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A High-Gain DC-DC Boost Converter for Solar Microgrid Systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

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Monitoring of Fire-Affected Buildings and Air-Quality Assessment: A Remote-Sensing Study Using Ground-Based interferometric Radar and Google-Earth-Engine

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Rangeland Ecology and Management 2025年 101卷 28-42页

作者： Zhang, Hao Zheng, Xiangtian Ghaffar, Bushra Quddoos, Abdul Naz, Iram Aslam, Rana Waqar Fatima, Zoia Abdullah-Al-Wadud, M. Zulqarnain, Rana Muhammad China Academy of Safety Science and Technology Beijing 100012 China Nanjing Institute of Technology School of Computer Engineering Nanjing 211167 China Department of Environmental Science Faculty of Sciences International Islamic University Islamabad 44000 Pakistan State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (LIESMARS) Wuhan University Wuhan 430079 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu Chennai - 602105 India

This study investigates the environmental and structural impacts of crop and rangeland residue burning in Punjab during April and May 2023, utilizing ground-based interferometric radar alongside atmospheric and land use datasets. The radar system was employed to monitor structural deformations in buildings affected by fire, providing critical insights into the broader impacts of the fires on infrastructure. Multiple ground-based interferometric radars for distributed monitoring can effectively obtain the three-dimensional deformation components of structural targets in real fire scenes. The monitoring results conform to the displacement variation law of existing portal steel frame structures during fire deformation, and can provide effective data support for building structure collapse warning under fire. This approach offers a novel method for assessing the structural integrity of buildings during and after fire events, complementing traditional environmental monitoring. Environmental data show a marked increase in atmospheric pollutants such as NO₂, CH₄, and CO in May 2023, directly linked to the residue burning. Active fire data from FIRMS and MODIS revealed a 98% increase in fire events compared to April, while land surface temperature analysis showed a rise of 1.54%, with affected areas reaching up to 45.9°C. LULC analysis indicated a 23.3% decrease in cropland and rangeland areas, reflecting extensive land clearing. These findings highlight the urgent need for sustainable agricultural management and policies to mitigate the environmental and infrastructural damage caused by residue burning in Punjab. © 2025 The Society for Range Management

关键词： Agriculture Fire & Air Quality Distributed monitoring Google Earth Engine Ground-based interferometric radar Sentinel-2

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A Virtual Network Mapping Algorithm Based on Time

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Chinese Journal of Electronics 2025年第1期23卷 31-36页

作者： Ming Jiang Zhiyang Zhao Min Zhang Jingfan Tang Chunming Wu Xiao Min Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Mathematics and Information Engineering College of Jiaxing University Jiaxing China

As one of the challenges for network virtualization, virtual network embedding which maps Virtual network (VN) to the substrate network and allocates resources according to the requirements of VN in an efficient way has gained great attention. Existing algorithms generally make their decision according to the present available substrate network resource, especially bandwidth. This paper proposes a time-based VN embedding algorithm. A probability model is formulated to obtain the maximum probability that the available resources of substrate network can be used by succeeding VN requests. The probability is set as the weight of the node and the greedy algorithm is employed to embed the virtual node. The reciprocal of the probability is set as the weight of the link and the shortest path algorithm is employed to embed the virtual link. Simulation experiments show that the proposed algorithm increases the acceptance rate and the revenue compared to the existing algorithms.

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A Hybrid Vectorized Merge Sort on ARM NEON 24th

A Hybrid Vectorized Merge Sort on ARM NEON

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhou, Jincheng Zhang, Jin Zhang, Xiang Xiao, Tiaojie Ma, Di Gong, Chunye School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer National University of Defense Technology Hunan Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China

ISBN: (纸本)9789819615506

Sorting algorithms are the most extensively researched topics in computer science and serve for numerous practical applications. Although various sorts have been proposed for efficiency, different architectures offer distinct flavors to the implementation of parallel sorting. In this paper, we propose a hybrid vectorized merge sort on ARM NEON, named NEON Merge Sort for short (NEON-MS). In detail, according to the granted register functions, we first identify the optimal register number to avoid the register-to-memory access, due to the write-back of intermediate outcomes. More importantly, following the generic merge sort framework that primarily uses sorting network for column sort and merging networks for three types of vectorized merge, we further improve their structures for high efficiency in an unified asymmetry way: 1) it makes the optimal sorting networks with few comparators become possible;2) hybrid implementation of both serial and vectorized merges incurs the pipeline with merge instructions highly interleaved. Experiments on a single FT2000+ core show that NEON-MS is 3.8 and 2.1 times faster than std::sort and boost::block_sort, respectively, on average. Additionally, as compared to the parallel version of the latter, NEON-MS gains an average speedup of 1.25. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Memory architecture

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Analysis of converting -circuit into -circuit

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EPJ Quantum Technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

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Traffic Prediction Using Lightweight Large Model in UAV-Assisted Mobile Computing for Time-Critical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Yunzhe Liu, Yinqiu Cui, Huajun Liu, Jinghuan Mao, Yingchi He, Xiaoming University of Electronic Science and Technology of China College of Communication and Information Engineering Chengdu China Nanyang Technological University School of Computer Science and Engineering Singapore PowerChina Beijing Engineering Corporation Limited Digital Intelligence Research Institute Beijing China Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing China

Unmanned Aerial Vehicles (UAVs) have emerged as an important category of consumer electronics, offering versatile applications and enhanced operational efficiency. In the context of Mobile Edge Computing (MEC), UAVs can improve task execution and service quality by providing dynamic allocation of computational and data processing resources at the network edge. However, one critical issue in UAV-assisted MEC is task traffic prediction, which directly impacts the efficiency of task offloading strategies. Inspired by the recent success of large models, we propose a novel Lightweight Large Model with Temporal-Spatial Attention (L-LMTSA), designed to maximize task traffic prediction accuracy and minimize the overall computation latency. Specifically, L-LMTSA integrates a Temporal Attention Model (TAM) and a Spatial Attention Model (SAM) to capture multi-dimensional information, such as task flow location and temporal characteristics. In addition, we develop a pre-trained lightweight GPT-2 model by employing a one-dimensional convolutional input encoding layer and fine-tuning its parameters for predictive accuracy. Additionally, we optimize the UAV's trajectory and task offloading strategy using a three-dimensional artificial potential field informed by the predicted task traffic. Extensive experiments demonstrate that L-LMTSA not only improves training speed and prediction accuracy but also significantly reduces computational latency, resulting in more efficient UAV-assisted MEC experiences for consumers. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning

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IEEE Transactions on Multimedia 2025年

作者： Wang, Xu He, Ziyan Zhang, Qiudan Yang, You Zhao, Tiesong Jiang, Jianmin Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Huangzhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China

Being able to estimate monocular depth for spherical panoramas is of fundamental importance in 3D scene perception. However, spherical distortion severely limits the effectiveness of vanilla convolutions. To push the envelope of accuracy, recent approaches attempt to utilize Tangent projection (TP) to estimate the depth of 360° images. Yet, these methods still suffer from discrepancies and inconsistencies among patch-wise tangent images, as well as the lack of accurate ground truth depth maps under a supervised fashion. In this paper, we propose a geometry-aware self-supervised 360° image depth estimation methodology that explores the complementary advantages of TP and Equirectangular projection (ERP) by an asymmetric dual-domain collaborative learning strategy. Especially, we first develop a lightweight asymmetric dual-domain depth estimation network, which enables to aggregate depth-related features from a single TP domain, and then produce depth distributions of the TP and ERP domains via collaborative learning. This effectively mitigates stitching artifacts and preserves fine details in depth inference without overspending model parameters. In addition, a frequent-spatial feature concentration module is devised to simultaneously capture non-local Fourier features and local spatial features, such that facilitating the efficient exploration of monocular depth cues. Moreover, we introduce a geometric structural alignment module to further improve geometric structural consistency among tangent images. Extensive experiments illustrate that our designed approach outperforms existing self-supervised 360° depth estimation methods on three publicly available benchmark datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

