Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

A Dataset for Breast Cancer Histopathological Image Classification.

Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database. The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.