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Research on Computer Processing Technology of Big Data Based on Cloud Environment 3

Research on Computer Processing Technology of Big Data Based...

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3rd international conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)

作者： Du, Chenglong Guangdong Univ Sci & Technol Dongguan 523083 Guangdong Peoples R China

ISBN: (纸本)9781665454551

With the development of The Times, the Internet has become an essential part of people's *** order to meet the needs of the market, it is necessary to continuously optimize and innovate in technology and display various advantages in the era of big *** methods such as distributed computing or parallel computing are limited by various conditions and cannot meet specific computing ***, in order to solve this problem, people gradually begin to adopt the computer processing technology of big data based on the background of cloud environment, so as to break these limitations and carry out data calculation more quickly and *** the background of the era of big data, the choice of vigorously promoting the use of advanced computer information processing technology can continue to ensure the security of different types of data storage and application, so that the application value of data information can be presented in an all-round way.

关键词： cloud environment computer technology big data processing calculate data storage information technology

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Enhancing Task Scheduling in Cloud Computing: A Multi-Objective Cuckoo Search Algorithm Approach 3

Enhancing Task Scheduling in Cloud Computing: A Multi-Object...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Shetty, Pallavi Veeraiah, Vivek Khidse, Swati Vitthal Rai, Mritunjay Gupta, Ankur Dhabliya, Dharmesh Dept. of Mca Karnataka Udupi India Sri Siddhartha Academy of Higher Education Dept. of Computer Science Karnataka Tumkur India Dr. Babasaheb Ambedkar Marathwada University Maharashtra Aurangabad India Shri Ramswaroop Memorial University Dept. of Ece UP Barabanki India Vaish College of Engineering Dept. of Cse Haryana Rohtak India Vishwakarma Institute of Information Technology Dept. of It Maharashtra Pune India

ISBN: (纸本)9798350330724

Services that are delivered and managed online employ cloud computing. Without taking into account the end user's actual location, cloud computing offers data access and storage devices. Clouds are quickly becoming as a crucial application platform for science. Due to the limitless number of nodes in the cloud environment, resources are inherently unstable, which has a significant impact on task execution and scheduling. Hardware, virtualization, distributed computing, and Internet-based service delivery improvements in recent years have all contributed to the evolution of cloud computing. Cloud computing may not entail many cutting-edge technology, but it does offer a novel approach to IT management. An essential and crucial issue in cloud systems is the proper scheduling of jobs that have been received across service providers. In cloud settings, the scheduling problem is NP-hard. The current system offers a scheduling algorithm that takes care of the main problems with job scheduling in the cloud. To plan the jobs in cloud computing for this project, the system suggested a method called Multi Objective Cuckoo Search. © 2023 IEEE.

关键词： Scheduling algorithms

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On the Role of ANN on the Estimation of Payload Inertia in parallel Kinematic Machine 3

On the Role of ANN on the Estimation of Payload Inertia in P...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Fuentes, José Alfonso Farías Giuletti, Nicola Carnevale, Marco Giberti, Hermes Università di Pavia Dept. Electrical Computer and Biomedical Engineering Pavia Italy Università di Pavia Dept. Industrial and Information Engineering Pavia Italy

ISBN: (纸本)9798350378009

In the robotic field, accurately estimating the inertial parameters of a robot payload is essential for improving path-following precision in model-based robot control. Nowadays, there are two main approaches to address this problem. One approach involves using direct measurement of the interaction between the robot End-Effector and the payload in terms of motions and exchanged forces-torques. The other approach involves a standard dynamic robot identification problem, considering the payload as an extension of the last robot link. Several of these techniques have been tested and consolidated for serial robots. However, results obtained for the estimation using parallel Kinematic Machines have been less accurate than those using serial robots, especially when no direct measurement is considered, due to the complexity of their kinematic chain and the greater impact of uncertainties in their geometrical and dynamic parameters. The purpose of this work is to evaluate the performance of an approach for estimating the inertial properties of the payload using a trained data-driven model, namely an Artificial Neural Network (ANN). This model takes as input the same information available to the machine when direct measurements are made (i.e., positions and forces exchanged between the End-Effector and the payload) and returns as output the inertia tensor and center of mass of the payload. The developed method is validated and compared with standard model-based approaches (e.g., Least Square Method based on the Newton-Euler equations). This work is the first step towards developing a novel approach for estimating the inertial properties of the robot payload using data-driven models with the inputs of the indirect method (i.e., actuator's motion and torque), in order to use as few sensors as possible. © 2024 IEEE.

关键词： Euler equations

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indexPDT: A High Scalable distributed Classification Approach with Novel Cache Structure for Geo-location 25

indexPDT: A High Scalable Distributed Classification Approac...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Sun, Zhijie Li, Jing Xie, Jun Zheng, Binfan Zeng, Li Zhao, Rongqian Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measured by the mobile communication network. The geographic location information can help to support network performance monitoring and evaluation. Considering accuracy and cost, we mainly adopt a hybrid location scheme combined with feature matching location and Weighted Centroid Correction Location (WCCL). As for feature matching location, over 20 billion samples gathered from tens of thousands of cells daily updated. Due to the vast data scale, feature analysis encounters a severe performance bottleneck. To address this problem, we design the indexed parallel decision tree (indexPDT) operator and integrate it into WindTensor, a self-innovated distributed machine learning (ML) engine. indexPDT is a classifier unit of the random forest (RF) algorithm with a novel cache structure. It performs structured cache processing on the dataset's meta-information, which can accompany the splitting of nodes. The cache structure can be quickly converted into statistical information to help find the optimal splitting point, effectively reducing memory usage and improving performance. Under the public datasets testing on 5 nodes, the mean speedup ratios are 86x and 3x compared with SparkML and XGBoost, respectively. In the Geo-location scenario, for a single cell, the speedup ratios are 82x and 4x compared with SparkML and XG Boost, respectively. © 2023 IEEE.

