Telfor Journal Vol.13 No.2 (2021)

Content

Editorial HyperLink
Editor-in-Chief: Prof. dr Aleksandar Nešković

Complex Signal Constellations in Cumulants-Based AMC: Statistics and Performance

M. S. Pajic, M. Veinovic, and V. D. Orlic
Topic:
Communications Systems
Abstract
In this paper various complex signal constellations are considered in the context of Automatic Modulation Classification (AMC) based on a higher-order normalized cumulants value. Most of the constellations have been addressed so far with fourth-order cumulants as AMC features only. The goal of this paper is to provide comparable values of sixth-order cumulants’ statistics for complex signal constellations as well, while resolving ambiguity in constellation shapes addressed at the same time and directing towards the criterion for estimation of expected classification performance.
Keywords
Constellation, complex signals, AMC, cumulants, statistics.
Full Text
HyperLink Complex Signal Constellations in Cumulants-Based AMC: Statistics and Performance
Page(s)
63-68
Doi
10.5937/telfor2102063P

Cloud-based System for Real-Time Reading of Smart Meters’ Data over 5G New Radio

M. Forcan, M. Maksimović, J. Forcan, and S. Jokić
Topic:
Communications Systems
Abstract
The potential of the combined application of Cloud computing and the fifth generation of cellular network technology (5G) in Smart Grid (SG) could be revolutionary in terms of empowering the Advanced Metering Infrastructure (AMI). The model of a real-time 5G-based communication system for remote reading of Smart Meters’ data is developed and presented in this paper. Online monitoring of power demand has been performed using the Cloud-based platform ThingSpeak. The proposed 5G communication model and online monitoring function have been tested and validated using a power demand variation scenario in a well-known IEEE 13 node network. The obtained results confirm the model accuracy and reveal the potential of AMI based on real-time data transmission between Smart meters (SM) and Cloud computing platform using a 5G communication network.
Keywords
5G, Cloud computing, Smart meter, Smart Grid.
Full Text
HyperLink Cloud-based System for Real-Time Reading of Smart Meters’ Data over 5G New Radio
Page(s)
69-74
Doi
10.5937/telfor2102069F

Influence of Emotion Distribution and Classification on a Call Processing for an Emergency Call Center

M. Bojanić, V. Delić, and A. Karpov
Topic:
Speech Processing
Abstract
The article addresses the influence of two aspects on speech emotion recognition utilization for an emergency call center: a frequency of a caller experiencing certain emotional state and classification methods used for speech emotion recognition. In situations when more simultaneous calls in an emergency call center are received, the aim is to detect more urgent callers, e.g. in a life threating situation, and give them priority in a callers’ queue. Three different emotion distributions based on the corpora from real-world emergency call centers are considered. The influence of those emotion distributions on the proposed call redistribution and subsequent time savings are reported and discussed. Regarding speech emotion classification, two approaches are presented, namely the linear Bayes classifier and a multilayer perceptron-based neural network. Their recognition results on the corpus of acted emotional Serbian speech are presented and potential application in an emergency call center is discussed.
Keywords
Affective computing, Call center, Speech emotion recognition.
Full Text
HyperLink Influence of Emotion Distribution and Classification on a Call Processing for an Emergency Call Center
Page(s)
75-80
Doi
10.5937/telfor2102075B

Long Short-Term Memory Prediction for COVID19 Time Series

M. S. Milivojević and A. Gavrovska
Topic:
Data Prediction
Abstract
Entire world has been dealing with the number of new Coronavirus 2 or COVID-19 cases. The spread of this severe acute respiratory syndrome has produced many concerns worldwide. Having data related to coronavirus available for tests, novel models for forecasting the number of new cases can be developed. In this paper, a long short-term memory (LSTM) based methodology is applied for such prediction. Here, experimental analysis is performed with the parameters, such as the number of layers and units of the network. The root mean squared error is calculated for data corresponding to the Republic of Serbia, as well as per different continents. The results show that LSTM model can be useful for further analysis and time series prediction.
Keywords
Prediction, neural network, LSTM, Acute Respiratory Syndrome, COVID-19, Root Mean Squared Error.
Full Text
HyperLink Long Short-Term Memory Prediction for COVID19 Time Series
Page(s)
81-86
Doi
10.5937/telfor2102081M

An Automated Framework for Runtime Analysis of Malicious Executables on Linux

I. Vurdelja, I. Blažić, D. Bojić, and D. Drašković
Topic:
Software Tools and Applications
Abstract
One way of testing a malware detection tool is to expose it to a large number of diverse malware samples and verify its detection accuracy. During these tests, the host system must not be harmed by malware and yet be able to analyze its harmful behavior. An additional challenge is to run a large number of executables in the shortest amount of time. Advanced malware can even stop its execution when detecting a simulation. This paper presents a framework for automated malware analysis on Linux. The proposed solution addresses these problems with parallel realistic simulations. Existing malware analysis methods are discussed, as well as technical details behind reliable execution simulations.
Keywords
Computer Security, Dynamic Analysis, Sandbox, Linux.
Full Text
HyperLink An Automated Framework for Runtime Analysis of Malicious Executables on Linux
Page(s)
87-91
Doi
10.5937/telfor2102087V