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Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy *** distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high *** address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage *** architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and *** on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw *** off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data *** provide a unified access interface for user-friendly system *** testing validates the system’s reliability and stable *** proposed approach significantly enhances storage capacity compared to standalone blockchain *** reliability tests consistently yield positive *** average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

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SteadySketch: Finding Steady Flows in Data Streams 31

SteadySketch: Finding Steady Flows in Data Streams

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Li, Xiaodong Fan, Zhuochen Li, Haoyu Zhong, Zheng Guo, Jiarui Long, Sheng Yang, Tong Cui, Bin School of Computer Science Peking University National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350399738

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub. © 2023 IEEE.

关键词： Steady flow

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques

Review on Predicting the Landslide Susceptibility using Mach...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Abhishek Sinha Shivam Kumar Giri Himanshu Yadav Sanjeev Kumar Prasad Priya Mishra School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generalizability challenges.

关键词： Deep learning Accuracy Reviews Explainable AI Computational modeling Predictive models Terrain factors Data models Real-time systems Reliability

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A Multi-objective Antenna Optimization Method Based on Preferred Parameter Spaces

A Multi-objective Antenna Optimization Method Based on Prefe...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Lin, Siyu Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

Machine learning has been successfully used in the field of multi-objective antenna design, enabling the enhancement of antenna performance and optimizing efficiency. However, the prediction within an exceptionally vast parameter space imposes significant demands on machine learning algorithms. Moreover, minor adjustments in dimensions can bring about significant alterations in antenna performance, which renders simultaneous optimization for all objectives exceedingly challenging. To solve the multi-objective antenna optimization problem, a multi-objective antenna optimization method based on a preferred parameter space is proposed. Specifically, this method utilizes the KNN algorithm to preliminarily optimize the antenna and acquire a high-quality dataset. Subsequently, the sensitivity of ten selected parameters based on the electromagnetic (EM) simulation results is calculated by the XGBoost algorithm. The value of the parameter sensitivity is acquired by averaging ten calculations. The top four ranked parameters are designated as variables while the rest are fixed as constants. This approach reduces the number of parameters for subsequent optimization, thus greatly improving the subsequent prediction accuracy while ensuring the antenna performance. Finally, the variables derived from XGBoost are input into the fruit-fly optimization algorithm (FOA) to find the near-optimal solution. Hence, the task of parameter optimization under the multi-objectives of the impedance bandwidth and circular polarization characteristics can be effectively accomplished. To further validate its performance, the proposed algorithm is applied to optimize the S-band miniaturized broadband circularly polarized antenna. Compared to the results obtained by electromagnetic simulation only, the optimized results exhibit a 17.95% improvement of impedance bandwidth and a 42.86% enhancement of axial ratio bandwidth. Numerical outcomes validate that the proposed method achieves multi-objective o

关键词： Circular polarization

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An Efficient Antenna Optimization Method Based on a Parameter Dimensionality Reduction Approach

An Efficient Antenna Optimization Method Based on a Paramete...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Zhao, Chenyang Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

As antenna structure grow in complexity, the problem of time-consuming in electromagnetic (EM) simulation have become more common. This problem undoubtedly brings a series of the challenges in raising the effectiveness of antenna optimization. In order to overcome the existing difficulties, an antenna optimization method based on RF-GWO method is proposed. This approach consists of three major components. Firstly, an artificial neural network (ANN) is used to build the dataset. Compared with the traditional sampling method, the data filtered by the artificial neural network (ANN) are closer to the optimization objective. Consequently, it can better responsive to the relationship between the antenna parameters and the optimization objective. Then, the Random Forest method (RF) is utilized to analyze the impact of the ten selected parameters based on the electromagnetic (EM) simulation results on the antenna performance. Random Forest Algorithm (RF) has strong interpretability, allowing for the assessment of the contribution of different parameters to the optimization objective, which can effectively reduce the number of design variables. Finally, the antenna optimization is performed utilizing the Grey Wolf Optimization (GWO) algorithm. Combined with the sensitivity analysis results, the Gray Wolf Algorithm (GWO) can effectively search for improved results in a faster manner. In order to evaluate the algorithm's performance, the proposed method is employed to optimize a multiband miniaturized microstrip antenna. The result demonstrates that substantial improvement in impedance bandwidth is achieved by the proposed method, with an approximate 29% increase compared to conventional manual optimization methods. This outcome has proved potential and effectiveness of the proposed method in practical implications of antenna optimization. © 2024 IEEE.

