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arXiv 2025年

作者： Liu, Chenyang Chen, Keyan Zhao, Rui Zou, Zhengxia Shi, Zhenwei The Department of Aerospace Intelligent Science and Technology School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Recently, generative foundation models have significantly advanced large-scale text-driven natural image generation and have become a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensing image-text datasets are small in scale and confined to specific geographic areas and scene types. Besides, existing text2image methods have struggled to achieve global-scale, multi-resolution controllable, and unbounded image generation. To address these challenges, this paper presents two key contributions: the Git-10M dataset and the Text2Earth foundation model. Git-10M is a global-scale image-text dataset comprising 10.5 million image-text pairs, 5 times larger than the previous largest one. The dataset contains essential resolution information and covers a wide range of geographic scenes and contains essential geospatial metadata, significantly surpassing existing datasets in both size and diversity. Building on Git-10M, we propose Text2Earth, a 1.3 billion parameter generative foundation model based on the diffusion framework to model global-scale remote sensing scenes. Text2Earth integrates a resolution guidance mechanism, enabling users to specify image resolutions. A dynamic condition adaptation strategy is proposed for training and inference to improve image generation quality. Text2Earth not only excels in zero-shot text2image generation but also demonstrates robust generalization and flexibility across multiple tasks, including unbounded scene construction, image editing, and cross-modal image generation. This robust capability surpasses previous models restricted to the basic fixed size and limited scene types. On the previous text2image benchmark dataset, Text2Earth outperfoms previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot Cls-OA metric. Our project page is https://chen-y

关键词： Optical remote sensing

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Triple Point Masking

arXiv

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arXiv 2024年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. Our code and models are available at https://***/liujia99/TPM. © 2024, CC BY.

关键词： Signal encoding

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An Adaptive Decision-Making Approach for Better Selection of a Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

arXiv

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arXiv 2023年

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using two blockchain platforms selected based on our proposed decision-making map approach for the selection of the top two suitable platforms for healthcare insurance frauds detection application, followed by an evaluation of their performances. Our classification shows that the largest percentage of blockchain platforms could be used for all types of application domains, and the second biggest percentage is to develop financial services only, even though generic platforms can be used, while a small number is for developing in other specific application domains. Our decision-making map revealed that Hyperledger Fabric is the best blockchain platform for detecting healthcare insurance frauds. The performance evaluation of the top two selected platforms indicates that Fabric surpassed Neo in all *** Codes 6804, 68M11, 68U35 © 2023, CC BY-NC-ND.

关键词： Smart contract

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing systems: Current Issues and Prospective Solutions

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

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Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach

arXiv

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arXiv 2023年

作者： Hong, Haokai Jiang, Min Lin, Qiuzhen Tan, Kay Chen Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China College of Computer Science and Software Engineering Shenzhen University China Department of Computing Hong Kong Polytechnic University Hong Kong

We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of real-world scenarios necessitating the optimization of hundreds of thousands, if not millions, of variables. However, the larger dimension in VLSMOPs intensifies the curse of dimensionality and poses significant challenges for existing large-scale evolutionary multiobjective algorithms, rendering them more difficult to solve within the constraints of practical computing resources. To overcome this issue, we propose a novel approach called the very large-scale multiobjective optimization framework (VMOF). The method efficiently samples general yet suitable evolutionary directions in the very large-scale space and subsequently fine-tunes these directions to locate the Pareto-optimal solutions. To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations. Furthermore, a technique is designed for fine-tuning directions specific to tracking Pareto-optimal solutions. To understand the designed framework, we present our analysis of the framework and then evaluate VMOF using widely recognized benchmarks and real-world problems spanning dimensions from 100 to 1,000,000. Experimental results demonstrate that our method exhibits superior performance not only on LSMOPs but also on VLSMOPs when compared to existing algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Performance Investigation of Instantaneous Reactive Power Control for 5 Level-Neutral Point Clamped Grid-Connected Photovoltaic Inverter

Performance Investigation of Instantaneous Reactive Power Co...

引用

Electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Oumaymah Elamri Alla Eddine Toubal Maamar Charaf Chermite Moulay Rachid Douiri Mostefa Kermadi Saad Mekhilef LAMIGEP multidisciplinary research laboratory École Marocaine des Sciences de l'Ingénieur (EMSI) of Marrakech Marrakech Morocco Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara of Boumerdes Boumerdes Algeria Physics Department Control and Computer Science for Intelligent Systems and Green Energy Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco School of Engineering University of Leicester Leicester United Kingdom Power Electronics and Renewable Energy Research Laboratory (PEARL) Kuala Lumpur Malaysia Swinburne University of Technology Hawthorn Vic Australia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The utilization of solar energy in grid-connected photovoltaic installations has become increasingly significant. This article introduces a photovoltaic system seamlessly integrated with a three-phase network. The system employing a five-level Neutral Point Clamped (NPC) voltage source inverter is designed for a single-step connection to enhance power quality in the distribution network. The control technique employed for the efficient functioning of the photovoltaic system is based on the notion of instantaneous reactive power. Notably, this strategy is straightforward, operates the system throughout the day, and eliminates the need for Phase-Locked Loop (PLL) usage. The Pulse-Width Modulated (PWM) Current Controller sends switching signals to the NPC multilevel inverter, which does a great job of dynamic performance under steady-state and transient situations. The proportional-integral controller regulates the voltage of the Uninterruptible Power Supply DC bus capacitors. The proposed system undergoes comprehensive simulation validation under various irradiation levels.

关键词： Photovoltaic systems Reactive power Voltage source inverters Switches Solar energy Steady-state Transient analysis

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

SARENA: SFC-Enabled Architecture for Adaptive Video Streamin...

引用

IEEE International Conference on Communications (ICC)

作者： Reza Farahani Abdelhak Bentaleb Christian Timmerer Mohammad Shojafar Radu Prodan Hermann Hellwagner Christian Doppler Laboratory ATHENA Institute of Information Technology Alpen-Adria-Universität Klagenfurt Austria The Department of Computer Science and Software Engineering Concordia University Canada 5GIC and 6GIC Institute for Communication Systems (ICS) University of Surrey United Kingdom

5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multime-dia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an _S_FC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in large-scale scenarios. Finally, we design a large-scale cloud-based testbed including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users' QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.

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A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences

arXiv

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arXiv 2022年

作者： Yang, Kaicheng Zhang, Ruxuan Xu, Hua Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Northeast Electric Power University China

Inter-modal interaction plays an indispensable role in multimodal sentiment analysis. Due to different modalities sequences are usually non-alignment, how to integrate relevant information of each modality to learn fusion representations has been one of the central challenges in multimodal learning. In this paper, a Self-Adjusting Fusion Representation Learning Model (SA-FRLM) is proposed to learn robust crossmodal fusion representations directly from the unaligned text and audio sequences. Different from previous works, our model not only makes full use of the interaction between different modalities but also maximizes the protection of the unimodal characteristics. Specifically, we first employ a crossmodal alignment module to project different modalities features to the same dimension. The crossmodal collaboration attention is then adopted to model the inter-modal interaction between text and audio sequences and initialize the fusion representations. After that, as the core unit of the SA-FRLM, the crossmodal adjustment transformer is proposed to protect original unimodal characteristics. It can dynamically adapt the fusion representations by using single modal streams. We evaluate our approach on the public multimodal sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experiment results show that our model has significantly improved the performance of all the metrics on the unaligned text-audio sequences. © 2022, CC BY.

关键词： Sentiment analysis

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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