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arXiv 2025年

作者： Meng, Yucong Yang, Zhiwei Song, Zhijian Shi, Yonghong Fu, Kexue Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy of Engineering and Technology Fudan University Shanghai200433 China Jinan China

The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViT, have shown substantial performance improvements for this task while encountering the dilemma between global receptive fields and efficient computation. To this end, this paper pioneers exploring Mamba, a new paradigm for long-range dependency modeling with linear complexity, for efficient and effective MRI reconstruction. However, directly applying Mamba to MRI reconstruction faces three significant issues: (1) Mamba’s row-wise and column-wise scanning disrupts k-space’s unique spectrum, leaving its potential in k-space learning unexplored. (2) Existing Mamba methods unfold feature maps with multiple lengthy scanning paths, leading to long-range forgetting and high computational burden. (3) Mamba struggles with spatially-varying contents, resulting in limited diversity of local representations. To address these, we propose a dual-domain multi-scale Mamba for MRI reconstruction from the following perspectives: (1) We pioneer vision Mamba in k-space learning. A circular scanning is customized for spectrum unfolding, benefiting the global modeling of k-space. (2) We propose a multi-scale Mamba with an efficient scanning strategy in both image and k-space domains. It mitigates long-range forgetting and achieves a better trade-off between efficiency and performance. (3) We develop a local diversity enhancement module to improve the spatially-varying representation of Mamba. Extensive experiments are conducted on three public datasets for MRI reconstruction under various undersampling patterns. Comprehensive results demonstrate that our method significantly outperforms state-of-the-art methods with lower computational cost. Implementation code will be available in https://***/XiaoMengLiLiLi/DM-Mamba. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Analysis of Metallization Effects of Pressure-Assisted Cu Nanoparticle Sintering on DBC by Experiment and Nanoscale Simulation

Analysis of Metallization Effects of Pressure-Assisted Cu Na...

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International Conference on (ICEPT) Electronic Packaging technology

作者： Shizhen Li Jing Jiang Xu Liu Shaogang Wang Zhonghua Zhang Huaiyu Ye Joint Lab for Advanced Semiconductor Packaging Technologies Joint Lab for Advanced Packaging and Testing Technology of Integrated Circuits School of Microelectronics Southern University of Science and Technology Shenzhen China Academy for Engineering&Technology Fudan University Shanghai China Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands School of Medical Informatics Daqing Campus Harbin Medical University Daqing China

Substrate metallization is a crucial factor affecting the mechanical properties of sintered nanoparticles in microelectronics applications, as it is essential for ensuring good adhesion between the substrate and the sintered material. In this study, we investigated the influence of metallization on pressure-assisted nanocopper sintering and analyzed the related mechanism of interaction using experiments and molecular dynamics simulation. In the first session, we bonded dummy dies on various substrates, including bare Cu, and substrates with Ag or Au metallization by nanocopper pressure-assisted sintering. The mechanical properties of the bonding layers were estimated using shear strength and SEM image analysis of fracture and cross-section morphologies under different sintering conditions. We found that the group of Cu-bare Cu have better bonding strength as the sintering temperature or assisted pressure is not high enough. However, as more energy input to the bonding layer, such as higher temperature or larger sintering pressure, the mechanical performance showed a significant increase. In the second session, a sintering model, which contained a single nanoparticle and substrate, was built to illustrate the effects of metallization from the perspective of solid-state wetting. The contact angle was estimated using a creative method, and the crystallization structure evolutions under different sintering conditions were analyzed. We found that the lattice boundary generated as the Cu nanoparticle coalescence with Ag or Au substrate, which may decrease the bonding strength. However, for Ag and Au metallization, limited interface diffusion can be observed at the neck region, where a few numbers of substrate atoms transmitted toward Cu nanoparticle, and the contact area was larger than that of bare Cu substrate. Finally, a simple uniaxial stretching simulation was conducted to prove the results of sintering simulation. This study provides valuable insights into the effec

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Multimodal Remote Sensing Image Registration Based on Local Phase Congruency and Keypoint Filtering

Multimodal Remote Sensing Image Registration Based on Local ...

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Chinese Automation Congress (CAC)

作者： Tao Ma Weijian Huang Linlin Du Chun Wang Shuangquan Ge Yunfei Liu Kun Yu Institute of Computer Application China Academy of Engineering Physics Mianyang China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan China

Robust registration is able to register the complementary information captured by multimodal images. Owning to the nonlinear intensity differences and repeating patterns in multimodal images, many classic descriptors are unable to achieve good performance. Aiming at this problem, a remote sensing image registration method based on key point description and filtering is proposed. First, this work utilizes a new keypoint descriptor with Log-Gabor filters to characterize the oriented phase congruency information in multimodal images. Then, a keypoint filtering is performed on all descriptors by mean-shift clustering to remove the repeating patterns. This approach is evaluated on three commonly used data sets, which are used for structure map construction and image registration comparisons. The experimental results show that the proposed method is able to preserve highly similar structure features and remove most repeating keypoints in multimodal images, which indicates this method is efficient and can achieve good registration performance.

