2020 10th Annual Computing and Communication Workshop and Conference (CCWC)最新文献

Location Privacy Protection in Online Query based-on Privacy Region Replacement 基于隐私区域替换的在线查询位置隐私保护

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031176

Yali Ji, Ruowei Gui, Xiaolin Gui, Dong Liao, Xinyang Lin

引用次数: 6

Dynamic Constellation Mapping and SER Performance for Asynchronized PNC Using QPSK 基于QPSK的异步PNC动态星座映射与SER性能

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031110

Aditi Singh, T. M. Syed, L. Joiner

{"title":"Dynamic Constellation Mapping and SER Performance for Asynchronized PNC Using QPSK","authors":"Aditi Singh, T. M. Syed, L. Joiner","doi":"10.1109/CCWC47524.2020.9031110","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031110","url":null,"abstract":"The increased throughput performance of physical-layer network coding (PNC) has led to rigorous research in this field for two-way relay systems or multi-way systems. For coherent detection of information received by the relay node and decoding the correct symbols transmitted by the user nodes after removing self-information, synchronization in the system is a critical requirement. This paper aims to study the symbol error rate (SER) performance of an asynchronized PNC system, i.e. a system with phase misalignments. The symbols of two end users arrive with a phase difference of $phi$. We study a mapping scheme for the combined signal at the relay based on its superimposed constellation structure incorporating the phase difference $phi$. An analytical bound on SER performance at the relay node in presence of phase difference $phi$ is derived. Simulation results showing identical SER performance for a fixed value of $phi$, independent of the individual link phase offsets, have been presented. It can be seen that mapping provides better spectral efficiency when compared to the de-noise and forward scheme with a slight tradeoff in SER performance compared to non-mapped case.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114661501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Applying k-means Method to the Modified Bottleneck Assignment Problem in Vector Case k-均值法在矢量情况下改进瓶颈分配问题中的应用

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031191

Y. Kamura

引用次数: 0

Monitoring and Alarm System Timing Analysis for an Interconnecting Grid 互联电网监控报警系统时序分析

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031167

Steve Chan, P. Nopphawan

引用次数: 1

Complex Motion Detection Based on Channel State Information and LSTM-RNN 基于通道状态信息和LSTM-RNN的复杂运动检测

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031214

Pengyu Zhang, Zhuoran Su, Zehua Dong, K. Pahlavan

引用次数: 5

Interface for Human Machine Interaction for assistant devices: A Review 辅助设备的人机交互界面:综述

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031244

Saifuddin Mahmud, Xiangxu Lin, Jong-Hoon Kim

引用次数: 32

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based Matrix-Vector Multiplication (MVM) 利用机器学习估计基于opencl的矩阵向量乘法(MVM)的利用率和吞吐量

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031173

Jannatun Naher, C. Gloster, C. Doss, Shrikant S. Jadhav

{"title":"Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based Matrix-Vector Multiplication (MVM)","authors":"Jannatun Naher, C. Gloster, C. Doss, Shrikant S. Jadhav","doi":"10.1109/CCWC47524.2020.9031173","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031173","url":null,"abstract":"OpenCL is a framework for writing programs that execute across heterogeneous platforms, including FPGAs. OpenCL allows users to write standardized C-like code for the host as well as for the hardware accelerators, thus reducing the programming challenge for FPGAs. Hardware descriptions can be written in OpenCL using different memory access and data partitioning strategies. Matrix-Vector Multiplication (MVM) is the critical computational bottleneck for many System of Linear Equations (SLEs) solvers. The MVM OpenCL kernel can be optimized by varying several design parameters in the OpenCL description, improving hardware performance. To effectively explore the design space, logic synthesis is performed after each iteration of setting design parameters to determine their impact on design area and performance. However, each of these synthesis runs can take multiple hours. Hence, manual design space exploration for a large number of designs is prohibitive. To address this challenge, a prediction of FPGA utilization and throughput can significantly reduce the design time. This paper presents a machine learning-based approach to estimating FPGA utilization and throughput for a given set of design parameter values. It also presents an optimized MVM implementation obtained after compiling, synthesizing, and executing over 100 designs. The Random Forest machine learning algorithm estimates the result and for 175 designs, the average error is. 0098%,. 0012%,. 0039%,. 0414%, and 123.21% for estimating Look-up Tables (LUTs), Digital Signal Processors (DSPs), memory bits, RAM blocks and throughput (GFLOPs) respectively.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Group Mutual Exclusion protocol for the Use Case of IoT-Blockchain Integration In Work-Safe Scenario 安全生产场景下物联网-区块链集成用例的组互斥协议

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031277

Q. Mamun, Muhammad Arif Khan

引用次数: 2

Machine Learning Techniques for Motion Analysis of Fatigue from Manual Material Handling Operations Using 3D Motion Capture Data 使用3D运动捕捉数据的人工材料搬运操作疲劳运动分析的机器学习技术

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031222

Geovanni Hernandez, Damian Valles, David C. Wierschem, Rachel M. Koldenhoven, G. Koutitas, F. A. M. Mediavilla, S. Aslan, Jesús A. Jiménez

{"title":"Machine Learning Techniques for Motion Analysis of Fatigue from Manual Material Handling Operations Using 3D Motion Capture Data","authors":"Geovanni Hernandez, Damian Valles, David C. Wierschem, Rachel M. Koldenhoven, G. Koutitas, F. A. M. Mediavilla, S. Aslan, Jesús A. Jiménez","doi":"10.1109/CCWC47524.2020.9031222","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031222","url":null,"abstract":"Industrial Revolution 4.0 is defined as the interconnection of Information, Communications Technologies (ICT), and factory floor workers. Workers in the material handling industry are often subject to repetitive motions that cause exhaustion (or fatigue) which leads to work-related musculoskeletal disorders (WMSDs). The most common repetitive motions are lifting, pulling, pushing, carrying and walking with load. In this research data is collected as time-stamped motion data using infrared cameras at a rate of 100Hz while a subject performs one of the repetitive motions (i.e. lifting). The data is a combination of xyz-coordinates of 39 reflective markers. This results in 117 data points for each frame captured. Since these motions occur over time for a duration of time, this data is used as input to a time-series machine learning (ML) model such as Recurrent Neural Network (RNN). Using this model, this paper evaluates machine learning techniques, based on RNN, to evaluate the fatigue factor caused by repetitive motions.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117307855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Investigating the Security Threats on Networked Medical Devices 调查网络医疗设备的安全威胁

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031212

David Zaldivar, L. Tawalbeh, Fadi Muheidat

引用次数: 11