快速小波分解算法在FPGA-SoC上的速度优化实现

A. Fite, M. Gromov, Tianyang Fang, J. Saniie
{"title":"快速小波分解算法在FPGA-SoC上的速度优化实现","authors":"A. Fite, M. Gromov, Tianyang Fang, J. Saniie","doi":"10.1109/eIT57321.2023.10187330","DOIUrl":null,"url":null,"abstract":"In ultrasonic nondestructive evaluation (NDE) of materials an essential step in characterizing an ultrasonic signal is decomposing the patterns of multiple interfering echoes. The Chirplet Transform (CT) is a powerful method to analyze the echoes in an ultrasonic signal. However, CT analysis is computationally heavy and impractical. Motivated by achieving real-time execution of the CT this research presents a speed-optimized implementation of the chirplet functions on FPGA. Chirplet echo generation used in Fast Chirplet Decomposition (FCD) Algorithm for ultrasonic signal analysis necessitates the frequent generation of chirplet functions with a 6-degree of freedom associated with chirplet parameters including the amplitude scaler; the time of arrival; the Gaussian envelope scaler; the phase of the chirplet; the center frequency and the frequency sweep. By minimizing the processing time of the chirplet generation, the FCD algorithm can be implemented efficiently on FPGA System-on-Chip (SoC). This study presents the hardware realization of the chirplet function on FPGA which is 37 times faster compared to using a Teensy 4.0 microcontroller, and 146 times faster than a highly popular Raspberry Pi 4.0 single board computer.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Speed-Optimized Implementation of Fast Chirplet Decomposition Algorithm on FPGA-SoC\",\"authors\":\"A. Fite, M. Gromov, Tianyang Fang, J. Saniie\",\"doi\":\"10.1109/eIT57321.2023.10187330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In ultrasonic nondestructive evaluation (NDE) of materials an essential step in characterizing an ultrasonic signal is decomposing the patterns of multiple interfering echoes. The Chirplet Transform (CT) is a powerful method to analyze the echoes in an ultrasonic signal. However, CT analysis is computationally heavy and impractical. Motivated by achieving real-time execution of the CT this research presents a speed-optimized implementation of the chirplet functions on FPGA. Chirplet echo generation used in Fast Chirplet Decomposition (FCD) Algorithm for ultrasonic signal analysis necessitates the frequent generation of chirplet functions with a 6-degree of freedom associated with chirplet parameters including the amplitude scaler; the time of arrival; the Gaussian envelope scaler; the phase of the chirplet; the center frequency and the frequency sweep. By minimizing the processing time of the chirplet generation, the FCD algorithm can be implemented efficiently on FPGA System-on-Chip (SoC). This study presents the hardware realization of the chirplet function on FPGA which is 37 times faster compared to using a Teensy 4.0 microcontroller, and 146 times faster than a highly popular Raspberry Pi 4.0 single board computer.\",\"PeriodicalId\":113717,\"journal\":{\"name\":\"2023 IEEE International Conference on Electro Information Technology (eIT)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Conference on Electro Information Technology (eIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/eIT57321.2023.10187330\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Electro Information Technology (eIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eIT57321.2023.10187330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在材料的超声无损检测中,表征超声信号的一个重要步骤是分解多重干扰回波的模式。小波变换(CT)是一种分析超声回波信号的有效方法。然而,CT分析计算量大且不切实际。为了实现CT的实时执行,本研究在FPGA上提出了一种速度优化的小波函数实现。用于超声信号分析的快速啁啾分解(FCD)算法中的啁啾回波生成需要频繁生成具有6个自由度的啁啾函数,该函数与包括幅度标度在内的啁啾参数相关;到达时间;高斯包络标量;啁啾的相位;中心频率和扫频。通过最小化小波产生的处理时间,FCD算法可以在FPGA片上有效地实现。本研究提出了在FPGA上实现啁啾功能的硬件实现,比使用Teensy 4.0微控制器快37倍,比非常流行的树莓派4.0单板计算机快146倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Speed-Optimized Implementation of Fast Chirplet Decomposition Algorithm on FPGA-SoC
In ultrasonic nondestructive evaluation (NDE) of materials an essential step in characterizing an ultrasonic signal is decomposing the patterns of multiple interfering echoes. The Chirplet Transform (CT) is a powerful method to analyze the echoes in an ultrasonic signal. However, CT analysis is computationally heavy and impractical. Motivated by achieving real-time execution of the CT this research presents a speed-optimized implementation of the chirplet functions on FPGA. Chirplet echo generation used in Fast Chirplet Decomposition (FCD) Algorithm for ultrasonic signal analysis necessitates the frequent generation of chirplet functions with a 6-degree of freedom associated with chirplet parameters including the amplitude scaler; the time of arrival; the Gaussian envelope scaler; the phase of the chirplet; the center frequency and the frequency sweep. By minimizing the processing time of the chirplet generation, the FCD algorithm can be implemented efficiently on FPGA System-on-Chip (SoC). This study presents the hardware realization of the chirplet function on FPGA which is 37 times faster compared to using a Teensy 4.0 microcontroller, and 146 times faster than a highly popular Raspberry Pi 4.0 single board computer.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Correlation of Egg counts, Micro-nutrients, and NDVI Distribution for Accurate Tracking of SCN Population Density Detection Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles ChatGPT: A Threat Against the CIA Triad of Cyber Security Smart UX-design for Rescue Operations Wearable - A Knowledge Graph Informed Visualization Approach for Information Retrieval in Emergency Situations Comparative Study of Deep Learning LSTM and 1D-CNN Models for Real-time Flood Prediction in Red River of the North, USA
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1