Com-in-AI

About

Artificial intelligence (AI) algorithms are typically used to solve the most challenging problems, achieving state-of-the-art performances and providing a substantial impact on all aspects of human life. However, because of their computational complexity and high number of parallel processing operations, AI algorithms require significant computational resources (processing power and memory), they consume a considerable amount of energy and require powerful hardware (such as GPUs, servers, clouds). All of these constraints, significantly limit their applicability. In recent years, there has been an increasing need for implementation of AI algorithms on devices with limited resources (memory space, processing power and available energy). Therefore, the problem of implementing complex AI algorithms on devices with limited resources is one of the most topical and challenging research problems in the field of AI. This project deals with solving the mentioned problem, where the overall objective is to propose and implement new approaches to reduce the complexity of AI algorithms using advanced quantization and compression methods, based on the strong expertise of the project team in the field. An important research question we will answer is how to make state-of-the-art DNN, with numerous parameters, more compact and efficient. We will exploit the fact that reduced precision of the DNN parameters, i.e. going from 32-bit floating point to a low-bit fixed point, can be achieved by thoughtful application of quantization and compression, which will result in a worthy compression ratio and, accordingly in the reduced storage and energy cost, and computational requirements. Majority of the research directions has been set toward achieving the highest possible compression ratio of DNN parameters without significant accuracy loss. Since this field of research is still in an early stage, significant improvements are possible. This project offers new approaches to advanced quantization and compression of DNN parameters in order to outperform state-of-the-art solutions. Besides their fundamental scientific importance in the field of AI, all theoretical results will also be fully applicable in practice. Hence, an important part of the project concept is to validate all the proposed theoretical results and to examine their applicability. This will be done by implementing all theoretical solutions on the developed DNN, as a part of the target use case. Eventually, all theoretical results will be implemented and validated and their performance will be evaluated within a real industrial application using data from a real industrial environment.

Abstract

Very topical issues present in generally very powerful AI algorithms related to decreasing computational complexity and memory resources that are of particular importance in portable and edge computing devices with limited memory and processing power are the driving force behind this project proposal. Specifically, this project contributes to a new and fastly growing research in the worldwide AI science solving these particular issues by prudent and deliberate application of quantization theory. Due to the topicality of these issues and the fact that related research is still in the early stage and deserves further investigation, our project team will explore and propose innovative methods of compression and quantization of DNN parameters (weights, biases, activations) and deep features. Also, the goal of the project is to develop a state-of-the-art deep neural network model with a high performance not only on the hardware usually used for AI applications but also on devices with limited computational resources and thus enabling them to support energy demanding and memory constraint applications. In order to achieve these goals, the project proposes an integrated approach to quantization and compression of DNN parameters, based on statistical modeling of the data per layers, as well as of the data subsets within layers and adaptation of the quantizers itself on the statistical characteristics of the input data. Moreover, from the exploration of the compression and quantization effects in DNNs vis-à-vis its accuracy, the benchmark of our methodology will be defined. The researchers’ interdisciplinary competence ensures successful development of innovative quantization, compression and learning methodologies that will enable reducing the complexity of AI algorithms and its much wider usage. The results obtained within this project will find a wide range of applications in both academia and industry, particularly in numerous latency-critical services.

Objectives

To achieve significant compression ratio regarding the full precision representation with the negligible or tolerant accuracy loss of DNN by novel quantizers and compression techniques for quantization and compression of DNN parameters;
To significantly decrease the data amount of DNN’s deep features with novel optimal quantization and compression methods at the same time to preserve high performance (accuracy) of DNN;
To increase the efficiency of the clustering and classification algorithms, by designing quantizers for Gaussian mixture model (GMM);
To verify developed quantization and compression methods through application based on DNN and data from the real industrial environment (to implement and evaluate all obtained scientific results);
To achieve a high level of visibility and knowledge sharing of the project results through a comprehensive set of Dissemination, Exploitation and Communication activities for wide-scale project promotion among wide target groups (scientific community, economy, etc).

Impacts

Future research in the field of AI because of offering innovative research approaches, ideas and directions, as well as significant scientific results;
Serbian science due to launching new research topics, increasing visibility, reputation and networking capacity of Serbian scientists;
Industry 4.0 due to providing more efficient, intelligent and automated industrial systems, by reducing the complexity of AI algorithms and therefore allowing their implementation on edge devices and much wider usage in industrial environments;
ELFAK curriculums, which will be enhanced and will therefore provide high-quality workforce to both, the academy and industry.

TEAM

The project team is fully compatible, since all team members have basic knowledge of quantization and coding theory, as well as of neural networks and AI algorithms, which will greatly facilitate their collaboration and communication and will improve work efficiency. Moreover, the project team has a great complementarity since each team member has a unique deep knowledge and strong expertise required for the project realization. Team members have been working successfully at the University of Niš, Faculty of Electronic Engineering, on joint scientific projects and papers for years and therefore know each other’s strengths and possibilities very well, which will give the optimal outputs of this project.
Professor Zoran Perić as a PI has a stunning academic and scientific career, as well as necessary management and administrative skills to lead this project. With years of experience and significant scientific and applicable results, a Full Professor Dejan Ćirić will give great contribution in the activities within this project related to AI (DNN design, application and evaluation), audio signal processing and sound acquisition. The special expertise of Associate Professor Aleksandra Jovanović is design of vector quantizers and also design of power efficient signal constellations. Therefore, it is expected that she will predominantly contribute to the part of the project related to data clustering and classification where the multidimensional space partition is an important issue. Assistant Professor Milan Dinčić has strong expertise in the design of VLC (variable-length coding) compression algorithms as well as in the statistical modeling of input data for DNNs and other AI algorithms. His contribution mostly will be in the domain of compression. Associate Professor Jelena Nikolić will contribute to this project with her expertise in speech compression and its application in machine learning and AI. In particular, she will give great contribution in the project activities related to quantization and compression of DNN parameters, where her expertise can be exploited greatly. Ph.D. student Nikola Vučić as a young researcher brings fresh energy to the project and provides support to the part related to quantization and compression. Ph.D. student Bojan Denić will contribute to the part of the project related to data classification, as it is closely related to his research areas. Professor Vladimir Despotović is an expert in machine learning, natural language processing and fractional calculus. His knowledge and extensive international experience considerably increase the capacity of the team to develop advanced methods of learning in AI. All project objectives will be realized through an interdisciplinary approach and knowledge synergy of all team members. Eventually, team members are carefully selected so that everyone has a precisely defined role in the project.

Zoran Perić

Principal Investigator

zoran.peric@elfak.ni.ac.rs
[ Info ]
[ Google Scholar ]
[ Publications ]

Full Professor Zoran Perić has an academic and scientific career longer than 30 years. He served as a vice-dean of the Faculty of Electronic Engineering in Niš for 11 years. Right now, professor Perić is a member of the council of the University of Niš. He has been the supervisor of 12 Ph.D. thesis and over 80 master and bachelor thesis, and an author of 310 papers, among them 135 are published in the journals from the SCI/SCIe list (with IF). His excellent research results were awarded by Telenor foundation in 2017, with the prize “Professor Ilija Stojanović” for the contribution in the field of telecommunications in the category of scientific papers published over the previous two years in renowned international journals. Professor Zoran Perić was a reviewer for numerous reputable IEEE and Elsevier journals, as well as a member of the Editorial Board of the journals Elektronika ir Elektrotechnika, Information and Facta Universitatis Series: Electronics and Energetics. He served as a Lead Guest Editor in Information journal (special issue: Signal Processing and Machine Learning) and in the journal Computational Intelligence and Neuroscience (special issue: Advanced Signal Processing and Adaptive Learning Methods) and also as an Editor-in-Chief in the journal Facta Universitatis Series: Electronics and Energetics. During his career, Dr Perić has participated in 8 national projects funded by the Ministry of Science and Technological Development of the Republic of Serbia, 2 bilateral projects between Serbia and Slovakia and in numerous international projects. He was also a PI of many sub-projects realized within the Faculty of Electronic Engineering. Moreover, he has established an international collaboration with numerous colleagues from the USA, Germany, Slovakia and other countries. Professor Perić with his valuable experience definitely has professional and personal abilities to lead this project. During these years, the area of AI has become very familiar to him, which has been proven by the impressive number of scientific papers. He will also bring the project management and administrative skills acquired during plenty of projects and positions.

Dejan Ćirić

dejan.ciric@elfak.ni.ac.rs
[ Info ]
[ Publications ]

Full Professor Dejan Ćirić brings into this project research and scientific experience of more than 21 years in the fields of Acoustics, Audio signals, Audio Analytics and AI. He is an author/co-author of more than 130 research papers (18 articles in refereed scientific journals). Professor Ćirić has participated in more than 25 national, international and projects financed by external companies both as a project leader and participant. Several of them are closely connected to the subject of this proposal (e.g., AI-based sound event detection, audio analytics of industrial sound, machine learning approach for sound source radiation control, etc.). He has international experience working as a guest researcher at IRCAM, Paris, France and as a research assistant and Ph.D. student at Acoustics, Department of Electronic Systems, Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark. His strong points relevant for this project also include experience with transfer of scientific research results into commercial products in the AI applications in sound.

Aleksandra Jovanović

aleksandra.jovanovic@elfak.ni.ac.rs
[ Info ]
[ Publications ]

Associate Professor Aleksandra Jovanović has strong expertise in signal compression algorithms, signal constellation design and signal detection problems. As a participant of several projects she developed different algorithms and models for signal compression and modulation. She verified the achieved results by publishing over 80 papers (27 of which are published in journals with IF) and one monograph. She was awarded by Telenor foundation in 2017. She received the best paper award Professor Ilija Stojanović for contribution in the field of Telecommunications in the category of scientific papers published over the past two years in renowned international journals. She was a reviewer for reputable journals, conferences and bilateral projects. The knowledge and experience she gained in quantization, compression and detection can be applied in AI to solve the topical problems in classification and clustering in data mining and pattern recognition.

Milan Dinčić

milan.dincic@elfak.ni.ac.rs
[ Info ]
[ Publications ]

Assistant Professor Milan Dinčić, received 2 Ph.D. degrees in two different areas (Telecommunications and Metrology). He finished his master study with the highest average grade of 10 and was awarded as the best graduate student of the University of Niš. Besides the title in higher education: Assistant Professor, he also received the scientific title: Scientific Associate. He is an author of 26 papers in the journals from the SCI/SCIe list. Dr. Dinčić achieved The Seal of Excellence for a H2020 project. He collaborates with colleagues from the academic community (IHP - Frankfurt (Oder) Germany, Universities of Luxembourg, Maribor, Skopje Macedonia), as well as with people from the industry. He has participated in 4 scientific projects and in one bilateral project between Serbia and Slovenia. Also, he was the project leader of the project SENSORS within The Program for Higher Education Development 2019. He was a reviewer for international scientific journals. For this project, particularly important is his strong expertise of quantization, coding and compression theory, and of acquisition, processing and modeling of sensors’ data (as input data for AI systems), as well as his extensive experience in working with measurement and laboratory equipment.

Jelena Nikolić

jelena.nikolic@elfak.ni.ac.rs
[ Info ]
[ Google Scholar ]

Associate Professor Jelena Nikolić, contributes to this project with her expertise in the field of quantization and speech compression and with her ability to integrate and apply the broad theoretical knowledge acquired in these fields in cutting edge research in the field of AI. She is an author of 127 papers, 54 in the journals from the SCI/SCIe list. Because of her excellent scientific publications, she was awarded by Telenor foundation in 2017. She received the best paper award Professor Ilija Stojanović for contribution in the field of Telecommunications in the category of scientific papers published over the past two years in renowned international journals. Dr. Jelena Nikolić was a reviewer for numerous reputable journals and distinguished textbooks. She is a member of the Editorial Board of the Journal of Advanced Computer Science & Technology and of the journal Mathematical Problems in Engineering. By participating in 9 projects, she established international scientific collaboration. As she follows the latest trends in science, has a large number of publications, she would significantly contribute to publications of the results planned for this project in reputable journals and conferences.

Nikola Vučić

nikola.vucic@elfak.ni.ac.rs
[ ORCID ]

Nikola Vučić graduated at the Faculty of Electronic Engineering in Niš as B.Sc. and M.Sc. in 2013 and 2014, respectively, with the best marks (average grade 10/10). He is a Ph.D. student mentored by PI. From 2018 he has been working as a Junior Research Assistant at the Faculty of Electronic Engineering in Niš. He has published 14 papers and 4 among them are in journals with IF. During his education, he has been awarded many times with prestigious prizes. Nikola Vučić has been involved in Electrical Engineering Students’ European Association- Local committee Niš (EESTEC LC Niš) for almost 10 years, whereby he got an experience in various tasks and positions (chairperson, contact person, finances, bookkeeping, legal issues, grant projects, public relations, international cooperation, logistics etc.). He is used to the team work in domestic and international projects. He has taken part in many workshops, trainings, conferences and other events both as a participant and organizer. Although a young researcher, he has a wealthy experience from various fields that could be used in this project.

Bojan Denić

bojan.denic@elfak.ni.ac.rs
[ Kobson ]

Bojan Denić currently works as a Junior Research Assistant at the Faculty of Electronic Engineering, University of Niš. His main research areas include scalar quantization, signal processing and fractional calculus. He is an author/coauthor of 27 scientific papers, 15 of them are published in international scientific journals with IF. He is engaged as a researcher to one national project funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, and one bilateral project. He was a reviewer for international scientific journals. Bojan Denić brings into this project the valuable experience achieved in the field of quantization and signal processing that can be applied to the problems of quantization and classification in AI.

Vladimir Despotović

vladimir.despotovic@uni.lu
[ Info ]
[ Google Scholar ]

Dr Vladimir Despotović currently works as a Postdoctoral Researcher at the University of Luxembourg. Previously he was engaged as Associate Professor at the University of Belgrade and Postdoctoral Researcher at the Paderborn University, Germany. His main research interests include speech/audio/image signal processing, machine learning, natural language processing and fractional calculus. He is an author of 92 papers, out of which 25 in the peer-reviewed journals with IF, one book, two book chapters and one patent. He was a PI of two international projects, member of the management committee of one COST project and engaged as a researcher on three national projects (one as a project coordinator). Vladimir Despotovic was granted an Erasmus Mundus Action 2 postdoctoral fellowship at the Paderborn University, Germany, for research on unsupervised spoken language understanding using machine learning techniques in 2014. During 2009 he received an OeAD scholarship at the Vienna University of Technology, Austria, for research on nonlinear prediction of speech signal. During 2008 and 2007 he was granted scholarships for research on identification of fractional order systems at the University of Kosice, Slovakia, from SAIA and the government of the Slovak Republic, respectively. He brings to this project extensive international experience on the use of machine learning and AI in various signal processing applications, as well as project management skills.

Previous projects related to the project

Members of our project team have participated in several projects closely related to the topic of this project. PI, P2 and P4 have been involved in the project Human-Machine Speech Communication, related to a detailed analysis of all problems in speech to machine communication in both directions with the aim to give machines intelligence and human-like capability to speak and understand speech. Also, PI, P2 and P4 have been involved in the project The Development of Dialogue Systems for Serbian and Other South Slavic Languages considered the development of dialogue systems using artificial neural networks, which is highly correlated with this project. PI, P5 and P6 have taken part in the bilateral project, Fractional calculus approach to machine learning, between Serbia and Slovakia. The project developed a novel approach to machine learning by introducing fractional-order calculus to optimization methods used in machine learning algorithms. P1 was a project leader of recently finished project SONO360 - Smart audio interface financed by the Innovation Fund of the Republic of Serbia. The project dealt with the development of a prototype of smart audio device capable of acquiring sound from 3D space applying direction of arrival (DoA) and beamforming technique using spherical microphone array, as well as detecting and recognizing domestic sound events based on a specific design of a DNN. There is a close relation to this project with regards to DNN design and implementation in the field of sound. The experience and knowledge gained working on those projects are very valuable for the accomplishment of the tasks and goals we have defined in this project.

News

Final project promotion

November 2022

On November 22, 2022 at the Faculty of Electronic Engineering, University of Niš, Final project promotion was organised, results were summarised and the possibilities of further cooperation were discussed.

Special Issue "Artificial Intelligence and Mathematical Methods"-MDPI Mathematics Journal

November 2022

Prof. Dr. Zoran H. Perić, Dr. Jelena Nikolić, Prof. Dr. Marko Petković and Prof. Dr. Vlado Delić are Editors of the Special Issue "Artificial Intelligence and Mathematical Methods" in MDPI Mathematics Journal.
>>> Link to the Journal <<<
Deadline for manuscript submissions is 28 February 2023.

Study visit

July 2022

Com-in-AI team members visited the University of Potsdam and the Leibniz Institute for High Performance Microelectronics in Frankfurt (IHP) at Oder. During the visit, closer cooperation was established with colleagues from Germany and it was agreed to jointly apply for future projects.

RTS visit

June 2022

Radio Television of Serbia (RTS) visited our team and faculty for the filming of Science in Motion: Development of Artificial Intelligence through Projects.The film shown on the RTS is also available on the YouTube channel https://www.youtube.com/watch?v=drJhUo8KXvE?t=469

Participation at conference

June 2022

The team members Dejan Ćirić, Jelena Nikolić and Nikola Vučić took part in the 57th International Conference on Information, Communication and Energy Systems and Technologies, ICEST 2022, Ohrid, North Macedonia, June 16-18, 2022. The team member Dejan Ćirić also took part in the 9th International conference IcETRAN held in Novi Pazar, Serbia on June 6-9, 2022.

Workshop organized

May 31st 2022

An important dissemination event within Q7 was the Workshop entitled “Trends in the development and applications of artificial intelligence”. The Workshop was held in a hybrid form, in vivo and online, at the Faculty of Electronic Engineering, University of Niš, on May 31, 2022. The Workshop presented the main trends in AI from different aspects (fundamental research, applications and commercialization). Members of the project team, experts from Serbia and abroad participated as lecturers, while there were about 40 participants in the audience (half of them in vivo and the other half online). All the lectures were recorded. The promotional materials purchased for the Workshop (cups, notebooks, pens and bags) were distributed among the presenters and participants.

Invited lecture organized

March 31st 2022

On March 31, 2022 at the Faculty of Electronic Engineering, University of Niš, prof. Dr. Reinhold Häb-Umbach from the University of Paderborn in Germany gave an invited lecture entitled "Computational Analysis of Sound Scenes and Events". This lecture presented a taxonomy of tasks in the field of sound recognition and provided a discussion on generic system architecture and evaluation metrics. Attendees were also able to learn about current research challenges in detection and classification of acoustic scenes and events. Finally, further plans for cooperation of the project team members with prof. Häb-Umbach was established.

Participation at conference

March 18th 2022

The team member Nikola Vučić took part in the International Symposium INFOTEH-JAHORINA 2022 Jahorina, East Sarajevo, Bosnia and Herzegovina, March 16-18, 2022.
The team member Jelena Nikolić took part in the online version of the International Symposium INFOTEH-JAHORINA 2022 Jahorina, East Sarajevo, Bosnia and Herzegovina, March 16-18, 2022.
The team member Nikola Vučić took part in the online version of the 7th International Mardin Artuklu Scientific Researches Conference, Mardin, Turkey, December 10-12, 2021.

Training School organized

February 17th-18th 2022

The most important dissemination activity during Q6 was Training school, organized as an on-line event in the period 17-18th of February 2022. The platform used for the event was MS Teams. The participation was free of charge. The immense promotion of the Training school took part over various channels: project web site - "News" tab (https://com-in-ai.elfak.rs/), SRO's student portal (https://sip.elfak.ni.ac.rs/article/konkursi/trening-skola-com-in-al-2022), mailing list of SRO’s employees, mailing list of members of Science and Technology Park Niš, Facebook page of the project (https://www.facebook.com/project.cominai) and SRO (https://www.facebook.com/elektronskifakultetnis), professional and personal contacts etc. As a result of this successful promotional campaign we have received a large number of registered participants (more than 120). We have managed to find very expertised presenters from the field of artificial intelligence for this event. Their presentations took the attention of the participants and enabled them to get new knowledge and skills from this field. All the lectures were recorded and will be available online as a precious base of knowledge. At the end of the Training school, the participants were asked to fill in the questionnaire form and to give their feedback about the event to the organizers. The promotional materials purchased for the Training school (cups, notebooks and bags) were distributed among the presenters and participants.

Training School - Тренинг школа

17. и 18. фебруар 2022.

У оквиру пројекта „Напредне методе квантизације, компресије и учења у вештачкој интелигенцији“ (Com-in-AI) који се средствима Фонда за науку Републике Србије реализује на Електронском факултету у Нишу, 17. и 18. фебруара 2022. године биће одржана оn-line тренинг школа под називом "Увод у квантизацију неуронских мрежа и примене". Главне теме тренинг школе биће:

Квантизација тежина неуронских мрежа;
Софтверска имплементација неуронских мрежа;
Хардверски акцелератори за неуронске мреже;
Имплементација неуронских мрежа на edge уређајима;
Примена неуронских мрежа у аудио области.

Предавања ће у складу с Агендом држати признати стручњаци из Србије и иностранства. Учешће је бесплатно. Тренинг школа ће бити организована у виду видео конференције, док ће више детаља бити послато пријављеним учесницима.
Позивамо Вас да се пријавите путем овог линка.

Promotional activities in Q5

December 14th 2021

Within this quarterly period, researchers from our project team took part at three conferences (TELFOR, SAUM and TELSIKS).

In September we also organized the project promotion within the SAUM conference. Namely, project leader prof. Perić and dr. Dinčić gave a presentation whereby they promoted the project Com-in-AI. They explained and summarized the project results and objectives to the visitors and pointed out the project importance for developing science in the field of AI in our country. During this event we exchanged ideas with other researchers and made new connections. To raise the reputation of the project we shared promotional materials (notebooks, pens, bags) at the project promotion at the SAUM conference.

An important meeting related to artificial intelligence and its development in Niš took place on 13th of October at Science and Technology Park of Niš whereby a recently established Artificial Intelligence Institute was presented. Many people from academia and industry attended the event, as well as students and other interested citizens. The contacts with the AI Institute and its leaders are strengthened. Project leader prof. Perić made a short oral presentation of the Com-in-AI project to the audience. The promotional materials were distributed to the visitors and the contacts with potential partners for future collaboration were exchanged.

Participation at conference

September 15th 2021

The team member Nikola Vučić took part in the online version of the 25th International conference ELECTRONICS 2021, where he presented a scientific paper selected for publication in the scientific journal Elektronika Ir Elektrotechnika:

Zoran Perić, Bojan Denić, Milan Savić, Nikola Vučić and Nikola Simić, "Binary quantization analysis of neural networks weights on MNIST dataset", Elektronika Ir Elektrotechnika, vol. 27, no. 4, pp. 55-61, 2021. https://doi.org/10.5755/j02.eie.28881

Participation at conferences and meetups

June 13th 2021

Team members P1 (prof. Ćirić) and P2 (prof. Jovanović) took part in the online version (due to the covid-19 pandemic) of 20th International Symposium INFOTEH-JAHORINA (INFOTEH).

Team members P5 (Nikola Vučić) took part in the online version of 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE), where he presented a scientific paper.

Team member P1 (prof. Ćirić) participated in the online event “AI meetup: Artificial intelligence – strategy, conditions and challenges” held on the 20th of April 2021, where he gave a presentation promoting the project Com-in-AI.

The first project presentation

October 9th 2020

The project Com-in-AI was presented on October 9th 2020 at Kalemegdan (Belgrade) within the Science Fund exposition dedicated to the promotion of the Program for the development of projects in the domain of AI. More details at link.

PROMOTION

The first project presentation

Procured promotional material

Conference presentation

Project promotion at SAUM

Artificial Intelligence Institute presentation

Training School organized

Workshop - Trends in the development and applications of AI

Study visit in Germany

Final project
promotion

The dissemination, exploitation and communication activities will be carried out continuously throughout the lifetime of the project, but also after the project completion, to ensure long-term effects of the project. The dissemination activities will be focused on promoting the scientific results, the potential usage and also the project in general among a wide scientific community as well as among the target groups relevant for exploitation of the achieved results. Workshops to be organized will target the scientific and the interested non-scientific stakeholders (industry, AI companies, Public authorities at national and local levels, relevant decision makers) to exchange ideas on the scientific, economic and social aspects as well as applicability of the project results and to discuss possible collaboration.

RESULTS

Publications

Zoran Perić, Milan Savić, Nikola Simić, Bojan Denić and Vladimir Despotović, "Design of a 2-bit neural network quantizer for Laplacian source", Entropy, vol. 23, Article ID: 933, 17 pages, 2021. https://doi.org/10.3390/e23080933
Zoran Perić, Bojan Denić and Vladimir Despotović, "Algorithm based on 2-bit adaptive delta modulation and fractional linear prediction for Gaussian source coding", IET Signal Processing, vol. 15, issue 6, pp. 410-423, 2021. https://doi.org/10.1049/sil2.12040
Milan Dinčić, Zoran Perić, Milan Tančić, Dragan Denić, Zoran Stamenković and Bojan Denić, "Support region of μ-law logarithmic quantizers for Laplacian source applied in neural networks", Microelectronics Reliability, vol. 124, Article ID:114269, 15 pages, 2021. https://doi.org/10.1016/j.microrel.2021.114269
Zoran Perić, Bojan Denić, Milan Savić, Nikola Vučić and Nikola Simić, "Binary quantization analysis of neural networks weights on MNIST dataset", Elektronika Ir Elektrotechnika, vol. 27, no. 4, pp. 55-61, 2021. https://doi.org/10.5755/j02.eie.28881
Aleksandra Ž. Jovanović, Zoran H. Perić, "Two-dimensional GMM-based clustering in the presence of quantization noise", Facta Universitatis, Series: Automatic Control and Robotics, vol. 20, no. 2, pp. 99-110, 2021. https://doi.org/10.22190/FUACR210321008J
Dejan Ćirić, Marko Janković, Marko Milenković and Miljan Miletić, "Cepstrum-based pitch detection of industrial product sound," presented at 8th International Conference IcETRAN 2021, Ethno village Stanišići, Bosnia and Herzegovina, September 08-10, 2021.
Dejan Ćirić, Zoran Perić, Jelena Nikolić, Nikola Vučić, "Intra-class and inter-class differences in mel-spectrogram images of DC motor sounds", in Proc. of 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), pp. 189-192, ISBN: 978-1-6654-2912-2, Niš, Serbia, October 20-22, 2021.
Zoran Perić, Bojan Denić, Milan Dinčić and Jelena Nikolić, "Robust 2-bit quantization of weights in neural network modeled by Laplacian distribution", Advances in Electrical and Computer Engineering, vol. 21, no. 3, pp.3-10, 2021. https://doi.org/10.4316/AECE.2021.03001
Zoran Perić, Bojan Denić, Milan Savić and Vladimir Despotović, "Design and analysis of binary scalar quantizer of Laplacian source with applications," Information, vol. 11, no. 11, 501, 18 pages, 2020. https://doi.org/10.3390/info11110501
Zoran Perić, Aleksandar Marković, Nataša Kontrec, Stefan Panić and Petar Spalević, "Novel composite approximation for the Gaussian Q-function," Elektronika Ir Elektrotechnika, vol. 26, no. 5, pp. 33-38, 2020. https://doi.org/10.5755/j01.eie.26.5.26012
Slobodan A. Vlajkov, Aleksandra Ž. Jovanović and Zoran H. Perić, "Improvement of energy efficiency of PAM constellation by applying optimal companding quantization in constellation design," in Proc. of 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2020, pp. 155-158, Niš, Serbia, September 10-12, 2020.
Slobodan A. Vlajkov, Aleksandra Ž. Jovanović and Zoran H. Perić, "The influence of compression parameter μ on the energy efficiency of PAM constellation based on μ-law companding quantization," in Proc. 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2020, pp. 159-162, Niš, Serbia, September 10-12, 2020.
Zoran Perić, Jelena Nikolić, Danijela Aleksić and Anastasija Perić, "Symmetric quantile quantizer parameterization for the Laplacian source: qualification for contemporary quantization solutions," Mathematical Problems in Engineering, vol. 2021, Article ID 6647135, 12 pages, 2021. https://doi.org/10.5755/j01.eie.26.5.26012
Zoran Perić, Goran Petković, Bojan Denić, Aleksandar Stanimirović, Vladimir Despotović and Leonid Stoimenov, "Gaussian source coding using a simple switched quantization algorithm and variable length codewords," Advances in Electrical and Computer Engineering, vol. 20, no. 4, pp. 11-18, 2020. https://doi.org/10.4316/AECE.2020.04002
Đorđe Damnjanović, Dejan Ćirić, Miljan Miletić and Dejan Vučić, "Usage of different wavelet families in DC motor sounds feature analysis," in Proc. of 7th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2020, pp. 43-48, ISBN: 978-86-7466-852-8, Belgrade, Čačak, Niš, Novi Sad, Serbia, September 28-29, 2020.
Marko Milivojčević and Dejan Ćirić, "Izdvajanje značajnih akustičkih karakteristika motora sa unutrašnjim sagorevanjem," u Zborniku radova 64. konferencije za elektroniku, telekomunikacije, računarstvo, automatiku i nuklearnu tehniku, ETRAN 2020, str. 49-53, ISBN: 978-86-7466-852-8, Beograd, Čačak, Niš, Novi Sad, Srbija, Septembar 28-29, 2020.
Zoran Perić, Nikola Vučić, Milan Dinčić, Dejan Ćirić, Bojan Denić and Anastasija Perić, "Design of uniform scalar quantizer for discrete input signals," in Proc. of 28th Telecommunications Forum (TELFOR), pp. 181-184, ISBN: 978-0-7381-4242-5, Belgrade, Serbia, November, 24-25, 2020.
Zoran Peric, Milan Savic, Milan Dincic, Nikola Vucic, Danijel Djosic and Srdjan Milosavljevic, "Floating point and fixed point 32-bits quantizers for quantization of weights of neural networks", 2021 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1-4, Bucharest, Romania, 25-27 March 2021. https://doi.org/10.1109/ATEE52255.2021.9425265
Aleksandra Ž. Jovanović, Zoran H. Perić and Jelena R. Nikolić, "Iterative algorithm for designing asymptotically optimal uniform scalar quantization of the one-sided Rayleigh density", IET Communications, vol. 15, no. 5, pp. 723-729, March 2021. https://doi.org/10.1049/cmu2.12114
Aleksandra Jovanović, Zoran Perić, Jelena Nikolić and Danijela Aleksić, "The effect of uniform data quantization on GMM-based clustering by means of EM algorithm", in Proc. of 20th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1-5, East Sarajevo, Bosnia and Herzegovina, March 17-19, 2021, ISBN: 978-99976-710-8-0, https://doi.org/10.1109/INFOTEH51037.2021.9400662
Đorđe Damnjanović, Dejan Ćirić and Zoran Perić, "Wavelet-based audio features of DC motor sound", Facta Universitatis - Series: Electronics and Energetics, vol. 34, no. 1, pp. 71–88, March 2021. https://doi.org/10.2298/FUEE2101071D [Database]
Dejan Ćirić, Zoran Perić, Jelena Nikolić and Nikola Vučić, "Audio signal mapping into spectrogram-based images for deep learning applications", 2021 20th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1-6, East Sarajevo, Bosnia and Herzegovina, March 17-19, 2021, ISBN: 978-99976-710-8-0. https://doi.org/10.1109/INFOTEH51037.2021.9400698
Marko T. Milojkovic, Andjela D. Djordjevic, Stanisa Lj. Peric, Miroslav B. Milovanovic, Zoran H. Peric and Nikola B. Dankovic, "Model predictive control of nonlinear MIMO systems based on adaptive orthogonal polynomial networks", Elektronika Ir Elektrotechnika, vol. 27, no. 2, 4-10. https://doi.org/10.5755/j02.eie.28780
Zoran Perić, Bojan Denić, Milan Savić, Milan Dinčić and Darko Mihajlov, "Quantization of weights of neural networks with negligible decreasing of prediction accuracy," Information Technology and Control, vol. 50, no. 3, 501, pp. 558-569, 2021. https://doi.org/10.5755/j01.itc.50.3.28468
Zoran Perić, Bojan Denić, Aleksandra Jovanović, Milan Savić, Nikola Vučić, Anastasija Nikolić, "A dual-mode 2-bit uniform scalar quantizer for data with Laplacian distribution", in Proc. of 29th Telecommunications Forum (TELFOR), pp. 214-217, ISBN: 978-1-6654-2584-1, Belgrade, Serbia, November 23-24, 2021.
Milan Dinčić, Zoran Perić, Milan Savić, Marko Milojković, Nikola Vučić, "SQNR analysis and classification accuracy of the 24-bit floating point representation of the Laplacian data Source applied for quantization of weights of a multilayer perceptron", In Proc. of the XV International Conference on Systems, Automatic Control and Measurements (SAUM), pp. 144-147, ISBN: 978-86-6125-243-3, Niš, Serbia, September 09-10, 2021.
Zoran Perić, Aleksandra Jovanović, Milan Dinčić, Milan Savić, Nikola Vučić, Anastasija Nikolić, "Analysis of 32-bit fixed point quantizer in the wide variance range for the Laplacian source", in Proc. of 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), pp. 181-184, ISBN: 978-1-6654-2912-2, Niš, Serbia, October 20-22, 2021.
Jelena Nikolić, Danijela Aleksić, Zoran Perić, Milan Dinčić, "Iterative algorithm for parameterization of two-region piecewise uniform quantizer for the Laplacian source," Mathematics, vol. 9, no. 23, 3091. https://doi.org/10.3390/math9233091
Jelena Nikolić, Zoran Perić, Danijela Aleksić, Stefan Tomić, Aleksandra Jovanović, "Whether the support region of three-bit uniform quantizer has a strong impact on post-training quantization for MNIST dataset?" Entropy, vol. 23, no. 12, 1699, 2021. https://doi.org/10.3390/e23121699
Zoran Perić, Aleksandra Jovanović, Milan Dinčić, Milan Savić, Nikola Vučić, Anastasija Nikolić, "Fixed point 32 bits quantizer for quantization of Laplacian source", The Book of Full Texts on Applied Sciences - 7th International Mardin Artuklu Scientific Researches Conference, pp. 935-942, ISBN: 978-625-8423-02-0, Mardin, Turkey, December 10-12, 2021.
Aleksandra Jovanović, Zoran Perić, Nikola Vučić, "Piecewise uniform quantization for one-dimensional two-component GMM", XXI International Symposium INFOTEH-JAHORINA 2022, Jahorina, East Sarajevo, Bosnia and Herzegovina, March 16-18, 2022.
Jelena Nikolić, Zoran Perić, Stefan Tomić, Danijela Aleksić, "On different criteria for optimizing the two-bit uniform quantizer", XXI International Symposium INFOTEH-JAHORINA 2022, Jahorina, East Sarajevo, Bosnia and Herzegovina, March 16-18, 2022.
Stefan Tomić, Jelena Nikolić, Zoran Perić, Danijela Aleksić, "Performance of post-training two-bits uniform and layer-wise uniform quantization for MNIST dataset from the perspective of support region choice", Mathematical Problems in Engineering, Vol. 2022, Article ID 1463094, 15 pages, 2022. https://doi.org/10.1155/2022/1463094
Nikola Simić, Siniša Suzić, Tijana Nosek, Mia Vujović, Zoran Perić, Milan Savić, Vlado Delić, "Speaker recognition using constrained convolutional neural networks in emotional speech", Entropy, vol. 24, no. 3, 414, 2022. https://doi.org/10.3390/e24030414
Jelena Nikolić, Stefan Tomić, Zoran Perić, Danijela Aleksić "Analysis of Neural Network Accuracy Degradation due to Uniform Weight Quantization of One or More Layers", 57th International Conference on Information, Communication and Energy Systems and Technologies, ICEST 2022, pp. 101-104, ISBN: 978-1-6654-8500-5, Ohrid, North Macedonia, June 16-18, 2022. https://doi.org/10.1109/ICEST55168.2022.9828602
Danijela Aleksić, Zoran Perić, "One-Bit Quantizer Parametrization for Arbitrary Laplacian Sources", Facta Universitatis Series Automatic Control and Robotics, vol. 21, no. 1, pp. 37-46, 2022. https://doi.org/10.22190/FUACR220321004А
Dejan Ćirić, Marko Janković, Miljan Miletić, "Sound Based DC Motor Classification by a Convolution Neural Network", 57th International Conference on Information, Communication and Energy Systems and Technologies, ICEST 2022, pp. 93-96, ISBN: 978-1-6654-8500-5, Ohrid, North Macedonia, June 16-18, 2022. https://doi.org/10.1109/ICEST55168.2022.9828682
Dejan G. Ciric, Zoran H. Peric, Marko Milenkovic, Nikola J. Vucic, "Evaluating Similarity of Spectrogram-like Images of DC Motor Sounds by Pearson Correlation Coefficient", Elektronika Ir Elektrotechnika, vol. 28, no. 3, pp. 37-44, 2022. https://doi.org/10.5755/j02.eie.31041
Nikola Vucic, Zoran Peric, Aleksandra Jovanovic, "Model of Improved Floating Point 32-bits Quantizer" 57th International Conference on Information, Communication and Energy Systems and Technologies, ICEST 2022, pp. 105-106, ISBN: 978-1-6654-8500-5, Ohrid, North Macedonia, June 16-18, 2022. https://doi.org/10.1109/ICEST55168.2022.9828586
Jelena Nikolić, Zoran Perić, "Novel Exponential Type Approximations of the Q-Function", Facta Universitatis Series Automatic Control and Robotics, vol. 21, no. 1, pp. 47-58, 2022. https://doi.org/10.22190/FUACR220401005N
Đorđe Damnjanović, Dejan Ćirić, Dejan Vujičić, "Feature Analysis for Industrial Product Sounds Using Discrete Meyer Wavelet", IX INTERNATIONAL CONFERENCE IcETRAN, Novi Pazar, Serbia, June 6-9, 2022. ISBN 978-86-7466-930-3.
Zoran H. Perić, Bojan D. Denić, Aleksandra Z. Jovanović, Srdjan Milosavljević, Milan S. Savić, "Performance Analysis of a 2-bit Dual-Mode Uniform Scalar Quantizer for Laplacian Source", accepted for publication in Journal of Information Technology and Control (ITC).
Zoran Perić, Danijela Aleksić, Jelena Nikolić and Stefan Tomić, "Two Novel Non-Uniform Quantizers with Application in Post-Training Quantization" Mathematics, vol. 10, no. 19, 3435, 2022. https://doi.org/10.3390/math10193435
Zoran Perić, Aleksandar Marković, Nataša Kontrec, Jelena Nikolić, Marko D. Petković, Aleksandra Jovanović, "Two interval upper bound Q-function approximations with applications", Mathematics, vol. 10, no.19, 3590, 2022. https://doi.org/10.3390/math10193590

