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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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Wireless-Assisted Automatic Online Spatial Calibration based on 5G TSN for sensor Fusion

Wireless-Assisted Automatic Online Spatial Calibration based...

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2023 ieee international conference on Signal Processing, Communications and computing, ICSPCC 2023

作者： Lei, Ming Rahman, Mahib Mathaikutty, Deepak Alvarez, Ignacio Intel Corporation

ISBN: (纸本)9798350316728

In the field of artificial intelligence (AI), sensor fusion [1] is becoming an increasingly important technology for many Internet of things (IoT) applications. For intelligent transportation system (ITS), we propose a wireless-assisted automatic online spatial calibration for sensor fusion. Due to the mobility of traffic objects such as vehicles, it is critical to make sure ground truth (GT) position and corresponding sample positions are measured based on raw data collected by different sensors at the same time. We use 5G time sensitive networking (TSN) to achieve this time alignment. Additionally, we illustrate how to perform automatic miscalibration detection and correction based on correctly grouped GT position and sample positions. There are many advantages of using our solution in practical sensor fusion systems that can significantly improve traffic safety and efficiency. © 2023 ieee.

关键词： Calibration

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Machine Learning Enabled FBG Optical sensor Applications 2

Machine Learning Enabled FBG Optical Sensor Applications

引用

2nd ieee international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Gupta, Soni Kumar, Bhupesh Mishra, Sumita Amity University Amity School of Engineering and Technology Rajasthan India Amity University Uttar Pradesh India Amity University Amity School of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9798350390179

This review paper develops the current understanding of machine learning applications in the Structural health monitoring industry using Fiber Bragg Grating (FBG) sensors. We describe how this synergy transforms strain measuring, temperature sensing, and structural health monitoring based on analysis of papers and case studies. In addition to aspects involving these sensors, the discussion of how integration can achieve accuracy, real-time capabilities, and predictive analytics also raises issues with data management complexity, the efficiency of the algorithms to be used, and the efficiency of integrating this into current systems. This research paper also presents a comprehensive investigation into using FBG optical sensor technology and machine learning models for the early detection and management of diabetes mellitus. Thus, as we suggest possible solutions and directions of further research in the form of conclusions, it seems to us that a growing trend will make in aviation operations safer than ever and more efficient. © 2024 ieee.

关键词： Fiber Bragg gratings

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Leveraging Machine Learning for Enhanced sensorPowered Green IoT with Federated Learning 15

Leveraging Machine Learning for Enhanced SensorPowered Green...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Devrani, Rekha Premkumar, R. Kumar, Shyam Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

Federated getting to know (FL) is a gadget studying generation which allows a user to train a single model across more than one dispensed system, with none of the person systems having to transfer the statistics sets, nor the version, to a vital device for education. It allows disbursed schooling of an unmarried model throughout more than one disbursed net of things (IoT) gadgets or users. It makes it feasible to improve the accuracy and scalability of our sensor-Powered inexperienced IoT applications. FL can consequently be instrumental in taking inexperienced IoT answers towards the character sources of environmental records, like air firstclass sensors, as opposed to amassing facts *** permits us to leverage powerful gadget learning models to enhance the accuracy and scalability of our sensor-Powered green IoT systems. By using federated models throughout education, we will grow the accuracy of our predictions and decrease the amount of effort and time we have to acquire and store sensor facts centrally. Moreover, FL lets us improve the version overall performance of green IoT packages by dispensing schooling and predictions to a disbursed community of sensor-based totally IoT devices. We also can, in addition, reduce the reliance on central systems with the aid of transferring the middle of our sensor-Powered inexperienced IoT packages from the cloud to allotted nodes. © 2024 ieee.

关键词： Generation Predictions Scalability sensor Statistics Throughout Unmarried

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distributed Power Parameter Measurement Method Using a Strong Tracking Factor Kalman Filter Based on Particle Swarm Optimization 5

Distributed Power Parameter Measurement Method Using a Stron...

