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Recenzovaný sborník příspěvků z 25. odborné konference z cyklu Integrované inženýrství v řízení průmyslových podniků na téma Datadriven management pro průmyslové podniky.
(Czech Technical University in Prague, 2025) Hlinák V.; Lhota J.
Surface-polyconvex models for soft elastic solids
(Elsevier Science, 2025) Horák M.; Šmejkal M.; Kružík M.
Soft solids with surface energy exhibit complex mechanical behavior, necessitating advanced constitutive models to capture the interplay between bulk and surface mechanics. This interplay has profound implications for material design and emerging technologies. In this work, we set up variational models for bulk-surface elasticity and explore a novel class of surface-polyconvex constitutive models that account for surface energy while ensuring the existence of minimizers. These models are implemented within a finite element framework and validated through benchmark problems and applications, including, e.g., the liquid bridge problem and the Rayleigh-Plateau instability, for which the surface energy plays the dominant role. The results demonstrate the ability of surface-polyconvex models to accurately capture surface-driven phenomena, establishing them as a powerful tool for advancing the mechanics of soft materials in both engineering and biological applications.
Using hyperspectral imaging to identify optimal narrowband filter parameters for construction and demolition waste classification
(Elsevier Science, 2025) Vítek S.; Zbíral T.; Nežerka V.
Hyperspectral imaging (HSI) is widely applied in various industries, enabling detailed analysis of material properties or composition through their spectral signatures. However, for classification of construction and demolition waste (CDW) materials, HSI is impractical since real-time sorting requires rapid data acquisition and lightweight classification. Instead, fitting selected narrowband filters onto standard cameras can achieve comparable results with substantially reduced computational overhead. In this study, reflectance data of common CDW materials were recorded using a hyperspectral camera, and a multilayer perceptron classifier was employed to evaluate different feature sets. The findings indicate that adding only two wavelengths beyond the RGB channels is sufficient for high-accuracy classification, with optimal filter central wavelengths identified at approximately 650-750 nm and 850-1000 nm across the tested bandwidths (5-50 nm) highlighting the importance of near-infrared regions for material discrimination.
FIMD: Fast Isolated Marker Detection for UV-Based Visual Relative Localisation in Agile UAV Swarms
(Springer, 2025) Vrba V.; Walter V.; Štěpán P.; Saska M.
A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a functionally equivalent FPGA streaming architecture. For the proposed CPU and GPU solutions, the mean processing time per pixel of input camera frames was accelerated by two to three orders of magnitude compared to the state of the art. For the localisation task, the proposed FPGA architecture offered the most significant overall acceleration by minimising the total delay from camera exposure to detection results. Additionally, the proposed solutions were evaluated on various 32-bit and 64-bit embedded platforms to demonstrate their efficiency, as well as their feasibility for applications using low-end UAVs and MAVs. Thus, it has become a crucial enabling technology for agile UAV swarming.
FIMD: Fast Isolated Marker Detection for UV-Based Visual Relative Localisation in Agile UAV Swarms
(Springer, 2025) Vrba V.; Walter V.; Štěpán P.; Saska M.
A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a functionally equivalent FPGA streaming architecture. For the proposed CPU and GPU solutions, the mean processing time per pixel of input camera frames was accelerated by two to three orders of magnitude compared to the state of the art. For the localisation task, the proposed FPGA architecture offered the most significant overall acceleration by minimising the total delay from camera exposure to detection results. Additionally, the proposed solutions were evaluated on various 32-bit and 64-bit embedded platforms to demonstrate their efficiency, as well as their feasibility for applications using low-end UAVs and MAVs. Thus, it has become a crucial enabling technology for agile UAV swarming.