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Calibration Software & Data Sets

Graph Signal Reconstruction Python library used in: "Graph Signal Reconstruction Techniques for Air Pollution Monitoring Platforms" paper

Python library used in the elaboration of the paper: "Graph Signal Reconstruction Techniques for Air Pollution Monitoring Platforms" submitted to the IEEE Transactions on Network Science and Engineering. The Python library implements the different methods used in the paper; laplacian-based interpolated regularization from Belkin et al., GSP low-pass based from Stankovic et al. and graph Kernel Ridge Regression from Romero et al. Finally, the file includes useful functions such as various graph kernels.

Software

Authors: Pau Ferrer-Cid, Jose M. Barcelo-Ordinas and Jorge Garcia-Vidal.

Calibration Software and Data Sets used in: "Multi-sensor data fusion calibration in IoT air pollution platforms" paper

Calibration software and data sets used in the elaboration of the paper: "Multi-sensor data fusion calibration in IoT air pollution platforms" submitted to the IEEE Internet of Things journal. There are two types of data sets; the first type contain data from several metal-oxide sensors that coincided in the same location, along with the ozone reference values for calibration purposes. While the second type contain data from one electro-chemical sensor and four metal-oxide sensors, with the ozone reference values.

Software and Data Sets

Authors: Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal, Anna Ripoll and Mar Viana.

Calibration Software and Data Sets used in: "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms" paper

Calibration software and data sets used in the elaboration of the paper: "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms" published in the IEEE Internet of Things journal. Four machine learning models are implemented for the calibration of ozone sensor. The models available are: the Multiple Linear Regression (MLR), the K-Nearest Neighbors (KNN), the Random Forest (RF) and the Support Vector Regression (SVR). The data sets correspond to nodes containing ozone MOX and EC sensors.

Software and Data Sets

Authors: Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal, Anna Ripoll and Mar Viana.