Veronica Mangia
Research Subjects
Research Interests
Veronica Mangia has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Alessandro Candiani
DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy
1 shared publication - Alessandro Grazioli
DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy
1 shared publication - Alessandro Tonelli
DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy
1 shared publication - Annamaria Cucinotta
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
1 shared publication - Davide Gorni
Research and Development Department, H&D S.r.l., 43124 Parma, Italy
1 shared publication - Francesco Pasquali
DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy
1 shared publication
Latest Publications
- Open Access
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 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.
Open AccessSensing 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 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.