77a7a32c862d9277a2b602959b9294d2 pmc_10347157.pdf 8c7349322a489702c4f55a8cb0b351c24901163f pmc_10347157.pdf 541f215e0e777368cb217a4b653a8cfe96105315d94ef14889d0fbbad989e9eb pmc_10347157.pdf Title: AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity Subject: This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor's quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (6-fold accuracy enhancement). Keywords: microwave sensor; coupled CSRR; active sensor; deep neural network; convolutional neural network; selectivity; material characterization; mixture sensing Author: Mohammad Abdolrazzaghi, Nazli Kazemi, Vahid Nayyeri and Ferran Martin Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Jul 7 14:19:57 2023 CEST ModDate: Fri Jul 7 14:31:55 2023 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 20 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3820269 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- UFYELT+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 FOIPVQ+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 AOGMQZ+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 TTYNSN+CMSY10 Type 1 Builtin yes yes no 23 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 41 0 STLEUE+CMEX10 Type 1 Builtin yes yes no 87 0 IYTFPY+PazoMath-Italic Type 1 Builtin yes yes no 91 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 95 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 102 0 XYRGLE+PazoMath Type 1 Builtin yes yes no 106 0 YGACWG+PazoMath-BoldItalic Type 1 Builtin yes yes no 132 0 LJCHJU+PazoMath-Bold Type 1 Builtin yes yes no 136 0 PXKVEW+URWPalladioL-BoldItal Type 1 Custom yes yes no 144 0 ArialMT TrueType WinAnsi no no no 150 0 ArialMT TrueType WinAnsi no no no 153 0 TimesNewRomanPSMT TrueType WinAnsi no no no 156 0 ArialMT TrueType WinAnsi no no no 254 0 [none] Type 3 Custom yes no no 257 0 ArialMT TrueType WinAnsi no no no 276 0 ArialMT TrueType WinAnsi no no no 279 0 ArialMT TrueType WinAnsi no no no 282 0 TimesNewRomanPSMT TrueType WinAnsi no no no 285 0 Symbol Type 1 Symbol no no no 288 0 TimesNewRomanPSMT TrueType WinAnsi no no no 289 0 OVNEXW+EURM10 Type 1 Builtin yes yes no 298 0 AMMHYO+CMBSY10 Type 1 Builtin yes yes no 401 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-02-02 02:33:37 CET RepresentationInformation: pmc_10347157.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-02-01 02:45:09 CET Size: 3820269 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 729 FreeObjects: 10 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Coupled CSRR Principle Destination: section.2 Children: Item: Title: Nodal Analysis Destination: subsection.2.1 Item: Title: Even/Odd Mode Analysis Destination: subsection.2.2 Item: Title: Regenerative Amplification Destination: section.3 Item: Title: Sensitivity and Resolution Enhancement Destination: section.4 Item: Title: Loss-Compensation Mechanism Destination: section.5 Item: Title: Material Characterization Destination: section.6 Children: Item: Title: Sensor Response: FAQ (Frequency, Amplitude, Quality-Factor) Destination: subsection.6.1 Item: Title: Generating Training Dataset Destination: subsection.6.2 Item: Title: Characterizing with a Deep Neural Network Destination: subsection.6.3 Item: Title: Convolutional Neural Network Destination: subsection.6.4 Item: Title: Conclusions Destination: section.7 Item: Title: Appendix A Destination: section.A. Item: Title: References Destination: equation.A..1 Info: Title: AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity Author: Mohammad Abdolrazzaghi, Nazli Kazemi, Vahid Nayyeri and Ferran Martin Subject: This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor's quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (6-fold accuracy enhancement). 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