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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm.

Juan Barrios-Avilés1, Alfredo Rosado-Muñoz2, Leandro D Medus3

  • 1Group for Digital Design and Processing, Department of Electronic Engineering, School of Engineering, Universitat de Valencia, Burjassot, 46100 Valencia, Spain. juan.barrios@uv.es.

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Summary
This summary is machine-generated.

This study introduces the Less Data Same Information (LDSI) bio-inspired filter to reduce event-based sensor data, cutting transmission load. The LDSI filter effectively minimizes data while preserving crucial information, demonstrating real-time processing capabilities on FPGA.

Keywords:
FPGA implementationbioinspired event filteringdynamic vision sensorevent data reductionevent-based sensorsneuromorphic systemsspike-based

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Area of Science:

  • Computer Vision and Sensor Data Processing
  • Bio-inspired Algorithms and Neuromorphic Engineering

Background:

  • Sensor data acquisition generates large volumes, potentially overloading communication channels.
  • Efficient data reduction is crucial for lowering hardware requirements and power consumption in networked sensor systems.

Purpose of the Study:

  • To propose and evaluate the Less Data Same Information (LDSI) filtering algorithm for event-based sensors.
  • To demonstrate data reduction without significant loss of relevant information.
  • To assess the algorithm's performance and real-time capabilities through simulations and FPGA implementation.

Main Methods:

  • Developed a bio-inspired filtering algorithm (LDSI) mimicking biological neuronal processing.
  • Configured the LDSI filter with varying parameters (weak, medium, restrictive).
  • Evaluated the filter using similarity detection and object tracking algorithms with DVS camera data.
  • Implemented the LDSI algorithm on a Xilinx Virtex6 FPGA for real-time performance analysis.

Main Results:

  • LDSI achieved up to 30% data reduction with no loss in similarity index for event data.
  • Data reduction reached 85% with a minor 15% penalty in similarity index.
  • The LDSI filter showed lower error rates (4.86 ± 1.87) in object tracking compared to background activity filters (5.01 ± 1.93).
  • FPGA implementation demonstrated real-time operation at 177 MHz with low resource usage (671 LUT, 40 Block RAM).

Conclusions:

  • The LDSI filter effectively reduces data from event-based sensors while retaining essential information.
  • The bio-inspired approach offers configurable data compression suitable for various applications.
  • FPGA implementation confirms the algorithm's suitability for real-time, low-power embedded systems.