{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84020"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84020","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Analog Circuits Based Machine Learning Classifier to Detect Counterfeit Currency Notes","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Madurai Narayanamurthy, Anish; 0000-0001-8082-0655"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sanyal, Arindam","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:07Z","date_published":"2022-06-21T15:47:07Z","updated_at":"2026-07-27T19:05:28Z","subjects":["artificial intelligence","electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84020","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sanyal, Arindam","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Madurai Narayanamurthy, Anish; 0000-0001-8082-0655"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:07Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["artificial intelligence","electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84020"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","A classifier based on Analog circuits is proposed in this work to detect counterfeit currency notes. The classifier is designed in 65nm Technology using CMOS transistors. Switched capacitor circuits are used to implement the Multiple and accumulate operation and a differential amplifier is used to generate the non-linearity used for classification. The classifier produces an accuracy of 95.25% when tested with the Bank Note Authentication dataset (UC Irvine Repository). It consumes a total power of 5.892µW while operating with a 1V supply voltage. The classifier is trained in MATLAB and implemented in Cadence Virtuoso and validated using simulations.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Analog Circuits Based Machine Learning Classifier to Detect Counterfeit Currency Notes"]}]}],"canonical_facts":{"dc:contributor":["Sanyal, Arindam","Electrical Engineering"],"dc:creator":["Madurai Narayanamurthy, Anish; 0000-0001-8082-0655"],"dc:date":["2022-06-21T15:47:07Z","2020"],"dc:description":["M.S.","A classifier based on Analog circuits is proposed in this work to detect counterfeit currency notes. The classifier is designed in 65nm Technology using CMOS transistors. Switched capacitor circuits are used to implement the Multiple and accumulate operation and a differential amplifier is used to generate the non-linearity used for classification. The classifier produces an accuracy of 95.25% when tested with the Bank Note Authentication dataset (UC Irvine Repository). It consumes a total power of 5.892µW while operating with a 1V supply voltage. The classifier is trained in MATLAB and implemented in Cadence Virtuoso and validated using simulations.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84020"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["artificial intelligence","electrical engineering"],"dc:title":["Analog Circuits Based Machine Learning Classifier to Detect Counterfeit Currency Notes"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:28Z"}