引用

5th ieee international conference on Power, Intelligent computing and systems, ICPICS 2023

作者： Yin, Xiang Fan, Chuanguang Yang, Xinyuan Fu, Gaoshan Zang, Siqi Li, Zhangcheng State Grid Xinjiang Electric Power Co. Ltd. Urumqi China PowerChina Huber Electric Engineering Co. Ltd. Wuhan China Xinjiang Transmission Power Co. Ltd. Urumqi China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9798350333442

distributed power sources can reduce transmission and network losses, improve grid stability and reliability, and lower system operation costs, which are of great significance for improving the economy and environmental protection of the power system. The signals in distributed power systems often contain various noise, harmonic, and inter-harmonic interferences, which seriously affect the accuracy of phasor measurement. To address this issue, a particle swarm optimization (PSO) algorithm is used to optimize the strong tracking factor based on the strong tracking Kalman filter (STKF), and measure the amplitude, frequency, and phase angle of power signals. The results show that compared with the strong tracking Kalman filter, the PSO-STKF algorithm improves the accuracy and convergence speed of phasor measurement. The research enriches the content of signal measurement in power systems. © 2023 ieee.

关键词： Phasor measurement units

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Efficient Power Management in Multicore systems using distributed On-Chip Switched-Capacitor Converters with DVFS 2

Efficient Power Management in Multicore Systems using Distri...

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2nd ieee international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Aiswarya, M. Veera Boopathy, E. Kiruba, S. Karthick, L.S. Kannadhasan, S. Gomalavalli, R. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of ECE Coimbatore India Rathinam Technical Campus Department of Electronics and Communication Engineering Coimbatore India Study World College of Engineering Department of Electronics and Communication Engineering Coimbatore India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Efficient power management is paramount in modern multicore systems to balance performance and energy consumption. This paper explores the integration of distributed On-Chip Switched-Capacitor Converters (DoS-DCCs) with Dynamic Voltage and Frequency Scaling (DVFS) support to address this challenge. DoS-DCCs offer a decentralized approach to power delivery, enabling localized conversion tailored to individual cores or clusters. Leveraging a switched-capacitor architecture, these converters provide benefits such as high efficiency and compatibility with standard CMOS processes. By integrating DVFS, DoS-DCCs empower cores to dynamically adjust their voltage and frequency, optimizing performance while minimizing energy usage. This paper presents the implementation of DoS-DCCs with DVFS support in multicore systems and discusses their potential to enhance power efficiency and performance scalability in modern computing environments. This paper presents a comprehensive exploration of the implementation and benefits of DoS-DCCs with DVFS support in multicore systems. Through simulation studies and experimental validation, the effectiveness of this approach is demonstrated in improving power efficiency and performance scalability. Furthermore, potential challenges and future research directions for advancing the integration of DoS-DCCs with DVFS in next-generation multicore architectures are outlined. Overall, the combination of distributed On-Chip Switched-Capacitor Converters and Dynamic Voltage and Frequency Scaling offers a compelling solution for efficient power management in multicore systems, paving the way for enhanced performance and energy efficiency in future computing platforms. © 2024 ieee.

关键词： Capacitor bank

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Using Machine Learning Regression Model to Predict the Optimum Election Algorithm for Parallel and distributed computing systems 3

Using Machine Learning Regression Model to Predict the Optim...

引用

3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Dhariwal, Naman Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9798350370867

Parallel and distributed computing has become a necessity when it comes to high-performance computing, artificial intelligence, big data analytics, machine learning, deep learning, signal processing and bioengineering. System engineers connect distributed systems, with large number of nodes, that then compute the solution for a problem concurrently. The distributed system has a leader that manages the overall coordination of these connected nodes. This research provides a novel approach to predict the most efficient and time-feasible election algorithm, that must be ran on a distributed system, using linear regression machine learning approach. This research implements the prediction process for three election algorithms and successfully predicts the most efficient and least time-consuming algorithm with 94.98% accuracy. Initially, the dataset for the regression model training was created by executing the three election algorithms parallelly using MPI running 16 parallel processors (cores). The research also presents three regression line equations, trained and modelled on this dataset, that can be used to predict the execution time for the three algorithms for a system with any number of nodes. The results are duly visualized, and regression lines are plotted for comparison. © 2023 ieee.

关键词： Forecasting

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AreaHash: A Balanced and fully scalable consistency hashing algorithm 24

AreaHash: A Balanced and fully scalable consistency hashing ...

引用

24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zeng, Yan Zhang, Jinbo Xue, Meiting Wan, Jian Zhang, Jilin Zhou, Li School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Cyberspace Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9798350319934

distributed systems use consistent hashing algorithms to solve load balancing problems because of their good balance, minimal interruptions, and high query rates. However, existing algorithms fail to maintain consistency under changes, require high memory for high query rates, or have poor scaling. Therefore, we propose consistent AreaHash, which virtualizes the hash space into a ring. The logically ordered ring nodes occupy the same area, allowing the AreaHash to expand the nodes without any change. The key calculated using the hashing algorithm can be mapped to a node by a simple shift calculation, reducing memory requirements. We make a reasonable theoretical analysis and perform high-level algorithm implementation with a memory footprint of only a few bytes per node. Experimental results indicate that AreaHash achieves a lookup rate of more than 40 million keys per second, even when the number of nodes on a single CPU exceeds one million. In addition, a lookup rate of more than 20 million keys per second is achieved when more than half of the nodes in the cluster are down. © 2022 ieee.

