance of undesired harmonics (zeros, even), while minimum number of H bridge modules with a lower rating of the capacitors is ***, the proposed AC/DC/AC converter is validated on an industrial network for supplying a specific load and tested within a MATlaB environment. © 2024 IEEE.

关键词： Power quality

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Development and Characterization of Zinc Dry Electrodes for Wearable Electrophysiology

Development and Characterization of Zinc Dry Electrodes for ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Cassia Rizq Alessandro D’Amico Aidan Truel Joelle Faybishenko Min Suk Lee Jeong-Hoon Kim Gert Cauwenberghs Virginia R. de Sa Department of Bioengineering University of California San Diego La Jolla USA Department of Cognitive Science University of California San Diego La Jolla USA Halıcıoğlu Data Science Institute University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Zinc dry electrodes were fabricated and investigated for wearable electrophysiology recording. Results from electrochemical impedance spectroscopy and electromyography functionality testing show that zinc electrodes are suitable for use in electrophysiology. Two electrode configurations were tested: a standard disc and a custom tripolar concentric ring configuration. However, no functional benefit was observed with the tripolar concentric ring electrodes as compared to the disc electrodes.

关键词： Electrodes Electromyography Recording engineering in medicine and biology Zinc Standards Testing Electrochemical impedance spectroscopy

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MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation

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computers in Biology and Medicine 2025年 185卷 109469-109469页

作者： Prabhu, Swathi Prasad, Keerthana Hoang, Thuong Lu, Xuequan Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Manipal School of Information Sciences Manipal Academy of Higher Education Karnataka Manipal576104 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University GeelongVICVIC 3220 Australia Department of Computer Science and IT La Trobe University MelbourneVICVIC 3086 Australia

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization. However, the existing literature has predominantly concentrated on identifying cellular irregularities with high magnification images and considering specific organs of squamous cell carcinoma origin. In contrast, relatively little attention has been given to recognizing structural abnormalities observable at low magnification images. In this paper, we consider squamous cell carcinoma histopathological images across different organs of origin captured at low magnification and these images are gathered from various centers to develop a robust model. We propose a novel deep neural network model (MultiSCCHisto-Net) that can detect squamous cell carcinoma of any organ irrespective of organ of origin. In addition, deep neural networks used for histopathological image analysis typically have many parameters, making them computationally expensive. To address this research gap, we incorporate knowledge distillation, which compresses knowledge from a complex teacher model (MultiSCCHisto-Net) into a smaller student model (MultiSCCHisto-Net-KD) while preserving performance and enhancing the generalization of the student model by learning from the teacher's intermediate layer features. Moreover, an explainable deep learning technique called gradient-weighted class activation mapping is incorporated to highlight the image areas that help to classify the sample into particular classes. This explainability significantly enhances our confidence in the proposed model outcomes. We evaluate the model's robustness using private multi-centric and publicly available datasets. Our results show that accur

关键词： Teaching

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Architecture Decisions in AI-based Systems Development: An Empirical Study

Architecture Decisions in AI-based Systems Development: An E...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Beiqi Zhang Tianyang Liu Peng Liang Chong Wang Mojtaba Shahin Jiaxin Yu School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China Department of Computer Science and Engineering University of California San Diego La Jolla USA School of Computing Technologies RMIT University Melbourne Australia

Artificial Intelligence (AI) technologies have been developed rapidly, and AI-based systems have been widely used in various application domains with opportunities and challenges. However, little is known about the architecture decisions made in AI-based systems development, which has a substantial impact on the success and sustainability of these systems. To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub. More specifically, we searched on SO with six sets of keywords and explored 32 AI-based projects on GitHub, and finally we collected 174 posts and 128 GitHub issues related to architecture decisions. The results show that in AI-based systems development (1) architecture decisions are expressed in six linguistic patterns, among which Solution Proposal and Information Giving are most frequently used, (2) Technology Decision, Component Decision, and Data Decision are the main types of architecture decisions made, (3) Game is the most common application domain among the eighteen application domains identified, (4) the dominant quality attribute considered in architecture decision-making is Performance, and (5) the main limitations and challenges encountered by practitioners in making architecture decisions are Design Issues and Data Issues. Our results suggest that the limitations and challenges when making architecture decisions in AI-based systems development are highly specific to the characteristics of AI-based systems and are mainly of technical nature, which need to be properly confronted.

