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Starovoitov V.,United Institute of Informatics Problems
Communications in Computer and Information Science | Year: 2017

The main objective of the paper is to present our comparative investigations of high dynamic range compression to demonstrate the SAR images on monitors with the standard dynamic range. To display the images we need to compress the input image range in 256 times with preservation of the most object details and maximal image contrast. We studied several well-known tone mapping methods developed for optical images and used some published no reference quality measures for evaluation of the obtained dynamic range compression results. © Springer International Publishing AG 2017.

10 November 2016, Amsterdam, NL: The Azerbaijani and Belarusian networks for research and education have connected to the GÉANT European network, allowing scientists and students in Azerbaijan and Belarus to exchange and collaborate with their peers from around the world. Enabling these connections, the EU-funded Eastern Partnership Connect (EaPConnect) project aims to create a world-class research and education (R&E) network in the Eastern Partnership region, connecting Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine to the GÉANT European backbone network. This is in line with the European Neighbourhood policy that supports increased interconnectivity and economic development for the European Union and its neighbours. Professor Rasim Alguliyev, Director of IIT of ANAS – the Institute of Information Technology of Azerbaijan National Academy of Sciences - says: "This link opens the door to new opportunities for Azerbaijan and brings our community closer to Europe. Together we can discover more solutions to the challenges we all face. I trust EaPConnect will bring positive changes across the region." Professor Alexander Tuzikov, General Director of UIIP NASB - the United Institute of Informatics Problems National Academy of Sciences of Belarus – comments : "This connection comes at a time when the need for more capacity and tailored services for our research and education community is high. We are proud to support the Belarusian and European researchers in their race for innovation and expect the outcomes to benefit us all." Strengthening inter-regional connectivity and research collaboration With Georgia and Armenia connecting earlier this year, the connection of Azerbaijan and Belarus strengthens the foundations of the Eastern Partnership (EaP) as a collaborative hub and allows the region to contribute to global research. Steve Cotter, CEO of GÉANT, adds: ‘’Connecting Belarus and Azerbaijan builds up our pool of talents and resources. The Eastern Partnership is evolving quickly and GÉANT is proud to be the first to connect to the region. With many opportunities to collaborate on research in climate change, seismology, high-energy physics or high-performance computing and e-learning, we can be sure our networks will allow better cooperation and bring forth the breakthroughs the world needs more quickly." Seizing the momentum, the R&E networking organisations of Azerbaijan, Armenia, Belarus, Georgia, Moldova and Ukraine worked together through EaPConnect to organise the first Eastern Partnership E-infrastructure Conference (EaPEC) in Tbilisi, Georgia, on 6-7 October 2016. The event provided human networking opportunities for participants from the region and featured major e-infrastructure and research projects such as the Human Brain Project, OpenAire and CERN. Conference website: www.eapec.eu FURTHER INFORMATION: The Azerbaijan link connects Baku to Budapest in Hungary with a bandwidth of 1 Gigabit per second, and allows more than 30 institutions to participate in global research. Running from Minsk to Poznan in Poland, the Belarus link offers 5 Gigabit per second capacity for data exchange across the border. About AzScienceNet and Institute of Information Technology of Azerbaijan National Academy of Sciences (IIT of ANAS) AzScienceNet, the national R&E networking organisation for Azerbaijan, founded by the Azerbaijan National Academy of Sciences, has been serving the research community since the 1980s. Being the first point of internet connectivity in the country, has led AzScienceNet to advanced global networking technology. Today, with a data centre and modern network connected via a 1Gbps direct EaPConnect link to GÉANT, AzScienceNet delivers most of the modern services such as connectivity, cloud, eduroam, videoconferencing, identity and security, distant education, web hosting, storage, TV broadcasting and VoIP. AzScienceNet is the e-infrastructure of national e-science and is developing the national open science cloud in correspondence with the European Open Science Cloud. AzScienceNet has always been strong thanks to its parent organisation, the Institute of Information Technology of ANAS. IIT is Azerbaijan’s leading research organisation for Computer Science, Information Technology and Information Society. Besides its research centres and graduate education (Masters and PhD), IIT has unique education opportunities in IT for PhD students of all disciplines, possible via distance education technology. IIT is one of 40 research institutes in the government-funded Azerbaijan National Academy of Sciences, which conducts basic and applied research, coordinates research activities and educates most of PhD students in the country, works closely with government and forms science policy. Website: http://ict.az/en About UIIP NASB United Institute of Informatics Problems National Academy of Sciences of Belarus United Institute of Informatics Problems (UIIP) is the leading organisation in Belarus in fundamental and applied research on information technologies: CAD/CAM/CAE systems, applied mathematics, high-performance parallel computing, bioinformatics and medical informatics, geoinformation systems, digital cartogragraphic systems, space informatics, GRID-technologies. The Institute is the provider of scientific and educational internet networks in Belarus. It takes part in state recommendations on information technologies implementation, scientific support of informatisation processes, prognosis in related science and technology fields in Belarus, high skill specialists training. UIIP NASB is a beneficiary partner of the EU-funded EaPConnect project. Website: www.uiip.bas-net.by/eng/ About EaPConnect The Eastern Partnership Connect (EaPConnect) project sets out to create a regional research and education (R&E) network in Eastern Europe and the Southern Caucasus. This will interconnect the national R&E networks (NRENs) in six Eastern Partnership (EaP) countries and integrate them with the pan-European GÉANT network. The partner countries are Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine. The overall objective is to decrease the digital divide, improve intra-regional connectivity and facilitate participation of local scientists, students and academics in global R&E collaborations. By interconnecting the R&E communities across the region and with their European counterparts, EaPConnect will create a gateway for talented individuals in the EaP countries to be truly global players. EaPConnect is managed by networking organisation GÉANT in collaboration with the NRENs in the six beneficiary partner countries, and includes ten associate partners from other world regions. The European Commission’s Directorate-General for Neighbourhood and Enlargements Negotiations (DG NEAR) is contributing 95% (€13m) to the cost of the EaPConnect project. Website: www.eapconnect.eu Twitter: @EaPConnect_News Facebook: @EaPConnectProject About GÉANT GÉANT is Europe’s leading collaboration on network and related e-infrastructure and services for the benefit of research and education, contributing to Europe’s economic growth and competitiveness. The organisation develops, delivers and promotes advanced network and associated e-infrastructure services, and supports innovation and knowledge-sharing amongst its members, partners and the wider research and education networking community. Website: www.geant.org Twitter: @GEANTnews Facebook: @GEANTcommunity About DG NEAR The mission of the Directorate-General for Neighbourhood and Enlargement Negotiations (DG NEAR) is to take forward the European Union’s neighbourhood and enlargement policies, as well as to coordinate relations with European Economic Area - European Free Trade Association (EEA-EFTA) countries insofar as Commission policies are concerned. By implementing assistance actions in Europe’s eastern and southern neighbourhood, DG NEAR supports reform and democratic consolidation, and strengthens the prosperity, stability and security around Europe. Website: http://ec.europa.eu/enlargement/about/directorate-general/index_en.htm This story is online at: https://www.eapconnect.eu/news-event/azerbaijan-belarus-connect-geant-eapconnect/

Sotskov Yu.N.,United Institute of Informatics Problems | Lai T.-C.,National Taiwan University
Computers and Operations Research | Year: 2012

We consider an uncertain single-machine scheduling problem, in which the processing time of a job can take any real value on a given closed interval. The criterion is to minimize the total weighted flow time of the n jobs, where there is a weight associated with a job. We calculate a number of minimal dominant sets of the job permutations (a minimal dominant set contains at least one optimal permutation for each possible scenario). We introduce a new stability measure of a job permutation (a stability box) and derive an exact formula for the stability box, which runs in O(n log n) time. We investigate properties of a stability box. These properties allow us to develop an O(n 2)-algorithm for constructing a permutation with the largest volume of a stability box. If several permutations have the largest volume of a stability box, the developed algorithm selects one of them due to a simple heuristic. The efficiency of the constructed permutation is demonstrated on a large set of randomly generated instances with 10≤n≤1000. © 2011 Elsevier Ltd. All rights reserved.

Gholami O.,Islamic Azad University | Sotskov Y.N.,United Institute of Informatics Problems
International Journal of Advanced Manufacturing Technology | Year: 2014

We consider a multistage processing system, which includes both identical (parallel) machines that can process the same set of operations and different machines that can process only different operation sets. A release time ri is given for each job Ji to be processed. For such a processing system, we minimize the makespan, i.e., problem IJ|ri |Cmax is considered. The problem IJ|ri |Cmax is an extension of the classical job-shop problem J |ri |Cmax for the case when parallel (or identical)machines are also given. Both problems J |ri |Cmax and IJ|ri |Cmax are strongly NPhard. A mixed graph model used for solving the problem J |ri |Cmax is generalized for the problem IJ|ri |Cmax. Using the mixed graph model, we developed a fast heuristic algorithm for solving the problem IJ|ri |Cmax. Computational experiments were conducted to evaluate the performance of the algorithm on the 22 benchmark instances and on the 40 new randomly generated instances of the problem IJ||Cmax. For the small and moderate instances, the exact values of the objective function were compared with those calculated by the proposed heuristic algorithm. The average relative error was not greater than 2.4 % for all instances with available optimal schedules. Computational results showed that the developed algorithm runs faster than some other heuristics being tested, and the schedules constructed by the developed algorithm have smaller makespans. © Springer-Verlag London 2013.

Gholami O.,Islamic Azad University | Gholami O.,United Institute of Informatics Problems | Sotskov Y.N.,United Institute of Informatics Problems
International Journal of Production Research | Year: 2014

Aparallel machines job-shop problem is a generalisation of a job-shop problem to the case when there are identical machines of the same type. Job-shop problems encountered in a flexible manufacturing system, train timetabling, production planning and in other real-life scheduling systems. This paper presents an adaptive algorithm with a learning stage for solving the parallel machines job-shop problem.Alearning stage tends to produce knowledge about a benchmark of priority dispatching rules allowing a scheduler to improve the quality of a schedule which may be useful for a similar scheduling problem. Once trained on solving sample problems (usually with small sizes), the adaptive algorithm is able to solve similar job-shop problems with larger size better than heuristics used as a benchmark at the learning stage. For using an adaptive algorithm with a learning stage, a job-shop problem is modelled via a weighted mixed graph with a conflict resolution strategy used for finding an appropriate schedule. We show how to generalise the mixed graph model for solving parallel machines job-shop problem. The proposed adaptive algorithm is tested on benchmark instances. © 2013 Taylor & Francis.

Hasani K.,Islamic Azad University | Kravchenko S.A.,United Institute of Informatics Problems | Werner F.,Otto Von Guericke University of Magdeburg
Computers and Operations Research | Year: 2014

We consider the problem of scheduling a set of non-preemptable jobs on two identical parallel machines such that the makespan is minimized. Before processing, each job must be loaded on a machine, which takes a given setup time. All these setups have to be done by a single server which can handle at most one job at a time. For this problem, we propose a mixed integer linear programming formulation based on the idea of decomposing a schedule into a set of blocks. We compare the results obtained by the model suggested with known heuristics from the literature. © 2013 Elsevier Ltd.

Kravchenko S.A.,United Institute of Informatics Problems | Werner F.,Otto Von Guericke University of Magdeburg
Journal of Scheduling | Year: 2011

The basic scheduling problem we are dealing with is the following. There are n jobs, each requiring an identical execution time. All jobs have to be processed on a set of parallel machines. Preemptions can be either allowed or forbidden. The aim is to construct a feasible schedule such that a given criterion is minimized. In this paper, we survey existing approaches for the problem class considered. © Springer Science+Business Media, LLC 2011.

Kravchenko S.A.,United Institute of Informatics Problems | Werner F.,Otto Von Guericke University of Magdeburg
Journal of Scheduling | Year: 2012

The basic scheduling problem we are dealing with in this paper is the following one. A set of jobs has to be scheduled on a set of parallel uniform machines. Each machine can handle at most one job at a time. Each job becomes available for processing at its release date. All jobs have the same execution requirement and arbitrary due dates. Each machine has a known speed. The processing of any job may be interrupted arbitrarily often and resumed later on any machine. The goal is to find a schedule that minimizes the sum of tardiness, i.e., we consider problem Q |r j, p j = p, pmtn | Σ T j whose complexity status was open. Recently, Tian et al. (J. Sched. 9:343-364, 2006) proposed a polynomial algorithm for problem 1 |r j, p j = p, pmtn | Σ T j. We show that both the problem P | pmtn | Σ T j of minimizing total tardiness on a set of parallel machines with allowed preemptions and the problem P |r j, p j = p, pmtn | Σ T j of minimizing total tardiness on a set of parallel machines with release dates, equal processing times and allowed preemptions are NP-hard. Moreover, we give a polynomial algorithm for the case of uniform machines without release dates, i.e., for problem Q | p j = p, pmtn | Σ T j. © Springer Science+Business Media, LLC 2010.

Novoselova N.,United Institute of Informatics Problems
Intelligent Data Analysis | Year: 2014

The stability techniques widely used in bioinformatics research estimate clusterings with a pre-defined number of clusters. But the complex nature of bio-molecular data necessitates the extension of the stability techniques in order to validate the whole clusters' hierarchy without strict setting of the number of clusters beforehand. In this paper we proposed a stability-based algorithm HClusterV to estimate the individual clusters of the dendrogram. It is based on a repetitive construction of the hierarchy of clusters followed by the calculation of the original consensus matrix. The proposed algorithm allows to overcome the deficiency of the previous approach and to improve the reliability of the stability indices. Experiments on two simulated datasets and further comparative analysis confirmed the advantages of our approach. The proposed HClusterV algorithm was evaluated on two real microarray datasets and gave the results consistent with the corresponding non-hierarchical stability-based methods and relevant biological knowledge. © 2014 - IOS Press and the authors. All rights reserved.

Gholami O.,United Institute of Informatics Problems | Sotskov Y.N.,United Institute of Informatics Problems
International Journal of Industrial Engineering Computations | Year: 2014

The problem of generating a train schedule for a single-track railway system is addressed in this paper. A three stage scheduling is proposed to reduce the total train tardiness. We derived an appropriate job-shop scheduling algorithm called DR-algorithm. In the first stage, by determining appropriate weights of the dispatching rules, a pre-schedule is constructed. In the second stage, on the basis of the pre-schedule, the departure times of the trains are modified to reduce the number of conflicts in using railway sections by different trains. In the third stage, a train speed control helps the scheduler to change the trains' speeds in order to reduce the train tardiness and to reach other objectives. The factual train schedule is based on the modified train speeds and on the modified departure times of the trains. The experimental running of the DR-algorithm on the benchmark instances showed this algorithm can solve train scheduling problems in a close to optimal way. In particular, the total train tardiness was reduced about 20% due to controlling train speeds and the departure times of the trains. © 2014 Growing Science Ltd. All rights reserved.

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