Signalling Transmission for Internet Television

Signalizace v sítích pracujících s internetovým protokolem (IP) je používána pro monitorování a řízení činnosti sítě. Tato práce se zabývá přenosem signalizace skrze IP sítě pro velké skupiny komunikujících prvků a navrhuje škálovatelné řešení, jak pro malá, tak pro velká vysílání internetových televize (IPTV). Hlavní přínos práce spočívá v návrhu algoritmů pro ustavení optimálního hierarchického stromu na základě dostupných zdrojů a s ohledem na geografickou a virtuální polohu jednotlivých stanic. Pro účely optimalizace byly použity jak simulace s parametry globální experimentální sítě Planetlab, tak byly navržené algoritmy a protokoly nasazeny do reálného provozu v této síti.A signalization in an Internet protocol environment is commonly used for monitoring quality of service and other parameters of a network. This thesis is involved in transmission of signalization through internet protocol networks and proposes scalable solution for small and even for large-scale internet television broadcasting. The main contribution of this thesis lies in design and validation of optimal hierarchical tree on the basis of resources assigned. This is done in respect to geographical distance, network distance of each particular member of the hierarchical structure. For the design of algorithms simulations and global experimental network were used.

Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks

The widespread use of Batch Normalization has enabled training deeper neural networks with more stable and faster results. However, the Batch Normalization works best using large batch size during training and as the state-of-the-art segmentation convolutional neural network architectures are very memory demanding, large batch size is often impossible to achieve on current hardware. We evaluate the alternative normalization methods proposed to solve this issue on a problem of binary spine segmentation from 3D CT scan. Our results show the effectiveness of Instance Normalization in the limited batch size neural network training environment. Out of all the compared methods the Instance Normalization achieved the highest result with Dice coefficient = 0.96 which is comparable to our previous results achieved by deeper network with longer training time. We also show that the Instance Normalization implementation used in this experiment is computational time efficient when compared to the network without any normalization method

Evaluation of Natural Robustness of Best Constant Weights to Random Communication Breakdowns

One of the most crucial aspects of an algorithmdesign for the wireless sensors networks is the failure tolerance.A high natural robustness and an effectively bounded executiontime are factors that can significantly optimize the overall energyconsumption and therefore, a great emphasis is laid on theseaspects in many applications from the area of the wireless sensornetworks. This paper addresses the robustness of the optimizedBest Constant weights of Average Consensus with a stoppingcriterion (i.e. the algorithm is executed in a finite time) and theirfive variations with a lower mixing parameter (i.e. slowervariants) to random communication breakdowns modeled as a stochastic event of a Bernoulli distribution. We choose threemetrics, namely the deviation of the least precise final estimatesfrom the average, the convergence rate expressed as the numberof the iterations for the consensus, and the deceleration of eachinitial setup, in order to evaluate the robustness of various initialsetups of Best Constant weights under a varying failureprobability and over 30 random geometric graphs of either astrong or a weak connectivity. Our contribution is to find themost robust initial setup of Best Constant weights according tonumerical experiments executed in Matlab. Finally, theexperimentally obtained results are discussed, compared to theresults from the error-free executions, and our conclusions arecompared with the conclusions from related papers

Chest X-ray Image Analysis using Convolutional Vision Transformer

In recent years, computer techniques for clinical imageanalysis have been improved significantly, especially becauseof the pandemic situation. Most recent approaches are focusedon the detection of viral pneumonia or COVID-19 diseases.However, there is less attention to common pulmonary diseases,such as fibrosis, infiltration and others. This paper introduces theneural network, which is aimed to detect 14 pulmonary diseases.This model is composed of two branches: global, which is theInceptionNetV3, and local, which consists of Inception modulesand a modified Vision Transformer. Additionally, the AsymmetricLoss function was utilized to deal with the problem of multilabelclassification. The proposed model has achieved an AUC of 0.8012and an accuracy of 0.7429, which outperforms the well-knownclassification models

The Transferable Methodologies of Detection Sleep Disorders Thanks to the Actigraphy Device for Parkinson's Disease Detection

Due to population aging, society is struggling with an increasing number of patients with neurodegenerative diseases. One of them is Parkinson's disease. Early detection of Parkinson's disease is very important since there is no cure and the treatment is more effective when administered early. Wearable devices can be of great help - they are cheap and reachable, they can last for many days without charging, can provide long time monitoring, and are minimally invasive to human life. In the paper, we briefly desribe the sensors and actigraphs suitable for the analysis of sleep disturbance in Parkinson's patients and noctural symptoms of Parkinson's disease. Moreover, we pointed out how to collect the data and what could have an influence on the final performance of the automatic models. Additionally, as the main aim of this paper, we have analysed and desribed the machine learning algorithms used in the area of analysis accelerometer singla for sleep / awake stages recognition or diseases which manifested in changes in sleep patterns. We though that these algorithms, because of the nature of Parkinon's patients' sleep patterns, will be simultaneously appropriate for the detection of Parkinon's disease

Is It Possible to Distinguish COVID-19 Cases and Influenza with Wearable Devices? Analysis with Machine Learning

The COVID-19 situation is enforcing the creation of the diagnosis and supporting methods for early detection, which could serve as screening tools. In this paper, we introduced the methodologies based on wearable devices and machine learning, which distinguishes between COVID-19 disease and two types of Influenza. We checked the results of binary classification for various scenarios and multiclass classification. The results were evaluated separately for the cases before the pandemic and in the middle of the pandemic. In the middle of the pandemic, the best classification accuracy was achieved when distinguishing between COVID-19 and Influenza cases with k-NN (the balanced accuracy was equal to 73%). The highest sensitivity was achieved for Logistic Regression - 61%. The successful distinction between Influenza types was achieved in 80 % for XGBoost and Decision Tree. Additionally, the balanced accuracy for multiclass classification was equal to 69 % for k-NN

Network Anomaly Detection With Temporal Convolutional Network and U-Net Model

Anomaly detection in network traffic is one of the key techniques to ensure security in future networks. Today, the importance of this topic is even higher, since the network traffic is growing and there is a need to have smart algorithms, which can automatically adapt to new network conditions, detect threats and recognize the type of the possible network attack. Nowadays, there are a lot of different approaches, some of them have reached relatively sufficient accuracy. However, the majority of works are being tested on old datasets, which do not reflect current network conditions and it leads to overfitted results. This is caused by high redundancy of the data and because they fail to reflect the performance of the latest methods in the real-world anomaly detection applications. In this work, we applied a couple of new methods based on convolutional neural networks: U-Net based and Temporal convolutional network based for network attack classification. We trained and evaluated methods on the old dataset KDD99 and the modern large-scale one CSE-CIC-IDS2018. According to results, Temporal convolutional network with LSTM has achieved accuracy 92% and 97% on the KDD99 and the CSE-CIC-IDS2018 respectively, the U-Net model has accuracy 93% and 94% on the KDD99 and the CSE-CIC-IDS2018 respectively. Additionally, we utilized the focal loss function in the Temporal convolutional network with Long Short-Term Memory model, which has positive effect on class imbalance in time-series data. We showed, that the Temporal convolutional network in combination with Long Short-Term Memory network and U-Net model can give higher accuracy compared to other network architectures for network traffic classification. In this work we also proved, that methods trained on the old dataset can easily overfit during training and achieve relatively good results on the testing set, but at the same time, these methods are not so successful on more complex and actual data