Rekonstrukcija signala iz nepotpunih merenja sa primenom u ubrzanju algoritama za rekonstrukciju slike magnetne rezonance

In dissertation a problem of reconstruction of images from undersampled measurements is considered which has direct application in creation of magnetic resonance images. The topic of the research is proposition of new regularization based methods for image reconstruction which are based on statistical Markov random field models and theory of compressive sensing. With the proposed signal model which follows the statistics of images, a new regularization functions are defined and four methods for reconstruction of magnetic resonance images are derived.У докторској дисертацији разматран је проблем реконструкције сигнала слике из непотпуних мерења који има директну примену у креирању слика магнетне резнонаце. Предмет истраживања је везан за предлог нових регуларизационих метода реконструкције коришћењем статистичких модела Марковљевог случајног поља и теорије ретке репрезентације сигнала. На основу предложеног модела који на веродостојан начин репрезентује статистику сигнала слике предложене су регуларизационе функције и креирана четири алгоритма за реконструкцију слике магнетне резонанце.U doktorskoj disertaciji razmatran je problem rekonstrukcije signala slike iz nepotpunih merenja koji ima direktnu primenu u kreiranju slika magnetne reznonace. Predmet istraživanja je vezan za predlog novih regularizacionih metoda rekonstrukcije korišćenjem statističkih modela Markovljevog slučajnog polja i teorije retke reprezentacije signala. Na osnovu predloženog modela koji na verodostojan način reprezentuje statistiku signala slike predložene su regularizacione funkcije i kreirana četiri algoritma za rekonstrukciju slike magnetne rezonance

crISPr-cas9 technology: from basic research to clinical application

Tehnologije za manipulaciju molekula DNK su omogućile brojna otkrića i prodore u biomedicinskim naukama. Ipak, metode za uvođenje ciljanih promena u genomu su do skora bile relativno komplikovane i nepristupačne najvećem broju naučnika. Primenom CRISPR-Cas9 tehnologije počela je revolucija u biomedicinskim naukama zato što su metode za genomsko inženjerstvo postale dostupne gotovo svakoj laboratoriji. Ova tehnologija je prešla veliki put od osnovnih istraživanja u vezi sa prokariotskim genomima pre nekoliko decenija, preko otkrića mehanizma stečenog imuniteta bakterija, da bi danas postala dominantna tehnologija za genomsko inženjerstvo. Fokus ovog rada je na praktičnom aspektu primene CRISPR-Cas9 tehnologije i njenim poređenjem u odnosu na alternative za uvođenje dvolančanih prekida u genomu (TALEN i ZFN nukleazama). Detaljno će se obraditi upotreba CRISPR-Cas9 tehnologije u bazičnim (naučnim) istraživanjima i u medicini. Razmatraće se i svi nedostaci trenutnih tehnologija za genomsko inženjerstvo, uključijući uvođenje nespecifičnih promena u genomu i načini prevazilaženja ovih nedostataka. Kroz brojne primere upotrebe CRISPR-Cas9 tehnologije će biti približeno zašto je ova metoda značajna ne samo za biomedicinske nauke, već za celokupno društvo.Technologies for DNA manipulation have enabled numerous breakthroughs in the field of biomedical sciences. Until recently, methods for genome editing have been too complicated and practically unavailable for the vast majority of research laboratories. CRISPR-Cas9 technology has started a revolution in the biomedical sciences since it enabled the use of genome engineering methods in almost every research laboratory. This technology has gone a long way from its discovery in bacterial genomes, through being identified as a part of the bacterial immune system, to the application it is most known today – genome engineering. This paper will focus on the practical aspects of using CRISPR-Cas9 and its comparison to similar methods for genome engineering (using TALEN and ZFN nucleases). This paper will cover the benefits and drawbacks of CRISPR-Cas9 genome editing including off-target cleavage, and the possibilities to overcome these drawbacks. The use of CRISPR-Cas9 technology in basic research and clinical studies will be covered in detail. Current research related to CRISPR-Cas9 technology will be covered to emphasize the importance of this method not only for life sciences, but for society as a whole.Zahvaljujem se Prof. Dr Dušanki Savić-Pavićević, Dr Meliti Vidaković i Dr Snežani Kojić na izuzetno korisnim sugestijama tokom pisanja rada. Autor je član „Returning expert“ programa (Centrum für internationale Migration und Entwicklung, SR Nemačka)

ALGORITMI ZA PROCEDURALNO GENERIRANJE SADRŽAJA U RAČUNALNIM IGRAMA

Glavna tema diplomskog rada je proceduralno generiranje sadržaja u računalnim igrama pomoću programskih algoritama, na praktičnom primjeru dvodimenzionalne igre izrađene pomoću Unity alata. U prvom dijelu rada su opisani odabrani softverski alati korišteni prilikom praktične izrade računalne igre, te njihove osnove komponente. Nakon toga su opisani glavni koncepti i ideja same igre, te osnovni elementi u sklopu Unity-a od kojih je ona izrađena. Svi elementi igre su praćeni s primjerima iz programskog koda, koji pokazuju interakciju između njih i pozadinskih skripti. Središnji dio rada sadrži opis pet različitih algoritama čija je osnovna zadaća kreiranje dijela sadržaja unutar igre pomoću koda u C# programskom jeziku, popraćen s praktičnim primjerima korištenja tih algoritama u igri. Na kraju rada su predstavljeni rezultati analize i usporedbe primijenjenih algoritama u četiri različite kategorije. Kompleksnost algoritama mjeri maksimalni broj koraka potrebnih za izvršavanje algoritama. Performanse algoritama su analizirane u sklopu opterećenja procesora i memorije za vrijeme izvršavanja algoritama tijekom pokretanja igre. Kategorije fleksibilnosti i igrivosti donose nešto subjektivniju analizu algoritama iz perspektive njihovog proširivanja, iskorištavanje u drugim igrama, te kvalitete sadržaja koji je generiran pomoću njih iz perspektive igrača.Main topic of this thesis is procedural generation of content in video games using programming algorithms, with practical application in a two-dimensional game developed using Unity software. First section describes software tools used for developing the game and their basic components. Main idea and concepts behind the game are described in the next section, including the main components in Unity used during game development. All game elements contain code examples, with the purpose of demonstrating interaction between them and programming scripts running in the background. Central section contains the description of five different algorithms used for procedural content generation using C# programming laguange, together with actual code examples showing the practical application of these algorithms during game development. The ending section of this thesis contains analysis and comparison between algorithms used in the game in four different categories. Algorithm complexity shows maximum number of iterations needed for execution of the chosen algorithm. Algorithm performance is shown using processor and memory usage during their running time. Flexibility and playability contain a more subjective analysis of algorithms in terms of their expandability, their ability to be used in other games, and the quality of game content that was generated using the algoritms, from the perspective of the game player

Uticaj procesnih parametara na transesterifikaciju kukuruznog ulja na bazno promovisanoj γ - alumini kao heterogenom katalizatoru

