National Repository of Grey Literature 216 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Tram Detection in Video by Neural Network
Golda, Vojtěch ; Špaňhel, Jakub (referee) ; Dyk, Tomáš (advisor)
This paper deals with tram detection in video using convolutional neural networks. The basic principles of their function are described. A number of distinct architectures are trained. The usefulness of the resulting models is subsequently compared. The output of this paper is a program capable of detecting trams in video.
Tram Detection in Video by Neural Network
Golda, Vojtěch ; Špaňhel, Jakub (referee) ; Dyk, Tomáš (advisor)
This paper deals with tram detection in video using convolutional neural networks. The basic principles of their function are described. A number of distinct architectures are trained. The usefulness of the resulting models is subsequently compared. The output of this paper is a program capable of detecting trams in video.
Geometric algebras and neural networks
Zapletal, Jakub ; Procházková, Jana (referee) ; Vašík, Petr (advisor)
This thesis deals with the use of geometric algebras in the field of neural networks. First, Conformal Geometric Algebra (CGA) and Geometric Algebra for Conics (GAC) and their Python implementations are introduced. The functioning of neural networks is then described, including an explanatory example. Finally, both topics are connected by using the appropriate library in the Python language, and the possibilities of geometric algebras for different models of neural networks are demonstrated on several examples.
Computational model of the environment of an autonomous vehicle
Doležel, Radek ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
The aim of this thesis is to develop a functional computational model for vehicle motion prediction based on a search of sensors and their locations on the vehicle, neural networks for computer vision, datasets for network learning, and programs for creating simulations and virtual environments. The paper describes the process of creating the vehicle virtual environment and simulation. In addition, sensor placement designs including their parameters are developed. Subsequently, the programmed vehicle trajectory prediction algorithm including learning and neural network implementation is presented. Finally, the results of the developed algorithm are presented.
Estimation of Algorithm Execution Time Using Machine Learning
Buchta, Martin ; Chlebík, Jakub (referee) ; Jaroš, Jiří (advisor)
This work aims to predict the execution time of k-Wave ultrasound simulations on supercomputers based on a given domain size. The program uses MPI and can be run on multiple nodes. Prediction models were developed using symbolic regression and neural networks, both of which trained on captured data and compared against each other. The results demonstrate that the models outperform existing solutions. Specifically, the symbolic regression model achieved an average error of 5.64% for suitable tasks, while the neural network model achieved an average error of 8.25% on unseen domain sizes and across all tasks, including those not optimized for k-Wave simulations. This work contributes a new, more accurate model for predicting execution time, and compares the effectiveness of neural networks and symbolic regression for this specific type of regression problem. Overall, these findings suggest that new models will have important practical applications in the field of k-Wave ultrasound simulations.
Use of Data Mining for Payment Identification
Bartoš, Stanislav ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This master thesis concentrates on design and implementation of a system for payment identification, even if the reliable identifier (e.g. variable symbol) is missing. Data mining techniques, such as classification and prediction, were used as a solution to this problem. This master thesis is company assignment for company "Platební instituce Roger a.s.".
Neural networks used in autonomous vehicles
Ryšavý, Jan ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
This bachelor thesis deals with the use of neural networks in autonomous vehicles. The first part of the thesis presents the basic principles of neural networks and learning methods that are used in autonomous vehicles. Then the thesis describes the architecture and functions of neural networks. The second part of the thesis also describes the different types of autonomous vehicles, their classifications and an overview of the sensors used by autonomous vehicles. The last part of the thesis deals with the implementation of neural networks in ECUs using programming languages and libraries, and applications such as object detection and marker recognition.
Visualizing Neuroevolution in Neural Network Learning
Bednář, Martin ; Janoušek, Vladimír (referee) ; Zbořil, František (advisor)
This thesis examines options for neural network learning achieved by means of neuroevolution, examines general functioning of neuroevolution, design and implementation of neuroevolution and marginally deals with design and implementation of feed-forward neural networks with fully connected layers. The goal of this thesis is to introduce program, that executes neuroevolutionary algorithm and separate graphic application, which encapsulates this program for easier use and for display of graphic output of the program visualizing problem-solving capabilities of neural networks created by neuroevolution. The end part of the thesis is devoted to experiments done on the created program.
Audio signal denoising using deep learning
Pacal, Tomáš ; Záviška, Pavel (referee) ; Mokrý, Ondřej (advisor)
This thesis deals with noise removal in audio signal using deep learning. The basic types of neural networks and their use in audio signal processing are described. The possibilities of implementing neural networks are tested in Matlab and Python. Subsequently, a~convolutional neural network model is proposed, according to which four different convolutional network architectures are implemented and then trained and tested on different types of noise. Based on these tests, one architecture was selected and subjected to a comparative test on a speech recording and then on a music recording, together with a noise reduction method using wavelet transform. The results are evaluated using both objective sound quality metrics and an informal listening test. The neural network achieved better results according to all the metrics used as well as in the listening test.
Advanced sleep quality estimation
Benáček, Petr ; Ředina, Richard (referee) ; Filipenská, Marina (advisor)
This thesis deals with the assessment of sleep quality using modern deep learning methods. The thesis describes metrics for automatic classification of sleep stages. A selected database of sleep data is discussed. Due to the low number of data in the wakefulness phase, different methods of data augmentation are described and implemented. Models based on 1D convolutional networks are the basis for the classification. As a result, models for binary classification and classification of 3 and 4 sleep phases are prepared. Finally, sleep quality metrics are calculated using these models and the results are compared with the literature.

National Repository of Grey Literature : 216 records found   1 - 10nextend  jump to record:
Interested in being notified about new results for this query?
Subscribe to the RSS feed.