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UW-Adapter: Adapting Monocular Depth Estimation Model in Underwater Scenes

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IEEE Transactions on Multimedia 2025年

作者： Ye, Xinchen Chang, Yue Xu, Rui Li, Haojie DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Liaoning China Shandong University of Science and Technology College of Computer Science and Engineering China

Estimating depth maps from monocular underwater images poses one of the most challenging problems in underwater applications. Due to the lack of large-scale paired underwater color-depth datasets for effective training, existing style transfer-based and self-supervision-based approaches can improve the performance of depth estimation to some extent, but they remain unsatisfactory. Leveraging the power of massive training datasets, foundation models designed for terrestrial monocular depth estimation have demonstrated superior performance across various scenes. These models provide rich prior knowledge of 3D perception, which can be valuable for underwater depth estimation. Upon this, we introduce tunable adapters (UW-Adapter) that tailor a pre-trained foundation model specifically for underwater depth estimation, customizing it to the unique characteristics of underwater imagery. Our approach involves freezing the parameters of the pre-trained model and updating only the adapters through self-supervision. To address the complex degradation of underwater images, we propose two adapters: the transmission adapter and the high-frequency adapter. These adapters incorporate depth clues and high-frequency information as prior knowledge, thereby enhancing the performance of pre-trained model in underwater depth estimation. Experimental results demonstrate that by integrating lightweight adapters into off-the-shelf depth estimation foundation models, our method achieves superior performance across multiple datasets. © 2025 IEEE.

关键词： Underwater photography

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MixSSC: Forward-Backward Mixture for Vision-based 3D Semantic Scene Completion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Meng Ding, Yan Liu, Yumeng Qin, Yunchuan Li, Ruihui Tang, Zhuo The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China

Vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, 3D modeling from a single view is an ill-posed problem, limited by the field of view and occlusion problems caused by image input. Moreover, existing methods tend to produce erroneous scene hallucinations and overly smooth boundary segmentation due to a lack of information. To address this problem, we propose MixSSC, which mixes the sparsity of forward projection with the denseness of depth-prior backward projection. The aim is to use sparse features to fill information-poor regions and dense features to enhance visible regions. Specifically, we develop the forward-backward mixture module, which enables the generation of scene mixture voxel representation by leveraging the benefits of both forward and backward projection. Subsequently, we design the semantic-spatial fusion module, which utilizes a coarse-to-fine approach to process mixture voxel features at the semantic-spatial level. Extensive experimental results on the SemanticKITTI, SSCBench-KITTI-360 and nuScenes datasets demonstrate the superiority of MixSSC. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Prompt-Driven Continual Graph Learning

arXiv

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arXiv 2025年

作者： Wang, Qi Zhou, Tianfei Yuan, Ye Mao, Rui Beijing Institute of Technology Zhuhai China School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Continual Graph Learning (CGL), which aims to accommodate new tasks over evolving graph data without forgetting prior knowledge, is garnering significant research interest. Mainstream solutions adopt the memory replay-based idea, i.e., caching representative data from earlier tasks for retraining the graph model. However, this strategy struggles with scalability issues for constantly evolving graphs and raises concerns regarding data privacy. Inspired by recent advancements in the prompt-based learning paradigm, this paper introduces a novel prompt-driven continual graph learning (PROMPTCGL) framework, which learns a separate prompt for each incoming task and maintains the underlying graph neural network model fixed. In this way, PROMPTCGL naturally avoids catastrophic forgetting of knowledge from previous tasks. More specifically, we propose hierarchical prompting to instruct the model from both feature- and topology-level to fully address the variability of task graphs in dynamic continual learning. Additionally, we develop a personalized prompt generator to generate tailored prompts for each graph node while minimizing the number of prompts needed, leading to constant memory consumption regardless of the graph scale. Extensive experiments on four benchmarks show that PROMPTCGL achieves superior performance against existing CGL approaches while significantly reducing memory consumption. Our code is available at https://***/QiWang98/PromptCGL. © 2025, CC BY.

关键词： Knowledge graph

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