关键词： Image registration Automation Information filters Feature extraction Computational efficiency Registers Remote sensing

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Experimental investigation of a multilayer nitride coating deposited on austenitic coating steel

Experimental investigation of a multilayer nitride coating d...

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International Conference on Future Trends in Materials and Mechanical engineering, ICFTMME 2022

作者： Jasper, S. Vijayakumar, D. Stalin, B. Rajesh, S. Jhansi Ida, S. Department of Mechanical Engineering Jeppiaar Institute of Technology Tamil Nadu Chennai631604 India Department of Mechanical Engineering Veltech Multitech Engineering College Tamil Nadu Chennai600062 India Department of Mechanical Engineering Anna University Regional Campus Tamil Nadu Madurai625019 India Department of Mechanical Engineering Kalasalingam Academy of Resaerch and Education Tamil Nadu Krishnankoil626128 India Department of Computer Science and Business Systems R.M.K Engineering College Tamil Nadu Kavaraipettai601206 India

Friction is the drawback in automotive and industrial machining parts and assembly. To overcome the wear loss due to friction the surface property must be enhanced using suitable technology. Arc deposition is a PVD type of coating which improves the property such as hardness, roughness, corrosion, and tribological on a server work environment. Titanium nitride (TiN) and titanium aluminum nitride (TiAlN) are hard ceramic coating materials. The mechanical and wear properties of stainless steel (SS304) coated with Method of cathodic arc deposition (CAD) The ability to slide against Ti-6Al-4 V alloy has improved significantly. The three coated samples' surface hardness, roughness, and wear rate are measured and compared. The applied tension (2-6 N), sliding velocity (0.32-0.75 m/s), and sliding distance (580-1500 m) tribological parameters are modified during the wear test with the ball-on-disc. In comparison to other types of coatings, On the SS316L surface, the TiN coating provides exceptional mechanical and tribological properties. © 2022 Elsevier Ltd. All rights reserved.

关键词： Titanium nitride

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Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

Influence of Lung Cancer Model Characteristics on Nanodrugs ...

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Advancement in technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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Fully Connected Reconfigurable Intelligent Surface Aided Rate-Splitting Multiple Access for Multi-User Multi-Antenna Transmission

arXiv

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arXiv 2022年

作者： Fang, Tianyu Mao, Yijie Shen, Shanpu Zhu, Zhencai Clerckx, Bruno School of Information Science and Technology ShanghaiTech University Shanghai China University of Chinese Academy of Sciences Beijing China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering Imperial College London United Kingdom

Rate-splitting multiple access (RSMA) has been recognized as a promising and powerful multiple access (MA) scheme, non-orthogonal transmission framework and interference management strategy for 6G. Inspired by the appealing spectral efficiency gain achieved by RSMA over conventional MA schemes in multi-user multi-antenna transmission, in this paper we introduce RSMA to reconfigurable intelligent surface (RIS)aided multiple-input single-out (MISO) broadcast channel (BC). To further enhance the spectral efficiency, a more generalized RIS architecture called fully connected RIS is considered. By jointly optimizing the scattering matrix of the fully connected RIS and the transmit beamformers to maximize the sum-rate, we show that the proposed fully connected RIS aided RSMA transmission scheme significantly improves the spectral efficiency compared with the conventional single connected RIS schemes and the schemes without RIS. It acts as a new benchmark for linearly precoded multi-user multi-antenna networks. © 2022, CC BY.

关键词： Efficiency

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Internet of Things based Smart Water Leakage Monitoring and Alert System

Internet of Things based Smart Water Leakage Monitoring and ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Keerthana S Elango S Judgi T Vivek K S Ponmurugan P Neelam Sanjeev Kumar Department of Electrical and Electronics Engineering J K K Nataraja College of Engineering and Technology Komarapalayam India Department of Electrical and Electronics Engineering Nandha Engineering College Erode Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology India Department of Electrical and Electronics Engineering Shree Venkateshwara Hi-Tech Engineering College Gobichettipalayam Department of Research Meenakshi Academy of Higher Education and Research Chennai Department of Biomedical Engineering Saveetha School of Engineering SIMATS Chennai India

It is critical to manage water supply in a safe and efficient manner. Significant amounts of water are lost each year as a result of leaks in water distribution systems (WDN). As a result, locating leaks in a reliable manner is critical. Water leaks can be detected using monitoring software that analyses the flow of water. Apps that track the user’s location can be extremely useful. If this technology is widely adopted, it will aid in the resolution of water management issues such as human error and waste of limited water resources. Water leaks can be located predictably using the Internet of Things (Internet of Things). Many countries struggle to get enough water, but the situation is not hopeless. It is critical to accurately locate water leaks in order to reduce system resistance.

