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Difference between revisions of "Sensor Fusion for Rockfall Sensor Node"

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Over the last years, [http://www.slf.ch SLF Davos] has been using embedded sensor nodes for measuring trajectories of falling rocks. The acquired data is used to improve simulations of rockfall incidents. However, the employed sensor nodes ([http://www.embedded.arch.ethz.ch/Projects/StoneNode StoneNode]) have come to their end-of-life and are currently being replaced with better and smaller sensors employing state-of-the-art technology. Over the last months, a new StoneNode has been developed at the IIS, and preliminary field test data has recently been obtained.
 
Over the last years, [http://www.slf.ch SLF Davos] has been using embedded sensor nodes for measuring trajectories of falling rocks. The acquired data is used to improve simulations of rockfall incidents. However, the employed sensor nodes ([http://www.embedded.arch.ethz.ch/Projects/StoneNode StoneNode]) have come to their end-of-life and are currently being replaced with better and smaller sensors employing state-of-the-art technology. Over the last months, a new StoneNode has been developed at the IIS, and preliminary field test data has recently been obtained.
  
In this master project you are going to analyze the obtained data from accelerometers/gyroscopes and pressure sensors, and develop sensor fusion approaches which would allow to partially or completely reconstruct the rock trajectories. Depending on the project progress, novel approaches introducing additional sources of information could be evaluated as well (e.g., videos shot by a drone, 3D terrain models, etc.).
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In this master project you are going to analyze the obtained data from accelerometers/gyroscopes and pressure sensors, and develop sensor fusion approaches which will allow partial or complete reconstruction of the rock trajectories. Depending on the project progress, novel approaches introducing additional sources of information could be evaluated as well (e.g., videos shot by a drone, 3D terrain models, etc.).
  
  

Revision as of 12:43, 6 November 2016

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Short Description

Over the last years, SLF Davos has been using embedded sensor nodes for measuring trajectories of falling rocks. The acquired data is used to improve simulations of rockfall incidents. However, the employed sensor nodes (StoneNode) have come to their end-of-life and are currently being replaced with better and smaller sensors employing state-of-the-art technology. Over the last months, a new StoneNode has been developed at the IIS, and preliminary field test data has recently been obtained.

In this master project you are going to analyze the obtained data from accelerometers/gyroscopes and pressure sensors, and develop sensor fusion approaches which will allow partial or complete reconstruction of the rock trajectories. Depending on the project progress, novel approaches introducing additional sources of information could be evaluated as well (e.g., videos shot by a drone, 3D terrain models, etc.).


Status: Available

  • Looking for 1-2 Master Students
Supervisors: Michele Magno, Michael Schaffner, Lukas Cavigelli, Andrin Caviezel (SLF Davos)

Prerequisites

  • PCB design experience (PCB course strongly recommended)
  • analog electronics and signal conditioning with operational amplifiers: amplifiers, filters, integrators etc.
  • basic knowledge of microcontroller programming (C, preferably Texas Instruments MSP430)
  • basic knowledge on signal processing is a plus.

Character

30% Theory
30% Implementation
40% Evaluation

Partners

SLF Davos

Professor

Luca Benini

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