This work focuses on a theoretical explanation of heart rhythm disorders and the possibility of their automatic detection using deep learning networks. For the purposes of this work, a total of 6884 10-second ECG recordings with measured eight leads were used. Those recordings were divided into 5 groups according to heart rhythm into a group of records with atrial fibrillation, sinus rhythms, supraventricular rhythms, ventricular rhythms, and the last group consisted of the others records. Individual groups were unbalanced represented and more than 85 % of the total number of data are sinus rhythm group records. The used classification methods served effectively as a record detector of the largest group and the most effective of all was a procedure consisting of a 2D convolutional neural network into which data entered in the form of scalalograms (classification procedure number 3). It achieved results of precision of 91%, recall of 96% and F1-score values of 0.93. On the contrary, when classifying all groups at the same time, there were no such quality results for all groups. The most efficient procedure seems to be a variant composed of PCA on eight input signals with the gain of one output signal, which becomes the input of a 1D convolutional neural network (classification procedure number 5). This procedure achieved the following F1-score values: 1) group of records with atrial fibrillation 0.54, 2) group of sinus rhythms 0.91, 3) group of supraventricular rhythms 0.65, 4) group of ventricular rhythms 0.68, 5) others records 0.65.

This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.

Sectors of security, safety, and defence require methods for identifying people on the individual level. Automation of these tasks has the potential of outperforming manual labor, as well as relieving workloads. The ever-extending surveillance camera networks, advances in human pose estimation from monocular cameras, together with the progress of deep learning techniques, pave the way for automated walking gait analysis as an identification method. This thesis investigates the use of 2D kinematic pose sequences to represent gait, monocularly extracted from a limited dataset containing walking individuals captured from five camera views. The sequential information of the gait is captured using recurrent neural networks. Techniques in deep metric learning are applied to evaluate two network models, with contrasting output dimensionalities, against deep-metric-, and non-deep-metric-based embedding spaces. The results indicate that the gait representation, network designs, and network learning structure show promise when identifying individuals, scaling particularly well to unseen individuals. However, with the limited dataset, the network models performed best when the dataset included the labels from both the individuals and the camera views simultaneously, contrary to when the data only contained the labels from the individuals without the information of the camera views. For further investigations, an extension of the data would be required to evaluate the accuracy and effectiveness of these methods, for the re-identification task of each individual. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

This thesis deal with an analysis of network traffic and its properties. In this thesis are discussed possibilities of prediction network traffic by FARIMA model, theory of chaos with Lyapunov exponent and by neural networks. The biggest attention was dedicated to prediction network traffic by neural networks. In Matlab with using Neural Network Toolbox were created, trained and tested recurrent networks for prediction specific types of network traffics, which was captured on local network. There were choosed Elman network, LRN and NARX network to test the prediction of network traffic, results were discussed. Thesis also introduce area of application ability prediction of network traffic, there is introduce design of system for dynamic allocation bandwidth with particular description of its prediction part. Thesis also states possible use designed system for dynamic allocation of bandwidth.

The aim of this thesis was to study problems of prediction of data in computer networks. Furthermore, this work deals with network traffic and analyzing its properties. In this study were analyzed the possibilities of network traffic prediction using Farima model, the theory of chaos with Lyapunov exponents and neural networks. Possibilities of prediction with the focus on neural network were discussed in detail here, mainly on recurrent neural networks. Prediction was performed in Matlab development environment in Neural Network Toolbox, where they were created, trained and evaluated neural network to predict specific types of network traffic. For testing were selected Elman network NARX network and general LRN recurrent network. The results were clearly organized into tables and plotted in graphical relationships before and after the use of predictive techniques designed to final evaluation.

Tato práce se zabývá automatickým generovaním popisů obrázků s využitím několika druhů neuronových sítí. Práce je založena na článcích z MS COCO Captioning Challenge 2015 a znakových jazykových modelech, popularizovaných A. Karpathym. Navržený model je kombinací konvoluční a rekurentní neuronové sítě s architekturou kodér--dekodér. Vektor reprezentující zakódovaný obrázek je předáván jazykovému modelu jako hodnoty paměti LSTM vrstev v síti. Práce zkoumá, na jaké úrovni je model s takto jednoduchou architekturou schopen popisovat obrázky a jak si stojí v porovnání s ostatními současnými modely. Jedním ze závěrů práce je, že navržená architektura není dostatečná pro jakýkoli popis obrázků.

This thesis is aimed to exploitation of fundamental analysis in automatic trading. Technical analysis uses historical prices and indicators derived from price for price prediction. On the opposite, fundamental analysis uses various information resources for price prediction. In this thesis, only quantitative data are used. These data sources are namely weather, Forex, Google Trends, WikiTrends, historical prices of futures and some fundamental data (birth rate, migration, \dots). These data are processed with LSTM neural network, which predicts stocks prices of selected companies. This prediction is basis for created trading system. Experiments show major improvement in results of the trading system; 8\% increase in success prediction accuracy thanks to involvement of fundamental analysis.

