This article deals with the system for modular provisioning the IP telephony devices. Theoretical part deals with issues in mass configuration and data synchronization, followed by the exact practical implementations of the provisioning solutions. The article also describes the design and implementation of the whole platform, including the subsequent testing of functionality with possible design of further improvements.
Besides facial expression or gestures, human speech is still the main channel of communication in ordinary human life. In addition to speech content, this signal also contains additional source / human status information. Gender, age, but also the emotional state of man can be extracted from spoken speech. This research is focused on the classification of the emotional state of man, the stress in particular. Accordingly, we have created a speech database of emergency phone calls. The database contains recordings of the Integrated Rescue System (IRS) of 112 emergency line from Czech Republic. It was designed to detect the stress from the human voice. Due to the detection of stress from a neutral (resting) state, the database was divided into neutral speech and human speech in stress. The neutral subgroup consists of voice recordings of the IRS operator. The stress subgroup is made up of people in danger. We have deliberately selected events with great stressful stimuli such as car accident, domestic violence, situations close to death, and so on. The speech signal is then pre-processed and analyzed for the feature extraction. The feature vectors represents classifier input data. Old-fashioned classification methods such as Support Vector Machine (SVM) or k-Nearest Neighbors (k-NN) classifiers and new artificial intelligence methods such as Convolutional Neural Networks (CNN) are used to detect and recognize human stress. The applications of achieved results are broad: from phone services through Smart Health to security components analysis.
It is estimated, that the number of connected Internet of Things (IoT) devices around the world could increase dramatically, with expectations ranging from 25 billion to 50 billion devices in 2025. As the IoT area is wider and wider and the number of connected IoT devices is higher every day, it appears that the issue of security is more up to date. The paper deals with LoRaWAN and Sigfox networks belonging to the LPWAN (Low Power Wide Area Network) category, where we focus on detection of the end device movement in a network based on the qualitative parameters of a radio signal. The result of this work is a software solution to notice the owner of the end device about the location change. As a testbed, we use LoRaWAN network, which is part of the infrastructure covering the area of Czech Republic. For Sigfox solution we use the public network provided by SimpleCell company and own solution to show actual parameters with which base station received messages. Detection serves as a measure against an attacker performing a spoofing attack or the physical movement of a statically-located end device activated by authentication methods. Based on the experimental simulation of the attacker's behavior, we have summed up the attack into individual points, according to which we subsequently constructed a countermeasure principle in the form of detection. This principle was applied to an algorithm that could be integrated into the gateway in case of LoRaWAN network and implemented as a separate element for Sigfox solution.
Software Defined Networks (SDN) are gaining attraction with the expanding use of complex data center infrastructures that accommodate the increasing demand for computational power related to much more feature-rich web applications and common use of deep learning algorithms. The increased set of features being used in the applications are reflected in the increased demands on network architectures starting with the higher network throughput, through the need for complex high-availability schemes and ending with near-perfect delay/loss communication characteristics. This increased demand resulted in the need for more flexible network architectures resulting in the major change in the networking paradigm and the related shift from traditional networks to software defined ones. The quality of service (QoS) in the networks and quality of experience (QoE) of the end-user services is a major topic of interest in the networking community resulting in several approaches implemented in the networks to ensure resource reservation or traffic prioritization. In this paper, we propose a way how to propagate the arbitrary qualitative parameter in the OpenFlow messages that would allow for easy monitoring of the quality of service and quality of experience. Moreover, we focus on the measurement of the quality of speech and the consecutive propagation of the information through the SDN network to allow SDN controllers and the OpenFlow capable switches controlled by them to react on the decreasing quality and support the services being carried through the network. The paper describes the way how the quality is measured, how the information is processed by the controller and how it is encapsulated in the OpenFlow messages. The assumptions are validated in the simulations based on the mininet simulation tool and Ryu SDN controller. The implications for the carried voice quality are discussed as well.