关键词： Location

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LoRa Modulation: A 2.4GHz Communication Strategy 3

LoRa Modulation: A 2.4GHz Communication Strategy

引用

3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Lavric, Alexandru Petrariu, Adrian I. Popa, Valentin Stefan Cel Mare University of Suceava Computers Electronics and Automation Department Romania Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan Cel Mare University Suceava Romania

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

In the past decade the internet that we know has changed from being governed by humans to inter-object devices that are able to communicate and share information by means of wireless communication protocols. This growth has led to a set of problems related to Internet of Things device coexistence that usually communicate on a shared radio channel. This paper focuses on LoRa modulation technology and its potential. The LoRa modulation was seen as a holy grail that came to solve many problems of the IoT concept, problems related to power energy efficiency and achieve large communication distances. In this paper, we present the performance evaluation of the LoRa 2.4GHz modulation considering the main advantages that it brings to IoT concept. Also, we present a Software Defined Radio (Srd) traffic generator for LoRa technology that can be used for the scalability evaluation of the LoRa 2.4GHz technology. The traffic generator is implemented and tested using Srd hardware. © 2022 IEEE.

关键词： Software radio

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A Bit Level Acceleration of Mixed Precision Neural Network 29

A Bit Level Acceleration of Mixed Precision Neural Network

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Qiu, Dehui Wang, Jing Hou, Yi Zhang, Weigong Gao, Lan Capital Normal University Information Engineering College Beijing China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350330717

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks. © 2023 IEEE.

关键词： bit level computing unit convolutional neural network FPGA hardware accelerator

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Design and Implementation of High-Speed Serial Transmission for ADC Based on JESD204B 3

Design and Implementation of High-Speed Serial Transmission ...

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3rd IEEE international conference on Intelligent Technology and Embedded systems, ICITES 2023

作者： Huang, Yawei Tian, Haishan Zhang, Mingru Sun, Yuxin Xie, Dan Tang, Chang School of Physics and Electronics Hunan Normal University Changsha China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

ISBN: (纸本)9798350343755

Currently, as the resolution of sensors such as radar continues to improve, the signal bandwidth is rapidly increasing. There is a growing demand for higher sampling rates and data transmission rates in Analog-to-Digital Converters (ADCs). However, the parallel transmission commonly used in ADCs faces issues such as high crosstalk and difficulties in transmission synchronization. In response to this problem, this paper proposes a method for the design and implementation of a high-speed serial data transmission interface based on JESD204B. Specifically, it utilizes Xilinx's ZYNQ-7 series FPGA and ADC as the core components and presents clock synchronization design, ADC circuit design, and FPGA 204B IP design methods. Experimental testing was conducted using selected link parameters (LMFS=4222) as an example. The experimental results demonstrate that correct JESD204B interface data transmission for four channels was achieved at a data rate of 10 Gbps, thus validating the feasibility of the proposed high-speed transmission link design based on JESD204B in this paper. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Integrating Blockchain for Supply Chain Management of Agricultural and Food Products 3rd

Integrating Blockchain for Supply Chain Management of Agricu...

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3rd international conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Cheripelli, Ramesh Prasannanjaneyulu, A.N.K. Department of Information Technology Vidya Jyothi Institue of Technology Hyderabad India Institute of Insurance and Risk Management Hyderabad India

ISBN: (纸本)9789819783540

Traceability is an essential part of agriculture supply chain management for guaranteeing the quality of food and boosting consumer happiness and loyalty. As a result of the problems associated with centralized data storage, we are transitioning to a system in which transparency and quality assurance are ensured at every stage of the supply chain, from framer to end user. The present supply chain model has some drawbacks, such as a lack of knowledge about the product’s travel history and origin and a lack of communication between the organizations of the supply chain. When different farmers and other interested parties use modern means of communication, everyone benefits. Blockchain technology improves supply chain security, transparency, and traceability by recording all supply chain transactions and making them permanently accessible to all parties. In this paper, we introduce a blockchain-based, distributed ledger system (DLT) for tracking assets that can guarantee the system’s honesty and openness. Most of the drawbacks of the old supply chain model were resolved by adopting this new one. We pre sented a decentralized supply chain concept with a smart contract for coordinating all supply chain transactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accountability Blockchain technology Credibility Food sustainability Reputation Security transparency Supply chain Traceability Trust

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Mutual information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling 23

Mutual Information as Intrinsic Reward of Reinforcement Lear...

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23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Zhang, Xianjie Sun, Jiahao Gong, Chen Wang, Kai Cao, Yifei Chen, Hao Liu, Yu Dalian University of Technology Dalian China University of Virginia Virginia United States Nanyang Technological University Singapore Institute of Automation Chinese Academy of Sciencec Beijing China

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services. Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), especially the regional dispatching of vehicles has a significant impact on matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get rides. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and request distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3% over the existing best on-demand ride pooling method. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Reinforcement learning

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Pawlowicz, Paulina Sunderam, Vaidy Hung, Che-Lun Kwiecien, Andrzej Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Emory University Department of Computer Science AtlantaGA United States National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei City Taiwan

ISBN: (纸本)9798350362480

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data. © 2024 IEEE.

关键词： information granules

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