Due to the frequent use of fossil fuels, which has negative effects on the environment, there is a need to find a new, environmentally acceptable replacements for fossil fuels. One of the possible replacement is the inclusion of green technologies, in order to obtain the type of fuel that would be acceptable from an environmental and economic point of view. Biodiesel represents renewable and less toxic substituent for fossil fuels, which consists of esters of higher fatty acids and depending on the environmental conditions, can be manufactured from various types of oils, both plant and animal origin. Since corn is one of the most cultivated plants in Serbia, the research included the transesterification reaction of corn oil on a heterogeneous catalyst. The paper examined the activity of heterogeneous base catalyst (CaO/γ-Al2O3) and the influence of various parameters on the conversion of corn oil. From the optimization of process parameters, it was found that the optimal conditions for transesterification of corn oil to 25% CaO/γ-Al2O3: molar ratio of methanol to oil 1:12; stirring speed 900rpm; reflux temperature of the methanol; reaction time of 6 hours; the amount of catalyst in the reaction of 5wt.%.Zbog sve učestalijeg korišćenja fosilnih goriva, koja imaju nepoželjne efekte na životnu sredinu, postoji potreba za pronalaženjem nove, ekološki prihvatljive zamene za fosilna goriva. Jednu od mogućih zamena predstavlja uključivanje zelenih tehnologija radi dobijanja vrste goriva koje bi bilo prihvatljivo sa ekološkog i ekonomskog aspekta. Bidizel predstavlja obnovljiv i manje toksičan substituent za fosilna goriva, koji se sastoji od estara viših masnih kiselina i u zavisnosti od podnevlja, može se proizvoditi od različitih vrsta ulja, kako biljnog, tako i životinjskog porekla. Obzirom da je kukuruz jedna od najviše gajenih vrsta biljaka u Srbiji, istraživanja su obuhvatila reakciju transesterifikacije kukuruznog ulja na heterogenom katalizatoru. U radu je ispitivana aktivnost heterogenog baznog katalizatora (CaO/γ-Al2O3) kao i uticaj različitih parametara na konverziju kukuruznog ulja. Optimizacijom procesnih parametara je ustanovljeno da su optimalni uslovi za transesterifikaciju kukuruznog ulja na 25% CaO/γ-Al2O3: Molarni odnos metanola prema ulju 1:12; brzina mešanja 900rpm; temperatura reakcije refluks metanola; vreme reakcije 6 sati; količina katalizatora u reakciji 5%

Risk education in Serbia

Natural disaster risk reduction can be achieved through vulnerability reduction, as well as through strengthening the resilience of the population. One of the segments leading to these aims is a proper risk education. It is the public (compulsory) education system that reaches the greatest number of participants and represents a good platform for the natural disaster knowledge transfer. Geography, as a complex subject that includes both natural and social components, is the most appropriate to transfer the knowledge necessary to improve the resilience. Research done in Serbia (detailed analyses of curricula, textbooks, teachers' role and pupils' knowledge) shows that children do learn about natural disasters but not in a way which provides usable knowledge

Preučevanje Gaussovih mešanih modelov za potrebe klasifikacije: raziskava na primeru klasifikacije napak v ležajih

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring

Optimizing the estimation of a histogram-bin width—application to the multivariate mixture-model estimation

A maximum-likelihood estimation of a multivariate mixture model\u27s parameters is a difficult problem. One approach is to combine the REBMIX and EM algorithms. However, the REBMIX algorithm requires the use of histogram estimation, which is the most rudimentary approach to an empirical density estimation and has many drawbacks. Nevertheless, because of its simplicity, it is still one of the most commonly used techniques. The main problem is to estimate the optimum histogram-bin width, which is usually set by the number of non-overlapping, regularly spaced bins. For univariate problems it is usually denoted by an integer valuei.e., the number of bins. However, for multivariate problems, in order to obtain a histogram estimation, a regular grid must be formed. Thus, to obtain the optimum histogram estimation, an integer-optimization problem must be solved. The aim is therefore the estimation of optimum histogram binning, alone and in application to the mixture model parameter estimation with the REBMIX&EM strategy. As an estimator, the Knuth rule was used. For the optimization algorithm, a derivative based on the coordinate-descent optimization was composed. These proposals yielded promising results. The optimization algorithm was efficient and the results were accurate. When applied to the multivariate, Gaussian-mixture-model parameter estimation, the results were competitive. All the improvements were implemented in the rebmix R package

Improved initialization of the EM algorithm for mixture model parameter estimation

A commonly used tool for estimating the parameters of a mixture model is the Expectation-Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density-estimation datasets and image-segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package

Uloga i delovanje elite u oslobađanju i usmeravanju društvene energije

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