61. Razširitev wi-fi omrežja Šolskega centra Krško-Sevnica z Mikrotik mrežno opremo : Diplomska nalogaZoran Tkavc, 2021, undergraduate thesis Abstract: V diplomski nalogi je opisano načrtovanje in izvedba brezžičnega omrežja z uporabo mrežne opreme proizvajalca MikroTik v prostorih Šolskega centra Krško-Sevnica. Novo Wi-Fi omrežje je bilo treba vključiti v obstoječe omrežje šolskih delavnic in ustrezno zavarovati pred nepooblaščenimi dostopi. Wi-Fi omrežje je namenjeno širši rabi, zato sem v teoretičnem delu naloge predstavil osnove delovanja Wi-Fi omrežij ter opisal nadzor dostopa z avtentikacijo, pri kateri ugotavljamo identiteto uporabnika. V empiričnem delu sem pri načrtovanju omrežja v Šolskem centru izhajal iz šolskih potreb in razpoložljive mrežne opreme. Centraliziran nadzor dostopa uporabnikov je izveden z uporabo FreeRADIUS strežnika. Za nadzor nad delovanjem omrežja sem uporabil aplikacijo Dude podjetja Mikrotik. Na koncu sem izvedel meritve, s katerimi sem ocenil izvedbo brezžičnega omrežja. Rezultati meritev so potrdili ustreznost načrtovanega Wi-Fi omrežja potrebam Šolskega centra Krško-Sevnica.
Keywords: Wi-Fi, RouterOS, FreeRADIUS, Mikrotik, VLAN, IP
Published in ReVIS: 29.06.2021; Views: 1299; Downloads: 109
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64. Modelling human cardiorespiratory system through heart-rate variability : Doctoral dissertationAlbert Zorko, 2020, doctoral dissertation Abstract: The modern computer resources and the data analysis methods allow for a biomedical data to be examined in a more detail than ever. The heart rate variability (HRV) is an easily accessible vital signal that offers a range of useful information about the person under a study. One such application regards an automatical determining whether a person is awake or asleep from the HRV data only. This is of an importance not just for medical but also for practical applications, such as a traffic safety or smart homes.
In this doctoral work we study the HRV data of 75 healthy individuals of a varying age and sex, recorded with a microsecond precision. We employ the empirical fact that heart and respiration cycles couples differently during a sleep and awake period. Namely, a respiratory modulation of a heart rhythm or a respiratory sinus arrhythmia (RSA) is more pronounced while asleep, as both sleep and RSA are connected to a strong vagal activity. Therefore, the onset of sleep can be recognized or perhaps even predicted by a carefully examining the cardio-respiratory coupling. We show that the above can indeed be used, at least in principle, to design an algorithmic method to automatically determine the state of a person's consciousness from the HRV data only.
On the methodological front we rely on quantifying the (self)similarity among the shapelets, the short chunks of the HRV time series, that allow for a consistent comparison among and within the time series. To establish a better benchmark, we also carry out a comprehensive analysis of the overall HRV dynamics depending on age and sex.
Our results include: (i) that a distinctive patterns of the HRV dynamics are consistent across age and sex, (ii) that they are not only an indicative of sleep and awake, but allow to pinpoint the change from awake to sleep and vice versa almost immediately, (iii) that the shapelet analysis is a viable tool to examine these data with a great precision. We conclude that a more systematic analysis involving more subjects could lead to a practical method for the prediction of the onset of sleep.
Keywords: algoritem, EKG, Holter, vzorčna entropija, razmerje signal-šum, klasifikacija
Published in ReVIS: 18.09.2020; Views: 2290; Downloads: 220
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66. Podatkovna analiza političnega diskurza predsednikov Republike Slovenije : magistrska nalogaElvira Žagar, 2019, master's thesis Abstract: Magistrska naloga obsega podatkovno analizo političnega diskurza predsednikov Republike Slovenije. Zajema inavguralne govore, združene govore ob slovesnosti ob Dnevu državnosti ter govore ob zaključku predsedniškega mandata; od prvega govora, ki ga je leta 1991 imel Milan Kučan, ter do zadnjega inavguracijskega govora iz leta 2017, sedanjega predsednika Boruta Pahorja. Magistrska naloga je empirično zastavljena, saj obsega podatkovno analizo korpusov govorov z namenom ugotavljanja frekventnosti pojavljanja določenih besed za ugotavljanje trenutnega stanja v državi skozi spremembe v političnem diskurzu in ugotavljanje retoričnega načina prepričevanja množic.
Keywords: inavguracijski govor, analiza inavguracijskih govorov, analiza političnega diskurza, podatkovna analiza, politični diskurz
Published in ReVIS: 13.11.2019; Views: 2427; Downloads: 175
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67. Outstanding problems in nonequilibrium statistical physics : doctoral dissertationMarco Faggian, 2019, doctoral dissertation Abstract: This PhD thesis is mainly devoted to the study of different problems in nonequilibrium systems that undergo certain phase transitions. The first part of this thesis constitutes a brief review of fundamental concepts in statistical
physics. Many of them, of course, will be useful in the remaining of the thesis. My research is thus articulated in three main chapters. The second chapter focuses on a numerical and experimental evidence of an absorbing phase
transition, so far associated with spatiotemporal dynamics provided in a purely temporal optical system. We provide a numerical and experimental study of an effective model for a bistable semiconductor laser, with long-delayed opto-electronic feedback and multiplicative noise showing the peculiar features of a critical phenomenon belonging to the directed percolation universality class.
In the third part of the thesis we present a random network of heterogeneous phase oscillators in which the links mediating the interactions are constantly rearranged with a characteristic timescale and an extremely low instantaneous connectivity. We will show that, provided strong coupling and fast enough rewiring are considered, the network is able to reach partial synchronisation even in the vanishing connectivity limit. The last part is dedicated to a systematic test of an effective thermodynamics approach proposed for the identification of critical phase transitions in nonequilibrium systems by making a formal analogy with equilibrium systems. When the authors apply it to experimental data of neurons, this method seems to bring a signature of a "special critical point". However, the approach has never been tested on synthetic data and for this reason we will test it on out of equilibrium toy models that display critical transition in a known range of parameters. In the concluding section I summarise and briefly comment my main results and sketch some directions for further research.
Keywords: statistična fizika, ravnotežna statistična fizika, neenakomerna statistična fizika, termodinamika, kritični pojavi, kritična točka, Kuramoto model, skupina za renormalizacijo, samoorganizirana kritičnost omrežja, Zipfov zakon, efektivni termodinamični pristop
Published in ReVIS: 28.05.2019; Views: 2690; Downloads: 172
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69. Automatic reconstruction of complex dynamical networks : doctoral dissertationMarc Grau Leguia, 2019, doctoral dissertation Abstract: A foremost problem in network science is how to reconstruct (infer) the topology of a real network from signals measured from its internal units. Grasping the architecture of complex networks is key, not only to understand their functioning, but also to predict and control their behaviour. Currently available methods largely focus on the detection of links of undirected networks and often require strong assumptions about the system. However, many of these methods cannot be applied to networks with directional connections. To address this problem, in this doctoral work we focus at the inference of directed networks. Specifically, we develop a model-based network reconstruction method that combines statistics of derivative-variable correlations with simulated annealing. We furthermore develop a data-driven reconstruction method based on a nonlinear interdependence measure.
This method allows one to infer the topology of directed networks of chaotic Lorenz oscillators for a subrange of the coupling strength and link density. Finally, we apply the data-driven method to multichannel electroencephalographic recordings from an epilepsy patient. The functional brain networks obtained from this approach are consistent with the available medical information.
Keywords: network reconstruction, simulated annealing, dynamical systems, nonlinear interdependence measure, EEG
Published in ReVIS: 19.04.2019; Views: 2503; Downloads: 147
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70. Multilevel complex systems approaches to computational linguistics : doctoral dissertationKristina Ban, 2018, doctoral dissertation Abstract: Complex systems are omnipresent in nature, society as well as in human culture. Last few decades saw an increase of interest for their study, particularly by using graph-theoretic methodologies. By identifying systems' units as nodes and modelling interactions between the units as links, the study of complex networks spread to a number of disciplines including sociology, biology and linguistics, to just mention a few. The research done in this doctoral dissertation falls in this context. The core of this doctoral work is the data-driven multilevel analysis of major human languages, which was done in two stages. First, we looked at the speed of growth of Wikipedias in 26 different languages over the span of 15 years. This involved creating and analysing a dataset with 14962 articles, each of which exists in all 26 languages. We found six well-defined clusters of Wikipedias that share common growth patterns, with their make-up robust against the method used for their determination. Interestingly, the identified clusters were found to have little correlation with the respective language families. Rather, our results suggest that growth of Wikipedias is primarily governed by an intricate set of other factors, from culture to information literacy. Second, to approach human languages at another independent level, we gathered a dataset comprising a list of syllables and a list of syllables words in 10 different languages, specifically: English, Dutch, German, Russian, Slovenian, Croatian, French, Spanish, Latin and Basque. These datasets were obtained from recognized repositories for each language and benchmarked in the same way. Syllable networks were created by looking at pairs of syllables that jointly compose at least one word. We then carried out a systematic network analysis, relying on both standard network analysis methods and more recent techniques, such as K-core analysis and graphlet statistics. Research revealed striking similarities between the architectures of syllable networks that belong to the same language family, along with expected differences between the families. Indeed, structures of syllable networks were found to well quantify the linguistic similarities among these 10 languages, exactly as known from classical linguistics. Most interestingly, we found that Basque language, whose classification is as of today still unknown, bares a strong resemblance to Latin, at least when syllable network representation is concerned. Earlier stages of this doctoral work involved comparing the performance of network alignment algorithms, used in bioinformatics for studying protein networks. Several alignment algorithms were compared by scoring their performance on standard protein datasets. It was found that three algorithms, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. Due to the change of doctoral adviser, this research topic was abandoned in favour of language/syllable networks. In sum, this doctoral work involved two distinct directions of research in network science, one related to developing the methodology of network analysis (alignment algorithms), and the other devoted to extracting new information from specifically designed datasets (syllable networks). Therefore, the original contribution of this work to science includes both theory and methodology. Future research avenues include advancement along both directions, most interesting being the application of network alignment methods to syllable datasets, which could reveal more precise quantification of structural differences among syllable networks.
Keywords: computational statistics, biostatistics, bioinformatics, machine learning, computational linguistics
Published in ReVIS: 21.12.2018; Views: 3179; Downloads: 139
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