Change search
Refine search result
123 1 - 50 of 116
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Abdukalikova, Anara
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Wiklund, Urban
    Umeå University, Umeå, Sweden.
    Detection of Atrial Fibrillation from Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers2018In: Computing in Cardiology 2018: Proceedings / [ed] Christine Pickett; Cristiana Corsi; Pablo Laguna; Rob MacLeod, IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F 1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.

  • 2.
    Alonso, Pedro
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Shridhar, Kumar
    Department of Computer Science, ETH Zürich, Zürich, Switzerland.
    Kleyko, Denis
    UC Berkeley, Berkeley, USA; Research Institutes of Sweden, Kista, Sweden.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing Enabled Embedding of n-gram Statistics2021In: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings, IEEE, 2021Conference paper (Refereed)
    Abstract [en]

    Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of n -gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n -gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, memory reduction was ca. 100 times and train and test speed-ups were over 100 times. Importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting strict dependency between the dimensionality of the representation and n-gram size, thus, opening a room for tradeoffs.

  • 3.
    Bandaragoda, Tharindu
    et al.
    Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    De Silva, Daswin
    Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Wiklund, Urban
    Umeå University, Umeå, Sweden.
    Alahakoon, Damminda
    Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing2019In: The 2019 IEEE Intelligent Transportation Systems Conference - ITSC, IEEE, 2019, p. 1664-1670Conference paper (Refereed)
    Abstract [en]

    Road traffic congestion in urban environments poses an increasingly complex challenge of detection, profiling and prediction. Although public policy promotes transport alternatives and new infrastructure, traffic congestion is highly prevalent and continues to be the lead cause for numerous social, economic and environmental issues. Although a significant volume of research has been reported on road traffic prediction, profiling of traffic has received much less attention. In this paper we address two key problems in traffic profiling by proposing a novel unsupervised incremental learning approach for road traffic congestion detection and profiling, dynamically over time. This approach uses (a) hyperdimensional computing to enable capture variable-length trajectories of commuter trips represented as vehicular movement across intersections, and (b) transforms these into feature vectors that can be incrementally learned over time by the Incremental Knowledge Acquiring Self-Learning (IKASL) algorithm. The proposed approach was tested and evaluated on a dataset consisting of approximately 190 million vehicular movement records obtained from 1,400 Bluetooth identifiers placed at the intersections of the arterial road network in the State of Victoria, Australia.

  • 4.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Lindgren, Per
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Riliskis, Laurynas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Road surface networks technology enablers for enhanced ITS2010In: 2010 IEEE Vehicular Networking Conference, VNC 2010: Jersey City, NJ ; 13-15 Dec 2010, Piscataway, NJ: IEEE Communications Society, 2010, p. 152-159Conference paper (Refereed)
    Abstract [en]

    The increased need for mobility has led to transportation problems like congestion, accidents and pollution. In order to provide safe and efficient transport systems great efforts are currently being put into developing Intelligent Transport Systems (ITS) and cooperative systems. In this paper we extend proposed solutions with autonomous on-road sensors and actuators forming a wireless Road Surface Network (RSN). We present the RSN architecture and design methodology and demonstrate its applicability to queue-end detection. For the use case we discuss the requirements and technological solutions to sensor technology, data processing and communication. In particular the MAC protocol is detailed and its performance assessed through theoretical verification. The RSN architecture is shown to offer a scalable solution, where increased node density offers more precise sensing as well as increased redundancy for safety critical applications. The use-case demonstrates that RSN solutions may be deployed as standalone systems potentially integrated into current and future ITS. RSN may provide both easily deployable and cost effective alternatives to traditional ITS (with a direct impact independent of penetration rate of other ITS infrastructures - i.e., smart vehicles, safe spots etc.) as well as provide fine grain sensory information directly from the road surface to back-end and cooperative systems, thus enabling a wide range of ITS applications beyond current state of the art.

    Download full text (pdf)
    FULLTEXT01
  • 5.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Lundberg Nordenvaad, Magnus
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gylling, Arne
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Mäkitaavola, Henrik
    Project: iRoad2011Other (Other (popular science, discussion, etc.))
  • 6.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    On the design of cooperative road infrastructure systems2008In: Reglermöte 2008: proceedings / [ed] Thomas Gustafsson; Wolfgang Birk; Andreas Johansson, Luleå: Luleå tekniska universitet, 2008Conference paper (Other academic)
    Abstract [en]

    This paper discusses the design of cooperative road infrastructure systems for infrastructure-based driving support functions. The background of such systems is mapped out and it is shown that there is a need for a cross disciplinary approach. Using an example of a support function, namely the overtaking support, it is shown that such a system is feasible. The different challenges and technological problems that are identified are given and the future work is indicated.

    Download full text (pdf)
    FULLTEXT01
  • 7.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    iRoad - cooperative road infrastructure systems for driver support2009In: 16th World Congress and Exhibition on Intelligent Transport Systems 2009: 16th ITS World Congress ; Stockholm, Sweden, 21 - 25 September 2009, Red Hook: Curran Associates, Inc., 2009Conference paper (Refereed)
    Abstract [en]

    This paper discusses the design and implementation of a cooperative road infrastructure systems, which uses an intelligent road surface. Using an overtaking assist feature as an example it is shown how such a feature can be designed and implemented on a road infrastructure and integrated with drivers and passengers using IMS. The feasibility of this feature is assessed from a functional and communication perspective. Moreover, first results from real-life tests on the Swedish highway E4 are presented which motivate the next research and development steps.

    Download full text (pdf)
    FULLTEXT01
  • 8.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Riliskis, Laurynas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hesler, Alban
    NEC.
    Modular design and performance ranking of communication protocols2009Report (Other academic)
    Abstract [en]

    In this deliverable we present a systematic approach towards designing modularized protocols and rank a contribution of their components to the overall system performance. In the nutshell, this approach is based onthree steps: 1.) identifying adjustable parameters in existing protocols, 2.) ranking their influence on the system-level performance metrics and 3.) defining protocol modules exposing the parameters of the highest rank. To this end we present the definition of the components for constructing MAC protocols based on ranking of the impact of adjustable parameters on the overall system performance. We also overview a ranking method for functional blocks of protocols on the routing layer.

