國立虎尾科技大學 |

In-material Processing of High Bandwidth Sensor Measurements using Modular Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	In-material Processing of High Bandwidth Sensor Measurements using Modular Neural Networks./
Author:	Hughes, Dana.
Description:	1 online resource (119 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355966114

In-material Processing of High Bandwidth Sensor Measurements using Modular Neural Networks.
Hughes, Dana.

In-material Processing of High Bandwidth Sensor Measurements using Modular Neural Networks. - 1 online resource (119 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2018.

Includes bibliographical references

Robotic materials are a novel class of materials that tightly integrate sensing, computing, and actuation into an engineered material or composite to allow the behavior of the material to be defined algorithmically. Robotic materials are constructed using an embedded network of computing nodes based on small, inexpensive microcontrollers. Examples of such materials include morphable airfoils which change shape in response to flight conditions or mission parameters, robotic skins with rich tactile sensing capabilities that recognize texture or touch gestures, clothing with tightly integrated sensing to assist with or augment the wearer's perception of the environment, or materials with dynamic camouflage capabilities.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355966114Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

In-material Processing of High Bandwidth Sensor Measurements using Modular Neural Networks.
LDR:04562ntm a2200385Ki 4500 001 916897
005 20180928111502.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355966114
035 $a (MiAaPQ)AAI10792412
035 $a (MiAaPQ)colorado:15430
035 $a AAI10792412
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Hughes, Dana. $3 1190762
245 1 0 $a In-material Processing of High Bandwidth Sensor Measurements using Modular Neural Networks.
264 0 $c 2018
300 $a 1 online resource (119 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Nikolaus Correll.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2018.
504 $a Includes bibliographical references
520 $a Robotic materials are a novel class of materials that tightly integrate sensing, computing, and actuation into an engineered material or composite to allow the behavior of the material to be defined algorithmically. Robotic materials are constructed using an embedded network of computing nodes based on small, inexpensive microcontrollers. Examples of such materials include morphable airfoils which change shape in response to flight conditions or mission parameters, robotic skins with rich tactile sensing capabilities that recognize texture or touch gestures, clothing with tightly integrated sensing to assist with or augment the wearer's perception of the environment, or materials with dynamic camouflage capabilities.
520 $a In this thesis, I develop a framework for in-material processing which tightly couples modularized deep neural networks and high-bandwidth sensors using a network of embedded, material-scale components. This framework enables materials to learn multiple desired responses to stimuli, avoiding the need for accurate modeling of the dynamics of the material and stimuli.
520 $a I utilize a modular neural network design consisting of convolutional (CNN) and long short-term memory (LSTM) layers implemented in each node in the material as a computational approach for robotic materials. This network architecture allows for nodes in the material to process local sensor values, maintain local state information, and communicate with nodes in a local neighborhood in the materials. A multiobjective optimization approach is employed to automatically design the neural network architectures which maximizes the performance of the network while ensuring hardware budgets, such as memory requirements, are maintained. A communication network design is also developed to allow network modules to learn a communication protocol that limits communication to a desired rate, ensuring in-network bandwidth constraints are maintained.
520 $a I demonstrate the suitability of this computational model for robotic materials using examples in several domains. An RF-based e-textile gesture input device capable of distinguishing between user control gestures is used to control arbitrary external devices. A tire with embedded piezoelectric sensing capabilites for use in high-performance autonomous vehicles performs state-of-the-art identification of terrains driven on. Two robotic skins are presented---one which is capable of detecting and localizing contact, and identifying the texture of the contacting objects; and a second which assists with avoiding collisions with obstacles and identifies affective touch gestures performed by a human collaborator. Finally, a distributed approach to human activity recognition is presented whose activity identification performance is comparable to a centralized approach, but can be implemented on hardware designed for wearable applications, as opposed to a GPU-enabled device. The examples shown demonstrate that robotic materials can perform significant in-material processing; are loosely coupled from a host system, communicating a minimal number of low-bandwidth events to the host; and can exhibit multifunctional behavior that is analyzed for safety or performance considerations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Robotics. $3 561941
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0771
690 $a 0800
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Colorado at Boulder. $b Computer Science. $3 1180184
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792412 $z click for full text (PQDT)