{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95628"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95628","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"In-sensor information processing for resource-limited platforms on flexible epidermal substrates","abstract":"Moving towards the age of big data, the demand for embedded processing has been drastically increasing to make inference and intelligent decisions at lower architectural layers. The myriad of health conditions that can be treated and analyzed via low-energy embedded information processing kernels drives the demand for biomedical circuits, with optimized performance and cost. A large class of these healthcare applications require digital signal processing algorithms to be implemented with strict resources, such as energy and silicon area. Shrinking technology nodes produce both higher computing performance and energy efficiency. However, energy delivery and communication circuitry have not benefited significantly from technology scaling due to different sets of figures of merit. In-sensor information processing can be utilized to lower the energy consumption of such systems by eliminating the redundant volume of data traffic between the sensors and the central processing station. This work focuses on embedding intelligence on the epidermal flexible substrates to extract and analyze critical biomedical information for in-situ diagnosis. The primary objective of this work is illustrating the advantages of epidermal electronics combined with robust information processing systems, at system and application level. The major challenge is the design of robust and efficient algorithms for reliable operation on resource limited hardware platforms and flexible substrate non-idealities. To do so, we developed the first in-sensor ECG and PPG processors on flexible epidermal substrates. The systems are first prototyped using discrete components, followed by an ASIC implementation. Measurement results show that the in-sensor information processing has reduced the transmitted data traffic by 150X, and the system energy consumption by 3.56X.","abstract_html":"Moving towards the age of big data, the demand for embedded processing has been drastically increasing to make inference and intelligent decisions at lower architectural layers. The myriad of health conditions that can be treated and analyzed via low-energy embedded information processing kernels drives the demand for biomedical circuits, with optimized performance and cost. A large class of these healthcare applications require digital signal processing algorithms to be implemented with strict resources, such as energy and silicon area. Shrinking technology nodes produce both higher computing performance and energy efficiency. However, energy delivery and communication circuitry have not benefited significantly from technology scaling due to different sets of figures of merit. In-sensor information processing can be utilized to lower the energy consumption of such systems by eliminating the redundant volume of data traffic between the sensors and the central processing station. This work focuses on embedding intelligence on the epidermal flexible substrates to extract and analyze critical biomedical information for in-situ diagnosis. The primary objective of this work is illustrating the advantages of epidermal electronics combined with robust information processing systems, at system and application level. The major challenge is the design of robust and efficient algorithms for reliable operation on resource limited hardware platforms and flexible substrate non-idealities. To do so, we developed the first in-sensor ECG and PPG processors on flexible epidermal substrates. The systems are first prototyped using discrete components, followed by an ASIC implementation. Measurement results show that the in-sensor information processing has reduced the transmitted data traffic by 150X, and the system energy consumption by 3.56X.","abstract_has_math":false,"creators":["Assem, Pourya"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Shanbhag, Naresh R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T17:02:15Z","date_published":"2017-03-01T17:02:15Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Application-specific integrated circuit (ASIC)","Integrated circuit (IC)","Near-field communication (NFC)","Pan-Tompkins Algorithm (PTA)","Photoplethysmogram (PPG)","Epidermal electronics"],"languages":["en"],"rights":["Copyright 2016 Pourya Assem"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95628","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shanbhag, Naresh R."]},{"key":"dc:creator","label":"Author","values":["Assem, Pourya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T17:02:15Z","2019-03-02T10:15:27Z","2016-07-07","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Application-specific integrated circuit (ASIC)","Integrated circuit (IC)","Near-field communication (NFC)","Pan-Tompkins Algorithm (PTA)","Photoplethysmogram (PPG)","Epidermal electronics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Pourya Assem"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95628"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Moving towards the age of big data, the demand for embedded processing has been drastically increasing to make inference and intelligent decisions at lower architectural layers. The myriad of health conditions that can be treated and analyzed via low-energy embedded information processing kernels drives the demand for biomedical circuits, with optimized performance and cost. A large class of these healthcare applications require digital signal processing algorithms to be implemented with strict resources, such as energy and silicon area. Shrinking technology nodes produce both higher computing performance and energy efficiency. However, energy delivery and communication circuitry have not benefited significantly from technology scaling due to different sets of figures of merit. In-sensor information processing can be utilized to lower the energy consumption of such systems by eliminating the redundant volume of data traffic between the sensors and the central processing station. This work focuses on embedding intelligence on the epidermal flexible substrates to extract and analyze critical biomedical information for in-situ diagnosis. The primary objective of this work is illustrating the advantages of epidermal electronics combined with robust information processing systems, at system and application level. The major challenge is the design of robust and efficient algorithms for reliable operation on resource limited hardware platforms and flexible substrate non-idealities. To do so, we developed the first in-sensor ECG and PPG processors on flexible epidermal substrates. The systems are first prototyped using discrete components, followed by an ASIC implementation. Measurement results show that the in-sensor information processing has reduced the transmitted data traffic by 150X, and the system energy consumption by 3.56X.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Pourya Assem, accepted the attached license on 2016-07-05 at 17:47.","The student, Pourya Assem, submitted this Thesis for approval on 2016-07-05 at 17:49.","This Thesis was approved for publication on 2016-07-07 at 12:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9769 on 2017-02-28 at 14:40:24","Made available in DSpace on 2017-03-01T17:02:15Z (GMT). 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The myriad of health conditions that can be treated and analyzed via low-energy embedded information processing kernels drives the demand for biomedical circuits, with optimized performance and cost. A large class of these healthcare applications require digital signal processing algorithms to be implemented with strict resources, such as energy and silicon area. Shrinking technology nodes produce both higher computing performance and energy efficiency. However, energy delivery and communication circuitry have not benefited significantly from technology scaling due to different sets of figures of merit. In-sensor information processing can be utilized to lower the energy consumption of such systems by eliminating the redundant volume of data traffic between the sensors and the central processing station. This work focuses on embedding intelligence on the epidermal flexible substrates to extract and analyze critical biomedical information for in-situ diagnosis. The primary objective of this work is illustrating the advantages of epidermal electronics combined with robust information processing systems, at system and application level. The major challenge is the design of robust and efficient algorithms for reliable operation on resource limited hardware platforms and flexible substrate non-idealities. To do so, we developed the first in-sensor ECG and PPG processors on flexible epidermal substrates. The systems are first prototyped using discrete components, followed by an ASIC implementation. Measurement results show that the in-sensor information processing has reduced the transmitted data traffic by 150X, and the system energy consumption by 3.56X.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Pourya Assem, accepted the attached license on 2016-07-05 at 17:47.","The student, Pourya Assem, submitted this Thesis for approval on 2016-07-05 at 17:49.","This Thesis was approved for publication on 2016-07-07 at 12:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9769 on 2017-02-28 at 14:40:24","Made available in DSpace on 2017-03-01T17:02:15Z (GMT). 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