  • 9.
    Brogle, Marc
    et al.
    University of Bern.
    Osipov, EvgenyLuleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.Braun, TorstenUniversity of Bern.Heijenk, GeertUniversity of Twente.
    Fourth ERCIM workshop on e-mobility2010Conference proceedings (editor) (Other academic)
    Abstract [en]

    ERCIM, the European Research Consortium for Informatics and Mathematics, aims to foster collaborative work within the European research community and to increase co-operation with European industry. In the ERCIM eMobility workshop, current progress and future developments in the area of eMobility should be discussed and the existing gap between theory and application closed. This volume contains all accepted papers of the fourth eMobility workshop, which has been held in Luleå, Sweden, on May 31, 2009. Papers from different areas have been selected for this workshop. The contributions discuss several topics of the ERCIM eMobility working group including, testbeds for mobile networks, performance optimization for cellular networks, QoS in V2B communication, reliability in ad-hoc networks, distributed resource discovery and use, traffic generation models for wireless networks, IMS clients, ICT support for mobility, VANETs, and mobile video conferencing.At this point, we want to thank all authors of the submitted papers and the members of the international program committee for their contribution to the success of the event and the high quality program. The proceedings are divided into two sections, full papers and short papers. While the latter present work in progress and ongoing research, the full papers have been carefully selected in a peer review process. The reviewers evaluated all papers and sent the authors the comments on their work.The invited talks featured presentations of different European research projects: ELVIRE presented by Marc Brogle, EU-Mesh presented by Vasilios Siris, Frederica presented by Kurt Baumann, GINSENG presented by Marilia Curado, Socrates presented by Hans van den Berg, Winemo presented by Yevgeni Koucheryavy, and Wisebed presented by Torsten Braun.Luleå University of Technology is the northernmost university of technology in Scandinavia and with world-class research and education. Its strength lies in its cooperation with companies and with the rest of the world around it. Contacts with companies and society help to develop research and education that satisfy the demands that the world around makes on the university and its students. Luleå University of Technology conducts research in the Faculty of engineering and the Faculty of arts and social sciences. Research at the University has an annual turnover of EUR 70 million and comprises 70 research subjects in 6 research profiles and 6 development profiles. The research is characterized by multidisciplinary cooperation between the University's research departments and close interaction with trade and industry and society. Both major international and national companies and small enterprises in the region are involved in the University's research and development projects. The companies also cooperate in the University's research centers. Torsten Braun, Marc Brogle, Geert Heijenk, Evgeny Osipov

    Download full text (pdf)
    FULLTEXT01
  • 10.
    Buttyan, Levente
    et al.
    BME.
    Acs, Gergely
    BME.
    Schaffer, Peter
    BME.
    Farkas, Karoly
    BME.
    Bencsath, Boldizsar
    BME.
    Thong, Ta Vinh
    BME.
    Laszka, Aron
    BME.
    Grilo, Antonio
    INOV.
    Hessler, Alban
    NEC.
    Riliskis, Laurynas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Perito, Daniele
    INRIA.
    Castelluccia, Claude
    INRIA.
    Dependability concepts, models, and analysis of networking mechanisms for WSANs2009Report (Other academic)
    Abstract [en]

    In this deliverable, we report on the results of Work Package 3 (Dependable Networking) obtained in the first year of the WSAN4CIP Project. These results are related to the identification of the design principles of dependable networking mechanisms for WSANs. In our work, and hence, in this deliverable, we follow the layered model of networking protocol stacks: We identify the most important dependability concepts and models at the physical, MAC (Medium Access Control), routing, and transport layers, and we analyze existing networking protocols from the different layers proposed in the literature with respect to the identified dependability properties.

    Download full text (pdf)
    FULLTEXT01
  • 11.
    Chaltseva, Anna
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Empirical cross-layer model of TCP throughput in multihop wireless chain2010Report (Other academic)
    Abstract [en]

    Analysis of TCP throughput in multihop wireless networks is a continuously important research topic. Yet a neat and practically useful formula for the TCP transfer rate similar to the macroscopic model of TCP in the Internet, however, capturing the cross-layer dependencies is unavailable for wireless networks. In this paper we statistically analyze the significance of parameters on physical, MAC and transport layers in a multihop wireless chains and derive a practically usable cross-layer throughput formula. The resulting model allows estimation of the throughput with less than 2% error.

    Download full text (pdf)
    FULLTEXT01
  • 12.
    Chaltseva, Anna
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Empirical predictor of TCP throughput on a multihop wireless path2010In: Smart spaces and next generation Wired/Wireless networking: third Conference on Smart Spaces, ruSMART 2010, and 10th international conference, NEW2AN 2010, St. Petersburg, Russia, August 23-25, 2010 ; proceedings / [ed] Sergey Balandin; Roman Dunaytsev; Yevgeni Koucheryavy, Encyclopedia of Global Archaeology/Springer Verlag, 2010, p. 323-334Conference paper (Refereed)
    Abstract [en]

    This paper addresses a question of predicting TCP throughput over a multihop wireless path. Since it is useful for a variety of applications it is desirable that TCP throughput prediction technique introduces low-overhead while avoiding active measurement techniques. Analytical derivation of the throughput predictor for multihop wireless networks is difficult if not impossible at all due to complex cross-layer dependencies. In this article we statistically analyze the significance of parameters on physical, MAC and transport layers in a multihop wireless chain and empirically derive a practically usable throughput predictor. The resulting model allows prediction of the throughput with less than 2% error.

  • 13.
    Chaltseva, Anna
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    On passive characterization of aggregated traffic in wireless networks2011Report (Other academic)
    Abstract [en]

    We present a practical measurement-based model of aggregated traffic intensity on microseconds time scale for wireless networks. The model allows estimating the traffic intensity for the period of time required to transmit data structures of different size (short control frames and a data packet of the maximum size). The presented model opens a possibility to mitigate the effect of interferences in the network by optimizing the communication parameters of the MAC layer (e.g. size of contention window, retransmission strategy, etc.) for the forthcoming transmission to minimize the packet collision probability and further increase network's capacity. We also discuss issues and challenges associated with PHY-layer characterization of the network state.

