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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6956 条记录，以下是771-780 订阅

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Joint Tensor Gradient and Sparsity-driven for Fast Robust Video SAR Imaging 11

Joint Tensor Gradient and Sparsity-driven for Fast Robust Vi...

引用

11th IEEE Asia-Pacific conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Wu, Yunqiang Gui, Shuliang Tian, Zengshan Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China

ISBN: (纸本)9798350326277

Due to the ability of providing continuous observation, the video synthetic aperture radar(ViSAR) has recently received increasing attention. Particularly focusing on imaging of fields that admit a sparse representation, we propose a tensor sparsity-driven method for video SAR imaging. Joint with high-dimension data processing, the proposed method significantly reduce computing burden. Compared with conventional algorithms, numerical simulations validate the effectiveness, accuracy and robustness of our algorithm. © 2023 IEEE.

关键词： high-dimension data processing sparsity-driven Video SAR

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Research and Design of Traffic Information processing Based on Hadoop Big data Architecture 2

Research and Design of Traffic Information Processing Based ...

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2nd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2023

作者： Niu, Ruiyuan Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350324358

As the information resources in the field of transportation become more and more huge, the traditional processing methods can hardly support the needs of real-time computing, low-latency query and statistical analysis of massive data. In the paper, a design method of intelligent traffic information processing based on Hadoop big data architecture is proposed. By analyzing the application requirements and the current situation in the field of intelligent transportation, the paper investigates the computation mode combined with big data processing technology to collect, process, analyze and utilize multi-user requirements and large-scale and massive traffic data, and realize the sharing and common use of big data, as well as efficient and convenient storage methods and accurate computation methods. In order to provide personalized services to people in a dynamic, timely and accurate manner. The final construction of traffic management, travel reference, monitoring and criminal investigation and other comprehensive, multi-faceted intelligent traffic information processing system. © 2023 IEEE.

关键词： Big data

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IoT integration with Big data and Cloud computing: A Multidisciplinary Approach 2

IoT integration with Big Data and Cloud Computing: A Multidi...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Sharma, Atul Aggarwal, Kapil Kumar Malik, Meena University Institute of Computing Chandigarh University Punjab Mohali India University School of Business Chandigarh University Punjab Mohali India Chandigarh University Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350369960

IoT (Internet of Things) has become the most popular technology nowadays which opens chances for home machines, wearable gadgets, and software to share and convey data on the Internet. Internet of things globally connected devices and gained popularity in the telecommunication industry. It has many applications in the area of healthcare, marketing, production, social application and smart home applications etc. But this technology also has some issues related to its implementation in various areas such as data processing and storage, security etc. In this paper, we start with general information of IoT, architecture, applications, and limitations and then the importance of the convergence of IoT, Cloud computing and Big data. Finally, this paper summarizes the researchers about how to leverage the benefits by combining these three technologies. © 2023 IEEE.

关键词： Big data Cloud computing Integration Internet of Things Sensors

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Construction of Network data processing Model Based on Mathematical Statistics and Wavelet Decomposition Algorithm

Construction of Network Data Processing Model Based on Mathe...

引用

2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Guo, Wenting Changjiang Institute of Technology Wuhan430212 China

ISBN: (纸本)9798350313314

With the rapid growth of people's demand for network, the scale of telecommunication network is becoming larger and larger, and the network technology is becoming more and more complex. However, the difference of equipment from different manufacturers and equipment management means makes network management more complex. Effective maintenance of the network and ensuring the operation quality of the network have become a huge challenge. The information age puts forward higher requirements for data security. Therefore, this paper proposes a network operation anomaly data mining model based on mathematical statistics and wavelet decomposition algorithm, including data extraction, data feature extraction, and anomaly data mining. Through the experiment, it is concluded that the model designed in this paper can basically detect abnormal data by more than 98.6%. Therefore, it proves that the network operation anomaly data mining model based on mathematical statistics designed in this paper is highly effective. © 2023 IEEE.

关键词： Wavelet decomposition

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A Write-Optimized PM-oriented B+-tree with Aligned Flush and Selective Migration 21

A Write-Optimized PM-oriented B+-tree with Aligned Flush and...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： He, Xianyu Zhang, Runyu Tian, Pengpeng Zhou, Lening Lian, Min Yang, Chaoshu Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350329223

Emerging Persistent Memories (PMs) usually have the severe drawback of expensive writes. Therefore, existing PM-oriented B+-trees mainly concentrate on alleviating the write overhead (i.e., reducing the writes to PM and the flush instructions). Unfortunately, these schemes still employ improper data organization in leaf nodes, which can lead to massive data migration during insertions or node splits. This paper proposes a write-optimized PM-oriented B+ -tree (WPB+ -tree) to solve the above problems. WPB+ -tree first adopts a buffer-assisted mechanism that temporarily stores the newly inserted data to reduce the overhead of data migration and flush. Second, WPB+ tree employs a selective migration of node entries to achieve less data migration when splitting a node. We implement the proposed WPB+ -tree with actual persistent memory. Compared with state-of-the-art solutions (i.e., FAST&FAIR and SSB-Tree), WPB+ -tree achieves up to 17%, 40%, and 20% higher throughput in the insert, search, and scan operations, respectively. © 2023 IEEE.

