JournalTOCs

Image Processing & Communications
Number of Followers: 18

This is an Open Access Journal

Open Access journal
ISSN (Print) 1425-140X - ISSN (Online) 2300-8709
Published by Institute of Telecommunications Bydgoszcz

[1 journal]

DDOS RECOGNITION METHOD FOR LIGHT IOT DEVICES IN 5G NETWORK
- Authors: Łukasz Apiecionek
  Pages: 5 - 14
  Abstract: The Internet of Things systems, as all kinds of networks, are susceptible to various kinds of attacks on 5G network, which hinder their functionalities and pose a threat to the security of their users. One of such attacks are Distributed Denial of Service Attacks, which are able to block the whole network and disable the functions of the devices within it. The method of recognizing and neutralizing such attacks presented in this paper is based on Ordered Fuzzy Numbers and it is easy for implementation.
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
GPU-ENABLED SOFTWARE ENVIRONMENT FOR PERFORMANCE SIMULATION OF SC
MACRODIVERSITY SYSTEM WITH TWO MICRODIVERSITY MRC RECEIVERS IN THE
PRESENCE OF κ-µ FADING
- Authors: Dragana S. Krstic, Suad N. Suljovic, Nenad Petrovic, Elmedin Biberovic
  Pages: 15 - 26
  Abstract: In this paper, the outage probability (Pout) of the selection combining (SC) macrodiversity (MD) system with two maximal ratio combining (MRC) microdiversity (mD) receivers in Gamma shadowed κ-µ fading environment is investigated. Each mD receiver has L input branches. For this system model, analytical expression for the probability density function (PDF) of the signal to noise ratio (SNR) at the output of the MD SC receiver, and the Pout of the MD SC receiver are calculated. The obtained results are graphically presented to accentuate parameters influence to the system performance. Finally, the Pout expression is adopted for purpose of quality of service (QoS) estimation inside Graphics Processing Unit (GPU) enabled software simulation environment aiming optimal planning of mobile networks in smart cities making use of deep learning for demand prediction and linear optimization.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
COMPARISON OF EVERYDAY EXPOSURE SITUATIONS TO RF RADIATION REGARDING
DIFFERENT SOURCES INCLUDING MOBILE COMMUNICATIONS WITH RESPECT TO 5G
- Authors: Peter Mandl, Pirmin Pezzei, Erich Leitgeb
  Pages: 27 - 38
  Abstract: In the near future, the demand of wireless communications will elevate tremendously. The rapid increasing number of mobile end devices will require a much higher data rate connection than nowadays e.g. to smart homes (Internet of Things, IoT) or to the Internet. The radiation power pattern of base stations and mobile end devices will completely change for the 5G Next Generation Mobile Network technology, which is expected to use frequency bands up to 100 GHz. The electro-magnetic exposure especially to human bodies will increase in the future, because most of the wireless Internet connections are realized in RF technology. In particular, the technology standard 5G and others are being promoted and realised worldwide, which rises a public discussion regarding the relevant non-ionizing electromagnetic radiation exposure for the population. To compare the everyday exposure situation to different RF radiation sources six different measurement campaigns are presented in this contribution. In particular this publication presents a comparison of the non-ionizing electromagnetic radiation exposure between a mobile base station, a mobile phone, mobile phone radiation in a moving vehicle, WLAN base stations, state of the art TV broadcast transmitters like DVB-T2 and 5G base stations as part of long-term measurements. In particular, the different power flux densities of the technologies mentioned are measured, compared and discussed with regard to the legal framework and limits.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
THE IMPACT OF LABEL NOISE ON THE CLASSIFICATION MODELS FOR HYPERSPECTRAL
IMAGES
- Authors: Meizhu Li, Shaoguang Huang, Aleksandra Pizurica
  Pages: 39 - 48
  Abstract: Supervised classification methods rely heavily on labeled training data. However, errors in the manually labeled data arise inevitably in practice, especially in applications where data labeling is a complex and expensive process, as is often the case in remote sensing. Erroneous labels affect the learning models, deteriorate the classification performances and hinder thereby subsequent image analysis and scene interpretation. In this paper, we analyze the effect of erroneous labels on spectral signatures of landcover classes in remotely sensed hyperspectral images (HSIs). We analyze also statistical distributions of the principal components of HSIs under label noise in order to interpret the deterioration of the classification performance. We compare the behaviour of different types of classifiers: spectral only and spectral-spatial classifiers based on different learning models including deep learning. Our analysis reveals which levels of label noise are acceptable for a given tolerance in the classification accuracy and how robust are different learning models in this respect.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
