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Item type:Item, Access status: Open Access , Metody strojového učení v rekonstrukci dat elektrické impedanční tomografie(Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií) Kouakouo Nomvussi, Serge Ayme; Mikulka, Jan; Kořínek, Radim; Dušek, Janiii Abstract Reconstructing clear and meaningful images from noisy or incomplete data is a funda- mental challenge in areas such as medical imaging, remote sensing, and computer vision. Tra- ditional methods like Total Variation and Gauss-Newton often fall short when confronted with complex shapes or high noise levels, leading to limited accuracy and loss of structural detail. This dissertation presents a new approach to image reconstruction using a Cascaded Ra- dial Basis Function Neural Network (CRBFNN). The method features a two-stage neural ar- chitecture. In the first subnetwork, DBSCAN clustering and K-Nearest Neighbors (KNN) are used for center and spread estimation, respectively, allowing the model to adapt to the underly- ing data structure. The second subnetwork applies a fixed spread to ensure stability and com- putational efficiency during the refinement of the final output. This design enables the network to respond adaptively to different noise patterns while preserving structural consistency in its predictions. Comprehensive experiments were carried out using simulated data affected by white Gaussian noise, impulsive noise, and contact noise. Across all conditions, the CRBFNN con- sistently demonstrated strong performance, achieving a Structural Similarity Index (SSIM) of up to 0.991, a Correlation Coefficient (CC) of 0.983, and a notably low training Mean Squared Error (MSE) of 0.00066. It also outperformed several modern techniques, including DenseNet, CWGAN-AM, and Enhanced CNN, both in accuracy and robustness. Beyond accuracy, the model offers practical advantages. Once trained, CRBFNN pro- duces high-resolution 2D conductivity maps in approximately 1.3 seconds using only CPU re- sources, making it suitable for real-time applications and integration into modular EIT systems. This research highlights CRBFNN as a reliable and efficient tool for image reconstruction under diverse and challenging conditions. Looking ahead, future work will aim to enhance computa- tional efficiency through hardware optimization and parallel processing, validate the method on real-world datasets with complex noise and structural variability, and extend the approach to 3D and dynamic imaging scenarios. Additionally, integrating CRBFNN with advanced deep learning architectures such as attention mechanisms, hybrid CNN-RBF models, or perceptual loss functions may further improve its ability to handle fine structural details and improve gen- eralization in diverse imaging environments.Item type:Item, Access status: Open Access , Digital speech biomarkers for assessing cognitive decline across neurodegenerative conditions(2025-10-31) Kováč, Daniel; Nováková, Ľubomíra; Mekyska, Jiří; Novotný, Kryštof; Brabenec, Luboš; Klobušiaková, Patrícia; Rektorová, IrenaThis study investigates speech impairments in individuals with mild cognitive impairment due to Alzheimer’s disease (MCI-AD), mild cognitive impairment with Lewy bodies (MCI-LB), and Parkinson’s disease with mild cognitive impairment (PD-MCI), compared to healthy controls (HC), aiming to identify linguistic and acoustic digital biomarkers that differentiate these groups. Monologue recordings were collected from 68 HC, 42 MCI-AD, 50 MCI-LB, and 47 PD-MCI participants (ON state). Participants were instructed to speak spontaneously for one and a half minutes. Speech was automatically transcribed, manually corrected, and analyzed using natural language processing to extract eight linguistic (lexical/syntactic) and four acoustic (prosodic) biomarkers. Group differences were assessed using the Mann–Whitney U test, with Spearman’s correlation used to examine associations with clinical and MRI measures (FDR-corrected). Machine learning models (XGBoost) were applied to evaluate the classificatory and predictive potential of speech features. Distinct speech patterns were observed across groups: MCI-AD participants exhibited reduced use of function words, resulting in increased content density, PD-MCI participants used shorter sentences and fewer coordinating conjunctions with longer pauses, and MCI-LB participants exhibited greater lexical repetition than MCI-AD. Altered speech features correlated with structural brain changes but not with global cognition (MoCA) or depressive symptoms (GDS). Sentence structure and pausing features showed strong interrelationships. Machine learning models showed that adding speech biomarkers improved classification performance compared to using clinical scores alone. In regression analyses, the models predicted MoCA with a normalized error of 10%, performing similarly on automatic and manually corrected transcripts. These findings suggest that speech biomarkers and traditional clinical assessments may offer complementary information about cognitive status and brain health, supporting their use in scalable, non-invasive cognitive monitoring.Item type:Item, Access status: Open Access , Selection of a suitable C02 sensor for indoor air quality assessment(ECON Publishing, 2025-09-30) Kučírek, Pavel; Šikula, OndřejThis article compares eight types of CO2 sensors using NDIR technology for accuracy, response time, and acquisition costs. The experiment included static and dynamic tests in a chamber and a duct. The Testo 935 and Rotronic CL11/CP11 sensors showed high accuracy and fast response, while the Winsen MH-Z16 offered the best performance-to-cost ratio. Other tested sensors – NetAtmo, Comet S3532, LaskaKit SCD41 and MH-Z14 exhibited inaccuracies, making them unsuitable for scientific applications.Item type:Item, Access status: Open Access , Effect of obstacles on the crawl space airflow(ECON Publishing, 2025-09-30) Pobucká, Slávka; Kučírek, Pavel; Žajdlík, Tomáš; Šuhajda, KarelOne type of building foundation is a crawl space. This system is widespread in Scandinavia, but in recent years it has been used in the Czech Republic. A crawl space is a building foundation system where there is an air gap between the building and the ground. Both the design of the structure and the proper use is important for the proper functioning of the crawl space. Any obstacle can affect the airflow in the crawl space. This paper deals with simulations of airflow and age of air with the placement of obstacles in the crawl space.Item type:Item, Access status: Open Access , A simulation-based case study of deciduous tree impacts on office building daylighting(ECON Publishing, 2025-09-30) Hakszer, Tomáš; Hanuliak, PeterThe presence of full-grown trees near buildings affects daylight availability and reduces glare. This study presents dynamic simulations that assess how deciduous trees at various distances (10 m, 15 m, 20 m) from the façade of an office building impact Useful Daylight Illuminance (UDI) and Daylight Glare Probability (DGP). The results show that closer trees (10 m) reduce glare significantly but also decrease desirable daylight levels to a large extent. Careful tree placement is crucial to balance daylighting and visual comfort in buildings. By considering trees early in the design process and utilising available calculation methods, we can effectively integrate them so as to enhance both the building and its surroundings.