关键词： Sensitivity analysis

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Bioinspired Clustering in UWSNs Using Monarch Butterfly Optimization Compared with Ant Colony Optimization Algorithm

Bioinspired Clustering in UWSNs Using Monarch Butterfly Opti...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Indira, G. Rani, Ruchika Chiranjevi, D. Husain, Saif O. Yogi, Rahul Nandhini, T.J. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Aditya Engineering College Department of Computer Science & Engineering India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

The Monarch Butterfly Optimization (MBO) algorithm for clustering in Underwater Wireless Sensor Networks (UWSNs) is investigated and compared to the performance of the Ant Colony Optimization (ACO) algorithm in this paper. As is evident from the outcomes depicted in the above graphs MBO consumes an average of 31% less energy than the ACO while lasting longer in the network. 7J or make a network get 790 round life cycle while ACO did 34. 5J and 730 rounds. The results indicate that MBO is more advantageous to cluster in the UWSNs making it a viable solution to boost up the network productivity and the overall energy utilization efficiency. © 2024 IEEE.

关键词： Ant colony optimization

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Investigating the Potential of Applying Permission Blockchain for IoT Networks 15

Investigating the Potential of Applying Permission Blockchai...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manjunath, H.R. Moharana, Alok Kumar Infimum School of Engineering and Technology Department of Physics Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This paper investigates the capability of applying permission block chain for IoT networks. Permission block chain is a dispensed ledger device that uses a consensus-based totally method to shop and manipulate records. Such a technique offers a comfy and dependable platform for trustless environments, permitting a comfy, open, and immutable statistics infrastructure. Moreover, permission block chain can allow authentication of statistics, creating a less complicated way for trusted entities to accept as true with facts from untrusted assets, along with IoT networks. Moreover, the paper explores the possible packages and demanding situations of permission block chain in deploying at ease, dependable, and electricity-efficient IoT networks. Demanding situations together with scalability, interoperability, strength performance, privateers, and safety need to be addressed when it comes to the development of permission block chain answers for IoT networks. In the end, tips and answers are mentioned, along with proposals for building a hybrid permission layer for IoT networks. © 2024 IEEE.

关键词： Authentication Complicated Infrastructure Permission Scalability Situations

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Bio-assembled smart nanocapsules for targeted delivery of KRAS shRNA and cancer cell bioimage

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Chinese Chemical Letters 2023年第1期34卷 270-273页

作者： Maonan Wang Zengchao Guo Jiayu Zeng Liu Liu Yihan Wang Jinpeng Wang Hongbing Lu Haijun Zhang Hui Jiang Xuemei Wang State Key Laboratory of Bioelectronics(Chien-Shiung Wu Lab) School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Department of Biomedical Engineering/Computer Application Fourth Military Medical UniversityXi’an 710032China Department of Oncology Zhongda HospitalMedical School of Southeast UniversityNanjing 210009China

The five-year survival rate for pancreatic cancer is less than 5%. However, the current clinical multimodal therapy combined with first-line chemotherapy drugs only increases the patient’s median survival from 5.0 months to 7.2 months. Consequently, a new strategy of cancer treatments is urgently needed to overcome this high-fatality disease. Through a series of biometric analyses, we found that KRAS is highly expressed in the tumor of pancreatic cancer patients, and this high expression is closely related to the poor prognosis of patients. It shows that inhibiting the expression of KRAS has great potential in gene therapy for pancreatic cancer. Given those above, we have exploited the possibility of targeted delivery of KRAS shRNA with the intelligent and bio-responsive nanomedicine to detect the special oxidative stress microenvironment of cancer cells and realize efficient cancer theranostics. Our observations demonstrate that by designing the smart self-assembled nanocapsules of melanin with fluorescent nanoclusters we can readily achieve the bio-recognition and bioimaging of cancer cells in biological solution or *** self-assembled nanocapsules can make a significant bio-response to the oxidative stress microenvironment of cancer cells and generate fluorescent zinc oxide Nanoclusters in situ for targeted cell bioimaging. Moreover, it can also readily facilitate cancer cell suppression through the targeted delivery of KRAS shRNA and low-temperature hyperthermia. This raises the possibility to provide a promising theranostics platform and self-assembled nanomedicine for targeted cancer diagnostics and treatments through special oxidative stress-responsive effects of cancer cells.

关键词： Smart bio-responsive nanocapsules Intelligent nanomedicine shRNA KRAS Fluorescent bioimaging Oxidative stress response

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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RANSACs for 3D Rigid Registration:A Comparative Evaluation

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1861-1878页

作者： Jiaqi Yang Zhiqiang Huang Siwen Quan Zhiguo Cao Yanning Zhang the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China the School of Electronic and Control Engineering Chang’an UniversityXi’an 710064China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China IEEE

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid *** sample consensus(RANSAC)and its variants are popular solutions to this *** there have been a number of RANSAC-fashion estimators,two issues remain ***,it is unclear which estimator is more appropriate to a particular ***,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain *** work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive *** objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation *** have been carried out on four standard datasets with different application scenarios,data modalities,and *** provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and *** on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

关键词： 3D rigid registration performance evaluation point cloud pose estimation

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