Difference between revisions of "User:Dpalossi"
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[[File:DP-UAV.jpeg|thumb|right|210px|Daniele Palossi]] | [[File:DP-UAV.jpeg|thumb|right|210px|Daniele Palossi]] | ||
Daniele is a Ph.D. student at the Dept. of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Daniele's research interest is in energy-efficiency on embedded real-time platforms for autonomous vehicles and advanced driver assistance systems, with particular emphasis on approximate computing and bio-inspired techniques. | Daniele is a Ph.D. student at the Dept. of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Daniele's research interest is in energy-efficiency on embedded real-time platforms for autonomous vehicles and advanced driver assistance systems, with particular emphasis on approximate computing and bio-inspired techniques. | ||
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+ | ==Contact Information== | ||
+ | * '''Office''': ETZ J76.2 | ||
+ | * '''e-mail''': [mailto:daniele.palossi@iis.ee.ethz.ch daniele.palossi@iis.ee.ethz.ch] | ||
+ | * '''phone''': (+41 44 63) 388 43 | ||
+ | * '''www''': IIS Staff page [http://www.iis.ee.ethz.ch/portrait/staff/dpalossi.en.html] | ||
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==Projects== | ==Projects== | ||
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[[Category:Software]] | [[Category:Software]] |
Revision as of 22:25, 19 February 2018
Contents
Daniele Palossi
Daniele is a Ph.D. student at the Dept. of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Daniele's research interest is in energy-efficiency on embedded real-time platforms for autonomous vehicles and advanced driver assistance systems, with particular emphasis on approximate computing and bio-inspired techniques.
Contact Information
- Office: ETZ J76.2
- e-mail: daniele.palossi@iis.ee.ethz.ch
- phone: (+41 44 63) 388 43
- www: IIS Staff page [1]
Projects
Available Projects
- Event-based navigation on autonomous nano-drones
- A Novel Execution Scheme for Ultra-tiny CNNs Aboard Nano-UAVs
- Improved Collision Avoidance for Nano-drones
- Deep Learning-based Global Local Planner for Autonomous Nano-drones
- Monocular Vision-based Object Following on Nano-size Robotic Blimp
- A Waypoint-based Navigation System for Nano-Size UAVs in GPS-denied Environments
- Covariant Feature Detector on Parallel Ultra Low Power Architecture
Projects In Progress