Hand detection application based on QRD RLS Lattice algorithm and its implementation on Xilinx Zynq Ultrascale+

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00558772" target="_blank" >RIV/67985556:_____/22:00558772 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/22:00358639

Výsledek na webu

<a href="http://nnw.cz/doi/2022/NNW.2022.32.005.pdf" target="_blank" >http://nnw.cz/doi/2022/NNW.2022.32.005.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2022.32.005" target="_blank" >10.14311/NNW.2022.32.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hand detection application based on QRD RLS Lattice algorithm and its implementation on Xilinx Zynq Ultrascale+

Popis výsledku v původním jazyce

The present paper describes hand detection application implemented on Xilinx Zynq Ultrascale+ device, comprising multi-core processor ARM Cortex A53 and FPGA programmable logic. It uses ultrasound data and is based on adaptive QRD RLS lattice algorithm extended with hypothesis testing. The algorithm chooses between two use-cases: (1) “there is a hand in front of the device” vs (2) “there is no hand in front of the device”. For these purposes a new structure of the identification models was designed. The model presenting use-case (1) is a regression model, which has the order sufficient to cover all incoming data. The model responsible for use-case (2) is a regression model, which has a smaller order than the model (1) and a certain time delay, covering the maximal distance where the hand can possibly appear. The offered concept was successfully verified using real ultrasound data in MATLAB optimized for parallel processing and implemented in parallel on four cores of ARM Cortex A53 processor. It was proved that computational time of the algorithm is sufficient for applications requiring real-time processing.n

Název v anglickém jazyce

Hand detection application based on QRD RLS Lattice algorithm and its implementation on Xilinx Zynq Ultrascale+

Popis výsledku anglicky

The present paper describes hand detection application implemented on Xilinx Zynq Ultrascale+ device, comprising multi-core processor ARM Cortex A53 and FPGA programmable logic. It uses ultrasound data and is based on adaptive QRD RLS lattice algorithm extended with hypothesis testing. The algorithm chooses between two use-cases: (1) “there is a hand in front of the device” vs (2) “there is no hand in front of the device”. For these purposes a new structure of the identification models was designed. The model presenting use-case (1) is a regression model, which has the order sufficient to cover all incoming data. The model responsible for use-case (2) is a regression model, which has a smaller order than the model (1) and a certain time delay, covering the maximal distance where the hand can possibly appear. The offered concept was successfully verified using real ultrasound data in MATLAB optimized for parallel processing and implemented in parallel on four cores of ARM Cortex A53 processor. It was proved that computational time of the algorithm is sufficient for applications requiring real-time processing.n

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/8A21009" target="_blank" >8A21009: Embedded storage elements on next MCU generation ready for AI on the edge</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

20

Strana od-do

73-92

Kód UT WoS článku

000821082700001

EID výsledku v databázi Scopus

2-s2.0-85134783820