The significant expansion of the Internet of Things (IoT) field and unique requirements of the IoT devices bring new technologies created exclusively to provide wireless connectivity for the IoT devices. Among these technologies, we can include LoRa technology. Unlike some other LPWANs, LoRa technology is an open standard, and it allows us to build private networks. We took advantage of that and developed our gateway. The paper deals with a proposal of network infrastructure and the hardware solution of the LoRaWAN gateway based on the second generation of monolithic microcomputer Raspberry Pi model B and fully compatible LoRaWAN 868 MHz iC880A concentrator. The concentrator is connected to Raspberry Pi via Serial Peripheral Interface (SPI). In 2018, five gateways were deployed to cover a nearly entire area of Ostrava city in the Czech Republic and its surrounding areas with LoRaWAN signal. Our solution uses The Things Network platform to connect to a global open crowd-sourced IoT data network. For the end-users comfort, we implemented a web application that serves as a backend for registration of the end-devices to the LoRaWAN network, and it provides access to the history of all uplink messages transmitted by end-devices and received by LoRaWAN network. The next part of the article discusses end-device proposed for network availability testing.
This article focuses on the analysis of the effect of long-time thermal load on the total losses of the selected fiber-optic couplers which have been thermal stressed for the thirty weeks at temperature 100 °C ± 5 °C. A total of six couplers with 10:90, 1:99 and 50:50 dividing ratio were tested. Measurements were made for two wavelengths (1310 nm and 1550 nm). The results obtained show how long-term thermal stresses affect the total losses of the optical couplers. This information could be interest for the practical implementations of the optical couplers.
The article describes a comparative measurement of a classical seismic sensor and a fiber-optic interferometric sensor for the perimetric applications. We created and proposed technically and financially the simplest interferometric sensor (type two-arm Mach Zehnder). A test polygon was created where were analyzed the vibration-acoustic manifestations caused by the 20 test subjects. The article describes original results that clearly point to the high sensitivity of the interferometric sensor.
Artificial neural networks affect our everyday life. But every neural network depends on the appropriate training set and setting of internal properties with hyperparameters. Even accurate and complete training set doesnt imply high performance of neural network algorithm. Tuning of hyperparameters is crucial for correct functionality, fast learning and high accuracy of neural networks. The hyperparameter selection relies on manual fine-tuning based on multiple full training trials. There are a lot of neural network implementation available for public and commercial use, but the setting of hyperparameters is often a neglected problem. Choosing the best structure and hyperparameters is the primary challenge in designing a neural network. This article describes a genetic algorithm for automatic selection of hyperparameters and their tuning for increasing the performance of neural networks without human interaction. The optimization algorithm accelerates the discovery of configuration, which is otherwise a time-consuming task. We evaluate the results of optimizations in comparison to naïve approach and compare pro and cons of different techniques.
The paper deals with the need to provide security of the VoIP (Voice over IP) architecture. It is not entirely trivial matter to ensure the security of the VoIP services and attacks on telecommunication solutions, built on VoIP technology, grow with an increasing number of active users. In many situations, it is necessary to detect and analyze these attacks, monitor their progress and then prepare an effective defense against them. The best way how to detect attacks on VoIP infrastructure is implementing VoIP Honeypots. We have developed our honeypot solution. The main motivation for the development of our own honeypot for VoIP service is a nonexistent actively developed project with a similar purpose, which is adapted to the new security threats and which is developed according to the needs of the telecommunications market. Honeypot for VoIP services is implemented purely in software and honeypot is able to deal with various types of attacks. The entire solution is based on a Linux platform and it is prepared in a virtual environment for the simplest deployment and clustering possible.
This article deals with the system for voice control of the UAV (Unattended Aerial Vehicle) accessories using the mobile device and an advanced communication platform. The paper provides an overview of projects realized in last period in field of voice-controlled drones and explains the applied approach for automatic speech recognition using hidden markov models. Authors describes also converting speech commands instructions for UAV control and necessary steps in practical testing and optimization of the whole system. The achieved results and conclusions are given in the final chapter of the article in which authors provide their experience gained within the experimental development.