Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications
Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/10/3552 |
| _version_ | 1797533383468777472 |
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| author | Alessandro Tonelli Veronica Mangia Alessandro Candiani Francesco Pasquali Tiziana Jessica Mangiaracina Alessandro Grazioli Michele Sozzi Davide Gorni Simona Bussolati Annamaria Cucinotta Giuseppina Basini Stefano Selleri |
| author_facet | Alessandro Tonelli Veronica Mangia Alessandro Candiani Francesco Pasquali Tiziana Jessica Mangiaracina Alessandro Grazioli Michele Sozzi Davide Gorni Simona Bussolati Annamaria Cucinotta Giuseppina Basini Stefano Selleri |
| author_sort | Alessandro Tonelli |
| collection | DOAJ |
| description | Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results. |
| first_indexed | 2024-03-10T11:14:46Z |
| format | Article |
| id | doaj.art-035c4cf5e22a4ebb8a677b03521cf319 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:14:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-035c4cf5e22a4ebb8a677b03521cf3192023-11-21T20:30:46ZengMDPI AGSensors1424-82202021-05-012110355210.3390/s21103552Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics ApplicationsAlessandro Tonelli0Veronica Mangia1Alessandro Candiani2Francesco Pasquali3Tiziana Jessica Mangiaracina4Alessandro Grazioli5Michele Sozzi6Davide Gorni7Simona Bussolati8Annamaria Cucinotta9Giuseppina Basini10Stefano Selleri11DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyDNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyDNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyDNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyDNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyDNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyDNAPhone S.R.L., Viale Mentana 150, 43121 Parma, ItalyH&D S.R.L., Strada Langhirano 264/1a, 43124 Parma, ItalyDipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, ItalyDipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, ItalyDipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, ItalyDipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, ItalySingle-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.https://www.mdpi.com/1424-8220/21/10/3552Raspberry PiCMOSraw dataphotometric analysisimagingoxidative stress |
| spellingShingle | Alessandro Tonelli Veronica Mangia Alessandro Candiani Francesco Pasquali Tiziana Jessica Mangiaracina Alessandro Grazioli Michele Sozzi Davide Gorni Simona Bussolati Annamaria Cucinotta Giuseppina Basini Stefano Selleri Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications Sensors Raspberry Pi CMOS raw data photometric analysis imaging oxidative stress |
| title | Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications |
| title_full | Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications |
| title_fullStr | Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications |
| title_full_unstemmed | Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications |
| title_short | Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications |
| title_sort | sensing optimum in the raw leveraging the raw data imaging capabilities of raspberry pi for diagnostics applications |
| topic | Raspberry Pi CMOS raw data photometric analysis imaging oxidative stress |
| url | https://www.mdpi.com/1424-8220/21/10/3552 |
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