    Download full text (pdf)
    FULLTEXT01
  • 14.
    Chaltseva, Anna
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    On passive characterization of aggregated traffic in wireless networks2012In: Wired/Wireless Internet Communication: 10th International Conference, WWIC 2012, Proceedings, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2012, p. 282-289Conference paper (Refereed)
    Abstract [en]

    We present a practical measurement-based characterization of the aggregated traffic on microseconds time scale in wireless networks. The model allows estimating the channel utilization for the period of time required to transmit data structures of different sizes (short control frames and a data packet of the maximum size). The presented model opens a possibility to mitigate the effect of interferences in the network by optimizing the communication parameters of the MAC layer (e.g. the size of contention window, retransmission strategy, etc.) for the forthcoming transmission. The article discusses issues and challenges associated with the PHY-layer characterization of the network state.

    Download full text (pdf)
    FULLTEXT01
  • 15.
    Cheng, Haibo
    et al.
    Shenyang Institute of Automation, Chinese Academy of Sciences,Lab. of Networked Control Systems,Shenyang,China.
    Han, Xiaoning
    Shenyang Institute of Automation, Chinese Academy of Sciences,Lab. of Networked Control Systems,Shenyang,China.
    Zeng, Peng
    Shenyang Institute of Automation, Chinese Academy of Sciences,Lab. of Networked Control Systems,Shenyang,China.
    Yu, Haibin
    Shenyang Institute of Automation, Chinese Academy of Sciences,Lab. of Networked Control Systems,Shenyang,China.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    ANN based Interwell Connectivity Analysis in Cyber-Physical Petroleum Systems2019In: Proceedings: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), IEEE, 2019, p. 199-205Conference paper (Other academic)
    Abstract [en]

    In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (O&G) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

  • 16.
    Cheng, Haibo
    et al.
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China. University of Chinese Academy of Sciences, Beijing 100049, China.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zeng, Peng
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Yu, Haibin
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 67289-67299Article in journal (Refereed)
    Abstract [en]

    In petroleum production system, interwell connectivity evaluation is a significant process to understand reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas field. In this paper, a novel long short-term memory (LSTM) neural network based global sensitivity analysis (GSA) method is proposed to analyse injector-producer relationship. LSTM neural network is employed to build up the mapping relationship between production wells and surrounding injection wells using the massive historical injection and production fluctuation data of a synthetic reservoir model. Next, the extended Fourier amplitude sensitivity test (EFAST) based GSA approach is utilized to evaluate interwell connectivity on the basis of the generated LSTM model. Finally, the presented LSTM based EFAST sensitivity analysis method is applied to a benchmark test and a synthetic reservoir model. Experimental results show that the proposed technique is an efficient method for estimating interwell connectivity.

  • 17.
    Cheng, Haibo
    et al.
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China. University of Chinese Academy of Sciences, Beijing 100049, China.
    Yu, Haibin
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Zeng, Peng
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Li, Shichao
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland.
    Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 19, article id 5659Article in journal (Refereed)
    Abstract [en]

    Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

  • 18.
    Dai, William
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Riliskis, Laurynas
    Stanford University, USA.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Aalto University, Helsinki, Finland.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    A Configurable Cloud-Based Testing Infrastructure for Interoperable Distributed Automation Systems2015In: IECON 2014: 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, USA , Oct. 29 2014-Nov. 1 2014, Piscataway, NJ: IEEE Communications Society, 2015, p. 2492-2498Conference paper (Refereed)
    Abstract [en]

    The interoperability between various automation systems is considered as one of the major character of future automation systems. Service-oriented Architecture is a possible interoperability enabler between legacy and future automation systems. In order to prove the interoperability between those systems, a verification framework is essential. This paper proposes a configurable cloud-based validation environment for interoperability tests between various distributed automation systems. The testing framework is implemented in a multi-layer structure which provides automated closed-loop testing from the protocol level to the system level. The testing infrastructure is also capable for simulating automation systems as well as wireless sensor networks in the cloud. Test cases could be automatically generated and executed by the framework.

  • 19.
    de Silva, D.
    et al.
    Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, Australia.
    Pang, Z.
    ABB Corporate Research, Västeras, Sweden.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Helsinki, Finland.
    Guest Editorial: Special Section on Developments in Artificial Intelligence for Industrial Informatics2019In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, no 6, p. 3690-3692Article in journal (Other academic)
    Abstract [en]

    The emergence of artificial intelligence (AI), empowered by robust computing infrastructure and abundance of data, maintains potential for radical transformation of human society, essentially a third phase in evolution. Numerous research endeavor, policy development, and thought-leadership are presently in progress aimed at discovering data-driven intelligent decision-making solutions for smart cities, smart grids, smart homes, and informed citizens as well as addressing potential risks posed by AI workplace automation. Joining this broad effort, this Special Section contributes six research articles that consolidate recent developments in AI for industrial informatics.

  • 20.
    De Silva, Daswin
    et al.
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.
    Ranasinghe, Weranja
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia; Austin Hospital, Heidelberg, Victoria, Australia.
    Bandaragoda, Tharindu
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.
    Adikari, Achini
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.
    Mills, Nishan
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.
    Iddamalgoda, Lahiru
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.
    Alahakoon, Damminda
    Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.
    Lawrentschuk, Nathan
    Austin Hospital, Heidelberg, Victoria, Australia.
    Persad, Raj
    North Bristol, NHS Trust, Bristol, United Kingdom.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Gray, Richard
    School of Nursing and Midwifery, La Trobe University, Victoria, Australia.
    Bolton, Damien
    Austin Hospital, Heidelberg, Victoria, Australia.
    Machine learning to support social media empowered patients in cancer care and cancer treatment decisions2018In: PLOS ONE, E-ISSN 1932-6203, Vol. 13, no 10, article id e0205855Article in journal (Refereed)
    Abstract [en]

    BACKGROUND

    A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines.

    METHODS AND FINDINGS

    We have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed.

    CONCLUSIONS

    Findings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.