Knowledge database

Here you can find a list of sorted literature relevant to the project topic:
>>> Link to the list of relevant literature <<<

Open data

It is well known that deep learning becomes the most profitable when applied to large training datasets. This is why audio data from industrial machines/products have been acquired and recorded during the course of the project. For the purpose of machine condition monitoring, among others, sounds of direct current (DC) motors and pumps from the home heating/air conditioning systems were recorded and stored. The acquired sounds have been used for implementing theoretical solutions on the developed DNNs, as a part of the target use case (prediction of the working condition of a tested machine).

Sounds of DC motors: Two different types of DC motors (type A and B) were recorded. All files were stored as mono audio in wav format, with sampling frequency of 16 kHz, and of duration of 18 s. There are 6591 files for the motor A, and 7343 files for the motor B. The overall size of the dataset is about 7.5 GB.
https://drive.google.com/drive/u/2/folders/1bGtR45gY5KoNmg4NR_3nD_KNNX36m3Ae

Sounds of water pumps: Sounds of two water pumps (good and bad) were measured, where the measurements were done using the condenser measurement microphone of class 1. The dataset consists of 2479 (for good pump) and 2483 (for bad pump) mono audio files in wav format, and its size is about 2 GB. Duration of each audio signal is 5 s, while the sampling frequency is 44.1 kHz.
https://drive.google.com/drive/u/2/folders/1NcUtjQX-M4U7Fm9FrP46nZrJsxQrnXXk