National Repository of Grey Literature 387 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Pre-industrial and post-industrial landscape in Moravia: concept and reality
Kolejka, Jaromír ; Batelková, Kateřina
The paper deals with the scientific concept of the terms the post-industrial and the pre-industrial landscape and presents available data sources needed for their identification in the Czech Republic. The overview shows the current state of knowledge and the research progress in the study of such historical landscapes. In brief, the methodology of their identification, mapping and classification is given. The post-industrial landscape Rosice-Oslavany and the pre-industrial landscape Bělečsko are presented as examples from the historical territory of Moravia. Their significance for cognitive, educational and conservation practices is emphasized.
Multichannel Emg Signal Processing For Gesture Recognition
Brázdil, Štěpán
This work deals with the complex solution of the problem concerning the design and implementation of the system for the collection, classification and visualization of EMG signal. Project is designed with respect to possible future applications like implementation of a real limb model controlled by the proposed system. The main goal of this work is developing of functional program code in Matlab that allows visualization basic upper limb gestures (e. g. grip, finger extension) in the graphical user interface.
Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area
Kuthan, Tomáš ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area Abstract The thesis is focused on the analysis of spectral characteristics of selected agricultural crops druring agriculutural season from time series of Sentinel -2 (A and B) and PlanetScope sensors in the model area situated around the settlements of Kolín and Kutná Hora. It is based on the assumption that the use of multiple dates of image data acquired crops in different phenological phases of the crops allows better identification of crop species (Lu et al., 2004). The aim of the thesis was to analyse the characteristics of the seasonal course of spectral features of selected agricultural crops (sugar beet, spring barley, winter barley, maize, spring wheat, winter wheat, winter rape) and to determine the period of the year suitable for the differentiation of individual crops. Another aim of the thesis was to classify these crops in the model area from time series of two above-mentioned sensors and to compare the accuracy of the pixel and object-oriented classification approach for multitemporal composites and the accuracy for monotemporal image from the term when the individual crops are clearly distinguishable. The training and validation datasets and the classification mask...
Classification of a point cloud from airborne and mobile scanning
Borový, Ján ; Kuruc, Michal (referee) ; Volařík, Tomáš (advisor)
This diploma thesis deals with the classification of the point clouds taken by different carrier and with various density. The terrain model and building models were created from provided data sets. Also the software equipment is described. Achieved outcomes of elaboration are presented in each corresponding chapter. In conclusion, the overall evaluation and assessment of the results of processing is done.
Introductoryprocessof formative assessment in elementaryschool EDUCAnet
Buršíková, Hana ; Mazáčová, Nataša (advisor) ; Sak, Petr (referee)
My Diploma Thesis "Process of the implementation of a formative assessment at the primary school EDUCAnet" deals with the possibilities of the school assessment and offers examples of the gradual change from the classical system to the formative evaluation. The theoretical part is aimed at the explanation of the basic terms connected with the assessment. It demonstrates different assessment methods and gradually specifies possibilities of the formative assessment. Practical part introduces assessment methods used at the primary school EDUCAnet. Final research summarizes EDUCAnet's students attitude to the assessment. To collect the data I used a questionnaire method, which is afterwards evaluated in graphs as well as in comments. The goal of my research is a detection of the formative assessment method, which is preferred by the students. In this context I am also interested in the motivation of the students and their understanding of the role of mistake in their education.
Estimating performance of classifiers from dataset properties
Todt, Michal ; Polák, Petr (advisor) ; Baruník, Jozef (referee)
The following thesis explores the impact of the dataset distributional prop- erties on classification performance. We use Gaussian copulas to generate 1000 artificial dataset and train classifiers on them. We train Generalized linear models, Distributed Random forest, Extremely randomized trees and Gradient boosting machines via H2O.ai machine learning platform accessed by R. Classi- fication performance on these datasets is evaluated and empirical observations on influence are presented. Secondly, we use real Australian credit dataset and predict which classifier is possibly going to work best. The predicted perfor- mance for any individual method is based on penalizing the differences between the Australian dataset and artificial datasets where the method performed com- paratively better, but it failed to predict correctly. 1
Tree species classification using sentinel-2 and Landsat 8 data
Havelka, Ondřej ; Štych, Přemysl (advisor) ; Kupková, Lucie (referee)
The main objectives of this master thesis are to evaluate and compare chosen classification algorithm for the tree species classification. With usage of satellite imagery Sentinel-2 and Landsat 8 is examined whether the better spatial resolution affects the quality of the resulted classification. According to past case studies and literature was chosen supervised algorithms Support Vector Machine, Neural Network and Maximum Likelihood. To achieve the best possible results of classification is necessary to find a suitable choice of parameters and rules. Based on literate was applied different settings which were subsequently evaluated by cross validation. All results are accompanied by tables, charts and maps which comprehensively and clearly summarize the answers to the main objectives of the thesis.
Neighborhood components analysis and machine learning
Hanousek, Jan ; Antoch, Jaromír (advisor) ; Maciak, Matúš (referee)
In this thesis we focus on the NCA algorithm, which is a modification of k-nearest neighbors algorithm. Following a brief introduction into classification algorithms we overview KNN algorithm, its strengths and flaws and what lead to the creation of the NCA. Then we discuss two of the most widely used mod- ifications of NCA called Fast NCA and Kernel (fast) NCA, which implements the so-called kernel trick. Integral part of this thesis is also a proposed algo- rithm based on KNN (/NCA) and Linear discriminant analysis titled TSKNN (/TSNCA), respectively. We conclude this thesis with a detailed study of two real life financial problems and compare all the algorithms introduced in this thesis based on the performance in these tasks. 1
Klasifikace na množinách bodů v 3D
Střelský, Jakub ; Mráz, František (advisor) ; Šikudová, Elena (referee)
Increasing interest for classification of 3D geometrical data has led to discov- ery of PointNet, which is a neural network architecture capable of processing un- ordered point sets. This thesis explores several methods of utilizing conventional point features within PointNet and their impact on classification. Classification performance of the presented methods was experimentally evaluated and com- pared with a baseline PointNet model on four different datasets. The results of the experiments suggest that some of the considered features can improve clas- sification effectiveness of PointNet on difficult datasets with objects that are not aligned into canonical orientation. In particular, the well known spin image rep- resentations can be employed successfully and reliably within PointNet. Further- more, a feature-based alternative to spatial transformer, which is a sub-network of PointNet responsible for aligning misaligned objects into canonical orientation, have been introduced. Additional experiments demonstrate that the alternative might be competitive with spatial transformer on challenging datasets. 1
Artificial neural networks for macroeconomic data analysis
Padrón Peňa, Ildefonso ; Mrázová, Iveta (advisor) ; Kuboň, David (referee)
The analysis and prediction of macroeconomic time-series is a factor of great interest to national policymakers. However, economic analysis and forecast- ing are not simple tasks due to the lack of a precise model for the economy and the influence of external factors, such as weather changes or political decisions. Our research is focused on Spanish speaking countries. In this thesis, we study dif- ferent types of neural networks and their applicability for various analysis tasks, including GDP prediction as well as assessing major trends in the development of the countries. The studied models include multilayered neural networks, recur- sive neural networks, and Kohonen maps. Historical macroeconomic data across 17 Spanish speaking countries, together with France and Germany, over the time period of 1980-2015 is analyzed. This work then compares the performances of various algorithms for training neural networks, and demonstrates the revealed changes in the state of the countries' economies. Further, we provide possible reasons that explain the found trends in the data.

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