BrainMap

Mr. Andrei Olaru

Associate Professor - UNIVERSITATEA NAȚIONALĂ DE ȘTIINȚĂ ȘI TEHNOLOGIE POLITEHNICA BUCUREȘTI

Researcher | Teaching staff | Scientific reviewer

Web of Science ResearcherID: not public

Expertise & keywords

AI Folk - Resource Management for Distributed AI Call name: P 1 - SP 1.1 - Proiecte de cercetare pentru stimularea tinerelor echipe independente - TE-2021
PN-III-P1-1.1-TE-2021-1422 2022 - 2024

Role in this project:

Coordinating institution: UNIVERSITATEA NAŢIONALĂ DE ŞTIINŢĂ ŞI TEHNOLOGIE POLITEHNICA BUCUREŞTI

Project partners: UNIVERSITATEA NAŢIONALĂ DE ŞTIINŢĂ ŞI TEHNOLOGIE POLITEHNICA BUCUREŞTI (RO)

Affiliation:

Project website: https://aifolk.upb.ro/

Abstract:

Într-un ecosistem de inteligență artificială distribuit, participanții au nevoie să caute, să transfere, și să împărtășească resurse legate de învățarea automată: observații, seturi de date, modele de învățare și experiențe folosind modelele respective. Aceasta asigură o distribuire a calculului peste resursele de calcul existente și o gestionare a datelor care protejează dreptul la viață privată.

Obiectivul nostru de cercetare este dezvoltarea unui model de cunoștințe și a unui protocol de interacțiune care permit unui sistem format din actori umani, software sau organizaționali să caute resursele de învățare automată de care au nevoie, să folosească aceste resurse în mod adecvat bazat pe descrierea lor, să îmbunătățească resursele și să împărtășească resursele cu alți actori. Ne bazăm în acest proiect pe experiența existentă în domeniile învățării federative (federated learning) și inteligenței artificiale de periferie (in-edge AI) și ne axăm pe două scenarii concrete: o aplicație de conducere autonomă și un scenariu de răspuns la dezastre.

Proiectului va avea ca rezultat o ontologie pentru descrierea resurselor de învățare automată, o metodologie pentru crearea căutărilor pentru resurse, un modul care implementează protocolul de interacțiune prin care actorii pot căuta, transfera și actualiza resurse de învățare automată, implementarea a două aplicații prototip folosind această abordare, și o metodologie generală de aplicare a abordării propuse în alte domenii ale IA.

Provably Correct Networks Call name: P 3 - SP 3.6 - Premierea participării în Orizont 2020
PN-III-P3-3.6-H2020-2016-0120 2018 - 2022

Role in this project:

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation:

Project website: http://nets.cs.pub.ro/researchgrants.html

Abstract:

This projects aims to sustain top-level research within the computer science department of Facultatea de Automatica si Calculatoare, Universitatea Politehnica din Bucuresti. The project will offer research scholarships to starting academics (assistant professors or post docs) already hired in the department or wishing to join the department and perform top level research. Selection of the awardees will be achieved in two phases, where internal evaluators will provide feedback in the first phase, and external ones will help select the best proposals in the second phase.

Future IT leaders for a multicultural, digital Europe Call name:
2019-1-FR01-KA203-063041 2019 - 2022

Role in this project: Partner team leader

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (); University of Passau (); Institut national des sciences appliquées de Lyon (); University of Milan ()

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Project website:

Abstract:

The project focuses on IT graduates’ employability and the way to better match their competencies with the fast evolving market’s needs.The European task force aims at gathering companies’ needs and priorities, and developing and implementing relevant evolutionary process of 4 seminars, beyond a classical exchange framework.

People detection and tracking for social robots and autonomous cars Call name: P 2 - SP 2.1 - Proiect experimental - demonstrativ
PN-III-P2-2.1-PED-2019-4995 2020 - 2022

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: https://aimas.cs.pub.ro/petra

Abstract:

Detecting, tracking, and recognizing people is a valuable capability for machines. However, these tasks are quite difficult to be achieved autonomously and, although significant results have been obtained, they are still a major technological challenge. People tracking, unlike other recognition and interpretation tasks, is difficult both from the point of view of the recognition and prediction of trajectory, and from the one of the identification of the ground truth.

The main objective of the PETRA project is the development of a software platform enabling the development of applications requiring people detection and tracking in real environments. The design and implementation of the platform and the set of supported algorithms for people detection and tracking will be such that they can be easily used and integrated in tasks performed by social robots in closed spaces, and also in tasks in open spaces, such as is the case for pedestrian detection and tracking. The scientific and technological challenge of the project is to start from our current developments on people detection and tracking in the contexts of user-robot interaction and autonomous driving, and develop and implement novel solutions based on deep learning approaches. Especially for people tracking, this is a significant challenge, less explored in the current state-of-the art, and aiming at improving results performances.

One of the main focuses of the project is to extensively test the implementations towards several difficult benchmarks, but also on our own data sets, and strive to achieve results better than current state-of-the-art.

ARTIFICIAL INTELLIGENCE UPGRADE FOR THE GENERIC DATA REDUCTION FRAMEWORK FOR SPACE SURVEILLANCE Call name: P 2 - SP 2.1 - Proiect de transfer la operatorul economic
PN-III-P2-2.1-PTE-2019-0554 2020 - 2022

Role in this project: Key expert

Coordinating institution: GMV INNOVATING SOLUTIONS S.R.L.

Project partners: GMV INNOVATING SOLUTIONS S.R.L. (RO); UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO); INSTITUTUL ASTRONOMIC (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: https://ai4gendared.ro:49339/

Abstract:

Space Surveillance and Tracking (SST) requires the development of observational campaigns to early identify Earth orbiting objects, estimate their orbital elements and monitor the evolution of their trajectories.

With a growing number of satellites being launched every year, there is an increasing interest in SST at worldwide level and especially across the EU. In this context, Romania is one of the few EU member states involved in such activities.

SST systems must implement an Image Data Reduction Subsystem, able to timely process the continuous exposures taken in an automatic and continuous way by optical telescopes, analyse these data sets to identify objects of interest (target objects) and retrieve accurate measurements of the apparent position and brightness of the detected objects. In the last step, the tool generates tracklet information for the identified target objects.

This proposal’s main goal is to upgrade GMV’s existing proprietary solution for an SST data reduction framework (GENDARED) with artificial intelligence algorithms for image processing, considered mature enough to be adapted to optical telescope imagery.

The consortium is led by GMV and perfectly complemented by the Faculty of Automatic Control and Computer Science, with a vast experience in machine learning and artificial intelligence algorithms for image processing, and by the Astronomical Institute of the Romanian Academy, as owner of SST telescopes, data provider and one of the tool’s possible end-users.

By integrating artificial intelligence techniques into the current GENDARED framework, we expect to improve its performance, reduce user involvement and computational costs, as well as increase pipeline autonomy.

The upgraded solution will be tested and validated in representative scenarios based on real telescope data, with the ultimate goal of converting the prototype into a product that will be validated in an SST operational environment.

The Robots and the Society: Cognitive Systems for Personal Robots and Autonomous Vehicles Call name: P 1 - SP 1.2 - Proiecte complexe realizate in consorții CDI
PN-III-P1-1.2-PCCDI-2017-0734 2018 - 2021

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO); INSTITUTUL DE CERCETARI PENTRU INTELIGENTA ARTIFICIALA ,,MIHAI DRAGANESCU'' (RO); INSTITUTUL DE MATEMATICA "SIMION STOILOW" AL ACADEMIEI ROMANE (RO); UNIVERSITATEA TEHNICA DIN CLUJ - NAPOCA (RO); UNIVERSITATEA "DUNAREA DE JOS" (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: http://aimas.cs.pub.ro/robin/

Abstract:

ROBIN is a user-centered project designing software systems and services for the use of robots in an interconnected digital society, and enables companies to develop complex, intelligent and ready-to-use products and services for these users, as well as for the society as a whole. The project covers a diverse range of robots: assistive robots for the support of people with special needs, social robots for interaction with store customers, and software robots that can be installed on intelligent vehicles to achieve autonomous car driving. The project combines advanced techniques and technologies of artificial intelligence, human-robot interaction, interactions with a pervasive intelligent environment, and Cloud processing.

ROBIN-Social develops integrated and easily configurable solutions for the personalization of assistive and social robots, with a cognitive and autonomous character.

ROBIN-Car develops computer vision methods, which resolves a large and sophisticated plethora of tasks for automated driving, and a prototype system which will be tested on an electrical vehicle.

ROBIN-Context creates a support platform for the semantic representation and efficient management of data that becomes context in scenarios of personalized robotic assistance and AADS.

ROBIN-Dialog develops a set of scenarios for micro-worlds and the technology for the Romanian language processing to achieve situational dialogs in these micro-worlds.

ROBIN-Cloud builds a support platform for collecting data coming from the sensors of robotic systems and IoT systems that offers Cloud Edge and Cloud Robotics computing.

The complex project, through the interaction of its developed components, brings significant contributions in fundamental research, applied research, innovation, and technological transfer, offers advanced scientific and technical services, and contributes to the enhancement of the institutional capacity of the 4 partners in the consortium.

Ecosystem for Research, Innovation and Development of ITC Products and Services, towards an IoT-Connected Society Call name:
POC_40_270 2016 - 2020

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Project website: http://netio.ro

Abstract:

The recent and explosive evolution of the Internet of Things (IoT) creates a new dimension for society and its members, and cities of the future are "smart cities" capable of interconnecting existing community members, processes, data and "smart objects". at their level. At the same time, the evolution of IoT stimulates productivity, creates business opportunities and conditions to reduce unprecedented production costs.

In this context, the aim of the NETIO project is to create an effective collaboration framework between specialists from University Politehnica of Bucharest and companies and to accelerate the transfer of knowledge from the academic environment to the industry

ActivRobo - Improving the understanding of scenes by recognition of human activities by assistive robots Call name:
Netio subsidiary contract 1268/22.01.2018 2018 - 2020

Role in this project: Project coordinator

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (); CENTRUL IT PENTRU STIINTA SI TEHNOLOGIE S.R.L. ()

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Project website:

Abstract:

The project aims to develop data fusion-based algorithms for the recognition of human activities and objects in a scene.

Space technologies used in the management of disasters and major crises, manifested at local, national and regional levels Call name: P 2 - SP 2.1 - SOLUTII - 3 - Tehnologii spaţiale în managementul dezastrelor şi crizelor majore
PN-III-P2-2.1-SOL-2016-03-0046 2017 - 2020

Role in this project: Key expert

Coordinating institution: INSTITUTUL NATIONAL DE CERCETARE - DEZVOLTARE IN INFORMATICA - ICI BUCURESTI

Project partners: INSTITUTUL NATIONAL DE CERCETARE - DEZVOLTARE IN INFORMATICA - ICI BUCURESTI (RO); UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO); UTI GRUP S.A. (RO); Ministerul Apararii Nationale prin Agentia de Cercetare pentru Tehnica si Tehnologii Militare (ACTTM) (RO); TERRASIGNA SRL (RO); INSTITUTUL DE STIINTE SPATIALE-FILIALA INFLPR (RO); INSTITUTUL ASTRONOMIC (RO); Academia Tehnica Militara (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: http://spero.ici.ro

Abstract:

Disaster management involves activities aiming to organize and manage resources and responsibilities to minimize human and material losses. The need for solid, real-time information to support authorities’ intervention, coordinate humanitarian activities and remove civilian security threats requires the use and analysis of satellite and multi-source data as well as fast mapping methods. The overall objective of the SPERO project - "Space technologies used in the management of disasters and major crises, manifested at local, national and regional levels" is to create a support platform for the management of emergency situations generated by natural disasters, industrial accidents, humanitarian crisis situations or extreme atmospheric and space phenomena. The project focuses on building a complex geo-spatial database, visualization, processing and analysis tools; inventory of risk areas and existing and necessary means for the management of major disasters and crises; integrated situational analysis at local, national and regional level; facilitating the access to these informational resources to structures with major crisis prevention and disaster response tasks; substantiating national and regional initiatives and policies and initiatives; Integration and capitalization of national expertise in support areas such as geo-spatial sciences, geodesy, cartography, photogrammetry, remote sensing, astrophysics, optical and video data processing, security and IT & C. Successful implementation of the project will have a significant impact on the profitability of the economic agents involved by increasing the annual turnover based on the services they offer in the proposed system. The achievement of the proposed system produces immediate positive economic effects by lowering the cost of crisis analysis and management, real-time and accurate estimation of the resources needed to solve crisis situations, as well as subsequent monitoring of the situations and problems generated. The results of the project will be disseminated and exploited through specific activities by the consortium partners.

Artificially intelligent ecosystem for self-management and sustainable quality of life in AAL Call name: P 3 - SP 3.5 - Programul "Active and Assisted Living"
AAL-CAMI 2015 - 2018

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO); CENTRUL IT PENTRU STIINTA SI TEHNOLOGIE S.R.L. (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: http://www.camiproject.eu/

Abstract:

CAMI is offering a fully integrated AAL solution by providing services for health management, home management and wellbeing (including socialization, and reduced mobility support). CAMI builds an artificial intelligence ecosystem, which allows seamless integration of any number of ambient and wearable sensors with a mobile robotic platform endowed with multimodal interaction (touch, voice, person detection), including a telepresence robot with manipulatory capabilities.

The services offered by CAMI ecosystem address both healthy individuals as well as those with age-related impairments. CAMI solution will reconcile the increased demand for care in the current aging society with limited resources by supporting an efficient and sustainable care system.

A Context Management Middleware Servicing Ambient Intelligence Applications Call name: P 2 - SP 2.1 - Proiect experimental - demonstrativ
PN-III-P2-2.1-PED-2016-1753 2017 - 2018

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: http://aimas.cs.pub.ro/consert

Abstract:

Ambient Intelligence (AmI) has nowadays matured enough to become the focus of industry-level projects and European support directives.

A foundational subdomain of AmI is that of context management and context-aware computing (provide applications and users with the right information, at the right time, and in the right way).

Context Management covers many research aspects from knowledge representation and reasoning issues to information management concerns. A clear separation of concerns between the main application functionality and contextual adaptation is needed.

Thus, the objective of this project is the creation of an open source context management middleware solution presenting rich features that alleviate context-aware application development.

Work is based on an existing platform called CONSERT, recently developed in the AIMAS research group of UPB. The current project improves upon CONSERT with: (1) performance improvement and modularisation of the reasoning engine to support applications of variable complexity requirements, (2) development of an integrated development environment (IDE) for context modeling and deployment to facilitate context-aware application programming based on the CONSERT Middleware, (3) advanced options for OSGi and Docker based deployments of CONSERT processing units to address dynamic change of context within an application (e.g. change of location, change of activity), and (4) development of two test applications: Smart Meeting Support for activity monitoring of students and teachers on a campus, and Smart Home User Support for adequate dynamic configuration of services offered in an ambient intelligence environment to assist users at home.

An open web-platform (CONSERT Store) will be setup where software components and configurations of CONSERT processing units can be shared by developers.

Protection and management of personal information in the Internet of Things Call name:
UPB-EXCELEN Ț Ă-2016 ID 391 2016 - 2017

Role in this project: Project coordinator

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Project website:

Abstract:

The Internet of Things brings many challenges related to security and to the protection of personal or personally identifiable information (PI and PII). The goal of this project is to develop a methodology for the distributed management of information in the Internet of Things, based on experience in the field of context-awareness and multi-agent systems.

Empowering Romanian Research on Intelligent Information Technologies Call name:
FP7-REGPOT-2010-1/264207 2010 - 2013

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website:

Abstract:

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects