This paper presents present an example of the method of selecting an optimal model of fixed access network and compare various well-known access network technology choices in heterogeneous environment, in order to selecting an optimal solution. Architecture scenarios and technologies used for experimenting are P2P (Point to Point)) based on Ethernet, P2MP (Point to MultiPoint) based on GPON (Gigabit Passive Optical Network) technology and FTTB/C (Fibre to The Building/Cabinet) based on VDSL technology. After short reminder of FOAN (Fibre Optics Access Network) architectures, topologies and technologies and brief comparisons of P2P vs. GPON and FTTH (Fibre to The Home) vs. FTTB, the sample areas, which are subject of analysis are described in detail. A comparison of proposed solutions in four chosen areas is shown.

This paper presents an end-to-end architecture for smart waste management, leveraging real-time data, IoT, AI, and machine learning to optimize operational efficiency and decision-making processes. The architecture is designed for both near real-time and batch data processing, ensuring continuous optimization and adaptation of waste collection routes and resource allocation. Machine learning models are employed to predict possible bad adverse scenarios and optimize operational plans. Additionally, business intelligence is utilized for data analysis and reporting, providing actionable insights based on real-time and historical data. The presented system is implemented on a scalable Kubernetes infrastructure, supporting the increasing data volumes and processing demands while maintaining system responsiveness and efficiency. This integrated approach demonstrates significant improvements in resource utilization, operational efficiency, and service delivery, highlighting the potential for smarter and more sustainable waste management practices. This research addresses the gap in combining IT architectures with AI models and IoT, paving the way for future advancements in smart waste management systems.

Recently, the necessity of video testing at the point of reception has become a challenge for video distributors. This paper presents a new system framework for managing the quality of video degradation detection. The system is based on objective video quality assessment metrics and unsupervised machine learning techniques that use the dimensionality reduction of time series. It was demonstrated that it is possible to detect anomalies in the video during video streaming in soft real time. In addition, the model discovers degradations based on the visible correlation between adjacent images in the video sequence regardless the quick or slow change of a scene in the sequence. With additional hardware manipulations on the equipment on the user side, the proposed solution can be used in practical implementations where the need for monitoring possible degradations during video streaming exists.

This article presents a simple software-developed model for calculating the relative frequency of individual symbols and the entropy of the Latin alphabet of a standardised language used by four South-Slavic origin ethnic groups in the Western Balkans in four countries. In addition, a method of applying the Shannon-Fano and Huffman source coding algorithms is presented, which takes into consideration the specificity of the observed alphabet in relation to the English one. The presented model is developed in the MATLAB programming language. The model is tested using an arbitrarily selected text.

This paper presents a model that enables the application of smart waste collection management using artificial intelligence to detect QR-codes on the video stream of surveillance cameras attached to waste collection trucks. A framework model proposal together with a detailed explanation of the key components of the system is shown. It also demonstrates the use of QR-code detection for identification of waste bins and its specific application in smart waste management system.

The aim of the paper is the quality of video streaming analysis in cases of using different video codecs in the environment of distributed computer systems with different QoS (Quality of Service). For the purposes of the analysis, several scenarios were set up in which video encoded with different codecs is transmitted by a virtual video streaming server to virtual clients. For each of the scenarios, an environment with different QoS (packet losses, latency, jitter) was simulated and the quality of the received video stream was evaluated for each video codec. The quality of the received decoded video stream was calculated using SSIM (Structural Similarity) and VMAF (Video Multimethod Assessment Fusion) video objective metrics and compared to the original video stream.

This paper presents some results of analysis of using several types of common Ethernet cables (LAN cables) in last section of access networks. The main goal of the article is to answer the question whether (if so, under what conditions and to what extent) we should consider the type of specific Ethernet cable when using it in a FTTB environment. Four branded and two unbranded CAT5e Ethernet cables are used for measurements. Additionally, a DSL cable with diameter of 0.4 mm is used for comparison purposes. The results are collected and mutually compared under similar loop conditions (good loop). All of the results of measurements are collected in operating conditions.

This paper presents some results of research on possibility of using LAN (Local Area Network) cables instead of DSL (Digital Subscriber Line) cables in access networks. The aim of this paper is to answer the question whether (and under which conditions) LAN cables can be used as a replacement for DSL cables, and whether they can provide the same quality of service (QoS). CAT5e Ethernet cables and xDSL cables with diameter of 0.4 mm were used for measurements. The results were collected and compared for two different lengths of cables and for different loop conditions (good and fault loop). All results of measurements are collected on real system in operating conditions.

This paper presents some results of research of reliability of using simple and low-cost Android-based handheld measurement units for DSL (Digital Subscriber Line) loop qualification a nd/or t roubleshooting. T he g oal o f t his p aper is provide the answer on key question whether such units can be used (and if so, under what circumstances) for quick and reliable evaluation of copper local loop properties, in respect to its ability for exploitation in certain DSL applications. A particular unit has been observing in several typical loop conditions (good loop, fault loop, etc.). Collected results were compared with those obtained from professional measurement equipment. It is shown that simple unit is more unfavourable to use compared to the professional one, in respect to determination of system actual performance. All results of measurements and observations are collected on real system in operation conditions.

The main aim of this paper is to analyse correlation between adjacent images in a video sequence. Adjacent images with a slow or fast changeable scene in a video have high correlation which shows consistency in the video sequence therefore it can be the proof of normal reproduction of video service. Due to QoS (Quality of service) problems, especially over lossy network, appearance of different visual degradations in frames (images) during delivery of video service to end users can happen very often. In that case, adjacent images in the video sequence have low correlation which can be used as an indicator the problem occurred in some part of the network. In addition, the paper analyses correlation correspondent to polygons i.e. parts of adjacent images in the video sequence in order to discover a degree of influence visual degradations to user’s QoE (Quality of Experience). In order to check this aim, tested degraded and non-degraded video sequence was captured using IPTV system of one significant market power provider and processed in offline mod with Python script created especially for this purpose.

The aim of this paper is comparing a simulation model with real IPTV (Internet Protocol TeleVision) scenario in access network. In real IPTV scenarios, it is known that packet losses appear suddenly and might have an "explosive" character, especially in DSL (Digital Subscriber Line) case. In addition, these packet losses usually appear in groups and lead to huge degradation of the video service, which decreases customer’s QoE (Quality of Experience) level. Hence, estimation of this degradation in access network is important and the paper explained one simulation model based on SSIM (Structural Similarity Index) analysis, which can be used as one perceptive video quality assessment by imitating a real environment with packet losses. To check this, we compared our simulation model with the real IPTV video distributed over DSL (Digital Subscriber Line) and exposed to different packet loss appearances.

During delivery of video service, most of management control systems are able to collect useful data from different OSI layers that help in indication quality of video service. Recently, a mathematical model has been proposed which, only with a help of data collected at PHY (physical) and MAC (Media Access Control) layers and after appearance of certain degradations in transmission channel, estimates QoS (Quality of Service) indicators and then objective QoE (Quality of Experience). In this paper, we analyse that mathematical model, but only in the case of sudden occurrence of significant disturbance in transmission channel. In addition, we have chosen AIC criterion instead of the vector one in order to define the size of L-value. Validation and verification of the model are done in DSL (Digital Subscriber Line) environment during IPTV (Internet Protocol Television) service delivering and NS2 respectively.

Channel coding is a common technique used to reduce bit-error rate (BER) in a communication channel. In cases where a certain block code is used, there is a known procedure for determining a residual BER (bit-error rate after encoding and decoding). Analysis in opposite direction should determine a block code parameters for optimising system performance in terms of reliability and throughput. This paper proposes an iterative method for addressed problem by introducing some auxiliary function, whose inverse can be written in closed form. We demonstrate the usage of proposed method in determining parameters of suitable binary BCH code to improve error probability during the transmission of BPSK signal over Rayleigh fading channel. The correctness of analytically obtained results are validated by simulation results.

This paper, on the basis of experimental research of the system in exploitation, identifies the main disadvantages of the existing troubleshooting scenarios for IPTV over xDSL. Also, this paper shows how the process of troubleshooting can be made more efficient in practice, with the already existing test solutions and other possibilities of test devices and xDSL transceivers.

The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.