  • 21.
    de Silva, Daswin
    et al.
    Research Center for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    Sierla, Seppo
    Department of Electrical Engineering and Automation, Aalto University.
    Alahakoon, Damminda
    Research Center for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yu, Xinghuo
    Royal Melbourne Institute of Technology, Australia.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Aalto University, Espoo, Finland.
    Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications2020In: IEEE Industrial Electronics Magazine, ISSN 1932-4529, E-ISSN 1941-0115, Vol. 14, no 2, p. 57-72Article, review/survey (Refereed)
    Abstract [en]

    Research, the universal pursuit of new knowledge, is embarking on a fresh journey into artificial intelligence (AI). Nature reports that AI arose nine places to the fourth-most popular search term and that search terms machine learning and deep learning appeared in the top 20 for the first time since 2018. It is pertinent for industrial informatics to embrace this renewed surge of interest in AI with a clear direction and purpose that engages scholars, practitioners, and professionals alike.

  • 22.
    Dudin, Alexander
    et al.
    BSU.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Dudin, Sergey
    BSU.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Socio-behavioral scheduling of time-frequency resources for modern mobile operators2013In: Modern Probabilistic Methods for Analysis of Telecommunication Networks: Belarusian Winter Workshops in Queueing Theory, BWWQT 2013, Minsk, Belarus, January 28-31, 2013. Proceedings, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2013, p. 69-82Conference paper (Refereed)
    Abstract [en]

    This article presents a mathematical foundation for scheduling of batch data produced by mobile end users over the time-frequency resources provided by modern mobile operators. We model the mobile user behavior by Batch Markovian Arrival Process, where a state corresponds to a specific user data activity (i.e. sending a photo, writing a blog message, answering an e-mail etc). The state transition is marked by issuing a batch of data of the size typical to the activity. To model the changes of user behavior caused by the environment, we introduce a random environment which affects the intensities of transitions between states (i.e., the probabilities of the user data activities). The model can be used for calculating probability of packet loss and probability of exceeding the arbitrarily fixed value by the sojourn time of a packet in the system conditional that the packet arrives to the system at moments when the random environment has a given state. This allows to compute the realistic values of these probabilities and can help to properly fix their values that can be guaranteed, depending on the state of the random environment, by a service provider

  • 23.
    Elkotob, Muslim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Enabling communication service reconfigurability via guided cross layering2009Report (Other academic)
    Abstract [en]

    A system architecture which natively supports cross-layer design in a general sense is an essential prerequisite for enabling communication services in the future heterogeneous Internet. A multitude of cross-layer approaches ranging from clean slate designs to actual implementations of cross-layer links in the standard TCP/IP stack have been suggested during the last decade. Yet, there is no agreement on a systematic integration of cross-layering into the actual Internet architecture. In this article we present a Guided Cross-layering Framework and a roadmap for its deployment in the Internet. We elaborate its key issue of identifying functional invariants in the present communication model. The invariants are the clean protocol stubs of the current TCP/IP stack. We describe the concept of meta-protocols and a design-deployment methodology for the framework. Our main postulate that promises an acceptance of the proposed architecture is the evolutionary, market-driven transformation of the current Internet architecture. On the example of the new ICSP (Integrated Communication and Signaling Protocol) protocol, we demonstrate the integration of CARD (Candidate Access Router Discovery), MIP (Mobile IP), and SIP (Session Initiation Protocol) in our framework which jointly optimize the performance and economic utilities of a multi-cell wireless network operator and the end user.

    Download full text (pdf)
    FULLTEXT01
  • 24. Elkotob, Muslim
    et al.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    iRide: a cooperative sensor and IP multimedia subsystem based architecture and application for ITS road safety2009In: Communications Infrastructure, Systems and Applications: First International ICST Conference, EuropeComm 2009, London, UK, August 11-13, 2009, Revised Selected Papers / [ed] R. Mehmood, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2009Conference paper (Refereed)
    Abstract [en]

    In this paper we present iRide (intelligent ride), an IP Multimedia Subsystem (IMS) application for warning drivers about hazardous situations on the road. iRide takes real-time information about road conditions and traffic situations from a wireless sensor network installed directly in the road surface. Upon logging to the iRide system, users start to receive periodic updates about the situation on the road along their route ahead. iRide is able to predict hazardous situations like slippery surface or dangerous distance to the nearest car and help drivers avoid accidents. We describe the service and the supporting network architecture of iRide. We discuss the major challenges associated with designing an IMS application for ITS, an intelligent transport system. Having a prototype implementation working on a small scale, we take it to the next step to perform system dimensioning and then verify the feasibility of having such a system using OPNET simulations.

  • 25.
    Gayathri, Madhavi
    et al.
    University of Moratuwa, Sri Lanka.
    Ariyaratne, Amanda
    University of Moratuwa, Sri Lanka.
    Kahawala, Sachin
    La Trobe University, Australia.
    De Silva, Daswin
    La Trobe University, Australia.
    Alahakoon, Damminda
    La Trobe University, Australia.
    Nanayakkara, Vishaka
    University of Moratuwa, Sri Lanka.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Yu, Xinghuo
    School of Engineering, RMIT University, Australia.
    Learning Rule Optimization and Comparative Evaluation of Accelerated Self-Organizing Maps for Industrial Applications2021In: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2021Conference paper (Refereed)
    Abstract [en]

    The emergence of low latency and high bandwidth 5G networks, alongside localized computation and data storage of edge computing are enabling real-time applications in industrial settings, such as smart grid, smart cities, and smart factories. The resolution, frequency and variety of data streams generated by such applications are not effectively processed and analysed by contemporary machine learning algorithms. This challenge is further complicated by the unlabelled and non-deterministic nature of the data streams. Hardware accelerated machine learning has been proposed to address some of these challenges but limited work has been published on unsupervised learning from unlabelled data. In this paper, we extend the hardware accelerated Self Organizing Map (SOM) algorithm by optimizing the learning rule for computational efficiency, followed by a comparative empirical evaluation with two other variants, tri-state SOM and integer SOM. We have used two datasets representative of real-time industrial applications in 5G networks and smart grids, for this evaluation.

  • 26.
    Glover, Tom Eivind
    et al.
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
    Lind, Pedro
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
    Yazidi, Anis
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Nichele, Stefano
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway; Department of Computer Science and Communication, Østfold University College, Halden, Norway.
    Investigating Rules and Parameters of Reservoir Computing with Elementary Cellular Automata, with a Criticism of Rule 90 and the Five-Bit Memory Benchmark2023In: Complex Systems, ISSN 0891-2513, Vol. 32, no 3, p. 309-351Article in journal (Refereed)
    Abstract [en]

    Reservoir computing with cellular automata (ReCAs) is a promising concept by virtue of its potential for effective hardware implementation. In this paper, we explore elementary cellular automata rules in the context of ReCAs and the 5-bit memory benchmark. We combine elementary cellular automaton theory with our results and use them to identify and explain some of the patterns found. Furthermore, we use these findings to expose weaknesses in the 5-bit memory benchmark as it is typically applied in ReCAs, such as pointing out what features it selects for or solving it using random vectors. We look deeply into previ-ously successful rules in ReCAs such as rule 90 and explain some of the consequences of its additive properties as well as the correlation between grid size and performance. Additionally, we present results from exhaustively exploring ReCAs on key parameters such as distrac-tor period, iterations and grid size. The findings of this paper should motivate the ReCAs community to move away from using the 5-bit memory benchmark as it is being applied today.

    Download full text (pdf)
    fulltext
  • 27. Granlund, Daniel
    et al.
    Gylling, Arne
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Brännström, Robert
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jacquier, Anna
    Elkotob, Muslim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Åhl, Anneli
    Project: BASICNET2008Other (Other (popular science, discussion, etc.))
    Abstract [en]

    Broadband Access Services In Converging NETworks

  • 28.
    Grytsenko, Vladimir I.
    et al.
    International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of Ministry of Education and Science of Ukraine, av. Acad. Glushkova, 40, Kiev, 03680, Ukraine.
    Rachkovskij, Dmitri A.
    International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of Ministry of Education and Science of Ukraine, av. Acad. Glushkova, 40, Kiev, 03680, Ukraine.
    Frolov, Alexander A.
    Technical University of Ostrava, 17 listopadu 15, 708 33 Ostrava-Poruba, Czech Republic.
    Gayler, Ross
    Independent researcher, Melbourne, VIC, Australia.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Neural Distributed Autoassociative Memories: A Survey.2017In: Cybernetics and Computer Engineering Journal, ISSN 0454-9910, Vol. 188, no 2, p. 5-35Article in journal (Refereed)
    Abstract [en]

    Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.

    The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).

    Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints.

    Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors. 

  • 29.
    Haputhanthri, Dilantha
    et al.
    Centre for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kahawala, Sachin
    Centre for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    De Silva, Daswin
    Centre for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    Kempitiya, Thimal
    Centre for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    Alahakoon, Damminda
    Centre for Data Analytics and Cognition at La Trobe University, Melbourne, Australia.
    Evaluating Complex Sparse Representation of Hypervectors for Unsupervised Machine Learning2022In: 2022 International Joint Conference on Neural Networks (IJCNN): 2022 Conference Proceedings, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    The increasing use of Vector Symbolic Architectures (VSA) in machine learning has contributed towards en-ergy efficient computation, short training cycles and improved performance. A further advancement of VSA is to leverage sparse representations, where the VSA-encoded hypervectors are sparsified to represent receptive field properties when encoding sensory inputs. The hyperseed algorithm is an unsupervised machine learning algorithm based on VSA for fast learning a topology preserving feature map of unlabelled data. In this paper, we implement two methods of sparse block-codes on the hyperseed algorithm, they are selecting the maximum element of each block and selecting a random element of each block as the nonzero element. Finally, the sparsified hyperseed algorithm is empirically evaluated for performance using three distinct bench-mark datasets, Iris classification, classification and visualisation of synthetic datasets from the Fundamental Clustering Problems Suite and language classification using n-gram statistics.

  • 30.
    Jardak, Christine
    et al.
    RWTH Aachen University, Kackertstrasse 9, D-52072 Aachen, Germany.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mähönen, Petri
    RWTH Aachen University, Kackertstrasse 9, D-52072 Aachen, Germany.
    Distributed information storage and collection for WSNs2007In: Proceedings, 4th IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Piscataway, NJ: IEEE Communications Society, 2007Conference paper (Refereed)
    Abstract [en]

    Distributed data storage is an important component of wireless sensor networks, which protects the mission critical information from unexpected node failures or malicious destruction of parts of the network. In this paper we present DISC, a protocol for distributed information storage and collection. The two major mechanisms in DISC which make our solution distinct from the related approaches are probabilistic choice of storing nodes and a search engine based on the usage of Bloom filters. In comparison to the deterministic choice of the backup node, the random selection strategy makes it virtually impossible for an attacker to determine and destroy the exact node keeping a particular piece of information. The usage of Bloom filters in the information search engine makes the navigation to a specific data fast and efficient. We show that with DISC the amount of recovered information is more than two times higher than that in deterministic storage schemes.

    Download full text (pdf)
    FULLTEXT01
  • 31. Johansson, Tomas
    et al.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Carr-Motyckova, Lenka
    Interference aware construction of multi- and convergecast trees in wireless sensor networks2008In: Next Generation Teletraffic and Wired/Wireless Advanced Networking: 8th International Conference, NEW2AN 2008 and ruSMART, St. Petersburg, Russia, September 3-5, 2008 ; proceedings / [ed] Sergey Balandin; Dimitri Moltchanov; Yevgeni Koucheryavy, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2008, p. 72-87Conference paper (Refereed)
    Abstract [en]

    In this paper we consider a problem of building a forwarding tree for multicast and convergecast traffic in short-range wireless sensor networks. Interference awareness and energy efficiency are the major design objectives for WSN protocols in order to maximize the network lifetime. The existing multicast algorithms aim at constructinglow-energy cost trees. Adding interference-awareness, however, leads to increased throughput and further reduces the energy consumption by avoiding unnecessary retransmissions due to interference-induced packetlosses. We propose a Localized Area-Spanning Tree (LAST) protocol for wireless short-range sensor networks. Unlike previous similar protocols, the LAST protocol reaches all the nodes in a given geographical area,rather than only specific individual nodes. When creating the tree, the protocol jointly optimizes the energy cost and the interference imposed by the structure.

    Download full text (pdf)
    FULLTEXT01
  • 32.
    Kahawala, Sachin
    et al.
    Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia.
    De Silva, Daswin
    Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia.
    Sierla, Seppo
    Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland.
    Alahakoon, Damminda
    Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia.
    Nawaratne, Rashmika
    Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jennings, Andrew
    Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland.
    Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing2021In: Energies, E-ISSN 1996-1073, Vol. 14, no 14, article id 4378Article in journal (Refereed)
    Abstract [en]

    Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.

  • 33.
    Kempitiya, Thimal
    et al.
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.
    Alahakoon, Damminda
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, 971 87, Sweden.
    Kahawala, Sachin
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.
    De Silva, Daswin
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.
    A Two-Layer Self-Organizing Map with Vector Symbolic Architecture for Spatiotemporal Sequence Learning and Prediction2024In: Biomimetics, E-ISSN 2313-7673, Vol. 9, no 3, article id 175Article in journal (Refereed)
    Abstract [en]

    We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm.

    Download full text (pdf)
    fulltext
  • 34.
    Kempitiya, Thimal
    et al.
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    De Silva, Daswin
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    Kahawala, Sachin
    Centre for Data Analytics and Cognition , La Trobe University, Melbourne, Australia.
    Haputhanthri, Dilantha
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    Alahakoon, Damminda
    Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Parameterization of Vector Symbolic Approach for Sequence Encoding Based Visual Place Recognition2022In: 2022 International Joint Conference on Neural Networks (IJCNN): 2022 Conference Proceedings, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    Sequence-based methods for visual place recognition (VPR) have great importance due to their ability of additional information capture through the sequences compared to single image comparison. Vector symbolic architecture (VSA) started to gain attention within these methods due to the unique capabilities for representing variable-length sequences using single high-dimensional vectors. But the effect of different sequence parameters for the visual place recognition task is yet to be explored. In this work, we explore the parametrization of sequence encoding with VSA in the SeqNet variant of sequence-based visual place recognition and introduce a new hierarchical VPR method, which utilizes the proposed parametrization. We show that with our parametrization the VSA realization of sequence-based visual place recognition achieves on par results to conventional algorithms, while featuring the capability of being implemented on novel neuromorphic hardware for efficient execution.

  • 35.
    Khattak, Rabiullah
    et al.
    Luleå University of Technology.
    Chaltseva, Anna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Riliskis, Laurynas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Bodin, Ulf
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Comparison of wireless network simulators with multihop wireless network testbed in corridor environment2011In: Wired/wireless internet communications: 9th IFIP TC 6 International Conference, WWIC 2011, Vilanova i la Geltrú, Spain, June 15-17, 2011 ; proceedings / [ed] Xavier Masip-Bruin, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2011, p. 80-91Conference paper (Refereed)
    Abstract [en]

    This paper presents a comparative study between results of a single channel multihop wireless network testbed and the network simulators ns-2 and ns-3. We explore how well these simulators reflect reality with their standard empirical radio modeling capabilities. The environment studied is a corridor causing wave-guiding propagation phenomena of radio waves, which challenges the radio models used in the simulators. We find that simulations are roughly matching with testbed results for single flows, but clearly deviate from testbed results for concurrent flows. The mismatch between simulations and testbed results is due to imperfect wireless propagation channel modeling. This paper reveals the importance of validating simulation results when studying single channel multihop wireless network performance. It further emphasizes the need for validation when using empirical radio modeling for more complex environments such as corridors.

  • 36.
    Kirilenko, Daniil E.
    et al.
    Moscow Institute of Physics and Technology, Moscow, Russia.
    Kovalev, Alexey K.
    HSE University, Moscow, Russia; Artificial Intelligence Research Institute FRC CSC RAS, Moscow, Russia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Panov, Aleksandr I.
    Moscow Institute of Physics and Technology, Moscow, Russia; Artificial Intelligence Research Institute FRC CSC RAS, Moscow, Russia.
    Question Answering for Visual Navigation in Human-Centered Environments2021In: Advances in Soft Computing: 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Mexico City, Mexico, October 25–30, 2021, Proceedings, Part II / [ed] Ildar Batyrshin, Alexander Gelbukh, Grigori Sidorov, Springer Nature, 2021, p. 31-45Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose an HISNav VQA dataset - a challenging dataset for a Visual Question Answering task that is aimed at the needs of Visual Navigation in human-centered environments. The dataset consists of images of various room scenes that were captured using the Habitat virtual environment and of questions important for navigation tasks using only visual information. We also propose a baseline for a HISNav VQA dataset, a Vector Semiotic Architecture, and demonstrate its performance. The Vector Semiotic Architecture is a combination of a Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model allows representing various aspects of an agent’s knowledge, and Vector Symbolic Architectures serve on a low computational level. The Vector Semiotic Architecture addresses the symbol grounding problem that plays an important role in the Visual Question Answering Task.

  • 37.
    Kleyko, Denis
    et al.
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA 94720 USA; Intelligent Systems Laboratory, Research Institutes of Sweden, 16440 Kista.
    Davies, Mike
    Neuromorphic Computing Laboratory, Intel Labs, Santa Clara, CA, USA.
    Frady, Edward Paxon
    Neuromorphic Computing Laboratory, Intel Labs, Santa Clara, CA, USA.
    Kanerva, Pentti
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA, USA.
    Kent, Spencer J.
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA, USA.
    Olshausen, Bruno A.
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA, USA.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rabaey, Jan M.
    Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA.
    Rachkovskij, Dmitri A.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. International Research and Training Center for Information Technologies and Systems, Kyiv, Ukraine.
    Rahimi, Abbas
    IBM Research–Zurich, Rüschlikon, Switzerland.
    Sommer, Friedrich T.
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA, USA; Neuromorphic Computing Laboratory, Intel Labs, Santa Clara, CA, USA.
    Vector Symbolic Architectures as a Computing Framework for Emerging Hardware2022In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 110, no 10, p. 1538-1571Article in journal (Refereed)
    Abstract [en]

    This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware, and it naturally expresses the types of cognitive operations required for artificial intelligence (AI). We demonstrate in this article that the field-like algebraic structure of VSA offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of VSA, “computing in superposition,” which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that VSA are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind VSA, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.

  • 38.
    Kleyko, Denis
    et al.
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA 94720, USA; Intelligent Systems Lab, Research Institutes of Sweden, 164 40 Kista, Sweden.
    Frady, Edward Paxon
    Neuromorphic Computing Lab, Intel Labs, Santa Clara, CA 95054 USA; Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA 94720, USA.
    Kheffache, Mansour
    Netlight Consulting AB, 111 53 Stockholm, Sweden.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware2022In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 33, no 4, p. 1688-1701Article in journal (Refereed)
    Abstract [en]

    We propose an approximation of echo state networks (ESNs) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed integer ESN (intESN) is a vector containing only n-bits integers (where n< 8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The proposed intESN approach is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs, classifying time series, and learning dynamic processes. Such architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss. The experiments on a field-programmable gate array confirm that the proposed intESN approach is much more energy efficient than the conventional ESN.

  • 39.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Comparison of Machine Learning Techniques for Vehicle Classification using Road Side Sensors2015In: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems: Las Palmas, 15-18 Sept. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 572-577, article id 7313192Conference paper (Refereed)
    Abstract [en]

    The main contribution of this paper is a comparison of different machine learning algorithms for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard using measurements of road surface vibrations and magnetic field disturbances caused by vehicles. The algorithms considered are logistic regression, neural networks, and support vector machines. They are evaluated on a large dataset, consisting of 3074 samples and hence, a good estimate of the actual classification rate is obtained. The results show that for the considered classification problem logistic regression is the best choice with an overall classification rate of 93.4%.

  • 40.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hostettler, Roland
    Department of Electrical Engineering and Automation, Aalto University.
    Lyamin, Nikita
    School of Information Technology, Halmstad University.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Wiklund, Urban
    Department of Biomedical Engineering and Informatics, Umeå University.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vehicle Classification using Road Side Sensors and Feature-free Data Smashing Approach2016In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1988-1993, article id 7795877Conference paper (Refereed)
    Abstract [en]

    The main contribution of this paper is a study of the applicability of data smashing – a recently proposed data mining method – for vehicle classification according to the “Nordic system for intelligent classification of vehicles” standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method’s development efforts could be achieved.

  • 41.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Khan, Sumeer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yong, Suet-Peng
    Department of Computer and Information Sciences, Universiti Teknologi PETRONAS.
    Modality Classification of Medical Images with Distributed Representations Based on Cellular Automata Reservoir Computing2017In: Proceedings - International Symposium on Biomedical Imaging, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1053-1056Conference paper (Refereed)
    Abstract [en]

    Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage of principles of hyper-dimensional computing and reservoir computing. It is demonstrated that the highest classification accuracy of the proposed method is on a par with the best classical method for the given dataset (83% vs. 84%). The major positive property of the proposed method is that it does not require any optimization routine during the training phase and naturally allows for incremental learning upon the availability of new training data.

  • 42.
    Kleyko, Denis
    et al.
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA 94720 USA; Intelligent Systems Lab, Research Institutes of Sweden, 164 40 Kista, Sweden.
    Kheffache, Mansour
    Netlight Consulting AB, 111 53 Stockholm, Sweden.
    Frady, E. Paxon
    Redwood Center for Theoretical Neuroscience, University of California at Berkeley, Berkeley, CA 94720 USA.
    Wiklund, Urban
    Department of Radiation Sciences, Biomedical Engineering, Umeå University, 901 87 Umeå, Sweden.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks2021In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 32, no 8, p. 3777-3783Article in journal (Refereed)
    Abstract [en]

    The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this brief, we focus on resource-efficient randomly connected neural networks known as random vector functional link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world data sets from the UCI machine learning repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small n-bits integers, which results in a computationally efficient architecture. Finally, through hardware field-programmable gate array (FPGA) implementations, we show that such an approach consumes approximately 11 times less energy than that of the conventional RVFL.

  • 43.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lyamin, Nikita
    Halmstad University.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Modified algorithm of dynamic frequency hopping (DFH) in the IEEE 802.22 standard2014In: Multiple Access Communications: 7th International Workshop, MACOM 2014, Halmstad, Sweden, August 27-28, 2014. Proceedings / [ed] Magnus Jonsson; Alexey Vinel; Boris Bellalta; Evgeny Belyaev, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 75-83Conference paper (Refereed)
    Abstract [en]

    IEEE 802.22 Cognitive Wireless Regional Area Networks is a first standard of wireless terrestrial system relying on cognitive radio concept and operating as an opportunistic system in the the vacant unoccupied frequency spaces of the licensed TV-frequency band. Concept of the proposed standard assumes special functionality to protect the operation of the primary licensed subscribers. Dynamic Frequency Hopping is the mechanism for providing connectionless operation of Wireless Regional Area Networks systems while ensuring protection of transmissions from the primary users. During its operation regular time gaps appear on the involved frequency channels. This paper introduces the concept of the efficient reuse of the vacant frequency resources appearing when using the Dynamic Frequency Hopping mode. The scheme for consecutive-parallel inclusion of the new Dynamic Frequency Hopping Communities-members in the Dynamic Frequency Hopping mode is presented. The proposed approach allows to significantly decrease transition time.

  • 44.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lyamin, Nikita
    Siberian State University of Telecommunications and Information Sciences.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Riliskis, Laurynas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Dependable MAC layer architecture based on holographic data representation using hyper-dimensional binary spatter codes2012In: Multiple Access Communications: 5th International Workshop, MACOM 2012, Maynooth, Ireland, November 19-20, 2012. Proceedings / [ed] Boris Bellalta, Heidelberg: Encyclopedia of Global Archaeology/Springer Verlag, 2012, p. 134-145Conference paper (Refereed)
    Abstract [en]

    In this article we propose the usage of binary spatter codes and distributed data representation for communicating loss and delay sensitive data in event-driven sensor and actuator networks. Using the proposed data representation technique along with the medium access control protocol the mission critical control information can be transmitted with assured constant delay in deployments exposing below 0 dB signal-to-noise ratio figures.

  • 45.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Brain-like classifier of temporal patterns2014In: International Conference on Computer and Information Sciences, ICCOINS 2014, Kuala Lumpur, Malaysia, June 03-05, 2014. Proceedings, Piscataway, NJ: IEEE Communications Society, 2014, p. 1-6Conference paper (Refereed)
    Abstract [en]

    In this article we present a pattern classification system which uses Vector Symbolic Architecture (VSA) for representation, learning and subsequent classification of patterns, as a showcase we have used classification of vibration sensors measurements to vehicles types. On the quantitative side the proposed classifier requires only 1 kB of memory to classify an incoming signal against of several hundred of training samples. The classification operation into N types requires only 2*N+1 arithmetic operations this makes the proposed classifier feasible for implementation on a low-end sensor nodes. The main contribution of this article is the proposed methodology for representing temporal patterns with distributed representation and VSA-based classifier.

  • 46.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    No Two Brains Are Alike: Cloning a Hyperdimensional Associative Memory Using Cellular Automata Computations2018In: Biologically Inspired Cognitive Architectures (BICA) for Young Scientists: First International Early Research Career Enhancement School on BICA and Cybersecurity (FIERCES 2017) / [ed] Alexei V. Samsonovich, Valentin V. Klimov, Cham: Springer, 2018, p. 91-100Conference paper (Refereed)
    Abstract [en]

    This paper looks beyond of the current focus of research on biologically inspired cognitive systems and considers the problem of replication of its learned functionality. The considered challenge is to replicate the learned knowledge such that uniqueness of the internal symbolic representations is guaranteed. This article takes a neurological argument “no two brains are alike” and suggests an architecture for mapping a content of the trained associative memory built using principles of hyperdimensional computing and Vector Symbolic Architectures into a new and orthogonal basis of atomic symbols. This is done with the help of computations on cellular automata. The results of this article open a way towards a secure usage of cognitive architectures in a variety of practical application domains.

  • 47.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    On bidirectional transitions between localist and distributed representations: The case of common substrings search using Vector Symbolic Architecture2014In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 41, p. 104-113Article in journal (Refereed)
    Abstract [en]

    The contribution of this article is twofold. First, it presents an encoding approach for seamless bidirectional transitions between localist and distributed representation domains. Second, the approach is demonstrated on the example of using Vector Symbolic Architecture for solving a problem of finding common substrings. The proposed algorithm uses elementary operations on long binary vectors. For the case of two patterns with respective lengths L1 and L2 it requires Θ(L1 + L2 – 1) operations on binary vectors, which is equal to the suffix trees approach – the fastest algorithm for this problem. The simulation results show that in order to be robustly detected by the proposed approach the length of a common substring should be more than 4% of the longest pattern.

  • 48.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Björk, Magnus
    Luleå University of Technology.
    Toresson, Henrik
    Luleå University of Technology.
    Öberg, Anton
    Luleå University of Technology.
    Fly-The-Bee: A Game Imitating Concept Learning in Bees2015In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 71, p. 25-30Article in journal (Refereed)
    Abstract [en]

    This article presents a web-based game functionally imitating a part of the cognitive behavior of a living organism. This game is a prototype implementation of an artificial online cognitive architecture based on the usage of distributed data representations and Vector Symbolic Architectures. The game emonstrates the feasibility of creating a lightweight cognitive architecture, which is capable of performing rather complex cognitive tasks. The cognitive functionality is implemented in about 100 lines of code and requires few tens of kilobytes of memory for its operation, which make the concept suitable for implementing in low-end devices such as minirobots and wireless sensors.

  • 49.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    De Silva, Daswin
    La Trobe University, Melbourne, Australia.
    Wiklund, Urban
    Umeå University, Umeå, Sweden.
    Alahakoon, Damminda
    La Trobe University, Melbourne, Australia.
    Integer Self-Organizing Maps for Digital Hardware2019In: 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019, article id N-20091Conference paper (Refereed)
    Abstract [en]

    The Self-Organizing Map algorithm has been proven and demonstrated to be a useful paradigm for unsupervised machine learning of two-dimensional projections of multidimensional data. The tri-state Self-Organizing Maps have been proposed as an accelerated resource-efficient alternative to the Self-Organizing Maps for implementation on field-programmable gate array (FPGA) hardware. This paper presents a generalization of the tri-state Self-Organizing Maps. The proposed generalization, which we call integer Self-Organizing Maps, requires only integer operations for weight updates. The presented experiments demonstrated that the integer Self-Organizing Maps achieve better accuracy in a classification task when compared to the original tri-state Self-Organizing Maps.

  • 50.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    De Silva, Daswin
    La Trobe University, Melbourne, Australia.
    Wiklund, Urban
    Umeå University, Umeå, Sweden.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Alahakoon, Damminda
    La Trobe University, Melbourne, Australia.
    Distributed Representation of n-gram Statistics for Boosting Self-organizing Maps with Hyperdimensional Computing2019In: Perspectives of System Informatics: 12th International Andrei P. Ershov Informatics Conference, PSI 2019, Novosibirsk, Russia, July 2–5, 2019, Revised Selected Papers / [ed] Nikolaj Bjørner; Irina Virbitskaite; Andrei Voronkov, Springer, 2019, p. 64-79Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach for substantial reduction of the training and operating phases of Self-Organizing Maps in tasks of 2-D projection of multi-dimensional symbolic data for natural language processing such as language classification, topic extraction, and ontology development. The conventional approach for this type of problem is to use n-gram statistics as a fixed size representation for input of Self-Organizing Maps. The performance bottleneck with n-gram statistics is that the size of representation and as a result the computation time of Self-Organizing Maps grows exponentially with the size of n-grams. The presented approach is based on distributed representations of structured data using principles of hyperdimensional computing. The experiments performed on the European languages recognition task demonstrate that Self-Organizing Maps trained with distributed representations require less computations than the conventional n-gram statistics while well preserving the overall performance of Self-Organizing Maps. 

123 1 - 50 of 116
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf