Research Archives - Page 7 of 12

17.02.2023.28.04.2023.

IEEE IT2023: Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023. The paper is titled “Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN” and authored by Andrej Cvijetić, Slobodan Djukanović and Andrija Peruničić

ABSTRACT : This paper addresses vehicle speed estimation using visual data obtained from a single video camera. The proposed method accurately predicts the speed of a vehicle, using the YOLO algorithm for vehicle detection and tracking, and a one-dimensional convolutional neural network (1D-CNN) for speed estimation. The YOLO algorithm outputs bounding boxes around detected objects in an image, which is, in our case, the vehicle whose speed is to be predicted. As input to our 1D-CNN speed estimation model, we introduce a novel feature based on the change of area of the bounding box around the vehicle. The feature, referred to as the changing bounding box area (CBBA), is obtained by calculating the area of the bounding box, frame-to-frame, as the vehicle approaches the camera. The shape of the CBBA curve remains closely the same for all vehicles, with differences conditioned by the value of the observed vehicle’s speed. The proposed method is trained and tested on the VS13 dataset. Experiments show that it is able to accurately predict the vehicle’s speed with an average error of 2.76 km/h, with the best performing vehicle having the average error of just 1.31 km/h. The proposed method exhibits the robustness as a key advantage, eliminating the need for prior knowledge of real-world dimensions such as the vehicle size, road width, camera distance and angle in relation to the road etc.

Link : https://ieeexplore.ieee.org/document/10078518

17.02.2023.28.04.2023.

IEEE IT2023: Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023.

The paper is titled “Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks” and authored by Ivana Čavor and Slobodan Djukanović.

ABSTRACT : This paper presents an approach to acoustic vehicle speed estimation using audio data obtained from single-sensor measurements. One-dimensional convolutional neural network (1D CNN) is used to estimate the vehicle’s speed directly from raw audio signal. The proposed approach does not require manual feature extraction and can be trained directly on unprocessed time-domain signals. The VS13 dataset, which contains 400 audio-video recordings of 13 different vehicles, is used for training and testing of the proposed model. Two training procedures have been evaluated and tested, one based on determining optimal number of training epochs and the other based on recording model state with minimal validation loss. The experimental results show that the average estimation error on VS13 is 9.50 km/h and 8.88 km/h, respectively.

Link : https://ieeexplore.ieee.org/document/10078724

15.02.2023.28.02.2023.

HPC4S3ME project presented at the IEEE IT2023

New reearch project HPC4S3ME was presented by prof. Tomo Popovic and dr. Luka Filipovic at the 27th IEEE International Conference – Information Technology – IT2023. The conference had a special session dedicated to presentation of project results for the projects implemented in Montenegro and the region. The conference program is available at the following link.

The full title of this new project is “Building scientific and innovation potential to utilize HPC and AI for S3 Smart Specialisation in Montenegro – HPC4S3ME” and it is funded by the through IPA II program, call reference EuropeAid/172-351/ID/ACT/ME.

Proposing the use of HPC for research in Montenegrin S3 priority domains

Project was presented by prof. Popovic and dr. Filipovic

The overall objective of HPC4S3ME project is to contribute to straightening research excellence by building scientific and innovation potential based on the use of high performance computing (HPC) and artificial intelligence (AI) for applications in industrial domains proposed by the Smart Specialisation Strategy (2019-2024) for Montenegro. The implementation of this project will provide a state-of-the-art environment for young researchers to gain experience in research and development in computer science, more specifically to apply machine learning (ML) and deep learning (DL) algorithms supported by HPC in order to create innovative information-communication technology (ICT) solutions for applications in agriculture and food value chain, health and tourism, energy and sustainable environment., i.e. priority domains identified by the smart specialisation strategy. More information on the project available at the HPC4S3ME website.

HPC4S3ME focuses on HPC capacity building and support for young researchers

28.01.2023.28.04.2023.

Journal Technology and Health Care: Verification of temperature, wind and precipitation fields for the high-resolution WRF NMM model over the complex terrain of Montenegro

Researchers from NCC Montenegro presented a paper at the “Special Issue for magazine Technology and health care: official journal of the European Society for Engineering and Medicine”. The paper titled “Verification of temperature, wind and precipitation fields for the high-resolution WRF NMM model over the complex terrain of Montenegro” is written by Zečević Aleksandar, Filipović Luka and Marčev Angel.

ABSTRACT :

BACKGROUND: The necessity of setting up high-resolution models is essential to timely forecast dangerous meteorological phenomena.

OBJECTIVE: This study presents a verification of the numerical Weather Research and Forecasting non-hydrostatic Mesoscale Model (WRF NMM) for weather prediction using the High-Performance Computing (HPC) cluster over the complex relief of Montenegro.

METHODS: Verification was performed comparing WRF NMM predicted values and measured values for temperature, wind and precipitation for six Montenegrin weather stations in a five-year period using statistical parameters. The difficult task of adjusting the model over the complex Montenegrin terrain is caused by a rapid altitude change in in the coastal area, numerous karst fields, basins, river valleys and canyons, large areas of artificial lakes on a relatively small terrain.

RESULTS: Based on the obtained verification results, the results of the model vary during time of day, the season of the year, the altitude of the station for which the model results were verified, as well as the surrounding relief for them. The results show the best performance in the central region and show deviations for some metrological measures in some periods of the year.

CONCLUSION: This study can give recommendations on how to adapt a numerical model to a real situation in order to produce better weather forecast for the public.

Link : https://content.iospress.com/articles/technology-and-health-care/thc229016

05.01.2023.19.12.2024.

HPC4S3ME IPA Project

The full title of this new project is “Building scientific and innovation potential to utilize HPC and AI for S3 Smart Specialisation in Montenegro – HPC4S3ME” and it is funded by the IPA II program, call reference EuropeAid/172-351/ID/ACT/ME.

The overall objective of HPC4S3ME project is to contribute to straightening research excellence by building scientific and innovation potential based on the use of high performance computing and artificial intelligence (AI) for applications in industrial domains proposed by the Smart Specialisation Strategy (2019-2024) for Montenegro. The implementation of this project will provide a state-of-the-art environment for young researchers to gain experience in research and development in computer science, more specifically to apply machine learning and deep learning algorithms supported by HPC in order to create innovative information-communication technology solutions for applications in agriculture and food value chain, health and tourism, energy and sustainable environment, namely the priority domains identified by the smart specialisation strategy. This is a two-year project and it starts in Jan 2023.

03.01.2023.28.01.2023.

MAIA – Montenegrin AI Association

MAIA – Montenegrin AI Association is a Non-Governmental organization, founded in September 2022 with an ambition to bring together the Montenegrin AI community. Our goal is to popularize Artificial Intelligence related research and spread awareness of its importance in our country, but also encourage our society to join the fast wave of AI innovation in the World. Several NCC Montenegro team members are taking part in this initiative. Check MAIA website for more details at the following link.

26.11.2022.06.01.2023.

A scientific paper at IEEE ACIT 2022 conference

Researchers from UDG and NCC Montenegro presented a scientific paper at the 2022 International Arab Conference on Information Technology (ACIT). The conference took place at the Al Ain University – Abu Dhabi Campus on November 22-24, 2022. The paper “Overcoming Limitations of Statistical Methods with Artificial Neural Networks” was authored by M. Grebovic, L. Filipovic, I. Katnic, M. Vukotic, and T. Popovic. More information about the conference is available here. The paper is available at IEEE Xplore at the following link.

ABSTRACT – Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-ahead linear processes to improve only short-term prediction’s accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.