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Hardware and software contributions for optimized detection of fluorescent labeled cytokines
- Source :
- 2017 International Semiconductor Conference (CAS).
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Paper presents hardware improvements, step by step, besides to software refinements for the fluorescent labelled cytokines detection, associated to bacterial meningitis diagnosis. The hardware optimization firstly concerns the implementation of a versatile hardware device with the possibility to obtain the finally best solution. The software refinement includes Monte Carlo combined with denoising algorithms. The logistic curve extracted from fluorescent labelled cytokine images, reduces the error vector from 629 to 2.4, offering the best set of parameters.
- Subjects :
- Computer science
business.industry
Noise reduction
Monte Carlo method
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
021001 nanoscience & nanotechnology
Fluorescence
030218 nuclear medicine & medical imaging
Set (abstract data type)
03 medical and health sciences
Statistical classification
ComputingMethodologies_PATTERNRECOGNITION
0302 clinical medicine
Software
Bacterial meningitis
0210 nano-technology
business
Computer hardware
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 International Semiconductor Conference (CAS)
- Accession number :
- edsair.doi...........212f51ad6fcfce3a4d51ab6be5bcc977
- Full Text :
- https://doi.org/10.1109/smicnd.2017.8101221