Impact of changes in blood pressure and pulse from human speech is disclosed in this article. The symptoms of increased physical activity are pulse, systolic and diastolic pressure. There are many methods of measuring and indicating these parameters. The measurements must be carried out using devices which are not used in everyday life. In most cases, the measurement of blood pressure and pulse following health problems or other adverse feelings. Nowadays, research teams are trying to design and implement modern methods in ordinary human activities. The main objective of the proposal is to reduce the delay between detecting the adverse pressure and to the mentioned warning signs and feelings. Common and frequent activity of man is speaking, while it is known that the function of the vocal tract can be affected by the change in heart activity. Therefore, it can be a useful parameter for detecting physiological changes. A method for detecting human physiological changes by speech processing and artificial neural network classification is described in this article. The pulse and blood pressure changes was induced by physical exercises in this experiment. The set of measured subjects was formed by ten healthy volunteers of both sexes. None of the subjects was a professional athlete. The process of the experiment was divided into phases before, during and after physical training. Pulse, systolic, diastolic pressure was measured and voice activity was recorded after each of them. The results of this experiment describe a method for detecting increased cardiac activity from human speech using artificial neural network.

In this paper the predictive model for speech quality estimation is described. This model allows its user to gain the information about the speech quality in VoIP networks without the need of performing the actual call and the consecutive time consuming sound file evaluation. This rapidly increases usability of the speech quality measurement especially in high load networks, where the actual processing of all calls is rendered difficult or even impossible. This model can reach its results that are highly conformant with the PESQ algorithm only based on the network state parameters that are easily obtainable by the commonly used software tools. Experiments were carried out to investigate whether different languages (English, Czech) have an effect on perceived voice quality for the same network conditions and the language factor was incorporated directly into the model.

Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

This paper concerns available steganographic techniques that can be used for sending hidden data through public network. Typically, in steganographic communication it is advised to use popular/often used method for sending hidden data and amount of that data need to be high as much as possible. We confirmed this by choosing a Domain Name System (DNS) as a vital protocol of each network and choosing Distributed denial of service (DDoS) attacks that are most popular network attacks currently represented in the world. Apart from characterizing existing steganographic methods we provide new insights by presenting two new techniques. The first one is network steganography solution which exploits free/unused protocols fields and is known for IP, UDP or TCP protocols, but has never been applied to DNS (Domain Name Server) which are the fundamental part of network communications. The second explains the usage of DNS Amplification DDoS Attack to send seamlessly data through public network. The calculation that was performed to estimate the total amount of data that can be covertly transferred by using these technique, regardless of steganalysis, is included in this paper.

Emotional states of humans and their impact on physiological and neurological characteristics are discussed in this paper. This problem is the goal of many teams who have dealt with this topic. Nowadays, it is necessary to increase the accuracy of methods for obtaining information about correlations between emotional state and physiological changes. To be able to record these changes, we focused on two majority emotional states. Studied subjects were psychologically stimulated to neutral - calm and then to the stress state. Electrocardiography, Electroencephalography and blood pressure represented neurological and physiological samples that were collected during patient’s stimulated conditions. Speech activity was recording during the patient was reading selected text. Feature extraction was calculated by speech processing operations. Classifier based on Gaussian Mixture Model was trained and tested using Mel-Frequency Cepstral Coefficients extracted from the patient's speech. All measurements were performed in a chamber with electromagnetic compatibility. The article discusses a method for determining the influence of stress emotional state on the human and his physiological and neurological changes.

The paper deals with presentation of the IVAS system within the 7FP EU INDECT project. The INDECT project aims at developing the tools for enhancing the security of citizens and protecting the confidentiality of recorded and stored information. It is a part of the Seventh Framework Programme of European Union. We participate in INDECT portal and the Interactive Video Audio System (IVAS). This IVAS system provides a communication gateway between police officers working in dispatching centre and police officers in terrain. The officers in dispatching centre have capabilities to obtain information about all online police officers in terrain, they can command officers in terrain via text messages, voice or video calls and they are able to manage multimedia files from CCTV cameras or other sources, which can be interesting for officers in terrain. The police officers in terrain are equipped by smartphones or tablets. Besides common communication, they can reach pictures or videos sent by commander in office and they can respond to the command via text or multimedia messages taken by their devices. Our IVAS system is unique because we are developing it according to the special requirements from the Police of the Czech Republic. The IVAS communication system is designed to use modern Voice over Internet Protocol (VoIP) services. The whole solution is based on open source software including linux and android operating systems. The technical details of our solution are presented in the paper.

The following books are reviewed: Cryptography for Security and Privacy in Cloud Computing (Rass, S.; 2013); Communications Networks: an Optimization, Control and Stochastic Networks Perspective (Srikant, R.; 2014).

This paper deals with the comparison of different methods of speech features extraction for a neural network classifier. We have used a Kohohen self-organizing feature map (SOM) for output-stage classifier which is a specific type of artificial neural nets. The result of this research deals with the accuracy of emotion classifier and compares the two input combinations.

The aim of this work is to design an application utilizing the QoS prediction model for Triple play services, the input parameters of which are used QoS policies and network state. In order to analyze the efficiency of individual policies designed for serving the packet queues, it was necessary to perform practical measurements in an overloaded network and monitor the total network delay, Jitter and packet loss. Results of the measurements after the conformance check were used for purposes of creating QoS prediction models for each type of service (voice, video and data).