Low Power FSAS utilizing the complex multitasking units of ML processors

Author(s)	Srinivasan Venugopalan, Ajay Kumar Naik Guguloth, Chandra Sekhar Kuluru, Ravi Sunkugalla
Country	India
Abstract	The study was conducted to analyze throughput of chamber-driven leaf study using manual-herbarium methods or with botanical scopes are popular in laboratory data extraction for classification of floral parameters to be utilized in toxic-studies. The intravascular studies of leaves are need for vital identifiers to determine their genetic roots and classify them in their nomenclature with character association. Products from them are highly dependent not only on their chemical behavior but also on their genetic and physical attributes. Picturesque information taken from cameras are offline data that consume more pixels that need to be compressed before transmission.
Keywords	AQI – Air Quality Index, FSAS- Foliar Sample Analyses System, MLP- Machine Learning Processors, BER- Bit Error Rate, GIS- Geographical Information Systems
Field	Computer > Data / Information
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-11
Cite This	Low Power FSAS utilizing the complex multitasking units of ML processors - Srinivasan Venugopalan, Ajay Kumar Naik Guguloth, Chandra Sekhar Kuluru, Ravi Sunkugalla - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.7362
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.7362
Short DOI	https://doi.org/gsv6jv

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