关键词： Decision making computer architecture Games Linguistics Software systems Proposals Artificial intelligence

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SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation

arXiv

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arXiv 2024年

作者： Tan, Shengbo Zhang, Zeyu Cai, Ying Ergu, Daji Wu, Lin Hu, Binbin Yu, Pengzhang Zhao, Yang Key Laboratory of Electronic Information Engineering College of Electronic and Information Southwest Minzu University Chengdu610225 China College of Engineering Computing and Cybernetics The Australian National University CanberraACT2601 Australia Hunan University of Technology Zhuzhou410072 China Department of Computer Science and Information Technology La Trobe University BundooraVIC3086 Australia

Medical imaging segmentation plays a significant role in the automatic recognition and analysis of lesions. State-of-the-art methods, particularly those utilizing transformers, have been prominently adopted in 3D semantic segmentation due to their superior performance in scalability and generalizability. However, plain vision transformers encounter challenges due to their neglect of local features and their high computational complexity. To address these challenges, we introduce three key contributions: Firstly, we proposed SegStitch, an innovative architecture that integrates transformers with denoising ODE blocks. Instead of taking whole 3D volumes as inputs, we adapt axial patches and customize patch-wise queries to ensure semantic consistency. Additionally, we conducted extensive experiments on the BTCV and ACDC datasets, achieving improvements up to 11.48% and 6.71% respectively in mDSC, compared to state-of-the-art methods. lastly, our proposed method demonstrates outstanding efficiency, reducing the number of parameters by 36.7% and the number of FLOPS by 10.7% compared to UNETR. This advancement holds promising potential for adapting our method to real-world clinical practice. The code will be available at https://***/goblin327/SegStitch. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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A Classification of Cross-layer Optimization Approaches in LoRaWAN for Internet of Things

A Classification of Cross-Layer Optimization Approaches in L...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Carl Christian Chaguile Melchizedek Alipio Miroslav Bures Department of Electronics and Computer Engineering De La Salle University 2401 Taft Avenue Malate Manila Philippines Department of Computer Science FEE Czech Technical University in Prague Karlovo namesti 13 Prague 2 Czechia

The Internet of Things (IoT) uses Low-Power Wide Area Networks (LPWAN) for applications that require long-range, energy-efficient, and low-cost end devices. LoRaWAN is one of the most popular LPWAN technologies because of its key features and openness making it highly suitable for IoT. Despite its exceptional features, some challenges faced by this technology are optimizing the protocol used for scheduling, low data rate, and duty cycle restrictions. One possible way to address these challenges is by using cross-layer optimization. This optimization technique violates the restrictions of the traditional OSI protocol stack giving freedom to its protocol layers. However, there is currently no summary of cross-layer methods implemented in LoRaWAN. This paper presents a classification of state-of-the-art cross-layer approaches that were used in optimizing the LoRaWAN technology in IoT. The cross-layer techniques were classified based on the merging of adjacent layers, direct communication between layers, and completely new abstractions. In addition, this paper identified the issues and challenges featured in these state-of-the-art cross-layer approaches. Finally, this paper serves as an overview of the performance of cross-layer optimization in LoRaWAN technology for IoT applications.

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UMS: Live Migration of Containerized Services across Autonomous Computing Systems

UMS: Live Migration of Containerized Services across Autonom...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Thanawat Chanikaphon Mohsen Amini Salehi HPCC Lab School of Computing and Informatics University of Louisiana at Lafayette LA USA Computer Science and Engineering Department HPCC Lab University of North Texas

