Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a “gas fingerprint”, which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

Paper presents a microscopic studies of activated sludge supported by automatic image analysis based on deep learning neural networks. The organisms classified as Arcella vulgaris were chosen for the research. They frequently occur in the waters containing organic substances as well as WWTPs...

The article contains the procedure of image acquisition, including sampling of analyzed material as well as technical solutions of hardware and preprocessing used in research. A dataset of digital images containing identified objects were obtained with help of automated mechanical system for...

The aim of the research was to develop a new device for monitoring and diagnosing functional disorders of the lower urinary tract, which will enable measurement of muscle tension (EMG) and electrical impedance tomography (EIT) with the possibility of electrostimulation of bladder reconstruction.

The main purpose of the article is to compare selected machine learning methods in Electrical Impedance Tomography. The paper studies the relationship between a number of training cases and Root Mean Squared Error loss in the EIT image reconstruction problem. The research was conducted with the Elastic Net, Least Angle Regression and Artificial Neural Network algorithms in R environment. Various tests have been performed, leading to many results and a discussion about a plateau in the model training plot.

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique increasingly applied in biomedical diagnostics. Traditionally, EIT systems are stationary and require specialist knowledge to operate and interpret results. This study presents the development and evaluation of an...