ePrints.FRI

    Indoor positioning using NFC technology

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    In this diploma thesis an example of the indoor positioning system using NFC technology is presented. The indoor signal of satellites, such as GPS, is not visible to the navigational devices, so the positioning there is not possible. The thesis covers the development of several applications including Android application for smartphones. The smartphone user reads ID from NFC label and gets his current position and the list of all possible destinations from the server. User can then select one destination and can receive the shortest path from the current position to the destination from the server. Mobile application navigates him between NFC labels on this shortest path until the destination is reached. The thesis also covers some mathematical and physical problems connected with the topic and possible solutions. In the last chapter the statistical analysis that can help find strong magnetic field anomalies is presented

    Cache and indexing of unstructured data

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    In this thesis algorithms for construction of data structures for a long texts, are introduced. In the beginning, the overview of the DNA structure is given, as an example of a long text. Several examples of data structures are described. In the same part of thesis, the memory hierarchy, which influences algorithm execution speed, is described. In the main part each algorithm is presented in its own chapter, where the construction process is divided into number of stages. Each stage is described and illustrated with concrete example. The last part gives the comparison of algorithms with respect to time and space complexity, both for the construction of data structures as queries. It also presents the results of time measurements and measurements of I/O accesses

    Vizualizacija bioelektromagnetnega polja človeka

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    Z razvojem tehnologije je postalo možno znanstveno raziskovanje nekaterih vidikov pojava avre (bioelektromagnetnega polja). Za pridobivanje podob aver prstov preiskovane osebe uporabljamo Kirlianov pojav, znan tudi pod imenom tehnika Vizualizacije Izločenih Plinov (angl. Gas Discharge Visualization - GDV). Ker razvijamo ekspertni sistem za postavljanje diagnoze iz podob GDV z uporabo tehnik strojnega učenja, ki se je izkazalo za uspešno tudi v klasični medicini, moramo iz pridobljenih podob izvleči kvantitativne informacije. Pričujoči članek zajema opis metod računalniškega vida, uporabljenih na podobah GDV z namenom dobiti avro celotnega telesa, kar predstavlja prvi korak k omenjenemu ekspertnemu sistemu

    Iskanje obrazov na osnovi barv s pomočjo statističnih metod razpoznavanja vzorcev

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    V zadnjem času postaja video nadzor vse pomembnejši in s tem tudi sistemi za iskanje in prepoznavo človeških obrazov na slikah. Zato se v magistrskem delu ukvarjam s problemom iskanja obrazov na slikah. Pri metodah za iskanje obrazov na podlagi barve smo velikokrat omejeni na človeške obraze samo določene polti, same metode pa so tudi zelo odvisne od osvetlitve. V magistrskem delu zato poskušam s pomočjo kromatičnega barvnega prostora odvisnost od osvetlitve zmanjšati. Preizkusil bom različne metode za barvno segmentacijo na osnovi parametričnega in neparametričnega modela. S pomočjo teh modelov bom poskušal modelirati kožno barvo pri različnih osvetlitvah in različnih kožnih polteh. Uspešnost metod bom primerjal z metodo, ki deluje v barvnem prostoru RGB na osnovi eksplicitno določenih mej. Za potrjevanje označenih kožnih regij bom uporabil metodo na osnovi videza, ki nam med vsemi metodami obljublja najboljše rezultate. Izdelal in preizkusil bom metodo BDF, ki na osnovi naučenega vzorca obraza in neobraza s pomočjo Bayesovega klasifikatorja najde frontalne obraze na sivinskih slikah. Glavna slabost metod na osnovi videza je njihova časovna zahtevnost, zato bom poskušal izdelati metodo, ki bo kombinirala pristop na osnovi barv in pristop na osnovi videza. S pomočjo tako izdelane metode bom poskušal doseči hitro in učinkovito iskanje frontalnih obrazov na barvnih slikah

    Bayesian Learning of Markov Network Structure

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    We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar complexity. Our Markov network model is represented as a set of consistent probability distributions on subsets of variables. Inference with such a model can be done efficiently in closed form for problems like class probability estimation. We also propose a highly efficient Bayesian structure learning algorithm for conditional prediction problems, based on integrating along a hill-climb in the structure space. Our prior based on the degrees of freedom effectively prevents overfitting

    Automatic recognition of facial expressions on iOS platform

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    In this diploma thesis we present an algorithm for automatic recognition of facial expressions in images from mobile devices with iOS operating system. In the first step we have to find a face region. In the next step we need to transform input data into a computer readable form. There are several methods for transforming the data. In the last step the data has to be stored and a computer model for predicting facial expressions has to be generated. Prediction is done by using Support Vector Machines. SVM method is fast and reliable. The final result of the work presented in the thesis is a library available to all developers. The library is easy to use and enables developers to get facial expression information with almost zero work. This solution was tested on labeled faces from Cohn-Kanade database and on labeled faces from Kaggle website. All algorithms in this work rely on OpenCV framework. The library works on almost all Apple mobile devices with iOS operating system

    Building animated 3D face models from range data

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    Towards a real time panoramic depth sensor

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    Recently we have presented a system for panoramic depth imaging with a single standard camera. One of the problems of such a system is the fact that we cannot generate a stereo pair of images in real time. This paper presents a possible solution to this problem. Based on a new sensor setup simulations were performed to establish the quality of new results in comparison to results obtained with the old sensor setup. The goal of the paper is to reveal whether the new setup can be used for real time capturing of panoramic depth images and consequently for autonomous navigation of a mobile robot in a room

    Singing Voice Intonation Analysis using Pitch Detection Algorithms

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    Pitch drift is a commonly known phenomenon among singers. It occurs in most a cappella groups. Reasons for the phenomenon are not known, despite that it is very common and unwanted. It is usually mistakenly argued that pitch drift depends on the singers skills. The objective of this thesis is to find the causes of pitch drift. We reviewed scientific publications related to the theme and analysed the changes in intonation on audio tracks, which we recorded in the context of this thesis. Tracks were analyzed with algorithms for pitch detection. We used an existing plug-in based on pYIN algorithm and our own modified implementation of algorithm based on autocorrelation. The analysis results have further confirmed the findings of some publications, saying that the intonation in the songs changes due to differences between the just intonation and the equal temperament. Changes occur when difference in tuning between two notes is negative. More such cases are there in the song, greater change in the intonation can occur

    System for audio capture and classification of baby cry samples

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    We explore multiclass classification of infants' cries and the relation between the age of the infant and the accuracy of classification. Additionally we explore secure cloud storage and cloud data processing. We compare several state-of-the-art multiclass classification models with recurrent neural networks. Classification accuracy was obtained on data from infants of various ages. For data storage and processing we used the Django Rest API and the opensource cloud platform OpenStack. Multiclass classification models successfully differentiated between different classes of crying, but no age effect has been found. We have demonstrated the aptness of the Django Rest API and OpenStack platform for data storing and processing in the cloud
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