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Design, Automation and Test in Europe Conference and Exhibition

作者： Chenlin Ma Xiaochuan Zheng Kaoyi Sun Tianyu Wang Yi Wang College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Radio Frequency Heterogeneous Integration

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Interlaced Magnetic Recording (IMR), a technology that improves storage density through track overlap, introduces significant latency due to Read-Modify-Write (RMW) operations. Writing to overlapped tracks affects underlying tracks, requiring additional I/O operations to read, back up, and rewrite them, resulting in significant head movement latency. We propose EF-IMR, a new architecture that ensures crash consistency in IMR while minimizing RMW latency and head movement. EF-IMR reduces head movement during RMW operations and decreases redundant RMW operations. Evaluations under real-world, intensive I/O workloads show that EF-IMR reduces RMW latency by 20.11 % and head movement latency by 89.37% compared to existing methods.

关键词： Tracking Europe Writing Magnetic heads computer crashes Delays Magnetic recording

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Efficient High-Precision Classification Algorithm for Radar Deceptive Jamming via Array Detection

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Progress in Electromagnetics Research C 2025年 155卷 29-35页

作者： Liu, Wenjie Qin, Wenfang Shen, Mingwei Han, Guodong College of Information Science and Engineering Hohai University Jiangsu Changzhou213200 China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing211106 China The 54th Research Institute of CETC Hebei Shijiazhuang050081 China

In complex electromagnetic scenarios where multiple deceptive jamming signals are simultaneously aliased in the time frequency domain, conventional single-channel electronic detection systems struggle to effectively separate and classify overlapping jamming sources. To address this limitation, this paper investigates an array detection-based classification scheme for multi-source jamming. First, due to the spatial degrees of freedom offered by array systems, the Direction-of-Arrival (DOA) of each jamming source is precisely estimated using the MUSIC algorithm. Then, the adaptive digital beamforming (ADBF) filters are designed based on the estimated DOAparameters, enabling spatial-domain extraction of individual jamming signals. Finally, the DOA information is integrated into the Pulse Description Word (PDW) of each separated jamming measurement, which can facilitate adaptive K-Means clustering with enhanced class separability. Simulation results demonstrate that the proposed method achieves a 22.4% improvement in classification accuracy compared to existing single-channel detection approaches, while maintaining computational efficiency. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Frequency response

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Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images

arXiv

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arXiv 2025年

作者： Fang, Sheng Li, Kaiyu Li, Zhe Zhao, Jianli Zhang, Xingli College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China

Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical remote sensing

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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Semantic consistency assisted change detection in remote sensing images

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National Remote Sensing Bulletin 2025年第4期29卷 1031-1045页

作者： Wu, Hucheng Wang, Renfang Qiu, Hong Wang, Feng Gao, Guang Wu, Dun College of Computer Science and Technology Ocean University of China Qingdao266100 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China Co. Ltd. Ningbo315042 China

Semantic change detection of remote sensing images plays an important role in ecological environment, land use, and land cover monitoring. In recent years, deep learning-based change detection methods are the hotspot of remote sensing intelligent interpretation concern;however, the existing three-branch semantic change detection methods lack consistency of change branch and semantic branches, which leads to the self-contradiction of the bitemporal semantic change detection. To address this problem, this study proposes a remote sensing image semantic change detection algorithm based on Siamese CNN and Transformer. In the encoding stage, the Siamese ResNet34 network is first designed to extract the multiscale features of the image, and the difference enhancement module is embedded to increase the attention of the change information;then, the semantic tokenizer is used to map the feature map into compact semantic tokens, and the Transformer encoder is used to combine the bitemporal semantics and the change information to model the"semantic change"consistency. In the decoding stage, different fine-grained semantic information is fused by the Transformer decoder using a hopping connection to generate a refined semantic feature map. Finally, the result of bitemporal semantic change is obtained after upsampling recovery and mask multiplication. Experimental results on the remote sensing semantic change detection public dataset SECOND and Landsat-SCD show that the proposed algorithm can effectively focus on the change region, maintain the consistency between the change results and the semantic results, and achieve excellent evaluation indexes and visual effects. We can draw the following conclusions: (1) The proposed difference enhancement module can enhance the difference characteristics of bitemporal remote sensing images and improve the network’s focus on change information. (2) The proposed bitemporal Transformer module maps the difference information and bitemporal sem