关键词： Wires Inspection Software Software reliability Internet of Things Electrical resistance measurement Water resources

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Reduced Energy Consumption in Cloud Data Center Using Machine Learning Algorithm Load Balancing 5

Reduced Energy Consumption in Cloud Data Center Using Machin...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Suneetha, T. Singh, Shalini Neeraja, B. Tufa, Sisay Wayu Thiyagarajan, N. Rao, A Kakoli Loyola Academy HOD Department of Computer Science and Internet of Things Alwal Telangana Secunderabad India Graphic Era Deemed to Be University Department of ECE Uttarakhand Dehradun India TVT Institute Addis Ababa Department of ICT Ethiopia Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Lloyd Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350398267

Cloud computing has made it possible for users to spend only for the volume of a service that they consume rather than purchasing a predetermined amount. Cloud computing and virtualization, when coupled, make it possible to access and share information technology resources whenever and wherever they are needed. When it comes to cloud computing, a dependable data centre infrastructure is unrivalled by anything else. In the context of this discussion, the term 'infrastructure' refers to the collection of computers, wiring, and power sources that together serve as a host for an organization's information and store it. When it comes to cloud computing, great overall performance has always been the most important factor, but this has resulted in an increase in the amount of energy that is required. The major objective is to achieve as low a level of power consumption as possible while maintaining as high a level of performance and quality of service as possible inside the carrier network. Our strategy calls for an in-depth study of the patterns of energy consumption exhibited by cloud-based systems. Authors perform an analysis of energy use and show how suitable optimization methods based on our models of energy consumption can assist cloud data centres in saving even more electricity. These methods are informed by our analysis of energy consumption. Because of capsule optimization, this system is able to generate more accurate projections of the costs that will be incurred in the future. The accuracy rate for the forecasting component is 97%. © 2022 IEEE.

关键词： Electric power utilization

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NeRV-3D-DC: A Nonlinear Dimensionality Reduction Visualization Method for 3D Chromosome Structure Reconstruction with High Resolution Hi-C Data

NeRV-3D-DC: A Nonlinear Dimensionality Reduction Visualizati...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gong, Haiyan Yang, Yi Zhang, Xiaotong Ma, Fuqiang Li, Minghong Chen, Zhengyuan Zhang, Sichen Chen, Yang University of Science and Technology Beijing School of Computer and Communication Engineering Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China Institute of Basic Medical Sciences School of Basic Medicine Chinese Academy of Medical Sciences Peking Union Medical College The State Key Laboratory of Medical Molecular Biology Department of Biochemistry and Molecular Biology Beijing100005 China University of Science and Technology Shunde Innovation School Foshan Beijing528399 China Electronic Information Industry Co. Ltd China

ISBN: (纸本)9781665468190

Chromosome structure plays an essential role in exploring the relationship between chromosome structure and gene transcription. In recent years, High-resolution Hi-C data allows us to explore the chromosome structures in 3D space. However, most of the existing chromosome structure reconstruction methods can not realize the 3D structure reconstruction at a high resolution (for example, 5 Kilobase (KB)). In this paper, we first proposed the NeRV-3D to reconstruct the 3D chromosome structure at a low resolution based on the nonlinear dimensionality reduction visualization algorithm. Then, we proposed the NeRV-3D-DC to reconstruct and visualize the 3D chromosome structure at a high resolution based on the divide-and-conquer method. Results show that NeRV-3D and NeRV-3DDC perform better than existing methods by comparing the 3D visualization effects and evaluation metrics on simulated and real Hi-C datasets. Implementation of NeRV-3D-DC is available on https://***/ghaiyan/NeRV-3D-DC © 2022 IEEE.

关键词： Visualization

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A Novel Optimization Strategies for Detecting and Preventing Distributed Denial of Service Attacks in Wireless Networks

A Novel Optimization Strategies for Detecting and Preventing...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Saurabh Kumar Jambi Ratna Raja Kumar Dipannita Mondal S. Hemelatha Geetha. B Mandar P. Diwakar Department of Commerce Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D. Y.Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India

allotted Denial of carrier (DDoS) assaults have end up a prime difficulty in trendy wireless networks. these assaults aim to disrupt the regular operation of networks by means of overwhelming them with excessive traffic from more than one assets. traditional DDoS detection and prevention strategies have established to be useless in wireless networks due to the dynamic and unpredictable nature of the wireless surroundings. To deal with this problem, novel optimization techniques were proposed to enhance the potential of wireless networks to discover and prevent DDoS *** of the key techniques is the use of wise algorithms which includes machine learning and synthetic intelligence. these algorithms can analyze and study from network site visitors patterns to pick out strange behavior this is indicative of a DDoS attack. by way of continuously tracking the network and adapting to converting traffic patterns, these algorithms are capable of as it should be locate and mitigate DDoS assaults in actual-time. any other method is the deployment of robust and dynamic defense mechanisms. those mechanisms include the usage of anomaly detection, traffic filtering, and traffic shaping. Anomaly detection strategies rent statistical techniques to hit upon patterns that deviate from ordinary network conduct. traffic filtering involves figuring out and blockading malicious site visitors, even as site visitors shaping manages and controls the flow of legitimate visitors to prevent congestion and enhance community overall performance.

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