Difference between revisions of "Smart sensor"
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Robin R. Murphy. Dempster–Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 14, NO. 2, APRIL 1998 197. | Robin R. Murphy. Dempster–Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 14, NO. 2, APRIL 1998 197. | ||
|Prerequisites=Projects involving both software, hardware, and electronics require much development/time | |Prerequisites=Projects involving both software, hardware, and electronics require much development/time | ||
| − | |Supervisor=Martin Cooney, | + | |Supervisor=Martin Cooney, Håkan Petterson |
|Author=Can Yang | |Author=Can Yang | ||
|Level=Master | |Level=Master | ||
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}} | }} | ||
Goal: small smart sensors | Goal: small smart sensors | ||
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Motivation: | Motivation: | ||
Current sensors are "static", detecting just one thing, such as light, sound, or touch, because of which in complex systems such as vehicles or homes many sensors are typically required. | Current sensors are "static", detecting just one thing, such as light, sound, or touch, because of which in complex systems such as vehicles or homes many sensors are typically required. | ||
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Alternatively, an ensemble of dynamic sensors could acquire more information than the same number of static sensors. | Alternatively, an ensemble of dynamic sensors could acquire more information than the same number of static sensors. | ||
Furthermore, if such sensors can be made small, various benefits would emerge: e.g., making arbitrary surfaces embedded with sensors for robots such as intelligent skin, and interesting tasks at the micro scale could potentially be facilitated, such as monitoring cells inside of a person. | Furthermore, if such sensors can be made small, various benefits would emerge: e.g., making arbitrary surfaces embedded with sensors for robots such as intelligent skin, and interesting tasks at the micro scale could potentially be facilitated, such as monitoring cells inside of a person. | ||
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Challenges: | Challenges: | ||
It is unknown how a sensor could be automatically changed into a different kind of sensor, a good strategy for changing a sensor or an ensemble of sensors, and how to achieve this at a small scale. | It is unknown how a sensor could be automatically changed into a different kind of sensor, a good strategy for changing a sensor or an ensemble of sensors, and how to achieve this at a small scale. | ||
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Approach: | Approach: | ||
The student will | The student will | ||
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-Design transitions between some typical sensors | -Design transitions between some typical sensors | ||
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-Design algorithms for calculating information/interestingness for each modality (possibly using change point detection) and for strategically allocating roles (like particles in a particle filter). | -Design algorithms for calculating information/interestingness for each modality (possibly using change point detection) and for strategically allocating roles (like particles in a particle filter). | ||
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-Design a strategy for the micro scale | -Design a strategy for the micro scale | ||
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-Implement a macro-scale proof-of-concept, which can be switched manually or algorithmically, and a smaller proof-of-concept (mini, or micro; nano would be cool but would probably not be possible) | -Implement a macro-scale proof-of-concept, which can be switched manually or algorithmically, and a smaller proof-of-concept (mini, or micro; nano would be cool but would probably not be possible) | ||
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-Evaluate | -Evaluate | ||
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-Thus, students will develop both software, and hardware/electronics | -Thus, students will develop both software, and hardware/electronics | ||
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Expected result: some ideas (new knowledge) of how to adapt different sensors, at different scales. Thesis, prototypes, video, hopefully a paper, possibly other such as code | Expected result: some ideas (new knowledge) of how to adapt different sensors, at different scales. Thesis, prototypes, video, hopefully a paper, possibly other such as code | ||
Latest revision as of 16:15, 1 February 2018
| Title | Smart sensor |
|---|---|
| Summary | Small smart sensors |
| Keywords | Smart sensors, adaptation, sensors |
| TimeFrame | First half of 2018 |
| References | Surya G. Nurzaman, Utku Culha, Luzius Brodbeck, Liyu Wang, Fumiya Iida. (2013) Active Sensing System with In Situ Adjustable Sensor Morphology. PLoS ONE 8(12):e84090. doi:10.1371/journal.pone.0084090
Robin R. Murphy. Dempster–Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 14, NO. 2, APRIL 1998 197. |
| Prerequisites | Projects involving both software, hardware, and electronics require much development/time |
| Author | Can Yang |
| Supervisor | Martin Cooney, Håkan Petterson |
| Level | Master |
| Status | Ongoing |
Goal: small smart sensors
Motivation: Current sensors are "static", detecting just one thing, such as light, sound, or touch, because of which in complex systems such as vehicles or homes many sensors are typically required. A "smart" sensor which could dynamically adapt itself (to detect different signals) could replace multiple static sensors, leading to less space taken, less cost, less work for installations, a greater ability to operate when changes occur, and possibly even easier repairs (self-healing). Alternatively, an ensemble of dynamic sensors could acquire more information than the same number of static sensors. Furthermore, if such sensors can be made small, various benefits would emerge: e.g., making arbitrary surfaces embedded with sensors for robots such as intelligent skin, and interesting tasks at the micro scale could potentially be facilitated, such as monitoring cells inside of a person.
Challenges: It is unknown how a sensor could be automatically changed into a different kind of sensor, a good strategy for changing a sensor or an ensemble of sensors, and how to achieve this at a small scale.
Approach: The student will
-Design transitions between some typical sensors
-Design algorithms for calculating information/interestingness for each modality (possibly using change point detection) and for strategically allocating roles (like particles in a particle filter).
-Design a strategy for the micro scale
-Implement a macro-scale proof-of-concept, which can be switched manually or algorithmically, and a smaller proof-of-concept (mini, or micro; nano would be cool but would probably not be possible)
-Evaluate
-Thus, students will develop both software, and hardware/electronics
Expected result: some ideas (new knowledge) of how to adapt different sensors, at different scales. Thesis, prototypes, video, hopefully a paper, possibly other such as code
