一种通用的、可配置的、高效的第一代和第二代离散小波包变换体系结构,具有超高速和低成本的FPGA实现

Mouhamad Chehaitly, M. Tabaa, F. Monteiro, A. Dandache
{"title":"一种通用的、可配置的、高效的第一代和第二代离散小波包变换体系结构,具有超高速和低成本的FPGA实现","authors":"Mouhamad Chehaitly, M. Tabaa, F. Monteiro, A. Dandache","doi":"10.1063/1.5138576","DOIUrl":null,"url":null,"abstract":"This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.","PeriodicalId":186251,"journal":{"name":"TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY: TMREES19Gr","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A generic, configurable and efficient architecture for first and second generation discrete wavelet packet transform with ultra-high speed and low-cost FPGA implementation\",\"authors\":\"Mouhamad Chehaitly, M. Tabaa, F. Monteiro, A. Dandache\",\"doi\":\"10.1063/1.5138576\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.\",\"PeriodicalId\":186251,\"journal\":{\"name\":\"TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY: TMREES19Gr\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY: TMREES19Gr\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1063/1.5138576\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY: TMREES19Gr","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/1.5138576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

这项工作是无线传感器网络领域更广泛项目的一部分,其中小波变换是传输功能的核心。本文的目标是提出一种新的DWT架构,其特点是具有高水平的性能和低成本的设计。这一目标的实现主要得益于DWT算法中不同过滤器之间硬件资源的智能共享。本文介绍了基于Mallat算法的第一代离散小波包变换(DWPT)和基于提升方案的第二代离散小波包变换(DWPT)的体系结构。这些架构使我们能够以高采样率(高达每秒750兆样本)计算DWPT,同时只需要有限的硬件资源,并且在DWPT / IDWPT(逆DWPT)变换的不同深度阶段之间或内部不需要内存存储。这项工作是无线传感器网络领域更广泛项目的一部分,其中小波变换是传输功能的核心。本文的目标是提出一种新的DWT架构,其特点是具有高水平的性能和低成本的设计。这一目标的实现主要得益于DWT算法中不同过滤器之间硬件资源的智能共享。本文介绍了基于Mallat算法的第一代离散小波包变换(DWPT)和基于提升方案的第二代离散小波包变换(DWPT)的体系结构。这些架构使我们能够以高采样率(高达每秒750兆样本)计算DWPT,同时只需要有限的硬件资源,并且在DWPT / IDWPT(逆DWPT)变换的不同深度阶段之间或内部不需要内存存储。
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
A generic, configurable and efficient architecture for first and second generation discrete wavelet packet transform with ultra-high speed and low-cost FPGA implementation
This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.
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
Theoretical simulation model of a proton exchange membrane fuel cell Study the effect of nanoic indium oxide (In2O3) on electrical properties of ZnO- based varistor Synthesis of copper oxide nanoparticles (CuO-NPs) and its evaluation of antibacterial activity against P. aeruginosa biofilm gene’s Comparative analysis regarding burning process for different fuels in hybrid rocket engines Antibacterial activity of chitosan/PAN blend prepared at different ratios
×
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