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Detection and Analysis of Violators in the Monitored Area
Sadílek, Jakub ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
The aim of this thesis is to create an internet application for detection and analysis of violators in the monitored area. Such an application then can be used for automated processing of records from security cameras or other captured videos from the guarded area. The first part of the work is focused on the theory of neural networks for object detection and classification in the image and recognition of people by their face. The next part describes used technologies for application development. The result is a client-server application with the possibility of configuration of processing, which allows detection of violators, identification of persons, object tracking and counting, path drawing, definition of the monitored area, usage of own detector, etc. Processed videos at the end can be played, downloaded or together with a list of detected violators shared on the internet via link.
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Preparation of the lead-free piezoceramic by non-conventional sintering methods
Sršeň, Maroš ; Prajzler, Vladimír (referee) ; Pouchlý, Václav (advisor)
Ceramic materials that exhibit piezoelectric properties currently have a variety of uses in various industries, such as the automotive industry or information technology. Leading materials are currently the best ceramic piezoelectrics, but these materials show considerable toxicity. This has led to the need to find health-safe and environmentally friendly materials based on lead-free piezoelectric materials. Among these materials, KNN-based materials prove to be good candidates. However, their preparation has its own specifications, and therefore the use of conventional sintering methods may not lead to the desired results. It is for this reason that research into the preparation of these materials using unconventional sintering methods that use electric current for sintering has come to the fore in recent years. One of these methods is the Spark Plasma Sintering method, which has therefore been intensively researched in recent years. Within the experimental part of the work, ceramic bodies based on KNN were prepared. Sintering was performed by a conventional method using a laboratory oven, as well as by an unconventional Spark Plasma Sintering method, and the results were compared. Compacting ceramic bodies with high relative density were obtained by sintering by both methods. It has been shown that the Spark Plasma Sintering method can be used to prepare lead-free piezoelectric materials with a high relative density in a relatively short time. It has also been confirmed that the Spark Plasma sintering method has several advantages over conventional sintering methods, and that the ceramic bodies obtained have a higher relative density when sintered by this method.
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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
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Estimating performance of disk arrays using predictive analytics
Vlha, Matej ; Mašek, Jan (referee) ; Burget, Radim (advisor)
Thesis focuses on disk arrays, where the goal is to design test scenarios to measure performance of disk array and use predictive analytics tools to train a model that will predict the selected performance parameter on a measured set of data. The implemented web application demonstrates the functionality of the trained model and shows estimate of the disk array performance.
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Speech-signal-based recognition of type of transmission channel
Kopřiva, Tomáš ; Burget, Radim (referee) ; Atassi, Hicham (advisor)
This work deals with the classification of five different transmission channels by speech signal processing. The channels considered are: GSM, two PSTN channels and two VoIP channels. For the training and testing purposes, a speech database for the transmission channels called SPLAB_TranCh was constructed. The speech signals of this corpus originally come from well-known TIMIT database, where each utterance passed through each mentioned transmission channel. The main objective of this work is to find optimal features and classification accuracy that yield best classification accuracy. Several types of features, including MFCC, LPCC and spectral characteristics were put under examination. The best suprasegmental features were identified by using mRMR algorithm. Several classifiers were tested as well. The results suggested that the classification of transmission channel can be performed with high accuracy (around 90 %). Influence of adverse effects, which can occur during transmission, is also examined. Considered types of distortions are: saturation, thresholding, echo, crackling noises and different colors of noises and filters.
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Analysis of experimental ECG
Mackových, Marek ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This thesis is focused on the analysis of experimental ECG records drawn up in isolated rabbit hearts and aims to describe changes in EKG caused by ischemia and left ventricular hypertrophy. It consists of a theoretical analysis of the problems in the evaluation of ECG during ischemia and hypertrophy, and describes an experimental ECG recording. Theoretical part is followed by a practical section which describes the method for calculating morphological parameters, followed by ROC analysis to evaluate their suitability for the classification of hypertrophy and at the end is focused on classification.
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Recognition of Home Appliances Based on Their Power Consumption Characteristics
Vaňková, Klára ; Černocký, Jan (referee) ; Schwarz, Petr (advisor)
The goal of this master's thesis is to design and implement a system for recognition of home appliances based on their power consumption characteristics. This system should identify the individual home appliances from measurements of the total household consumption. The acquired data could be used for statistics of usage of a particular appliance and subsequent detection of errors or non-standard behavior of the measured device. An important part of my work is a design and hardware implementation of a unit for measuring and a system for processing the measured signal. The first version of my project uses pulse output of an electrometer to measure the energy. This method does not provide a sufficient sample rate but it's a quick way to obtain data for processing and analysis. The second version monitors the power consumption with a multi-purpose AC converter which measures active and reactive power with the desired sample rate. The data is then processed and recognized by two classifiers - HMM and KNN.
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Robust detection of keywords in speech signal
Vrba, Václav ; Sysel, Petr (referee) ; Atassi, Hicham (advisor)
The master thesis is divided into two parts theoretical and practical. The theoretical part is focused on methods of analysis and detection of speech signals. In the practical part the system for isolated word recognition was created in Matlab. The system is speaker independent separately for men and women. Also two speech databases were created for further use in the aircraft cockpit. Tests and evaluations were performed even with added noise.
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Automatické rozpoznávání zpěvu ptáků
Břenek, Roman
This master thesis deals with methods of automatic recognition of bird species by their voices. In first, I defined the database of records and created a reference data by handmade evaluation. The next step is to find the optimal features for describing a bird singing. I use a Human Frequency cepstral Coefficients (HFCC). For the best accuracy of recognition is necessary to correctly classify a bird's vocalization from a non-vocalization segments. The VAD system is based on an algorithm k-Nearest Neighbours. The last step describes the system based on Hidden Markov Models which allows to recognize the concrete bird species from the parts of bird's singing.
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