Tato zpráva popisuje  experimentální práci na statistické jazykovém modelování pomocí rekurentních neuronových sítí (RNN). Je zde předložen důkladný přehled dosud publikovaných prací, následovaný popisem algoritmů pro trénování příslušných modelů. Většina z popsaných technik byla implementována ve vlastním nástroji, založeném na knihovně Theano. Byla provedena rozsáhlá sada experimentů s modelem Jednoduché rekurentní sítě (SRN), která odhalila některé jejich dosud nepublikované vlastnosti. Při statické evaluaci modelu byly dosažené výsledky relativně cca. o 2.7 % horší, než nejlepší publikované výsledky. V případě dynamické evaluace však bylo dosaženo relativního zlepšení o 1 %. Dále bylo experimentováno i s modelem Strukturně omezené rekurentní sítě, ale ten se nepodařilo natrénovat k předpokládáným výkonům. Konečně bylo navrženo rozšíření SRN, pojmenované Náhodně prořidlá rekurentní neuronová síť. Experimentálně bylo potvrzeno, že RS-RNN dosahuje lepších výsledků v učení vlastního trénovacího korpusu a kombinace několika RS-RNN modelů přináší o 30 % větší zlepšení než kombinace stejného počtu SRN.

Die Technik der intravitrealen Gentamicin Injektion darzulegen, die Auswirkungen dieser auf die klinischen Symptome von Uveitiden, sowie die möglichen unmittelbaren Komplikationen (innerhalb von 24 Stunden) und längerfristigen Komplikationen (30 bis 780 Tage) die mit dieser Technik verbunden sein können, zu beschreiben. Zusätzlich wurde der okuläre und systemische Leptospiren- Status ermittelt und der Einfluss dieser auf das Behandlungsergebnis untersucht.:Table of Contents 1 INTRODUCTION 1 2 LITERATURE OVERVIEW 2 2.1 Etiology and pathogenesis 2 2.1.1 Proposed etiologies 2 2.1.2 ERU: an immune mediated disease 3 2.2 Leptospirosis and ERU 4 2.2.1 Genetic predisposition for ERU 6 2.3 Definition of ERU 7 2.3.1 Classification and syndromes 7 2.3.2 Clinical symptoms 8 2.4 Diagnostic testing for ERU (Leptospira) 8 2.4.1 Sample collection (aqueous humor, vitreous humor, serum) 8 2.4.2 Methodology 9 2.4.2.1 Microagglutination test (MAT) 9 2.4.2.2 Polymerase chain reaction (PCR) 10 2.4.2.3 Cultures 10 2.5 Treatment of ERU 10 2.5.1 Medical management 10 2.5.2 Intravitreal and suprachoroidal injections 11 2.5.2.1 Intravitreal rapamycin injections 11 2.5.2.2 Intravitreal triamcinolone injections 11 2.5.2.3 Suprachoroidal triamcinolone injections 12 2.5.2.4 Low-dose intravitreal gentamicin injections 12 2.5.3 Surgical procedures 13 2.5.3.1 Suprachoroidal cyclosporine implants 13 2.5.3.2 Pars plana vitrectomy 14 3 PUBLICATIONS 16 3.1 Intravitreal injection of low-dose gentamicin for the treatment of recurrent or persistent uveitis in horses: Preliminary results 16 3.2 Medical and Surgical Management of Equine Recurrent Uveitis 29 4 DISCUSSION 47 5 ZUSAMMENFASSUNG 51 6 SUMMARY 52 7 REFERENCES 53 / To describe the intravitreal gentamicin injection technique, report the effects of the injection on the clinical signs of uveitis and to describe the associated peri-injection (within 24 hours) and post-injection complications (30 to 780 days). Additionally, evaluation of the systemic and ocular Leptospira status and its effects on the treatment outcome was performed.:Table of Contents 1 INTRODUCTION 1 2 LITERATURE OVERVIEW 2 2.1 Etiology and pathogenesis 2 2.1.1 Proposed etiologies 2 2.1.2 ERU: an immune mediated disease 3 2.2 Leptospirosis and ERU 4 2.2.1 Genetic predisposition for ERU 6 2.3 Definition of ERU 7 2.3.1 Classification and syndromes 7 2.3.2 Clinical symptoms 8 2.4 Diagnostic testing for ERU (Leptospira) 8 2.4.1 Sample collection (aqueous humor, vitreous humor, serum) 8 2.4.2 Methodology 9 2.4.2.1 Microagglutination test (MAT) 9 2.4.2.2 Polymerase chain reaction (PCR) 10 2.4.2.3 Cultures 10 2.5 Treatment of ERU 10 2.5.1 Medical management 10 2.5.2 Intravitreal and suprachoroidal injections 11 2.5.2.1 Intravitreal rapamycin injections 11 2.5.2.2 Intravitreal triamcinolone injections 11 2.5.2.3 Suprachoroidal triamcinolone injections 12 2.5.2.4 Low-dose intravitreal gentamicin injections 12 2.5.3 Surgical procedures 13 2.5.3.1 Suprachoroidal cyclosporine implants 13 2.5.3.2 Pars plana vitrectomy 14 3 PUBLICATIONS 16 3.1 Intravitreal injection of low-dose gentamicin for the treatment of recurrent or persistent uveitis in horses: Preliminary results 16 3.2 Medical and Surgical Management of Equine Recurrent Uveitis 29 4 DISCUSSION 47 5 ZUSAMMENFASSUNG 51 6 SUMMARY 52 7 REFERENCES 53

No description available.