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Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm

Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the com...

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Autores principales: Barrios-Avilés, Juan, Rosado-Muñoz, Alfredo, Medus, Leandro D., Bataller-Mompeán, Manuel, Guerrero-Martínez, Juan F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308842/
https://www.ncbi.nlm.nih.gov/pubmed/30477237
http://dx.doi.org/10.3390/s18124122
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author Barrios-Avilés, Juan
Rosado-Muñoz, Alfredo
Medus, Leandro D.
Bataller-Mompeán, Manuel
Guerrero-Martínez, Juan F.
author_facet Barrios-Avilés, Juan
Rosado-Muñoz, Alfredo
Medus, Leandro D.
Bataller-Mompeán, Manuel
Guerrero-Martínez, Juan F.
author_sort Barrios-Avilés, Juan
collection PubMed
description Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI—Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a “transparent mode” to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error (4.86 ± 1.87) when compared to the background activity filter (5.01 ± 1.93). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 × 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data).
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spelling pubmed-63088422019-01-04 Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm Barrios-Avilés, Juan Rosado-Muñoz, Alfredo Medus, Leandro D. Bataller-Mompeán, Manuel Guerrero-Martínez, Juan F. Sensors (Basel) Article Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI—Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a “transparent mode” to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error (4.86 ± 1.87) when compared to the background activity filter (5.01 ± 1.93). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 × 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data). MDPI 2018-11-24 /pmc/articles/PMC6308842/ /pubmed/30477237 http://dx.doi.org/10.3390/s18124122 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Barrios-Avilés, Juan
Rosado-Muñoz, Alfredo
Medus, Leandro D.
Bataller-Mompeán, Manuel
Guerrero-Martínez, Juan F.
Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
title Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
title_full Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
title_fullStr Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
title_full_unstemmed Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
title_short Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm
title_sort less data same information for event-based sensors: a bioinspired filtering and data reduction algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308842/
https://www.ncbi.nlm.nih.gov/pubmed/30477237
http://dx.doi.org/10.3390/s18124122
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