Search

The work presents image reconstruction in ultrasonic transmission tomography using the Block-Wise-Transform-Reduction method. The system consists of a tomography platform using ultrasound built by the authors, and an algorithm that can reconstruct images in a distributed system. The algorithm applies image compression techniques (Discrete Cosine Transformation) for each block of the image separately. This allows location object in the analyzed area for real-time image reconstruction using an ultrasound tomographic device. The idea behind the method used is that the reconstruction process is directly connected to the compression process.

Over the years, the issue of air pollution is increasingly published in line with the seriousness of this problem for human health. However, air quality is just one of many issues that are solved by Environmental Computer Science. The areas in which Environmental Computer Science deals are the foundation for the creation of Environmental Decision Support Systems. Such systems are complex, extensive and multidisciplinary IT solutions. The aim of the research was to develop a comprehensive system based on the Internet of Things architecture, which is used to monitor environmental conditions. The first stage of this research includes the creation of a system for collecting, storing and analyzing data in the area of air quality tests in the city.

The article presents a novel approach to ultrasound tomography in industrial applications. In order to visualize the interior of a tank (reactor) filled with tap water, a single neural network enhanced with autoencoder was used. A novelty is the use of an autoencoder to improve the quality of the measurement vector. Thanks to the use of the autoencoder for denoising the input measurements in connection with the appropriately adapted neural network, the quality of the output image was improved. A robust algorithm was developed that properly reconstructs hidden objects in monochrome images with high efficiency.

This article presents a method of using the logistic regression to solve the inverse problem in electrical impedance tomography. Multicollinearity between predictors in linear models make it difficult to precisely forecast for output variable. To reduce the problem of highest correlation between measures obtained from electrodes the elasticnet method was applied.