Containerized services deployed within various computing systems, such as edge and cloud, desire live migration support to enable user mobility, elasticity, and load balancing. To enable such a ubiquitous and efficient service migration, a live migration solution needs to handle circumstances where users have various authority levels (full control, limited control, or no control) over the underlying computing systems. Supporting the live migration at these levels serves as the cornerstone of interoperability, and can unlock several use cases across various forms of distributed systems. As such, in this study, we develop a ubiquitous migration solution (called UMS) that, for a given containerized service, can automatically identify the feasible migration approach, and then seamlessly perform the migration across autonomous computing systems. UMS does not interfere with the way the orchestrator handles containers and can coordinate the migration without the orchestrator involvement. Moreover, UMS is orchestrator-agnostic, i.e., it can be plugged into any underlying orchestrator platform. UMS is equipped with novel methods that can coordinate and perform the live migration at the orchestrator, container, and service levels. Experimental results show that for single-process containers, the service-level approach, and for multi-process containers with small $(< 128 \mathbf{MiB})$ memory footprint, the container-level migration approach lead to the lowest migration overhead and service downtime. To demonstrate the potential of UMS in realizing interoperability and multi-cloud scenarios, we examined it to perform live service migration across heterogeneous orchestrators, and between Microsoft Azure and Google Cloud.

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Effect of Underlap with Fixed Gate Length: GaN-Based Double-Gate MOSFETs 2

Effect of Underlap with Fixed Gate Length: GaN-Based Double-...

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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Hasan, Md. Rokib Islam, Md. Rabiul Bhuiyan, Tanjim Masroor Nibir, Muhib Ashraf Hasan, Md. Emran Hossain, Tanvir American International University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Deakin University Dept. Electrical and Renewable Engineering Melbourne Australia City University Dept of Computer Science and Engineering Dhaka Bangladesh La Trobe University School of Engineering and Mathematical Science Melbourne Australia

ISBN: (纸本)9781665415767

The effect of gate length 8 nm with underlap of double-gate MOSFET has been designed for VLSI Technology. The evaluation process was followed by NEGF (non-equilibrium Green's function) formalism using SILVACO ATlaS followed to ITRS-2013. The investigations on the threshold voltage, Subthreshold Slope, ION, I_{OFF}, I_{ON}/I_{OFF}, DIBL, and switching characteristics of the electric field have been done with the simulation results. In the simulation, adopting symmetrical distances from the source to gate and gate to drain (S-G and G-D) by fixing the gate length identical are cited as underlap. Here, the observation has been done for LUN =(0 to 4 nm) underlap length. GaN and HfO2 have been chosen as channel material and dielectric material, respectively. The Proposed device structure indicates that GaN-based DG-MOSFETs for L_{G}=S nm with various underlap lengths is a promising candidate for the aspect of modern VLSI applications. © 2021 IEEE.

关键词： MOSFET devices

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Phenotype Based Surrogate-Assisted Multi-objective Genetic Programming with Brood Recombination for Dynamic Flexible Job Shop Scheduling

Phenotype Based Surrogate-Assisted Multi-objective Genetic P...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Fangfang Zhang Yi Mei Su Nguyen Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington New Zealand Centre for Data Analytics and Cognition La Trobe University Melbourne Victoria Australia

ISBN: (纸本)9781665487696

Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem with a large number of real-world applications such as component production in manufacturing. Genetic programming (GP), as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DFJSS. Brood recombination has been shown its effectiveness to improve the performance of GP by generating more offspring and preselecting only promising individuals into the next generation. However, evaluating more individuals requires more computational cost. Phenotype based surrogate models have been successfully used with GP to speed up the evaluation in single-objective dynamic job shop scheduling. However, its effectiveness on multi-objective dynamic job shop scheduling is unknown. To fill this gap, this paper proposes a novel surrogate-assisted multi-objective GP based on the phenotype of GP individuals for DFJSS. Specifically, we first use phenotypic vector to represent the behaviour of GP individuals in DFJSS. Second, K-nearest neighbour based surrogates are built according to the phenotypic characterisations and multiple fitness values of the evaluated individuals. last, the built surrogate models are used to predict the fitness of newly generated offspring in GP with brood recombination. The results show that with the same training time, the proposed algorithm can achieve significantly better scheduling heuristics than the compared algorithm. The analyses of population diversity, feature importance, and the number of non-dominated individuals have also shown the effectiveness of the proposed algorithm in different aspects.

关键词： Training Job shop scheduling Processor scheduling Heuristic algorithms Sociology Genetic programming Production

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