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Development of a Practical Electrical Tomography System for Flexible Contact Sensing Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of a Practical Electrical Tomography System for Flexible Contact Sensing Applications./
作者:	Russo, Stefania.
面頁冊數:	1 online resource (157 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Tomography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28470925click for full text (PQDT)
ISBN:	9798505520291

Development of a Practical Electrical Tomography System for Flexible Contact Sensing Applications.
Russo, Stefania.

Development of a Practical Electrical Tomography System for Flexible Contact Sensing Applications. - 1 online resource (157 pages)

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of Salford (United Kingdom), 2018.

Includes bibliographical references

Tactile sensing is seeing an increase in potential applications, such as in humanoid and industrial robots; health care systems and medical instrumentation; prosthetic devices; and in the context of human-machine interaction. However, these applications require the integration of tactile sensors over various objects with different surface shapes. This emphasises the need of developing sensors which are flexible in contrast with the common rigid type. Moreover, flexible sensing research is considered to be in its infancy. Many technological and system issues are still open, mainly: conformability; scalability; system integration; high system cost; sensor size; and power consumption.In light of the above, this thesis is concerned with the development of a flexible fabric-based contact sensor system. This is done through an interdisciplinary approach whereby electronics, system engineering, electrical tomography, and machine learning have been considered. This results in a practical flexible sensor that is capable of accurately detecting contact locations with high temporal resolution; and requires low power consumption. The sensor is based on the principle of electrical tomography. This is essential since this technique allows us to eliminate electrodes and wiring from within the sensing area, confining them to the periphery of the sensor. This improves flexibility all while eliminating electrode fatigue and deterioration due to repeated loading. We start by developing an electrical tomography sensor system. This comprises of a piezoresistive flexible fabric material, a data acquisition card, and a custom printed circuit board for managing both current injection and data collection. We show that current injection and voltage measurement protocols respond differently to different positions of the input contact region of interest, consequently affecting the overall performance of the tomography sensor system. Then, an approach for classifying contact location over the sensor is presented. This is done using supervised machine learning, namely discriminant analysis. Accurate touch location identification is achieved, along with an increase in the detection speed and sensor versatility. Finally, the sensor is placed over different surfaces in order to show and validate its efficiency.The main finding of this work is that electrical tomography flexible sensor systems present a very promising technology, and can be practically and effectively used for developing inexpensive and durable flexible sensors for tactile applications. The main advantage of this approach is the complete absence of wires in the internal area of the sensor. This allows the sensor to be placed over surfaces with different shapes without losing its functionality. The sensor's applicability can be further improved by using machine learning strategies due to their ability of empirical learning and extracting meaningful tactile information.The research work in this thesis was motivated by the problems faced by industrial partners which were part of the sustainable manufacturing and advanced robotics training network in Europe (SMART-e).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798505520291Subjects--Topical Terms:

836553
Tomography.
Index Terms--Genre/Form:

542853
Electronic books.

Development of a Practical Electrical Tomography System for Flexible Contact Sensing Applications.
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245 1 0 $a Development of a Practical Electrical Tomography System for Flexible Contact Sensing Applications.
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500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Meziani, Samia-Nefti.
502 $a Thesis (Ph.D.)--University of Salford (United Kingdom), 2018.
504 $a Includes bibliographical references
520 $a Tactile sensing is seeing an increase in potential applications, such as in humanoid and industrial robots; health care systems and medical instrumentation; prosthetic devices; and in the context of human-machine interaction. However, these applications require the integration of tactile sensors over various objects with different surface shapes. This emphasises the need of developing sensors which are flexible in contrast with the common rigid type. Moreover, flexible sensing research is considered to be in its infancy. Many technological and system issues are still open, mainly: conformability; scalability; system integration; high system cost; sensor size; and power consumption.In light of the above, this thesis is concerned with the development of a flexible fabric-based contact sensor system. This is done through an interdisciplinary approach whereby electronics, system engineering, electrical tomography, and machine learning have been considered. This results in a practical flexible sensor that is capable of accurately detecting contact locations with high temporal resolution; and requires low power consumption. The sensor is based on the principle of electrical tomography. This is essential since this technique allows us to eliminate electrodes and wiring from within the sensing area, confining them to the periphery of the sensor. This improves flexibility all while eliminating electrode fatigue and deterioration due to repeated loading. We start by developing an electrical tomography sensor system. This comprises of a piezoresistive flexible fabric material, a data acquisition card, and a custom printed circuit board for managing both current injection and data collection. We show that current injection and voltage measurement protocols respond differently to different positions of the input contact region of interest, consequently affecting the overall performance of the tomography sensor system. Then, an approach for classifying contact location over the sensor is presented. This is done using supervised machine learning, namely discriminant analysis. Accurate touch location identification is achieved, along with an increase in the detection speed and sensor versatility. Finally, the sensor is placed over different surfaces in order to show and validate its efficiency.The main finding of this work is that electrical tomography flexible sensor systems present a very promising technology, and can be practically and effectively used for developing inexpensive and durable flexible sensors for tactile applications. The main advantage of this approach is the complete absence of wires in the internal area of the sensor. This allows the sensor to be placed over surfaces with different shapes without losing its functionality. The sensor's applicability can be further improved by using machine learning strategies due to their ability of empirical learning and extracting meaningful tactile information.The research work in this thesis was motivated by the problems faced by industrial partners which were part of the sustainable manufacturing and advanced robotics training network in Europe (SMART-e).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Tomography. $3 836553
650 4 $a Accuracy. $3 3559958
650 4 $a Electrodes. $3 629151
650 4 $a Prostheses. $3 3683608
650 4 $a Discriminant analysis. $3 560438
650 4 $a Communication. $3 524709
650 4 $a Artificial intelligence. $3 516317
650 4 $a Printed circuit boards. $3 3560292
650 4 $a Robots. $3 529507
650 4 $a Friendship. $3 611043
650 4 $a Robotics. $3 519753
650 4 $a Motivation. $3 532704
650 4 $a Principal components analysis. $3 565921
650 4 $a Neural networks. $3 677449
650 4 $a Sensors. $3 3549539
650 4 $a Clothing. $3 3682213
650 4 $a Medical equipment. $3 3560831
650 4 $a Algorithms. $3 536374
650 4 $a Textiles. $3 3559975
650 4 $a Textile research. $3 2153103
650 4 $a Medical imaging. $3 3172799
650 4 $a Statistics. $3 517247
650 4 $a Individual & family studies. $3 2122770
650 4 $a Random variables. $3 646291
650 4 $a Classification. $3 595585
650 4 $a Data collection. $3 3561708
650 4 $a Human-computer interaction. $3 560071
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0459
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690 $a 0574
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of Salford (United Kingdom). $3 3556058
773 0 $t Dissertations Abstracts International $g 83-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28470925 $z click for full text (PQDT)

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