关键词： Change detection

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Decentralized Self-Auditing Multiple Cloud Storage in Compressed Provable Data Possession

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yang, Yang Chen, Yanjiao Xiong, Ping Chen, Fei Chen, Jing Zhongnan University of Economics and Law School of Information Engineering Wuhan China Zhejiang University College of Electrical Engineering Hangzhou China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Wuhan University School of Cyber Science and Engineering Wuhan China

As cloud storage becomes popular, more and more users tend to outsource their data to powerful cloud severs. To prevent a single point of failure, users prefer to store data on multiple cloud servers from different cloud service providers. However, after outsourcing data to cloud servers, users lose the control of their data, which may incur many serious security issues, such as abnormal data tampering and deleting. It is necessary for users to audit multiple cloud storage aperiodically. In this paper, we aim to design a decentralized self-auditing solution for multiple cloud storage, in which cloud servers can audit the integrity of each other, and thus no third-party entity is required. Firstly, based on basic algebra, our protocol realizes decentralized self-auditing multiple cloud storage that only involves encrypted user data. Secondly, we design a proof exchanging mechanism, making any number of cloud servers form the same final integrity proof with a linear number of interactions. Thirdly, our solution can achieve cloud dynamics, which can freely enable and disable a cloud server to provide storage service. At last, security proof and performance evaluation show that the proposed protocol has provable security and high efficiency. © 2004-2012 IEEE.

关键词： Cloud security

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Attention-guided CenterNet deep learning approach for lung cancer detection

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computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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Enhancing Semantic Awareness by Sentimental Constraint with Automatic Outlier Masking for Multimodal Sarcasm Detection

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IEEE Transactions on Multimedia 2025年

作者： Yuan, Shaozu Wei, Yiwei Zhou, Hengyang Xu, Qinfu Chen, Meng He, Xiaodong Meituan Beijing China at Karamay College of Petroleum Engineering Karamay China Qingdao Institute of Software College of Computer Science and Technology China JD AI Research Beijing China

Multimodal sarcasm detection, aiming to uncover sarcastic sentiment behind multimodal data, has gained substantial attention in multimodal communities. Recent advancements in multimodal sarcasm detection (MSD) methods have primarily focused on modality alignment with pre-trained vision-language (V-L) model. However, text-image pairs often exhibit weak or even opposite semantic correlations in MSD tasks. Consequently, directly aligning these modalities can potentially result in feature shift and inter-class confusion, ultimately hindering the model's ability. To alleviate this issue, we propose the Enhancing Semantic Awareness Model (ESAM) for multimodal sarcasm detection. Specifically, we first devise a Modality-decoupled Framework (MDF) to separate the textual and visual features from the fused multimodal representation. This decoupling enables the parallel integration of the Sentimental Congruity Constraint (SCC) within both visual and textual latent spaces, thereby enhancing the semantic awareness of different modalities. Furthermore, given that certain outlier samples with ambiguous sentiments can mislead the training and weaken the performance of SCC, we further incorporate Automatic Outlier Masking. This mechanism automatically detects and masks the outliers, guiding the model to focus on more informative samples during training. Experimental results on two public MSD datasets validate the robustness and superiority of our proposed ESAM model. © 2025 IEEE.

关键词： Visual languages

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Towards Drought Modeling in South Asia: Machine Learning Approaches, Challenges and Opportunities

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IEEE Access 2025年

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, M.A. Rajab, Khairan Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering Istanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state- of-the-art in ML and DL (Complex Machine learning and deep learning models) for drought modeling, highlighting current challenges such as incomplete and inconsistent datasets, Lack of explainability and interpretability in complex ML/DL models, unavailability of benchmarks. Based on these challenges, this review suggests the following techniques to address these challenges: Data integration (Data fusion), distributed machine learning (Federated Learning) and explainable AI (XAI, SHAP, LIME, etc.). © 2013 IEEE.

关键词： Federated learning

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

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