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Classification of metals by means of Laser-induced Breakdown Spectroscopy and chemometric methods
Képeš, Erik ; Hrdlička, Aleš (referee) ; Novotný, Jan (advisor)
Táto diplomová práca sa zaoberá klasifikáciou kovov pomocou spektroskopie laserom indukovanej plazmy (LIBS) a chemometrických metód. Práca poskytuje prehľad o štúdiách na danú tému. Sú vybrané tri široko používané chemometrické klasifikačné metódy: "Soft Independent Modeling of Class Analogy" (SIMCA), "Partial Least Squares Discriminant Analysis" (PLS-DA) a variácia umelých neurónových sietí (ANN), "Feedforward Multilayer Perceptron". Rôzne prístupy k prieskumovej analýze su tiež preskúmané. Metódy sú stručne opísané. Následne sú klasifikátory experimentálne porovnané.
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Recognition of Skin Diseases on Fingers of a Human Hand
Šesták, Martin ; Orság, Filip (referee) ; Drahanský, Martin (advisor)
This bachelor thesis deals with the design of methods and application for recognition of the fingerprints from diseased and healthy fingers and the subsequent detection of selected diseases. The first part describes basic principles of biometrics, introduction to fingerprints and their processing, and introduction to the topic of fingerprints affected by skin diseases. The next section describes the implementation of the application and its results. The application was tested on a database of research group STRaDe, from the Department of Intelligent Systems, Faculty of Information Technology, Brno University of Technology, which contains approximately 380 test fingerprints.
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Searching for Objects in Pictures
Motlík, Matúš ; Žák, Marek (referee) ; Zbořil, František (advisor)
This bachelor's thesis deals with solving the problem of searching simple objects in pictures. The aim was to create a program that will be able to search simple objects in grayscale images. In this theses are described progressively steps of image processing. There are described preprocessing of image, segmentation, description of segmented data, classification and searching for objects in image.
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Bioinformatics Tool for Prediction of Protein Solubility
Hronský, Patrik ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
This master's thesis addresses the solubility of recombinant proteins and its prediction. It describes the subject of protein synthesis, as well as the process of recombinant protein creation. Recombinant protein synthesis is of great importance for example to pharmacologic industry. This synthesis is not a simple task and it does not always produce viable proteins. Protein solubility is an important factor, determining the viability of the resulting proteins. It is of course favourable for companies, that take part in recombinant protein synthesis, to focus their effort and their resources on proteins, that will be viable in the end. In this regard, bioinformatics is of great help, as it is capable, with the help of machine learning, of predicting the solubility of proteins, for example based on their sequences. This thesis introduces the reader to the basic principles of machine learning and presents several machine learning methods, used in the field of protein solubility prediction. It deals with the definition of a dataset, which is later used to test selected predictors, as well as to train the ensemble predictor, which is the main focus of this thesis. It also focuses on several specific protein solubility predictors and explains the basic principles upon which they are built, as well as the results of their testing. In the end, it presents the ensemble predictor of protein solubility.
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Mapping of Match Tables from P4 Language to FPGA Technology
Kekely, Michal ; Matoušek, Jiří (referee) ; Kořenek, Jan (advisor)
This thesis deals with design and implementation of mapping of match action tables from P4 language to FPGA technology. Goal of the thesis was to describe key principles, which need to be understood in order to design such a mapping and function of algorithms needed, apply these principles by implementing them and analyze the speed and memory requirements of such an implementation. Outcome provides configurable hardware unit capable of classifying packets and connection between the unit and match action tables from P4 language. The implementation is based on DCFL algorithm and requires less memory compared to HiCuts and HyperCuts algorithms while being comparably fast at worst-case scenarios.
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Traffic Signs Detection
Ťapuška, Tomáš ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This bachelor's thesis is about traffic sign detection in picture. There are written some known methods, their advantages and disadvantages. There is present implementation of the system for traffic sign detection. There are present in the last chapter some tests that were done on the system with using testing set, which was created specialy for this purpose.
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Information Technologies in Psychology
Ličko, Jozef ; Grézl, František (referee) ; Smrž, Pavel (advisor)
We focus on characteristic traits recognition of the autor from his written text. This thesis, in particular, deals with the implementaion of Kreitler psychosemantics method. The result of our work includes our own vocabulary, that is used to assign one of the parameters from the method. Implemented solution is successful when used on a set of words that was used as a source for the vocabulary construction.
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Extraction of Landscape Elements from Remote Sensing Data
Ferencz, Jakub ; Kalvoda, Petr (referee) ; Hanzl, Vlastimil (advisor)
This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.
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Analysis of Parkinson's disease using segmental speech parameters
Mračko, Peter ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
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