16 citations · 29 across the 15 of their papers we have counts for
16 papers
Adaptive Unequal Error Protection for Semantic Split Learning over Wireless Channels
Vukan Ninkovic, Dejan Vukobratovic, Dragisa Miskovic +1
We propose a task-aware semantic split learning (SL) framework for wireless edge-cloud inference, in which the reliability of transmitted latent representations is dynamically adap…
End-to-End O-RAN Testbed for Edge-AI-Enabled 5G/6G Connected Industrial Robotics
Sasa Talosi, Vladimir Vincan, Srdjan Sobot +5
Connected robotics is one of the principal use cases driving the transition towards more intelligent and capable 6G mobile cellular networks. Replacing wired connections with highl…
Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness
Vukan Ninkovic, Tamara Sobot, Vladimir Vincan +3
Wireless transmission of high-dimensional 3D point clouds (PCs) is increasingly required in industrial collaborative robotics systems. Conventional compression methods prioritize g…
Improving Customer Service with Automatic Topic Detection in User Emails
Bojana Bašaragin, Darija Medvecki, Gorana Gojić +2
This study introduces a novel natural language processing pipeline that enhances customer service efficiency at Telekom Srbija, a leading Serbian telecommunications company, throug…
JuliaGrid: An Open-Source Julia-Based Framework for Power System State Estimation
Mirsad Cosovic, Ognjen Kundacina, Muhamed Delalic +6
Modern electric power systems have an increasingly complex structure due to rise in power demand and integration of diverse energy sources. Monitoring these large-scale systems, wh…
COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms
Vukan Ninkovic, Dejan Vukobratovic, Dragisa Miskovic +1
The significance of distributed learning and inference algorithms in Internet of Things (IoT) network is growing since they flexibly distribute computation load between IoT devices…
UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments
Vukan Ninkovic, Dejan Vukobratovic, Dragisa Miskovic
Distributed learning and inference algorithms have become indispensable for IoT systems, offering benefits such as workload alleviation, data privacy preservation, and reduced late…
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition
Ognjen Kundacina, Vladimir Vincan, Dragisa Miskovic
This paper introduces a novel two-stage active learning (AL) pipeline for automatic speech recognition (ASR), combining unsupervised and supervised AL methods. The first stage util…
Overview of Deep Learning Methods for Retinal Vessel Segmentation
Gorana Gojić, Ognjen Kundačina, Dragiša Mišković +1
Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases. With the fast development of deep learnin…
Non-adversarial Robustness of Deep Learning Methods for Computer Vision
Gorana Gojić, Vladimir Vincan, Ognjen Kundačina +2
Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shif…
Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
Ognjen Kundacina, Mirsad Cosovic, Dragisa Miskovic +1
As phasor measurement units (PMUs) become more widely used in transmission power systems, a fast state estimation (SE) algorithm that can take advantage of their high sample rates…
Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
Ognjen Kundacina, Gorana Gojic, Mirsad Cosovic +2
Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measureme…
Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms
Ognjen Kundacina, Gorana Gojic, Mile Mitrovic +2
Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation.…
Distributed Inference over Linear Models using Alternating Gaussian Belief Propagation
Mirsad Cosovic, Dragisa Miskovic, Muhamed Delalic +2
We consider the problem of maximum likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massiv…
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Ognjen Kundacina, Miodrag Forcan, Mirsad Cosovic +5
Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for…
Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dragisa Miskovic +1
Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iter…