A NOVEL DATA REPRESENTATION FOR MUSIC GENERATION WITH DEEP LEARNING
- Authors: Nermin Naguib J. Siphocly, El-Sayed M. El-Horbaty, Abdel-Badeeh M. Salem
  Pages: 49 - 60
  Abstract: Deep learning techniques are currently gaining high prominence in the field of computer music generation. The main objective of this paper is to propose a novel, imagebased, data representation for music that is tailored for training deep learning models. Specifically, we suggest using color encodings to represent music notes inside images. We develop an intelligent accompaniment music generator using the pix2pix Generative Adversarial Network (GAN). We compare the effect of our suggested data representation technique on the pix2pix network learning as opposed to the traditional binary encoding scheme used in the literature. We also suggest a post-processing technique for enhancing the quality of the generated music. Additionally, we introduce two automatic evaluation metrics for assessing the generated music based on dissimilarity and musical harmony. Finally, we suggest a variation of our proposed data representation and devise a comparison between the two representations. Our experimental results show that our music representation achieved better results on pix2pix GANs over the traditional representations, reaching a loss function value of 0.001. Moreover, by evaluating the music generated by our system, the results show that our proposed post-processing technique enhanced the musical harmony and the dissimilarity of the generated music by 51.26% and 81.98% respectively.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
NETWORK PLANNING LEVERAGING AVERAGE BIT ERROR PROBABILITY AND CHANNEL
CAPACITY OF MACRO DIVERSITY SYSTEM IN GAMMA SHADOWED RAYLEIGH FADING
CHANNEL
- Authors: Suad Suljovic, Dejan Milic, Nenad Petrovic, Stefan Panic, Samir Konicanin
  Pages: 61 - 70
  Abstract: This paper considers a system of macro diversity (MD) that consists of a macro receiver of SC diversity (MD SC) and two micro MRC (mD MRC) receivers, which operate in a correlated Gamma-shadowed Rayleigh multipath fading environment. The combination of the maximum L-branch ratio (SNR signal-to-noise) is realized at the micro level, and the optional selection combiner (SC) with two base stations is performed at the macro level. The closed-form expression for the moment-generating function (MGF) of the SC macro diversity output signal envelope is calculated. This result is used to study important system performance parameters such as downtime probability, average bit error probability (ABEP), average output signal value, and amount of fading (AoF). The paper also calculates the channel capacity (CC) of the MD SC receiver. The results are graphically illustrated showing the influence of different system parameters on performance, as well as the improvement due to the use of a combination of micro and macro diversity systems. Furthermore, the derived expressions are leveraged within the GPU-enabled mobile network modeling, planning and simulation environment for Quality of Service (QoS) parameter value determination.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
β-VARIATIONAL AUTOENCODERS FOR LEARNING INVERTIBLE LOCAL IMAGE
DESCRIPTORS
- Authors: Nina Zizakic, Aleksandra Pizurica
  Pages: 71 - 78
  Abstract: In this paper, we propose an efficient method for learning a local image descriptor and its inversion function using a modified version of a variational autoencoder (VAE) - a β-VAE. We examine different values of β in the loss function of the β-VAE to find the an optimal balance between incentivising the similarities between input patches to be preserved in latent space, and ensuring good reconstruction of the patches from their encodings in latent space. Our proposed descriptor demonstrates patch retrieval comparable to the reference autoencoder-based local image descriptor, and also shows improved reconstruction of patches from their encodings.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)
LORAWAN NETWORK SIMULATIONS IN PYTHON
- Authors: Maciej Piechowiak, Piotr Zwierzykowski2
  Pages: 79 - 86
  Abstract: The Internet of Things is changing the approach to data transmission and protocol design as well as network services. That is why it is considered to be another technological revolution. The challenge faced by designers of IoT solutions is to determine the scalability of a given technology, with particular emphasis on unlicensed bandwidth (ISM) transmission in highly urbanized areas. Because the construction and implementation of a wireless network for Internet of Things in each of the presented technologies is expensive and time consuming, it must be preceded by an assessment of performance using computer simulations. The literature contains various approaches to modeling the mechanisms of the MAC layer of LoRa technology and implementation in the LoRaWAN network. The article provides an overview of representative LoRa MAC network simulators. It presents and comments the most important research results obtained by the authors of the mentioned simulators.
  
  PubDate: 2021-11-02
  Issue No: Vol. 24, No. 1 (2021)