关键词： Consistent hashing distributed systems fully scalable load balancing resource efficient

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SpDISTAL: Compiling distributed Sparse Tensor Computations

SpDISTAL: Compiling Distributed Sparse Tensor Computations

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international conference for High Performance computing, Networking, Storage and Analysis (HPC)

作者： Yadav, Rohan Aiken, Alex Kjolstad, Fredrik Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665454445

We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and computation distribution. Thus, it enables distributed execution of sparse tensor algebra expressions with a wide variety of sparse data structures and data distributions. SpDISTAL is implemented as a C++ library that targets a distributed task-based runtime system and can generate code for nodes with both multi-core CPUs and multiple GPUs. SpDISTAL generates distributed code that achieves performance competitive with hand-written distributed functions for specific sparse tensor algebra expressions and that outperforms general interpretation-based systems by one to two orders of magnitude. © 2022 ieee.

关键词： Computer Science Programming Parallel Programming

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HyperSack: distributed Hyperparameter Optimization for Deep Learning using Resource-Aware Scheduling on Heterogeneous GPU systems 31

HyperSack: Distributed Hyperparameter Optimization for Deep ...

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31st Annual ieee international conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Alnaasan, Nawras Ramesh, Bharath Yao, Jinghan Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. Department of Computer Science and Engineering The Ohio State University ColumbusOH United States

ISBN: (纸本)9798331509095

Hyperparameter Optimization (HPO) can unlock the full potential of Deep Learning (DL) models;however, it is considered one of the most compute-intensive tasks in the DL domain due to multi-dimensional search spaces and complex neural network architectures. A common method for accelerating HPO workloads is parallelizing training jobs on multiple computing devices, such as modern GPUs in High-Performance computing (HPC) environments. Nonetheless, existing HPO parallelization strategies underutilize powerful GPU devices, like the NVIDIA A100 and H100, especially for training lightweight Deep Neural Networks. Resource-sharing mechanisms can improve GPU utilization;nonetheless, naïve adaptations for HPO workloads lead to poor performance. Therefore, we propose HyperSack—a distributed HPO framework for dynamic and resource-aware scheduling on heterogeneous GPU-based HPC systems with resource elasticity and fault tolerance. HyperSack reduces the execution time of HPO workloads by orchestrating the placement of DL training jobs on GPU devices with different computational capabilities. It supports different hardware architectures, HPO workloads, and scheduling policies. Our evaluations on vision and language models HPO workloads show up to 2.8x performance improvement in execution time on A100 GPUs, 4.0x on H100 GPUs, and 3.9x on a combination of 12 A100 and 4 H100 GPUs using HyperSack over standard HPO parallelization methods. © 2024 ieee.

关键词： Graphics processing unit

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distributed Computations with Layered Resolution 11

Distributed Computations with Layered Resolution

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11th ieee international conference on Cloud Networking (CloudNet)

作者： Esfahanizadeh, Homa Cohen, Alejandro Medard, Muriel Shamai (Shitz), Shlomo MIT RLE Cambridge MA 02139 USA Technion Fac Elect & Comp Engn Haifa Israel

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

Modern computationally-heavy applications are often time-sensitive, demanding distributed strategies to accelerate them. On the other hand, distributed computing suffers from the bottleneck of slow workers in practice. distributed coded computing is an attractive solution that adds redundancy such that a subset of distributed computations suffices to obtain the final result. However, the final result is still either obtained within a desired time or not, and for the latter, the resources that are spent are wasted. In this paper, we introduce the novel concept of layered-resolution distributed coded computations such that lower resolutions of the final result are obtained from collective results of the workers - at an earlier stage than the final result. This innovation makes it possible to have more effective deadline-based systems, since even if a computational job is terminated because of timing, an approximated version of the final result can be released. Based on our theoretical and empirical results, the average execution delay for the first resolution is notably smaller than the one for the final resolution. Moreover, the probability of meeting a deadline is one for the first resolution in a setting where the final resolution exceeds the deadline almost all the time, reducing the success rate of the systems with no layering.

关键词： Technological innovation conferences Redundancy Delays distributed computing

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