关键词： Cost reduction

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Spark-KNN Parallel Pattern Recognition Technique for Substation Secondary Cable data 3

Spark-KNN Parallel Pattern Recognition Technique for Substat...

引用

3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Lu, Weizhou Huang, Haiying Luo, Huan Guangdong Power Grid Maoming Power Supply Bureau China

ISBN: (纸本)9798350382426

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data. © 2023 IEEE.

关键词： Online monitoring Secondary cable data Spark-KNN

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2nd international conference on Women Researchers in Electronics and computing, WREC 2023

2nd International Conference on Women Researchers in Electro...

引用

2nd international conference on Women Researchers in Electronics and computing, WREC 2023

ISBN: (纸本)9789819970766

The proceedings contain 56 papers. The special focus in this conference is on Women Researchers in Electronics and computing. The topics include: Analysis of Heart Disease Prediction Using Various Machine Learning algorithms;Analysis of Agricultural Commodities Prices Using BART: A Machine Learning Technique;deep Learning Model-Based Approach for Agricultural Crop Price Prediction in Indian Market;enhanced Intracranial Tumor Strain Prediction and Detection Using Transfer and Multilevel Ensemble Learning;detection and Classification of Blood Cancer Using Deep Learning Framework;deep Learning-Based Multi-label Image Classification for Chest X-Rays;fine-Tuning the Deep Learning Models Using Transfer Learning for the Classification of Lung Diseases from Chest Radiographs;a Systematic Approach for Effective Apgar Score Assessment in 1 and 5 min Using Manifold Machine Learning algorithms;SOT-MRAM Memories for Energy Efficient Embedded and AI Applications;data Pre-processing Techniques for Brain Tumor Classification;a Novel Approach for Detection of Lumpy Virus;internet of Healthcare Things-Enabled Open-Source Non-invasive Wearable Sensor Architecture for Incessant Real-Time Pneumonia Patient Monitoring;VANET Security Optimization with Blowfish Algorithm and Adversarial Transfer Learning;LoRa-IoT-Based Smart Weather data Acquisition and Prediction for PV Plant Using Machine Learning;entity Perception Using Remotely Piloted Aerial Vehicle;RF-MEMS SPDT Capacitive Switch: Accelerating the Performance in B5G Applications;Investigation of Bulk, Electronic and Transport Properties of Armchair Silicene Nanoribbon as Liquefied Petroleum Gas Combustion Indicator: A DFT Study;Recent Advancement of Artificial Intelligence in COVID-19: Prediction, Diagnosis, Monitoring, and Drug Development;analysis of Corner Truncated Rectangular Microstrip Patch Antenna for IoT Applications;dual-Polarized Textile Antenna for Full-Duplex Wearable Applications.

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Real-Time Network Monitoring: A Big data Approach 14

Real-Time Network Monitoring: A Big Data Approach

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Saravanan, S. Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs. © 2023 IEEE.

关键词： Network performance

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Research on Wireless Encrypted Communication Based on Multi-Channel Transmission 3

Research on Wireless Encrypted Communication Based on Multi-...

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3rd international conference on Intelligent Communications and computing, ICC 2023

作者： Li, Yuefeng Zhang, Xuewei Zhang, Ziqiang Huang, Qiangqiang Yan, Rong Li, Fen Zhang, Jingyu Yin, Yuan Beijing Aerospace Xinfeng Mechanical Equipment Co. Ltd Beijing China

ISBN: (纸本)9798350308327

With the development of the information age and the popularization of the internet, the ways of obtaining information are becoming increasingly rich, and the users' information security awareness is also constantly improving. The secure transmission of information has gradually become an inevitable requirement and tendency. Currently, most of the encryption algorithms utilize single-channel encryption for transmission, and sophisticated algorithms are mostly used for commercial purpose, which are not very economical for ordinary users. Besides, no matter how complex the encryption algorithm is, there is always a risk of being deciphered. Furthermore, once some commercial or industrial confidences are hijacked and deciphered, it will cause unpredictable losses. This paper proposes a wireless encrypted communication method based on multi-channel transmission. The proposed method integrates 'unpacking, multi-channel transmission, combining' operations on transmitting data, by applying corresponding algorithms. The data is transmitted in the form of 'fragments', which increases information entropy during the transmission process, and then improves the security of information transmission. Experimental results show that the method proposed has obvious advantages compared to the traditional data encryption transmission methods. At the same time, it provides a new low-cost, easy-to-operate solution for secure information transmission. © 2023 IEEE.

关键词： Security of data

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MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA data STORAGE 30

MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA DATA ST...

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30th IEEE international conference on Image processing (ICIP)

作者： Pic, Xavier Dimopoulou, Melpomeni San Antonio, Eva Gil Antonini, Marc Cote Azur Univ I3S Lab Nice France CNRS UMR 7271 Sophia Antipolis France

ISBN: (纸本)9781728198354

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.

关键词： image compression DNA JPEG 2000 arithmetic coder

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