About: Signature Analysis for MEMS Pseudorandom Testing Using Neural Networks     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • Zaměření tohoto příspěvku je ve vyvinutí přídavného a levného integrovaného testu pro MEMS systémy. Předložená metoda je založena na zpracování Impulsové Odezvy (IR) pomocí natrénované neuronové sítě pro předpovídání souboru MEMS parametrů, které jsou v současnosti konvenčně zjišťovány drahým měřením. Použití neuronových sítí nám dovoluje použít nízkodimenzionální IR signaturu. MEMS struktura kombinuje elektro-termální vybuzení a piezorezistivní snímání. Tento model chování byl nasimulován pomocí Matlabu pro účely experimentu. Výsledky ukazují, že predikce neuronových sítí jsou v excelentní shodě s výsledky simulace chování modelu. (cs)
  • The aim of this work is to develop a low-overhead, low-cost built-in test for Micro Electro Mechanical Systems (MEMS). The proposed method relies on processing the Impulse Response (IR) through trained neural networks, in order to predict a set of MEMS performances, which are otherwise very expensive to measure using the conventional test approach. The use of neural networks allows us to employ a low-dimensional IR signature, which results in a compact built-in test. A MEMS structure combining electro-thermal excitation and piezoresistive sensing was chosen as our case study. A behavioral model of this structure was built using Matlab for the purpose of the experiment. The results demonstrate that the neural network predictions are in excellent agreement with the simulation results of the behavioral model.
  • The aim of this work is to develop a low-overhead, low-cost built-in test for Micro Electro Mechanical Systems (MEMS). The proposed method relies on processing the Impulse Response (IR) through trained neural networks, in order to predict a set of MEMS performances, which are otherwise very expensive to measure using the conventional test approach. The use of neural networks allows us to employ a low-dimensional IR signature, which results in a compact built-in test. A MEMS structure combining electro-thermal excitation and piezoresistive sensing was chosen as our case study. A behavioral model of this structure was built using Matlab for the purpose of the experiment. The results demonstrate that the neural network predictions are in excellent agreement with the simulation results of the behavioral model. (en)
Title
  • Signature Analysis for MEMS Pseudorandom Testing Using Neural Networks
  • Analýza signálu pro MEMS pseudonáhodné testování použitím neuronových sítí (cs)
  • Signature Analysis for MEMS Pseudorandom Testing Using Neural Networks (en)
skos:prefLabel
  • Signature Analysis for MEMS Pseudorandom Testing Using Neural Networks
  • Analýza signálu pro MEMS pseudonáhodné testování použitím neuronových sítí (cs)
  • Signature Analysis for MEMS Pseudorandom Testing Using Neural Networks (en)
skos:notation
  • RIV/49777513:23220/08:00500550!RIV09-MSM-23220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM4977751310)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 394686
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23220/08:00500550
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • MEMS testing; neural networks; feature selection (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [5E6E6BE82AC3]
http://linked.open...v/mistoKonaniAkce
  • Annecy, Francie
http://linked.open...i/riv/mistoVydani
  • Annecy
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 12th IMEKO TC1-TC7 joint Symposium on Man, Science & Measurement
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kupka, Lukáš
  • Tůmová, Olga
  • Mir, Salvador
  • Rufer, Libor
  • Simeu, Emmanuel
  • Stratigopoulos, Haralampos
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Université de Savoie
https://schema.org/isbn
  • 2-9516453-8-4
http://localhost/t...ganizacniJednotka
  • 23220
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.116 as of Feb 22 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3239 as of Feb 22 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 68 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software